Current statistical post-processing methods for probabilistic weather
forecasting are not capable of using full spatial patterns from the numerical
weather prediction (NWP) model. In this paper we incorporate spatial wind speed
information by using convolutional neural networks (CNNs) and obtain
probabilistic wind speed forecasts in the Netherlands for 48 hours ahead, based
on KNMI's deterministic Harmonie-Arome NWP model. The probabilistic forecasts
from the CNNs are shown to have higher Brier skill scores for medium to higher
wind speeds, as well as a better continuous ranked probability score (CRPS) and
logarithmic score, than the forecasts from fully connected neural networks and
quantile regression forests. As a secondary result, we have compared the CNNs
using 3 different density estimation methods (quantized softmax (QS), kernel
mixture networks, and fitting a truncated normal distribution), and found the
probabilistic forecasts based on the QS method to be best.Comment: 44 pages, 5 figure