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Fuqing Zhang, Yonghui Weng, Jason A. Sippel, Zhiyong Meng, and Craig H. Bishop

beyond ( Evensen 2003 ; Hamill 2006 ). For the past few years, the feasibility and performance of the EnKF have been demonstrated through both simulated and real-data observations ranging from convective scales using radar observations (e.g., Snyder and Zhang 2003 ; Zhang et al. 2004 ; Dowell et al. 2004 ; Tong and Xue 2005 ) to mesoscale and regional scales (e.g., Zhang et al. 2006a ; Torn et al. 2006 ; Meng and Zhang 2007 ; Fujita et al. 2007 ; Meng and Zhang 2008a , b ). The benefits of

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Hyun Mee Kim and Byoung-Joo Jung

. Nevertheless, SVs with moist physics and norm have not been fully compared with SVs with dry physics and norm for TCs. Moreover, because SVs with moist physics tend to have smaller structures than SVs with dry physics ( Ehrendorfer et al. 1999 ) and tend to reside near the TC ( Barkmeijer et al. 2001 ), the sensitive regions detected by SVs with moist physics and norm may be different from those detected by SVs with dry physics and norm. Because of the remote environmental effects (e.g., Peng and Reynolds

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Sim D. Aberson

(FNMOC) Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Japanese Meteorological Agency (JMA) Global Spectral Model (GSM), are 14%, 14%, and 19%, respectively. Four aircraft released dropwindsondes in and around tropical cyclones in the west Pacific during The Observing System Research and Predictability Experiment (THORPEX) Pacific Area Regional Campaign (T-PARC), the Tropical Cyclone Structure-08 (TCS08) experiment, and DOTSTAR, and multiple aircraft also participated in

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Hyun Mee Kim and Byoung-Joo Jung

may be different, and have not been compared. Kim and Jung (2009) demonstrated that the moist physics and corresponding moist TE norm may result in SV sensitivities concentrated near the TC center and, in the above physics and norm configurations, the large sensitivities remote from the TC center caused by environmental effects may not be detected well enough. At the same time, Kim and Jung (2009) demonstrated that the remote large-scale influence (e.g., midlatitude trough or subtropical ridge

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Matthieu Plu

ones for which the Regional Specialized Meteorological Centers (RSMC) or other warning centers issue and broadcast cyclone forecasts. RSMC analyses will serve as reference in the present study. For the purpose of comparing the statistics of the three different models, an homogeneous dataset ( Tsuyuki et al. 2002 ) is built, in which a forecast is taken into account if and only if the tropical cyclone is present in the reference and is detected (using the tracking algorithm of appendix A ) in every

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Peter Black, Lee Harrison, Mark Beaubien, Robert Bluth, Roy Woods, Andrew Penny, Robert W. Smith, and James D. Doyle

1. Introduction Widely used tropical cyclone (TC) models include regional air–sea coupled dynamical models such as COAMPS-TC ( Jin et al. 2014 ), HWRF ( Tallapragada et al. 2014 ; Kim et al. 2014 ) and GFDL ( Bender et al. 2007 ; Gall et al. 2011 ); global dynamical models such as GFS and ECMWF; and statistical–dynamical intensity-prediction models such as SHIPS, the Statistical Typhoon Intensity Prediction Scheme (STIPS), the Logistic Growth Equation Model (LGEM), and the rapid intensity

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Munehiko Yamaguchi, Takeshi Iriguchi, Tetsuo Nakazawa, and Chun-Chieh Wu

. 2001 ; Yamaguchi et al. 2009 ). Meanwhile, there was an attempt to reduce TC track forecast uncertainty itself under The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) in 2008. One of the main goals of T-PARC is to lessen forecast uncertainty of TCs, which may cause severe weather events on a time scale of 1 day to 2 weeks. For this purpose, dropwindsonde ( Hock and Franklin 1999 ) observations by aircraft were deployed in an effort to

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Mu Mu, Feifan Zhou, and Hongli Wang

North Atlantic Observing System Research and Predictability Experiment Regional Campaign (NATReC; Petersen et al. 2006 ). Results from these field experiments showed that the forecast skills are improved by assimilation of targeted observations ( Langland et al. 1999b ; Langland 2005 ; Buizza et al. 2007 ). A key issue in adaptive observation is the determination of the sensitive areas where additional observations are expected to yield a better forecast than observations taken in other regions

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Shin-Gan Chen, Chun-Chieh Wu, Jan-Huey Chen, and Kun-Hsuan Chou

Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC; Elsberry and Harr 2008 ). In addition, the value and impact of targeted observations on improving TC track forecasts or reducing forecast errors have been evaluated and addressed in many studies (e.g., Aberson 2003 ; Wu et al. 2007b ; Yamaguchi et al. 2009 ; Harnisch and Weissmann 2010 ; Weissmann et al. 2011 ), while assimilating observational data into the numerical models is likely to lead

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