Abstract
In this study, we aim to assess the skill of a stochastic weather generator (SWG) to forecast precipitation in several cities of Western Europe. The SWG is based on random sampling of analogs of the geopotential height at 500 hPa. The SWG is evaluated for two reanalyses (NCEP and ERA5). We simulate 100-member ensemble forecasts on a daily time increment. We evaluate the performance of SWG with forecast skill scores and we compare it to ECMWF forecasts. Results show significant positive skill scores (continuous rank probability skill score and correlation) for lead times of 5 and 10 days for different areas in Europe. We found that the low predictability of our model is related to specific weather regimes, depending on the European region. Comparing SWG forecasts to ECMWF forecasts, we found that the SWG shows a good performance for 5 days. This performance varies from one region to another. This paper is a proof of concept for a stochastic regional ensemble precipitation forecast. Its parameters (e.g. region for analogs) must be tuned for each region in order to optimize its performance.
Highlights
Ensemble weather forecasts were designed to overcome the issues of meteorological chaos, from which small uncertainties in 15 initial conditions can lead to a wide range of possible trajectories (Sivillo et al, 1997; Palmer, 2000)
This has justified the development of stochastic weather generators (SWG), which are stochastic processes that emulate the behavior of key climate variables (Ailliot et al, 2015)
We found that the values of continuous ranked probability score (CRPS) of European Centre for Medium-Range Weather Forecasts (ECMWF) forecast and SWG forecast are 80%, 39% 50% and 40 % equal or near to zero for respectively Orly, Berlin, Madrid and Toulouse, which indicates the small variations of the CRPS
Summary
Ensemble weather forecasts were designed to overcome the issues of meteorological chaos, from which small uncertainties in 15 initial conditions can lead to a wide range of possible trajectories (Sivillo et al, 1997; Palmer, 2000). Forecasts issued by meteorological centers are obtained by computing several simulations with perturbed initial conditions, in order to sample uncertainties Those experiments are rather costly in terms of computing resources and are generally limited to a few tens of members (Hersbach et al, 2020; Toth and Kalnay, 1997), which can hinder a proper estimate of probability 20 distributions of trajectories. From a mathematical point of view, computing the probability distribution of the trajectories of a (deterministic) system makes the underlying assumption that the system behaves like a stochastic process, for which statistical properties are defined 25 naturally (Ruelle, 1979; Eckmann and Ruelle, 1985) This has justified the development of stochastic weather generators (SWG), which are stochastic processes that emulate the behavior of key climate variables (Ailliot et al, 2015). SWGs were developed and used to estimate the probability distributions of climate variables such as temperature, solar radiation, and precipitation through extensive simulations (Richardson, 1981)
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