PRECIPITATION ESTIMATION FROM INFRARED SATELLITE IMAGERY

Transcription

1 PRECIPITATION ESTIMATION FROM INFRARED SATELLITE IMAGERY A.M. BRASJEN AUGUST

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3 PRECIPITATION ESTIMATION FROM INFRARED SATELLITE IMAGERY MASTER S THESIS AUGUST 2014 A.M. BRASJEN Department of Geoscience and Remote Sensing Faculty of Civil Engineering and Geosciences Delft University of Technology SUPERVISORS: Dr. J.F. Meirink Royal Netherlands Meteorological Institute Department of Climate Research Earth Observation and Climate Prof. dr. A.P. Siebesma Delft University of Technology Faculty of Civil Engineering and Geosciences Department of Geoscience and Remote Sensing Atmospheric Physics 3

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5 ABSTRACT Since the introduction of the first geostationary satellites, researchers have tried to estimate precipitation from satellite imagery. Despite the fact that precipitation rates derived from satellite imagery are inherently indirect, satellite-based rainfall estimates have been a valid addition to existing rain gauge and weather radar networks for a long time: a single satellite is able to monitor an area much larger than an extensive weather radar network, and also covers the oceans. Nowadays, many algorithms estimating precipitation from satellite imagery exist. Yet, most algorithms either utilize visible imagery or are only able to estimate precipitation for deep convective clouds. In order for these algorithms to really contribute to the information of weather radars, an algorithm is needed that is able to produce precipitation rates both during daytime and nighttime for all types of clouds. In this thesis a new algorithm is proposed: the Nighttime Infrared Precipitation Estimation (NIPE) model, which utilizes only infrared imagery to estimate precipitation rates for both convective and stratiform clouds. The basis of this algorithm is a cloud type dependent precipitation index in which a number of SEVIRI infrared brightness temperatures and brightness temperature differences are incorporated, along with a number of factors correcting for the moisture content of the atmosphere and the cloud structure. In order to take into account that lower clouds do not produce as much precipitation as deep convective clouds, the calculated precipitation index is converted to a precipitation rate using histogram mapping with a conversion chart dependent on the cloud type. The algorithm is trained with precipitation data from the Dutch weather network, and verified with a separate dataset. Comparing the results of the NIPE model to other existing methods using infrared imagery, the NIPE model has a larger skill score in classifying a satellite pixel as precipitating or nonprecipitating, equal to approximately 24%. In addition, the precipitation estimates deviate on average by a factor 3.3 from the observations and are overall unbiased. The number of precipitation occurrences is slightly overestimated, mainly for deep convective situations, yet this overestimation is smaller than for other methods. In addition, the cloud type dependency introduces discontinuities as well as under- and overestimations of the precipitation rate along the borders between different cloud types. Apart from solving these issues, future research should focus on verifying the performance of the NIPE model on a larger domain across Western Europe and Africa. 5

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7 CONTENTS INTRODUCTION ) METEOSAT ) Introduction ) SEVIRI ) SEVIRI channels ) SEVIRI channel combinations ) Conversion of radiances and instrumental noise ) AVAILABLE METHODS ) Introduction ) Convective rainfall rate estimation ) Rainfall rate estimation with visible imagery ) Precipitation probability estimation with infrared imagery ) RADIATIVE TRANSFER ) Introduction ) Radiative transfer simulations ) METHOD ) Introduction ) Training and verification dataset ) Correlation study ) Channel selection precipitation index ) Noise filtering ) Optimization of coefficients ) Conversion from precipitation index to precipitation rate ) RESULTS ) Introduction ) Correlation study ) Model performance ) Case study ) OTHER APPLICATIONS ) Introduction ) Model performance: daytime analysis ) Case study: Western Europe CONCLUSION & DISCUSSION Research summary Verification results Future research and recommendations BIBLIOGRAPHY

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9 INTRODUCTION Due to the large spatiotemporal variability in rainfall, measuring precipitation has proved to be an inherently difficult problem. Yet the problem s high relevance has urged most countries to develop an extensive rain gauge network, which in the Netherlands consists of over 350 observation stations see Figure I.1. Despite the extent of these networks, remote and mountainous regions tend to be underrepresented and the networks do very seldom contain observations at sea. In order to solve these issues, researchers have been focusing on developing alternative methods for a long time. Figure I.1) Dutch weather radar and rain gauge network Since the discovery of weather radars during World War II, the temporal and spatial resolution of precipitation observations has greatly improved: the spatial resolution of the Dutch weather radar network is approximately 1 km 2, whereas the rain gauges are located approximately 10 km from each other and only provide a local estimate. However, as opposed to rain gauges, conventional weather radars do not provide direct measurements of precipitation. Since the reflectivity of the radar signal not only depends on the number of cloud droplets which can be associated with the precipitation rate but also on the size distribution, one of these two parameters has to be pre-determined. Usually, the size distribution is assumed to be constant, although this parameter varies a lot between clouds producing drizzle and deep convective clouds. Although the introduction of weather radar provided a huge improvement in monitoring thunderstorms, hurricanes and other weather systems, most radar networks are confined to land, and do not offer any information of storm and frontal developments over sea. Additionally, only developed countries have invested in creating a weather radar network covering the entire country; the coverage of weather radars in for example Africa, South America and Asia is generally limited. Therefore, since satellite images became available during the late 1960s, a lot of research has been done into estimating precipitation from both visible and infrared imagery provided by these satellites. 9

10 Despite the slightly lower spatial and temporal resolution, the area monitored by a single satellite is much larger than the area covered by radar networks comprising hundreds of stations, and more importantly, it covers the large oceans. However, the rainfall estimates from these satellites are inherently indirect, since infrared radiation is not able to penetrate the clouds and the reflection of visible radiation does not depend on the droplet concentration. Initially, research concerning visible imagery was mainly focused on correlating cloud patterns in the visible spectrum with precipitation occurrences (Griffith et al. 1978). Soon it was discovered that the brightest clouds generally produce the most rainfall, since the reflectivity of a cloud is a quite accurate measure for its geometrical thickness. However, while studying cloud patterns and comparing them with radar echoes, it became clear that the area of most clouds is a lot larger than the area of precipitation and that clouds generally produce more rainfall while developing than when they reach their mature state (Scofield 1987). Therefore, monitoring cloud development became relevant, favouring the use of geostationary satellites rather than polar-orbiting satellites, which do not offer a continuous view on the same area. Today, a large part of the globe is monitored using geostationary satellites, including (see Figure I.2): Geostationary Operational Environmental West (GOES-West) and GOES-East operated by the National Oceanic and Atmospheric Administration (NOAA) situated over North and South America and the Eastern Pacific; Meteosat operated by EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) observing Europe and Africa and Meteosat-East covering the Indian Ocean and Middle Asia Multi-Functional Transport Satellite (MT-SAT) operated by the Japanese Meteorological Agency (JMA) observing East Asia, Australia and the Western Pacific. Due to the curvature of the Earth, the spatial resolution decreases for larger latitudes from typically 1 to 4 km at the nadir point by a factor 4 for latitudes of 60. For latitudes larger than 81 the Earth s surface is not even visible anymore. Therefore, for polar regions polar-orbiting satellites are still needed to provide images with sufficient resolution. Figure I.2) Coverage of Earth by geostationary satellites In addition to the network of geostationary satellites, a number of polar-orbiting satellites devoted to measuring precipitation have been active, some of which are equipped with additional measurement devices which do not only observe visible and infrared radiation but are also able to detect cloud emissivities in the microwave spectrum, of which the SSM/I is the most well-known example. With the microwave data, quantities like integrated water vapour path, liquid water path and precipitation rates can be retrieved, yielding a more direct measurement than with visible and infrared imagery only. Other missions like the Tropical Rainfall Measuring Mission (TRMM) and the recently launched Global 10

11 Precipitation Measurement (GPM) mission also include an active profiling precipitation radar, which is obviously even more sensitive to precipitation. Due to the fact that these satellites usually have a limited viewing angle and swath width, the period between two measuring instants of the same area is usually in the order of days see Figure I.3 and therefore these satellites are not very useful in real time monitoring of weather systems, but merely designed for gaining more insight into cloud and precipitation processes and developing climatology statistics. Figure I.3) The area covered by three orbits of TRMM in yellow and three orbits of GPM Core Observatory in blue (source: NASA) Approximately simultaneously with the attempts to estimate precipitation using visible imagery, another way was developed utilizing the infrared data from geostationary satellites. Thunderstorms with high cloud tops and associated cold cloud top temperatures are very bright on these images and it turns out that when these cloud tops are colder, rainfall is more intense, yet there is no guarantee that every cold cloud is a thunderstorm (Scofield 1987). The developed algorithms have proved to be very useful in monitoring extreme weather events and possible flash floods, but they are not able to reproduce the precipitation originating from lower clouds associated with frontal movements. A usual approach to solve this problem is combining visible and infrared imagery. However, visible imagery is only roughly half a day available and for continuity in monitoring precipitation systems and developing climatological statistics, it is essential that a valid precipitation estimate can be made during nighttime, regardless of whether it is originating from a thunderstorm or not. Therefore, the goal of the research, as presented in this thesis, is to create an algorithm which is able to produce a proper precipitation estimate by only using infrared imagery for both deep-convective clouds and stratiform clouds. Since there is primarily a lack of accurate precipitation estimates during nighttime, this research will be mainly focused on nighttime conditions. In order to investigate the tools available to solve this problem, this report will start with an introduction into the measuring equipment of the Meteosat Second Generation (MSG) satellite the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and describe the characteristics of the channels of this imager. Since the measured infrared brightness temperatures are often used as a brightness temperature difference with respect to another channel, the most common channel differences will be introduced, after which the instrumental noise of the infrared channels will be discussed. In Chapter 2 an overview is presented of a selection of currently available methods that provide an estimation of precipitation using either visible or infrared imagery, or a combination of the two. These 11

12 methods act as a starting point for the algorithm presented in this thesis and their limitations and advantages will be discussed. With the atmospheric response to infrared radiation already introduced in Chapter 1 in a qualitative way, Chapter 3 is dedicated to gaining more insight in the quantitative response. The radiative transfer simulations presented in this chapter will provide a guideline which SEVIRI channels and channel differences are most suited to be included in the model. In Chapter 4 the model proposed in this thesis is presented, by adapting and combining currently available methods and introducing new aspects and corrections. The estimated precipitation rates calculated with the developed algorithm will be verified with weather radar data in Chapter 5, and compared to the performance of existing methods. This verification will be focused on both the magnitude of the estimated precipitation rates and the correct classification of clouds as precipitating or non-precipitating. In the verification study a number of case studies are discussed too, to show the limitations and characteristics of the model. The verification study is extended in Chapter 6 for situations outside the main scope of the research: daytime conditions and a larger domain. This additional verification study is primarily done to put the results of the verification study in Chapter 5 into perspective, which is used in the discussion and recommendations concluding this thesis. 12

13 11.1 METEOSAT INTRODUCTION Shortly after the first geostationary GOES satellites were launched by NASA in the late 1970s, the European Space Agency (ESA) began its own program for geostationary meteorological satellites. The first Meteosat satellite was launched in 1977 and provided images of the Earth s atmosphere in three spectral channels: one visible channel, one water vapour channel and one infrared channel. In 2002, after seven versions of this first generation Meteosat satellite, the first Meteosat Second Generation (MSG) satellite was launched. The current satellites, providing images of Europe and Africa, turn out to be a large improvement compared to their predecessors: not only has the temporal resolution been increased from 30 to 15 minutes, on this new generation satellites twelve spectral channels are available and the spatial resolution has increased from approximately five to three kilometres at the nadir point. The current generation of Meteosat satellites is expected to last at least until 2022, after which the satellites will be replaced by Meteosat Third Generation (MTG) satellites which are currently being developed. Figure 1.1) SEVIRI s view on the Earth, enabling the monitoring of cloud development over Africa, Europe and parts of South America and Asia. 1.2 SEVIRI The most important instrument of the MSG satellites is the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Every 15 minutes, the Earth is observed with eleven spectral channels, accompanied by a high-resolution visible (HRV) channel which is able to cover only half of the viewing area of the 13

14 other channels. The instrument uses the spinning motion of the satellite itself needed to stabilize the satellite combined with a rotating mirror, to scan the entire full disk. Usually a scan takes around 12 minutes after which the infrared channels are calibrated with an on-board blackbody in order to decrease errors due to degradation of the instrument (Schmetz et al. 2002). An overview of the spectral channels of SEVIRI is presented in Table 1.1. Table 1.1) Overview of the characteristics of the spectral channels of the SEVIRI instrument (source: Schmetz et al. 2002) # Channel Spectral range (µm) Type 1 VIS window 2 VIS window 3 NIR window 4 IR window 5 WV water vapour 6 WV water vapour 7 IR window 8 IR ozone 9 IR window 10 IR window 11 IR carbon dioxide 12 HRV window 1.3 SEVIRI CHANNELS Although at first glance, the selection of the channels seems fairly arbitrary, each channel has its own characteristics and legacy to ensure backward compatibility with the previous generations satellites VISIBLE CHANNELS The visible channels VIS0.6 and VIS0.8 are chosen in such a way that they are comparable with the visible channels on the GOES and AVHRR satellites. They are primarily used for cloud recognition using backscattered solar radiation. VIS0.8 has a better recognition of land surface structures than VIS0.6 due to the higher spectral reflectance of soil and leafs. This property also ensures that transparent clouds are better visible in VIS0.6 due to the less reflecting surface (EUMETSAT 2013). Figure 1.2) Reflectance of the SEVIRI VIS0.6 channel for 18 October :00 UTC over Western Europe 14

15 Although not sensitive to radiation in the spectrum visible to the human eye, channel NIR1.6 utilizes the backscattering of solar radiation in a similar way as VIS0.6 and VIS0.8. The channel is chosen in such a way that it is centred on a wavelength for which the absorption for ice particles is much larger than the absorption for water particles see Figure 1.3. Combined with the other visible channels, water clouds will be more highlighted in these images, which makes it possible to distinguish them from ice clouds and snow covered land surface. Figure 1.3) Complex part of the refractive index as a function of wavelength with in blue and red the coefficient for water and ice respectively. The grey bands in the diagram denote the three visible / near-infrared SEVIRI channels (1) VIS0.6, (2) VIS0.8 and (3) NIR1.6. (source: EUMETSAT 2013) CHANNEL IR3.9 Channel IR3.9 is also a channel used by GOES and AVHRR, and is the only channel in which both solar and terrestrial radiation play an important role during daytime, complicating the behaviour of the channel. This is even more complicated by the fact that the channel contains significant carbon dioxide absorption. The main application of this channel is the identification of fog and low stratus clouds. Since the emissivity for water clouds at 3.9 µm is lower than at 10.8 µm corresponding with SEVIRI channel 9, see Figure 1.5 channel IR3.9 exhibits a higher reflection of the colder atmosphere above the cloud compared to channel IR10.8. Similarly to the fog detection, thin cirrus clouds can be distinguished using the different absorption characteristics of ice at 3.9 µm and 10.8 µm. Another advantage of the IR3.9 channel is the sensitivity to cloud phase and cloud particle size, as the reflection of solar radiation is higher for water particles than for ice particles and the reflection decreases as the particle size becomes larger. Figure 1.4) Brightness temperature of the SEVIRI IR3.9 channel for 2 February :00 UTC. The red colours in this image correspond with a high brightness temperature and usually no clouds, whereas bluish colours indicate cold cloud tops. 15

16 Figure 1.5) Complex part of the refractive index as a function of wavelength with in blue and red the coefficient for water and ice respectively. The grey bands in the diagram denote the infrared SEVIRI channels WATER VAPOUR CHANNELS Channels WV6.2 and WV7.3 are two channels which both are located inside the water vapour absorption band, with channel WV6.2 located in the most absorbing part of this band. As a consequence the signal in WV6.2 is mainly from the top of the troposphere peaking at approximately 375 hpa, since any infrared radiation in this band emitted from the surface will be absorbed by the abundant water vapour in the lowest layers of the atmosphere, see also Figure 1.6. For channel WV7.3 this effect is less prominent, yielding that surface features are still visible, but the signal peak is located at around 550 hpa. Figure 1.6) Atmospheric weighting function for each SEVIRI channel based on the mid-latitude summer standard atmosphere. (source: EUMETSAT 2013) These absorption features allow the possibility of tracking cloud features at different levels, from which the wind speed and direction at the peaking levels can be derived. However, since the concentration of water vapour in the atmosphere is not constant, both channels mainly provide 16

17 information regarding the water vapour amount in the upper part of the troposphere, from which the distinction between different air masses can be made. Figure 1.7) Brightness temperature of the SEVIRI WV6.2 channel for 2 February :00 UTC. Due to the fact that the signal is dominated by the upper troposphere, this image does not show the low clouds over the North Sea as visible in Figure 1.4, but displays other water vapour patterns from which the circulation in the upper layers of the troposphere can be determined. Please note the difference in colour scale with respect to Figure WINDOW CHANNELS Channels IR8.7, IR10.8 and IR12.0 are all so-called window channels, all centred on wavelengths for which there is little to no absorption of radiation in the atmosphere. As a result, these channels can all be used to determine the temperature of the highest reflecting surface, whether this is an opaque cloud or the land or sea surface. However, the centre wavelengths of these channels are chosen in such a way that they allow the detection of thin cirrus clouds, due to a slightly different absorption for water and ice SOUNDING CHANNELS The remaining channels IR9.7 and IR13.4 are located in the ozone and carbon dioxide absorption band respectively. In combination with the window channels, channel IR9.7 can provide information of the evolution of ozone concentrations in time. For channel IR13.4 the peak signal is located at approximately 850 hpa, and a similar approach as with the water vapour channels can be applied to determine the low level winds. Furthermore, techniques have been developed to use the information from IR13.4, WV6.2 and WV7.3 and a window channel to determine the altitude of semi-transparent cirrus clouds. 1.4 SEVIRI CHANNEL COMBINATIONS As already mentioned in the previous section, the infrared channel responses from SEVIRI are often used as differences between two channels. In most cases channel IR10.8 is used as a reference, since this channel is influenced by hardly any absorption in the atmosphere and many other satellites contain an infrared channel with approximately the same spectral properties CHANNEL IR3.9 IR10.8 Despite the fact that the brightness temperatures of channel IR3.9 are influenced by solar radiation during daytime, together with channel IR10.8 it forms one of the most utilized differences (EUMETSAT 2013). This is mainly caused by the different emissivity for water and ice droplets at both wavelengths, 17

18 which is smaller for IR3.9 than for IR10.8. For optically thin clouds, these emissivity characteristics are relevant, as a few cloud droplets alter the channel response in a different way for each channel. For optically thicker clouds, the difference in channel response vanishes, since almost the entire channel response is determined by the cloud droplets and therefore only relies on the temperature of the cloud. As the difference in emissivity at 3.9 µm and 10.8 µm is also different for water and ice clouds, channel combination IR3.9 IR10.8 is therefore also sensitive to the phase of the cloud droplets and as a consequence a good detector of thin cirrus clouds. Figure 1.8) Brightness temperature difference IR3.9 IR10.8 for 2 February :00 UTC. Red areas correspond with high thin semi-transparent clouds, whereas dark blue areas as observed in South Sweden can be associated with fog and low clouds. Pale blue areas correspond with clear sky conditions, and green areas with medium to high optically thick clouds. The noisy areas in the image can be associated with very high clouds with cold cloud tops for which noise in channel IR 3.9 becomes dominant. In addition, channel IR3.9 is the only infrared channel that is significantly sensitive to the cloud droplet size. The absorption for this channel is relatively small compared to the other infrared channels, yet large enough for most of the radiation to be absorbed near the cloud top. As the net reflectance (i.e. the ratio between scattering and absorption) is inversely proportional to the cloud droplet effective radius, clouds with smaller droplet sizes have a relatively smaller brightness temperature at 3.9 µm, yielding a smaller channel difference IR3.9 IR10.8. In case of low stratus or fog clouds consisting of very small particles, the differences in absorption result in an even more negative channel difference than for other clouds or clear sky, which is the reason why this channel difference is often used as an indicator for these types of clouds. However, a negative channel difference does not guarantee the presence of fog, since differing surface emissivity characteristics at 3.9 µm and 10.8 µm complicate the situation. Especially desert areas and, to a lesser extent, oceans and other large water surfaces, have a very low emissivity at 3.9 µm as is displayed in Figure 1.9, yielding very large negative brightness temperature differences. Therefore, thresholds dependent on surface vegetation are usually applied in the automatic detection of fog and low stratus clouds. 18

19 Figure 1.9) Emissivity as a function of wavelength for different surface types. The grey bands in the diagram correspond with the infrared SEVIRI channels. (source: EUMETSAT 2013) OPTICAL THICKNESS The thin cirrus detection as described in the previous section can also be applied using brightness temperature differences IR12.0 IR10.8 and IR8.7 IR10.8, since the emissivity at 12.0 µm is much higher for both water and ice particles compared to IR10.8, whereas the emissivity at 8.7 µm is lower than at 10.8 µm. Although the sensitivity to cloud droplet size is lacking for these channels, a large negative brightness temperature difference in case of IR12.0 IR10.8 or a large positive difference in case of IR8.7 IR10.8, corresponds with optically thin clouds, and is therefore useful in separating cirrus clouds from optically thick thunderstorms with cold tops CLOUD TOP HEIGHT The differences in the weighting functions as presented in section 1.3 can be utilized to determine the cloud top height. For this determination mainly sounding channels and water vapour channels are useful, especially CO 2 channel IR13.4 and water vapour channel WV6.2. Since the latter is particularly sensitive above 500 hpa, channel difference WV6.2 IR10.8 also serves as a good indicator of convective storms with overshooting tops. Figure 1.10) Brightness temperature difference WV6.2 IR10.8 for 2 February :00 UTC. High cloud tops are highlighted as red areas, whereas blue areas indicate clear sky conditions OTHER COMBINATIONS AND APPLICATIONS Apart from channel combinations to detect desert dust and volcanic ash, which will not be discussed in this thesis, there is one other popular channel difference, consisting of the two water vapour channels WV6.2 and WV7.3. Although the difference between the weighting functions of both channels causes the brightness temperature difference WV6.2 WV7.3 to be an indicator of cloud top height for high clouds, the main application for clear sky conditions is the characterisation of air type. 19

20 As water vapour channels WV6.2 and WV7.3 show the amount of upper air moisture at two different layers, a less negative difference with respect to clear sky conditions is associated with descending dry stratospheric air. The channel combinations are often used in so-called RGB composites, aimed at combining the individual channel responses in such a way that the information contained in the brightness temperatures becomes clear in a glance. Several RGB composite schemes have been proposed with different purposes (Lensky and Rosenfeld 2008). In Figure 1.11 a number of these RGB composites are displayed. The first image uses the differences WV6.2 WV7.3, IR3.9 IR10.8 and NIR1.6 VIS0.6 in order to represent the amount of moisture, particle size and optical thickness in one image at the same time and thereby provides an indication of severe convection and the stage of these convective storms. The image in Figure 1.11b is designed for distinguishing fog, cumulonimbus and cirrus clouds by using brightness temperature differences IR12.0 IR10.8, IR10.8 IR3.9 and channel response IR10.8 for optical thickness, particle size and cloud top temperature respectively. The last image in Figure 1.11 is based on the aforementioned combination WV6.2 WV7.3, IR9.7 IR10.8 and WV6.2 in order to show the atmospheric circulation and different air masses. 1.5 CONVERSION OF RADIANCES AND INSTRUMENTAL NOISE CONVERSION OF RADIANCES The SEVIRI instrument measures the radiance of each channel with 10-bit precision in mw m -2 sr -1 (cm -1 ) -1, after which these radiances are usually converted to reflectances for visible channels, or to brightness temperatures for the infrared channels. The expression for the calculation of the reflectance is given by: (1.1a) in which is the reflectance of the channels VIS0.6, VIS0.8 and NIR1.6, is the measured radiance in mw m -2 sr -1 (cm -1 ) -1, is the solar zenith angle and is the extra-terrestrial solar flux at wavelength given by: (1.1b) with given by the value in Table 1.2 and the distance between the Earth and Sun given by: ( ) (1.1c) in which JD is the day number. The conversion from radiance to brightness temperature is done using the following Planck function: ( ) ( ) (1.2) in which is the brightness temperature in K, and are channel dependent coefficients given in Table 1.2, is the central wavenumber of the channel and constants and are given by mw m -2 sr -1 (cm -1 ) -4 and K (cm -1 ) -1 respectively. 20

21 Figure 1.11a) RGB composite from SEVIRI imagery with channel combinations WV6.2 WV7.3, IR3.9 IR10.8, NIR1.6 VIS0.6 for 21 October :00 UTC. Yellow areas as observed over Northern Italy and West of Spain indicate deep convective clouds with small ice particles. Red areas West of Spain correspond with high level clouds for which the size of the ice particles is generally larger and can therefore be associated with thunderstorms in the dissipating stage. Purple and violetpurple areas over Central Spain and France indicate non-precipitating thin cirrus clouds. Figure 1.11b) RGB composite from SEVIRI imagery with channel combinations IR12.0 IR10.8, IR10.8 IR9.7 and IR10.8 for 2 February :00 UTC. Pale green areas correspond with fog, whereas noisy yellow-red areas can be associated with cumulonimbus clouds and dark blue areas are indicative of cirrus clouds. Figure 1.11c) RGB composite from SEVIRI imagery with channel combinations WV6.2 WV7.3, IR9.7 IR10.8 and WV6.2 for 2 February :00 UTC. White areas correspond with thick high-level clouds, green and green-yellow areas as observed over Southern Italy are indicative of a warm airmass, whereas purple regions like Northern Europe indicate a cold airmass. Red features in the images are associated with the Jetstream and a high potential vorticity. 21

22 Table 1.2) Overview of the channel dependent extra-terrestrial solar fluxes, coefficients and and the central wavenumber for the channels of the SEVIRI instrument (source: EUMETSAT 2013 and Lensky and Rosenfeld 2008) # Channel (mw sr -1 (cm -1 ) -1 ) (K) (cm -1 ) 1 VIS VIS NIR IR WV WV IR IR IR IR IR DAYTIME IR3.9 CORRECTION For channel IR3.9 the measured radiance during daytime also contains a contribution from solar radiation. In order to calculate the brightness temperature of the infrared component of the radiance, a correction has to be applied as is described in Lensky and Rosenfeld (2008). It relies on the assumption of sufficiently thick clouds for which the emissivity at 10.8 µm is equal to unity and the transmission at 3.9 µm is equal to zero. The total radiance at both wavelengths is then given by: (1.3a) (1.3b) in which is again the brightness temperature as a function of the temperature given by equation (1.2), is the upward transmission of radiation above the cloud, is the sum of the downward and upward transmission and is the reflectance of the channel which now can be estimated by: (1.4a) in which can be calculated using the inverted Planck function, with: (1.4b) Since the window at channel IR3.9 incorporates part of the CO 2 absorption band, transmission coefficients and depend on both the absorption due to water and CO 2. This CO 2 absorption can be estimated using channel IR13.4. The upward transmission of this channel can be estimated with: ( ) (1.5a) As a result, the absorption at this channel is given by. Since CO 2 absorption in channel IR13.4 is higher than in IR3.9, this absorption coefficient is multiplied by an empirical factor of 0.8 in 22

23 order to calculate the absorption coefficient : and subsequently the upward transmission coefficient (1.5b) The transmission coefficient is now given by: ( ) ( ) (1.5c) in which is the satellite zenith angle. The first term in this expression represents the CO 2 transmittance from the cloud top to the satellite, the second term is the transmittance from the sun to the cloud top and the last term is the water vapour absorption, adapted from Rosenfeld and Lensky (1998). represents the precipitable water above a cloud top of 20 C for the current atmosphere and the precipitable water for a saturated atmosphere, with absorption coefficient equal to INSTRUMENTAL NOISE As can be deduced from equation (1.2), the measured radiance increases non-linearly with the temperature, and this non-linear effect is the most prominent for small wavelengths. As a result, channel IR3.9 suffers from significant instrumental noise for temperatures lower than 220 K as is visualized in Figure This effect should be taken into account when choosing suitable channel combinations, since a linear combination with channel IR3.9 will produce noisy pictures if temperatures are sufficiently low. For the other infrared channels, the instrumental noise will increase as the brightness temperature approaches lower values, yet the noise levels for these channels at low temperatures are in the order 0.5 K and therefore less significant than for channel IR 3.9. Figure 1.12) Instrumental noise as function of brightness temperature for channel IR

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25 AVAILABLE METHODS INTRODUCTION As soon as geostationary satellite images became available in the late 1960s, scientists have been trying to develop schemes which utilize visible and infrared imagery in order to estimate precipitation events. Today, many different methods exist, which can be classified into three different categories which coincide with the sections of this chapter in which they will be discussed: Algorithms estimating rainfall just for deep convective systems with infrared imagery in order to be able to monitor potential extreme precipitation events and flash floods (section 2.2) Algorithms estimating rainfall with visible imagery which is only available during daylight (section 2.3) Algorithms estimating rainfall with only infrared imagery for all types of weather systems (section 2.4) In this chapter the current state of research for each of these three categories will be discussed, in which the most common algorithms will be used as an example to describe the strengths and limitations of these methods. CONVECTIVE RAINFALL RATE ESTIMATION INTRODUCTION Most research utilizing infrared geostationary satellite imagery has been focused on developing methods in order to estimate rainfall rates from convective systems. The first algorithms in this category go back to the early 1970s, with the Griffith-Woodley technique as the most prominent example (Griffith et al. 1978). All methods combined with visible imagery or not utilize the same principle: the colder the infrared cloud top temperature, the higher the precipitation rate. Furthermore, in most algorithms the convective cloud structure and life cycle (see Figure 2.1) is taken into account as well as is described in Scofield (1987): Precipitation rates are the highest in the areas with overshooting tops, the anvil generally does not produce precipitation Convective clouds whose anvils are expanding produce more rainfall than clouds for which the cloud area decreases Clouds whose cloud tops become warmer are associated with little to no precipitation Clouds in more humid conditions produce more rainfall than clouds in dry conditions In this section three convective rainfall estimation methods will be discussed, developed by NOAA/NESDIS, EUMETSAT and NWC SAF respectively. 25

26 Figure 2.1) Typical life cycle of a thunderstorm: a) developing stage, b) mature stage and c) dissipating stage. The arrows indicate vertical air circulation (source: Ahrens 2012) OPERATIONAL GOES INFRARED RAINFALL ESTIMATION TECHNIQUE (NOAA/NESDIS) The National Environmental Satellite Data and Information Service (NESDIS) of NOAA has been operating an infrared rainfall estimating technique since the late 1980s in order to support meteorologists from the National Weather Service (NWS) in forecasting flash floods and extreme precipitation events. As the current U.S. weather radar network although being one of the most extensive networks in the world suffers from typical radar problems such as ground clutter, blocking by mountains, anomalous propagation of the radar signal in the atmosphere and differing relationships depending on the precipitation regime, the developed infrared rainfall estimation technique has proved to be very useful in monitoring cloud complexes producing a lot of precipitation. Another advantage of the technique is the fact that hurricanes and other storm complexes can be monitored a long time before they reach the area of coverage of the weather radar network. The operational rainfall estimation technique as described in Vicente et al. (1998), utilizes the 10.7 µm brightness temperatures from both the GOES-8 and GOES-9 geostationary satellites in order to estimate the precipitation rate. The conversion is done by applying a predetermined power law relationship between cloud top temperatures and rainfall rates determined from radar. For this power law, only clouds which were manually identified as being part of convective systems were taken into account. In order to separate the earlier mentioned effects of the cloud structure and life cycle from the general principle the colder the infrared cloud top temperature, the higher the precipitation rate, only the coldest infrared temperatures were used to match with the highest rainfall rate. In Figure 2.2 the average radar rainfall rates are displayed for a 1-K binned series of infrared cloud top temperatures, accompanied by the resulting regression fit given by: 26

27 ( ) (2.1) in which precipitation rate is expressed in mm hr -1 and cloud top temperature is expressed in K. Figure 2.2) Binned rainfall rates as a function of cloud top temperature accompanied by the exponential fit of equation (2.1) as used in the NOAA/NESDIS algorithm (source: Vicente et al. 1998) Since parameterizing the rainfall rate by a single regression curve is too much of a simplification of all the cloud physical processes, three correction factors are introduced. The first correction factor accounts for the notion that clouds in humid environments produce more rainfall than clouds in dry atmospheric conditions. This so-called moisture correction factor is given by the product of the average atmospheric relative humidity in the layer from the surface up to a level of 500 hpa expressed as a percentage, and the precipitable water in this same layer expressed in inches of water, both derived from numerical weather prediction models: (2.2) The value of typically ranges between 0 and 2, ranging from very dry to very moist conditions. The calculated rainfall rate based on the infrared cloud top temperature is multiplied by this moisture correction factor, with the exception of very low temperatures ( < 210 K) for which the correction is only applied as long as it decreases the precipitation rate. In order to incorporate the cloud structure and life cycle, two corrections have been proposed by Vicente et al. (1998), the cloud growth rate correction factor and the cloud top gradient correction factor. The cloud growth rate correction factor is determined by collocating pixels from two subsequent infrared images. In case the coldest pixels in the second image become colder, the convective system is supposed to be intensifying, which indicates heavy precipitation and therefore means that the rainfall rate estimated with the power law relationship is accurate. If the coldest infrared pixels are getting warmer in the second image, the cloud is in its dissipating stage and the estimated precipitation rate is set to zero. Since this correction factor relies on the availability of two consecutive infrared images which cannot be guaranteed, another correction factor is proposed. The cloud top gradient correction factor determines for a single infrared image the local temperature gradient within the clouds, by estimating the spatial second derivative of the temperature. In case an infrared pixel corresponds with a local minimum in temperature, it is assumed to be one of the overshooting tops and the estimated precipitation rate remains unchanged. For local maxima in temperature, the rainfall rate is set to zero, as well as for pixels which are neither a minimum nor a maximum. 27

28 In the case studies analysed by Vicente et al. (1998), both the cloud growth rate and the cloud top gradient correction factor are applied separately, and although visually (see also Figure 2.3) apparent differences between both methods exist, no statistical preference for either one of the correction factors could be determined. Figure 2.3) Precipitation rates estimated with the NOAA/NESDIS algorithm for Mid-East United States at 18 March :00 to 23:00 with different correction factors applied: a) precipitation rate determined from radar with rain gauge correction b) precipitation rate determined from cloud top temperature using equation (2.1) c) cloud top temperature and moisture correction factor d) cloud top temperature, moisture correction factor and cloud top gradient correction factor e) cloud top temperature, moisture correction factor and cloud growth rate correction factor (source: Vicente et al. (1998)) MULTISENSOR PRECIPITATION ESTIMATE (EUMETSAT) The Multisensor Precipitation Estimate (MPE) developed by EUMETSAT does not incorporate various correction factors in order to account for the effect of cloud structure and life cycle and is in that respect probably less accurate than the NOAA/NESDIS rainfall estimation technique. Yet the method is worth mentioning, since it uses a dynamical relationship between infrared cloud top temperature and precipitation rate (Heinemann et al. 2002). This dynamical relationship is established by using precipitation rates derived from the polar-orbiting Special Sensor Microwave / Imager (SSM/I) which are collocated with infrared cloud top temperatures for windows of five degrees latitude and longitude and of six to twelve hours in time. Based on the proposition that colder cloud top temperatures yield higher precipitation rates, histogram mapping is applied to the infrared brightness temperatures using the cumulative distribution of both the cloud top temperatures and the distribution of the precipitation rates. The highest precipitation rates are automatically assigned to the lowest IR temperatures, without checking whether these high precipitation rates originally were collocated with low cloud top temperatures. For convective systems this approach works quite well as Figure 2.4a shows in which the histogram mapping for a case over West Africa is displayed. However, 28

29 Figure 2.4b shows the histogram mapping for a case over the South Atlantic Ocean in which the precipitation was mainly produced by a warm front, yielding an precipitation rate estimate which will probably perform quite poorly. It can therefore be concluded that incorporating microwave rainfall rate information will improve the performance of a single regression curve, whose limitations arise due to seasonal and spatial variations in precipitation patterns. However, in situations which cannot be regarded as purely convective, using microwave rainfall rate information may decrease the accuracy of the algorithm. a) b) Figure 2.4) Two examples of histogram matching in the MPE algorithm. On the left a situation for a convective situation over West Africa, on the right a situation in which the precipitation was mainly caused by an overpassing warm front in the South Atlantic Ocean. (source: Heinemann et al. 2003) CONVECTIVE RAINFALL RATE (NWC SAF) The Convective Rainfall Rate (CRR) product of the Satellite Application Facility for Nowcasting (NWC SAF) uses a similar approach as the NOAA/NESDIS rainfall rate estimation technique: a regression curve is fitted between SEVIRI brightness temperatures and rainfall rates determined from weather radars in Spain, Hungary and the Baltic States (NWC SAF 2013b). However, the regression curve is extended with information from the 6.2 µm water vapour channel of SEVIRI and in daylight conditions visible reflectances. During nighttime, the calibration function is given by: (2.3a) in which is expressed in mm hr -1, which is a similar exponential function as introduced with the NOAA/NESDIS rainfall rate technique, only with a different coefficient and a different exponent for infrared cloud top temperature. The function is given by: [ { } ] (2.3b) [ ( ) ] (2.3c) The function makes sure that the precipitation rate increases in case overshooting tops are present. These overshooting tops are associated with a negative value for, as noted by Schmetz et al. (1997) and described in the previous chapter. Function results in a wider bellcurve around = 215 K, indicating a higher precipitation occurrence for cloud top temperature in this regime see also Figure

30 Figure 2.5) Fitted precipitation rate as a function of cloud top temperature and brightness temperature difference as introduced in equation (2.3). Accompanied with the moisture correction and cloud top gradient correction, this fit forms the basis of the Convective Rainfall Rate algorithm. Subsequently, a similar procedure is followed as with the NOAA/NESDIS technique. First the calculated precipitation rate is multiplied by the same moisture correction factor as described in Vicente et al., after which the cloud growth rate correction factor is applied, although slightly less strict than in the NOAA/NESDIS algorithm: instead of setting the precipitation rate to zero, the original value is multiplied by a factor of If the cloud growth rate correction factor cannot be applied due to a lack of two consecutive images for example the cloud top gradient correction factor is applied. This correction factor is also applied in a less stringent way: precipitation rate located at cloud top temperature maxima are multiplied by a factor 0.25, whereas pixels which are neither a minimum nor a maximum are scaled with a value of 0.5. In Figure 2.6 the result of this Convective Rainfall Rate is displayed for the situation of 2 February :00 UTC, which is the same timestamp as used for the displayed SEVIRI brightness temperatures and brightness temperature differences in Chapter 1. Figure 2.6) Precipitation rates estimated with the Convective Rainfall Rate algorithm for 2 February :00 UTC 30

31 2.3 RAINFALL RATE ESTIMATION WITH VISIBLE IMAGERY INTRODUCTION As already mentioned, estimating precipitation rates from infrared imagery seems to work only properly for deep convective cloud systems, since only for those clouds the cloud top temperature is a well-performing indicator of the intensity of precipitation. Although this information is very valuable for extreme weather events, the algorithms are insensitive to precipitation generated by, for example, weak frontal systems and lower, more stratiform clouds. In order to be able to deliver a complete image of precipitation, algorithms have been developed which rely on the backscattering of solar radiation in the visible and near-infrared spectrum. An example of such a model is the MSG Cloud Physical Properties (MSG CPP) product developed at the KNMI as part of the Satellite Application Facility for Climate Monitoring (CM SAF) MSG CLOUD PHYSICAL PROPERTIES (CM SAF) The primary goal of this product, as its name suggests, is to estimate properties for clouds, from which other quantities like precipitation and surface solar irradiance are inferred. The algorithm can be split into three parts: in the first step a distinction is made between cloud-free, cloud-contaminated and cloud-filled pixels. This step does not rely only on the visible reflectances in the 0.6 µm, 0.8 µm and 1.6 µm channels of SEVIRI, but also on additional infrared radiances from the 3.8 µm, 8.7 µm, 10.8 µm and 12 µm channels. Subsequently, as described by Roebeling et al. (2006), primary cloud properties cloud phase, cloud optical thickness, effective radius and cloud top temperature are derived using a predetermined look up table generated with the Double Adding KNMI (DAK) radiative transfer model. In the last step, secondary cloud properties like cloud water path, droplet number concentration, are derived and precipitation rate is calculated. The cloud water path is calculated as described by Roebeling et al. (2008) from the retrieved optical thickness and particle effective radius, using the following expression: (2.4a) in which is the density of liquid water or ice respectively. Subsequently, the precipitation rate in the MSG CPP model is parameterized as described by Roebeling and Holleman (2009) using the derived cloud water path and cloud top temperature using the following empirical relationship: [ ] ( ) (2.4b) in which is expressed in g m -2, the constant denotes the minimum rainfall rate equal to 0.05 mm hr -1 and represents the height of the rain column in kilometres. This height is calculated from the cloud top temperature of the pixel and the maximum in the image which is assumed to correspond with a thin water cloud: (2.4c) in which represents the wet adiabatic lapse rate of about 6.0 K km -1 and is the minimum rain column height set to 0.7 km. 31

32 In Figure 2.7 the parameterized precipitation rate is displayed for various height columns as a function of cloud water path. Figure 2.7) Parameterization of the precipitation rate as a function of the cloud water path for different values for the height column, in which blue lines correspond with small values for height column and a dark grey line with a high value of. After calculating the precipitation rate using the described procedure, the precipitation rate is limited to a maximum of 40 mm hr -1 and set to zero for cloud water paths smaller than 150 g m -2 and clouds with an effective radius smaller than 16 µm, in order to separate relatively optically thick, non-precipitating clouds with small particles from precipitating clouds that consist of relatively large particles. In Figure 2.8 the result of the MSG Cloud Physical Properties algorithm is displayed for the situation of 18 October :00 UTC, which is the same timestamp as used for the displayed SEVIRI reflectances in Chapter 1. Figure 2.8) Precipitation rates estimated with the MSG CPP algorithm for 18 October :00 UTC 2.4 PRECIPITATION PROBABILITY ESTIMATION WITH INFRARED IMAGERY INTRODUCTION Since the goal of this research is developing an algorithm that is able to estimate precipitation rates for all weather systems during the entire day, methods relying solely on infrared imagery are the most interesting for this study. One of these models is the Precipitating Clouds (PC) product developed by the NWC SAF, which will therefore be discussed more extensively than the previous methods. Although this method does not generate a precipitation rate as output, but a precipitation probability, 32

33 the underlying procedures before calculating the output variable are still relevant for this research. Since the Precipitating Clouds product is not the only algorithm in the collection of the NWC SAF and it requires a cloud mask and a cloud type for its input, first the algorithms in order to derive the cloud mask and the cloud type (both developed by the NWC SAF as well) will be described CLOUD MASK The cloud mask (CMa) routine is developed to separate cloud-filled areas from cloud-free pixels, but also to distinguish cloudy features from snow/ice, dust and volcanic ash. During nighttime, the algorithm cannot rely on visible reflectances, and therefore it only incorporates infrared channels of 3.9 µm, 8.7 µm, 10.8 µm and 12.0 µm and for sea conditions the sea surface temperature (SST). Furthermore, viewing angles of the satellite and the surface temperature derived from numerical weather prediction models are used as an input see also Figure 2.9. land/ice sea opaque clouds T T T surf SST clim thin cirrus T T T T T T low clouds T T desert: T T T T T T cloud edges local spatial texture cloud free T T opaque clouds cloud contaminated cloud filled Figure 2.9) Flowchart Cloud mask As described in NWC SAF (2013a), the first test is the only step that is different for sea and land pixels: for sea, the calculated sea surface temperature derived from and and climatological SST information is compared to a threshold. In case the SST is smaller than the threshold, the pixel is set to cloudy, but in case climatological SST information suggests the ocean could be frozen ( < K), the procedure for a land pixel is followed. This procedure relies on comparing the brightness temperature with a threshold determined by the surface temperature from NWP information, corrected with a function that incorporates the satellite zenith angle and atmospheric water vapour content. Subsequently, the second third, and fourth test are applied, in which the brightness temperature differences, and are compared to a threshold, allowing for the detection of thin cirrus clouds, as described in Chapter 1. These thresholds have been pre-determined by radiative transfer simulations for different satellite zenith angles, integrated atmospheric water vapour contents and land/sea conditions. The fifth, sixth and seventh test are designed to extract low water clouds and low clouds which are shadowed by higher clouds, using the fact that the water cloud emissivity at 3.9 µm is slightly lower than at 10.8 µm, yielding a positive value for channel difference. For ocean pixels, this test is repeated with the difference, which exhibits similar results, yet attains a slightly better contrast for ocean pixels. The seventh test is only applied for desert areas in Africa, which exhibits 33

34 almost the same channel difference for clear sky and low cloud conditions. By contrast, the emissivity at 8.7 µm for the desert surface is approximately equal to the emissivity at 3.9 µm, which is not the case for low clouds for which the emissivity at 8.7 µm is substantially higher. Lastly, a so-called local spatial texture test is performed in order to detect cloud edges, small broken clouds and so far undetected thin cirrus by calculating the spatial standard deviation in a block of pixels and, again, comparing this to a threshold, which is slightly higher for land pixels than for ocean pixels due to the larger general homogeneity of ocean surfaces. After this final test, pixels that have been classified as cloudy in any of these tests are separated into the categories cloud filled and cloud contaminated based on the channel difference in order to separate opaque clouds with an emissivity close to unity from fractional or semi-transparent clouds. Regardless of the flag of the pixel after these tests, separate tests for determining whether the pixel should be classified as desert dust or volcanic ash are performed these tests will not be discussed in this thesis CLOUD TYPE As precipitation rates for low clouds are generally substantially lower than for deep convective systems, and thin cirrus clouds rarely produce any precipitation, it is very valuable to know not only whether a pixel contains a cloud, but also what type of cloud that is. The cloud type (CT) algorithm is designed to further specify the cloud-filled pixels determined in the Cloud Mask procedure and classify these pixels into categories. The classification scheme at nighttime separates the cloud-filled pixels into low fractional, high semi-transparent and opaque clouds after which the clouds are reclassified into subcategories sea also Figure level of transparency test for opaque clouds T T 11 thick semitransparent altitude classification T T T T T T T T T T T opaque cloud 10 mod. thick opaque cloud 9 13 semi-transp. thin semitransparent low fractional T very low opaque low opaque medium opaque high opaque very high opaque opaque cloud Figure 2.10) Flowchart Cloud type As described in NWC SAF (2013a), Low fractional and high semi-transparent clouds are separated from opaque clouds by testing the channel differences and against a threshold. In case of a high semitransparent cloud and a channel difference smaller than the threshold, the cloud is either too transparent to detect properly, or too dense and therefore an opaque cloud. In case of a fractional cloud, the same reasoning applies: either the fraction of cloud cover is too small to be 34

35 detected, or the fraction is too close to unity and the cloud should therefore be classified as an opaque cloud. The distinction between high transparent and low fractional clouds is for obvious reasons made by looking at the cloud top temperature, but also by assessing the channel difference, which is usually larger for thin cirrus clouds than for low clouds. Based on the channel response of the 10.8 µm, a further distinction can be made for the semi-transparent clouds: the more transparent the cloud, the higher the brightness temperature will be due to the increased contribution of the surface signal. Therefore, semitransparent clouds are categorized in to three classes: thick, moderately thick and thin. During daytime, there is also a possibility to distinguish cirrus clouds overlaying low clouds using reflectances from visible channels. Since the focus of this research is mainly on nighttime conditions, this class is left out of consideration. In case a cloud-filled pixel is not classified as a low fractional cloud or as a semi-transparent cloud, it is categorized as an opaque cloud, for which five classes exist: very high, high, medium, low and very low. This classification relies only on testing brightness temperature against a number of thresholds which are determined by the temperature profile derived from NWP input. Generally speaking, the following pressure ranges for the clouds exist: Very high opaque clouds: High opaque clouds: Medium opaque clouds: Low opaque clouds: Very low opaque clouds: cloud top pressure lower than 300 hpa cloud top pressure between 300 hpa and 450 hpa cloud top pressure between 450 hpa and 650 hpa cloud top pressure between 650 hpa and 800 hpa cloud top pressure higher than 800 hpa Figure 2.11 shows the output of the Cloud Type classification algorithm for the same situation as used for the Convective Rainfall Rate product of Figure 2.7 and the brightness temperatures of Chapter 1. Figure 2.11) Cloud type determined with the Cloud Type algorithm for 2 February :00 UTC. The numbers in the legend of the image correspond with the cloud types as defined in Table PRECIPITATION PROBABILITY In order to train the Precipitation Clouds algorithm, French rain gauge data was used to determine the observed precipitation frequency for each cloud type (NWC SAF 2012). The results of this analysis are presented in Table 2.1, and as could be expected, frequencies are not equal for each cloud type. Furthermore, it should be noticed that either the cloud mask product does not work flawlessly or there is a mismatch between rain gauge and satellite pixels, since for pixels classified as cloud free, precipitation is observed, although very occasionally. 35

36 Table 2.1) Overview of observed precipitation frequency (precipitation rates higher than 0.1 mm hr -1 ) derived from French rain gauge data for each cloud type as specified in section (source: NWC SAF 2012) # Cloud type Precipitation frequency (%) cloud free 1 cloud free land cloud free sea snow/ice land 4.0 opaque clouds 4 very low opaque low opaque medium opaque high opaque very high opaque 43.6 semi-transparent clouds 9 thin semi-transparent moderately thick semi-transparent thick semi-transparent cirrus above lower cloud 5.1 fractional clouds 13 fractional 1.2 As is discussed in NWC SAF (2012), the probability of precipitation is estimated by calculating a precipitation index in which channel brightness temperatures and channel differences are combined linearly in such a way that it shows a high correlation with precipitation probability. For nighttime conditions, this precipitation index is given by: (2.5) in which is the surface temperature as derived from NWP input. The coefficients to have been optimized in order to maximize the correlation between precipitation index and precipitation occurrence and are given in Table 2.2. Table 2.2) Overview of coefficients to from equation (2.4) Using these coefficients, histograms can be built in which the precipitation probability is calculated as a function of the precipitation index. Since not every cloud type has the same probability to produce precipitation, four different histograms are built: Histogram 1: Histogram 2: Histogram 3: Histogram 4: medium opaque clouds high and very high opaque clouds thick semi-transparent clouds cirrus above lower clouds 36

37 This automatically implies that all other cloud types are regarded as non-precipitating. Using the generated histograms as a lookup table, the precipitation probability can be calculated as is displayed in Figure As a guideline, a value of 20 to 30 percent is recommended as the threshold between non-raining and raining clouds. Figure 2.12) Precipitation probability estimated with the Precipitating Clouds algorithm for 2 February :00 UTC. 37

38 38

39 RADIATIVE TRANSFER INTRODUCTION The use of infrared brightness temperature differences in RGB composites as discussed in Chapter 1 is mainly meant for interpreting cloud situations in a qualitative way, whereas the aim of this research is to find a precipitation estimate which is not only visually in correspondence with radar imagery, but also contains an accurate approximation of the rainfall rate. In the determination of the cloud mask and cloud type as described in Chapter 2, these channel differences have already been utilized using thresholds. These thresholds have been determined with radiative transfer simulations, which is the approach used in this chapter to decide which combinations can be used to determine whether a cloud is thick enough to precipitate and how the responses of these channel combinations differ for each cloud type. RADIATIVE TRANSFER SIMULATIONS INTRODUCTION NWC SAF s cloud mask and cloud type algorithms utilize the RTTOV radiative transfer model, which is an abbreviation for Radiative Transfer for the Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder. This fast radiative transfer model has been designed originally by Eyre (1991), but recently newer versions of the code have been developed by EUMETSAT s Numerical Weather Prediction Satellite Application Facility (NWP SAF). The newest version RTTOV v11.1 is able to perform rapid simulations of radiances for both infrared and microwave spaceborne radiation sensors. For these simulations the vertical atmospheric profile of the temperature and humidity is needed as an input, along with optional concentrations of other gases like ozone (O 3 ), carbon dioxide (CO 2 ) or nitrous oxide (N 2 O) and optional profiles of liquid water and ice content in order to be able to simulate clouds. In addition to these atmospheric profiles, surface conditions like surface temperature, relative humidity, land cover or simulated emissivity parameters can be supplied. For many radiation sensors on satellites coefficient files are available, in which the channel responses for those sensors are specified, making it quite easy to perform accurate simulations for a desired platform RTTOV SIMULATIONS Since the simulated radiances are highly dependent on the supplied atmospheric profiles and surface conditions, the performed simulations in this chapter are based on the mid-latitude International Standard Atmosphere (ISA). The ISA vertical profiles of temperature and relative humidity are displayed in Figure 3.1 as a function of pressure, for 54 different levels ranging from 1050 hpa up to hpa. The surface conditions are chosen to be equal to the atmospheric conditions at the lowest level of 1050 hpa. The other surface conditions are set to the default land conditions, which means no wind, satellite and solar zenith angles equal to zero and the simulations are performed with no solar radiation. 39

40 Figure 3.1) Vertical profiles of the temperature and humidity as defined by the International Standard Atmosphere (ISA) for mid-latitudes The two conditions for precipitation to fall are a sufficient amount of liquid cloud water and an average cloud droplet size large enough for the droplets to reach the ground. As the precipitation parameterization used by the MSG CPP product of the CM SAF suggests, the quantities linked to these conditions are the liquid water path and the cloud effective radius. In the following experiment these two quantities will be varied along with the cloud top pressure, in order to be able to determine which channels and channel differences are useful for which cloud types in the estimation of precipitation EXPERIMENTAL SETUP For this experiment, the atmospheric profiles are interpolated to 222 levels for which the pressure levels above 50 hpa stay as specified by the original 54 level standard atmosphere dataset and the pressure levels below 50 hpa down to 1050 hpa are separated by a 5 hpa spacing. Subsequently, the temperature and humidity profiles are interpolated using a piecewise cubic hermite interpolation polynomial in order to prevent that the interpolated values attain negative values which could occur with a normal spline interpolation. In the experiment, the cloud top pressure is varied from 900 hpa up to 100 hpa with a 10 hpa spacing. For each cloud top pressure, the liquid water path is varied assuming a constant liquid water content of 0.25 g m -3. In order to determine the cloud base pressure, the chosen pressure coordinates need to be converted to altitude coordinates. This is done by assuming hydrostatic equilibrium: (3.1a) in which in the air density both the water vapour content and the liquid water content are taken into account. With this expression the altitude of each level can be calculated, and subsequently the altitude of the cloud base can be estimated by iteratively solving the equation: (3.1b) After converting this cloud base altitude to a pressure level the liquid water content at the pressure levels between cloud base and cloud top is set to the constant value of 0.25 g m -3 except for the lowest level for which the liquid water content is set to a value in such a way that a linear interpolation of the liquid water path based on the specified pressure levels will return the required value for. In this simulation the liquid water path is varied logarithmically from 0.1 g m -2 to 5000 g m -2 with 100 steps. 40

41 The effective radius is varied for each cloud top pressure and liquid water path using the six pre-determined cloud types which are specified in Table 3.1. Table 3.1) Overview of the six built-in cloud types in RTTOV v11. The specified effective radius and standard deviation have been derived from Matricardi (2006), the effective radius for Cirrus clouds is calculated within the RTTOV v11 model using the Baran scheme. # Name (µm) (µm) Type 1 Stratus continental water 2 Stratus maritime water 3 Cumulus continental clean water 4 Cumulus continental polluted water 5 Cumulus maritime water 6 Cirrus ice Since the used atmospheric profiles for temperature and humidity do not allow any cloud formation, simply adding a cloud to these profiles by adjusting the liquid water content, does not produce a very representative weather situation. However, since the most important contribution to the infrared channel response is from the cloud itself and not from the altered surrounding atmosphere, it is assumed that the simulation results are sufficiently indicative of the influence of the cloud effective radius and liquid water path on the observed brightness temperatures SIMULATED CHANNEL RESPONSES: LIQUID WATER PATH In Figure 3.2 the simulated brightness temperatures are displayed for each infrared channel of the SEVIRI instrument as a function of the liquid water path for the first cloud type Stratus continental. The colour coding in the diagrams corresponds with the brightness temperatures for different cloud top pressure with blue representing a pressure of 100 hpa and red a cloud top pressure of 900 hpa. 41

42 Figure 3.2) Simulated SEVIRI brightness temperatures as function of the liquid water path. The different lines denote the responses measured for different cloud top pressures, in which the red lines indicate a high cloud top pressure associated with low clouds and the blue lines correspond with a low cloud top pressure. From the diagrams in Figure 3.2 it can be concluded that the brightness temperatures are highly sensitive to increasing liquid water path, in case the liquid water path is smaller than approximately 100 g m -2 and the cloud top pressure is sufficiently high. For a liquid water path higher than 100 g m -2, the channel response rapidly saturates and becomes insensitive to extra cloud water SIMULATED CHANNEL DIFFERENCES: LIQUID WATER PATH In Figure 3.3 a selection of brightness temperature differences is displayed in a similar way as in Figure

43 Figure 3.3) Simulated SEVIRI brightness temperature differences as function of the liquid water path. As in Figure 3.2, the different lines correspond with the cloud top pressure with the same definition: the red lines indicate a high cloud top pressure and the blue lines correspond with a low cloud top pressure. The diagrams from Figure 3.3 show a similar image regarding the saturation of the brightness temperature signal. However, in the diagrams the characteristics of the channel differences as described in Chapter 1 become apparent. Channel difference IR3.9 IR10.8 shows the acclaimed sensitivity to optical thickness, especially for high semi-transparent clouds. Comparable behaviour can be found for difference IR12.0 IR10.8, with the extra advantage that the brightness temperature difference for opaque clouds is almost independent from the cloud top pressure, an effect which complicates the behaviour of channel difference IR8.7 IR10.8 and to a lesser extent IR3.9 IR10.8. It is also eminent that channel combinations WV6.2 IR10.8 and IR13.4 IR10.8 can be used quite well for estimations of cloud top height as long as the clouds are of sufficient optical thickness SIMULATED CHANNEL DIFFERENCES: EFFECTIVE RADIUS In Figure 3.4 the channel differences from Figure 3.3 are displayed as a function of effective radius for a cloud top pressure of 100 hpa. Using equation (2.4a), the horizontal axis in these diagrams is changed from liquid water path to optical thickness. 43

44 Figure 3.4) Simulated SEVIRI brightness temperature differences as function of the liquid water path for a cloud top pressure of 100 hpa. In this diagram the different lines correspond with different settings for the cloud droplet size distribution The aforementioned sensitivity to cloud droplet effective radius in channel IR3.9 as described in Chapter 1, is clearly visible in the images of Figure 3.4. For water clouds it can be stated that the higher the effective radius, the more prominent the sensitivity to optically thin clouds is for channel difference IR3.9 IR 10.8, yet this sensitivity is lost as soon as the cloud is sufficiently opaque. For channel difference IR12.0 IR 10.8, it is the other way around: this brightness temperature differences attains a maximum sensitivity for very small particle sizes. The diagrams in Figure 3.5 show the channel differences for the different cloud droplet sizes in case the cloud top pressure of 900 hpa in used. From these diagrams the acclaimed sensitivity of channel difference IR3.9 IR10.8 can be inferred regarding fog detection. The sensitivity to effective radius makes sure that for optically thin clouds, there is a clear distinction between clouds with very small droplets considered fog and clouds with larger average droplet sizes. It should however be noted that the brightness temperature differences for a cloud top pressure of 900 hpa are an order of magnitude smaller than for equal to 100 hpa. Combined with the instrumental noise of the SEVIRI instrument, it should be concluded that the sensitivity to effective radius is limited for clouds at lower altitudes. Figure 3.5) Simulated SEVIRI brightness temperature differences as function of the liquid water path for a cloud top pressure of 900 hpa. In this diagram the different lines correspond with different settings for the cloud droplet size distribution. 44

45 The results of the simulations performed in this chapter indicate that with only using the radiative transfer properties of the infrared imagery, it is impossible to determine the precipitation rate as a function of the liquid water path or optical thickness. The infrared signal quickly saturates for values of larger than approximately 100 g m -2, which coincides with the lower threshold value for the parameterization of the precipitation rate as function of the liquid water path as used in the MSG Cloud Physical Properties model. Yet, utilizing the radiative transfer characteristics of each channel and more importantly, the differences in absorption between the channels, it is possible to determine the clouds that are optically thin and therefore not producing any precipitation, and can therefore serve as an exclusion mechanism. 45

46 46

47 METHOD INTRODUCTION The most promising, currently available technique to estimate precipitation from infrared satellite imagery is NWC SAF s Precipitating Clouds algorithm. Although it only produces a precipitation probability, the algorithm is able to produce an estimate for almost every cloud type, unlike the Convective Rainfall Rate product from the NWC SAF for example. Furthermore, the setup of the algorithm based on determining a precipitation index that is higher in case precipitation is more likely, suggests that is should be fairly easy to change the last conversion step from precipitation index to precipitation probability to a conversion from index to precipitation rate. TRAINING AND VERIFICATION DATASET In order to quantify the performance of the developed model in this chapter a dataset has been created consisting of the SEVIRI satellite imagery of the entire year 2013 for which only the images obtained at full hours (e.g. 00:00 UTC, 01:00 UTC) are taken into account. With this dataset any seasonal effects influencing the verification can be eliminated, and since the timescale of developing storms and passing weather fronts is in the order of hours, the chosen update frequency provides enough varying weather situations while the size of the dataset is kept limited. The precipitation data calculated by the developed model will be compared to Dutch weather radar data. The Dutch weather radar network consists of two identical C-band weather radars located at De Bilt and Den Helder and is operated by the KNMI. The measured reflectivities of these two radars are filtered for ground clutter after which the two images are combined in a radar composite mapped to a polar projection with a spatial resolution of 1 kilometre. In order to compare the radar observations to the SEVIRI output, the measured radar reflectivities are averaged and interpolated to the SEVIRI grid, which has a resolution of approximately 5.5 kilometres in the latitudinal direction and 3.5 kilometres in the longitudinal direction in the Netherlands. Since it takes approximately ten minutes for the SEVIRI scanning to reach the area covered by the Dutch radar network, the radar imagery of ten minutes past a full hour (e.g. 00:10 UTC, 01:10 UTC) is used for verification. Selecting only the SEVIRI pixels which coincide with the outer edges of the radar coverage area yields a domain of 206x114 pixels, of which 78% is covered by radar. a) b) Figure 4.1) a) Area of radar coverage by the Dutch weather radar network in the SEVIRI projection b) Time offset between start time of SEVRI scan and time that the scanning passes the Dutch domain 47

48 Since the precipitation index in the Precipitating Clouds algorithm presented in Chapter 3 utilizes the surface temperature retrieved from numerical weather prediction input, the dataset is extended with relevant NWP fields. The source of this data is ECMWF s ERA Interim reanalysis dataset sampled at a grid size of with a time step of 3 hours. The obtained NWP input is again interpolated to the SEVIRI grid. As the primary goal of the research is aimed at developing an algorithm for nighttime conditions, only the pixels are taken into account for which the solar zenith angle is larger than 90 degrees. This test is performed by calculating the solar zenith angle according to the PSA algorithm described in Blanco- Muriel et al. (2001). The dataset is split into two individual datasets of approximately the same size: a training dataset and a verification dataset. The distinction between the two is made by the week number: odd week numbers belong to the training dataset, whereas even week numbers are incorporated in the verification dataset. Since the average life time of a mid-latitude cyclone is in the order of a few days, the verification and training dataset will contain different weather systems, yet the period of a week for separation makes sure that seasonal effects and incidental dry or wet months are spread over both datasets. 4.3 CORRELATION STUDY CORRELATION PRECIPITATION INDEX AND PRECIPITATION RATE First step in the development of an improved algorithm is to determine whether the proposed precipitation index is a good measure to estimate the precipitation rate. Assuming the precipitation index to be defined as in the Precipitating Clouds algorithm: (4.1) the correlation can be calculated between this precipitation index and the precipitation rate. With the verification dataset described in the previous section, it is possible to correlate the calculated precipitation index with the precipitation rate calculated from the averaged and interpolated radar reflectivity. In Table 4.1 the correlation coefficient is displayed for the precipitation index and the precipitation rate for each cloud type. This calculation is done by averaging the radar precipitation rate in 100 bins of precipitation index ranging from 0 to 100 and subsequently computing the weighted correlation coefficient. The fourth column in Table 4.1 represents the correlation coefficient if the bin-averaged precipitation frequency is compared to the precipitation index, whereas the fifth column denotes the correlation coefficient with the precipitation rate if only cases in which the precipitation rate is larger than zero are taken into account. 48

49 Table 4.1) Overview of the correlation coefficients for each cloud type individually and all cloud types combined. The first column represents the frequency of cloud type occurrence, the second column the frequency of precipitation for that particular cloud type and the third column corresponds with the bin-averaged correlation between precipitation index and precipitation rate. The fourth column shows the correlation coefficient with precipitation index and precipitation occurrence and the last column displays the correlation between precipitation rate and precipitation index for precipitating cases only. The coloured bars in the first two columns correspond with the observed numeric values of the cloud type frequency and precipitation frequency, in the last three columns the length of the bars correspond with the absolute value of the correlation coefficient, negative values have a diagonal pattern. # Name (%) (%) 4 very low opaque low opaque medium opaque high opaque very high opaque thin semi-transparent mod. thick semi-transparent thick semi-transparent fractional all cloud types Comparing the precipitation frequencies from Table 4.1 to the precipitation frequencies of Table 2.1 which are the frequencies the Precipitation Clouds is tuned with, it becomes apparent that the precipitation frequencies measured by the Dutch weather radar network are slightly lower than observed with the French rain gauge network, although the trend that higher, more opaque clouds precipitate more often is still visible. It is also clear that the algorithm has been optimized for all cloud types together, as the overall correlation scores are a lot larger than the correlation coefficients for each individual cloud type. The fact that the correlation between precipitation index and precipitation rate is negative for each individual cloud type and positive overall, can be explained by the fact that the cloud types do not have the same range for. The overall correlation coefficient merely reflects the general trend of clouds with lower cloud temperatures producing more precipitation. In order to investigate the results further, it is helpful to look at the bin-averaged precipitation rate as function of the precipitation index to see whether the low correlation scores arise from either no dependence between and or a non-linear relationship which is not wellrepresented by the correlation coefficient statistics. Figure 4.2 shows the bin-averaged precipitation rate as function of the precipitation index along with the weight of each bin. 49

50 Figure 4.2) Bin-averaged precipitation rate as function of the precipitation index for each cloud type as defined in Table 4.1 in blue accompanied by the linear regression line in dotted blue. The red areas correspond with the relative number of elements in each bin and are therefore a measure for the weight of the bin in the determination of the correlation coefficient. Based on the both visual inspection of the data and the correlation coefficient scores listed in Table 4.1, one can state that the chosen precipitation index from the Precipitation Clouds algorithm performs quite well if no distinction is made between the cloud types, for which the correlation coefficients are all fairly high. Furthermore, it is also obvious that the precipitation index is best suited for determining the precipitation probability, as the correlations for are all larger than the correlation with precipitation rate. It is also apparent that this precipitation index is not able to represent the variation in precipitation rate properly, since the correlation coefficients are negative or close to zero for all the cloud types individually CORRELATION INFRARED BRIGHTNESS TEMPERATURES AND PRECIPITATION RATE The problems with the precipitation index discovered in the previous section could originate from the fact that the weighting coefficients to might not be an optimal fit to the used weather radar data. Another possibility is that other infrared channels and channel combinations should be taken into account in the proposed linear combination of equation (4.1). In order to investigate which channels are most indicative of precipitation the correlation study is extended with an analysis for each infrared brightness temperature and the brightness temperature differences introduced in section 1.4. In Table 4.2 and Table 4.3 the results of this correlation study are presented in a similar way as in Table 4.1, yet in Table 4.2 positive correlation coefficients are highlighted with a diagonal pattern. This is due to the fact that negative correlation coefficients are expected since a lower brightness temperature usually indicates more precipitation, as opposed to the correlation with the precipitation index as displayed in Table

51 Table 4.2) Correlation between brightness temperatures and bin-averaged precipitation rates. The coefficients are displayed for each cloud type individually and all cloud types combined. Similar to the convention in Table 4.1, the coloured bars correspond with the absolute value of the correlation coefficients, however, in this table positive values have a diagonal pattern. # Name IR3.9 WV6.2 WV7.3 IR8.7 IR9.7 IR10.8 IR12.0 IR very low opaque low opaque medium opaque high opaque very high opaque thin Ci mod. thick Ci thick Ci fractional all cloud types Table 4.3) Correlation between selected brightness temperature differences and bin-averaged precipitation rates. The coefficients are displayed for each cloud type individually and all cloud types combined. In this table the length of the coloured bars correspond with the absolute value of the correlation coefficients, with negative values highlighted by a diagonal pattern. # Name IR3.9 - IR8.7 - IR12.0- WV6.2- IR9.7 - IR13.4- WV6.2- IR10.8 IR10.8 IR10.8 IR10.8 IR10.8 IR10.8 WV7.3 4 very low opaque low opaque medium opaque high opaque very high opaque thin Ci mod. thick Ci thick Ci fractional all cloud types From Table 4.2 and Table 4.3 it can be inferred why the precipitation index as defined by the Precipitating Clouds algorithm does not produce a proper estimate of the precipitation rate for every cloud type. As opposed to what the coefficient of for the surface temperature suggests, a positive correlation coefficient between and precipitation rate is found for each cloud type. Furthermore, the correlation between channel difference IR12.0 IR10.8 is only positive for high clouds, which could explain why the precipitation index behaves poorly for very low and low clouds. The correlation coefficients from Table 4.2 indicate that not every channel is useful as an indicator of the cloud top temperature and naturally, this has to do with the characteristics of the SEVIRI infrared channels. Generally speaking brightness temperature IR10.8 proves to be the best indicator of cloud top temperature, along with channel IR12.0. Furthermore, it can be stated that mainly for high opaque 51

52 clouds the cloud top temperature is an important indicator of the average rain rate. These clouds are primarily associated with deep convection and therefore a correlation with cloud top temperature can be expected. For lower clouds, precipitation is more often caused by frontal movements, associated with weaker convection and therefore less dependent on the cloud top temperature. The results for brightness temperature difference IR3.9 IR10.8 from Table 4.3 show why a precipitation index tuned for each cloud type could be a great improvement. Whereas this channel difference is sensitive to optical thickness for high clouds and therefore of critical use in thin cirrus detection, the sign of the correlation coefficient is different for low clouds, where the channel difference can be used to distinguish non-precipitating fog from normal low clouds. Since it is not wise to choose the channels and channel differences for each cloud type just by looking at the correlation coefficient, in Figure 4.3 and Figure 4.4 the bin-averaged precipitation rate is displayed as function of the individual channels and selected channel differences. Figure 4.3) Bin-averaged precipitation rate as function of the SEVIRI channels for each cloud type. Please note the logarithmic scale for the average precipitation rate. 52

53 Figure 4.4) Bin-averaged precipitation rate as function of selected SEVIRI brightness temperature differences and the surface temperature for each cloud type. Please note the logarithmic scale for the 4.4 average precipitation rate. CHANNEL SELECTION PRECIPITATION INDEX BASIC CHANNEL SELECTION From the correlation coefficients displayed in Table 4.2 and Table 4.3 and associated visual inspection of Figure 4.3 and Figure 4.4 it can be deduced that the model can be greatly improved by designing a cloud-type dependent precipitation index highest correlation with the precipitation rate in which the channels and channel differences with the are taken into account. Using this approach, it is also possible to incorporate precipitation from very low, low opaque and fractional low clouds, which are poorly represented by the precipitation index used in the Precipitation Clouds algorithm. Precipitation from thin and moderately thick semi-transparent clouds is neglected in this algorithm, since the associated observed precipitation frequencies are small and the observed precipitation from these cloud types can probably be attributed to underlying low clouds. As it is very hard to extract information from multi-layer clouds during nighttime it is assumed that these cloud types are too thin for precipitation. From here on, the newly developed model will be referred to as the Nighttime Infrared Precipitation Estimation (NIPE). The general assumptions for each cloud type are that the precipitation rate is high in case of: High optical thickness or liquid water path High cloud top pressure Low cloud top temperature 53

54 Using the classification of Table 4.4 in which each infrared brightness temperature or brightness temperature difference is listed with the quantity it is most sensitive to, the channels and differences with the highest correlation are selected for each of the three aforementioned fields. Table 4.4) SEVIRI channels and channel differences categorized with respect to the quantity with most sensitivity. # Channel Classification # Channel difference Classification 4 IR IR 3.9 IR WV IR 8.7 IR WV IR 12.0 IR IR WV 6.2 IR IR IR 9.7 IR IR IR 13.4 IR IR WV 6.2 WV 7.3 Airmass 11 IR 13.4 Based on the results of Table 4.2 and Table 4.3 and with the classification displayed in Table 4.4, a selection of channels and channel differences for the adapted precipitation index of the Nighttime Infrared Precipitation Estimation model has been made for each cloud type, which is displayed in Table 4.5. Table 4.5) Selection of SEVIRI channels and channel differences for each cloud type # Name 4 very low opaque IR 10.8 IR 13.4 IR 10.8 IR 8.7 IR 10.8 IR 3.9 IR low opaque IR 10.8 IR 9.7 IR 10.8 IR 8.7 IR 10.8 IR 3.9 IR medium opaque IR 10.8 IR 9.7 IR 10.8 IR 8.7 IR high opaque IR 10.8 WV 6.2 IR 10.8 IR 12.0 IR 10.8 IR 3.9 IR very high opaque IR 10.8 WV 6.2 IR 10.8 IR 12.0 IR 10.8 IR 3.9 IR thick semi-transparent IR 10.8 WV 6.2 IR 10.8 IR 9.7 IR 10.8 IR 3.9 IR fractional IR 10.8 IR 13.4 IR 10.8 IR 9.7 IR 10.8 IR 12.0 IR 10.8 Channel IR10.8 is chosen to be representing the cloud top temperature for each cloud type, since this channel generally has the largest correlation with precipitation rate and is widely used in existing methods. For the cloud top pressure, the brightness temperature difference IR13.4 IR10.8 is chosen for lowest clouds very low opaque and fractional since this difference has the largest range for these cloud types and a sufficiently high correlation. For mid-level clouds, the brightness temperature difference IR9.7 IR 10.8 is chosen, as the increase in precipitation rate for this channel difference is larger compared to the other candidates. For high-level clouds channel 54

55 combination WV 6.2 IR 10.8 is chosen, due to the acclaimed sensitivity to overshooting tops which is primarily important for these cloud types. Regarding the sensitivity to liquid water path and optical thickness, channel difference IR3.9 IR10.8 is chosen for each cloud type for which this channel combination is relevant: for low-level clouds this channel combination can be used to separate non-precipitating fog from precipitating low clouds, for high-level semi-transparent clouds this channel difference is a good detector of ice and limited optical thickness and for high-level opaque clouds channel difference IR3.9 IR10.8 generally produces noisy results due to the associated low temperatures in channel IR3.9. The addition of channel differences IR12.0 IR10.8 and IR8.7 IR10.8 is done by looking at the diagrams of Figure 4.4 from which it can be deduced that the precipitation rate increases more monotonously for channel combination IR12.0 IR10.8 for high-level clouds, and increases more monotonously for channel combination IR8.7 IR10.8 for low-level clouds ADDITION OF CORRECTIONS In addition to the channel combinations displayed in Table 4.5, a number of other quantities are taken into account in the Nighttime Infrared Precipitation Estimation model. First of all, the surface temperature is added to the linear combination for each cloud type, although the associated correlation might not be as high for each cloud type. The premise behind the addition of the parameter is the fact that a high surface temperature causes increased convection, which generally indicates higher precipitation rates. The water vapour channel difference WV6.2 WV7.3 is higher in case the upper troposphere is warmer, contains more moisture and therefore could be associated with higher chances of precipitation. This channel combination is added for each cloud type, which is supported by the high correlation coefficients as found in Table 4.3 except for clouds classified as fractional. Furthermore, inspired by the moisture correction used by NOAA/NESDIS algorithm and the Convective Rainfall Rate product, the product of the vertically averaged relative humidity and the precipitable water in the layer between surface and 500 hpa is used in order to assign higher precipitation rates to weather situations in which the atmosphere contains more moisture. For high and very high opaque clouds, which can be associated with convective situations in most cases, the cloud top temperature gradient is also taken into account. Whereas the aforementioned NOAA/NESDIS and CRR algorithms apply this correction after determining the precipitation rate, by multiplying the pixels which are not a temperature minimum with a certain factor or setting these to zero, a slightly different approach is used in this model. In order to be able to rate the extremity of the minimum, the cloud top temperature gradient correction factor is calculated in the following way: { ( ) (4.2a) in which denotes the second derivative of the cloud top temperature at pixel and denotes the Hessian defined by: (4.2b) 55

56 The second derivatives, and are all determined by taking finite differences of the IR10.8 field which is a good approximation of the actual cloud top temperature. Since a pixel can be classified with the aid of and as: a maximum, if a minimum, if a saddle point, if and and equation (4.2a) gives a positive value for in case pixel is a minimum, which increases if the minimum gets more prominent. In order to let the value of range between approximately -3 and +3, the logarithm of the Hessian with base 10 is used. 4.5 NOISE FILTERING INSTRUMENTAL NOISE As already mentioned in section 1.5.3, SEVIRI infrared imagery suffers from noise as a result of the limited resolution in the spectral imager. For most of the individual channels channel IR3.9 is an exception this effect is not of large significance, as the noise levels generally do not exceed a value of 0.5 K. However, some brightness temperature differences selected in the precipitation index exhibit only a very limited range in values. As an example of this limited range, the brightness temperature difference IR12.0 IR10.8 is displayed in Figure 4.5. The values of this particular brightness temperature difference generally range between -4 and 0 K, which causes the noise in both channels to be of quite large significance. If the raw difference image of Figure 4.5 is used, the associated noise will propagate into the precipitation index and subsequently into the mapped precipitation rates, resulting in noisy images and neighbouring pixels which are alternately dry and wet in cases where the radar images shows a enclosed area. Figure 4.5) Brightness temperature difference IR12.0 IR10.8 for 8 September 05:00 UTC over the Netherlands WIENER FILTER A widely-used approach to decrease noise in digital images is the application of a so-called Wiener filter. This adaptive filter uses the statistics of the local environment of a certain pixel to determine the amount of smoothing necessary. For pixels with a large local variance compared to the estimated noise level, smoothing is negligible, whereas for pixels with a small local variance, intensified smoothing is applied. Unlike other smoothing filters like a local-plane fit, sharp gradients in the unfiltered images are preserved using the method. With being the average of the local 56

57 environment, the local variance and the estimated variance of the noise at the pixel, the filtered valued of brightness temperature is given by: { (4.3) ESTIMATION OF NOISE Crucial in the application of the Wiener filter is a good estimate of the instrumental noise. The noise is estimated assuming the noise in the 10-bit analog-to-digital converter used to measure the spectral radiance is equal to 1 bit. With the linear calibration function for each channel converting the 10-bit value to radiance, the noise in the brightness temperature can be estimated using: ( ( ) ) (4.4) ( ) in which is the propagated noise in the radiance due to the calibration function given by the values of Table 4.6, is the measured radiance at pixel and constants,,, and are given by the values in Table 1.2. Table 4.6) Overview of the calibration slope of the conversion from the 10-bit digital photon count to radiance. With the assumed noise level of 1 bit, this calibration slope equals the propagated noise in the radiance in mw m -2 sr -1 (cm -1 ) -1. # Channel 4 IR WV WV IR IR IR IR IR Using equation (4.4), the noise for each pixel and channel can be calculated given the brightness temperature. These values can also be used for the estimation of the noise in brightness temperature difference images, as the noise for IR12.0 IR10.8 for example is given by: (4.5) With the estimated noise, the Wiener filter can be applied for each channel and channel combination. In Figure 4.6 the brightness temperature difference IR12.0 IR10.8 is displayed for the same epoch as in Figure 4.5, with the Wiener filter applied. The local environment used to determine the local average and variance is set to a block of 5x5 pixels around each pixel. With these settings, the 57

58 variations in brightness temperature difference in the centre of the image are much smoother, whereas the sharp edges are still preserved. Figure 4.6) Brightness temperature difference IR12.0 IR10.8 for 8 September 05:00 UTC over the Netherlands for which the Wiener filter as described in section is applied. 4.6 OPTIMIZATION OF COEFFICIENTS OPTIMIZATION INITIALIZATION Since the definition of the precipitation index in the Nighttime Infrared Precipitation Estimation model has been altered from the definition used in the Precipitation Clouds product, the coefficients have to be re-optimized. As for every optimization, a proper first guess is needed in order to let the optimization process converge. A convenient and intuitive choice would be to choose the correlation coefficients listed in Table 4.2 as a first guess. Since the variations in the channels and channel differences are different for each parameter, the chosen channels and differences are scaled with using the difference between the 1 st and 99 th percentile of the parameter in the training dataset: (4.6) HISTOGRAM MAPPING OF PRECIPITATION RATE Using the first guess of the coefficients equal to the associated correlation coefficient, the new precipitation index can be calculated. Subsequently, the calculated indices are converted to precipitation rates using histogram mapping. In order to do so, the cumulative distribution function (CDF) of precipitation rates for each cloud type is determined. Successively, the cumulative distribution function of the precipitation index is calculated, after which for each pixel the value of the CDF of the index is determined. Lastly, the determined CDF value is matched with a precipitation rate with the same value for the CDF. This way, the highest values of the precipitation index will be converted to the highest values for the precipitation rate. Obviously, this process only works if the relationship between the precipitation rate and the index is a monotonous increasing function, which is what the optimization of coefficients should ensure OPTIMIZATION FUNCTION In order to determine what the most optimal choice of coefficients is, an optimization function should be designed. A vast range of options are possible, the most well-known would be a simple least-squares fit. However, this method does not appear very useful for this problem as the difference 58

59 Model Model between an estimated rain rate of 0.1 mm hr -1 and an observed rain rate of 0 mm hr -1 is valued as much as the difference between an estimated rain rate of 9.9 mm hr -1 and an observed rain rate of 10 mm hr -1. Since an important goal of this research is to be able to represent the precipitating area accurately, another optimization method is chosen. This function is based on the confusion matrix which can be used to determine how well the estimation of precipitating and non-precipitating pixels works. The confusion matrix of the estimated and observed precipitation rate for high opaque clouds with the original estimate is given by Table 4.6. Table 4.6) Confusion matrix of the initial estimate for high opaque clouds Observations Observations wet dry all wet dry all wet wet dry dry all all Since precipitation can be regarded to be a low frequency event, which is endorsed by the on average five percent precipitation probability, a convenient way to test the performance of the model is the Threat Score or Critical Success Index ( ) which is defined as: (4.7) This scores ranges from zero for very poor estimates in which there is no overlap between the estimated and observed precipitation area, up to one for a perfect forecast. Optimizing the Critical Success Index means that the model output visually looks most like the radar observations. However, one could comment that in this way the calculated precipitation rates are not taken into account in the optimization. As was concluded from the RTTOV simulations presented in Chapter 3, infrared imagery is not very sensitive to variations in optical thickness once the cloud is opaque enough, which is for almost all precipitating clouds the case. Therefore, the precipitation index is designed in such a way that in case the cloud is classified to be precipitating, the characteristics of the histogram mapping coincide with the principles of the NOAA/NESDIS and Convective Rainfall Rate products. This is the main reason that the cloud top temperature is incorporated in the precipitation index for each cloud type OPTIMIZATION PROCESS The optimization is done by using the built-in minimizing function fminsearch of MatLab. This function is an implementation of the Nelder-Mead simplex algorithm, which uses a simplex of points for vector of parameters. This method is particularly useful for solving non-linear problems which require certain robustness. Since the chosen optimization function is non-linear and discontinuous, the properties of this minimizer are profitable. In order to attain the highest during the optimization process, the function is minimized. The chosen tolerances for the minimizing function and changes in the coefficients are set to 10-3, which ensures that after the generally quick convergence the optimizer does not spend most of its time determining the position of the minimum with high, but unnecessary precision. 59

60 correlation coefficients CDF R coefficients a i calculate PI for each pixel calculate CDF value of PI for each pixel convert CDF values to R calculate CSI for entire training dataset adapt coefficients a i Figure 4.6) Flowchart of optimization process OPTIMIZATION RESULTS The results of the optimization process as described above are displayed in Table 4.7, accompanied by the relative change in the coefficients with respect to the initial value. In addition, a leave-out analysis has been performed in which for each coefficient, the other coefficients are re-optimized while maintaining the coefficient at zero. Lastly, the initial and after optimization are displayed in order to show the influence of the optimization process. The sensitivity mentioned in the tables is an indicator of the certainty in the quantitative value of the final coefficients. It is defined as the decrease in with respect to the optimized value of if the change in the coefficient with respect to the initial coefficient is increased by another 1 percent. This decrease in is calculated for each coefficient and averaged, resulting in the sensitivity value. Table 4.7) Overview of the optimized coefficients of the precipitation index for each cloud type. The third column corresponds with the initial value equal to the correlation coefficient, the fourth column displays the optimized coefficients and the fifth column the relative change with respect to the initial value. The last column shows the increase in 4) VERY LOW OPAQUE in case the quantity is left out of the optimization. Initial : 0.49% Final : 0.98% Sensitivity: % Quantity Type Initial coefficient Optimized coefficient Relative change Leave-out analysis IR % -69.1% IR13.4 IR % -14.2% IR8.7 IR % +9.5% IR3.9 IR % +3.4% WV6.2 WV7.3 Airmass % +0.9% % -17.2% % -29.7% 60

61 5) LOW OPAQUE Initial : 3.46% Final : 7.98% Sensitivity: 0.03% Quantity Type Initial Optimized Relative Leave-out coefficient coefficient change analysis IR % +0.7% IR9.7 IR % +1.1% IR8.7 IR % -13.4% IR3.9 IR % -4.1% WV6.2 WV7.3 Airmass % +0.9% % -3.8% % -38.7% 6) MEDIUM OPAQUE Initial : 15.26% Final : 19.67% Sensitivity: 0.007% Quantity Type Initial Optimized Relative Leave-out coefficient coefficient change analysis IR % +0.4% IR9.7 IR % -2.6% IR8.7 IR % +0.2% WV6.2 WV7.3 Airmass % -5.2% % -8.0% % -15.9% 0 7) HIGH OPAQUE Initial : 29.07% Final : 34.32% Sensitivity: 0.01% Quantity Type Initial Optimized Relative Leave-out coefficient coefficient change analysis IR % +0.7% WV6.2 IR % +1.6%. IR12.0 IR % +1.6% IR3.9 IR % -4.3% WV6.2 WV7.3 Airmass % +1.1% % +3.5% % -14.4% % 0-1.6% 61

62 8) VERY HIGH OPAQUE Initial : 34.25% Final : 43.58% Sensitivity: 0.01% Quantity Type Initial Optimized Relative Leave-out coefficient coefficient change analysis IR % +0.2% WV6.2 IR % -1.3%. IR12.0 IR % -1.4% IR3.9 IR % -9.3% WV6.2 WV7.3 Airmass % -1.9% % -0.5% % -3.0% % 0-9.8% 11) THICK SEMI-TRANSPARENT Initial : 17.93% Final : 21.81% Sensitivity: 0.14% Quantity Type Initial Optimized Relative Leave-out coefficient coefficient change analysis IR % +2.4% 0 WV6.2 IR % -0.4%.. IR9.7 IR % +3.6% 0 IR3.9 IR % -6.2% 3 WV6.2 WV7.3 Airmass % +0.2% % -1.0% % -18.4% 13) FRACTIONAL Initial : 2.38% Final : 3.52% Sensitivity: 0.004% Initial Optimized Relative Leave-out Quantity Type coefficient coefficient change analysis IR % -5.8% 0 IR13.4 IR % -25.9%.. IR9.7 IR % -5.9% 0 IR12.0 IR % +7.2% 3 WV6.2 WV7.3 Airmass % +25.5% % +1.6% % +4.2% Analyzing the results as displayed in Table 4.7, it becomes apparent that the optimization process improves the for each cloud type significantly, yet the increase is not as large for each cloud type. For example, the initial for cloud types very low opaque, low opaque and fractional was rather poor, and although the increase is significant the for the optimized coefficient is still below 10 percent. In addition, the low sensitivity for especially very low opaque and fractional clouds shows that another set of optimized coefficients would probably produce similar results, which also explains the slightly less drastic changes in the coefficients with respect to the initial coefficients compared to the other cloud types with a large sensitivity. For the cloud types low opaque and thick semi-transparent respectively four and three coefficients even change sign compared to the initial value. Combined with 62

63 the high value of the sensitivity, this indicates that the optimization process has most likely found a local maximum in which this set of optimized coefficients performs best in resembling the radar precipitation events in the training dataset. Furthermore, it is hard to interpret these sign conversions, which is primarily caused by the fact that the linear combination of channels and channel differences introduces the possibility of new channel differences like IR9.7 IR8.7 or IR13.4 IR12.0 which could have favourable or unfavourable properties for this particular dataset. This causes a coupling between the coefficients which makes it almost impossible to separate the change in influence for each channel difference. However, the sign conversion of the coefficients for the surface temperature in five of the seven cases remains a striking feature, but this sign conversion was also visible in the Precipitating Clouds algorithm, where it also did not agree with the sign of the correlation coefficient. Another curious sign conversion regards the parameter for cloud type high opaque. Since the high sensitivity in for this cloud type and the large difference between the optimized and initial coefficients, accompanied by three sign conversions, it should be concluded that this set of coefficients also has attained a local maximum in. The leave-out analysis shows for most of the coefficients a relative decrease in as expected, of which the relatively large decrease when leaving the parameter out of the selected channels is most apparent. In some cases however, the increases in case one of the selected channels and channel differences is left out of the analysis. These increases are the largest for cloud types very low opaque and fractional for which the optimization sensitivity and final is small. Since the is rather insensitive to changes in the coefficients for these cloud types, the maximizing algorithm has trouble finding the optimal set of coefficients, and this effect is more dominant for a larger number of degrees of freedom POST-PROCESSING OF COEFFICIENTS Using the coefficients as displayed in Table 4.7 in the calculation of the precipitation index will result in a different range for each cloud type in which the values of the precipitation index will lie. Analogous to the definition in the Precipitating Clouds and for convenience, the calculated precipitation indices for the training dataset are scaled from 0 to 100, along with the coefficients displayed in Table 4.7. The scaling procedure also involves a constant offset, which is displayed in Table 4.8 for each cloud type, along with the scaled values of the coefficients determined in the optimization process. 63

64 Table 4.8) Overview of the optimized scaled coefficients and offset. The third line for each cloud type displays the scale parameter mentioned in equation (4.4). # Name 4 very low IR13.4- IR8.7- IR3.9- WV6.2- IR10.8 opaque IR10.8 IR10.8 IR10.8 WV low IR9.7- IR8.7- IR3.9- WV6.2- IR10.8 opaque IR10.8 IR10.8 IR10.8 WV medium IR9.7- IR8.7- WV6.2- IR10.8 opaque IR10.8 IR10.8 WV high WV6.2- IR12.0- IR3.9- WV6.2- IR10.8 opaque IR10.8 IR10.8 IR10.8 WV very high WV6.2- IR12.0- IR3.9- WV6.2- IR10.8 opaque IR10.8 IR10.8 IR10.8 WV thick semitransparent IR10.8 IR10.8 IR10.8 WV7.3 WV6.2- IR9.7- IR3.9- WV6.2- IR fractional IR13.4- IR9.7- IR12.0- WV6.2- IR10.8 IR10.8 IR10.8 IR10.8 WV CONVERSION FROM PRECIPITATION INDEX TO PRECIPITATION RATE As described in section 4.6.2, histogram mapping is used to convert the calculated precipitation indices to precipitation rates. Using the optimized coefficients the cumulative distribution function (CDF) of the precipitation index can be calculated for each cloud type in the training dataset. In a similar way, the CDF of the observed precipitation rates can be determined. With these two CDFs a conversion chart can be built for each cloud type. In Figure 4.7 these conversion diagrams are displayed. 64

65 Figure 4.7) Conversion from precipitation index to precipitation rate for each cloud type based on matching the CDFs of and for the training dataset 65

66 66

67 RESULTS INTRODUCTION Using the optimized coefficients determined in the previous chapter, the precipitation index can be calculated for each situation. In this chapter the performance of the Nighttime Infrared Precipitation Estimation algorithm with optimized coefficients is determined, by performing a correlation study in a similar way as the performance of the NWC SAF s Precipitating Clouds algorithm has been tested in section 4.3. Subsequently, the model performance is studied more extensively by calculating skill scores with respect to classifying a pixel as precipitating or non-precipitating, after which the modelled precipitation rates for precipitation pixels are compared to the observed radar rain rates. With these statistics it is possible to compare the developed Nighttime Infrared Precipitation Estimation algorithm to existing nighttime algorithms like the Precipitating Clouds and Convective Rainfall Rate algorithm in a quantitative way. However, it is also very useful to study the model performance in a more qualitative way by comparing the generated model images with observed radar images. The last section of this chapter is therefore devoted to a case study in which a number of characteristic precipitation situations are studied. CORRELATION STUDY Analogous to the approach used in section 4.3, the performance of the Nighttime Infrared Precipitation Estimation model is studied with a correlation study between the used precipitation index of the developed algorithm against the modelled precipitation rates. In Table 5.1 the correlation coefficient is displayed for the precipitation index and the precipitation rate for each cloud type, along with the correlation coefficient for which the bin-averaged precipitation frequency is compared to the precipitation index. Lastly, the correlation coefficient is calculated which is determined analogously to, yet in this case only precipitation rates larger than zero are taken into account. As opposed to Table 4.1, the correlation coefficients for the combined set of all cloud types are left out of the analysis, since the definition of the precipitation index for each cloud type is different and the values of the precipitation indices can therefore not be compared between different cloud types. Comparing the results in Table 5.1 with the correlation coefficients calculated with the precipitation index as defined in the Precipitating Clouds algorithm in Table 4.1, it becomes apparent that the correlation coefficients for the individual cloud types with the newly developed Nighttime Infrared Precipitation Estimation model generally are higher compared to the Precipitating Clouds algorithm. Only the correlation coefficients for cloud types very low opaque and thick semi-transparent are lower than for the Precipitating Clouds algorithm, yet there are no negative correlations found for the new model. This trend is also visible when looking at the correlation coefficients and. Only the correlation coefficient for cloud type fractional is lower than in the Precipitating Clouds algorithm, with overall fewer negative correlation values. 67

68 Table 5.1) Overview of the correlation coefficients for each cloud type individually. The first column represents the frequency of cloud type occurrence, the second column the frequency of precipitation for that particular cloud type and the third column corresponds with the bin-averaged correlation between precipitation index and precipitation rate. The fourth column shows the correlation coefficient with precipitation index and precipitation occurrence and the last column displays the correlation between precipitation rate and precipitation index for precipitating cases only. The coloured bars in the first two columns correspond with the observed numeric values of the cloud type frequency and precipitation frequency, in the last three columns the length of the bars correspond with the absolute value of the correlation coefficient, negative values have a diagonal pattern. # Name (%) (%) 4 very low opaque low opaque medium opaque high opaque very high opaque thick semi-transparent fractional Similar to the diagrams of Figure 4.2, bin-averaged precipitation rates the precipitation index along with the weight of the bin in Figure 5.1. are plotted as a function of Figure 5.1) Bin-averaged precipitation rate as function of the precipitation index for each cloud type as defined in Table 5.1 in blue accompanied by the linear regression line in dotted blue. The red areas correspond with the relative number of elements in each bin and are therefore a measure for the weight of the bin in the determination of the correlation coefficient. Based on the comparison between Figure 4.2 and Figure 5.1 for each cloud type individually, one can state that the Nighttime Infrared Precipitation Estimation model is indeed better suited in determining the precipitation rate compared to the Precipitating Clouds algorithm, yet there are still some improvements possible. The relatively low correlation coefficient for cloud type very high opaque is caused by the fact that for high values of the bin-averaged rain rate levels off, thereby probably 68

69 introducing an overestimation of the area of precipitation for this cloud type. In addition, the diagrams for cloud types very low opaque and fractional show a rather noisy increase of the average precipitation rate as function of, accompanied by very small values of. Since these cloud types have a relatively low frequency of precipitation, it is very well possible that the model is only able to reproduce the precipitation patterns of the training dataset, and that the applicability outside this training dataset is limited. 5.3 MODEL PERFORMANCE PRECIPITATION CLASSIFICATION PERFORMANCE As already mentioned in the introduction of this chapter, the performance of the Nighttime Infrared Precipitation Estimation model will be tested in two ways: by comparing the classification of wet and dry satellite pixels to the observed radar precipitation classification and by studying the accuracy of the determined precipitation rates for raining pixels. In order to test the classification, the critical success index already introduced in section is one of the most illustrative parameters. In addition to this parameter two other verification measures are introduced, the classification bias and the Gilbert Skill Score. The classification bias is in this report defined as: (5.1) in which denotes the number of pixels falsely classified as precipitating and is the number of pixels falsely classified as dry. In case the entire image is set to be non-precipitating, the classification bias will be equal to -1, whereas will be equal to 1 in case every pixel is regarded to be precipitating. In this way, the is a measure of the overestimation of the precipitating area. A model which randomly assigns pixels to be precipitating or non-precipitating will most likely produce non-zero values for the critical success index. In order to determine the added value of using the Nighttime Infrared Precipitation Estimation model, the Gilbert Skill Score or Equitable Threat Score is introduced: (5.2a) in which is the critical success index of a perfect forecast equal to 1 and is the estimated critical success index of a random forecast given by: (5.2b) The number of correctly classified precipitating pixels by a random model can be estimated with the probability of a wet forecast and the probability of a wet observation : (5.2c) The Gilbert Skill Score is equal to zero if the model has no added value with respect to random classification and increases to one for a perfect forecast. It should be noted that the skill score can also attain negative values in case the model performs worse than a random model. Since the scanning time of SEVIRI and the weather radar are not perfectly synchronized, it is possible that precipitating areas are not located at the same position during both measurements. In addition to these displacement effects, the relatively high satellite zenith angle to the Meteosat satellite over the 69

70 Netherlands causes a parallax effect which could project clouds with very high tops up to a few pixels more northerly than they are actually located. In order to account for these negative influences on the Gilbert Skill Score, the skill is calculated for a local grid of 3x3 pixels: if for a precipitating SEVIRI pixel one of the surrounding pixels in this local grid is precipitating according to the radar observations, the pixel is regarded to be correctly classified. In Table 5.2 the model classification performance is displayed for the Nighttime Infrared Precipitation Estimation model and the reference models Convective Rainfall Rate and Precipitating Clouds. Since the Precipitating Clouds produces only a precipitation probability, a threshold of a probability of 30 percent is used in the calculation, as suggested in NWC SAF In addition to the overall performance, the model performance is studied for each individual cloud type as well. Table 5.2) Overview of the model classification performance for three different algorithms: the newly developed Nighttime Infrared Precipitation Estimation (NIPE) model, NWC SAF s Convective Rainfall Rate (CRR) algorithm and NWC SAF s Precipitating Clouds (PC) product. Analogous to the Table 5.1, the first two columns represent the frequency of cloud type occurrence, and observed frequency of precipitation occurrence. The modelled precipitation frequency is displayed for each model, along with the critical success index, classification and Gilbert Skill Score. # Name (%) (%) Model (%) (%) (%) (%) overall NIPE CRR PC very low NIPE opaque CRR PC low NIPE opaque CRR PC medium NIPE opaque CRR PC high NIPE opaque CRR PC very high NIPE opaque CRR PC thick semitransparent NIPE CRR PC fractional NIPE CRR PC

71 Comparing the critical success indices for each individual cloud type to the optimized value displayed in Table 4.7, it is obvious that the decreases when the model is applied to the verification dataset. Generally speaking, there is a decrease of a few percent, yet this decrease causes the for cloud type very low opaque to be almost equal to zero, which results in a very limited skill score. In addition, the modelled frequency of precipitation events is slightly higher than the observed value, with the exception of very high opaque clouds for which the modelled frequency is almost a factor two larger than the observed frequency. This effect was already predicted in the analysis of Figure 5.1, in which it becomes apparent that for high precipitation indices there are few precipitation events, although these are converted to high precipitation rates during the histogram mapping. The overestimation of precipitation from very high opaque clouds causes the skill score to be relatively small compared to other cloud types. The results in Table 5.2 show a clear increase in both and for the Nighttime Infrared Precipitation Estimation algorithm compared to the existing Convective Rainfall Rate and Precipitating Clouds products. This is not only the case for the overall model performance, but for each individual cloud type as well. In addition to these improvements, the modelled precipitation frequency is closer to the observed precipitation frequency and the classification is less extreme. The results for the Convective Rainfall Rate algorithm should be placed in some perspective, since this product is mainly aimed at estimating precipitation from clouds with cold cloud top temperatures. This results in a negligible detection of precipitation from cloud types other than high opaque, very high opaque and thick semi-transparent, for which the cloud top temperature is a dominant parameter. However, this algorithm still suffers from an overestimation of precipitation from very high opaque clouds, with a very limited overall skill score as a consequence. The Precipitating Clouds product also features precipitation from non-convective clouds, but does not include precipitation from cloud types very low opaque, low opaque and fractional as is visible in Table 5.2. Although the overall performance of the model is quite good, the skill scores of the individual cloud types that are covered in the model are still quite low, with significant overestimation of precipitation from high and very high opaque clouds and underestimation of precipitation from medium opaque clouds PRECIPITATION RATE ACCURACY Apart from the correct classification of SEVIRI pixels as precipitating or non-precipitating, another goal of the Nighttime Infrared Precipitation Estimation algorithm is providing an accurate estimate of the precipitation rate for precipitating pixels. To quantify these results, the median and 75 th percentile are calculated for both the radar observations and the estimated precipitation rates. In addition to these percentiles, the relative bias and absolute ratio between radar observations and modelled precipitation rates are calculated for each cloud type. In order to value differences between a modelled precipitation rate of 0.05 mm hr -1 and observation of 0.5 mm hr -1 as much as a difference between equal to 5 mm hr -1 and of 0.5 mm hr -1, the relative bias and absolute ratio are defined as: ( ) ( ) (5.3a) ( ) (5.3b) 71

72 It should be noted that the bias and ratio can only be calculated for cases in which both and is larger than zero. For convenience, the same threshold of 0.01 mm hr -1 is used in these calculations. The results of the analysis are displayed in Table 5.2 for the Nighttime Infrared Precipitation Estimation and Convective Rainfall Rate algorithms. Since the Precipitating Clouds product only produces rainfall probabilities, this algorithm has to be left out of the analysis. For the Convective Rainfall Rate product only the results for cloud types high opaque, very high opaque and thick semi-transparent are taken into account, as these cloud types are the only ones for which the algorithm has a significant level of skill see also Table 5.2. Table 5.3) Overview of the model precipitation rate accuracy for both the Nighttime Infrared Precipitation Estimation (NIPE) model and NWC SAF s Convective Rainfall Rate (CRR) algorithm. The first two columns denote the median and 75 th percentile of the observed rainfall rate distribution, whereas the fourth and fifth column correspond with the median and 75 th percentile of the modelled precipitation rate distribution. The last two columns represent the bias and ratio for precipitating cases. # Name (mm hr -1 ) (mm hr -1 ) Model (mm hr -1 ) (mm hr -1 ) (%) (%) overall NIPE CRR very low NIPE opaque CRR low NIPE opaque CRR medium NIPE opaque CRR high NIPE opaque CRR very high NIPE opaque CRR thick semitransparent NIPE CRR fractional NIPE CRR The results in Table 5.3 reveal that on average the estimated precipitation rates of the Nighttime Infrared Precipitation Estimate model are in rather good agreement with the radar observations, the differences can be attributed mostly to difference in the precipitation distribution between training and verification dataset. Exception is cloud type very high opaque for which the differences are primarily caused by the conversion from precipitation index to precipitation rate. As already noticed in the correlation study of section 5.2, high precipitation indices for this cloud type are erroneously coupled to high precipitation rates, causing both an overestimation of the precipitation area and an overestimation of the precipitation rate. The differences between training and verification dataset are also apparent for cloud type very low opaque, which as already discussed in section not only causes a very low skill score regarding the classification, but also introduces a relatively large bias in the estimated precipitation rates. The absolute ratio is both for each individual 72

73 cloud type and overall approximately equal to 3.3, which means that on average the observed rainfall rate deviates from the modelled rate by a factor three. Comparing the results of the Convective Rainfall Rate algorithm to the observations, it is evident that this algorithm not only suffers from large overestimation of the precipitation area, but also from a significant overestimation of the precipitation rates, especially for cloud type very high opaque. As a consequence the ratio is large for this algorithm as well. The results regarding the median and 75 th percentile seem to be somewhat contradicting with the relative bias in some cases: the modelled median and 75 th percentile are both larger than the observed value for the overall statistics and cloud types high opaque and fractional, whereas the relative bias is lower for these types. It is therefore useful to have a closer look at the precipitation rate distribution of the observations and the modelled values, which is displayed in Figure 5.2. Figure 5.2) Percentile plot of the precipitation rate distribution for both the training and verification radar observations dataset in blue and red. The model values determined by the Nighttime Infrared Precipitation Estimation model are displayed in green. The extent of the vertical lines shows the range of the precipitation rate distribution from the 0 th percentile up to the 99.9 th percentile. The horizontal bars crossing these vertical lines correspond with the 5 th percentile up to the 95 th percentile with a step size of 5 percent. The numbers on the horizontal axis correspond with the number convention of cloud types used throughout this report. From Figure 5.2 it can be deduced that the overall modelled precipitation rate distribution is approximately equal to the observed radar precipitation rate distribution, for which there are also no large differences between training and verification dataset. However, the differences between model and observations are more prominent for cloud type very low opaque as was already noticed due to the low skill score and high relative bias and absolute ratio. As is visible, this cannot not only be attributed to a poor description of these clouds in the model, but is a result of differences in precipitation rate distribution between the training and verification dataset as well. This difference between both datasets is also a significant contribution to the negative bias for cloud type low opaque, although compared to the training dataset this bias would still be negative. Last cloud type for which the precipitation rate distribution differs significantly from the radar verification dataset is very high opaque, the causes for these differences have already been covered. In addition to the values of relative bias and ratio displayed in Table 5.2 it is useful to study for which values of the precipitation rate the deviation is the largest. In order to be able to study this, a normalized scatter density plot is displayed in Figure

74 Figure 5.3) Modelled precipitation rate as function of observed precipitation rate. The dark blue area marks the 25%-confidence area, the lighter blue areas correspond with the 50%-, 75%- and 95%- confidence area. From the diagram in Figure 5.3 it can be deduced that the modelled precipitation rate is mainly negatively biased for very low precipitation rates and for precipitation rates around 5 mm hr -1, although there are relatively few pixels for which precipitation rates of this magnitude are found. The uncertainty is approximately equal for the entire range of precipitation rates. 5.4 CASE STUDY INTRODUCTION The results as presented in the previous sections are very useful in quantifying the performance of the Nighttime Infrared Precipitation Estimation model and comparing this performance with the skill and accuracy of other existing algorithms. However, in order to determine whether the developed algorithm produces images comparable to observed weather radar images, it is necessary to perform a number of case studies. With these case studies it can be inferred in what situations the model performs well, and when the Nighttime Infrared Precipitation Model has problems reproducing the radar image. Hence, in this section a number of case studies will be discussed for a variety of precipitation areas. In Table 5.4 the general statistics of these case studies are displayed. 74

75 Table 5.4) Overview of the model performance of the Nighttime Infrared Precipitation Estimation for a number of case studies. Analogous to the statistics used in section 5.3 to describe the model performance, for each case study the, and are calculated in order to determine to classification performance. The quality of the precipitation rate estimate is tested with the bias and ratio. # Date Time (%) (%) (%) (%) (%) 1 18 May : April : July : May : October : CASE STUDY 18 MAY :00 The first case study concerns a relatively large precipitation complex located over the northern part of the Netherlands. In Figure 5.4a and 5.4b the observed radar precipitation rate is displayed along with the precipitation rates as determined with the Nighttime Infrared Precipitation Estimation model. At first glance, an overestimation of the precipitation area can be observed, which can also be deduced from the relatively large as displayed in Table 5.3. However, this overestimation is mostly located at the edges of the radar domain, where the weather radar suffers from a lower sensitivity to low precipitation rates. If the modelled precipitation rate is compared to Cloud Type see Figure 5.4c it becomes apparent that the overestimation is caused by the fact that nearly every pixel along the band of precipitation with cloud type 6 medium opaque, 7 high opaque and 8 very high opaque is classified as precipitating. This effect has already been discussed in Chapter 2 in which it was noticed that only a small part of a large convective area produces precipitation. Although the precipitation rate is slightly underestimated in the centre of the cloud, at the northeast edge of this cloud there are modelled precipitation rates of over 20 mm hr -1 which is an overestimation of approximately factor four. These high precipitation rates are caused by the fact that the high cloud top temperature gradient factor the approximately equal. in these regions is much higher compared to in the centre of the cloud, whereas the cloud top temperature for both cloud tops is a) b) 75

76 c) d) Figure 5.4) Case study 18 May :00 UTC: a) precipitation rate as observed with the Dutch weather radar network; b) precipitation rate calculated with the Nighttime Infrared Precipitation Estimation model; c) cloud type classification and d) CDF value of the precipitation index CASE STUDY 12 APRIL :00 In Figure 5.5a and 5.5b the observed radar precipitation rate for a complex of showers over the Netherlands is displayed along with the modelled precipitation. The for these kinds of weather conditions is generally very low, although the number of precipitating pixels in both images is approximately equal. This is due to the fact that the cloud type classification for these situations is the largest influence on the modelled precipitation rate. Comparing the cloud type patterns in Figure 5.5c to the observed precipitation rate in Figure 5.5a, it becomes apparent that these patterns correlate only slightly. Due to the fact that cloud type 6 medium opaque generally produces more precipitation than 5 low opaque, most of the modelled precipitating pixels are limited to the extent of an enclosed area of cloud type medium opaque, whereas the radar observations show a lot of shower complexes going over borders between cloud types. a) b) c) d) Figure 5.5) Case study 12 April :00 UTC: a) precipitation rate as observed with the Dutch weather radar network; b) precipitation rate calculated with the Nighttime Infrared Precipitation Estimation model; c) cloud type classification and d) CDF value of the precipitation index. 76

77 5.4.4 CASE STUDY 27 JULY :00 In the evening of 27 July, a large complex of thunderstorms marked the ending of a heat wave over the Netherlands. The shape of this complex is clearly visible on the estimated precipitation rate displayed in Figure 5.6b, but the extent of the actual precipitating area is overestimated by a large amount compared to Figure 5.6a. This is a more illustrative example of the problem already observed in the first case study and already introduced in Chapter 2. For thunderstorms with a large anvil, the proposition that the colder the cloud top temperature, the higher the precipitation rate, no longer holds, as the precipitating area is limited to only a small part of the cloud. In addition, the corrections applied in the Nighttime Infrared Precipitation Estimation model to correct for this phenomenon are not sufficient as the anvil of this thunderstorm is thick enough to be considered an opaque cloud. As a result, the model is insensitive to the cloud structure and a large part of the thunderstorm is erroneously classified as precipitating. In addition to the cloud structure effect, there is also a discontinuity visible in the precipitating area induced by the cloud type classification. On the western side of the cloud, the precipitation rates are relatively larger than in the centre of the cloud, caused by the fact that this part of the cloud is classified as 7 high opaque. Since these pixels are located on the top end of their CDFs, the highest rain rates of this cloud type are assigned to these pixels, which would have been lower, if these pixels would have been classified as 8 very high opaque. a) b) c) d) Figure 5.6) Case study 27 July :00 UTC: a) precipitation rate as observed with the Dutch weather radar network; b) precipitation rate calculated with the Nighttime Infrared Precipitation Estimation model; c) cloud type classification and d) CDF value of the precipitation index CASE STUDY 25 MAY :00 In addition to overestimations of the precipitation area, in some cases the Nighttime Infrared Precipitation Estimation model suffers from underestimations. In the evening of 25 May 2013, a large area of relatively light precipitation covers the Netherlands see Figure 5.7a and 5.7b. The model produces an estimate with almost no overlap in precipitation area apart from some showers on the northern and eastern side of the domain. Again, this can be related to the cloud type classification, as 77

78 most of the modelled pixels with precipitation are of type 6 medium opaque, whereas there is little to no precipitation originating from the pixels of type 7 high opaque. Due to the fact that the precipitating pixels of cloud type medium opaque are located at a relatively high altitude compared to the rest of the pixels in this cloud type, whereas the non-precipitating pixels of cloud type high opaque are located at a relatively low altitude, high precipitation rates are assigned to the medium opaque clouds in this example and the high opaque cloud pixels remain dry. a) b) c) d) Figure 5.7) Case study 25 May :00 UTC: a) precipitation rate as observed with the Dutch weather radar network; b) precipitation rate calculated with the Nighttime Infrared Precipitation Estimation model; c) cloud type classification and d) CDF value of the precipitation index CASE STUDY 22 OCTOBER :00 In some cases the overestimation of the precipitation area cannot be explained by the cloud type structure or cloud type discontinuities. In Figure 5.8a and 5.8b, the observed and modelled precipitation rates are displayed for the case of 22 October Although the modelled precipitation area coincides with the observed precipitation area to a large extent, the magnitude of the precipitation rate in the centre of the northernmost cloud is much lower than observed. In addition to this, the modelled radar image classifies a large part over the North Sea as precipitating whereas the radar image shows barely any signal there. Since the cloud over the North Sea is sufficiently thick to be opaque, it is not classified as semi-transparent, although it does not produce any precipitation. In these cases it is impossible to determine the actual thickness of the cloud and the model subsequently overestimates the precipitation rate, since the cold cloud top temperature become dominant in this case. 78

79 a) b) c) d) Figure 5.8) Case study 22 October :00 UTC: a) precipitation rate as observed with the Dutch weather radar network; b) precipitation rate calculated with the Nighttime Infrared Precipitation Estimation model; c) cloud type classification and d) CDF value of the precipitation index. 79

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81 OTHER APPLICATIONS INTRODUCTION Although the Nighttime Infrared Precipitation Estimation model is designed and trained for nighttime conditions over the Netherlands, the setup of the algorithm provides the possibility to calculate precipitation estimates for other domains as well. The infrared channel responses are similar for daytime and nighttime conditions, with the only exception being channel IR3.9, which is influenced by a solar contribution during the day. Using the daytime correction for the IR3.9 channel introduced in Chapter 1 it is possible to apply the Nighttime Infrared Precipitation Estimation model for daytime conditions as well, and compare the results to algorithms which use visible information in their precipitation estimates. The results of this analysis are particularly interesting to determine whether the use of only infrared imagery is a valuable alternative for the other algorithms which have a separate daytime and nighttime model introducing discontinuities along the threshold value for the switch between these models. In addition to the daytime analysis, the model can also be applied to a larger domain Western Europe or the entire full disk. Although there is no radar coverage for this entire domain and quantitative evaluations of performance are therefore impossible, it is valuable to compare the model output to existing algorithms to determine whether the Nighttime Infrared Precipitation Estimation model produces comparable results for areas outside the Netherlands. MODEL PERFORMANCE: DAYTIME ANALYSIS INTRODUCTION Analogous to the approach used in section 5.3, the model performance of the Nighttime Infrared Precipitation Estimation algorithm will be evaluated both on the classification of precipitating and non-precipitating pixels and the deviation of the modelled precipitation rate estimates from the observed radar rainfall rates. Again, the estimates are compared with the existing algorithms Precipitating Clouds and Convective Rainfall Rate, although the definition for these models is slightly different in daytime conditions, as both models incorporate information from the SEVIRI channels VIS0.6 and NIR1.6 in their estimates. In addition, the estimates from the Nighttime Infrared Precipitation Estimation model are compared to the output of CM SAF s MSG Cloud Physical Properties algorithm, which primarily uses visible imagery in the retrieval of cloud properties. The verification dataset for daytime conditions is defined in a similar way as the original verification dataset for nighttime conditions. Only images obtained at full hours during a day with an even week number are taken into account in the verification dataset and compared to radar observations which were obtained ten minutes a full hour. The only difference is the criterion for the solar zenith angle: as opposed to the original dataset in which only pixels for which the solar zenith angles larger than 90 degrees are selected, the daytime verification dataset is limited to pixels with a solar zenith angles less than 78 degrees. This value coincides with the upper limit of the MSG Cloud Physical Properties algorithm and also makes sure that the Convective Rainfall Rate and Precipitating Clouds algorithms utilize their daytime algorithm the upper limit for these algorithms is 80 degrees. 81

82 Although it should be possible to use the conversion chart as displayed in Figure 4.7, this will introduce large deviations between the modelled precipitation rates and radar observations. This is due to the fact that higher temperatures during the day will cause a different range for a number of channels and channel differences and thereby influence the average value of the precipitation index. For that reason, the conversion chart of Figure 4.7 is determined for daytime conditions, with the same definition for the precipitation index, yet with a different conversion from precipitation index to precipitation rate determined from the cumulative distribution functions of both parameters in the training dataset PRECIPITATION CLASSIFICATION PERFORMANCE The precipitation classification performance of the Nighttime Infrared Precipitation Estimation model is evaluated in a similar way as in section Using the same definitions, the critical success index, the ratio of false positive and false negatives and skill score can be calculated for the Nighttime Infrared Precipitation Estimation model and the three reference models. The results are displayed in Table 6.1. Comparing the results from Table 6.1 to the results from Table 5.2, it is obvious that although the general precipitation statistics like precipitation frequency are comparable to the nighttime conditions the Nighttime Infrared Precipitation Estimation model performs worse during the day. The skill score overall and for almost every individual cloud type typically decreases by a few percent. Since a separate histogram mapping is applied for daytime conditions and therefore cannot be the cause of this decrease in performance, it should be attributed to the fact that the set of optimized coefficients is not optimal for daytime conditions. If the results for the daytime algorithms of the Precipitating Clouds and Convective Rainfall Rate product are compared to their nighttime equivalents, a distinct trend is visible. Overall, both models attain a higher skill score when visible imagery is incorporated in the model: the skill score for the CRR model increases from 10.57% up to 21.93%, whereas the skill score for PC increases from 19.09% to a value of 26.88%. This increase in performance is also visible in each individual cloud type, for which both the precipitation frequencies are more in correspondence with the observed values and the individual skill scores are higher compared to the nighttime version. The increase in skill is most striking for cloud type 7 high opaque for the Precipitating Clouds algorithms, which increases from almost no skill to a reasonable skill score of 18.55%. Despite the increased performance of the Precipitating Clouds and Convective Rainfall Rate algorithms with respect to their nighttime versions, they still feature an overestimation for every cloud type for which these products produce a precipitation estimate. Although this overestimating decreases slightly, incorporating visual imagery in the model does not solve the entire problem. This effect is also noticed in the performance of the MSG Cloud Physical Properties algorithm, which marks a pixel as precipitating in case both the liquid water path and cloud droplet effective radius is sufficiently high. Regarding the skill scores for each individual cloud type, this model is the best performing model of the four, yet the overestimation of the precipitating area causes the overall skill score to be slightly lower than the skill score of the Precipitating Clouds product. 82

83 Table 6.1) Overview of the model classification performance in daytime conditions for four different algorithms: the developed Nighttime Infrared Precipitation Estimation (NIPE) model, NWC SAF s Convective Rainfall Rate (CRR) algorithm, NWC SAF s Precipitating Clouds (PC) product and CM SAF s MSG Cloud Physical Properties (MSG CPP) algorithm. The column definitions are the same as in Table 5.2. # Name (%) (%) Model (%) (%) (%) (%) overall NIPE CRR PC MSG CPP very low NIPE opaque CRR PC MSG CPP low NIPE opaque CRR PC MSG CPP medium NIPE opaque CRR PC MSG CPP high NIPE opaque CRR PC MSG CPP very high NIPE opaque CRR PC MSG CPP thick semitransparent NIPE CRR PC MSG CPP fractional NIPE CRR PC MSG CPP PRECIPITATION RATE ACCURACY Although the precipitation classification performance of the Nighttime Infrared Precipitation Estimation model during daytime is not as good as the other algorithms which incorporate visible imagery in their calculations, it is important to have a look at the accuracy of the precipitation rate estimates as well. Since both the Convective Rainfall Rate product and the MSG Cloud Physical Properties algorithm uses a parameterization to determine the precipitation rate, overestimations are likely to occur, as was already observed in the nighttime analysis of the Convective Rainfall Rate 83

84 product in section In Table 6.2 the results of the quantitative comparison between model and radar are displayed in a similar fashion as in Table 5.3. The definitions of the relative bias and absolute ratio are identical to the ones used in section Table 6.2) Overview of the model precipitation rate accuracy in daytime conditions for three different algorithms: the Nighttime Infrared Precipitation Estimation (NIPE) model, NWC SAF s Convective Rainfall Rate (CRR) algorithm, and CM SAF s MSG Cloud Physical Properties (MSG CPP) algorithm. The first two columns denote the median and 75 th percentile of the observed rainfall rate distribution, whereas the fourth and fifth column correspond with the median and 75 th percentile of the modelled precipitation rate distribution. The last two columns represent the bias and ratio for precipitating cases. # Name (mm hr -1 ) (mm hr -1 ) Model (mm hr -1 ) (mm hr -1 ) (%) (%) overall NIPE CRR MSG CPP very low NIPE opaque CRR MSG CPP low NIPE opaque CRR MSG CPP medium NIPE opaque CRR MSG CPP high NIPE opaque CRR MSG CPP very high NIPE opaque CRR MSG CPP thick semitransparent CRR NIPE MSG CPP fractional NIPE CRR MSG CPP The slightly reduced performance of the Nighttime Infrared Precipitation Estimation model during daytime conditions as already observed in the classification of precipitating pixels is also visible in the results of Table 6.2. Although the relative bias is quite accurate for each individual cloud type except 4 very low opaque the average absolute ratio is a few percent larger than for nighttime conditions compare also Table 5.3. Although incorporating visual imagery into the Convective Rainfall Rate algorithm increases the overall skill score regarding the classification of precipitating and non-precipitating pixels, the 84

85 effect on the precipitation rate estimates is only limited. Overall, there is a slight decrease in the relative bias and ratio, however, the differences for the most important cloud types for this algorithm 7 high opaque and 8 very high opaque increase. The issues with using a parameterization instead of histogram mapping are also visible in the estimated precipitation rate of the MSG Cloud Physical Properties product: the rainfall rates calculated by this model are all strongly biased, with an absolute ratio that is generally larger than the ratio that is obtained with the nighttime version of the Nighttime Infrared Precipitation Estimation model. 6.3 CASE STUDY: WESTERN EUROPE The Nighttime Infrared Precipitation Estimation model has been trained and optimized with weather radar data from the Dutch radar network, but the model should also deliver proper precipitation rates for a larger domain, provided that the average precipitation rate distribution as a function of precipitation index is similar to the distribution obtained from the training and verification dataset. Considering that most of the storm complexes passing over the Netherlands also produce precipitation in other parts of Western Europe, the model should be applicable to a larger domain. Although no verification is possible with Dutch weather radar data, the performance of the Nighttime Infrared Precipitation Estimation model can be roughly estimated by comparing the results of the model with the existing Precipitating Clouds and Convective Rainfall Rate products. In Figure 6.1a the precipitation rate estimate of 2 February :00 of the Nighttime Infrared Precipitation Estimation model is displayed for the domain of Western Europe, along with the estimates provided by the Precipitating Clouds product and Convective Rainfall Rate algorithm. The time of this image is the same as used in Chapter 1 and 2 for the discussion of the SEVIRI channels and the available methods. a) b) 85

86 c) d) Figure 6.1) Precipitation rates estimated with the a) Nighttime Infrared Precipitation Estimation algorithm, b) Precipitating Clouds model and c) Convective Rainfall Rate product for 2 February :00 UTC, accompanied by d) cloud classification as defined with the NWC SAF s Cloud Type algorithm. Comparing the estimate of the Nighttime Infrared Precipitation Estimation model with the precipitation rate as calculated with the Convective Rainfall Rate algorithm, it is apparent that the precipitating area of the complex over the Alps is reduced, but considering the overestimation of the Convective Rainfall Rate algorithm this should not necessarily indicate undetected precipitation, since large parts of the precipitation as indicated by the CRR algorithm are classified as cloud type 11 thick semi-transparent. In addition, the model is able to detect some of the scattered showers over the Bay of Biscay and the North Sea, which are also featured in the Precipitating Clouds model. Despite the observed overestimation of precipitation rates within the CRR algorithm as discussed in section it cannot be stated that the precipitation rates produced with the NIPE model are more accurate. Although it is reasonable to assume that rainfall rates produced by the CRR algorithm are positively biased in this case, the magnitude of the precipitation rates calculated with the NIPE model for the convective situation over the Alps hardly exceeds a level of 2 mm hr -1, which is probably an underestimation of the actual precipitation rate. 86

87 CONCLUSION & DISCUSSION RESEARCH SUMMARY The goal of the research described in this thesis has been to develop an algorithm which is able to estimate precipitation rates with reasonable accuracy by using only infrared satellite imagery. Currently available methods utilizing satellite imagery to estimate precipitation either rely on visual information (MSG Cloud Physical Properties) which is not available during the night, only produce estimates for convective situations (Convective Rainfall Rate product), or only provide probabilities for precipitation to occur instead of precipitation rates (Precipitating Clouds product). An algorithm able to produce precipitation rates with just infrared satellite imagery would be a perfect alternative for expensive weather radar networks with only limited range, since infrared imagery is available for a large part of the Earth both during daytime and nighttime. Inspired by the existing methods aimed at estimating precipitation with satellite information, the algorithm of NWC SAF s Precipitating Clouds product is used as a starting point for improvement. Other methods using only infrared imagery are all based on the notion that colder cloud top temperatures correlate with higher precipitation rates, a premise that only holds for deep convective situations. Parameterizations based on the cloud top temperature therefore are only able to produce reliable precipitation rate estimates for clouds with very cold cloud tops and can never be a valid alternative for weather radars. In addition, the radiative transfer analysis reported in Chapter 3 has proved that a parameterization as function of optical thickness or liquid water path as used in the MSG Cloud Physical Properties is also not an option: infrared imagery is sensitive to liquid water path for values up to approximately 100 g m -2, and for higher values the signal rapidly saturates. The parameterization used in the MSG CPP model features a threshold liquid water path of 150 g m -2 for precipitation to occur, so using the radiative transfer properties of infrared imagery to estimate the precipitation rate is not possible it can only be used to distinguish precipitating clouds from non-precipitating clouds. Although a parameterization or retrieval based on radiative transfer properties is not an option to estimate the precipitation rate, it is possible to combine the information from various SEVIRI infrared channels to extract cloud opacity, cloud top temperature and cloud top height, which all show a correlation with observed precipitation rates. In the precipitation index introduced in the Precipitating Clouds product these quantities are combined in such a way that a higher precipitation index correlates with a higher frequency of precipitation The calculated precipitation index in the Precipitating Clouds product is converted to a precipitation probability with a mapping dependent on cloud type. Using the same cloud type classification algorithm, it was demonstrated in the correlation study of section 4.3 that the precipitation index shows a high correlation with the precipitation frequency of all cloud types combined, but that it lacks this correlation for individual cloud types, in particular for lower opaque clouds. Therefore, a new cloud type dependent precipitation index has been proposed in which, apart from cloud opacity, cloud top temperature and height, also cloud top structure and surface and atmospheric conditions are taken into account, with for each cloud type a different selection of SEVIRI brightness temperatures and brightness temperature differences. These SEVIRI channels and channel 87

88 differences have been selected by both looking at the simulated radiative transfer responses and a correlation study with observed precipitation rates over the Netherlands for the year In addition, instead of converting this precipitation index to a precipitation probability, the indices are converted to a precipitation rate using histogram mapping. The coefficients in new precipitation index have been optimized to maximize the critical success index which rates correctly classified precipitating pixels to incorrectly classified precipitating pixels. VERIFICATION RESULTS The precipitation rates estimated with the newly developed model referred to as the Nighttime Infrared Precipitation Estimation (NIPE) model have been verified with hourly weather radar observations over the Netherlands during nighttime for the year 2013, along with the existing methods in order to compare the performance. This verification and comparison study has been performed for the two main purposes of this research: produce a precipitation estimate for which both the classification of precipitating and non-precipitating pixels is correct and the estimated precipitation rates are of reasonable accuracy. For both aspects the Nighttime Infrared Precipitation Estimation model performs better than the existing methods, with overall higher skill scores, fewer incorrect precipitation classifications and a smaller relative precipitation rate deviation. Despite the improved performance, the overall skill score of 23.28% and overall absolute precipitation rate ratio of 331% shows that the results of the Nighttime Infrared Precipitation Estimation deviate from the radar observations by a relatively large amount. In addition, the skill scores for lower clouds are very small, causing the added value of incorporating these cloud types into the model to be only limited. Although the incorrect classification of precipitating pixels is significant, the histogram mapping used in the algorithm makes sure that the average results are only slightly positively biased, whereas the parameterization used in the Convective Rainfall Rate algorithm produces a large overestimation regarding both the precipitating area and the precipitating rates. However, the observed overestimation in both the Precipitating Clouds model and the Convective Rainfall Rate algorithm can also be attributed to a different training dataset as these models have been optimized for French rain gauge and Spanish weather radar data respectively. The case studies of section 5.4 reveal that, although the overall skill score rarely exceeds a level of 20%, the Nighttime Infrared Precipitation Estimation model is still able to represent the precipitation area with reasonable accuracy. The fact that there is a significant classification bias for deep convective clouds arises from the shape of these clouds: a relatively small part of the cloud produces precipitation, but is surrounded by a large anvil. If this anvil is of sufficient optical thickness, infrared imagery is not able to distinguish this non-precipitating part from the precipitating centre of the cloud despite the introduced cloud top temperature gradient correction, as the thunderstorms in the case studies displayed in section 5.4 are all fairly flat at the top with no clearly visible overshooting tops. Introducing a cloud type dependent precipitation index and conversion function results in inaccuracies too. For clouds at an altitude located closely to a threshold value in the cloud type classification, discontinuities in the precipitation rate estimation coinciding with the cloud type borders are inevitable. This is due to the fact that cloud top height and cloud top temperature are incorporated in the precipitation index and cause the pixels on both sides of the threshold to be on the lower and upper part of the histogram for their cloud type, respectively. This can lead to a scattered precipitation pattern in cases where the radar observations suggest an enclosed area. 88

89 The analysis of the performance with the Nighttime Infrared Precipitation Estimation model during daytime and subsequent verification and comparison study provides some perspective for the limited value of the skill score during nighttime. The skill for the Precipitating Clouds product increases by almost six percent in case visible imagery is incorporated, causing the overall skill score for this algorithm at daytime to be a few percent higher than for the Nighttime Infrared Precipitation Estimation model during nighttime. In addition, the MSG Cloud Physical Properties algorithm which predominantly uses visible imagery has a skill score during daytime which is only 1.5 percent higher than the NIPE model during the night. The decrease in skill score for the Nighttime Infrared Precipitation Estimation model during daytime can be largely attributed to the fact that the precipitation index is optimized for nighttime conditions and a large number of the used channels in this index attain different values during the day. It is therefore expected that the performance of the NIPE model should reach similar skill scores for daytime conditions, if the model is optimized for this period as well. Since the skill score of the Nighttime Infrared Precipitation Estimation model during nighttime is only slightly lower than the skill scores of products incorporating visible imagery in their algorithms, it can be concluded that the NIPE model can be a valuable addition to the set of precipitation estimators with satellite imagery, even during daytime. As observed in the case studies performed in section 5.4, at the edges of the radar domain, the sensitivity of the weather radar network to precipitation decreases. In Figure D.1 the frequency of precipitation during the night for the year 2013 is displayed, in which the same threshold value of precipitation rates larger than 0.01 mm hr -1 is used. Figure D.1) Precipitation frequency as observed with the Dutch weather radar network, in which a precipitation rate of 0.01 mm hr -1 is used as threshold value for precipitating events. The observed precipitation frequency in Figure D.1 shows a clear dependence on detection of precipitation as function of distance to one of the radars, whereas no significant differences throughout the domain are to be expected. Training the model with this data therefore should introduce an underestimation of precipitation probability, as most of the precipitating clouds along the edges of the radar detection domain are incorrectly classified as non-precipitating. In addition, despite the fact that there is some noise filtering done in the proximity of the radar, both the weather radar in Den Helder and the radar in De Bilt still feature a distinct radial noise pattern, which inevitable propagates in the model. Since the probability of precipitation detection decreases gradually for larger distances from the radars, data from rain gauges should be consulted to determine to what distance the radar observations still deliver an acceptable probability of detection or radar data calibrated with rain gauges should be used as a reference.. 89