标题： 利用深度学习改进定量降水预报的后处理：从高分辨率观测中学习降水物理特性 显示英文标题

标题： Improving Post-Processing for Quantitative Precipitation Forecasting Using Deep Learning: Learning Precipitation Physics from High-Resolution Observations

摘要： 定量降水预报（QPF）的准确性仍然是数值天气预报（NWP）中的主要挑战之一，主要是由于通过参数化方案表示大气微物理过程的全部复杂性存在困难。 本研究介绍了一种基于深度学习的后处理模型DL-QPF，该模型通过直接从高分辨率雷达估算降水数据中学习来诊断气象预报中的降水场。 DL-QPF模型采用了一种结合U-Net生成器和判别器的Patch-条件生成对抗网络（Patch-cGAN）架构。 生成器学习与降水相关的气象特征，而来自判别器的对抗损失鼓励生成真实的降雨模式和分布。 训练使用了韩半岛三年的暖季数据，输入变量来源于欧洲中期天气预报中心（ECMWF）集成预报系统高分辨率预报（IFS-HRES）。 模型验证针对多个参考模型进行，包括全球模型（IFS-HRES、KIM）、区域模型（KIM-Regional、KIM-LENS）以及基于AI的模型（GraphCast）。 多雨量阈值的验证结果显示，DL-QPF实现了接近1的频率偏差和更优的成功比率。 特别是在强降雨事件中，DL-QPF的表现优于传统的NWP和一个AI模型，显示出在捕捉高强度降水方面的技能提升。 本研究强调了基于观测数据驱动的深度学习方法在后处理QPF中的潜力。 通过直接从观测数据中学习，DL-QPF减少了系统偏差并增强了预测降水分布的真实性。 这些结果展示了该模型提高QPF真实性的潜力。 显示英文摘要

摘要： Accurate quantitative precipitation forecasting (QPF) remains one of the main challenges in numerical weather prediction (NWP), primarily due to the difficulty of representing the full complexity of atmospheric microphysics through parameterization schemes. This study introduces a deep learning-based post-processing model, DL-QPF, which diagnoses precipitation fields from meteorological forecasts by learning directly from high-resolution radar estimates precipitation. The DL-QPF model is constructed using a Patch-conditional Generative Adversarial Network (Patch-cGAN) architecture combined with a U-Net generator and a discriminator. The generator learns meteorological features relevant to precipitation, while the adversarial loss from the discriminator encourages the generation of realistic rainfall patterns and distributions. Training is performed on three years of warm-season data over the Korean Peninsula, with input variables derived from ECMWF's Integrated Forecasting System High-Resolution forecast (IFS-HRES). Model verification is conducted against multiple reference models, including global (IFS-HRES, KIM), regional (KIM-Regional, KIM-LENS), and AI-based (GraphCast) forecasts. Verification across multiple rainfall thresholds shows that DL-QPF achieves a frequency bias near one and superior success ratios. Particularly for heavy and intense rainfall events, DL-QPF outperforms both conventional NWP and an AI model, demonstrating improved skill in capturing high-intensity precipitation. This study highlights the potential of observational data-driven deep learning approaches in post-processing QPF. By directly learning from observations, DL-QPF reduces systematic biases and enhances the realism of forecasted rainfall distributions. These results demonstrate the model's potential to enhance QPF realism.