This study presented an evaluation of tropical cyclone(TC) intensity forecasts from five global ensemble prediction systems(EPSs) during 2015-2019 in the western North Pacific region. Notable error features include th...This study presented an evaluation of tropical cyclone(TC) intensity forecasts from five global ensemble prediction systems(EPSs) during 2015-2019 in the western North Pacific region. Notable error features include the underestimation of the TC intensity by ensemble mean forecast and the under-dispersion of the probability forecasts.The root mean square errors(brier scores) of the ensemble mean(probability forecasts) generally decrease consecutively at long lead times during the five years, but fluctuate between certain values at short lead times.Positive forecast skill appeared in the most recent two years(2018-2019) at 120 h or later as compared with the climatology forecasts. However, there is no obvious improvement for the intensity change forecasts during the 5-year period, with abrupt intensity change remaining a big challenge. The probability forecasts show no skill for strong TCs at all the lead times. Among the five EPSs, ECMWF-EPS ranks the best for the intensity forecast, while NCEPGEFS ranks the best for the intensity change forecast, according to the evaluation of ensemble mean and dispersion.As for the other probability forecast evaluation, ECMWF-EPS ranks the best at lead times shorter than 72 h, while NCEP-GEFS ranks the best later on.展开更多
基金National Key R&D Program of China(2017YFC1501604)National Natural Science Foundation of China (41875114)+1 种基金Shanghai Science&Technology Research Program (19dz1200101)Fundamental Research Funds of the STI/CMA (2020JB06)。
文摘This study presented an evaluation of tropical cyclone(TC) intensity forecasts from five global ensemble prediction systems(EPSs) during 2015-2019 in the western North Pacific region. Notable error features include the underestimation of the TC intensity by ensemble mean forecast and the under-dispersion of the probability forecasts.The root mean square errors(brier scores) of the ensemble mean(probability forecasts) generally decrease consecutively at long lead times during the five years, but fluctuate between certain values at short lead times.Positive forecast skill appeared in the most recent two years(2018-2019) at 120 h or later as compared with the climatology forecasts. However, there is no obvious improvement for the intensity change forecasts during the 5-year period, with abrupt intensity change remaining a big challenge. The probability forecasts show no skill for strong TCs at all the lead times. Among the five EPSs, ECMWF-EPS ranks the best for the intensity forecast, while NCEPGEFS ranks the best for the intensity change forecast, according to the evaluation of ensemble mean and dispersion.As for the other probability forecast evaluation, ECMWF-EPS ranks the best at lead times shorter than 72 h, while NCEP-GEFS ranks the best later on.
