VALIDATION RESULTS OF THE OPERATIONAL LSA-SAF SNOW COVER MAPPING

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1 VALIDATION RESULTS OF THE OPERATIONAL LSA-SAF SNOW COVER MAPPING Niilo Siljamo, Otto Hyvärinen Finnish Meteorological Institute, Erik Palménin aukio 1, P.O.Box 503, FI HELSINKI Abstract Hydrological processes and climate are highly affected by the seasonal snow cover. Snow areal extent is essential information both for hydrology and climatology. EUMETSAT's Land Surface Analysis Satellite Applications Facility (LandSAF) has been producing daily snow cover products since New version 2 of the algorithm has been in operational use since We compare different versions of LandSAF snow cover product with NOAA/NESDIS snow cover product in Europe and extend the earlier analysis to NOAA/NESDIS has operated one of the first snow services that employ both optical and microwave satellite data and in situ observations. However, the algorithm is not fully automatic and it sometimes needs human intervention. Our analysis shows that the new LandSAF algorithm agrees well with NOAA/NESDIS IMS product. Both products present a reasonable and realistic snow cover analysis in clear sky conditions, particularly during the winter season and it is no longer feasible to use one of them as preferred ground truth. Further validation must rely on surface observations such as SNORTEX campaign measurements and SYNOP observations from weather stations. Limits of using these observations for validation purposes are discussed. Some validation results based on these are presented. Geostationary satellites have also resolution restrictions in polar areas. To improve the snow cover analysis a new version of the algorithm for MetOP/AVHRR data is under development and some experimental results of the winter are presented. INTRODUCTION Since 2007 EUMETSAT's LSA SAF has been producing daily snow cover product with a baseline algorithm for the areas covered by MSG/SEVIRI instrument. The old version was based on the cloud mask product of the EUMETSAT's Nowcasting Satellite Application Facility (NWC SAF). The aim of NWC SAF cloud mask is to classify cloud cover. Thus the snow detection is only a rather limited byproduct. This approach has some severe limitations; hence a new version has been developed at Finnish Meteorological Institute (FMI). This significantly changed and improved version of the snow cover algorithm has been used to generate the snow cover product of LSA SAF since summer The snow detection algorithm produces snow cover product over the MSG/SEVIRI image area which is divided to four different geographical regions (Europe, North Africa, South Africa, and South America) as all LSA SAF products. In the future a product using polar orbiting METOP satellite data will also be developed. The best reference for satellite product validation would be in situ measurements, but such data is almost impossible to collect in large scale because of the serious limitations in the way the snow cover is reported in the weather observations. The presence of snow is not always reported in many stations, and the absence of snow is not usually reported at all. Therefore we used NOAA/NESDIS IMS product (Helfrich et al, 2007) as a baseline to which both LSA SC products are compared. NOAA/ NESDIS IMS product uses several other data sources which include also microwave instruments. These can be used to detect dry snow under the clouds or in bad lighting conditions. However, the algorithm is not fully automatic.

2 ALGORITHM DEVELOPMENT The algorithm employs six SEVIRI channels (0.6, 0.8, 1.6, 3.9, 10.8 and 12.0 µm), sun and satellite zenith and azimuth angles, land cover type and land surface temperature classification produced by LSA SAF. The capabilities of channels around 1.6 µm and 3.9 µm to discriminate low clouds and snow have been widely reported (Matson 1991, Kidder 1984). The development of the snow cover classification algorithm was started by subjective classification of selected areas in representative MSG/SEVIRI images. We used several images between November 2006 and August 2007 from which we selected samples of snow covered and snow free areas, different cloud types and also areas where the surface type could be seen through clouds. Over half a million MSG/SEVIRI pixels were classified to form a data set for algorithm development. The actual extent of snow cover was determined subjectively using ground observations and MODIS images. Figure 1 shows two examples of scatter plots demonstrating how the various classes differ from each other. These plots suggest the possibility to automatically classify MSG/SEVIRI images to snow covered and snow free classes. Figure 1: Two scatterplots of the development data set. On the vertical axis both plots show the radiance ratio of SEVIRI channels 2 and 3. On the left the horizontal axis is brightness temperature difference of channels 10 and 4. On the right horizontal axis is the sun azimuth angle. The plots in Figure 1 and other similar plots where used to develop a set of thresholding rules which are used for classification. All pixels are by default unclassified and then several tests are applied one by one. As a result each pixel is classified or remains unclassified. The pixel is unclassified if it is too dark, cloudy or in the area where satellite zenith angle is too high. There are also rules which remove obvious misclassifications such as pixels where the land surface is too warm to contain snow. The class of partial or probable snow is used if the pixel is both snow free and snow covered during the same day or if the snow cover in the pixel is patchy or otherwise partial. The daily Snow Cover (SC) product classifies each pixel as snow free, partially snow covered or totally snow covered based on MSG/SEVIRI data. For the daily LSA SAF snow cover product, all snow cover maps which are produced every 15 minutes are combined. The algorithm counts the number of different classifications for each pixel and then determines the final daily classification if there have been reasonable amount of cloud free observations during the day. ALGORITHM VALIDATION The algorithm was tested using data from from January 2007 to February The current snow cover product was compared to the old LSA SAF snow cover products and NOAA/NESDIS IMS products. Figure 2 shows both IMS and LSA-SAF snow cover products and SEVIRI RGB image for January 26th In the Figure 3 we have an example of the product in smaller area on top of the

3 satellite image. These images show that the new LSA SC system produces realistic snow cover fields and the edges of the snow covered areas are detected quite well. Simple comparisons such as these do not tell us much about the quality of the product, but we can use time series of several quality measures, such as probability of detection, false alarm ratio (FAR) and skill scores. The circles in the middle panel of the Figure 2 show some differences between IMS and LSA-SAF snow product. Circle 1 shows an example of a possible hand drawn edge in the IMS snow product. In the area 2 in Denmark IMS classifies the area as snow covered and LSA-SAF as snow free. Based on satellite image it seems that the latter one is correct. In the area 3 we have another example of such differences. Area 4 shows an example of mountain area, although LSA-SAF snow product has not been developed for mountain regions where several factors, such as topography, make the snow detection difficult. Figure 2: On the top SEVIRI channel combination 321, on the middle NOAA/NESDIS IMS snow product and on the bottom LSASAF snow cover product for January 26th, In both satellite products snow is white and snow free is green. In the LSASAF product red marks unclassified pixels. In the middle panel some examples of differences between snow cover products are circled.

4 Figure 3: Example of the LSA SAF snow cover product from Southern France, 29th January The picture shows a comparison of SEVIRI RGB 321 image (at 1000UTC) in background. On top of that the dots show the daily snow cover product. Each pixel is classified, but only cloudy (magenta) and snow covered pixels are marked (white is snow and blue is partial snow). Clear pixels without dots are snow free. In the satellite image snow is blue. Figures 4 to 6 present a set of time series plots, which we can use to evaluate the differences between the products from January 2007 to February The skill score commonly used for summarizing the 2*2 contingency table for categorical events, such as presence of snow, is the Heidke Skill Score (HSS, defined for example in Jolliffe and Stephenson, 2003). The HSS shows that during the winter the new LSA SC is not very far from IMS snow cover (Figure 4). The old version of the LSA SAF snow cover product seems to be less reliable and accuracy seems to be quite low. During the summer HSS is much lower, because there are relatively more differences between the products when there are only a few snow covered pixels which are also more probably misclassifications caused by clouds, for example. This can be clearly seen also in the BIAS (Figure 5) and False Alarm Ratio (Figure 6) which show that the new LSA SAF snow cover and IMS do not have significant differences during the summer. The new version of the LSA-SAF snow cover product does reduce the BIAS when compared to the old version during the winter. The same conclusion can made made based on False Alarm Ratio. Although we have seen that there are misclassifications in the IMS product, same applies also to LSASAF snow cover product. This comparison between the two show that the quality of both products is quite good, but we can not conclude which one is more reliable. For that we need comparisons with land surface observations, which are difficult to obtain for large areas.

5 Figure 4: Heidke Skill Score of the old and new LSA SAF snow cover product when compared to IMS product between January 2007 and February Red is old LSA-SAF snow cover product, black is the new version. Dark shades mark the days when there is reasonable amount of snow covered pixels for comparison, light shades mark the day when there are so few snow covered pixels that the comparison is not reliable. Usually these days are during the summer. Figure 5: BIAS of the old and new LSA SAF snow cover product when compared to IMS product between January 2007 and February Red is old LSA-SAF snow cover product, black is the new version. Dark shades mark the days when there is reasonable amount of snow covered pixels for comparison, light shades mark the day when there are so few snow covered pixels that the comparison is not reliable. Usually these days are during the summer.

6 Figure 6: False Alarm Ratio of the old and new LSA SAF snow cover product when compared to IMS product between January 2007 and February Red is old LSA-SAF snow cover product, black is the new version. Dark shades mark the days when there is reasonable amount of snow covered pixels for comparison, light shades mark the day when there are so few snow covered pixels that the comparison is not reliable. Usually these days are during the summer. CONCLUSIONS AND OUTLOOK Our analysis shows that the new version is better than the old version and agrees reasonably well with NOAA/NESDIS IMS product. Both products identify well the major features of the snow cover, although there are differences in the snow border line. It is not possible to conclude which of the two snow cover products (IMS and LSA SAF SC) is better without validation with surface observations. Our current plan is to use weather station observations and national snow cover maps from Finland and other countries for further validation. ACKNOWLEDGEMENTS The work was financially supported by EUMETSAT in the project Satellite Application Facility on Land Surface Analysis. REFERENCES Derrien, M. and LeGléau, H., MSG/SEVIRI cloud mask and type from SAFNWC, International Journal of Remote Sensing, vol. 26, no. 21, pp , Helfrich, S., McNamara, D., Ramsay, B., Baldwin, B., and Kasheta, T., Enhancements to, and forthcoming developments in the interactive multisensor snow and ice mapping system (IMS), Hydrological Processes, no. 21, pp , Jolliffe, I.T. and Stephenson,, D.B., Forecast Verification: A Practitioner s Guide in Atmospheric Science, John Wiley & Sons, 2003.

7 Kidder, S.Q., and Wu, H.-T., Dramatic contrast between low clouds and snow cover in daytime 3.7 m imagery, Monthly Weather Review, pp , LSA SAF, Product User Manual Snow Cover, 2007, SAF/LAND/FMI/PUM/SC/2.8, available from Matson, M., NOAA satellite snow cover data, Global and Planetary Change, vol. 4, pp , Météo-France, User Manual for the PGE v1.3 (Cloud Products) of the SAFNWC/MSG: Scientific part, 2007,