| 摘 要： |
This study evaluates three precipitation products including the Tropical Rainfall Measuring Mission (TRMM), Multi-satellite Precipitation Analysis (TMPA), Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE, hereafter abbreviated as APH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network Climate Data Record (PERSIANN-CDR, hereafter abbreviated as PCDR) using surface precipitation gauge (SPG) data of 43 stations over five climatic zones of Pakistan for the period 1998 - 2015 on multiple temporal scales. Multiple statistical performance metrics and categorical statistics were utilized for evaluating the precipitation products such as mean error (ME), mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (CC), standard deviation (SD), probability of detection (POD), critical success index (CSI), and false alarm ratio (FAR), whereas probability distribution function (PDF) technique was employed to evaluate the precipitation intensities, and Mann-Kendall (MK) test was used for assessing the trends. Blend of over- and underestimation between SPG-TMPA, SPG-APH, and SPG-PCDR were perceived in different climatic zones on all temporal scales. MAE and RMSE of daily were higher than monthly and annual temporal scales. TMPA demonstrated slightly better results in comparison to APH and PCDR in all five climatic zones by analyzing precipitation intensity. The magnitude of the trend was less in the first half (1998 - 2006) as compared to the second half (2007 - 2015). All precipitation products performed better in climatic zones situated in plain areas in comparison to high mountainous regions. It is concluded that TMPA can be a better substitute of SPG for agriculture modeling, weather analysis, and water resource management studies. |
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