LANDSAF SNOW COVER MAPPING USING MSG/SEVIRI DATA

Niilo Siljamo and Otto Hyvärinen

Finnish Meteorological Institute, Erik Palménin aukio 1, P.O.Box 503, FI-00101 Helsinki, Finland

Abstract

Land Surface Analysis Satellite Applications Facility (LandSAF) has been producing daily snow cover products for two years with a baseline algorithm using MSG/SEVIRI data. A new improved version of the product has been developed.

INTRODUCTION

Snow cover affects several surface parameters such as albedo and fluxes which are important in numerical weather prediction and other applications. Traditional methods used for snow cover analysis are limited by sparse in-situ measurements. Satellite based methods can be used to produce snow cover analysis for large areas without surface measurements, although these methods have also some limitations. Clouds, vegetation and lack of sunlight are all limiting factors in visible and infra-red channels. On the other hand microwave instruments can detect snow through clouds, but their resolution is much more coarse.

LandSAF snow cover is at the time of writing in a pre-operational status. A new version 2 of the product was needed because there were some obvious weaknesses in the snow detection of the version 1. Version 2 is still under active development, but even the current development version seems to produce much more realistic snow cover fields than the earlier version.

ALGORITHM

The MSG/SEVIRI data used by LandSAF Snow cover algorithm v2.03 are radiances (0.6, 0.8 and 1.6 μm channels), brightness temperatures (3.9, 10.8 and 12.0 µm channels), satellite and sun angles and LandSAF land surface temperature (LST). The ability of channels around 1.6 µm (Matson 1991) and 3.9 µm (Kidder and Wu 1984) to discriminate between low clouds and snow is well known. They are used in modern satellite image processing schemes like Nowcasting Satellite Application Facility Cloud Masks (Météo-France, 2007). Snow cover is derived for each 15 minute MSG/SEVIRI cycle. These instantaneous snow cover maps are combined to produce the daily LandSAF snow cover product.

For the algorithm development over 500 000 individual MSG/SEVIRI pixels were classified subjectively to form the training data set. These pixels represent both snow free and snow covered areas, different vegetation types and cloud types between November 2006 and August 2007. This data set was then used for classification algorithm development. The actual extent of snow cover was determined subjectively using all available data, such as true color MODIS images.

Figures 1 and 2 show two scatterplots. Such plots were used for algorithm development. These figures show that it is might be possible to sort pixels in MSG/SEVIRI images automatically to snow covered and snow free classes using different combinations of the data.

SNOW COVER DURING THE SPRING 2007

The algorithm was tested using data from spring 2007. From January to April every 15th day was selected. The results are in the figures 3 and 4. Especially in January the Northern part of Europe is unclassified due to darkness.

Figure 1: Radiance ratio of channels 3 and 2 vs. brightness temperature difference of channels 10 and 4 show a reasonably clear separation of clouds and cloud free areas. Different cloud types are marked as red, black and cyan. Clear snow free is green, cloudy snow free yellow, clear snow blue and cloudy snow magenta.

Figure 2: Radiance ratio of channels 3 and 2 vs. sun azimuth angle. Snow free and snow covered pixels are well separated. Same colors are used in figures 1 and 2.

Figure 3: The test run of the mid-winter 2007 shows the development of the snow cover during the spring. White and grey are different snow cover types, green is snow free. Orange marks the areas which are unclassified for different reasons. Usually these areas were cloud covered in every image during the day. It is also possible that these areas were too dark or the algorithm could not decide whether the area is snow covered or not.

Figure 4: The test run of the spring 2007 shows the development of the snow cover during the spring. White and grey are different snow cover types, green is snow free. Orange marks the areas which are unclassified for different reasons. Usually these areas were cloud covered in every image during the day. It is also possible that these areas were too dark or the algorithm could not decide whether the area is snow covered or not.

VALIDATION OF THE PRODUCT

The new version of the LandSAF snow cover product is not yet fully validated, but some preliminary results can be presented. Two days were selected for comparison with MSG/SEVIRI images and NOAA/NESDIS snow cover product.

As an example of snow cover in Central and Southern Europe January 26th, 2007 was selected (Figure 5). According to the MSG/SEVIRI image there were snow in large areas in France and Spain, but also large cloud covered areas. When all daily images were processed, large unclassified areas were still remaining. The LandSAF snow cover is very similar to NOAA/NESDIS snow cover of the same day in the cloud free areas. The other example is March 28th, 2007 (figure 6.) which was a very clear day in Northern and Eastern Europe. Also in this comparison both NOAA/NESDIS and LandSAF snow covers are in very good agreement. The border of the snow covered area in Scandinavia and Finland can be compared also to MODIS images of the same day. There are small differences between LandSAF and NOAA/NESDIS in this area, which illustrates that it is quite difficult to detect snow in forest areas. It seems that LandSAF snow cover in this area might be better of these two in this particular day.

Figure 5: Upper panel show the MSG/SEVIRI image (channel combination 321) of the noon January 26th 2007. Snow on the ground can be seen as cyan shades. Middle panel shows the daily LandSAF snow cover for the same day. Lower panel shows the NOAA/NESDIS snow analysis for the same day.

Figure 6: Upper panel show the MSG/SEVIRI image (channel combination 321) of the noon March 28th 2007. Snow on the ground can be seen as cyan shades. Middle panel shows the daily LandSAF snow cover for the same day. Lower panel shows the NOAA/NESDIS snow analysis for the same day.

SUMMARY

A new algorithm for snow cover detection using MSG/SEVIRI data has been developed. The algorithm is still under development, but early results show some skill when compared to other snow cover products.

REFERENCES

Kidder, S. Q. and Wu, H.-T., (1984) Dramatic contrast between low clouds and snow cover in daytime 3.7 µm imagery. Monthly Weather Review, 112, pp 2345-2346Matson, Michael, (1991) NOAA satellite snow cover data. Global and Planetary Change, 4, 1-3, pp 213-218Météo-France, (2007) User Manual for the PGE01-02-03 v1.3 (Cloud Products) of the SAFNWC / MSG: Scientific part. <http://nwcsaf.inm.es/>