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.

翻译：统计后处理已被证明是改善不同气象变量集合预报的有效工具。案例研究表明，后处理可以在优化表征预报技巧的适当评分规则的同时，弥补集合预报通常存在的离散度不足及潜在偏差问题。这些积极效应的代价通常表现为锐度降低：中心预报区间宽度和预报不确定性增大，尤其在较短预报时效中尤为明显。本研究旨在通过向神经网络损失函数添加惩罚项，降低上述现象对基于神经网络参数化后处理方法的影响。我们使用从EUPPBench基准数据集下载的欧洲中期天气预报中心2米温度集合预报数据，验证其与实际观测的一致性来证明所提出技术的效果。在此预测分布为高斯分布，我们采用连续等级概率评分（CRPS）作为损失函数。案例研究证实，与未使用惩罚项计算得到的预测分布宽度相比，名义中心预报区间宽度显著降低（8.2%-12.5%），同时概率预报的平均CRPS与预报均值的RMSE均未出现下降。