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Lead-time-continuous statistical postprocessing of ensemble weather forecasts
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DOI：10.1002/qj.4701.png)
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Abstract
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Numerical weather prediction (NWP) ensembles often exhibit biases and errors in dispersion, so they need some form of postprocessing 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 postprocessing methods in the literature are applied either at a fixed lead time or by fitting 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 postprocessing models and the usability of fitted models. This article investigates the lead-time dependence of postprocessing methods in the idealized Lorenz'96 system as well as temperature and wind-speed forecast data from the Met Office Global and Regional Ensemble Prediction System (MOGREPS-G). 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 with the classical lead-time-separated models, whilst saving substantial computation time. Statistical postprocessing methods for recalibrating forecasts are usually fitted individually for each lead time at which a forecast is available. This, however, is computationally expensive and restricts the usability of models. Here we study the lead-time dependence of Ensemble Model Output Statistics-a postprocessing method-and develop lead-time-continuous postprocessing models that are usable to correct forecasts at different lead times simultaneously. These models save substantially on computation time and show improved performance in small data situations/running-window training schemes. image
Keywords:
ensemble prediction
probabilistic weather forecasting
recalibration
statistical postprocessing
temperature
wind speed
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