Abstract
An object-based verification approach is employed to assess the performance of the commonly used high-resolution satellite precipitation products: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Climate Prediction center MORPHing technique (CMORPH), and Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42RT. The evaluation of the satellite precipitation products focuses on the skill of depicting the geometric features of the localized precipitation areas. Seasonal variability of the performances of these products against the ground observations is investigated through the examples of warm and cold seasons. It is found that PERSIANN is capable of depicting the orientation of the localized precipitation areas in both seasons. CMORPH has the ability to capture the sizes of the localized precipitation areas and performs the best in the overall assessment for both seasons. 3B42RT is capable of depicting the location of the precipitation areas for both seasons. In addition, all of the products perform better on capturing the sizes and centroids of precipitation areas in the warm season than in the cold season, while they perform better on depicting the intersection area and orientation in the cold season than in the warm season. These products are more skillful on correctly detecting the localized precipitation areas against the observations in the warm season than in the cold season.
Highlights
Precipitation is a crucial hydrologic variable that links the atmosphere with surface land processes
The object-based method is applied on three satellite precipitation products against ground references for the warm and cold seasons
This is mainly due to the limitations in retrieving precipitation with passive microwave when the land surface is covered by snow or ice [41]
Summary
Precipitation is a crucial hydrologic variable that links the atmosphere with surface land processes. Ground-based instruments, such as rain gauges and ground radar, are commonly used to measure precipitation. Such instruments can provide reliable local precipitation measurements, which, have limited range and availability. With the advances in satellite technology, many remote-sensing algorithms have been developed to estimate precipitation at quasi-global scales. The satellite precipitation products provide precipitation estimations with high temporal and spatial resolutions that are applicable for watershed management, flood monitoring, and hydrologic modeling, such as Precipitation Estimation from Remotely Sensed Information using Artificial Neural. Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) [4], and Integrated. Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) [5].
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