institute of meteorology and water management hydrological applications of cosmo model andrzej mazur...

INSTITUTE OF METEOROLOGYAND WATER MANAGEMENT

Hydrological applications of COSMO model

Andrzej MazurInstitute of Meteorology and Water Management

Centre of Numerical Weather Forecasts61 Podleśna str., PL-01673 Warsaw, Poland


Contents

4. Conclusions

1. Goal2. Methods

3. Results


1. Forecast of precipitation (of course)…

What is needed of DMO for precipitation-runoff models?

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 evapotranspiration

Goal


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obsn

forec

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Methods


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Tk

fky bh

1ka1k

fk QPP

fk

okk yye

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kTkk rw hPh

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ak ekbb

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ak w PkkP

where:y - measurement vectorb - multiple regression coefficients (time dependent)h - predictors - model forecast values Q - error covariancer - observational errorP - forecast covariancee - forecast errorw - temporary scalark - Kalman gain


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

Results


End of COSMO version 3.05 runs, June 30, 2007

Results of June 30, 2007 – air temperature (morning and afternoon model runs, DMO and corrected results)


Bias of June 30, 2007 – air temperature (morning and afternoon model runs, DMO and corrected results)



Results of June 30, 2007 – air temperature. Morning model run, AR results (left-most charts), DMO (middle) and measurement-derived fields (right-most) for 06:00 (upper) and 18:00 UTC (lower).


First six months of COSMO version 4.0 runs

Results of January 01, 2008 – air temperature (morning and afternoon model runs, DMO and corrected results)


Bias of January 01, 2008 – air temperature (morning and afternoon model runs, DMO and corrected results)



Results of Jan. 01, 2008 – air temperature. Morning model run, AR results (left-most charts), DMO (middle) and measurement-derived fields (right-most) for 06:00 (upper) and 18:00 UTC (lower).



Output, hour bias RMSE

AR, 06:00 -0.406 2.522

DMO, 06:00 -0.513 2.740

AR, 12:00 -1.476 3.228

DMO, 12:00 -1.615 4.305

AR, 18:00 -1.298 3.570

DMO, 18:00 -1.942 4.976


AR, 06:00 0.241 1.750

DMO, 06:00 0.690 1.870

AR, 12:00 0.169 1.906

DMO, 12:00 1.274 2.538

AR, 18:00 0.313 1.786

DMO, 18:00 1.741 2.697

Bias and RMSE values of DMO and AR-corrected results for selected hour of forecastJune 30, 2007 January 01, 2008


Results of Aug. 04, 2008 – air temperature. Morning model run, AR results (left-most charts), DMO (middle) and measurement-derived fields (right-most) for 06:00 (upper) and 18:00 UTC (lower).

Heavy storms over Poland, August 04, 2008



Results of Aug. 15, 2008 – air temperature. Morning model run, AR results (left-most charts), DMO (middle) and measurement-derived fields (right-most) for 06:00 (upper) and 18:00 UTC (lower).



AR, 06:00 -0.107 2.193

DMO, 06:00 -0.177 2.438

AR, 12:00 -1.073 3.804

DMO, 12:00 -4.552 5.906

AR, 18:00 -1.935 3.873

DMO, 18:00 -5.133 6.448


AR, 06:00 0.126 2.307

DMO, 06:00 0.130 2.438

AR, 12:00 0.664 2.579

DMO, 12:00 0.986 2.989

AR, 18:00 0.433 2.436

DMO, 18:00 0.683 2.915

Bias and RMSE values of DMO and AR-corrected results for selected hour of forecastAugust 04, 2008 August 15, 2008



Results of June 30, 2007 – precipitation (morning and afternoon model runs, DMO and corrected results)



Bias of June 30, 2007 – precipitation (morning and afternoon model runs, DMO and corrected results)



Results of June 30, 2007 – 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.


Results of January 01, 2008 – precipitation (morning and afternoon model runs, DMO and corrected results)



Bias of January 01, 2008 – precipitation (morning and afternoon model runs, DMO and corrected results)



Results of Jan. 01, 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.



Bias and RMSE values of DMO and AR-corrected results for selected hour of forecastJune 30, 2007 January 01, 2008


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


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


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.



Bias and RMSE values of DMO and AR-corrected results for selected hour of forecastAugust 04, 2008 August 15, 2008


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


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


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.

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.



Bias and RMSE values of DMO and AR-corrected results for selected hour of forecast, May 04, 2005, June 10, 2005 August 09, 2005.


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


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


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


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).


Temperature forecast corrections, 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 aside 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 (clearly visible) 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.

Results are sensitive to data continuity of some kind – the more data available, the faster scheme converges and further changes of coefficients are smaller and smaller. If there was a significant lack of data, rapid changes of coefficients and statistic values was seen.

ConclusionsHydrological applications of COSMO model

Thank you for your attention.IMGW01-673 Warszawa, ul.: Podleśna 61tel.: (022) 56 94 134fax: (022) 56 94 356mobile: 0 503 122 [email protected]

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institute of meteorology and water management hydrological applications of cosmo model andrzej mazur...

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