Improving Convective Precipitation Forecasts Using Ensemble‐Based Background Error Covariance in 3DVAR Radar Assimilation System

Abstract

Skillful quantitative precipitation forecast using the numerical weather prediction model relies on an accurate estimate of the atmospheric state as an initial condition. Variational assimilation methods (VAR) have the potential to provide improved initial state estimation to the numerical weather prediction model using observations, prior data (background), and their respective error covariance. The quality of variational assimilation hinges on the background error statistics (BES) as it weights the error in prior state and determines the spread of assimilated observations in model space. Traditional approaches used to model stationary BES in a three-dimensional variational assimilation system often fail to represent the model error in BES. In this study, we have proposed an ensemble method using Stochastically Perturbed Parameterization Tendency to represent the model error in BES. The characteristics of the proposed BES are compared with the traditional approaches using the National Meteorological Centre method for different control variables choices. We have further tested the performance of the proposed method in improving the skill of precipitation forecast for an extreme rainfall event, which caused devastating flood over Chennai city, India, on December 2015. Results demonstrate that the use of the proposed method results in better forecast skill of convective precipitation in terms of both position and intensity than traditional National Meteorological Centre-based BES. Best results are obtained when zonal and meridional momentum control variables are used for modeling ensemble-based BES.

Comparison of different momentum control variables on assimilating radar observations for the forecasts of a dispersive convective event

“…EXP_CV5 and EXP_CV7 assimilate radar observations (both reflectivity and radial velocity) every 3 h from 0600 UTC to 1200 UTC, taking the analysis field assimilated at 1200 UTC as the initial field and integrating forward for 6 h. The difference is that the former uses the BE matrix of the CV5 scheme for assimilation, while the latter uses the BE matrix of the CV7 scheme for assimilation. Current studies generally agree that the BE matrix obtained statistically using the NMC method overestimates the characteristic length scale (Liu et al, 2005;Li et al, 2012;Sun et al, 2016;Stanesic et al, 2019;Thiruvengadam et al, 2020). Therefore, in the two assimilation experiments, the length scales are reduced to half of the default value, which are set to 0.5.…”

Radar reflectivity and radial velocity assimilation in a hybrid ETKF‐3DVAR system for prediction of a heavy convective rainfall

“…The BES have a significant part in balancing and spreading of assimilated observation within a 3D-Var system. Thiruvengadam et al (2019;2020b) have found that control variables in BES have a significant role in increasing the precipitation forecast skill of a convective-scale 3D-Var radar assimilation system. The studies have further indicated that one of the crucial means for improving the convective precipitation forecast skill is through proper representation of BES within the 3D-Var framework.…”

“…Among the various assimilation methods, three‐dimensional variational data assimilation (3D‐Var) is the usual approach in many operational and research centres (Courtier, 1998; Gauthier et al ., 1999; Lindskog et al ., 2004; Routray et al ., 2013; Maiello et al ., 2014; Mohanty and Gopalakrishnan, 2016) for assimilating DWR data due to its computational efficiency (Rabier, 2005). However, recent studies (Thiruvengadam et al ., 2019; 2020b) have shown the sensitivity of the 3D‐Var radar assimilation system to the structure of background error statistics (BES). The BES have a significant part in balancing and spreading of assimilated observation within a 3D‐Var system.…”