A novel bias correction framework of TMPA 3B42 daily precipitation data using similarity matrix/homogeneous conditions

Bahram Choubin; Shahram Khalighi-Sigaroodi; Ashok Mishra; Massoud Goodarzi; Shahaboddin Shamshirband; Esmatullah Ghaljaee; Fan Zhang

doi:10.1016/j.scitotenv.2019.133680

A novel bias correction framework of TMPA 3B42 daily precipitation data using similarity matrix/homogeneous conditions

Sci Total Environ. 2019 Dec 1:694:133680. doi: 10.1016/j.scitotenv.2019.133680. Epub 2019 Jul 30.

Authors

Bahram Choubin¹, Shahram Khalighi-Sigaroodi², Ashok Mishra³, Massoud Goodarzi⁴, Shahaboddin Shamshirband⁵, Esmatullah Ghaljaee¹, Fan Zhang⁶

Affiliations

¹ Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
² Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran. Electronic address: khalighi@ut.ac.ir.
³ Glenn Department of Civil Engineering, Clemson University, Clemson, SC, USA.
⁴ Soil Conservation and Watershed Management Research Institute (SCWMRI), AREEO, Tehran, Iran.
⁵ Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. Electronic address: shahaboddin.shamshirband@tdtu.edu.vn.
⁶ Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China.

PMID: 31394326
DOI: 10.1016/j.scitotenv.2019.133680

Abstract

Reduction of bias in remotely sensed precipitation products is a major challenge in environment modeling, hydrology, and managing the water resources. Various bias correction techniques are applied to reduce the bias from pixel to gauge data. However, a successful methodology to improve bias correction on the daily scale is often challenging and limited. We present a methodology that can be used to correct the daily bias in remote sensing rainfall data, and to demonstrate the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 data was used. The proposed bias correction method is based on the concept of similarity (homogeneous) conditions developed based on the periodicity and different percentile-based precipitation amount, and by identifying the best donor pixel to transfer bias correction factor to a specific ungauged pixel (the receptor pixel) based on the similarity (elevation, latitude, and longitude). Bias correction factors were obtained using the mean bias-removal (MBR) and multiplicative ratio (MR) techniques in the cells of the similarity matrix. The proposed methodology demonstrates a significant removal of bias associated with TMPA 3B42 data sets and it is capable of removing the bias in daily precipitation data on an average by 57% (51%) in the gauged pixels, and 25% (22%) in the ungauged pixels for MBR (MR) method.

Keywords: Daily bias correction; Mean bias-removal; Multiplicative ratio; Similarity matrix; TMPA 3B42.