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High Resolution Snow Analysis for COSMO

High Resolution Snow Analysis for COSMO. jean-marie.bettems@meteoswiss.ch COSMO General Meeting 18-21 September 2007. 20040310. Satellite data. Near real time, high resolution, composite, partial snow cover Based on: Meteosat SEVIRI. 20070325. Snow depth analysis.

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High Resolution Snow Analysis for COSMO

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  1. High Resolution Snow Analysis for COSMO jean-marie.bettems@meteoswiss.ch COSMO General Meeting18-21 September 2007

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

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

  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. 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. Satellite data 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 Introduction

  7. clouds snow 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 mm 4 IR3.92m 5 IR 6.2 mm 6 IR 7.3mm 7 IR 8.7 mm 8 IR 9.7 mm 9 IR10.8 mm 10 IR12.0 mm 11 IR13.4 mm 12 HRV 0.7 mm

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

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

  11. Satellite data Examples high resolution normal resolution

  12. 20051204 20061204 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%)

  13. Snow analysis: method DWD software package for computing snow depth maps, adapted and optimised at MeteoSwiss for use with MSG SEVIRI. • Cressman analysis • Interpolation between observations of snow depth • depending on observation density: • use interpolated snow depth only, • or add interpolated precipitation • or add model snow depth • 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. Case study 24.05.2007: Alps SLF Snow analysis

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

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

  17. Snow analysis: alternative interpolation method 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 Rmax and within vertical distance Hmax • 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)

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

  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

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