Abstract
AbstractEnsemble weather forecasting generally suffers from bias and under‐dispersion, which limit its predictive power. Several post‐processing methods have been developed to overcome these limitations, and an intercomparison is needed to understand their performance. Four state‐of‐the‐art methods are compared in post‐processing precipitation and air temperature of the Global Ensemble Forecasting System (GEFS) reforecasts using a simple bias correction (BC) method as a reference. These methods include extended logistic regression (ExLR), generator‐based post‐processing (GPP), Bayesian model averaging (BMA) and affine kernel dressing (AKD). All these methods are tested over 659 national standard meteorological stations in China. The research concerns are the influence of region and forecast date and the role of BC on ensemble weather forecasting. It was found that: (1) the deterministic methods (GPP and ExLR) are more skilful than the probabilistic methods (BMA and AKD) in obtaining the well‐calibrated and skilful ensemble forecasts; (2) the forecast skill of the post‐processed ensemble weather forecasts is comparably high in the northern arid areas for precipitation, while the forecast skill for air temperature is only low in the Qinghai‐Tibetan Plateau area; (3) the skill difference of the post‐processed forecasts on different forecast date is only evident for air temperature, while not apparent for precipitation; and (4) only correcting bias for the ensemble weather forecasts can achieve about 0–70% (for precipitation) and 30–100% (for air temperature) forecast skill improvement for deterministic methods.
Highlights
Ensemble weather forecasting (EWF) has been a growing field of numerical weather prediction (NWP) since the1990s due to the fast-increasing computation resources (Gneiting and Raftery, 2005)
The results showed that the predictive probability distribution function (PDF) from the Bayesian model averaging (BMA) is well calibrated and sharp compared with the raw ensemble forecasts
The results showed that the predictive PDF is well calibrated and sharp, the probability of precipitation (PoP) forecasts is much better calibrated than those based on the raw ensemble, and the estimates of high-precipitation amount probability are better than the results using the logistic regression (LR)
Summary
Ensemble weather forecasting (EWF) has been a growing field of numerical weather prediction (NWP) since the. Their results showed that the generated probabilistic forecasts are more skilful and reliable than the raw ensemble weather forecasts. The results showed that the EVMOS could produce the ensemble consistent with the observations of the NGR, and even outperforms the NGR when the raw ensemble is highly skewed, or the extreme event occurred Both deterministic and probabilistic post-processing methods need to be compared for a better understanding of the advantages and disadvantages of these methods. The study evaluated and compared four state-of-theart post-processing methods: the BMA, AKD, weather GPP and ExLR, in order to post-process precipitation and air temperature over a large study area covering different climates and topographies. Since one week is the maximum lead time for skilful precipitation forecasts (Liu and Coulibaly, 2011; Chen et al, 2014), the GEFS reforecasts with seven lead days were used in the study
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