Lead-time-continuous statistical postprocessing of ensemble weather forecasts

Abstract

Numerical weather prediction (NWP) ensembles often exhibit biases and errors in dispersion, so they need some form of post processing to yield sharp and well-calibrated probabilistic predictions. The output of NWP models is usually at a multiplicity of different lead times and even though information is often required on this range of lead times, many post-processing methods in the literature are either applied at a fixed lead time or by fit ting individual models for each lead time. However, this is 1) computationally expensive because it requires the training of multiple models if users are interested in information at multiple lead times and 2) prohibitive because it restricts the data used for training post-processing models and the usability of fitted models. This paper investigates the lead-time dependence of post-processing methods in the idealized Lorenz ’96 system as well as temperature and wind speed forecast data from the Met Office’s MOGREPS-G ensemble prediction system. The results indicate that there is substantial regularity between the models fitted for different lead times and that one can fit models that are lead-time-continuous that work for multiple lead times simultaneously by including lead time as a covariate. These models achieve similar, and in small data situations, even improved performance compared to the classical lead-time-separated models, whilst saving substantial computation time.