Abstract Ensemble multi-satellite precipitation datasets (ESPDs) are alternative to satellite-based precipitation products (SPs), which tend to reduce the errors, combine advantages of individual SPs, and have higher accuracy for hydrological applications. The current study proposes and evaluates a dynamic WALS-ESPD developed using the Weighted Average Least Square (WALS) algorithm, which has 0.25° spatial and daily temporal resolutions across glacial, humid, arid and hyper-arid regions of Pakistan during 2000–2015. WALS-ESPD is developed using three SPs, Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Climate Prediction Center MORPHing technique (CMORPH), and one re-analysis product, Era-Interim. Mean Bias (MB), Mean Absolute Error (MAE), unbiased Root Mean Square Error (ubRMSE), Correlation Coefficient (R), Kling-Gupta efficiency (KGE score), and Theil's U are used to evaluate the performance of WALS-ESPD both spatially and temporally. Moreover, the skill scores of statistical metrics are used to assess the WALS-ESPD performance against two previously developed ESPDs, DBMA-ESPD and DCBA-ESPD. TMPA dominated all SPs with average weights of 0.317, 0.341, 0.314, and 0.326 across the glacial, humid, arid and hyper-arid regions. TMPA dominated pre-monsoon (30.26%) and monsoon (35.82%) seasons, while PERSIANN-CDR dominated post-monsoon (27.58%) and winter (29.82%) seasons. WALS-ESPD performed relatively poor across the glacial and humid regions, and during monsoon and pre-monsoon seasons. Skill scores of WALS-ESPD against DBMA-ESPD show better performance of WALS-ESPD in all four regions, especially across the glacial region with the maximum MB, MAE, and ubRMSE scores of 27.36%, 28.34%, and 27.67%, respectively. Meanwhile, WALS-ESPD performed better than DCBA-ESPD in the whole glacial region and most part of other regions, while DCBA-ESPD dominated WALS-ESPD at few stations across humid, arid, and hyper-arid (south-east) regions.

中文翻译：

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一种新的基于加权平均最小二乘法的集合多卫星降水数据集的开发及其对巴基斯坦的综合评价

摘要 集合多星降水数据集（ESPDs）是星基降水产品（SPs）的替代方案，具有减少误差、结合单个SPs优势、水文应用精度更高的特点。当前的研究提出并评估了使用加权平均最小二乘法 (WALS) 算法开发的动态 WALS-ESPD，该算法在 2000-2015 年期间在巴基斯坦的冰川、潮湿、干旱和超干旱地区具有 0.25° 的空间和每日时间分辨率。WALS-ESPD 是使用三个 SP 开发的，热带降雨测量任务 (TRMM) 多卫星降水分析 (TMPA) 3B42V7，使用人工神经网络的遥感信息进行降水估计 - 气候数据记录 (PERSIANN-CDR)，气候预测中心 MORPHing技术（CMORPH），和一种重新分析产品，Era-Interim。平均偏差 (MB)、平均绝对误差 (MAE)、无偏均方根误差 (ubRMSE)、相关系数 (R)、Kling-Gupta 效率（KGE 分数）和 Theil's U 用于评估 WALS-ESPD 的性能在空间和时间上。此外，统计指标的技能分数用于评估 WALS-ESPD 相对于两个先前开发的 ESPD，DBMA-ESPD 和 DCBA-ESPD 的性能。TMPA 以 0.317、0.341、0.314 和 0.326 的平均权重在冰川、潮湿、干旱和超干旱地区占主导地位。TMPA 主导季风前 (30.26%) 和季风 (35.82%) 季节，而 PERSIANN-CDR 主导季风后 (27.58%) 和冬季 (29.82%) 季节。WALS-ESPD 在冰川和潮湿地区以及季风和季风季节期间的表现相对较差。WALS-ESPD 与 DBMA-ESPD 的技能分数在所有四个区域中都显示出 WALS-ESPD 的更好性能，尤其是在冰川区域，MB、MAE 和 ubRMSE 分数分别为 27.36%、28.34% 和 27.67%。同时，WALS-ESPD在整个冰川区和其他大部分地区的表现优于DCBA-ESPD，而DCBA-ESPD在潮湿、干旱和超干旱（东南）地区的少数站点上主导了WALS-ESPD。