Impact of Optimally Growing Initial Errors on the Mesoscale Predictability of Heavy Precipitation Events along the Mei-Yu Front in China

Abstract

Abstract Based on the conditional nonlinear optimal perturbation (CNOP) approach, the impact of the optimally growing initial errors on the mesoscale predictability of typical mei-yu front heavy precipitation events over eastern China was explored. First, based on a nonlinear optimization system built using the high-resolution Weather Research and Forecasting Model and particle swarm optimization algorithm, the CNOPs for three heavy precipitation cases were obtained. The CNOPs as the optimally growing initial errors caused the largest forecast errors and made the 24-h accumulated precipitation stronger than any other kind of initial errors. Moreover, the CNOPs showed faster growth and saturation than the random errors in space, highlighting the importance of the initial error with specific spatial structure in the meso- and convective-scale processes. Despite different CNOPs having particular spatial structures, the large amplitudes of the CNOPs at lower levels were mainly located in the rainband along the mei-yu front. Although the spectral energies of the CNOPs increased with increasing scales, the forecast error growth for the CNOPs generally followed the well-known three-stage conceptual model. Moreover, the large-scale and large-amplitude initial errors in the CNOPs were the most influential in terms of the forecast quality. This suggests that reducing large-scale initial errors can potentially improve the forecast accuracy. However, the mesoscale predictability of the mei-yu front heavy precipitation events is inherently limited, for which the moist convection was found to be the main reason.