Incorporating a Large‐Scale Constraint Into Radar Data Assimilation to Mitigate the Effects of Large‐Scale Bias on the Analysis and Forecast of a Squall Line Over the Yangtze‐Huaihe River Basin

Abstract

When there is an obvious large‐scale bias between a regional simulation and its driving global analysis, the regional model will provide inaccurate background information for radar data assimilation, which may eventually yield location errors associated with predicted precipitation. A case study of a squall line over the Yangtze‐Huaihe river basin presents such a situation. In this regard, we propose an approach to incorporate a large‐scale constraint into radar data assimilation to mitigate the effects of large‐scale bias on analysis and forecast results, in which global analysis data are introduced into the regional model using the spectral nudging technique to improve the quality of the first guess and background error statistics in radar data assimilation. A series of experiments are conducted with the Weather Research and Forecasting model and its three‐dimensional variational system to investigate the effectiveness of the proposed approach to introduce a large‐scale constraint into radar data assimilation. The experimental results demonstrate that the introduction of global analysis data can effectively correct the large‐scale bias and significantly improve the forecast skill of large‐scale patterns and convection initiation. The background error covariance (BE) obtained with the large‐scale constraint plays an important role in improving the assimilation effect. The length scales of BE are reduced after the large‐scale bias is removed, which represents a partial solution to the overestimation of BE reported in previous studies. In addition, applying a larger nudging wave number to radar data assimilation domain is not appropriate because the use of a larger wave number can negatively impact the three‐dimensional variational analysis.

A Dynamic Blending Scheme to Mitigate Large‐Scale Bias in Regional Models

“…This method has been shown to reduce near‐surface wind errors (Vincent & Hahmann, ), improve the simulation of large‐scale circulation patterns and the related precipitation distribution (Cha & Lee, ), and decrease the bias in typhoon tracks in the northwestern Pacific (Feser & von Storch, ). This method was applied by Yue et al () to a RUC system for convective‐scale severe weather prediction, yielding encouraging results in a case study. The other two methods, variation blending and digital filter blending, were specifically developed for use in RUC systems to improve the forecast background in cycled data assimilation.…”

“…To mitigate such systematic large-scale bias, methods have been developed to introduce large-scale information from a global model (GM) into an RM simulation, given that GM forecasts are not subject to lateral boundary issues (Benjamin et al, 2016;Hsiao et al, 2012;Vendrasco et al, 2016;Wang et al, 2014;Yang, 2005b;Yue et al, 2018). The goal of these methods is to mitigate the large-scale bias by blending large-scale waves from a GM with small-scale waves from an RM, thereby retaining the advantages of both modeling ©2020.…”

Constraining the Large‐Scale Analysis of a Regional Rapid‐Update‐Cycle System for Short‐Term Convective Precipitation Forecasting

“…However, the rather brutal merging of fields from different models could introduce shock in the transition zone near the cutoff wavelength. Additional initialization processes (e.g., Lynch & Huang, ) or the nudging technique (Yue et al, ) had to be used to remedy the issue.…”