Radar Data Assimilation with WRF 4D-Var. Part I: System Development and Preliminary Testing

Abstract

The major goal of this two-part study is to assimilate radar data into the high-resolution Advanced Research Weather Research and Forecasting Model (ARW-WRF) for the improvement of short-term quantitative precipitation forecasting (QPF) using a four-dimensional variational data assimilation (4D-Var) technique. In Part I the development of a radar data assimilation scheme within the WRF 4D-Var system (WRF 4D-Var) and the preliminary testing of the scheme are described. In Part II the performance of the enhanced WRF 4D-Var system is examined by comparing it with the three-dimensional variational data assimilation system (WRF 3D-Var) for a convective system over the U.S. Great Plains. The WRF 4D-Var radar data assimilation system has been developed with the existing framework of an incremental formulation. The new development for radar data assimilation includes the tangent-linear and adjoint models of a Kessler warm-rain microphysics scheme and the new control variables of cloud water, rainwater, and vertical velocity and their error statistics. An ensemble forecast with 80 members is used to produce background error covariance. The preliminary testing presented in this paper includes single-observation experiments as well as real data assimilation experiments on a squall line with assimilation windows of 5, 15, and 30 min. The results indicate that the system is able to obtain anisotropic multivariate analyses at the convective scale and improve precipitation forecasts. The results also suggest that the incremental approach with successive basic-state updates works well at the convection-permitting scale for radar data assimilation with the selected assimilation windows.

Dynamical Precipitation Downscaling for Hydrologic Applications Using WRF 4D-Var Data Assimilation: Implications for GPM Era

“…Because of the growing interest in the WRFDA system and associated community-based developments, the WRFDA system has been equipped with extensive capability to assimilate various types of observations. The WRFDA system has DA options such as three-dimensional variational data assimilation (3D-Var), 4D-Var, and hybrid variational-ensemble DA that permit assimilating a wide range of observations including in situ measurements, Doppler radar reflectivity, precipitation, and radiances (Barker et al 2012;Wang et al 2013). For example, the 3D-Var assimilation of conventional ground-based data and radiance observations has been used for improving precipitation forecasts at various spatial resolutions (Ha et al 2011;Ha and Lee 2012;Hsiao et al 2012;Liu et al 2012;Routray et al 2010;Schwartz et al 2012;Xu and Powell 2012).…”

Recent Progress of the NHM-4DVAR towards a Super-High Resolution Data Assimilation

“…In this study, initial conditions are given only for cloud and rain waters but they are soon converted to ice materials with the nonlinear model (JMANHM). As shown in Kawabata et al (2011) and Wang et al (2013), warm rain initial condition seems acceptable for most deep convection predictions, and Wang et al (2012) indicated that a simplified warm rain scheme can be used for high-resolution 4D-Var in a short time assimilation window.…”

A scale-dependent blending scheme for WRFDA: impact on regional weather forecasting

“…WRFDA is developed and maintained at the National Center for Atmospheric Research (NCAR) and has been widely used both in research communities and operational centers (Barker et al, 2012;Huang et al, 2009;Wang et al, 2013b). A brief description of WRFDA will be presented in the next section.…”