Seven-Day Intensity and Intensity Spread Predictions in Bifurcation Situations with Guidance-On-Guidance for Western North Pacific Tropical Cyclones
被引:6
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Tsai, Hsiao-Chung
[1
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Elsberry, Russell L.
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机构:
Univ Colorado Colorado Springs, Hazards Ctr, Trauma, Hlth, Colorado Springs, CO 80919 USA
Naval Postgrad Sch, Dept Meteorol, Monterey, CA 93943 USATamkang Univ, Dept Water Resources & Environm Engn, New Taipei, Taiwan
Elsberry, Russell L.
[2
,3
]
机构:
[1] Tamkang Univ, Dept Water Resources & Environm Engn, New Taipei, Taiwan
[2] Univ Colorado Colorado Springs, Hazards Ctr, Trauma, Hlth, Colorado Springs, CO 80919 USA
[3] Naval Postgrad Sch, Dept Meteorol, Monterey, CA 93943 USA
An objective technique to detect and predict intensity bifurcation situations in a five-day Weighted Analog Intensity forecast technique for the western North Pacific (WAIP) has been extended to seven days. A hierarchical cluster analysis is applied to the N analog intensities to separate them into two clusters, which are considered to represent a substantial intensity bifurcation if a threshold maximum velocity difference of 15 kt is satisfied. Two important modifications have been made to develop the bifurcation version for seven-day WAIP forecasts. First, the number of track analogs has been increased from 10 analogs to 16 analogs, which results in larger sample sizes and better performance. Second, separate intensity bias corrections are calculated for the two cluster WAIP forecasts rather than using the same 16-analog intensity bias correction. If an always perfect selection of the correct cluster WAIP forecast of each bifurcation situation is made, a substantial improvement in the intensity mean absolute errors is achieved relative to the original WAIP forecasts based on all 16 of the best analogs. These perfect-cluster selection WAIP forecasts have smaller bias errors and are more highly correlated with the verifying intensities at all forecast intervals through 168h. Furthermore, the Probability of Detection is improved for the perfect-cluster selection and more realistic intensity spreads are specified. A simple guidance-on-guidance technique is demonstrated to assist the forecasters in selecting the correct WAIP cluster forecast in bifurcation situations.
机构:
Shanghai Typhoon Institute and Laboratory of Typhoon Forecast Technique,CMAShanghai Typhoon Institute and Laboratory of Typhoon Forecast Technique,CMA
余晖
陆益
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Shanghai Typhoon Institute and Laboratory of Typhoon Forecast Technique,CMA
Chinese Academy of Meteorological SciencesShanghai Typhoon Institute and Laboratory of Typhoon Forecast Technique,CMA
陆益
陈佩燕
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Shanghai Typhoon Institute and Laboratory of Typhoon Forecast Technique,CMAShanghai Typhoon Institute and Laboratory of Typhoon Forecast Technique,CMA
陈佩燕
周伟灿
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机构:Shanghai Typhoon Institute and Laboratory of Typhoon Forecast Technique,CMA
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Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R ChinaChinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R China
Song, Jinjie
Klotzbach, Philip J.
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Colorado State Univ, Dept Atmospher Sci, Ft Collins, CO 80523 USAChinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R China
Klotzbach, Philip J.
Duan, Yihong
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Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R ChinaChinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R China
机构:
Chinese Acad Meteorol Sci, Beijing 100081, Peoples R China
CMA, Shanghai Typhoon Inst, Shanghai 200030, Peoples R China
CMA, Lab Typhoon Forecast Tech, Shanghai 200030, Peoples R ChinaChinese Acad Meteorol Sci, Beijing 100081, Peoples R China
Yu Hui
Lu Yi
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Chinese Acad Meteorol Sci, Beijing 100081, Peoples R China
CMA, Shanghai Typhoon Inst, Shanghai 200030, Peoples R China
CMA, Lab Typhoon Forecast Tech, Shanghai 200030, Peoples R ChinaChinese Acad Meteorol Sci, Beijing 100081, Peoples R China
Lu Yi
Chen Pei-yan
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Chinese Acad Meteorol Sci, Beijing 100081, Peoples R ChinaChinese Acad Meteorol Sci, Beijing 100081, Peoples R China
Chen Pei-yan
Zhou Wei-can
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Wuxi Environm Sci & Engn Res Ctr, Wuxi 214000, Jiangsu, Peoples R ChinaChinese Acad Meteorol Sci, Beijing 100081, Peoples R China
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Department of Atmospheric and Ocean Sciences, Institute of Atmospheric Sciences,Fudan UniversityDepartment of Atmospheric and Ocean Sciences, Institute of Atmospheric Sciences,Fudan University
Yanchen ZHOU
Jiuwei ZHAO
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Department of Atmospheric and Ocean Sciences, Institute of Atmospheric Sciences,Fudan UniversityDepartment of Atmospheric and Ocean Sciences, Institute of Atmospheric Sciences,Fudan University
Jiuwei ZHAO
Ruifen ZHAN
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Department of Atmospheric and Ocean Sciences, Institute of Atmospheric Sciences,Fudan University
Fujian Key Laboratory of Severe Weather
Shanghai Typhoon Institute of China Meteorological Administration
Big Data Institute for Carbon Emission and Environmental Pollution, Fudan UniversityDepartment of Atmospheric and Ocean Sciences, Institute of Atmospheric Sciences,Fudan University
Ruifen ZHAN
Peiyan CHEN
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Shanghai Typhoon Institute of China Meteorological AdministrationDepartment of Atmospheric and Ocean Sciences, Institute of Atmospheric Sciences,Fudan University
Peiyan CHEN
Zhiwei WU
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Department of Atmospheric and Ocean Sciences, Institute of Atmospheric Sciences,Fudan University
Big Data Institute for Carbon Emission and Environmental Pollution, Fudan UniversityDepartment of Atmospheric and Ocean Sciences, Institute of Atmospheric Sciences,Fudan University
Zhiwei WU
Lan WANG
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Fujian Key Laboratory of Severe WeatherDepartment of Atmospheric and Ocean Sciences, Institute of Atmospheric Sciences,Fudan University