Baran, Sándor (University of Debrecen)

Probabilistic methods in weather forecasting

Recently, all major weather prediction centres provide forecast ensembles of different weather quantities, which are obtained from multiple runs of numerical weather prediction models with various initial conditions and model parametrizations. However, ensemble forecasts often show an underdispersive character and may also be biased, so that some post-processing is needed to account for these deficiencies. Probably the most popular modern post-processing techniques are the Bayesian model averaging (BMA) and the ensemble model output statistics (EMOS), which provide estimates of the density of the predictable weather quantity.

 

We present an overview of BMA and EMOS models for post-processing ensemble forecasts of various weather quantities, show the basic methods of forecast evaluation, and via several case studies focusing on wind speed,  illustrate the predictive performance of these approaches. 

 

Date: Sep. 13, Tuesday 4:15pm

Place: BME, Building „Q”, Room QBF13

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