Spatio-Temporal Characteristics of Heavy Precipitation Forecasts fromECMWF in Eastern China

This study examines the spatio-temporal characteristics of heavy precipitation forecasts in eastern China fromthe European Centre for Medium-Range Weather Forecasts (ECMWF) using the time-domain version of the Method forObject-based Diagnostic Evaluation (MODE-TD). A total of 23 heavy rainfall cases occurring between 2018 and 2021 areselected for analysis. Using Typhoon “Rumbia” as a case study, the paper illustrates how the MODE-TD method assessesthe overall simulation capability of models for the life history of precipitation systems. The results of multiple tests withdifferent parameter configurations reveal that the model underestimates the number of objects’ forecasted precipitationtracks, particularly at smaller radii. Additionally, the analysis based on centroid offset and area ratio tests for differentclassified precipitation objects indicates that the model performs better in predicting large-area, fast-moving, and long lifespan precipitation objects. Conversely, it tends to have less accurate predictions for small-area, slow-moving, and short lifespan precipitation objects. In terms of temporal characteristics, the model overestimates the forecasted movement speedfor precipitation objects with small-area, slow movement, or both long and short lifespans while underestimating it forprecipitation with fast movement. In terms of temporal characteristics, the model tends to overestimate the forecastedmovement speed for precipitation objects with small-area, slow movement, or both long and short lifespans while un derestimating it for precipitation with fast movement. Overall, the model provides more accurate predictions for theduration and dissipation of precipitation objects with large-area or long-lifespan (such as typhoon precipitation) whilehaving large prediction errors for precipitation objects with small-area or short-lifespan. Furthermore, the model’s simu lation results regarding the generation of precipitation objects show that it performs relatively well in simulating thegeneration of large-area and fast-moving precipitation objects. However, there are significant differences in the forecastedgeneration of small-area and slow-moving precipitation objects after 9 hours.

Spatio-Temporal Characteristics of Heavy Precipitation Forecasts fromECMWF in Eastern China

This study examines the spatio-temporal characteristics of heavy precipitation forecasts in eastern China fromthe European Centre for Medium-Range Weather Forecasts (ECMWF) using the time-domain version of the Method forObject-based Diagnostic Evaluation (MODE-TD). A total of 23 heavy rainfall cases occurring between 2018 and 2021 areselected for analysis. Using Typhoon “Rumbia” as a case study, the paper illustrates how the MODE-TD method assessesthe overall simulation capability of models for the life history of precipitation systems. The results of multiple tests withdifferent parameter configurations reveal that the model underestimates the number of objects’ forecasted precipitationtracks, particularly at smaller radii. Additionally, the analysis based on centroid offset and area ratio tests for differentclassified precipitation objects indicates that the model performs better in predicting large-area, fast-moving, and long lifespan precipitation objects. Conversely, it tends to have less accurate predictions for small-area, slow-moving, and short lifespan precipitation objects. In terms of temporal characteristics, the model overestimates the forecasted movement speedfor precipitation objects with small-area, slow movement, or both long and short lifespans while underestimating it forprecipitation with fast movement. In terms of temporal characteristics, the model tends to overestimate the forecastedmovement speed for precipitation objects with small-area, slow movement, or both long and short lifespans while un derestimating it for precipitation with fast movement. Overall, the model provides more accurate predictions for theduration and dissipation of precipitation objects with large-area or long-lifespan (such as typhoon precipitation) whilehaving large prediction errors for precipitation objects with small-area or short-lifespan. Furthermore, the model’s simu lation results regarding the generation of precipitation objects show that it performs relatively well in simulating thegeneration of large-area and fast-moving precipitation objects. However, there are significant differences in the forecastedgeneration of small-area and slow-moving precipitation objects after 9 hours.