Abstract
The current neighborhood probability (NP) method mainly considers the spatial displacement error in high-resolution precipitation forecasts, but the problem of the forecast time exceeding or lagging behind the observed field has not been properly solved. Therefore, a temporal factor was introduced into the NP method in this paper, and precipitation forecasts were evaluated in different spatial-temporal neighborhoods based on the improved NP method and fractions skill score (FSS), combined with the relative operating characteristic (ROC) curve. The results indicated that the forecasting accuracy of the ensemble forecast was higher than the control forecast. The neighborhood ensemble probability (NEP) and probability matched mean (PMM) methods were superior to the traditional ensemble mean (EM) method in forecasting heavy rainfall, which compensated for the limitations of the heavy rainfall forecasting ability of EM. For such squall line processes, a spatial scale of 15–45 km neighborhood radius could effectively rectify the displacement error of precipitation. There was a corresponding relationship between temporal scale and rainfall intensity in convective-scale precipitation forecast, so the temporal uncertainty of different levels of precipitation could be captured by different temporal scales. The spatial and temporal scales had interdependent influences on precipitation forecast effects, which could be affected by the intrinsic spatial-temporal scale of convective-scale weather systems. The improved NP method could simultaneously reflect the spatial and temporal uncertainties of convective-scale precipitation forecasts in high-resolution models, achieving a comprehensive assessment of spatial-temporal scale and providing probabilistic forecast results that match different levels of precipitation.
Highlights
As the demand for accurate forecasting of extreme weather is constantly increasing, convective-scale weather forecasting is becoming increasingly important
This study conducted a convection-allowing ensemble forecast experiment combined with a strong squall line process, using the probability matched mean (PMM) method to focus on extreme precipitation
The temporal dimension was introduced into the neighborhood probability (NP) method, and the probabilistic forecast field was produced by the improved NP method
Summary
As the demand for accurate forecasting of extreme weather is constantly increasing, convective-scale weather forecasting is becoming increasingly important. The refined convective-scale weather forecast is facing notable challenges due to its small spatial-temporal scale and the high non-linearity of the dynamic and physical processes. It is of vital importance to develop high-resolution convection-allowing ensemble forecasts. The UK Met Office [1]. Has conducted convection-allowing ensemble forecast experiments using downscaling and short perturbation rapid cycling methods. In Germany, scholars used the Consortium for Small Scale. Modeling (COSMO) model to conduct some convection-allowing ensemble forecast experiments [2,3]. In America, the National Center for Atmospheric Research (NCAR) and the Center for Analysis
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