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1 This article was downloaded by: [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] On: 3 January, At: 8:3 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1795 Registered office: Mortimer House, 37-1 Mortimer Street, London W1T 3JH, UK International Journal of Remote Sensing Publication details, including instructions for authors and subscription information: Inner convective system cloud-top wind estimation using multichannel infrared satellite images R.G. Negri a, L.A.T. Machado a & R. Borde b a Centre for Weather Forecasting and Climate Research, National Institute of Space Research (INPE/CPTEC), Rodovia Presidente Dutra km, Cachoeira Paulista, SP, Brazil b European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), Darmstadt, Germany Published online: Jan. To cite this article: R.G. Negri, L.A.T. Machado & R. Borde () Inner convective system cloudtop wind estimation using multichannel infrared satellite images, International Journal of Remote Sensing, 35:, 51-7 To link to this article: PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the Content ) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &

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3 International Journal of Remote Sensing, Vol. 35, No., 51 7, Inner convective system cloud-top wind estimation using multichannel infrared satellite images Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January R.G. Negri a *, L.A.T. Machado a, and R. Borde b a Centre for Weather Forecasting and Climate Research, National Institute of Space Research (INPE/CPTEC), Rodovia Presidente Dutra km, Cachoeira Paulista, SP, Brazil; b European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), Darmstadt, Germany (Received 31 May 1; accepted 1 November 13) Knowledge of deep convective system cloud processes and dynamic structures is a key feature in climate change and nowcasting. However, the horizontal inner structures at the cloud tops of deep convective systems are not well understood due to lack of measurements and the complex processes linked to dynamics and thermodynamics. This study describes a new technique to extract inner cloud-top dynamics using brightness temperature differences. This new information could help clarify ring and U or V shape structures in deep convection and be potentially useful in nowcasting applications. Indeed, the use of high-resolution numerical weather prediction (NWP) models, which now include explicit microphysical processes, requires data assimilation at very high resolution as well. A standard atmospheric motion vector tracking algorithm was applied to a pair of images composed of combinations of Spinning Enhanced Visible and Infra-red Imager (SEVIRI) channels. Several ranges of channel differences were used in the tracking process, such intervals being expected to correspond to specific cloud-top microphysics structures. Various consistent flows of motion vectors with different speeds and/or directions were extracted at the same location depending on the channel difference intervals used. These differences in speed/direction can illustrate local wind shear situations, or correspond to expansion or dissipation of cloud regions that contain high concentrations of specific kinds of ice crystals or droplets. The results from this technique were compared to models and ancillary data to advance our discussion and inter-comparisons. Also, the technique proposed here was evaluated using SEVIRI images simulated by the radiative transfer model RTTOV with input data from the UK Met Office Unified Model. A future application of the new data is exemplified by showing the relationship between wind divergence calculated from the new atmospheric motion vector and convective cloud top intensification. 1. Introduction Good knowledge of deep convective system dynamics, thermodynamics, and cloud processes is a key feature in nowcasting and high space/time weather forecast model resolution. However, the inner flows at the top of deep convection towers are not well understood due to several uncertainties linked to the dynamics (Cotton and Anthes 1989). Understanding and retrieving the dynamics of mesoscale convective systems could be important in improving parameterization at these scales or for monitoring severe storms. The patterns of winds associated with mesoscale convective systems are an important feature in improving short-range weather forecasting within short time scales. This kind of weather system *Corresponding author. renato.galante.cptec.inpe.br Taylor & Francis

4 5 R.G. Negri et al. Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January produces high amounts of precipitation, which contributes a large amount to the Earth s total precipitation, and therefore such systems are important for climate studies. Today, the estimation of winds using geostationary satellite images is well established, but it is mainly done operationally to estimate synoptic-scale winds. Studies on how to improve spatial coverage and detect mesoscale flows more precisely are important because models run operationally using high spatial resolution. For instance, the methodology presented here aims to detect wind at a horizontal scale of around 5 km and at min intervals. The Centre for Weather Forecasting and Climate Research, National Institute of Space Research (CPTEC/INPE) operational scheme estimates wind at a horizontal scale of around 1 km, which is related to a mean flow from 3 min to 1 h. During recent decades, many studies have been done to detect ice, water, and mixed-phase cloud situations using satellite- and ground-based observations (Pilewskie and Twomey 1987; Ackerman et al. 199; Strabala, Ackerman, and Menzelw 199; Chylek and Borel ; Chylek et al. ). These methods can be divided into three groups. The first uses only thermal infrared radiances, the second uses visible and near-infrared reflectances, and the third combines visible, near-infrared, and thermal radiances. Thermal infrared-based methods have the capability to retrieve cloud phase information during both day and night, but visible and near-infrared methods can only be performed during daylight hours. Arking and Childs (1985) obtained cloud thermodynamic phase information using radiances from channels 3.7 and 1.8 µm of the Advanced Very High Resolution Radiometer (AVHRR) on board a National Oceanic and Atmospheric Administration (NOAA) satellite. Strabala, Ackerman, and Menzelw (199) developed a tri-spectral cloud-phase algorithm combining the 8, 11, and 1 μm bands. This method is based on a dispersion diagram of the brightness temperature differences 8 11 μm versus 11 1 μm. Their results showed the potential to identify the thermodynamic cloud phase (ice or water clouds and mixed) in many situations. Baum et al. () improved the trispectral thermal infrared-based method proposed by Strabala, Ackerman, and Menzelw (199) by the addition of reflectances of.3, 1.3, and 1.9 µm spectral bands aimed at increasing the accuracy of thin cirrus cloud retrievals. Chylek and Borel () proposed a technique based on the ratio between two channels, one in the visible region around.8 µm and the other in the near infrared around 1. µm, to classify mixed-phase clouds. Chylek et al. () compared near-infrared and thermal-infrared cloud thermodynamics phase detection techniques. The near-infrared technique was based on the ratio between visible and near-infrared bands, while the thermal infrared technique uses the brightness temperature difference (BTD) between two thermal infrared bands. These results suggest that BTD alone is suitable for cloud phase detection, it being problematic only over surface areas covered by ice or snow. Wolters, Roebeling, and Feijt (8) investigated the suitability of three multi-spectral cloud phase satellite-based retrieval methods using the Spinning Enhanced Visible and Infra-red Imager (SEVIRI) radiometer on board the Meteosat 8 satellite. The authors compared the retrievals (a thermal infrared method based on the 8 11 µm bands, an International Satellite Cloud Climatology Project (ISCCP)-like method based on infrared brightness temperature threshold, and other methods based on comparing iteratively observed satellite reflectances at. and 1. µm with look-up tables of reflectances simulated by a radiative transfer model with ground-based cloud-phase determination methods from cloud radar and lidar data, assessing the quality of instantaneous cloud-phase retrieval, monthly average water/ice clouds occurrence and diurnal cycles of cloud-phase. They found that the SEVIRI µm BTD method reproduced the diurnal cycle of ice and water better than the other channel combination retrieval methods.

5 International Journal of Remote Sensing 53 Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January Other methods based on polar satellites or using other electromagnetic bands, such as microwaves (e.g. Tropical Rainfall Measuring Mission (TRMM)) play an important role in cloud classification. However, these methods are not suitable for the proposed application discussed here, because a time resolution higher than min is necessary, which is possible only with geostationary satellites. Satellite-derived winds, also known as atmospheric motion vectors (AMVs), are produced from all operational geostationary satellites and have been studied since the 19s (Fujita 198; Hubert and Whitney 1971). For a better description of the history and generation process of satellite AMVs, see Menzel (1). The AMV is a good way to retrieve wind in the synoptic scale, it being widely used in numerical weather prediction (NWP) models as atmospheric initial state parameters. Bedka and Mecikalski (5) showed the possibility of identifying motions associated with and induced by convective clouds (scales from to km where ageostrophic flow becomes important) using a modified satellite wind algorithm to detect mesoscale winds. The authors called these new AMVs mesoscale atmospheric motion vectors (MAMVs). When cloud-top microphysical components, ice crystals and water droplets, can be identified in a geostationary satellite image sequence, the different areas of the cloud top where any of these components are predominant can be tracked. Tracking these structures, information about their horizontal movements and also, indirectly, some information about the vertical flows can be estimated. Following this assumption, the present study shows the potential of identifying and tracking areas dominated by ice, water, or a mixed phase in deep convective cloud tops using SEVIRI channel combinations. Therefore, some information about the dynamics of the inner structures in mesoscale cloud-top systems can be assessed, as well as improvement of the spatial resolution of the winds fields. Section gives a full description of the mesoscale cloud-top wind detection technique. Section 3 presents a comparison between winds extracted using this technique and the Centre for Medium-Range Weather Forecasts (ECMWF) high-resolution model winds, as well as using synthetic SEVIRI images. This section also discusses the relationship between the high-level wind divergence calculated from this new wind technique and the time evolution of the brightness temperatures. Conclusions are presented in Section.. SEVIRI brightness temperature difference and the tracking scheme The new technique described here aims to detect horizontal movements of deep convection cloud-tops at a minimum of km horizontal scale (the horizontal scale depends on the target window used for the wind estimate). This technique assumes that the combinations of infrared pairs of SEVIRI channels allow the isolation of specific cloud components (droplets of different sizes or ice crystals with different shapes and sizes and mixed-phase cloud-top regions) and then the tracking of the displacement of these structures. The difference between two SEVIRI infrared channels, the brightness temperature difference (BTD), gives us a new array of data where each pixel represents a specific cloud component. In principle, any pair of spectral bands that have very different imaginary refraction indexes for ice and water can be used to identify ice and water in a cloud. The satellite-based cloud particle distinction is based on the difference in the spectral reflectivity of ice and water (Chylek and Borel ). The objective of the BTD technique is to find a suitable spectral region in the thermal infrared where water and ice or particle size have significant differences in emissivity (Chylek et al. ). Indeed, emissivity of any material is related to its absorbance, which is linked to the imaginary part of the refractive index (Hale and Query 1973; Warren 198; Gosse, Labrie,

6 5 R.G. Negri et al. Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January and Chylek 1995). As this study uses SEVIRI measurements, it was necessary to select the best combinations from all available SEVIRI infrared channels (3.9,., 7.3, 8.7, 9.7, 1.8, 1, and 13. µm). The infrared image spatial resolution is 3 km in the sub-satellite point. Hereafter, we define AMV obtained by the BTD technique as BTD-AMV, and those estimated using any SEVIRI channel alone as SEVIRI-AMV. The channel combination µm was also evaluated but the BTD-AMV amount was null or too small to be analysed. The noise due to sensor saturation makes it difficult to identify and track high-level cloud-top structures using this channel combination. In addition, the changes in the solar radiation that reaches the clouds during the daylight period have a strong influence on BTD throughout the min image interval, making the tracking processes difficult. For the daylight period, the sunlight backscattered by liquid water clouds greatly contributes to the total radiance observed at the 3.9 µm wavelength. This reflected shortwave solar radiation superimposes the thermal differences of clouds usually seen in the atmospheric window IR channels, making the use of the 3.9 µm channel combined with the other window IR channel inappropriate in daytime. In future work, this channel combination can be tested by estimating the flows of low- to mid-level clouds surrounding the deep convection clusters. Inoue (1985) showed that the brightness temperature difference between 11 and 1 µm can be used to detect semi-transparent cirrus clouds. Combinations µm (Baum et al. ) and µm (Inoue 1985; Saunders and Kriebel 1988; Strabala, Ackerman, and Menzelw 199) are useful in classifying pixels related to ice crystals or cloud droplets, and. 1.8 µm is useful in detecting penetrative cloud scenarios (Machado et al. 1998). Figure 1 shows an Brightness temperature difference (K) Brightness temperature difference (K) IR 1.8 3/8/:3 UTC 8 IR8.7-IR1.8 3/8/:3 UTC Brightness temperature difference (K) WV.-IR1.8 3/8/:3 UTC Brightness temperature difference (K) IR1.8-IR1. 3/8/:3 UTC Figure 1. Brightness temperature difference for 3 August :3 UTC. SEVIRI 1.8 µm image (top, left), BTD. 1.8 µm (top, right), BTD µm (bottom, left), and BTD µm (bottom, right). Note the different colour ranges for each plot. Missing values (white) represent locations where at least one brightness temperature was warmer than 5 K or colder than 1 K in any SEVIRI channel.

7 International Journal of Remote Sensing 55 Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January example of each of the three channel combinations used in this study, as well as a SEVIRI window IR image from 1.8 µm as reference. These three channel differences allow monitoring of the general features found in deep convective cloud tops and their surrounding areas: the overshooting core at the centre of the system, the cirrus associated with high-level mesoscale wind divergence, and the lower water and mixed-phase clouds. We can see from Figure 1 that the BTD. 1.8 (top-right corner) clearly identifies the penetrative tops normally related to deep convective vertical fluxes and water vapour injected into the stratosphere (yellow and orange tones). This characteristic is not captured by the other two BTDs, as shown in Figure 1. Cirrus clouds, westward of the deep convective area, are very well identified by the BTDs µm and µm (yellow and orange tones). The BTD µm shows cloud regions composed mainly of water (dark blue), mixed phase (light blue), and ice (other colours). The BTD µm can be negative under strong surface-based temperature inversions: when viewing barren surfaces (e.g. deserts) under clear-sky conditions; when viewing volcanic ash and certain non-volcanic mineral-based aerosols (e.g. dust); when cloud tops overshoot the tropopause due to stratospheric temperature inversion; and due to instrument noise. High water-vapour burdens can mask the negative BTD µm signal when viewing an actual ash cloud (Pavolonis et al. ). Situations involving surface temperature are discarded because a threshold of 5 K was applied to each thermal image before calculating the BTD. Every pixel warmer than the predefined minimum value has been eliminated. For SEVIRI BTD µm, the minimum value observed was approximately 1 K, occurring mainly on overshooting cloud tops. In these regions, the spatial variance of the BTD values compromises the pattern recognition algorithm, which is the basis of the tracking process, leading to the finding of random targets and, consequently, BTD-AMVs which do not represent realistic wind. However, the occurrence of negative values from this channel s difference is very low. The 5 K threshold also eliminates very thin cirrus. To estimate the smaller scale flows associated with very thin cirrus, negative BTD, a more specific cloud mask should be added to the procedure. Once the new BTD image is prepared, a tracking algorithm is applied to the images. The algorithm used for tracking was adapted from the CPTEC/INPE scheme (Negri and Machado 8). All pixels with values outside the defined BTD range (surface and low clouds) receive a random value in order to lessen their influence in the tracking crosscorrelation calculations. To detect mesoscale movements, the target window size must be decreased in comparison with the operational wind extraction scheme. The CPTEC/INPE operational scheme uses a target window of 3 3 pixels for the µm water vapour and for the 3.9 and 1. µm window infrared channels of the Geostationary Operational Environmental Satellite (GOES)-1/1 satellites. The inner cloud dynamics tracking algorithm considers the following assumptions. (a) It uses only image pairs instead of the triplet used in the operational CPTEC/INPE scheme. The characteristic time scale of the movements to be detected is very short, so it does not make sense to use a third image, as is usually done in a conventional AMV-generation algorithm. (b) A small target window is used. Various values between 5 and pixels have been tested and a size of 8 8 pixels was chosen. Bedka and Mecikalski (5) used target windows with sizes of 5 5 pixels when using km resolution IR images. However, such a window size is too small for our calculations due to the sparse BTD population in the cloud top boundaries. We must choose an image segment as small as possible when we are interested in tracking small-scale movements,

8 5 R.G. Negri et al. Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January but also large enough to contain a well-defined pattern that can be easily identified during the tracking process. (c) The process allows the target window to have an overlap with the neighbouring windows. Several overlaps have been tested and an optimal value of 5% (two pixel rows or lines in the 8 8 target window) was chosen. The overlap increases the vector size but the threshold must be chosen carefully to prevent situations where the same structure gives two or more vectors, leading to a false spatial coherence. (d) Our process is only applied to the top of convective systems because it has a mesoscale structure. To avoid warmer values or unrealistic cloud values, this technique is applied only for brightness temperatures warmer than 1 K or colder than 5 K. Pixels outside this interval are eliminated by replacing their values by a random number so as not to influence the tracking process. (e) A minimum number of valid pixels is required to allow the target window to be used. The maximum value of disposed pixels is set to 35% of the pixel population within the target window. Considering the 5% target window overlap (item c), the % of remaining pixels are only observed in each target window, ensuring that overlapping target windows are largely an independent set of pixels, allowing a BTD-AMV of a different part of the cloud top. Table 1 describes the channel combinations used to compute the winds and the applications and interpretation as a function of the BTD range. The procedures employed in this study do not use any quality control and height assignment. We focus only on the possibility of extracting extra information that can be obtained through the usual AMV techniques. Suitable automatic quality control should be developed in the future. The existing filters are not useful because their purpose is the consideration of only large-scale wind patterns, leading to the identification of many physically coherent BTD-AMVs as bad wind vectors. One possible application of the BTD-AMV would be in high-resolution numerical model assimilation, which could be the focus of a future study. Table 1. Brightness temperature difference and its applications and range for detection. SEVIRI BTD Applications BTD range/structure µm Cloud height relative to tropopause; overshooting clouds µm Cloud classification: water clouds, mixed-phase and ice clouds BTD > K : overshooting (but > K for tracking features) K < BTD K : highly developed cloud top BTD K : less developed convective cloud top BTD 1 K : water clouds 1 < BTD.5 K : mixed-phase clouds BTD >.5 K : ice clouds µm Cloud type; cirrus detection BTD > 5 K : thin cirrus (Strabala, Ackerman, and Menzelw 199) < BTD 5 K water, mixed phase, and thick cirrus BTD < sensor noise to cold brightness temperature or ash (Pavolonis et al. )

9 International Journal of Remote Sensing 57 Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January 3. Analyses and validation This section describes the analyses done to verify the quality of the AMV estimated by the BTD images. First, the BTD-AMV algorithm was evaluated using MSG imagery generated with a high-resolution NWP simulation, and the BTD-AMVs were derived from these simulated images. Using this approach it was possible to compare BTD-AMVs within the NWP winds, which can be considered the truth wind, and verify whether the BTD-AMV technique is able to detect wind flows associated with deep convective cloud tops. Next, the technique was applied over three days having large convective activity: 3,, and August. For each day, only a small segment of the whole MSG full disk image was used to isolate the strongest convective activity, to clarify the use of this tool (Figure ). The size of each zone was defined to capture all of the convective complexes during the life cycle for each case. The wind fields obtained were compared with wind profiles from the ECMWF high-resolution forecast model. Also, an indirect verification of the technique s ability was carried out, where the high-level wind divergence, calculated by the winds obtained by the proposed technique, showed a correlation with cloud life cycles BTD-AMVs from UK Met Office Unified Model simulated SEVIRI radiances The BTD-AMV was applied to a set of simulated SEVIRI radiances using the RTTOV (Radiative Transfer Model for TOVS) with the UK Met Office s Unified Model (UK-UM) atmospheric profiles as input data. The UK-UM was run with high time and spatial resolutions as part of the CASCADE project (NERC 8). CASCADE is a funded consortium project focused on studying organized convection in the tropical atmosphere using large-domain cloud system-resolving model simulations. These high-resolution model data allowed us to simulate SEVIRI radiances at higher spatial resolution than the actual 3 km SEVERI image. The., 8.7, 1.8, and 1 µm SEVIRI channels were simulated for July, from 13: to 13:5 UTC, to verify the technique presented in this article. The SEVIRI thermal channels were simulated using the radiative transfer model RTTOV version 9.3 (R9REP 1). SEVIRI radiances were simulated using the same UK-UM spatial resolutions of km and min. Version 7.1 of the UK-UM used in the CASCADE project and its configuration is described by Lean et al. (8). The atmospheric profiles used as input to RTTOV in this study are shown in Table. The first step was to build virtual UK-UM SEVERI images for the two time steps. The comparison between the model winds and the BTD-AMVs was done by means of a comparison of the u/v wind components within the NWP wind profile, looking for the pressure level where both BTD-AMV u/v components were lower. This comparison is known as best-fit adjustment and is usually done to compare normal AMVs within NWP analysis or forecasts to validate AMV datasets. In this section, the best-fit adjustment was used to verify whether the winds estimated by the BTD-AMV technique matched the NWP wind at a physically consistent pressure level. Here, only BTD-AMV vectors having scalar velocity less than 3 m s 1 compared with the UK-UM wind profile at some level were best-fit adjusted. The sensitivity of wind speed detection by the tracking algorithm, using full disk SEVIRI images (3 3 km each min), is around 3 m s 1 at sub-satellite points, so this threshold makes the comparison within the NWP very restricted. The BTD-AMV pressure was then set to the best-fit pressure found in the UK-UM fields. If this condition was not satisfied, the best-fit level was not considered and the BTD-AMV was set to an undefined pressure level. The algorithm was also computed using the UK-UM SEVIRI simulations in., 8.7, 1.8,

10 58 R.G. Negri et al Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January Figure. The three areas over the African tropical zone used for technique evaluation on 3 (top), (centre), and (bottom) August. Images from SEVIRI IR 1.8 µm channel are plotted as background.

11 International Journal of Remote Sensing 59 Table. 3D fields UK-UM profiles used as input for the RTTOV simulations. D fields Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January Pressure (hpa) Temperature (K) Specific cloud, liquid Specific cloud, frozen Cloud cover at each level (%) Ice cloud fraction (%) Water cloud fraction (%) Temperature at 1.5 m (K) Specific humidity at 1.5 m Surface temperature (K) Surface pressure (hpa) Land/sea mask and 1 µm channels using all criteria adopted for the BTD-AMV, as for instance, the brightness temperature between 1 and 5 K. This best-fit adjusted technique is hereafter termed BFP (best-fit pressure). Once the displacements of the clouds resolved by the cloud scheme of the UK-UM are directly related to the UK-UM winds, the AMVs estimated by a tracking algorithm must be close to the model s winds. For some locations, differences in direction and speed due to mismatch in the tracking algorithm or cloud model microphysical inconsistencies are expected. Figures 3 and show examples of the BTD-AMV and SEVERI-AMV, obtained from CASCADE/RTTOV SEVIRI simulations, for July, 13: to 13:5 UTC, over a small area of the tropical sub-saharan region. BTD-AMV is frequently different from collocated SEVIRI-AMV. No quality control filter was applied to the BTD-AMVs, so it is likely that some of the additional BTD-AMVs would not be valid vectors. However, for many situations these vectors are physically realistic. Due to the higher spatial resolution ( km against 3 3 km for real SEVIRI images), the wind fields can be estimated more precisely. The increase in AMV spatial density was smaller than expected, being clearly visible in Figure 3. The simulations are spatially smoother than real SEVIRI images, mainly in the inner cloud top portions, making automated tracking processes very difficult. The tracking procedure is based on spatial pattern recognition, and homogeneous targets (here, cloud segments) are harder to track due to ambiguous results. This homogeneity also compromises the channel combination, the resultant BTD images also being spatially smoother than the original ones. Also, the NWP cloud microphysical species distribution is simple if compared with the real situation. These two deficiencies in the model explain the lack of BTD-AMVs in the inner portion of the cloud tops, as well as the concentration of BTD- AMVs that fail in the best-fit adjustment in these areas. The description of the cloud boundary dynamics makes it possible to calculate the mass flux at the cloud top. Generally, the winds are very physically coherent, showing cloud top expansions at the moment strong convective cells are developing. BTD AMVs are sometimes more numerous than SEVERI-AMVs, and they show shear conditions of two very spatially consistent flows. The BTD-AMVs from the CASCADE simulations are located mainly over the cloud top boundaries, which are usually cirrus clouds. These BTD-AMVs are less consistent spatially than the default AMVs, but they are selfconsistent in time and space, indicating that this is not an error of the tracking algorithm. Table 3 presents the statistics of AMVs and BTD-AMVs that fail on the best-fit adjustment for each CASCADE/SEVIRI simulated channel and its channel combinations. The amount of mismatch for all channels and channel combinations is basically the same

12 R.G. Negri et al. Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January WV /7/3: UTC IR87 /7/3: UTC IR18 /7/3: UTC IR1 /7/3: UTC WV /7/3:3 UTC IR87 /7/3:3 UTC IR18 /7/3:3 UTC IR1 /7/3:3 UTC WV /7/3:5 UTC IR87 /7/3:5 UTC IR18 /7/3:5 UTC IR1 /7/3:5 UTC Figure 3. SEVERI-AMV for 13: to 13:5 UTC, July extracted from CASCADE/ RTTOVS SEVIRI simulations. From top to bottom:., 8.7, 1.8, and 1 µm SEVIRI channels. Vectors which best fit the wind of the model at some level are plotted in blue and those which do not fit are plotted in red. for the AMVs (around 1%) and BTD-AMVs, except for the WV. µm channel, which has the highest failure rate (around %). This higher value is explained by the clear sky wind vectors that are known as being noisier. The higher number of AMVs for the WV channel is mainly due to the possibility of tracking water vapour AMVs over clear sky situations. The BFP failure rate is very similar when using a SEVIRI channel and using a channel combination. This indicates that the use of BTD images enables wind estimation

13 International Journal of Remote Sensing 1 1 WV IR18 /7/3: UTC WV IR18 /7/3:3 UTC WV IR18 /7/3:5 UTC 3 Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January IR87 IR18 /7/3: UTC IR18 IR1 /7/3: UTC IR87 IR18 /7/3:3 UTC IR18 IR1 /7/3:3 UTC IR87 IR18 /7/3:5 UTC IR18 IR1 /7/3:5 UTC Figure. BTD-AMV for 13: to 13:5 UTC, July extracted from CASCADE/RTTOVS SEVIRI simulations. From top to bottom:. 1.8, , and µm BTD. Vectors which best fit the wind of the model at some level are plotted in blue and those which do not fit are plotted in red. with similar capability to the well-known default procedure using a single channel. We must remember that the SEVIRI/BTD-AMV used here had not passed through a quality control as is usually done for operational procedures. The failure rate is low and indicates that the technique is reasonable in regard to the numerical method. Table 3. Fractions of SEVIRI-AMV and BTD-AMV that fail in the best-fit adjustment for the CASCADE/SEVIRI simulations, total AMVs samples, and number of wind fields analysed. SEVIRI or BTD-AMVs are indicated by the image (or combination) used for estimation. Image type BFP fail (%) Total AMVs AMV fields (n) WV. IR1.8 μm IR8.7 IR1.8 μm 1.7 IR1.8 IR1 μm IR1.8 μm IR8.7 μm 1. 7 WV. μm. 9 IR1 μm

14 R.G. Negri et al. The application of the wind estimative technique based on channel combinations in a simulation scenario shows that it is possible and can be applied to real images, as will be shown in the next section. Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January 3.. ECMWF NWP comparison After the simulated scenario verification, the BTD-AMV technique was applied to real SEVIRI images and the wind vector obtained was compared with the ECMWF) NWP wind profiles. The comparison between the BTD-AMVs and the ECMWF NWP winds was done as described in Section 3.1 by best-fit adjustment. However, in this section, the purpose of this comparison was to understand whether wind vectors found within the same area were at different levels (associated with wind shear) or represented a different wind structure (apparent movement due to microphysical changes) at the same cloud top level. Once the NWP winds reproduce mesoscale winds, a BTD-AMV associated with an apparent movement due to microphysical changes will not match the model s profile, failing in the best-fit adjustment or matching the NWP wind profile at an unrealistic level. The ECMWF NWP forecast fields with 1 1 horizontal grid resolution and 91 vertical levels, have been used following similar procedures employed by Mahfouf and Rabier (). Neither SEVIRI nor BTD AMVs were assimilated in the ECMWF NWP runs used here. This example employs analysis or forecast fields, and is always chosen at the time nearest to the wind field being compared. Table presents the fraction of AMVs that failed BFP adjustment as well as the total number of AMVs of each single SEVIRI channel or BTD image and the number of AMV fields considered. The fraction of the SEVIRI/BTD-AMVs that failed on the BFP adjustment varied by around 3%. The fraction with the largest failure rate occurred for SEVIRI WV. µm AMVs, %, which is similar to that shown in the previous section, but much higher. The difference in grid resolution of the NWP explains this difference. BTD-AMVs, in the scale they are computed, do not represent MCS displacement but the area expansion of the convective system that is related to the upper level divergence (Machado and Laurent ). The MCS displacement that moves as a function of the steering level (Moncrieff and Miller 197) will not match a specific level of the mean wind described by the NWP. However, the winds described in this methodology are those contributing to this upper level divergence, but introducing the details of this complex inner structure in the cloud top. The cloud top has smaller scales of updrafts in the active cloud top regions, or the penetrative regions (Machado et al. 8), or the regions in dissipation stage. As no quality control test was applied to the SEVIRI-AMV and Table. Results of the best-fit pressure (BFP) between SEVERI-AMV and BTD-AMV with ECMWF data, total AMVs samples, and number of wind fields analysed. The SEVIRI or BTD- AMVs are indicated by the image (or combination) used for estimation. Image type BFP fail (%) Total AMVs AMV fields (n) WV. IR1.8 μm ,33 55 IR8.7 IR1.8 μm 9. 11,95 55 IR1.8 IR1 μm.7 138, 55 IR1.8 μm 9.,77 55 IR8.7 μm 8.8,3 55 WV. μm. 3,9 55 IR1 μm ,33 55

15 International Journal of Remote Sensing 3 Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January BTD-AMV, the similar failure rate of BFP indicates a very similar noise level for the BTD-AMV technique and the SEVIRI-AMV. BFP failure rates were initially expected to be larger for BTD-AMV because the resulting BTD images are noisier than the usual SEVIRI images, and this technique allows the retrieval of cloud top dynamics that are not necessarily reproduced by the ECMWF NWP. However, the failure rates are basically the same as all the IR AMV fields and always higher for the WV AMVs. A fraction of these vectors is likely describing the cloud top boundary displacements or perhaps the growth or displacement of an area domain by a specific kind of ice crystal near the cloud top. Under these situations, it is expected that the NWP will not reproduce this cloud boundary displacements, so the AMV related to this signal will not match the NWP at any level or the match will be at an unrealistic level. Figure 5 shows histograms of the BFP differences between each of the three BTD-AMVs used in this study and their respective SEVIRI-AMV single channel for situations where at least one SEVIRI-AMV, from any channel, is at the same position (same target window location). All collocated SEVIRI-AMVs and BTD-AMVs were compared, with each possible pair (SEVIRI-AMV, BTD-AMV) giving one BFP difference value each. Situations where more than one SEVIRI-AMV is at the same position than one or more BTD-AMV is unlikely and happens only at cloud tops. Because only very high cloud tops are the focus of the BTD technique, these SEVIRI/BTD-AMVs will describe the wind flow in a narrow pressure layer. The left column in Figure 5 shows the differences between the pressure levels assigned to SEVERI-AMV and Frequency (%) Frequency (%) Frequency (%).8... IR87 IR18 BFP difference IR87 IR18 SPD diff. BTD_AMV below IR87 IR18 SPD diff. BTD_AMV above.. Frequency (%) BTD dif: SEVIRI BTD_AMV (hpa) SEVIRI_AMV BTD_AMV (ms 1 ) IR18 IR1 BFP difference IR18 IR1 SPD diff. BTD_AMV below Frequency (%).... Frequency (%) Frequency (%)... 1 SEVIRI_AMV BTD_AMV (ms 1 ) IR18 IR1 SPD diff. BTD_AMV above BTD dif: SEVIRI BTD_AMV (hpa) SEVIRI_AMV BTD_AMV (ms 1 ) SEVIRI_AMV BTD_AMV (ms 1 ) WV IR18 BFP difference WV IR18 SPD diff. BTD_AMV below WV IR18 SPD diff. BTD_AMV above BTD dif: SEVIRI BTD_AMV (hpa) Frequency (%)... 1 SEVIRI_AMV BTD_AMV (ms 1 ) Frequency (%) SEVIRI_AMV BTD_AMV (ms 1 ) Figure 5. BFP and speed difference histograms between SEVERI-AMV and BTD-AMV for three channel combinations: IR 8.7 IR 1.8 µm (top), IR 1.8 IR 1. µm (centre), and WV. IR 1.8 µm (bottom). The left column shows the pressure height differences between SEVERI-AMV and the BTD-AMV (negative values denote that BTD-AMV is at a lower altitude than default AMV). The middle column shows the speed differences between SEVERI-AMV and BTD-AMV only for the AMV pairs at which the BTD-AMV is at a lower altitude than SEVIRI-AMV, and the right column shows the speed differences for the opposite situation. A positive speed difference means that the BTD-AMV is slower that the respective SEVIRI-AMV.

16 R.G. Negri et al. Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January BTD-AMV. Negative values mean that BTD-AMV is set to a lower level than SEVIRI- AMV. The middle column shows the speed differences between SEVERI-AMV and BTD-AMV only for situations where BTD-AMV is at a lower level (when SEVIRI- AMV minus BTD-AMV BFP differences are negative), and the right column shows the speed differences for the opposite cases. The BFP histogram shows a maximum peak at hpa, meaning that in the majority of the cases, both vectors are at nearly the same level but are slightly different. The BTD-AMV gives information about the wind field spatial structure in the cloud top. The histogram shows that nearly all values are ±1 hpa. It also shows that vectors retrieved at the same location can similarly indicate information about the wind shear in the cloud top. Positive speed differences in histograms mean that the BTD-AMV is slower than its collocated SEVIRI-AMV and negative differences have the opposite meaning. Figure 5 (middle and right-hand columns) shows that the BTD-AMV has a larger number of vectors with lower speeds when it is at a lower level than the collocated SEVIRI-AMV, and higher speeds when it is assigned to higher levels than the collocated SEVIRI-AMV. This result is physically consistent since, generally, higher level winds are associated with a high level of wind divergence. This physical consistency shows that this methodology gives additional details about the wind structure near the convective cloud tops. Figures and 7 show SEVERI-AMV and BTD-AMV, respectively, extracted for 3 August, from : to :3 UTC. To clarify the wind fields, we selected a very small image segment and plotted each kind of vector separately over its own image. Vectors which failed in the best-fit adjustment are plotted in red while those which got a BFP are plotted in blue. Refer to Table 1 for BTD intervals and their respective physical means. BTD-AMVs are more numerous over the cloud top boundaries, where cirrus clouds (BTD µm >.5 K and BTD µm > 1 K) show a typically high level of wind divergence. In the BTD images, the spatial patterns are better defined on the horizontal boundaries than on the inner zone of the convective cloud top. When the tracking process is based on cloud pattern recognition, BTD-AMV is estimated mostly at the top boundaries. An opposite scenario happens for the SEVIRI default images, where the spatial patterns are well defined all over the cloud top, leading to estimation of SEVIRI-AMV (channels 8.7, 1.8, and 1 µm) mainly over the inner region of the top cloud. In most cases, SEVIRI-AMVs are generally not estimated over the cloud top boundaries, unlike BTD-AMVs. The spatial and temporal consistency of SEVIRI and BTD AMV are illustrated in Figures and 7, which show that many AMVs are physically realistic, based on the expected wind flow that they represent as well as by their spatial consistence. For example, the red SEVIRI-AMV on the southern portion of the convective system describes the southwardly cloud expansion that can be easily verified by the background BTD image. This behaviour is also captured by the BTD-AMV technique, but not as well as using SEVIRI-AMV. However, the opposite situation occurs at the northwestern point, where the BTD-AMV better captures upper-level divergences and expanding areas than individual SEVIRI channels. This clearly illustrates the additional information that can potentially be captured using the BTD technique, complementing existing AMVs. The example shown in Figures and 7 is a mesoscale convective system in the first hours of its life cycle, where a strong horizontal expansion is occurring. The temporal consistency of these movements and their agreement with realistic pressure levels of the ECMWF NWP at the southern and northwestern boundaries indicate that physical information exists that can improve the description of the cloud processes. The following

17 International Journal of Remote Sensing 5 Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January WV 3/8/5: UTC IR87 3/8/5: UTC IR18 3/8/5: UTC IR1 3/8/5: UTC WV 3/8/5: UTC IR87 3/8/5: UTC IR18 3/8/5: UTC IR1 3/8/5: UTC section will explore an application of the BTD-AMV technique for nowcasting to prove that these techniques extract wind in a physically consistent manner WV 3/8/5:3 UTC IR87 3/8/5:3 UTC IR18 3/8/5:3 UTC IR1 3/8/5:3 UTC Figure. SEVIRI-AMV for : to :3 UTC, 3 August. From top to bottom:., 8.7, 1.8, and 1 µm SEVIRI channels used. Vectors assigned to the best-fit pressure from the ECMWF model at some level are plotted in blue, and those which do not fit are plotted in red Relationship between divergence and BT D-AMV This section presents a verification of the relationship between the high levels of wind divergence, calculated from BTD-AMV, and the co-located evolution of mean brightness temperature.

18 R.G. Negri et al. Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January WV IR18 3/8/5: UTC IR87 IR18 3/8/5: UTC IR18 IR1 3/8/5: UTC WV IR18 3/8/5: UTC IR87 IR18 3/8/5: UTC IR18 IR1 3/8/5: UTC The high level of wind divergence is directly related to the development of clouds, especially the deep convection (see Machado and Laurent ). When a high level wind divergence is present and it is associated with cloud development, a decrease in local mean brightness temperature is expected because the cloud tops are becoming higher and colder. An inverse situation is expected when high-level wind convergence is present. A way to verify whether the proposed BTD-AMV technique is capable of detecting the wind flows associated with convection development is to analyse the life cycle of the convection and the wind divergence calculated from BTD-AMVs in the same location. Following this hypothesis, the horizontal divergence field was computed using BTD- AMV. IR 1.8 µm only for BTD between 3 and K. Using this BTD range, deeper cloud tops were excluded and only clouds with potential for growing/overshooting in the following hours were taken in account, being the best case to evaluate whether the proposed technique is physically consistent. The trend in brightness temperature was calculated by the MSG infrared window at 1.8 μm. For this analysis, a sequence of METEOSAT-8 images with min intervals, from 1:5 to :5 UTC for 3 August was used. The first step was to find regions with sufficient density of BTD-AMVs and interpolate these vectors in space to further calculate the divergence field using discrete WV IR18 3/8/5:3 UTC IR87 IR18 3/8/5:3 UTC IR18 IR1 3/8/5:3 UTC Figure 7. BTD-AMV for : to :3 UTC, 3 August. From top to bottom:. 1.8, , and µm BTD. Vectors assigned to the best-fit pressure from the ECMWF model at some level are plotted in blue, and those which did not fit are plotted in red

19 International Journal of Remote Sensing 7 Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January calculations. The brightness temperature trend was computed for each point where divergence was assessed. The brightness temperature trend (ΔBTir) in the IR 1.8 µm channel was assessed by calculating the mean brightness temperature in a pixel image segment. The ΔBTir was computed for, 3, and 5 min after the time of the previous image used to compute BTD-AMV. For each of these three time steps, the image segment was centred at the location where the cloud segment was used to calculate BTD-AMVs, and consequently the wind divergence, to take into account the advection of the MSC as a whole. The definition of the possible position in each time step was estimated by the large-scale wind flow. Negative values of ΔBTir indicate a decrease in mean brightness temperature, so the clouds are in a developing stage, while positive values indicate situations where clouds are dissipating. To verify the expected relationship for different periods of the convection life cycle, the whole image sequence was divided in successive subsets of h each (1:5 to 1:5 UTC, 1:5 to :5 UTC, and so on). Figure 8 presents the relationship between the horizontal BTD-AMV divergence and the mean brightness temperature as described, where the plots of each subset are presented on each line. In the first two periods (1:5 to 1:5 and 1:5 to :5 UTC), a concentration along the vertical axis is observed, indicating weak divergence or convergence, i.e. as expected for this period of the day in tropical Africa. For the next period (:5 to 1:5 UTC), the values of the bi-dimensional histogram are concentrated in the two quadrants where the brightness temperature trend is negative (development of cloud tops). In the next period (1:5 to :5 UTC), the correlation between wind divergence and decrease in mean brightness temperature is clear, remaining so for the next period (:5 to :5 UTC). For the final period, a correlation between wind convergence and decrease in mean brightness temperature is still clear. One can expect an increase in Tir but, instead, a decrease predominates as an effect of the cirrus that remains after dissipation of the deep convection. This analysis shows that the proposed technique is able to detect a well-known characteristic of the convection life cycle and indicates a possible application for nowcasting.. Conclusions This study tested the possibility of detecting inner/outer movements of deep convective cloud tops through the application of a modified AMV tracking algorithm to various infraredmeteosat-8 channel combinations. High-spatial resolution numerical simulations done by UK-UM, where clouds were explicitly resolved, were used to test the ability of the BTD-AMV algorithm, retrieved BTD-AMVs were compared to corresponding ECMWF NWP wind profiles, and a physically consistent relationship between the wind divergence calculated from a specific BTD-AMV group and the convection life cycle was found. The results show that the proposed technique detects physically consistent movements that are sometimes different from the flow extracted by single SEVIRI channels at the same place, giving additional information on cloud processes. These motions correspond to local wind shear situations, divergence, or convergence zones, or apparent movements where zones with specific microphysical properties grow or dissipate. In many ways, BTD-AMV complements SEVIRI-AMV, indicating a promising use for increasing the spatial coverage of the usual AMVs and also providing higher-resolution wind fields that may be used for assimilation in high-resolution NWP models in the future.

20 8 R.G. Negri et al. WV IR18 3 Aug BTD: 3 to K Downloaded by [Instituto Nacional De Pasquisas], [Luiz A.T. Machado] at 8:3 3 January min after 3 min after 5 min after BTD AMV Div. (S 1 ) 1 5 BTD AMV Div. (S 1 ) 1 5 BTD AMV Div. (S 1 ) 1 5 min after 3 min after 5 min after BTD AMV Div. (S 1 ) 1 5 BTD AMV Div. (S 1 ) 1 5 BTD AMV Div. (S 1 ) 1 5 min after 3 min after 5 min after BTD AMV Div. (S 1 ) 1 5 BTD AMV Div. (S 1 ) 1 5 BTD AMV Div. (S 1 ) 1 5 min after 3 min after 5 min after BTD AMV Div. (S 1 ) 1 5 BTD AMV Div. (S 1 ) 1 5 BTD AMV Div. (S 1 ) 1 5 min after 3 min after 5 min after BTD AMV Div. (S 1 ) 1 5 BTD AMV Div. (S 1 ) 1 5 BTD AMV Div. (S 1 ) 1 5 min after 3 min after 5 min after BTD AMV Div. (S 1 ) 1 5 BTD AMV Div. (S 1 ) 1 5 BTD AMV Div. (S 1 ) 1 5 1:5 to 1:5 UTC 1:5 to :5 UTC :5 to 1:5 UTC 1:5 to :5 UTC :5 to :5 UTC :5 to :5 UTC Frequency (%) Figure 8. Relationship between brightness temperature trend (ΔBTir) in the IR 1.8 µm channel and horizontal BTD-AMV divergence for BTD WV. IR 1.8 µm in the range 3 to K for (left), 3 (centre), and 5 (right) min after divergence calculation. Data from 1:5 to :5 UTC, with min time resolution, from 3 August were used, being divided into successive periods of h (each line represents one of these periods).