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Hyun Mee Kim and Byoung-Joo Jung

′ are the zonal, meridional, and vertical wind perturbations, respectively; θ ′ is the potential temperature perturbation; p ′ is the pressure perturbation; N , θ , ρ , and c s are Brunt–Väisälä frequency, potential temperature, density, and speed of sound, respectively, at the reference level; and x , y , and σ denote zonal, meridional, and vertical coordinate, respectively. The moist TE is defined by combining the dry TE in (1) and the moisture term in Ehrendorfer et al. (1999) as

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Carolyn A. Reynolds, Melinda S. Peng, and Jan-Huey Chen

lower latitudes at which these cases occur). All results shown so far have been for dry SVs. Dry SVs have been used in previous intercomparison studies ( Majumdar et al. 2006 ; Reynolds et al. 2007 ; Wu et al. 2009a ) and these will be used for T-PARC. However, it is of interest, of course, to see the impact of inclusion of moisture in the SV calculation. Figures 4e,f show the potential and kinetic energy components of the initial-time SV sensitivity for the forecast for TC Shanshan from 16

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Mu Mu, Feifan Zhou, and Hongli Wang

uncertainties in wind, temperature, and pressure, rather than initial moisture error. Considering the impact of accounting for moisture in the above work related to moist SVs, it would appear worthwhile to extend the current study to the moist CNOPs and investigate the role of initial moisture errors in future work. Furthermore, it is of interest to compare CNOP with other methods, such as adjoint sensitivities and the ensemble transform Kalman filter (ETKF). In addition, in order to advance the success of

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

prominent at 250 hPa, but it can be also seen from about 200 to 500 hPa though the impact becomes less distinct away from the 250-hPa level. Figures 8a–f show the initial fields of NODROP and ALLDROP and the differences between them, regarding specific humidity at 500 (left) and 850 hPa (right), where analysis increments are relatively large. Compared with observations shown in Figs. 1c,d , it is revealed that the initial field of NODROP has less moisture content almost all around the typhoon at both

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

sensor used in the RD-94s. Nonetheless, a comparison of humidity profiles in Figs. 4a,b between RD-94 and XDD sondes shows consistent features in the humidity measurements: 1) the upper moist layer at the base of a temperature inversion (3–3.5 km; Fig. 4a ); 2) the dry adiabatic layer from the top of the marine layer inversion (1.25 km) to the base of the upper moist layer (2.7 km); 3) the strong moisture gradient from the top of the marine layer inversion to the top of the adiabatic marine layer

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

) are applied in the TLM and adjoint integrations, but the effect of moisture is neglected. The forward and backward integrations were executed by the MM5 forecast model and the adjoint model, respectively, to calculate the sensitivity of the response function to model input variables. The initial and boundary conditions of the MM5 model and the data for PV diagnosis are acquired from the NCEP GFS global analysis (1° × 1°). For Typhoon Shanshan, 0000 UTC on three successive days from 14 to 16

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Sim D. Aberson

(2002) reported that, in a small sample of cases from Hurricane Debby (2000), the dropwindsonde moisture data were as important as the wind vector data in improving forecasts in the Met Office (UKMO) model, with the temperature data being relatively unimportant. Further research on targeting will be reported elsewhere. The following is an updated impact assessment of numerical track and intensity guidance during the first 10 yr of G-IV operations. General descriptions of the two model systems

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

assimilation methods with or without initial vortex bogussing, our ability to initialize a tropical cyclone with dynamically consistent structure and intensity remains limited, even with the assimilation of radar observations (e.g., Zou and Xiao 2000 ; Pu and Braun 2001 ; Xiao et al. 2007 ). Numerical weather prediction models also have known difficulties in their “spinup” of a tropical cyclone or hurricane vortex with appropriate moisture, diabatic, and divergence structures at the initial time. Part

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