Federal Department of Home Affairs FDHAFederal Office of Meteorology and Climatology MeteoSwiss

High Resolution Snow Analysis

for COSMO

jean-marie.bettems@meteoswiss.ch

COSMO General Meeting18-21 September 2007

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Satellite data

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

Based on: Meteosat SEVIRI

20040310

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Snow depth analysis

Near real time, high resolution, snow depth anaysis

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

20070325

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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)

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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

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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

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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 m10 IR 12.0 m11 IR 13.4 m12 HRV 0.7 m

clouds

snow

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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)

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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)

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Satellite data

20040310 13:12 UTC

20040310 13:12 UTC, snow fraction

20040308-20040310, composite snow fraction

20040308-20040310, composite quality

Examples

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Satellite dataExamples

high resolution

normal resolution

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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

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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

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Case study 24.05.2007: Alps

SLF Snow analysis

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In-situ observations

Current data set

• mainly synop

• sparse

Potential additional data set

• considerably more data, but …

• … several data providers• … several data formats

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Case study: additional observations20070325, COSMO-2 observations from

aLMo database

observations from aLMo database and additional data set

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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 method

222 dzwdydxds

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Cressman analysis

Snow analysis: alternative interpolation method

altitudinal interpolation

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

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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