  Statistical postprocessing techniques are nowadays key components of the
forecasting suites in many National Meteorological Services (NMS), with for
most of them, the objective of correcting the impact of different types of
errors on the forecasts. The final aim is to provide optimal, automated,
seamless forecasts for end users. Many techniques are now flourishing in the
statistical, meteorological, climatological, hydrological, and engineering
communities. The methods range in complexity from simple bias corrections to
very sophisticated distribution-adjusting techniques that incorporate
correlations among the prognostic variables. The paper is an attempt to
summarize the main activities going on this area from theoretical developments
to operational applications, with a focus on the current challenges and
potential avenues in the field. Among these challenges is the shift in NMS
towards running ensemble Numerical Weather Prediction (NWP) systems at the
kilometer scale that produce very large datasets and require high-density
high-quality observations; the necessity to preserve space time correlation of
high-dimensional corrected fields; the need to reduce the impact of model
changes affecting the parameters of the corrections; the necessity for
techniques to merge different types of forecasts and ensembles with different
behaviors; and finally the ability to transfer research on statistical
postprocessing to operations. Potential new avenues will also be discussed.
