A new verification approach for nowcasting based on intensity and spatial-temporal feature correction.

Abstract

Forecast verification is very important in the nowcasting operation and technical development of strong convective weather. The current conventional verification method for nowcasting uses a binary classification event verification method, which exists with double punishment, leading to low scoring issues. In order to make up for the shortcomings of conventional verification methods and explore the potential value of forecasting, based on the characteristics and requirements of strong convective weather nowcasting operations, this paper proposes a neighborhood verification method that considers spatial scale, time scale, and intensity error information simultaneously, based on the spatial neighborhood fraction skill score (FSS) verification method. The paper designs an intensity neighborhood correction scheme, a time neighborhood correction scheme, and a comprehensive correction scheme that considers both intensity and time neighborhoods. By introducing spatial neighborhood probability, the strict spatial matching requirement is weakened. Based on the Gaussian membership function in fuzzy logic, the value of forecasting grid below the threshold is explored. Time neighborhood adaptive weighting strategy is adopted to solve the problem of early and delayed forecasts. The evaluation and verification of the optical flow radar extrapolation nowcasting results show that compared to the traditional "point-to-point" verification indicators, the proposed indicators can increase the tolerance of verification, provide more useful information, and are more in line with the needs of practical application.