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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

High Resolution Snow Analysis for COSMO

jean-marie.bettems@meteoswiss.ch

COSMO General Meeting18-21 September 2007

satellite data

20040310

Satellite data

Near real time, high resolution, composite, partial snow cover

Based on: Meteosat SEVIRI

snow depth analysis

20070325

Snow depth analysis

Near real time, high resolution, snow depth anaysis

Based on: in-situ observations, Meteosat mask, COSMO model

summary
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)
summary deliverables
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
satellite data1
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

satellite data2

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

slide8

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)

slide9

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)
satellite data3
Satellite data

Examples

20040310 13:12 UTC

20040308-20040310, composite snow fraction

20040310 13:12 UTC, snow fraction

20040308-20040310, composite quality

satellite data4
Satellite data

Examples

high resolution

normal resolution

slide12

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%)
snow analysis method
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
slide15

Current data set

  • mainly synop
  • sparse

Potential additional data set

  • considerably more data, but …
  • … several data providers
  • … several data formats

In-situ observations

snow analysis alternative interpolation method
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)

snow analysis alternative interpolation method1
Snow analysis: alternative interpolation method

Cressman analysis

altitudinal interpolation

(note: different geographic projection, no influence of satellite data)

outlook
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