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Rolf H. Langland, Christopher Velden, Patricia M. Pauley, and Howard Berger

1. Introduction Improving the forecast of tropical cyclone (TC) tracks remains a challenging problem in numerical weather prediction. During the 2005 season, for example, the Navy Operational Global Atmospheric Prediction System (NOGAPS) provided 72-h forecasts with average track errors of about 170 n mi for TCs in the Northern Hemisphere—this compares to average track errors of about 220 n mi in 2000 ( Goerss et al. 2004 ). The improved TC track forecasts available today are largely because of

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Sim D. Aberson

previous 20–25 yr and suggested that operational missions would be effective in operational numerical forecast error reduction. In 1996, NOAA procured a Gulfstream IV-SP jet aircraft (G-IV), and put it to use in operational “synoptic surveillance” missions in the environments of tropical cyclones that threaten the continental United States, Puerto Rico, the U.S. Virgin Islands, and Hawaii. A new dropwindsonde, based on the Global Positioning System, was developed by the National Center for

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Sim D. Aberson

aircraft ( Burpee et al. 1996 ). NOAA procured a Gulfstream IV-SP jet aircraft (G-IV) in 1996 and put it to use in operational “synoptic surveillance” missions in the environments of tropical cyclones that threaten the contiguous United States, U.S. Caribbean territories, and Hawaii. The missions led to 10%–15% reductions in GFS track forecast error during the critical watch and warning period before possible landfall (within the first 60 h), and small impacts in Geophysical Fluid Dynamics Laboratory

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

short-term forecast error ( Parrish and Derber 1992 ) are ill-suited for the highly flow-dependent background error covariances associated with tropical cyclones. In addition, operational models generally have insufficient model resolution to effectively incorporate high-resolution convective-scale observations (such as those from radars) for cloud-resolving hurricane prediction. Physical (diabatic) initializations using rainfall, radar, and/or satellite observations are a promising approach

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Kun-Hsuan Chou, Chun-Chieh Wu, Po-Hsiung Lin, Sim D. Aberson, Martin Weissmann, Florian Harnisch, and Tetsuo Nakazawa

Global Forecast System The operational version of the GFS was used on each mission. This implies that the model resolution and physics processes varied with time from 2003 to 2008 ( Aberson 2010 ). In 2003, the GFS horizontal resolution was T254, and the vertical coordinate extended from the surface to about 2.7 hPa with 64 (L64) unequally spaced sigma levels on a Lorenz grid ( Caplan et al. 1997 ; Surgi et al. 1998 ). The resolution was increased to T382L64 (~38 km horizontally) in 2005. The NCEP

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

fashion using the forward and adjoint propagators linearized about a particular forecast. For further discussion of SV diagnostics and their utility in atmospheric sciences, see Palmer et al. (1998) and references therein. The SVs are calculated using the tangent and adjoint models of the Navy Operational Global Atmospheric Prediction System (NOGAPS; Hogan and Rosmond 1991 ; Peng et al. 2004 ) with a total energy metric at both the initial and final time ( Rosmond 1997 ). For most of the results

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Matthieu Plu

models. They estimated a mean predictability per basin. Because of the remarkable improvement of operational models for cyclone track prediction in the very recent years, it is relevant to assess the inherent global predictability with other methods based on up-to-date numerical models. Moreover, some global models, like the one from the European Centre for Medium-Range Weather Forecasts (ECMWF), have such improved that one can wonder if they have not reached the predictability limit for tropical

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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

1. Introduction One method of improving numerical weather prediction (NWP) model forecasts of tropical cyclones (TCs) is to produce better initial conditions by combining observations with a model forecast via data assimilation. Most operational data assimilation systems employ quasi-fixed error statistics to spread observation information to model grid points, which are often not appropriate for the TC environment. Given this difficulty, several different techniques have emerged where either a

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