Global component analysis of errors in three satellite-only global precipitation estimates

Abstract. Revealing the error components of satellite-only precipitation products (SPPs) can help algorithm developers and end-users understand their features improve retrieval algorithms. Here, two decomposition schemes are employed to explore IMERG-Late, GSMaP-MVK, PERSIANN-CCS SPPs over different seasons, rainfall intensities, topography classes. Global maps total bias (total mean squared error) its three (two) independent depicted for first time. The evaluation results similar regions discussed, it is found that one region cannot be extended another region. Hit and/or false biases major in most overland globally. systematic contributes less than 20 % areas. Large errors primarily due missed precipitation. It show topographic patterns terms random errors. Notably, among SPPs, GSMaP-MVK shows strongest dependency four scores. A novel metric, namely normalized component (NEC), proposed as a means isolate impact on Potential methods improving satellite retrievals adjustment models discussed.