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  • American Meteorological Society  (2)
  • Unknown  (2)
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  • American Meteorological Society  (2)
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  • Unknown  (2)
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  • 1
    Online Resource
    Online Resource
    American Meteorological Society ; 2018
    In:  Weather and Forecasting Vol. 33, No. 5 ( 2018-10), p. 1299-1315
    In: Weather and Forecasting, American Meteorological Society, Vol. 33, No. 5 ( 2018-10), p. 1299-1315
    Abstract: This study proposes a statistical regression scheme to forecast tropical cyclone (TC) intensity at 12, 24, 36, 48, 60, and 72 h in the northwestern Pacific region. This study utilizes best track data from the Shanghai Typhoon Institute (STI), China, and the Joint Typhoon Warning Center (JTWC), United States, from 2000 to 2015. In addition to conventional factors involving climatology and persistence, this study pays close attention to the land effect on TC intensity change by considering a new factor involving the ratio of seawater area to land area (SL ratio) in the statistical regression model. TC intensity changes are investigated over the entire life-span, over the open ocean, near the coast, and after landfall. Data from 2000 to 2011 are used for model calibration, and data from 2012 to 2015 are used for model validation. The results show that the intensity change during the previous 12 h (DVMAX), the potential future intensity change (POT), and the area-averaged (200–800 km) wind shear at 1000–300 hPa (SHRD) are the most significant predictors of the intensity change for TCs over the open ocean and near the coast. Intensity forecasting for TCs near the coast and over land is improved with the addition of the SL ratio compared with that of the models that do not consider the SL ratio. As this study has considered the TC intensity change over the entire TC life-span, the proposed models are valuable and practical for forecasting TC intensity change over the open ocean, near the coast, and after landfall.
    Type of Medium: Online Resource
    ISSN: 0882-8156 , 1520-0434
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2018
    detail.hit.zdb_id: 2025194-4
    Location Call Number Limitation Availability
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  • 2
    Online Resource
    Online Resource
    American Meteorological Society ; 2011
    In:  Monthly Weather Review Vol. 139, No. 8 ( 2011-08), p. 2668-2685
    In: Monthly Weather Review, American Meteorological Society, Vol. 139, No. 8 ( 2011-08), p. 2668-2685
    Abstract: The methods of parameterizing model errors have a substantial effect on the accuracy of ensemble data assimilation. After a review of the current error-handling methods, a new blending error parameterization method was designed to combine the advantages of multiplicative inflation and additive inflation. Motivated by evolutionary algorithm concepts that have been developed in the control engineering field for years, the authors propose a new data assimilation method coupled with crossover principles of genetic algorithms based on ensemble transform Kalman filters (ETKFs). The numerical experiments were developed based on the classic nonlinear model (i.e., the Lorenz model). Convex crossover, affine crossover, direction-based crossover, and blending crossover data assimilation systems were consequently designed. When focusing on convex crossover and affine crossover data assimilation problems, the error adjustment factors were investigated with respect to four aspects, which were the initial conditions of the Lorenz model, the number of ensembles, observation covariance, and the observation interval. A new data assimilation system, coupled with genetic algorithms, is proposed to solve the difficult problem of the error adjustment factor search, which is usually performed using trial-and-error methods. The results show that all of the methods can adaptively obtain the best error factors within the constraints of the fitness function.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    RVK:
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2011
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
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