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Hung Ming Cheung, Chang-Hoi Ho, Minhee Chang, Dasol Kim, Jinwon Kim, and Woosuk Choi

Abstract

Despite tremendous advancements in dynamical models for weather forecasting, statistical models continue to offer various possibilities for tropical cyclone (TC) track forecasting. Herein, a track-pattern-based approach was developed to predict a TC track for a lead time of 6–8 days over the western North Pacific (WNP), utilizing historical tracks in conjunction with dynamical forecasts. It is composed of four main steps: 1) clustering historical tracks similar to that of an operational 5-day forecast in their early phase into track patterns, and calculating the daily mean environmental fields (500-hPa geopotential height and steering flow) associated with each track; 2) deriving the two environmental variables forecasted by dynamical models; 3) evaluating pattern correlation coefficients between the two environmental fields from step 1 and those from dynamical model for a lead times of 6–8 days; and 4) producing the final track forecast based on relative frequency maps obtained from the historical tracks in step 1 and the pattern correlation coefficients obtained from step 3. TCs that formed in the WNP and lasted for at least 7 days, during the 9-yr period 2011–19 were selected to verify the resulting track-pattern-based forecasts. In addition to the performance comparable to dynamical models under certain conditions, the track-pattern-based model is inexpensive, and can consistently produce forecasts over large latitudinal or longitudinal ranges. Machine learning techniques can be implemented to incorporate nonlinearity in the present model for improving medium-range track forecasts.

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Woosuk Choi, Chang-Hoi Ho, Jinwon Kim, Hyeong-Seog Kim, Song Feng, and KiRyong Kang

Abstract

A seasonal prediction model of tropical cyclone (TC) activities for the period August–October over the North Atlantic (NA) has been developed on the basis of TC track patterns. Using the fuzzy c-means method, a total of 432 TCs in the period 1965–2012 are categorized into the following four groups: 1) TCs off the U.S. East Coast, 2) TCs over the Gulf of Mexico, 3) TCs that recurve into the open ocean of the central NA, and 4) TCs that move westward in the southern NA. The model is applied to predict the four TC groups separately in conjunction with global climate forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2). By adding the distributions of the four TC tracks with precalculated weighting factors, this seasonal TC forecast model provides the spatial distribution of TC activities over the entire NA basin. Multiple forecasts initialized in six consecutive months from February to July are generated at monthly intervals to examine the applicability of this model in operational TC forecasting. Cross validations of individual forecasts show that the model can reasonably predict the observed TC frequencies over NA at the 99% confidence level. The model shows a stable spatial prediction skill, proving its advantage for forecasting regional TC activities several months in advance. In particular, the model can generate reliable information on regional TC counts in the near-coastal regions as well as in the entire NA basin.

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Woosuk Choi, Chang-Hoi Ho, Doo-Sun R. Park, Jinwon Kim, and Johnny C. L. Chan

Abstract

Prediction of tropical cyclone (TC) activity is essential to better prepare for and mitigate TC-induced disasters. Although many studies have attempted to predict TC activity on various time scales, very few have focused on near-future predictions. Here a decrease in seasonal TC activity over the North Atlantic (NA) for 2016–30 is shown using a track-pattern-based TC prediction model. The TC model is forced by long-term coupled simulations initialized using reanalysis data. Unfavorable conditions for TC development including strengthened vertical wind shear, enhanced low-level anticyclonic flow, and cooled sea surface temperature (SST) over the tropical NA are found in the simulations. Most of the environmental changes are attributable to cooling of the NA basinwide SST (NASST) and more frequent El Niño episodes in the near future. The consistent NASST warming trend in the projections from phase 5 of the Coupled Model Intercomparison Project (CMIP5) suggests that natural variability is more dominant than anthropogenic forcing over the NA in the near-future period.

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Joo-Hong Kim, Chang-Hoi Ho, Hyeong-Seog Kim, and Woosuk Choi

Abstract

Fourteen named tropical cyclones (TCs) formed in the western North Pacific (WNP) in 2010, representing the lowest count since 1951. Both low activity during the typhoon season (June–October) and quiescence during the pre- and posttyphoon seasons were major contributing factors. Despite overall low activity, TC activity along land boundaries was enhanced because the overall genesis locations of TCs shifted to the north and west and a majority of them affected the coastal countries in the WNP. These features are attributed to the expansion of the subtropical high and weakening of the monsoon trough associated with the rapid transition of the 2009/10 El Niño to the 2010/11 La Niña. The National Typhoon Center (NTC) in South Korea utilizes the recently developed track-pattern-based model of the hybrid statistical–dynamical type as the operational long-range TC forecast system. This model fairly forecast the anomalous spatial distribution of TC track density for the 2010 typhoon season. A higher-than-normal track density was successfully forecast near Korea and Japan. This is attributed to the overall skillful forecast of TC count for each pattern by the NTC model, though some deficiencies in forecasting extremes for some patterns are evident. The total seasonal genesis frequency integrated over the seven patterns is well below normal (about 16.4) close to the observations. The fair predictability in 2010 using the NTC model is attributed to the skillful forecast of the ENSO transition by the National Centers for Environmental Prediction’s Climate Forecast System, in addition to the validity of the NTC model itself.

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