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Sim D. Aberson

1. Introduction The need for data acquisition over data-sparse tropical oceans to improve tropical cyclone analysis and forecasting has long been known (e.g., Riehl et al. 1956 ). The National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division (HRD) significantly improved numerical track forecasts using data from 20 “synoptic flow” experiments between 1982 and 1996 to gather observations in the tropical cyclone core and environment using NOAA WP-3D (P-3) research

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Hyun Mee Kim and Byoung-Joo Jung

) used in Kim and Jung (2009) . Section 2 describes the experimental framework. The results are presented in section 3 . Section 4 contains a summary and discussion. 2. Experimental framework a. Model and physical processes This study uses the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), together with the MM5 adjoint modeling system ( Zou et al. 1997 ) and a Lanczos algorithm, to calculate SVs. The model domain for this study is

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

the environment is favorable for ET development when upper-level TC outflow enhances the equatorward entrance region of a downstream jet. These results are also consistent with those of Riemer et al. (2008) , who studied the downstream impact of TCs during extratropical transitions using a full-physics atmospheric model with idealized initial conditions. They showed that the evolution of the upper-level pattern can be interpreted as a Rossby wave train excited by the interaction of the TC with

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

skin sea surface temperature (SSTir) coincident with the atmospheric profile observations. This is becoming an increasingly critical observational input as a new generation of coupled air–sea TC prediction models demand data inputs from the ocean as well as from the atmosphere. The advent of GPS dropsonde atmospheric profiling ( Hock and Franklin 1999 ; Franklin et al. 2003 ; Wang et al. 2015 ) has played a key role in contributing to this need and in demonstrating improved model track and

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

assimilation method: EnKF The EnKF implemented in the WRF model is the same as that in Meng and Zhang (2008a , b ) except that no multischeme ensemble is used for this study. This version of the filter was originally implemented in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), which is documented in Zhang et al. (2006a) . It uses the covariance relaxation of Zhang et al. (2004) to inflate the background error covariance. Unlike the

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Mark Buehner, P. L. Houtekamer, Cecilien Charette, Herschel L. Mitchell, and Bin He

1. Introduction Variational data assimilation approaches are used at many numerical weather prediction (NWP) centers for operationally assimilating meteorological observations to provide a single “best” estimate of the current atmospheric state (e.g., Parrish and Derber, 1992 ; Rabier et al. 2000 ; Gauthier et al. 2007 ; Rawlins et al. 2007 ). The resulting analysis is used to initialize deterministic forecast models to produce short- and medium-range forecasts. Observations

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Munehiko Yamaguchi, Ryota Sakai, Masayuki Kyoda, Takuya Komori, and Takashi Kadowaki

are expected to improve deterministic TC track forecasts and also provide the uncertainty information ( WMO 2008a ), based on ensemble mean and ensemble spread, respectively (e.g., Jeffries and Fukada 2002 ; Vijaya Kumar and Krishnamurti 2003 ; Sampson et al. 2006 ; Goerss 2007 ). For the western North Pacific basin, Goerss et al. (2004) have shown that the consensus of three models, the Navy Operational Global Atmospheric Prediction System (NOGAPS; Hogan and Rosmond 1991 ; Goerss and

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Ryan D. Torn and Gregory J. Hakim

observations are taken from European Centre for Medium-Range Weather Forecasts (ECMWF) statistics, and we assume that dropsonde errors are characterized by radiosondes. These experiments also assimilate targeted dropsondes deployed by the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division (HRD; e.g., Aberson 2002 ) and the RAINEX field campaign. Since raw dropsonde data often contains high-frequency temporal noise that can be problematic for data assimilation, we assimilate

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Hyun Mee Kim and Byoung-Joo Jung

al. 2006 ) do not have an explicit vertical wind perturbation term in the TE norm because these studies used a hydrostatic model. b. Model and physical processes To calculate SVs, this study used the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), together with the MM5 tangent linear and adjoint modeling system ( Zou et al. 1997 ) and a Lanczos algorithm. The model domain for this study is a 50 × 50 horizontal grid (centered at 33°N

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

6 . 2. Brief overview of DOTSTAR and Typhoon Conson a. The DOTSTAR project and its data for Typhoon Conson DOTSTAR is a field experiment conducted by the National Taiwan University and the Central Weather Bureau of Taiwan, along with the National Oceanic and Atmospheric Administration (NOAA) since 2002 ( Wu et al. 2005 ). DOTSTAR has collected adaptive airborne dropwindsonde observations for typhoons that may affect the Taiwan area, aiming at the improvement of typhoon track forecasts with its

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