There exist significant challenges in accurately predicting unusual tropical cyclone(TC)tracks.This study applies the orthogonal conditional nonlinear optimal perturbations(O-CNOPs)method to the Weather Research and F...There exist significant challenges in accurately predicting unusual tropical cyclone(TC)tracks.This study applies the orthogonal conditional nonlinear optimal perturbations(O-CNOPs)method to the Weather Research and Forecast(WRF)model to improve ensemble forecast reliability of unusual TC tracks.Ensemble forecast experiments were conducted for twenty-three forecast periods of five TCs,all of which exhibited sharp turns,to examine the effectiveness of O-CNOPs.Results demonstrate that the O-CNOPs method outperforms the singular vectors(SVs)and bred vectors(BVs)methods by providing more stable and reliable improvements in TC track forecasting skills,from both deterministic and probabilistic perspectives.Notably,the OCNOPs shows a superior ability to generate ensemble members that accurately predict the sharp turns of TCs at lead times from one to five days.These results highlight the superiority of the O-CNOPs method over the SVs and BVs methods in enhancing the forecasting accuracy of TC tracks,particularly for forecasting unusual TC tracks.This study underscores the potential of OCNOPs to be extended to real-time TC forecasting and to play an important role in operational track forecasts.展开更多
基金jointly supported by the National Natural Science Foundation of China(Grant Nos.41930971)the International Partnership Program of the Chinese Academy of Sciences(Grant Nos.060GJHZ2022061MI)supported by the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab)。
文摘There exist significant challenges in accurately predicting unusual tropical cyclone(TC)tracks.This study applies the orthogonal conditional nonlinear optimal perturbations(O-CNOPs)method to the Weather Research and Forecast(WRF)model to improve ensemble forecast reliability of unusual TC tracks.Ensemble forecast experiments were conducted for twenty-three forecast periods of five TCs,all of which exhibited sharp turns,to examine the effectiveness of O-CNOPs.Results demonstrate that the O-CNOPs method outperforms the singular vectors(SVs)and bred vectors(BVs)methods by providing more stable and reliable improvements in TC track forecasting skills,from both deterministic and probabilistic perspectives.Notably,the OCNOPs shows a superior ability to generate ensemble members that accurately predict the sharp turns of TCs at lead times from one to five days.These results highlight the superiority of the O-CNOPs method over the SVs and BVs methods in enhancing the forecasting accuracy of TC tracks,particularly for forecasting unusual TC tracks.This study underscores the potential of OCNOPs to be extended to real-time TC forecasting and to play an important role in operational track forecasts.
