SNOW COVER MAPPING USING METOP/AVHRR AND MSG/SEVIRI

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1 SNOW COVER MAPPING USING METOP/AVHRR AND MSG/SEVIRI Niilo Siljamo, Markku Suomalainen, Otto Hyvärinen Finnish Meteorological Institute, P.O.Box 503, FI Helsinki, Finland Abstract Weather and meteorological processes are affected by the varying snow cover. Snow areal extent is essential information for weather forecasting and nowcasting. Successful snow detection is possible using data from geostationary satellites (e.g. LSA SAF snow cover product based on MSG/SEVIRI data). However geostationary products have limited resolution in polar regions. Polar orbiting satellites offer some advantages which might be used to improve snow cover products in these areas. The methods used for the development of the LSASAF MSG/SEVIRI snow cover algorithm have been modified and applied to the development of the LSA SAF Metop/AVHRR snow cover algorithm. The current version has been in testing phase in the LSA SAF system since March 2011 and has been operational since Both snow cover products were developed as a part of the LSASAF. Since March 2012 they have been part of the HydroSAF. In this paper, we describe the current MSG/SEVIRI and Metop/AVHRR snow detection algorithms and show some validation results using IMS snow product and surface observations of the snow coverage. CURRENT STATUS OF THE FORMER LSA SAF SNOW COVER PRODUCTS The MSG/SEVIRI snow cover product is operational and available from the LSA SAF web site ( The Metop/AVHRR PDU based snow cover product is currently generated for internal use in the LSA SAF system. These PDU based SC products will be used to generate the global Metop/AVHRR snow cover product in the future. ALGORITHM The MSG/SEVIRI SC 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 scatterplots such as in the Figure 1 have been 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.

2 AVHRR instruments have been in use since 1970 s and provide excellent data set for time series analysis. AVHRR instruments measure the reflectance of the Earth in 6 relatively wide spectral bands while the SEVIRI instrument has 12 channels. Some spectral bands which would be useful in snow detection are not available. In the Metop/AVHRR SC algorithm the classification is done separately in forests and open areas. The rules are based on several 2D-plots drawn from brightness temperatures and three different ratios of radiances on different channels. There are overall 18 rules, of which 5 apply to open areas only and 7 to forests only. The last 6 rules apply to both land cover types and make sure that no clouds or dark areas are classified. Figure 1: Two scatterplots of the MSG/SEVIRI 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. Different colours mark the classification used for the pixel. Blue is snow, green is snow free and other colours are different cloud classes. The Figure 2 shows an example of the Metop/AVHRR snow cover product during the spring The snow free and snow covered areas are detected quite reliably although forest and the melting season create additional problems in the area covered by the image. The preliminary version of the global product in the Figure 3 shows that the snow in the high latitudes is detected quite well also in North America and Asia. VALIDATION Snow cover products should be validated using the surface observations of the snow coverage. Unfortunately such data is not widely available. Usually the weather stations measure snow depth which is not directly related to snow coverage especially during the melting season and in the forest areas. The data collected during the Snow Edge and Extent Retrieval campaign will be used for validation of both MSG/SEVIRI and Metop/AVHRR snow cover products. The MSG/SEVIRI SC product is also compared to NOAA/NESDIS Interactive Multisensor Snow and Ice Mapping System (IMS) snow product. The comparison results of the MSG/SEVIRI SC and IMS are presented as time series in Figure 4. The maximum extent of snow is in February; the snow starts to melt in mid-march and has melted almost completely by the end of May. The remaining snow is found on mountaintops and glaciers, which are mostly outside the scope of these SAF snow cover products. New snow starts to accumulate in November. The surface area that is classified varies both because of the varying cloud cover and also with the season, as during the winter the zenith angle can be too high to enable classification for all day in northern regions. In addition, the results should be given less emphasis when the absolute

3 amount of snow is very small in summer, as the results vary greatly owing to even slight differences in snow cover between products. Therefore, days when there are more than 20 times more correct rejections than other classes are depicted with a different colour in the rest of the plots in Figure 4. Figure 2: PDU based Metop/AVHRR snow cover at March 25 th, UTC. Red areas are unclassified for various reasons. White marks the snow, green is snow free and light blue is partial snow. The edge of the snow cover is recognized quite well. Figure 3: Global compilation of the PDU based Metop/AVHRR snow cover products during March 25 th, The snow covered areas of the Northern Hemisphere are detected accurately. However, there are some thunderstorms (i.e. ice clouds) over the Amazon region in the South America, which are not classified correctly as clouds.

4 Figure 4: Amount of cloud-free land pixels (blue dashed line, IMS; red circle, version 1; black circle, version 2), and amount of snow-covered pixels (blue line, IMS; red line, version 1; black line, version 2). (b) Bias, (c) FAR, (d)h, (e) PC, and (f) HSS for version 1 (red circle) and version 2 (black circle) when compared with the IMS product. When the correct rejections exceed the other classes by more than 20 times, version 1 is shown in pink and version 2 in gray crosses. Vertical dotted lines show the transition from version 1 to version 2.02 (red), version 2.05 (gray), and version 2.10 (black). Curves show the two-month moving average of the data. From Siljamo and Hyvärinen (2011).

5 SNOW EXTENT AND EDGE RETRIEVAL (SEER) Reliable data of the snow coverage is not widely available, because direct measurements of the snow coverage are difficult to obtain automatically. To collect some snow coverage data and to test feasibility of the collecting coverage data from a moving vehicle the Snow Extent and Edge Retrieval (SEER) campaign was arranged in April By driving from snow covered area to snow free area and back we were able to collect snow coverage data. The snow coverage near the road was estimated and saved with location, photographs and some auxiliary measurements. The vehicle and the driving route during the campaign are presented in the Figure 5. During the campaign snow was melting fast and the edge of the snow covered area was moving daily. Reasonably full snow cover could be found from eastern and northern parts of Finland. Figure 5: The measuring vehicle used during the SEER campaign on the left. The SEER route is shown on the right. Green markers show the start and stop of each measuring day. (Map Google 2011) Although a rough estimate of the percentage of the snow covered area can be estimated from a moving car, there are several factors which must be considered when using the data. There are no natural undisturbed snow near the roads, where the snow removed from the road can change the properties of the snow along the roads. The snow tends to melt fasters near the road, also. The snow coverage should be estimated by observing the snow cover at least meters from the road to be able to avoid disturbed snow. Other factors to be taken in to account are land cover type, lakes and other water bodies which can be frozen or open and topography. The roads are not usually built on rough terrain or wetlands which also change the snow cover properties. The observed snow coverage was averaged for each MSG/SEVIRI and Metop/AVHRR pixel. Figure 6 shows the results of a preliminary comparison of the observed snow coverage and snow product classification for each pixel along the SEER route. The snow cover products resemble the observed snow coverage well although the effects of topography, lakes and land cover class are not yet considered.

6 Figure 6: The comparison of the SEER snow coverage data to MSG/SEVIRI snow cover (on the left) and Metop/AVHRR snow cover (on the right). Vertical stripes show the surface classification on the satellite product in each pixel along the SEER route. Dark green shows snow free pixels, light green partially snow covered pixels and grey snow covered pixels. White stripes are unclassified (usually cloudy) pixels. Black lines show the sliding average of the snow coverage estimates along the SEER route. Table 1 presents the preliminary statistical results (bias, hit rate, false alarm rate, false alarm ratio, proportion correct, critical success index and Heidke skill score) of the comparison of the SEER observations and satellite snow cover classifications. The products employ a partial snow cover class which is used quite rarely. To be able to estimate the practical interpretation of the partial snow cover class the statistics have been calculated separately for different interpretations. During the SEER campaign best results were achieved when the partial class was excluded. This might be related to the warm weather and wet melting snow during the SEER campaign and also to the high proportion of forests in the campaign area. Table 1: The statistical measures of the MSG/SEVIRI SC (on the left) and Metop/AVHRR SC (on the right) when compared to the SEER snow coverage data. Each column shows the results using a different interpretation of the partial snow cover class of the satellite snow cover products. Best results are achieved when partial class is excluded. For the comparison SEER observations were considered as snow free if the coverage was less than 25% and snow covered if the coverage was more than 25%. SEER observation vs. MSG/SEVI RI SC SEVIRI snow SEVIRI no snow SEVIRI SC excluded SEER observation vs. Metop/AVH RR SC AVHRR snow AVHRR no snow AVHRR SC excluded Bias Bias H H F F FAR FAR PC PC CSI CSI HSS HSS SUMMARY The former LSA SAF snow cover products have been part of HSAF since the March Currently operational product will be generated in the LSA SAF system.

7 Current versions of the MSG/SEVIRI global daily snow cover product and the Metop/AVHRR PDU based snow cover product show good agreement with observations. Daily global version of the Metop/AVHRR snow cover product is still under development, but preliminary results are very promising. REFERENCES AND FOOTNOTES Siljamo, N. and Hyvärinen, O., (2011) New Geostationary Satellite-Based Snow-Cover Algorithm. J. Appl. Meteorol. Climatol., 50, June 2011, , doi: /2010JAMC2568. ACKNOWLEDGEMENTS This work is financially supported by the EUMETSAT.