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 ...
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.
