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Kazutoshi Sato, Atsuyoshi Manda, Qoosaku Moteki, Kensuke K. Komatsu, Koto Ogata, Hatsumi Nishikawa, Miki Oshika, Yuriko Otomi, Shiori Kunoki, Hisao Kanehara, Takashi Aoshima, Kenichi Shimizu, Jun Uchida, Masako Shimoda, Mitsuharu Yagi, Shoshiro Minobe, and Yoshihiro Tachibana

by a global climate model and JMA operational nonhydrostatic mesoscale model (henceforth referred to as the global and mesoscale analyses, respectively), provided by JMA (2015b) . The global analysis data comprised 6-hourly tropospheric wind velocity, temperature, and relative humidity at the surface, 1000-, 925-, 850-, 700-, 600-, 500-, 400-, and 300-hPa levels on a 0.5° latitude × 0.5° longitude grid. The mesoscale analysis data comprised 3-hourly tropospheric wind velocity, temperature, and

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Larry W. O’Neill, Tracy Haack, and Theodore Durland

-level wind forecasts have been evaluated in several modeling studies ( Kindle et al. 2002 ; Haack et al. 2005 ; Pullen et al. 2006 , 2007 ; Hong et al. 2011 ), and the coupling of wind stress curl and divergence to spatial variations in SST has been documented in the U.S. West Coast region by Haack et al. (2008) . These studies have demonstrated that COAMPS is capable of predicting all-weather surface winds with good accuracy and with high spatial and temporal resolution. For this study, the model

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Yi-Hui Wang and W. Timothy Liu

AIRS data from the winter months between December 2003 and February 2013 are analyzed. Both the TRMM and AIRS data are provided by the NASA Goddard Earth Sciences Data and Information Services Center. To assess the satellite-derived results, we used reanalysis data from the ERA-Interim (e.g., Dee et al. 2011 ), which are produced by the European Centre for Medium-Range Weather Forecasts and are available since 1979 onward at a 0.75° resolution. The atmospheric variables of the ERA-Interim data

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Kotaro Katsube and Masaru Inatsu

K. Saito , 1991 : Description of a non-hydrostatic model developed at the Forecast Research Department of the MRI. MRI Tech. Rep. 28, 238 pp. Jones , S. , and Coauthors , 2003 : The extratropical transition of tropical cyclones: Forecast challenges, current understanding, and future directions . Wea. Forecasting , 18 , 1052 – 1092 , doi: 10.1175/1520-0434(2003)018<1052:TETOTC>2.0.CO;2 . Kain , J. , and J. Fritsch , 1993 : Convective parameterization for mesoscale models: The

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Hidetaka Hirata, Ryuichi Kawamura, Masaya Kato, and Taro Shinoda

-001 . 10.2151/sola.2013-001 Japan Meteorological Agency , 2013 : Outline of the operational numerical weather prediction at the Japan Meteorological Agency (March 2013). Japan Meteorological Agency, accessed 21 November 2017, http://www.jma.go.jp/jma/jma-eng/jma-center/nwp/outline2013-nwp/index.htm . Kuo , Y.-H. , R. J. Reed , and S. Low-Nam , 1991a : Effects of surface energy fluxes during the early development and rapid intensification stages of seven explosive cyclones in the western

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Larry W. O’Neill, Tracy Haack, Dudley B. Chelton, and Eric Skyllingstad

.1175/1520-0493(1997)125<1414:TNRLSC>2.0.CO;2 Hogan , T. F. , and T. E. Rosmond , 1991 : The description of the U.S. Navy Operational Global Atmospheric Prediction System’s spectral forecast model . Mon. Wea. Rev. , 119 , 1786 – 1815 , doi: 10.1175/1520-0493(1991)119<1786:TDOTNO>2.0.CO;2 . 10.1175/1520-0493(1991)119<1786:TDOTNO>2.0.CO;2 Holton , J. R. , 1992 : An Introduction to Dynamic Meteorology. 1st ed. Academic Press, 511 pp. Hoskins , B. J. , and P. J. Valdes , 1990 : On the existence of storm-tracks . J

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Ryusuke Masunaga, Hisashi Nakamura, Bunmei Taguchi, and Takafumi Miyasaka

additional product of the Japanese 55-yr Reanalysis (JRA-55) project of the Japan Meteorological Agency (JMA) called JRA-55CHS ( Masunaga et al. 2018 ). The atmospheric forecast model of JRA-55CHS is the same as the one used for the main product of JRA-55 ( Kobayashi et al. 2015 ; Harada et al. 2016 ) and JRA-55 Conventional ( Kobayashi et al. 2014 ). The horizontal resolution is TL319 (equivalent to ~55-km resolution) with 60 sigma-pressure hybrid vertical levels. For the lower-boundary condition, JRA

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Hidetaka Hirata, Ryuichi Kawamura, Masaya Kato, and Taro Shinoda

that the cyclone type that appeared and rapidly developed over the northwestern Pacific Ocean, the so-called Pacific Ocean–ocean (PO–O) cyclones, was more reinforced by the effect of latent heating than were other types. This may be because the PO–O cyclones occur under moister environments. From the viewpoint of forecasting explosive cyclone development, Kuwano-Yoshida and Enomoto (2013) demonstrated that the underestimation of latent heat release in a numerical model is a primary factor in PO

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Hyodae Seo

, experiments, and data a. Model description The Scripps Coupled Ocean–Atmosphere Regional (SCOAR) model ( Seo et al. 2007a , 2014 , 2016 ; http://hseo.whoi.edu/scoar ) is a regional coupled climate model that couples the Weather Research and Forecast (WRF; Skamarock et al. 2008 ) Model to the Regional Ocean Modeling System (ROMS; Haidvogel et al. 2000 ; Shchepetkin and McWilliams 2005 ). The interacting boundary layer is based on the bulk formula ( Fairall et al. 1996 ; 2003 ), which calculates the

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Hyodae Seo, Arthur J. Miller, and Joel R. Norris

description We utilize the Scripps Coupled Ocean–Atmosphere Regional (SCOAR) model ( Seo et al. 2007b , 2014 ). SCOAR currently couples one of two weather models, the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ) or the Regional Spectral Model (RSM; Juang and Kanamitsu 1994 ), to the Regional Ocean Modeling System (ROMS; Haidvogel et al. 2000 ; Shchepetkin and McWilliams 2005 ). This study uses the WRF–ROMS version of SCOAR ( Seo et al. 2014 ). The interacting boundary layer

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