一种用于多源降水融合和改进季节性及极端估计的双阶段TransUNet深度学习框架
A Dual-TransUNet Deep Learning Framework for Multi-Source Precipitation Merging and Improving Seasonal and Extreme Estimates
AI总结：
本文提出了一种双阶段TransUNet框架，用于多源降水融合和改进季节性及极端事件估计，通过融合多种数据源提升预测精度和极端事件检测能力。
摘要：
多源降水产品（MSPs）从卫星检索和再分析中广泛用于水文气候监测，但空间异质性偏差和对极端事件的有限技能仍然限制了其水文用途。本文开发了一种基于双阶段TransUNet的多源降水融合框架（DDL-MSPMF），整合了六个MSPs和四个ERA5近地表物理预测变量。第一阶段分类器估计每日降水发生概率，第二阶段回归器融合分类器输出和所有预测变量，以0.25度分辨率对中国2001-2020年的每日降水量进行估计。与多种深度学习和混合基线模型相比，TransUNet-TransUNet配置在季节性性能（R=0.75；RMSE=2.70 mm/day）方面表现最佳，并相对于单一回归器设置提高了鲁棒性。对于强降水（>25 mm/day），DDL-MSPMF在大多数东部中国地区提高了公平威胁分数，并更好地再现了2021年7月郑州暴雨的时空模式，表明其在极端事件检测方面的改进超越了季节均值校正。使用TPHiPr对青藏高原的独立评估进一步支持了其在数据稀少地区的适用性。SHAP分析突显了降水发生概率和地表气压的重要性，提供了物理可解释的诊断。所提出的框架为降水融合和极端事件评估提供了一种可扩展且可解释的方法。
英文摘要：
Multi-source precipitation products (MSPs) from satellite retrievals and reanalysis are widely used for hydroclimatic monitoring, yet spatially heterogeneous biases and limited skill for extremes still constrain their hydrologic utility. Here we develop a dual-stage TransUNet-based multi-source precipitation merging framework (DDL-MSPMF) that integrates six MSPs with four ERA5 near-surface physical predictors. A first-stage classifier estimates daily precipitation occurrence probability, and a second-stage regressor fuses the classifier outputs together with all predictors to estimate daily precipitation amount at 0.25 degree resolution over China for 2001-2020. Benchmarking against multiple deep learning and hybrid baselines shows that the TransUNet - TransUNet configuration yields the best seasonal performance (R = 0.75; RMSE = 2.70 mm/day) and improves robustness relative to a single-regressor setting. For heavy precipitation (>25 mm/day), DDL-MSPMF increases equitable threat scores across most regions of eastern China and better reproduces the spatial pattern of the July 2021 Zhengzhou rainstorm, indicating enhanced extreme-event detection beyond seasonal-mean corrections. Independent evaluation over the Qinghai-Tibet Plateau using TPHiPr further supports its applicability in data-scarce regions. SHAP analysis highlights the importance of precipitation occurrence probabilities and surface pressure, providing physically interpretable diagnostics. The proposed framework offers a scalable and explainable approach for precipitation fusion and extreme-event assessment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
