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