Cyclone-track based seasonal prediction for South Pacific tropical cyclone activity using APCC multi-model ensemble prediction

2018 ◽  
Vol 51 (9-10) ◽  
pp. 3209-3229 ◽  
Author(s):  
Ok-Yeon Kim ◽  
Johnny C. L. Chan
Keyword(s):  
Tropical Cyclone ◽  
South Pacific ◽  
Seasonal Prediction ◽  
Tropical Cyclone Activity ◽  
Ensemble Prediction ◽  
Model Ensemble ◽  
Cyclone Track ◽  
Cyclone Activity

scholarly journals Prospects for Improving the Operational Seasonal Prediction of Tropical Cyclone Activity in the Southern Hemisphere

2012 ◽  
Vol 02 (03) ◽  
pp. 298-306 ◽  
Author(s):  
Yuriy Kuleshov ◽  
Yan Wang ◽  
Jemishabye Apajee ◽  
Robert Fawcett ◽  
David Jones
Keyword(s):  
Tropical Cyclone ◽  
Southern Hemisphere ◽  
Seasonal Prediction ◽  
Tropical Cyclone Activity ◽  
Cyclone Activity

Multi-model ensemble forecasting of North Atlantic tropical cyclone activity

2016 ◽  
Vol 53 (12) ◽  
pp. 7461-7477 ◽  
Author(s):  
Gabriele Villarini ◽  
Beda Luitel ◽  
Gabriel A. Vecchi ◽  
Joyee Ghosh
Keyword(s):  
Tropical Cyclone ◽  
North Atlantic ◽  
Tropical Cyclone Activity ◽  
Ensemble Forecasting ◽  
Model Ensemble ◽  
Cyclone Activity ◽  
Atlantic Tropical Cyclone

scholarly journals Seasonal forecasts of North Atlantic tropical cyclone activity in the North American Multi-Model Ensemble

2017 ◽  
Vol 53 (12) ◽  
pp. 7169-7184 ◽  
Author(s):  
Julia V. Manganello ◽  
Benjamin A. Cash ◽  
Kevin I. Hodges ◽  
James L. Kinter
Keyword(s):  
Tropical Cyclone ◽  
North Atlantic ◽  
North American ◽  
Tropical Cyclone Activity ◽  
Model Ensemble ◽  
Seasonal Forecasts ◽  
Cyclone Activity ◽  
The North ◽  
Atlantic Tropical Cyclone

scholarly journals Subseasonal Tropical Cyclone Genesis Prediction and MJO in the S2S Dataset

2018 ◽  
Vol 33 (4) ◽  
pp. 967-988 ◽  
Author(s):  
Chia-Ying Lee ◽  
Suzana J. Camargo ◽  
Fréderic Vitart ◽  
Adam H. Sobel ◽  
Michael K. Tippett
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
South Pacific ◽  
Seasonal Prediction ◽  
Skill Score ◽  
Ensemble Prediction ◽  
Tropical Cyclone Genesis ◽  
Probabilistic Prediction ◽  
Skill Scores

Abstract Subseasonal probabilistic prediction of tropical cyclone (TC) genesis is investigated here using models from the Seasonal to Subseasonal (S2S) Prediction dataset. Forecasts are produced for basin-wide TC occurrence at weekly temporal resolution. Forecast skill is measured using the Brier skill score relative to a seasonal climatology that varies monthly through the TC season. Skill depends on models’ characteristics, lead time, and ensemble prediction design. Most models show skill for week 1 (days 1–7), the period when initialization is important. Among the six S2S models examined here, the European Centre for Medium-Range Weather Forecasts (ECMWF) model has the best performance, with skill in the Atlantic, western North Pacific, eastern North Pacific, and South Pacific at week 2. Similarly, the Australian Bureau of Meteorology (BoM) model is skillful in the western North Pacific, South Pacific, and across northern Australia at week 2. The Madden–Julian oscillation (MJO) modulates observed TC genesis, and there is a relationship, across models and lead times, between models’ skill scores and their ability to accurately represent the MJO and the MJO–TC relation. Additionally, a model’s TC climatology also influences its performance in subseasonal prediction. The dependence of the skill score on the simulated climatology, MJO, and MJO–TC relationship, however, varies from one basin to another. Skill scores increase with the ensemble size, as found in previous weather and seasonal prediction studies.

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