1. Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss High Resolution Snow Analysis for COSMO jean-marie.bettems@meteoswiss.ch COSMO General Meeting 18-21 September 2007

2. 2 Satellite data Near real time, high resolution, composite, partial snow cover Based on: Meteosat SEVIRI 20040310

3. 3 Snow depth analysis Near real time, high resolution, snow depth anaysis Based on: in-situ observations, Meteosat mask, COSMO model 20070325

4. 4 Summary Fractional snow cover is derived from satellite automatically, in near-real time, at 2 km resolution. A snow depth map is produced daily over western and central Europe on a 2.2 km grid.  both are unique  snow depth map is more realistic than current products; Meteosat information generates more realistic small scale structures by adding or removing snow patches  improved COSMO near surface weather in winter (e.g. 2m T)

5. 5 Summary - Deliverables Snow depth analysis for COSMO-7 in production Snow depth analysis for COSMO-2 in pre-production Scientific (EUMETSAT final report) and technical documentation available

6. 6 Satellite data Introduction General problems: obscurance of the surface by clouds confusion of ice clouds and snow (similar spectral signatures) Solution: high temporal frequency  MSG SEVIRI EUMETSAT Fellowship: detect dynamic behaviour of clouds for improving the discrimination between clouds and snow (with respect to spectral classification alone) detect all cloud-free instances to reduce obscurance of surface by clouds map snow cover automatically and in near-real time

7. 7 Satellite data SEVIRI characteristics Coarse to medium spatial resolution: 5-6 km and 1.5-2 km (HRV) High temporal resolution: each 15 minutes, only day-time images used Adequate spectral resolution: 12 spectral channels, 10 used: 1 VIS 0.635  m 2 VIS 0.81  m 3 NIR 1.64  m 4 IR 3.92  m 5 IR 6.2  m 6 IR 7.3  m 7 IR 8.7  m 8 IR 9.7  m 9 IR 10.8  m 10 IR 12.0  m 11 IR 13.4  m 12 HRV 0.7  m clouds snow

8. 8 Classification scheme: Satellite data classification result (white : snow; dark gray : clouds) temporal standard deviation, channel 3 (dark: low; bright: high) multi-channel colour composite (red: snow or ice clouds)

9. 9 Satellite data Snow cover products: instantaneous snow map daily composite snow map: all cloud free instances from 1 day combined running composite snow map: continuously updated with the latest cloud-free information (each pixel displays the latest instance that the pixel was cloud-free) quality flag taking into account snow depth at time of occultation  q=f(time,sza,n) Properties: fully automatic processing in near-real time (new image processed 2.5 hours after acquisition, each 15 minutes) fractional snow cover normal SEVIRI resolution (5-6 km) and high SEVIRI resolution (1.5-2 km)

10. 10 Satellite data 20040310 13:12 UTC 20040310 13:12 UTC, snow fraction 20040308-20040310, composite snow fraction 20040308-20040310, composite quality Examples

11. 11 Satellite data Examples high resolution normal resolution

12. 12 Satellite data Results winter 2005/2006: normal resolution: 94% correlation with in situ observations consistent quality over the whole period March + April 2007: normal resolution: 95% correlation (only Alps: 83%) high resolution: 96% correlation (only Alps: 87%) 20051204 20061204

13. 13 Snow analysis: method DWD software package for computing snow depth maps, adapted and optimised at MeteoSwiss for use with MSG SEVIRI. 1. Cressman analysis Interpolation between observations of snow depth 2. depending on observation density: use interpolated snow depth only, or add interpolated precipitation or add model snow depth 3. compare with satellite data always use latest version of running composite SEVIRI snow map resample SEVIRI snow map to model space only use satellite information that has high quality remove/add snow from Cressman analysis to match SEVIRI information

14. 14 Case study 24.05.2007: Alps SLF Snow analysis

15. 15 In-situ observations Current data set mainly synop sparse Potential additional data set considerably more data, but … … several data providers … several data formats

16. 16 Case study: additional observations 20070325, COSMO-2 observations from aLMo database observations from aLMo database and additional data set

17. 17 Snow depth generally increases with surface altitude  use local gradients for interpolation: for each model grid point (x,y,z): find np observation sites with smallest distance, only use sites within horizontal distance R max and within vertical distance H max make linear regression for these sites: snow depth = a + b  z (weight the contribution of each site with the inverse of d) use this regression line to compute the snow depth at (x,y,z) (only when enough of the np sites display snow, e.g. half of them) Snow analysis: alternative interpolation method

18. 18 Cressman analysis Snow analysis: alternative interpolation method altitudinal interpolation (note: different geographic projection, no influence of satellite data)

19. 19 Outlook Merge latest DWD snow analysis modifications with new software Access to additional in-situ observations Currently DWD data can not be decoded Interpolation with altitudinal gradient more realistic than Cressman interpolation over steep topography, but enough observations with snow must be present use gradient-interpolation to identify bad observations merge gradient-interpolation with cressman analysis, e.g. with weighted mean Use partial snow cover in COSMO/TERRA Use EUMETSAT SAF snow albedo in COSMO Introduce a more sophisticated snow model

20. 20 Recent developments (1) Meteosat derived snow mask, standard resolution (5-6 km) running composite map on aLMo7 domain routinely produced comparison with in-situ data: 94% correlation for winter 05/06 consistent quality over the whole period comparison with MODIS and AVHRR products similar quality MSG has significantly less cloudy pixels when deriving a daily product

21. 21 Recent developments (2) Meteosat derived snow mask, high resolution (1.5-2 km) available on aLMo7 domain, including topography correction composite HR snow map for period 9-10.3.2004 : low res cloud mask + temporal stability of HRV pixels + spatial consistency with low res mask

22. 22 Recent developments (3) Quality flag associated with snow masks Black: best quality (1) White: not to be trusted (0) Quality flag  Q(age of pixel information [scale=1d], solar zenith angle, proximity of clouds)

23. 23 Recent developments (4) Version 2.0 of M.Buchhold snow analysis code Combine in-situ obs (snow height, precip), first guess, snow mask Correction of some bugs (snwmsk.F, grpeva.F) Tuning of in-situ analysis (grpeva.F) Usage of snow mask largely re-written (snwmsk.F) Re-setting of some parameters Horizontal scale used in Cressmann analysis: 80km Minimum snow depth introduced by Cressmann analysis: 10mm Search radius for setting snow depth caused by snow mask: 250km Default snow depth caused by snow mask: 100mm Test suite aLMo7 with these latest developments, winter 2005/2006 Calculated and ‚verified‘