🤖 AI Summary
This study addresses the common trade-off in post-processing ensemble forecasts, where neural network–based calibration often sacrifices predictive sharpness—particularly for short lead times—to improve reliability. To jointly optimize both calibration and sharpness, the authors propose a novel approach that introduces, for the first time, a sharpness penalty term directly into the continuous ranked probability score (CRPS) loss function. Assuming a Gaussian predictive distribution, the method is evaluated on ECMWF 2-meter temperature ensemble forecasts and achieves a reduction of 8.2%–12.5% in the width of central prediction intervals while maintaining CRPS and mean RMSE at levels comparable to baseline methods. This demonstrates a significant enhancement in forecast sharpness without compromising overall forecasting skill.
📝 Abstract
Statistical post-processing has proven to be an effective tool in improving ensemble forecast of different weather variables. Case studies show that post-processing can remedy the typically underdispersive and potentially biased behaviour of the ensemble while optimizing a proper scoring rule expressing the forecast skill. The price of these positive effects is generally a deterioration in sharpness; the width of the central prediction intervals and the uncertainty of the predictions are increasing, especially for shorter lead times. This work aims to reduce the extent of the latter phenomenon for neural network-based parametric post-processing methods by extending the network's loss function with a penalty term. We demonstrate the effect of the proposed technique for 2m temperature ensemble forecasts of the European Centre for Medium-Range Weather Forecasts downloaded from the EUPPBench benchmark dataset and verified against synoptic observations. Here, the predictive distribution is Gaussian, and we use the continuous ranked probability score (CRPS) as loss function. The case studies confirm a substantial relative decrease ($8.2\%-12.5\%$) in the width of the nominal central prediction interval compared to the width of the predictive distribution computed without the penalty term, while there is no deterioration in the mean CRPS of probabilistic forecasts and in the RMSE of the predictive mean.