Estimation of Satellite Rainfall Error Variance Using Readily Available Geophysical Features

The present study addresses the estimation of error variance (mean square error, MSE) of three satellite rainfall products: i) Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product of 3B42RT; ii) Climate Prediction Center (CPC) Morph (CMORPH); and iii) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Nonlinear regression model is used to fit the response variable (satellite rainfall error variance) with explanatory variable (satellite rainfall rate) by grouping them as function of three key geophysical features: topography, climate, and season. The results of the study suggest that the error variance of a rainfall product is strongly correlated with rainfall rate and can be expressed as a power-law function. The geophysical feature based error classification analysis helps in achieving superior accuracy for prognostic error variance quantification in the absence of ground truth data. The multiple correlation coefficients between the estimated and observed error variance over an independent validation region (Upper Mississippi River basin) and time period (2007-2010) are found to be 0.75, 0.86, and 0.87 for 3B42RT, CMORPH, and PERSIANN-CCS products, respectively. In another validation region (Arkansas-Red River basin), the correlation coefficients are 0.59, 0.89, and 0.92 for the same products, respectively. Results of the assessment of error variance models reveal that the type of error component present in a satellite rainfall product directly impacts the accuracy of estimated error variance. The model estimates the error variance more accurately when the precipitation error components are mostly hit bias or false precipitation, while for a product with extensive missed precipitation, the accuracy of estimated error variance is significantly compromised. The study clearly demonstrates the feasibility of quantifying the error variance of satellite rainfall products in a spatially and temporally varying manner using readily available geophysical features and rainfall rate. The study is a path finder to a globally applicable and operationally feasible methodology for error variance estimation at high spatial and temporal scales for advancing satellite rainfall applications in ungauged basins.