scholarly journals Objective Estimation of the 24-h Probability of Tropical Cyclone Formation

2009 ◽  
Vol 24 (2) ◽  
pp. 456-471 ◽  
Author(s):  
Andrea B. Schumacher ◽  
Mark DeMaria ◽  
John A. Knaff
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Input Parameter ◽  
Probabilistic Prediction ◽  
Linear Discriminant ◽  
Tropical Cyclone Formation ◽  
Formation Probability ◽  
Prediction Scheme ◽  
Skill Scores ◽  
Probabilistic Forecasts

Abstract A new product for estimating the 24-h probability of TC formation in individual 5° × 5° subregions of the North Atlantic, eastern North Pacific, and western North Pacific tropical basins is developed. This product uses environmental and convective parameters computed from best-track tropical cyclone (TC) positions, National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) analysis fields, and water vapor (∼6.7 μm wavelength) imagery from multiple geostationary satellite platforms. The parameters are used in a two-step algorithm applied to the developmental dataset. First, a screening step removes all data points with environmental conditions highly unfavorable to TC formation. Then, a linear discriminant analysis (LDA) is applied to the screened dataset. A probabilistic prediction scheme for TC formation is developed from the results of the LDA. Coefficients computed by the LDA show that the largest contributors to TC formation probability are climatology, 850-hPa circulation, and distance to an existing TC. The product was evaluated by its Brier and relative operating characteristic skill scores and reliability diagrams. These measures show that the algorithm-generated probabilistic forecasts are skillful with respect to climatology, and that there is relatively good agreement between forecast probabilities and observed frequencies. As such, this prediction scheme has been implemented as an operational product called the National Environmental Satellite, Data, and Information Services (NESDIS) Tropical Cyclone Formation Probability (TCFP) product. The TCFP product updates every 6 h and displays plots of TC formation probability and input parameter values on its Web site. At present, the TCFP provides real-time, objective TC formation guidance used by tropical cyclone forecast offices in the Atlantic, eastern Pacific, and western Pacific basins.

scholarly journals An Operational Rapid Intensification Prediction Aid for the Western North Pacific

2018 ◽  
Vol 33 (3) ◽  
pp. 799-811 ◽  
Author(s):  
John A. Knaff ◽  
Charles R. Sampson ◽  
Kate D. Musgrave
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Rapid Intensification ◽  
Linear Discriminant ◽  
Probabilistic Forecasts ◽  
Weighted Probability ◽  
Joint Typhoon Warning Center

Abstract This work describes tropical cyclone rapid intensification forecast aids designed for the western North Pacific tropical cyclone basin and for use at the Joint Typhoon Warning Center. Two statistical methods, linear discriminant analysis and logistic regression, are used to create probabilistic forecasts for seven intensification thresholds including 25-, 30-, 35-, and 40-kt changes in 24 h, 45- and 55-kt in 36 h, and 70-kt in 48 h (1 kt = 0.514 m s−1). These forecast probabilities are further used to create an equally weighted probability consensus that is then used to trigger deterministic forecasts equal to the intensification thresholds once the probability in the consensus reaches 40%. These deterministic forecasts are incorporated into an operational intensity consensus forecast as additional members, resulting in an improved intensity consensus for these important and difficult to predict cases. Development of these methods is based on the 2000–15 typhoon seasons, and independent performance is assessed using the 2016 and 2017 typhoon seasons. In many cases, the probabilities have skill relative to climatology and adding the rapid intensification deterministic aids to the operational intensity consensus significantly reduces the negative forecast biases.

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.

A comparison of tropical cyclone formation over the western North Pacific in August between 1996 and 2014

2022 ◽  
Vol 266 ◽  
pp. 105952
Author(s):  
Xi Cao ◽  
Renguang Wu ◽  
Jing Xu ◽  
Yifeng Dai ◽  
Mingyu Bi ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Tropical Cyclone Formation

scholarly journals Reassessing the Use of Inner-Core Hot Towers to Predict Tropical Cyclone Rapid Intensification*

2015 ◽  
Vol 30 (5) ◽  
pp. 1265-1279 ◽  
Author(s):  
Xiao-Yong Zhuge ◽  
Jie Ming ◽  
Yuan Wang
Keyword(s):  
Tropical Cyclone ◽  
Inner Core ◽  
Prediction Method ◽  
Vertical Shear ◽  
Intensity Change ◽  
False Alarms ◽  
Rapid Intensification ◽  
Central Pacific ◽  
Prediction Scheme ◽  
Skill Scores

Abstract The hot tower (HT) in the inner core plays an important role in tropical cyclone (TC) rapid intensification (RI). With the help of Tropical Rainfall Measurement Mission (TRMM) data and the Statistical Hurricane Intensity Prediction Scheme dataset, the potential of HTs in operational RI prediction is reassessed in this study. The stand-alone HT-based RI prediction scheme showed little skill in the northern Atlantic (NA) and eastern and central Pacific (ECP), but yielded skill scores of >0.3 in the southern Indian Ocean (SI) and western North Pacific (WNP) basins. The inaccurate predictions are due to four scenarios: 1) RI events may have already begun prior to the TRMM overpass. 2) RI events are driven by non-HT factors. 3) The HT has already dissipated or has not occurred at the TRMM overpass time. 4) Large false alarms result from the unfavorable environment. When the HT was used in conjunction with the TC’s previous 12-h intensity change, the potential intensity, the percentage area from 50 to 200 km of cloud-top brightness temperatures lower than −10°C, and the 850–200-hPa vertical shear magnitude with the vortex removed, the predictive skill score in the SI was 0.56. This score was comparable to that of the RI index scheme, which is considered the most advanced RI prediction method. When the HT information was combined with the aforementioned four environmental factors in the NA, ECP, South Pacific, and WNP, the skill scores were 0.23, 0.32, 0.42, and 0.42, respectively.

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