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

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.