论文标题
使用条件生成对抗网络计算从确定性天气预测中计算合奏的扩散
Computing the ensemble spread from deterministic weather predictions using conditional generative adversarial networks
论文作者
论文摘要
集合预测系统是天气预报的宝贵工具。实际上，通过运行确定性控制预测的几种扰动来获得集合预测。但是，整体预测与高计算成本有关，并且通常涉及统计后处理步骤以提高其质量。在这里，我们建议使用基于深度学习的算法来学习集合预测系统的统计属性，集合扩展，仅给定确定性控制预测。因此，一旦受过训练，就不再需要昂贵的整体预测系统来获得将来的整体预测，并且合奏的统计属性可以从单个确定性的预测中得出。我们将经典的Pix2Pix体系结构调整为三维模型，并尝试使用共享的潜在空间编码器模型，并针对500 HPA地理电位高度的数年操作（集合）天气预报进行训练。结果表明，受过训练的模型确实允许仅从控制预测中获得高度准确的集合扩展。
Ensemble prediction systems are an invaluable tool for weather forecasting. Practically, ensemble predictions are obtained by running several perturbations of the deterministic control forecast. However, ensemble prediction is associated with a high computational cost and often involves statistical post-processing steps to improve its quality. Here we propose to use deep-learning-based algorithms to learn the statistical properties of an ensemble prediction system, the ensemble spread, given only the deterministic control forecast. Thus, once trained, the costly ensemble prediction system will not be needed anymore to obtain future ensemble forecasts, and the statistical properties of the ensemble can be derived from a single deterministic forecast. We adapt the classical pix2pix architecture to a three-dimensional model and also experiment with a shared latent space encoder-decoder model, and train them against several years of operational (ensemble) weather forecasts for the 500 hPa geopotential height. The results demonstrate that the trained models indeed allow obtaining a highly accurate ensemble spread from the control forecast only.
