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Remote Sens. 2016, 8(11), 899; doi:10.3390/rs8110899

Using a Kalman Filter to Assimilate TRMM-Based Real-Time Satellite Precipitation Estimates over Jinghe Basin, China

1
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
2
College of Computer and Information Engineering, Hohai University, Nanjing 210098, China
3
Department of Space Microwave Remote Sensing System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Academic Editors: Wolfgang Wagner and Prasad S. Thenkabail
Received: 19 July 2016 / Revised: 2 October 2016 / Accepted: 24 October 2016 / Published: 2 November 2016
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
View Full-Text   |   Download PDF [15567 KB, uploaded 2 November 2016]   |  

Abstract

In this study, efforts are focused on the comparison and validation of standard Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products—Version-7 3B42RT estimates before and after assimilation by using a Kalman filter with independent rain gauge networks located within the Jinghe basin of China. Generally, the direct comparison of TMPA precipitation estimates to 200 collocated rain gauges from 2006 to 2008 demonstrate that the spatial and temporal rainfall characteristics over the region are well captured by the assimilation estimates. Especially, results also show that using Kalman filter to assimilate TRMM-based multi-satellite real-time precipitation estimates tends to perform well over regions, where gauge network is rather sparse. Last, this study highlights that accurate detection and estimation of precipitation in the summer season by Kalman filter, particularly for nonlinear convective precipitation events, is still a challenging task for the future development of assimilation technique for improving the satellite-based precipitation accuracy. View Full-Text
Keywords: precipitation; satellite; data assimilation; Karlman Filter precipitation; satellite; data assimilation; Karlman Filter
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, J.; Yong, B.; Ren, L.; Wang, W.; Chen, B.; Lin, J.; Yu, Z.; Li, N. Using a Kalman Filter to Assimilate TRMM-Based Real-Time Satellite Precipitation Estimates over Jinghe Basin, China. Remote Sens. 2016, 8, 899.

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