1. INSTITUTE OF METEOROLOGY AND WATER MANAGEMENT
Hydrological applications of COSMO model
Andrzej Mazur
Institute of Meteorology and Water Management
Centre of Numerical Weather Forecasts
61 Podleśna str., PL Warsaw, Poland

2. Hydrological applications of COSMO model
Contents
1. Goals
2. Methods
3. Results
4. Conclusions

3. Hydrological applications of COSMO model
Goals
What is needed of DMO for precipitation-runoff models?
1. Forecast of precipitation (of course)…
2. … together with air temperature
While precipitation is obviously the main driving factor for a hydrological model, the temperature data provides information on the state of the precipitation and the available potential for evaporation
Moreover,…

4. Hydrological applications of COSMO model
Methods

5. Hydrological applications of COSMO model
where: y - measurement vector b - multiple regression coefficients (time dependent) h - predictors - model forecast values Q - error covariance r - observational error P - forecast covariance e - forecast error w - temporary scalar k - Kalman gain

6. Hydrological applications of COSMO model
Results
Results of experiments for selected days/periods for temperature and precipitation:
- June 30, 2007 – change from COSMO version 3.05 to 4.0
- January 01, 2008 – six months of COSMO version 4.0 runs
- August 04, 2008 – heavy storm over Poland (part I)
- August 15, 2008 – heavy storm over Poland (part II)
and only for precipitation – increased resolution computations, heavy precipitation (mainly convective type):
- May 04, 2005
- June 10, 2005
- August 09, 2005

7. Hydrological applications of COSMO model
End of COSMO version 3.05 runs, June 30, 2007
Results of June 30, 2007 – precipitation (morning and afternoon model runs, DMO and corrected results)

8. Hydrological applications of COSMO model
End of COSMO version 3.05 runs, June 30, 2007
Bias of June 30, 2007 – precipitation (morning and afternoon model runs, DMO and corrected results)

9. Hydrological applications of COSMO model
First six months of COSMO version 4.0 runs
Results of January 01, 2008 – precipitation (morning and afternoon model runs, DMO and corrected results)

10. Hydrological applications of COSMO model
First six months of COSMO version 4.0 runs
Bias of January 01, 2008 – precipitation (morning and afternoon model runs, DMO and corrected results)

11. Hydrological applications of COSMO model
Bias and RMSE values of DMO and AR-corrected results for selected hour of forecast
June 30, January 01, 2008
Output, hour
bias
RMSE
AR, 06:00
0.318
1.526
DMO, 06:00
0.328
1.566
AR, 12:00
0.092
0.662
DMO, 12:00
0.105
0.674
AR, 18:00
0.153
0.901
DMO, 18:00
0.176
0.915
Output, hour
bias
RMSE
AR, 06:00
0.311
1.154
DMO, 06:00
0.774
1.307
AR, 12:00
0.223
1.248
DMO, 12:00
0.894
1.349
AR, 18:00
1.450
2.118
DMO, 18:00
1.774
2.358

12. Hydrological applications of COSMO model
Heavy storms over Poland, August 04, 2008
Results of Aug. 04, 2008 – precipitation. Morning model run, AR results (left), DMO (right) for 06:00 (upper) and 18:00 UTC (lower). Measured sum of precipitation marked with crosses with size proportional to amount.

13. Hydrological applications of COSMO model
Heavy storms over Poland, August 15, 2008
Results of Aug. 15, 2008 – precipitation. Morning model run, AR results (left), DMO (right) for 06:00 (upper) and 18:00 UTC (lower). Measured sum of precipitation marked with crosses with size proportional to amount.

14. Hydrological applications of COSMO model
Bias and RMSE values of DMO and AR-corrected results for selected hour of forecast
August 04, August 15, 2008
Output, hour
bias
RMSE
AR, 06:00
1.010
4.146
DMO, 06:00
1.727
4.504
AR, 12:00
0.215
3.079
DMO, 12:00
0.473
3.221
AR, 18:00
1.437
5.264
DMO, 18:00
2.089
5.648
Output, hour
bias
RMSE
AR, 06:00
0.998
6.251
DMO, 06:00
1.874
6.563
AR, 12:00
0.258
4.854
DMO, 12:00
-0.945
6.472
AR, 18:00
2.332
9.983
DMO, 18:00
3.473
10.148

15. Hydrological applications of COSMO model
Increased resolution model runs
Results of May 04, 2005 – precipitation. Morning model run, AR results (left), DMO (right) for 06:00 (upper) and 18:00 UTC (lower). Measured sum of precipitation marked with crosses with size proportional to amount.

16. Hydrological applications of COSMO model
Increased resolution model runs
Results of June 10, 2005 – precipitation. Morning model run, AR results (left), DMO (right) for 06:00 (upper) and 18:00 UTC (lower). Measured sum of precipitation marked with crosses with size proportional to amount.

17. Hydrological applications of COSMO model
Increased resolution model runs
Results of Aug. 09, 2005 – precipitation. Morning model run, AR results (left), DMO (right) for 06:00 (upper) and 18:00 UTC (lower). Measured sum of precipitation marked with crosses with size proportional to amount.

18. Hydrological applications of COSMO model
Bias and RMSE values of DMO and AR-corrected results for selected hour of forecast,
May 04, 2005, June 10, August 09, 2005.
Output, hour
bias
RMSE
AR, 06:00
3.225
7.452
DMO, 06:00
3.779
7.862
AR, 12:00
0.461
7.298
DMO, 12:00
1.727
9.049
AR, 18:00
3.295
7.701
DMO, 18:00
5.165
8.427
Output, hour
bias
RMSE
AR, 06:00
2.970
8.503
DMO, 06:00
3.986
11.517
AR, 12:00
2.134
6.775
DMO, 12:00
2.413
6.953
AR, 18:00
3.972
11.681
DMO, 18:00
4.659
13.720
Output, hour
bias
RMSE
AR, 06:00
-0.186
3.324
DMO, 06:00
-1.190
5.858
AR, 12:00
-1.472
5.316
DMO, 12:00
-3.636
9.077
AR, 18:00
0.352
7.676
DMO, 18:00
-1.686
9.964

19. Hydrological applications of COSMO model
Air temperature RMSE changes during period August 01, 2008 to August 21, 2008 (DMO and AR results shown).
Precipitation RMSE changes during period August 01, 2008 to August 21, 2008 (DMO and AR results shown).

20. Hydrological applications of COSMO model
Conclusions
Temperature forecast corrections (even based on redundant predictors) is easier to develop, seem also to be more stable during a learning process (no sudden/drastic changes of coefficients over the entire period).
Method – even in this simple approach – is able to “detect” and correct not only any factor „out” of the model, but also systematic errors in its results.
The change of COSMO model version (from 3.05 to reference version 4.0) has not significant influence on time-evolvement of coefficients.
Precipitation seems to be well-posed as far as the predictors are concerned. Geographical coordinates, elevation, time of measurement and previous measured and forecasted values seem to be fairly set for the purpose.
Of course, artificial, but obvious constrain has always to be applied – corrected forecast value of precipitation must not be less than zero.
Additional problem with precipitation forecast correction - correction process is hardly able to “create” any amount of precipitation from “nothing”. If DMO forecast predicts no rain at a certain point, it’s not possible to obtain a non-zero (or significant amount of) precipitation using AR scheme.

21. Thank you for your attention.
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