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Mathias Drton, Caren Marzban, Peter Guttorp, and Joseph T. Schaefer


Tornadic activity in four U.S. regions is stochastically modeled based on data on tornado counts over the years 1953–98. It is shown that tornadic activity on a given day is mostly affected by the activity on the previous day. Hence, the process can be modeled as a Markov chain. A parametric nonhomogenous Markov chain model is developed based on the well-known increase of tornadic activity in the spring and summer months. This model, with only eight parameters, describes tornadic activity quite well. The interpretability of the estimated parameters allows a diagnosis of the regional differences in tornadic activity. For instance, a comparison of the values of the parameters for the four regions suggests that in the South tornado persistence is specific mostly to the early part of the year. Finally, within the framework of probabilistic forecast verification, it is shown that the Markov chain model outperforms the climatological model, even though the former is far simpler in terms of the number of parameters (8 and 366, respectively). The superior performance of the model is confirmed in terms of several measures of performance in all four regions. The exception is the southern Tornado Alley, where the reliability of the model forecasts is nonsignificantly inferior to that of the climatological ones.

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Le Bao, Tilmann Gneiting, Eric P. Grimit, Peter Guttorp, and Adrian E. Raftery


Wind direction is an angular variable, as opposed to weather quantities such as temperature, quantitative precipitation, or wind speed, which are linear variables. Consequently, traditional model output statistics and ensemble postprocessing methods become ineffective, or do not apply at all. This paper proposes an effective bias correction technique for wind direction forecasts from numerical weather prediction models, which is based on a state-of-the-art circular–circular regression approach. To calibrate forecast ensembles, a Bayesian model averaging scheme for directional variables is introduced, where the component distributions are von Mises densities centered at the individually bias-corrected ensemble member forecasts. These techniques are applied to 48-h forecasts of surface wind direction over the Pacific Northwest, using the University of Washington mesoscale ensemble, where they yield consistent improvements in forecast performance.

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