북서태평양 태풍 진로의 계절예측시스템 자동화 구축 및조기 예측성의 검증
Development of the Atomated Prediction System for Seasonal Tropical Cyclone Activity over the Western North Pacific and its Evaluation for Early Predictability
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초록
The automated prediction system for seasonal tropical cyclone (TC) activity isestablished at the National Typhoon Center of the Korea Meteorological Administration(KMA) to provide effective operation and control of the system for user who lacks knowledgeof the system. For automation of the system, two procedures which include subjective decisionsby user are performed in advance, and their output data are provided as input data. Toprovide the capability to understand the operational processes for operational user, the inputand output data are summarized with each process, and the directory structure is reconstructedfollowing KMA’s standard. We introduce a user interface using namelist input parameters toeffectively control operational conditions which is fixed or should be manually set in the previousversion of the prediction system. To operationally use early prediction which become availablethrough the automation, its performances are evaluated according to initial conditiondates. As a result, high correlations between the observed and predicted TC counts are kept forall track clusters even though advancing the initial condition date from May to January.
Abstract
The automated prediction system for seasonal tropical cyclone (TC) activity isestablished at the National Typhoon Center of the Korea Meteorological Administration(KMA) to provide effective operation and control of the system for user who lacks knowledgeof the system. For automation of the system, two procedures which include subjective decisionsby user are performed in advance, and their output data are provided as input data. Toprovide the capability to understand the operational processes for operational user, the inputand output data are summarized with each process, and the directory structure is reconstructedfollowing KMA’s standard. We introduce a user interface using namelist input parameters toeffectively control operational conditions which is fixed or should be manually set in the previousversion of the prediction system. To operationally use early prediction which become availablethrough the automation, its performances are evaluated according to initial conditiondates. As a result, high correlations between the observed and predicted TC counts are kept forall track clusters even though advancing the initial condition date from May to January.
참고문헌 (11)
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