Short-term forecasting of a midlatitude convective storm by the assimilation of single-doppler radar observations

The McGill University radar data assimilation system is used to initialize a convective storm at high resolution (1 km) from single-Doppler radar observations. In this study, the background term in the assimilation system is improved. Specifically, by assuming the correlation of the errors of the control variables to be isotropic and homogeneous, the background error covariance matrix is modeled by a recursive filter. In addition, a 3-h-prior high-resolution model forecast is used as the background field. The analysis fields from the assimilation system successfully trigger the convective storms in the radar-observed regions from a single assimilation window. Without data assimilation, the modeled storms did not occur at the right time and place. To account for the rapid evolution of the convective storms and to correct the forecast errors with time, a cycling process is applied for a very short-term forecast. It is found that the first assimilation window can maintain the prediction of the storms for less than 1 h. The cycling process helps to maintain the intensity of the storm cells for a longer period of time. However, a comparison of radar observations with the 90-min simulation indicates an error in the position of the convective cells. The error of the radial component of the wind field between the observation and the simulation is larger at the upper levels.Awavelet analysis between the observation and simulated reflectivities indicates that the forecast is able to adequately predict the convective scale (~10-20 km) during the first 20 min, whereas the simulation has more predictability at the longer scale (> .30 km) beyond 20 min.