On the Optimal Use of WSR-88D Doppler Radar Data for Variational Storm-Scale Data Assimilation

University Of Oklahoma Norman Campus, Norman OK

Investigators

Abstract

Three-dimensional variational data assimilation (3DVAR) is reaching a considerable state of maturity, and is being used operationally at several operational meteorological centers, though mainly in the context of large-scale hydrostatic flows. Such techniques cannot directly be extended to nonhydrostatic flows on the meso- and storm-scales for a variety of reasons (e.g., different dynamic constraints, the lack of a similar balance among variables, and large differences of weather phenomena). At the latter scales, and, especially for intense buoyant convection that is both highly nonhydrostatic and intermittent, the WSR-88D Doppler is the only operational instrument capable of providing high spatial and temporal resolution observations. The assimilation of such data into storm-resolving models, including the retrieval of quantities that cannot be observed directly by radars, is quite challenging, yet of great practical significance for the future of operational forecasting. Under this award, the Principal Investigator will attack three key aspects of radar data assimilation within a theoretical and computational 3DVAR framework including eventual use by the Weather Research and Forecasting (WRF) model. 1. The primary objective is to determine how WSR-88D radar data can best be used in 3DVAR, in combination with other observations, for the prediction of convective phenomena. The Principal Investigator will develop enhanced 3DVAR assimilation techniques which will include the ability to retrieve unobserved variables such as wind, pressure, and temperature fields. Two separate retrieval methods will be developed, the first for obtaining wind and thermodynamic variables simultaneously, and the second in a sequential mode. It is planned to develop a simple balance equation for different analysis variables and explore a suitable data assimilation time window for storm scale phenomenon. 2. In the context of specifying background error, which is a critical element of variational data assimilation, the Principal Investigator will investigate what improvements might be gained by specifying the error in terms of displacement (phase) and amplitude. Because of the intermittency of storm-scale structures, correction of the displacement errors is critical, and thus a variational phase correction method will be developed to ameliorate the background displacement error before it is used in 3DVAR analysis. 3. It is critical to have properly quality-controlled radar data. In this context, velocity folding is a challenge in using WSR-88D Doppler observations. The Principal investigator will develop a variational algorithm in which de-aliasing is performed locally using wind gradient information. The cost function will include the background wind field from a previous data assimilation cycle, and/or a modified Velocity Azimuth Display (VAD) technique; the gradient of observed radial velocity with respect to range and azimuth; and a smoothness constraint to reduce errors caused by ground clutter. The key to the method is that, by operating on gradients of velocity rather than the velocity itself, aliasing ambiguities are readily identified and eliminated. This methodology will be compared to several existing algorithms applied to both idealized and real data. The research findings will have a direct path to operations through the PI's involvement as one of the lead scientists in developing the variational data assimilation system for the new community-wide WRF model. Although some of the work to be performed herein will use the CAPS Advanced Regional Prediction System owing to its greater level maturity, emphasis will be placed on testing and development using the WRF.