标题： 印度夏季季风期间高分辨率网格降水数据集与密集雨量计网络的评估 显示英文标题

标题： Evaluation of High-Resolution Gridded Precipitation Datasets Against a Dense Rain Gauge Network During the Indian Summer Monsoon

摘要： 遥感技术的进步导致了多种卫星衍生降水产品的开发;然而，在科学和业务研究中使用之前，必须评估其准确性。 本研究全面评估了六个广泛使用的数据集PERSIANN CCS、CHIRPS、MSWEP、IMERG、AgERA5和GSMaP ISRO，这些数据集与卡纳塔克邦密集的雨量计网络进行对比，卡纳塔克邦是印度南部一个气候条件多样且地形复杂的州。 分析集中在2011年至2022年的印度夏季季风季节。 为了补充传统指标，应用了复杂网络理论的工具来研究降雨的空间组织和连接模式。 采用功能气候网络方法构建降雨相关网络，而事件同步作为一种非线性度量，量化了极端事件的同时发生。 大多数产品再现了大尺度季风特征，但它们在表示强度类别和极端值方面的能力各不相同。 GSMaP ISRO在子区域中表现出最高的相关性、最低的偏差和RMSE，而PERSIANN CCS在西高止山脉地区表现出系统性误差，尽管在内陆平原的相关性有所提高。 基于网络的分析再次确认了GSMaP ISRO在复制空间相关结构方面的技能，在由大尺度过程主导的区域表现出高一致性，在受局部动力学影响的区域则表现出较低的一致性。 观测到的降雨网络显示沿海地区和中央卡纳塔克邦之间有很强的同步性，表明极端事件的大范围同时发生，而马拉德地区显示出较弱的连接性，表明局部事件。 GSMaP ISRO密切再现了这种度分布，反映了使用IMD网格数据集进行的修正。 未来的工作应改进亚日和局部降雨估计，尤其是在复杂地形中。 显示英文摘要

摘要： Advancements in remote sensing have led to development of several satellite-derived precipitation products; however, their accuracy must be evaluated before use in scientific and operational studies. This study comprehensively assesses six widely used datasets PERSIANN CCS, CHIRPS, MSWEP, IMERG, AgERA5, and GSMaP ISRO against a dense rain gauge network across Karnataka, a southern Indian state characterized by diverse climatic conditions and complex topography. The analysis focuses on the Indian summer monsoon season for 2011 to 2022. To complement traditional metrics, tools from complex network theory were applied to investigate spatial organization and connectivity patterns of rainfall. A functional climate network approach was used to construct rainfall correlation networks, while event synchronization, a nonlinear measure, quantified the co occurrence of extreme events. Most products reproduced large scale monsoon features, yet their ability to represent intensity categories and extremes varied. GSMaP ISRO showed the highest correlation, lowest bias, and RMSE across subregions, whereas PERSIANN CCS exhibited systematic errors, particularly in Western Ghats, though correlations improved over interior plains. Network-based analysis reaffirmed GSMaP ISROs skill in replicating spatial correlation structures, capturing high coherence in regions dominated by large-scale processes and lower coherence in areas influenced by localized dynamics. The observed rainfall network revealed strong synchronization between the coastal region and central Karnataka, indicating broad spatial co occurrence of extremes, while the Malnad region showed weaker connectivity, suggesting localized events. GSMaP ISRO closely reproduced this degree distribution, reflecting corrections using IMD gridded dataset. Future work should improve sub-daily and localized rainfall estimates, especially in complex terrain.