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

understand detailed three-dimensional TC structures and TC surrounding environments, and to produce more accurate initial fields for NWP models with the supplementary observation data. In T-PARC, adaptive sampling techniques ( Buizza and Montani 1999 ; Majumdar et al. 2006 ; Wu et al. 2009b ) were used, aimed at maximizing the impact of the observations on NWP. Prior to the above field experiment, we investigated the impact of additional observations on TC track forecasts using JMA’s data assimilation

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Chun-Chieh Wu, Jan-Huey Chen, Sharanya J. Majumdar, Melinda S. Peng, Carolyn A. Reynolds, Sim D. Aberson, Roberto Buizza, Munehiko Yamaguchi, Shin-Gan Chen, Tetsuo Nakazawa, and Kun-Hsuan Chou

conducted under the support of the National Science Council (NSC) in Taiwan ( Wu et al. 2005 ). Four objective methods, adapted from those used to study the winter storms, have been employed for the targeted observations for these surveillance missions of TCs. These products are derived from four distinct techniques. First is the ensemble deep-layer mean (DLM) wind variance of the deep-layer steering flows based on the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecasting

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Sim D. Aberson

of the entire target. Assimilation of the data subset from fully sampled targets produced a statistically significant track forecast error reduction of up to 25%. This technique, therefore, produced larger improvements in dynamical model tropical cyclone forecast tracks than were possible by assimilating all available data from the missions. This result highlighted the suboptimality of data assimilation schemes used at the time. The main region of failure of current techniques seems to be at the

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

( Krishnamurti et al. 2001 ), though its effectiveness in spinning up a full hurricane vortex for cloud-resolving hurricane prediction remains to be fully explored. The ensemble Kalman filter (EnKF) is a state-estimation technique that uses short-term ensemble forecasts to estimate flow-dependent background error covariance or other probabilistic aspects of the background forecast. It was first proposed by Evensen (1994) and has been adopted for data assimilation of many disciplines in the geosciences and

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Sim D. Aberson

observing system experiment techniques used in the study. Section 3 examines the dropwindsonde data impact on track forecasts globally and by region. Section 4 delves into individual tropical cyclones and forecasts and the reasons for the impacts. Section 5 details the study’s conclusions. 2. Overview and procedures A total of 18 tropical cyclones (6 in the Atlantic, 3 in the east Pacific, 8 in the west Pacific, and 1 in the Indian Ocean) occurred between 27 August and 2 October 2008, according to

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

1. Introduction The accuracy of tropical cyclone (TC) forecasts is of great concern to both civilian and military interests. The forecast of the TC itself is of obvious concern for regions that may be directly impacted by the storm, but in some cases the storm forecast may significantly affect remote regions as well. It has been shown that when TCs recurve into the midlatitudes, they can have a substantial impact on the midlatitude environment well downstream, and the extratropical transition

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

NWP system, the 3-yr running mean of position errors of 5-day predictions in 2007 (451 km, the average of 2005, 2006, and 2007) is smaller than that of 3-day predictions in 1997 (472 km, the average of 1995, 1996, and 1997), indicating that we have succeeded in obtaining a 2-day lead time of deterministic TC track predictions over the past 11 yr (see Fig. 1 ). While the accuracy of TC track forecasts has improved significantly, ensemble techniques have been attracting much attention because they

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Matthieu Plu

modeling. One is the bogus technique ( Heming et al. 1995 ), which consists in forcing the assimilation of pseudo-observations (wind or pressure) into the model initial state. The pseudo-observations are deduced from an idealized cyclone structure observed by satellite imagery. These specific techniques generally improve cyclone forecasts ( Heming 2009 ). Section 2 recalls the general principle of the L82 method and presents the data. Section 3 shows the results for three numerical models. Before

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