%0 Journal Article
%T Impact of a Diagnostic Pressure Equation Constraint on Tornadic Supercell Thunderstorm Forecasts Initialized Using 3DVAR Radar Data Assimilation
%A Guoqing Ge
%A Jidong Gao
%A Ming Xue
%J Advances in Meteorology
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/947874
%X A diagnostic pressure equation constraint has been incorporated into a storm-scale three-dimensional variational (3DVAR) data assimilation system. This diagnostic pressure equation constraint (DPEC) is aimed to improve dynamic consistency among different model variables so as to produce better data assimilation results and improve the subsequent forecasts. Ge et al. (2012) described the development of DPEC and testing of it with idealized experiments. DPEC was also applied to a real supercell case, but only radial velocity was assimilated. In this paper, DPEC is further applied to two real tornadic supercell thunderstorm cases, where both radial velocity and radar reflectivity data are assimilated. The impact of DPEC on radar data assimilation is examined mainly based on the storm forecasts. It is found that the experiments using DPEC generally predict higher low-level vertical vorticity than the experiments not using DPEC near the time of observed tornadoes. Therefore, it is concluded that the use of DPEC improves the forecast of mesocyclone rotation within supercell thunderstorms. The experiments using different weighting coefficients generate similar results. This suggests that DPEC is not very sensitive to the weighting coefficients. 1. Introduction A dynamic consistent initial condition is very important for making a quality storm-scale numerical weather prediction (NWP) forecast. For this purpose, a large number of studies have been focused on utilizing high-resolution radar data to provide better storm-scale initial conditions (e.g., [1¨C7]). Since radars primarily observe the radial velocity and reflectivity, most state variables have to be ˇ°retrievedˇ± in the data assimilation (DA) process. This makes the assimilation of radar data a very challenging problem. Three-dimensional variational (3DVAR), four-dimensional variational (4DVAR), and ensemble Kalman filter (EnKF) methods have been applied to the previously mentioned radar DA problem. The 4DVAR method uses a NWP model as a strong constraint and hence naturally produces a dynamically consistent analysis. Sun and Crook [8, 9] and Sun [10] have shown encouraging results using a 4DVAR cloud model. However, it is very difficult to develop and maintain complex adjoint codes for NWP models. Complex ice microphysics, which are important for storm-scale applications but contain discontinuities and strong nonlinearities, introduce more difficulties in this situation. All of these difficulties limit the adoption of the 4DVAR method in storm-scale NWP operations. The EnKF technique is expected to
%U http://www.hindawi.com/journals/amete/2013/947874/