How well can we estimate error variance of satellite precipitation data around the world?

• Nonlinear regression model is proposed to estimate satellite rainfall error variance.
• Correlation between predicted and observed error variance ranged from 0.46 to 0.98.
• The spatial distribution of observed error variance has been adequately captured.
• Satellite rainfall error can be depicted even in the absence of reference data.

Providing error information associated with existing satellite precipitation estimates is crucial to advancing applications in hydrologic modeling. In this study, we present a method of estimating the square difference prediction of satellite precipitation (hereafter used synonymously with “error variance”) using regression model for three satellite precipitation products (3B42RT, CMORPH, and PERSIANN-CCS) using easily available geophysical features and satellite precipitation rate. Building on a suite of recent studies that have developed the error variance models, the goal of this work is to explore how well the method works around the world in diverse geophysical settings. Topography, climate, and seasons are considered as the governing factors to segregate the satellite precipitation uncertainty and fit a nonlinear regression equation as a function of satellite precipitation rate. The error variance models were tested on USA, Asia, Middle East, and Mediterranean region. Rain-gauge based precipitation product was used to validate the error variance of satellite precipitation products. The regression approach yielded good performance skill with high correlation between simulated and observed error variances. The correlation ranged from 0.46 to 0.98 during the independent validation period. In most cases (~ 85% of the scenarios), the correlation was higher than 0.72. The error variance models also captured the spatial distribution of observed error variance adequately for all study regions while producing unbiased residual error. The approach is promising for regions where missed precipitation is not a common occurrence in satellite precipitation estimation. Our study attests that transferability of model estimators (which help to estimate the error variance) from one region to another is practically possible by leveraging the similarity in geophysical features. Therefore, the quantitative picture of satellite precipitation error over ungauged regions can be discerned even in the absence of ground truth data.