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Atmosphere 2015, 6(9), 1307-1326; doi:10.3390/atmos6091307

Spatiotemporal Interpolation of Rainfall by Combining BME Theory and Satellite Rainfall Estimates

1
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
4
Institute of Islands and Coastal Ecosystems, Zhejiang University, Hangzhou 310058, China
5
China Centre for Resources Satellite Data and Application, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Academic Editor: Shaun Lovejoy
Received: 2 July 2015 / Accepted: 8 August 2015 / Published: 7 September 2015
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Abstract

The accurate assessment of spatiotemporal rainfall variability is a crucial and challenging task in many hydrological applications, mainly due to the lack of a sufficient number of rain gauges. The purpose of the present study is to investigate the spatiotemporal variations of annual and monthly rainfall over Fujian province in China by combining the Bayesian maximum entropy (BME) method and satellite rainfall estimates. Specifically, based on annual and monthly rainfall data at 20 meteorological stations from 2000 to 2012, (1) the BME method with Tropical Rainfall Measuring Mission (TRMM) estimates considered as soft data, (2) ordinary kriging (OK) and (3) cokriging (CK) were employed to model the spatiotemporal variations of rainfall in Fujian province. Subsequently, the performance of these methods was evaluated using cross-validation statistics. The results demonstrated that BME with TRMM as soft data (BME-TRMM) performed better than the other two methods, generating rainfall maps that represented the local rainfall disparities in a more realistic manner. Of the three interpolation (mapping) methods, the mean absolute error (MAE) and root mean square error (RMSE) values of the BME-TRMM method were the smallest. In conclusion, the BME-TRMM method improved spatiotemporal rainfall modeling and mapping by integrating hard data and soft information. Lastly, the study identified new opportunities concerning the application of TRMM rainfall estimates. View Full-Text
Keywords: Bayesian maximum entropy (BME); TRMM; spatiotemporal analysis; soft data; rainfall/precipitation Bayesian maximum entropy (BME); TRMM; spatiotemporal analysis; soft data; rainfall/precipitation
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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MDPI and ACS Style

Shi, T.; Yang, X.; Christakos, G.; Wang, J.; Liu, L. Spatiotemporal Interpolation of Rainfall by Combining BME Theory and Satellite Rainfall Estimates. Atmosphere 2015, 6, 1307-1326.

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