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

The Science of the Total Environment
Bahram ChoubinFan Zhang

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 removi...Continue Reading

Citations

Jun 18, 2020·Environmental Monitoring and Assessment·Salahuddin M Jaber, Mahmoud M Abu-Allaban
Oct 27, 2020·Environmental Monitoring and Assessment·Cheekati SireeshaMadan K Jha

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