3DVAR and Cloud Analysis with WSR-88D Level-II Data for the Prediction of the Fort Worth, Texas, Tornadic Thunderstorms. Part II: Impact of Radial Velocity Analysis via 3DVAR

Abstract

In this two-part paper, the impact of level-II Weather Surveillance Radar-1988 Doppler (WSR-88D) radar reflectivity and radial velocity data on the prediction of a cluster of tornadic thunderstorms in the Advanced Regional Prediction System (ARPS) model is studied. Radar reflectivity data are used primarily in a cloud analysis procedure that retrieves the amount of hydrometeors and adjusts in-cloud temperature, moisture, and cloud fields, while radial velocity data are analyzed through a three-dimensional variational (3DVAR) data assimilation scheme that contains a 3D mass divergence constraint in the cost function. In Part I, the impact of the cloud analysis and modifications to the scheme are discussed. In this part, the impact of radial velocity data and the mass divergence constraint in the 3DVAR cost function are studied. The case studied is that of the 28 March 2000 Fort Worth tornadoes. The addition of the radial velocity improves the forecasts beyond that experienced with the cloud analysis alone. The prediction is able to forecast the morphology of individual storm cells on the 3-km grid up to 2 h; the rotating supercell characteristics of the storm that spawned two tornadoes are well captured; timing errors in the forecast are less than 15 min and location errors are less than 10 km at the time of the tornadoes. When forecasts were made with radial velocity assimilation but not reflectivity, they failed to predict nearly all storm cells. Using the current 3DVAR and cloud analysis procedure with 10-min intermittent assimilation cycles, reflectivity data are found to have a greater positive impact than radial velocity. The use of radial velocity does improve the storm forecast when combined with reflectivity assimilation, by, for example, improving the forecasting of the strong low-level vorticity centers associated with the tornadoes. Positive effects of including a mass divergence constraint in the 3DVAR cost function are also documented.

Impacts of Beam Broadening and Earth Curvature on Storm-Scale 3D Variational Data Assimilation of Radial Velocity with Two Doppler Radars

“…The ARPS 3DVAR system, described in Gao et al (2002Gao et al ( , 2004 and Hu et al (2006b), is used for this purpose. The ARPS 3DVAR system is capable of analyzing radar radial velocity data along with conventional observations.…”

Meteorological Studies with the Phased Array Weather Radar and Data Assimilation Using the Ensemble Kalman Filter

“…The case studied is the tornadic supercell in Oklahoma on May 8, 2003. This case was selected mainly because it was previously investigated and analyzed using the ARPS 3DVAR and cloud analysis (Hu 2005), which can provide a base line for comparisons. In the multiscale approach, EnKF analysis was performed using both 3 km and 1.5 km (1 km for some experiments) horizontal resolutions.…”

Effect of Doppler Radial Velocity Data Assimilation on the Simulation of a Typhoon Approaching Taiwan: A Case Study of Typhoon Aere (2004)

“…Some regional models and non-hydrostatic models were built with the 3D-VAR system used for radar data assimilation (Gao et al 1999;Lindskog et al 2004;Hu and Xue 2006;Montmerle and Faccani 2009). Xiao et al (2005) developed a radar data assimilation method for MM5 and demonstrated that the method of radar wind data assimilation is useful for 6-h rainfall forecasts on a front.…”