An analogy-based method for strong convection forecasts in China using GFS forecast data

Abstract

This paper describes an analogue-based method for producing strong convection forecasts with conventional outputs from numerical models. The method takes advantage of the good performance of numerical models in predicting synoptic-scale weather situations. It calculates the convective parameters as predictors to detect the favorable-occurrence environment of strong convections. Times in the past when the forecast parameters are most similar to those forecast at the current time are identified by searching a large historical numerical dataset. The observed strong convection situations corresponding to those most similar times are then used to form strong convection forecasts for the current time. The method is applied as a postprocess of the NCEP Global Forecast System (GFS) model. The historical dataset in which the analogous situations are sought comprises two years of summer (June–September) GFS 6- to 48-h forecasts. The strong convection forecast is then generated every 6 h over most regions of China, provided the availability of strong convection observations. The results show that the method performs well in predicting strong convections in different regions of China. Through comparison with another postprocessing strong convection forecast method, it is shown that the convective-parameter threshold problem can be solved by employing the analogy method, which considers the local historical conditions of strong convection occurrence.