Study on the Technique of Data Assimilation and Nowcasting of Severe Convective Weather
It is still extremely difficult and challenging for accurate prediction of convective weather systems. In order to improve the service ability in severe weather monitoring and prediction, the following studies have been carried out recently. The feature recognition algorithms for new mesocyclone and tornado vortex are developed and proved to be successful in identifying tornado vortex characteristics in more than a dozen tornado cases. Extracted from Doppler radar volume scan data, more than twenty parameters are used in the study on the automatic recognition and warning technology of classified severe convective weather (downburst, tornado, hail and short-time intense precipitation). Rapidly updating cycle forecast system can effectively improve the quality of model initial values, which is very suitable for short-time forecast application. For the sake of improving severe thunderstorm prediction, a novel pseudo-observation and assimilation approach involving water vapor mass mixing ratio is proposed to better initialize numerical weather prediction (NWP) at convection-resolving scales. The blending technology, which is expected to overcome the deficiency of the short-time quantitative precipitation forecast (QPF) by a mesoscale NWP model at convective scales and the rapidly descending skill of rainfall forecast based on radar extrapolation method beyond the first few hours, is under development, and it would have potential in enhancing the ability of rainfall forecast within the nowcasting period.