A Study of Rain Forecast Error Structure Based on Radar Observations over a Continental-Scale Spatial Domain

Abstract

Abstract This study examines the univariate error covariances of hourly rainfall accumulations using two different NWP models and a mosaic of radar reflectivity over a continental-scale domain. The study focuses on two main areas. The focus of the first part of the paper is on the ensemble-based and the innovation-based error variance and correlation estimations. An ensemble of forecasts and a set of observations provide the basis for estimating the errors in two different ways. The results indicate that both ensemble- and innovation-based methods lead to comparable variance estimations, while the local error correlation estimates have larger differences due to the sensitivity of calculations to the gradient of the variance field. The second part of the paper uses innovations for identifying the errors. The focus of this part is on a prognostic method for estimating the error statistics from the background based on the Bayesian inference technique. The case study shows that the predictive model produces a similar result regarding the magnitude and the dispersion of variance in comparison with the innovation and ensemble-based variances. This study represents a step toward estimating local error variances and local error correlations to construct a nonhomogeneous and precipitation-dependent error covariance matrix of rainfall. These results will be used in a future paper in the design of a 2D-VAR Assimilation Method for Blending Extrapolated Radars (AMBER) with NWP precipitation forecast to form a precipitation nowcasting model.