Ensemble weather predictions from global forecast systems require
statistical postprocessing in order to remove systematic errors and to
obtain reliable probabilistic forecasts. Many traditional postprocessing
methods are based on statistical models that make parametric assumptions
about the forecast distribution and/or the relationship (e.g. linearity)
between predictors and predictands. A number of recent papers, however,
have demonstrated for ensemble temperature and wind speed forecasts that
more accurate predictions can be obtained using artificial neural
networks (ANNs) for statistical post-processing. Here, we propose a
statistical post-processing approach for precipitation forecasts that is
built around an artificial neural network (ANN) and addresses the
statistical peculiarities of precipitation as well as the challenges
that come with the low signal-to-noise ratio encountered at subseasonal
forecast lead times.

Our basic approach uses only precipitation forecasts from a numerical
weather prediction model and geographic information as predictors. In a
subseasonal forecast context, however, predictors like geopotential
height at 500 hPa (Z500) and total column water (TCW) may be more
useful since the NWP models may have better skill in predicting
large-scale weather patterns than surface weather variables at a
specific location. We therefore propose an extension of our basic
framework that uses a convolutional neural network to process images of
Z500 and TCW over a larger domain and uses them as predictors for
localized precipitation amounts.