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Danish Meteorological Institute

Danish Meteorological Institute. EPS Forecast of Weather Scenarios and Probability Presented at www.dmi.dk by Michael Steffensen Acknowledgments: Gorm Dybkjær Danish Meteorological Institute, who made much of the work in building the product.

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Danish Meteorological Institute

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  1. Danish Meteorological Institute EPS Forecast of Weather Scenarios and Probability Presented at www.dmi.dk by Michael Steffensen Acknowledgments: Gorm Dybkjær Danish Meteorological Institute, who made much of the work in building the product.

  2. Danish ByVejr (CityWeather) consists of three different forecast approaches • First 24 hours (day 1) are based on the Danish version of HIRLAM updated 4 times a day • Day 2 to 5 based on ECMWF T511 updated once a day • Day 7 to 9 based on ECMWF EPS updated once a day

  3. ByVejr is located at the www.dmi.dk frontpage

  4. Each red dot is a city with a local forecast.

  5. The upper graph is HILAM and lower graph is T511 HIRLAM T511

  6. Presentation of EPS forecast should • Look like the previous forecast, days in columns, weather symbol, temperature and wind speed • Show the probability for the occurrence of the combination of weather parameters shown in the column, called a weather scenario • Show the three most likely alternative weather scenarios The result is shown in the next three slides

  7. probability day temperature night temperature wind speed

  8. Probability of the combination of parameters shown in a scenario • Is determined for temperature and precipitation together, since these are the most important for the public • Three regimes for precipitation are used. <1mm/24hr, between 1 and 5mm/24hr and >5 mm/24hr meaning: no rain, some rain and more rain • Temperature is determined in a 5 degree interval. • The most probable 2D square is choosen as first scenario • The intervals for night temperature and wind speed are determined as the spread of the EPS members making up the respective 2D square

  9. Probability of the combination of parameters shown in a scenario • The second most probable 2D square not covering the first square is chosen as the second scenario • The third scenario is chosen similarly

  10. Verification • Next slide show the ranked histogram with the raw data in blue an calibrated data in red. • The ranked histogram for the raw data is very asymmetric. • Bin 52 for raw data is very high indicating that the ensemble generally is to cold. • This is corrected by a linear translation of all members, determined by the difference between the Kalman filtered ensemble mean and the ensemble mean:shift = Kal<EPS> - <EPS> • Precipitation is also asymmetric, but not corrected.

  11. <EPS> Kal<EPS>

  12. Verification, Reliability • The most popular probabilities are around40, 50 ,60 and 70 percent, because of the combination of parameters • Though still overestimating probabilities, the translation of the ensemble gives some improvements in reliability • The overestimation of probabilities are probably due to too small spread of the ensemble

  13. translated ensemble raw ensemble

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