In the operational forecasting of tropical cyclones(TCs),decoding TC warning messages from global centers,along with extracting,organizing,and storing useful track observations and forecasts,are fundamental tasks.The ...In the operational forecasting of tropical cyclones(TCs),decoding TC warning messages from global centers,along with extracting,organizing,and storing useful track observations and forecasts,are fundamental tasks.The technical core lies in accurately identifying distinct TC individuals through automated programming methods.Based on the statistical characteristics of historical distances between TC individuals,this study designs a novel method for automatic identification of TC individuals and establishes a database of TC track observations and forecasts by integrating the persistent features from various elements in TC warning messages.This method accurately identifies each TC individual and assigns it a unique database number through a two-step process:initially,through the'Same Center same Number Comparison(SCNC)'identi-fication method,followed by the'Spatio-Temeporal Distance Comparison(STDC)'identification method.On this basis,we obtain a well-organized and comprehensive dataset that covers entire TC life time.Over the past decade,the operational practice has demonstrated that this method is accurate and efficient,providing solid data support for the TC forecasting operation,the assessment of TC forecasting accuracy,the compilation of TC yearbook,and TC-related research.展开更多
基金support of the Innovation and Development Special Program of the China Meteorological Administration(CXFZ2024J006)Shanghai Science and Technology Commission Project(23DZ1204701)+1 种基金the National Key Research and Development Program of China(2021YFC3000805)the Typhoon Scientific and Technological Innovation Group of the China Meteorological Administration(CMA2023ZD06).
文摘In the operational forecasting of tropical cyclones(TCs),decoding TC warning messages from global centers,along with extracting,organizing,and storing useful track observations and forecasts,are fundamental tasks.The technical core lies in accurately identifying distinct TC individuals through automated programming methods.Based on the statistical characteristics of historical distances between TC individuals,this study designs a novel method for automatic identification of TC individuals and establishes a database of TC track observations and forecasts by integrating the persistent features from various elements in TC warning messages.This method accurately identifies each TC individual and assigns it a unique database number through a two-step process:initially,through the'Same Center same Number Comparison(SCNC)'identi-fication method,followed by the'Spatio-Temeporal Distance Comparison(STDC)'identification method.On this basis,we obtain a well-organized and comprehensive dataset that covers entire TC life time.Over the past decade,the operational practice has demonstrated that this method is accurate and efficient,providing solid data support for the TC forecasting operation,the assessment of TC forecasting accuracy,the compilation of TC yearbook,and TC-related research.
