Precipitation forecasts are less accurate compared to other meteorological fields because several key processes affecting precipitation distribution and intensity occur below the resolved scale of global weather prediction models. This requires to use higher resolution simulations. To generate an uncertainty prediction associated with the forecast, ensembles of simulations are run simultaneously. However, the computational cost is a limiting factor here. Thus, instead of generating an ensemble system from simulations there is a trend of using neural networks. Unfortunately the data for high resolution ensemble runs is not available. We propose a new approach to generating ensemble weather predictions for high-resolution precipitation without requiring high-resolution training data. The method uses generative adversarial networks to learn the complex patterns of precipitation and produce diverse and realistic precipitation fields, allowing to generate realistic precipitation ensemble members using only the available control forecast. We demonstrate the feasibility of generating realistic precipitation ensemble members on unseen higher resolutions. We use evaluation metrics such as RMSE, CRPS, rank histogram and ROC curves to demonstrate that our generated ensemble is almost identical to the ECMWF IFS ensemble.

翻译：降水预报相较于其他气象场精度较低，这是因为影响降水分布和强度的若干关键过程发生在全球天气预报模式可解析尺度以下，因此需要使用更高分辨率的模拟。为生成与预报相关的不确定性预测，需同时运行多组模拟构成的集合系统。然而，计算成本在此成为限制因素。因此，一种新兴趋势是利用神经网络替代基于模拟的集合系统。遗憾的是，高分辨率集合模拟所需数据难以获取。本文提出了一种无需高分辨率训练数据即可生成高分辨率降水集合预报的新方法。该方法采用生成对抗网络学习降水的复杂模式，生成多样且逼真的降水场，从而仅利用已有的控制预报即可生成具有真实感的降水集合成员。我们验证了在未见过的更高分辨率下生成逼真降水集合成员的可行性。通过RMSE、CRPS、秩直方图及ROC曲线等评估指标，证明所生成的集合与ECMWF IFS集合几乎完全一致。