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Atmosphere 2015, 6(8), 983-1005; doi:10.3390/atmos6080983

Topography and Data Mining Based Methods for Improving Satellite Precipitation in Mountainous Areas of China

1
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
2
State Key Lab of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Academic Editor: John Boland
Received: 16 February 2015 / Revised: 14 July 2015 / Accepted: 14 July 2015 / Published: 24 July 2015
(This article belongs to the Special Issue Climate Variable Forecasting)
View Full-Text   |   Download PDF [3913 KB, uploaded 24 July 2015]   |  

Abstract

Topography is a significant factor influencing the spatial distribution of precipitation. This study developed a new methodology to evaluate and calibrate the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) products by merging geographic and topographic information. In the proposed method, firstly, the consistency rule was introduced to evaluate the fitness of satellite rainfall with measurements on the grids with and without ground gauges. Secondly, in order to improve the consistency rate of satellite rainfall, genetic programming was introduced to mine the relationship between the gauge rainfall and location, elevation and TMPA rainfall. The proof experiment and analysis for the mean annual satellite precipitation from 2001–2012, 3B43 (V7) of TMPA rainfall product, was carried out in eight mountainous areas of China. The result shows that the proposed method is significant and efficient both for the assessment and improvement of satellite precipitation. It is found that the satellite rainfall consistency rates in the gauged and ungauged grids are different in the study area. In addition, the mined correlation of location-elevation-TMPA rainfall can noticeably improve the satellite precipitation, both in the context of the new criterion of the consistency rate and the existing criteria such as Bias and RMSD. The proposed method is also efficient for correcting the monthly and mean monthly rainfall of 3B43 and 3B42RT. View Full-Text
Keywords: satellite rainfall; evaluation; calibration; topography; data mining satellite rainfall; evaluation; calibration; topography; data mining
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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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MDPI and ACS Style

Xia, T.; Wang, Z.-J.; Zheng, H. Topography and Data Mining Based Methods for Improving Satellite Precipitation in Mountainous Areas of China. Atmosphere 2015, 6, 983-1005.

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