The Model SNOW 4. A Tool to Operationally Estimate Precipitation Supply

Transcription

1 The Model SNOW 4 A Tool to Operationally Estimate Precipitation Supply Uwe BöhmB hm,, Thomas Reich, Gerold Schneider Deutscher Wetterdienst, Dep. Hydrometeorology, Germany

2 Conceptual Design of SNOW 4

3 Conceptual Design of SNOW 4 SNOW 4 is a model for simulating the evolution of the snow cover internally computes the snow cover energy and mass balance quantifies the amount of rain and melt water from snow requires meteorological input (observations & forecasts) provides input for hydrological & hydraulic modelling produces operational forecasts for up to 3 days every 6 hours forms an interface between meteorological and flood forecasts

4 Model Physics

5 Model Physics LW H LE Glob LW P liq P sol α T surf Energy balance Latent heat v T T d M p - melting heat LW - longwave downward radiation LW - longwave upward radiation Glob - global radiation P liq - rain P sol - snowfall H - sensible heat flux LE - latent heat flux T - air temperature T d - dewpoint temperature - snow surface temperature T surf T T soil λsnow α V - snow cover mean temperature - soil surface temperature - snow heat conductivity - albedo - wind speed λ snow T M p Snow cover T soil Soil

6 Model Physics Measurement correction P liq P sol Mass balance E Snow compaction algorithm according to BERTLE, ) W n W liq W ice E D n Ret N d snow water equivalent snow liquid water amount snow ice amount evaporation from snow snow depth retention precipitation supply cover D n W n Ice W ice W liq Liquid water Ret Nd Snow cover Soil 1) Bertle, F.A., 1966: Effect of snow compaction on runoff from rain on snow. United States Department of the Interior, Bureau of Reclamation: Water Resources Technical Publication, Engineering Monograph No. 35.

7 Modules, Information Flow, Configuration

8 Modules, Information Flow, Configuration Analysis part Forecast part

9 Modules, Information Flow, Configuration Model area SNOW 4 Model area 2010/ x 1000 km 2 Enlarged area 2011/12 (under construction) 1100 x 1000 km 2

10 Model Evaluation

11 Model Evaluation General verification - water equivalent for the winter 2010/2011- (dates on which at least 25 measurement stations recorded snow) N µ RMSE Primary statistics of regionalised values [mm] σ = 29.2 Absolute differences [mm water equivalent W n ] SNOW 4 regionalised Values COSMO-EU regionalised Values Relative differences [% of the mean µ of the primary statistics ] SNOW 4 regionalised Values COSMO-EU regionalised values N - mean number of considered grid points µ - mean RMSE - root mean square error

12 Model Evaluation Site-specific verification water equivalent for the winter 2010/2011 North German lowland station Berlin-Dahlem (Met.Inst), 46m (51m) Water Equivalent Wn [mm] observed regionalised COSMO-EU SNOW 4 Differences Mean SNOW 4 regionalised: -0.5 mm Mean COSMO-EU regionalised: 4.4 mm Date

13 Model Output Users

14 Model Output Users Federal Institute for Hydrology State Company for Flood Protection and Water Management Saxony-Anhalt State Environmental Offices Bavaria, Baden-Württemberg, Brandenburg, Hesse, Rhineland-Palatine, Saarland, Saxony Flood Forecast Centres Bavaria, Baden-Württemberg, Lower Saxony, Saxony-Anhalt Flood Information Centre Bavaria Flood Warning Centre Rhineland-Palatine Flood Centre Saxony Water Management Association for the Rivers Emscher and Lippe Administration of the Austrian State Vorarlberg Hydrological and hydraulic modelling Flood forecast Customers Purpose Governance of water management systems (e.g. dams) Prevention of hazards & disaster mitigation

15 Model Output Users Output Supply Precipitation supply [mm] 7 January UTC forecast 6 Jan UTC 0h 24h data provision via ftp server plots of a result subset are available via the WaWIS homepage (password protected, specific user group) Special User Water Management Snowmelt Forecast Snow Results

16 Summary

17 Summary & Outlook Summary SNOW 4 computes precipitation supply at high spatial and temporal resolution (1km 2, 1h) SNOW 4 provides customers with current, region-specific forecasts every 6 hours Validation for winter 2010/2011 gave evidence of the model s performance and its added value (both mean and RMSE) In general good quality of snow cover evolution modelling Specific situations indicate needs for model enhancements (higher-level warm air advection, thawing over frozen soil, in mountainous regions..)

18 Thank you for your attention!