Weather forecasting is based on the outputs of deterministic numerical weather forecasting models. Multiple runs of these models with different initial conditions result in forecast ensembles which are used for estimating the distribution of future atmospheric variables. However, these ensembles are usually under-dispersive and uncalibrated, so post-processing is required. In the present work, Bayesian model averaging (BMA) is applied for calibrating ensembles of temperature forecasts produced by the operational limited area model ensemble prediction system of the Hungarian Meteorological Service (HMS). We describe two possible BMA models for temperature data of the HMS and show that BMA post-processing significantly improves calibration and probabilistic forecasts although the accuracy of point forecasts is rather unchanged.
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Research was supported by the Hungarian Scientific Research Fund under Grant No. OTKA NK 101680 and by the TÁMOP-4.2.2.C-11/1/KONV-2012-0001 project. The project has been supported by the European Union, co-financed by the European Social Fund. The authors are indebted to Tilmann Gneiting for his useful suggestions and remarks, to Máté Mile and Mihály Szűcs from the HMS for providing the data and to Éva Remete for preparing Fig. 2 of the manuscript. Last but not least the authors are very grateful to the Editor and Reviewers for their valuable comments.
Communicated by B. Ahrens.
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Baran, S., Horányi, A. & Nemoda, D. Probabilistic temperature forecasting with statistical calibration in Hungary. Meteorol Atmos Phys 124, 129–142 (2014). https://doi.org/10.1007/s00703-014-0314-8
- Root Mean Square Error
- Ensemble Member
- Bias Correction
- Ensemble Forecast
- Bayesian Model Average