A Statistical Model for the Uncertainty Analysis of Satellite Precipitation Products

Earth-observing satellites provide a method to measure precipitation from space with good spatial and temporal coverage, but these estimates have a high degree of uncertainty associated with them. Understanding and quantifying the uncertainty of the satellite estimates can be very beneficial when using these precipitation products in hydrological applications. In this study, the generalized normal distribution (GND) model is used to model the uncertainty of the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) precipitation product. The stage IV Multisensor Precipitation Estimator (radar-based product) was used as the reference measurement. The distribution parameters of the GND model are further extended across various rainfall rates and spatial and temporal resolutions. The GND model is calibrated for an area of 5 degrees x 5 degrees over the southeastern United States for both summer and winter seasons from 2004 to 2009. The GND model is used to represent the joint probability distribution of satellite (PERSIANN) and radar (stage IV) rainfall. The method is further investigated for the period of 2006-08 over the Illinois watershed south of Siloam Springs, Arkansas. Results show that, using the proposed method, the estimation of the precipitation is improved in terms of percent bias and root-mean-square error.