1. Kalman filtering at HNMS Petroula Louka Hellenic National Meteorological Service louka@hnms.gr

2. 11th COSMO General Meeting The main goal is the estimation of the bias y t as a function of the forecasting model direct output (m t ): The coefficients (x i,t ) are the parameters that have to be estimated by the filter and v t the Gaussian non systematic error. The state vector of the filter is the one formed by the coefficients Kalman filter method

3. 11th COSMO General Meeting The observation matrix takes the form: The system and observation equations, are: The variance matrices of the system equation, W t, and the observation equation, V t, are estimated based on the sample of the last 7 values of and : Kalman filter method (cont)

4. 11th COSMO General Meeting COSMO runs with a resolution of ~7km. SKIRON runs with similar resolution. –It is a non-hydrostatic model similar to COSMO. –It is based on Eta/NCEP model further developed by the University of Athens. ECMWF deterministic forecasts at 25km resolution. In order to fulfill the demands of the Forecasting Centre, Kalman filtering is applied, operationally, to NWPs (at the nearest model location): –2m maximum and minimum temperature forecasts, and –10m maximum wind speed COSMO forecasts (still at a pilot stage). NWP products at HNMS

5. 11th COSMO General Meeting Statistical analysis of maximum and minimum values of 2m temperature (modeled and filtered) of all three models for the whole 2008 divided by season for all available observation stations. Statistical analysis of maximum 10m wind for summer 2009. Statistical analysis

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10. Preliminary results for wind

11. 11th COSMO General Meeting Wind power applications – ANEMOS European project –Galanis G., Louka P., Katsafados P., Pytharoulis I., and Kallos G.: Applications of Kalman filters based on non-linear functions to numerical weather predictions. Ann. Geophys., 2006, 24, 2451-2460. –Louka P., Galanis G., Siebert N., Kariniotakis G., Katsafados P. Pytharoulis I., Kallos G.: Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering. J. Wind Eng. Ind. Aerodyn., 2008, 96, 2348-2362. Applications

12. 11th COSMO General Meeting Kalman filtering fulfills its scope removing the systematic bias from the modelled data. Even when applied to temperature forecasts of a global model it seems to “correct” them adequately providing similar values with those of the filtered regional forecasts. When considering wind, a non-linear Kalman filter (order of 3) is applied. Although, it is still in a pilot stage, this operational use seems to perform quite well. Remarks

13. 11th COSMO General Meeting Kalman filtering performs well, when the error is systematic. How, can it be improved when a “large” change in the weather occurs? How can we increase the accuracy of filtered data for more than 2 days ahead? How can Kalman filter applications fulfill the requirements of the forecasters?

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15. Preliminary results for wind