This page has only limited features, please log in for full access.

Unclaimed
Bo Chen
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 02 November 2016 in Remote Sensing
Reads 0
Downloads 0

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.

ACS Style

Jiaqi Chen; Bin Yong; Liliang Ren; Weiguang Wang; Bo Chen; Jianan Lin; Zhongbo Yu; Ning Li. Using a Kalman Filter to Assimilate TRMM-Based Real-Time Satellite Precipitation Estimates over Jinghe Basin, China. Remote Sensing 2016, 8, 899 .

AMA Style

Jiaqi Chen, Bin Yong, Liliang Ren, Weiguang Wang, Bo Chen, Jianan Lin, Zhongbo Yu, Ning Li. Using a Kalman Filter to Assimilate TRMM-Based Real-Time Satellite Precipitation Estimates over Jinghe Basin, China. Remote Sensing. 2016; 8 (11):899.

Chicago/Turabian Style

Jiaqi Chen; Bin Yong; Liliang Ren; Weiguang Wang; Bo Chen; Jianan Lin; Zhongbo Yu; Ning Li. 2016. "Using a Kalman Filter to Assimilate TRMM-Based Real-Time Satellite Precipitation Estimates over Jinghe Basin, China." Remote Sensing 8, no. 11: 899.

Journal article
Published: 23 May 2016 in Remote Sensing
Reads 0
Downloads 0

The Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) products have been widely used, but their error and uncertainty characteristics over diverse climate regimes still need to be quantified. In this study, we focused on a systematic evaluation of TMPA’s error characteristics over mainland China, with an improved error-component analysis procedure. We performed the analysis for both the TMPA real-time and research product suite at a daily scale and 0.25° × 0.25° resolution. Our results show that, in general, the error components in TMPA exhibit rather strong regional and seasonal differences. For humid regions, hit bias and missed precipitation are the two leading error sources in summer, whereas missed precipitation dominates the total errors in winter. For semi-humid and semi-arid regions, the error components of two real-time TMPA products show an evident topographic dependency. Furthermore, the missed and false precipitation components have the similar seasonal variation but they counter each other, which result in a smaller total error than the individual components. For arid regions, false precipitation is the main problem in retrievals, especially during winter. On the other hand, we examined the two gauge-correction schemes, i.e., climatological calibration algorithm (CCA) for real-time TMPA and gauge-based adjustment (GA) for post-real-time TMPA. Overall, our results indicate that the upward adjustments of CCA alleviate the TMPA’s systematic underestimation over humid region but, meanwhile, unfavorably increased the original positive biases over the Tibetan plateau and Tianshan Mountains. In contrast, the GA technique could substantially improve the error components for local areas. Additionally, our improved error-component analysis found that both CCA and GA actually also affect the hit bias at lower rain rates (particularly for non-humid regions), as well as at higher ones. Finally, this study recommends that future efforts should focus on improving hit bias of humid regions, false error of arid regions, and missed snow events in winter.

ACS Style

Bin Yong; Bo Chen; Yudong Tian; Zhongbo Yu; Yang Hong. Error-Component Analysis of TRMM-Based Multi-Satellite Precipitation Estimates over Mainland China. Remote Sensing 2016, 8, 440 .

AMA Style

Bin Yong, Bo Chen, Yudong Tian, Zhongbo Yu, Yang Hong. Error-Component Analysis of TRMM-Based Multi-Satellite Precipitation Estimates over Mainland China. Remote Sensing. 2016; 8 (5):440.

Chicago/Turabian Style

Bin Yong; Bo Chen; Yudong Tian; Zhongbo Yu; Yang Hong. 2016. "Error-Component Analysis of TRMM-Based Multi-Satellite Precipitation Estimates over Mainland China." Remote Sensing 8, no. 5: 440.

Journal article
Published: 09 January 2015 in Remote Sensing
Reads 0
Downloads 0

The impact of one or two missing passive microwave (PMW) input sensors on the end product of multi-satellite precipitation products is an interesting but obscure issue for both algorithm developers and data users. On 28 January 2013, the Version-7 TRMM Multi-satellite Precipitation Analysis (TMPA) products were reproduced and re-released by National Aeronautics and Space Administration (NASA) Goddard Space Flight Center because the Advanced Microwave Sounding Unit-B (AMSU-B) and the Special Sensor Microwave Imager-Sounder-F16 (SSMIS-F16) input data were unintentionally disregarded in the prior retrieval. Thus, this study investigates the sensitivity of TMPA algorithm results to missing PMW sensors by intercomparing the “early” and “late” Version-7 TMPA real-time (TMPA-RT) precipitation estimates (i.e., without and with AMSU-B, SSMIS-F16 sensors) with an independent high-density gauge network of 200 tipping-bucket rain gauges over the Chinese Jinghe river basin (45,421 km2). The retrieval counts and retrieval frequency of various PMW and Infrared (IR) sensors incorporated into the TMPA system were also analyzed to identify and diagnose the impacts of sensor availability on the TMPA-RT retrieval accuracy. Results show that the incorporation of AMSU-B and SSMIS-F16 has substantially reduced systematic errors. The improvement exhibits rather strong seasonal and topographic dependencies. Our analyses suggest that one or two single PMW sensors might play a key role in affecting the end product of current combined microwave-infrared precipitation estimates. This finding supports algorithm developers’ current endeavor in spatiotemporally incorporating as many PMW sensors as possible in the multi-satellite precipitation retrieval system called Integrated Multi-satellitE Retrievals for Global Precipitation Measurement mission (IMERG). This study also recommends users of satellite precipitation products to switch to the newest Version-7 TMPA datasets and the forthcoming IMERG products whenever they become available.

ACS Style

Bin Yong; Bo Chen; Yang Hong; Jonathan J. Gourley; Zhe Li. Impact of Missing Passive Microwave Sensors on Multi-Satellite Precipitation Retrieval Algorithm. Remote Sensing 2015, 7, 668 -683.

AMA Style

Bin Yong, Bo Chen, Yang Hong, Jonathan J. Gourley, Zhe Li. Impact of Missing Passive Microwave Sensors on Multi-Satellite Precipitation Retrieval Algorithm. Remote Sensing. 2015; 7 (1):668-683.

Chicago/Turabian Style

Bin Yong; Bo Chen; Yang Hong; Jonathan J. Gourley; Zhe Li. 2015. "Impact of Missing Passive Microwave Sensors on Multi-Satellite Precipitation Retrieval Algorithm." Remote Sensing 7, no. 1: 668-683.