Proceedings - C-SRNWP Project

many stations (Fig. 2b). In the single-station scatter plots, this results in vertical bands (Fig.
2c), caused by channeled flow that shows widely varying wind directions in the model.
a) Observations b) aLMo2 analyses c)
Figure 2: Wind-direction: Histograms of a) observations and b) aLMo analyses; c) scatter-plot model versus
observation (Site: Plaffeien)
aLMo2 tends to over-predict the wind speed in general, and specifically at locally influenced,
sheltered stations. Only few mountain-top stations are underestimated, because the gridaverage
in the model does not represent the local acceleration effect of a mountain top.
Overall, the dynamic range of the wind speed in aLMo2 seems too narrow.
This study is a first screening of the quality of aLMo2 near-surface winds. It shows that the
fine-grid aLMo2 is at least as good as or systematically better than the coarser-resolution
aLMo. One can conclude that aLMo2 has the potential to replace the current statistic tool for
simulating local wind.
Thunderstorm–prediction from aLMo output using boosting (D. Perler and O.
Marchand)
Introduction
We present a new approach to weather model output interpretation. We use Adaptive
Boosting [FS97] to train a set of simple base classifiers with historical data from weather
model output, SYNOP messages and lightning data. The resulting overall method then
interprets weather model forecast output and generates for each timestep and gridpoint a
certainty measure between zero and one for how likely a thunderstorm is to occur. We
compare resulting cross–validation scores to existing systems and approaches and find that we
can significantly improve the POD to 72% and FAR to 34%. The method has several
advantages over existing approaches, namely expert systems and other statistical methods:
• the superior cross–validation results,
• the short learning time (less than 10 minutes sequential runtime for our experiments
on a standard PC),
• the possibility to interpret the results of the underlying statistical analysis, and,
• the simplicity to add any predictors to a running system.
Boosting
Boosting is a new approach to machine learning that employs the power of many simple
classification schemes called base classifiers. These base classifiers then take a vote for the
final classification 2 . Depending on the training performance the base classifiers receive a
2 Much like the Ask-the-audience option (joker of the game) in the popular TV “Who wants to be a millionaire?”
158