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Open AccessArticle

Object-Based Assessment of Satellite Precipitation Products

Department of Geosciences and Environment, California State University Los Angeles, Los Angeles, CA 90032, USA
Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California Irvine, Irvine, CA 92697, USA
Author to whom correspondence should be addressed.
Academic Editors: Prashant K. Srivastava, Richard Gloaguen and Prasad S. Thenkabail
Remote Sens. 2016, 8(7), 547;
Received: 11 April 2016 / Revised: 13 June 2016 / Accepted: 22 June 2016 / Published: 27 June 2016
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. View Full-Text
Keywords: precipitation; verification; object-based; remote sensing precipitation; verification; object-based; remote sensing
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MDPI and ACS Style

Li, J.; Hsu, K.-L.; AghaKouchak, A.; Sorooshian, S. Object-Based Assessment of Satellite Precipitation Products. Remote Sens. 2016, 8, 547.

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