Abstract
Using radar observations, the performances of the ensemble square root filter (EnSRF) and an indirect three-dimensional variational (3DVar) data assimilation method were compared for a mesoscale convective system (MCS) that occurred in the Front Range of the Rocky Mountains, Colorado (USA). The results showed that the root mean square innovations (RMSIs) of EnSRF were lower than 3DVar for radar reflectivity and radial velocity and that the spread of EnSRF was generally consistent with its RMSIs. EnSRF substantially improved the analysis of the MCS compared with an experiment without radar data assimilation, and it produced a slight but noticeable improvement over 3DVar in terms of both coverage and intensity. Forecast results initiated from the final analysis revealed that EnSRF generally produced the best prediction of the MCS, with improved quantitative reflectivity and precipitation forecast skills. EnSRF also demonstrated better performance than 3DVar in the prediction of neighborhood probability for reflectivity at thresholds of 20 and 35 dBZ, which better matched the observed radar reflectivity in terms of both shape and extension. Additionally, the humidity, temperature, and wind fields were also improved by EnSRF; the largest error reduction was found in the water vapor field near the surface and at upper levels.
Highlights
Mesoscale convective systems (MCSs) are severe convective storms that can cause injury and damage property
To evaluate the performances of the 3DVar and the ensemble square root filter (EnSRF) analyses quantitatively, the root mean square innovations (RMSIs) and the ensemble spread were calculated for radar re ectivity and radial velocity during the 1 h assimilation period (Figure 5). e RMSIs provide a measure of the overall t of the model state to the observations, and the ensemble spread can be used to examine analysis uncertainty. e calculation was limited to regions where re ectivity was >15 dBZ
E analysis results showed that EnSRF reduced the RMSIs compared with 3DVar and that its ensemble spread and RMSI values were of comparable magnitude for both re ectivity and radial velocity in the analysis–forecast cycles
Summary
Mesoscale convective systems (MCSs) are severe convective storms that can cause injury and damage property. Xiao et al [1, 2] developed a Weather Research and Forecasting DA (WRFDA) 3DVar system to assimilate radial velocity and radar reflectivity by considering the total water vapor mixing ratio as a control variable, which improved quantitative precipitation forecasts for a hurricane. Their system is limited to warm rain microphysics. An advantage of this new approach is that it avoids linearization errors attributable to the nonlinear relationship between reflectivity and microphysical variables It has been used in several recent studies and it has demonstrated capability in improving short-term forecasts of convective storms [5,6,7]. It cannot overcome the inherent weakness of the 3DVar method that results from the neglect of the flow-dependent nature of the background error covariance (BEC). is problem is most severe in storm-scale
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