Comparison of cloud statistics from Meteosat with regional climate model data

Comparison of cloud statistics from Meteosat with regional climate model data R. Huckle, F. Olesen, G. Schädler Institut für Meteorologie und Klimaforschung, Forschungszentrum Karlsruhe, Germany (roger.huckle@imk.fzk.de / Phone: +49 7247 82 3822) Abstract The satellites of the METEOSAT series have continuously measured the state of the atmosphere as well as the land and sea surfaces over the past 30 years. With the second generation (MSG) in operation and the third generation (MTG) in preparation another 30 years of consistent measurements can be expected. This unique data set is ideal for climate analysis. A cloud detection algorithm using spectral information from all seven window channels was developed for MSG-SEVIRI. The algorithm is based on the APOLLO cloud detection scheme for AVHRR. The cloud data derived from the satellite data was compared with the modelled clouds in the CLM (climate version of the local model from the DWD). In a first step convective situations were compared. The agreement on convective days is approx. 10 15% worse than on days without convection, the variation in time and space being quite large. Overview Little is known of the impact of a global temperature rise on the atmospheric water cycle. A higher air temperature leads to an increase of moisture capacity of the air. This additional possible water vapour content in the atmosphere can have different impacts on the water cycle: more water vapour can intensify single precipitation events or the annual precipitation sum can be increased. But also a shift in precipitation distribution is possible, as due to an increase of water vapour condensation and cloud forming can take place at different locations. In case of convective situations more energy in form of condensation heat is available leading to an intensification of thunderstorms. If this would result e.g. into an overshooting into the tropopause region (including the entrainment of moist air) or in a broadening of the basis is not clear. The forming of clouds and with it the radiation balance can change due to more water in the atmosphere. An increase of the amount of clouds is possible as well as a shift in time and place. A change in distribution and occurrence of different cloud types could take place. An increase of clouds would affect the radiation budget of the atmosphere. Sunlight reflecting at the top of the clouds leaves less short wave radiation reaching the ground. Additional clouds on the other hand would reduce the cooling of the lower air layers at night. Such changes in the radiation balance can itself have an influence on the atmospheric water cycle. A multi annual analysis of the cloud cover and structure as well as statistics concerning cloud types can reveal information on such changes. Cloud detection in MSG data To have an automatic and standardised analysis of existing MSG data an algorithm to detect clouds was developed. For this pixel based cloud mask spectral information of all seven window channels form SEVIRI were used. The algorithm is based on the APOLLO cloud detection scheme for AVHRR (Saunders and Kriebel, 1988). The algorithm uses single channel threshold tests, channel differences and ratios. For 24 hour cloud detection the infrared channels of the MSG are dedicated. From the two SEVIRI (Spinning Enhanced Visible and InfraRed Imager) channels in the terrestrial atmospheric window channel 9 with a wave length centred at 10.8 µm is best used, as it is least affected by water vapour. The first test in the cloud detection algorithm uses the Top Of Atmosphere (TOA) Brightness

Temperature (BT) in this channel. The current temperature is compared with a background temperature for the cloud free case. If the measured BT is lower than the certain threshold compared to the background temperature the pixel is flagged as cloudy. However it is not trivial to obtain this background temperature. The algorithm developed at the IMK uses a 30 day sliding window for every pixel and time slot centred at the current day. From this 30 day series the maximum temperature is taken. This results in a diurnal curve of the maximum temperature for each pixel. To avoid jumps from one time slot to the next, the maximum temperatures are used in a thermal surface parameter model (TSP) (Göttsche and Olesen, 2001). During the day (sunshine hours at the specific location) the model uses a sinusoidal wave and during the night (cooling) a declining e-function. From a total of seven model parameters, such as temperature minimum and maximum, sun rise and set etc. an idealised diurnal temperature wave is calculated. The resulting values are used as a background temperature. The TSP model can only give results where there is a diurnal wave already in the input data. The use over sea surfaces is therefore not possible, but the variability from one slot to the next is small anyway. At these points the maximum values are used without the TSP. The second 24 hour test is the so called thin cirrus test for cirrus with an optical thickness < 1.5. In this split-window test the channels 9 and 10 (10.8 µm and 12.0 µm) are used. Thin cirrus clouds have different absorption, transmission and emissivity characteristics in these two wave lengths. At 10.8 µm thin cirrus is grey, it has some transmissivity, where as at 12.0 µm thin cirrus is nearly black, hence it has no transmissivity. This leads to a higher BT for thin cirrus at 10.8 µm, as a bigger part of the radiation from the earth s surface reaches the sensor. If a certain threshold in the channel difference is exceeded, the pixel is flagged as cloudy. For clouds with a large vertical extension the surface radiation is completely absorbed. Even the opposite effect takes place; cirrus clouds with an optical thickness > 1.5 are colder in 10.8 µm than in 12.0 µm, as due to the lower emissvity less radiation is emitted. The third test can only be used during the day and uses channel 1 with a centre wave length at 0.6 µm. This channel is better for cloud detection than the second channel in the visible spectrum at 0.8 µm. In both channels the reflectivity of clouds (ice and water) is very high and that of water away from sun glint areas is very low. The important difference is found over vegetated areas. The chlorophyll in plants has two absorption maxima, one is at 0.4 0.5 µm (blue spectral range) and a second one at 0.6 0.7 µm (red spectral range), therefore plants appear green as the reflectivity of chlorophyll is highest in the green spectrum. The second absorption maximum is right in the range of SEVIRI channel 1. Thus vegetated areas have a low reflectivity in this channel. At 0.8 µm the reflectivity is much higher. In analogy to the first test, a sliding 30 day window is used, but this time collecting the minimum value. These values have a small diurnal variation and only moderate changes from one slot to the next. If the measured value exceeds a threshold based on the background value the pixel is flagged as cloudy for this time slot. The detection of fog or low stratus was nearly impossible with Meteosat first generation, as fog is only marginally cooler than the underlying surface. With MSG this is now possible due to new channels. The channel difference between channel 9 and 4 (3.9 µm) or 7 (8.7 µm) respectively can be used for fog detection. The difference between channel 9 and 7 can be used day and night, but gives not so good results. The channel difference between 9 and 4 can only be used during night, as channel 4 reflects solar radiation. The algorithm uses only this difference for the night scheme as for the fog detection during day the visible channel is sufficient enough. Fog or low stratus has an emissivity of about 1 at 10.8 µm and between 0.8 and 0.9 at 3.9 µm. For a difference above 5 K a pixel is classed as foggy. Another new channel in the SEVIRI instrument is channel 3 in the near infrared (1.6 µm). This channel can be used for the detection of snow during day time. The reflectivity of water clouds are more ore less the same in channel 1 to 3, the ratio therefore is about 1. Ice clouds and snow however are nearly black at 1.6 µm, thus no sun light is reflected. The ratio between the two channels in the visible spectrum and that in near infrared changes to about 2. For the algorithm developed at the IMK the snow detection is only used on pixels flagged as cloudy by the previous tests. To differentiate between snow on the ground and ice clouds pixels with a temperature above -10 C are treated as snow and are then set to non cloudy. The automatic snow detection during night is very problematic and not implemented in this algorithm. Automatic clouds detection has difficulties during dawn and in coastal areas. In a coastal pixel there is always water and land. The exact location of a pixel has some variance over time and therefore the ratio of land and water in a costal pixel changes. The spectral characteristics of these two surfaces mix

and precise cloud detection is more difficult. During dawn the tests for either day or night can not be used sufficiently, and the cloud detection is more difficult. Other difficulties arise in the IR-threshold test if land surfaces are not as warm as the background value would indicate due to earlier rain and missing solar irradiation. This can lead to an increase of false detections e.g. behind a cold front with predominant cell convection. To reduce these false detections pixels only flagged in the IR-threshold test are checked with the 0.6 µm channel. If the value is very close to the 30-day minimum the probability of a cloud is very small. With this pixels that are only slightly beneath the threshold in the IR test will be assigned as cloud free. The result is a binary cloud mask for every pixel at every time slot, either cloudy or non cloudy. A pixel is cloudy as soon as one test is positive for clouds, but all other test are done nonetheless. Additionally information is stored which test detected clouds. Comparison of cloud masks from satellite data and numerical weather prediction model The results of the MSG cloud mask were compared with modelled data from the CLM, the climate version of the local model from the DWD. The time period compared is from March to August 2005. The model area reaches from the Adriatic in the southeast to the North Sea in the northwest (fig. 1). It includes the Po lowlands, all of the Alps and southern Germany; it reaches from the Czech Republic in the east to Paris in the west. The main focus of the modelling and the comparison lays on southwest Germany (including the Upper Rhine valley, the Black forest and the Swabian Alp) and the Vosges mountains in France (fig. 2). Earlier studies have shown that with a southerly flow extreme precipitation was modelled when the Alps were not included in the modelling region; therefore the current model area includes the Alps in the complete north to south extension. Fig. 1: Topography of the CLM model area This first part of the analysis is concentrated on convective weather situations in southwest Germany. The comparison was carried out only for days with convective clouds forming. Within the six months there were 69 days. Every 30 minutes the results were compared, which leads to a total of over 3300 comparisons. The 69 days were spread over the six months as follows: March 12, April 8, Mai 9, June 12, July 12, August 15.

Fig. 2: Topography of the area of interest with the Black Forest, the Rhine Valley, the Vosges Mountains and Swabian Alp 80 77 74 71 Fig. 3: Agreement in % (scale on the right) between CLM and cloud detection on days with convective weather situations (March-August). 68

Table 1: Detailed comparison between CLM clouds and clouds detected in MSG data Period/Region Agreement Clouds only CLM Clouds only MSG March August (convective) 73 % 16 % 8 % March August (all) 80 % 10 % 8 % March August (not convective) 84 % 5 % 8 % March (convective) 74 % 17 % 6 % April (convective) 72 % 8 % 17 % Mai (convective) 77 % 10 % 5 % June (convective) 73 % 15 % 10 % July (convective) 65 % 26 % 7 % August (convective) 75 % 16 % 6 % March August (convective)/ Rhine Valley March August (convective)/ Black Forest March August (convective)/ East of Black Forest March August (convective)/ East of Lake Constance 71 % 21 % 7 % 73 % 14 % 12 % 68 % 20 % 11 % 80 % 13 % 5 % July (convective)/ Rhine Valley 55-62 % 28-40 % 2-9 % August (convective)/ Rhine 69-74 % 21-29 % 2-4 % Valley July (convective)/ Black Forest 60-65 % 20-34 % 6-12 % August (convective)/ Black 80-84 % 9-16 % 4-10 % Forest July (convective)/ East of Black Forest August (convective)/ East of Black Forest July (convective)/ East of Lake Constance August (convective)/ East of Lake Constance 61-67 % 20-29 % 7-11 % 63-67 % 22-26 % 5-10 % 80-84 % 12-14 % 2-4 % 70-75 % 15-20 % 5-13%

The over all agreement in the study area is at 80%, for convective days this drops to 73% (fig. 3) and for non convective days it rises to 84%. The difference between convective and non convective days is highest to the east of the Black Forest and lowest near the Lake Constance (fig. 4). With the exception of April the clouds only present in the MSG data is below 10%. The biggest variations are in the amount of clouds only found in the CLM data and not in the MSG data. This varies from 8% in April to as high as 26% in July, for more details see table 1. For all convective days the best agreement is found to the east of Lake Constance. Over the Black Forest the agreement is average, but the amount of clouds only detected in the MSG data is also very high. The worst agreement is to the east of the Black Forest. The difference in the agreement between July (fig. 5) and August (fig. 6) is quite large. This is mainly due to a high amount of clouds only modelled in the CLM data and not detected by the MSG cloud detection algorithm. Especially in the Rhine Valley the amount of clouds only found in the CLM data is very high. Over the Black Forest and the Rhine Valley the agreement is significantly better during August than in July. To the East of the Black Forest the values are similar in both months. Only at the eastern end of Lake Constance the agreement is better in July. On a daily basis there are also large variations in the agreement. Some areas can have an agreement of lower than 10% on one day and more than 90% on the next. 18 15 12 9 Fig. 4 Difference in percentage-points between convective and non convective days from March to August 2005 6

87 78 69 60 51 Fig. 5 Agreement in % on convective days in July 2005 91 83 75 67 59 Fig. 6 Agreement in % on convective days in August 2005

Conclusion and outlook The quality of the cloud detection on convective days is good, as most clouds are high reaching and therefore very cold at the top, this makes it easy to identify them with the IR-threshold test. During the day this is supplemented with the test in the visible spectrum. Nevertheless there are occasions on which the cloud detection algorithm for MSG detects too many clouds. Especially during the night the algorithm sometimes is too sensitive. On average 8% of the time only clouds in the MSG data are detected. Some of this has to be counted towards the algorithm picking up too many clouds. At 16% of the time the modelled clouds have no counterpart in the clouds detected by the algorithm for MSG. As the algorithm tends to be very sensitive to clouds it is very likely that most of these clouds are wrongly modelled. As the results of the modelled clouds are poor in some places there is more research necessary concerning spatial resolution and convection parameterisation in the model. Especially the too many clouds to the west of the Black Forest indicate a problem in the model. The cloud detection algorithm for MSG has to be checked, especially for its too high sensitivity at night. Surprisingly high is the variation from month to month. Over the Black Forest the variation in the agreement is especially high. During July the agreement there is very low (approx. 60%). The MSG data has less than 50% clouds but in the CLM more than 65% of the time clouds are modelled. But for August the agreement is above 80%. The further development of the cloud detection algorithm for MSG includes the step away from the pixel by pixel basis towards a segment based analysis. This provides additional information. Besides the spectral signature of a cloud spatial, structure, context and neighbourhood information can be used for the analysis. The additional information gained through the process of segmentation enables the classification of spectral similar clouds into different classes. A cirrus can then be identified as a condensation trail (long and straight) or a part of a thunderstorm (sickle form and in neighbourhood to a CB). The potential of the algorithm has been shown in NOAA/AVHRR case studies and has been successfully used with MSG data. The use of this technique enables the statistical analysis of cloud occurrence and types over large areas and long time series. Changes in the cloud cover and type can be detected. Literature Göttsche, F. M., Olesen, F. S. (2001). Modelling of diurnal cycles of brightness temperature extracted from METEOSAT data. Remote Sensing of Environment, 76, 337-348 Saunders, R. W., Kribel, K. T. (1988). An improved method for detecting clear sky and cloudy radiances from AVHRR data. International Journal of Remote Sensing, 9 (1), 123-150.