Abstract：
The present research endeavors to examine the effectiveness of four gridded precipitation datasets, namely Integrated Multi-satellite Retrievals for GPM (IMERG), Tropical Precipitation Measuring Mission (TRMM), Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), with the observed rainfall data of eight rain gauge stations of India Meteorological Department (IMD) from 2001 to 2019 in Kosi River basin, India. Various statistical metrics, contingency tests, trend analysis, and rainfall anomaly index were utilized at daily, monthly, seasonal, and annual time scales. The categorical metrics namely probability of detection (POD) and false alarm ratio (FAR) indicate that MERRA-2 and IMERG datasets have the highest level of concurrence with the observed daily data. Statistical analysis of gridded datasets with observed dataset of IMD showed that the performance of the IMERG dataset is better than MERRA-2, PERSIANN, and TRMM datasets with \"very good\" coefficient of determination (R2) and Nash-Sutcliffe Efficiency values for monthly data. Trend analysis of gridded seasonal data of IMERG showed similar trends of observed seasonal data whereas other dataset differs. IMERG also performed well in identifying wet and dry years based on annual data. Discrepancies of the satellite sensor in capturing the precipitation have also been discussed. Thus, the IMERG dataset can be effectively used for hydro-meteorological and climatological investigations in cases of lack of observed datasets.
摘要：
本研究试图检验四个网格化降水数据集的有效性，即GPM综合多卫星检索(IMERG)，热带降水测量任务（TRMM），现代研究和应用回顾性分析第2版(MERRA-2)，使用人工神经网络(PERSIANN)从遥感信息中估算降水，利用印度气象部门（IMD）2001年至2019年在科西河流域的八个雨量计站的观测降雨数据，印度。各种统计指标，应急测试,趋势分析,每天使用降雨异常指数，每月,季节性,和年度时间尺度。分类指标，即检测概率（POD）和误报率（FAR）表明MERRA-2和IMERG数据集与观察到的每日数据具有最高的并发水平。用观察到的IMD数据集进行网格数据集的统计分析表明，IMERG数据集的性能优于MERRA-2，PERSIANN，和TRMM数据集具有“非常好”的确定系数（R2）和每月数据的Nash-Sutcliffe效率值。IMERG的网格季节性数据的趋势分析显示，观察到的季节性数据的趋势相似，而其他数据集不同。IMERG在根据年度数据确定干湿年份方面也表现良好。还讨论了卫星传感器在捕获降水方面的差异。因此，在缺乏观测数据集的情况下，IMERG数据集可有效用于水文气象和气候学调查。
