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

1. Introduction Starting in 2003, the research program “Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region” (DOTSTAR) marked the beginning of an era of tropical cyclone (TC) surveillance and targeted observations in the western North Pacific using GPS dropwindsondes ( Wu et al. 2005 ). This program is built upon work pioneered by the National Oceanic and Atmospheric Administration Hurricane Research Division to improve TC track forecasts in the Atlantic ( Burpee et al

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Matthieu Plu

1. Introduction For several decades, forecast errors in tropical cyclone tracks have been regularly decreasing ( Avila et al. 2006 ). Nowadays, cyclone forecasts heavily rely on numerical weather prediction. The rapid increasing amount of observations, especially from satellites, as well as the improvement of numerical models and of their assimilation schemes are certainly the main reasons for this error decrease. Nevertheless, there should be a nonzero limit for tropical cyclone track error

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

1. Introduction Additional observations made in most sensitive regions are expected to reduce uncertainties in the initial condition and thus decrease errors in numerical forecasts. This concept is referred to as the targeted (adaptive) observation, which has been one of the most active research and forecasting issues for improving tropical cyclone (TC) predictions ( Langland 2005 ; Wu 2006 ). In practice, many targeted observation methods have been commonly applied to design optimal flight

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

1. Introduction Landfalling hurricanes are among the deadliest and costliest natural hazards. Over the past decade, significant progress has been made in short-range track forecasts of tropical cyclones. The current-day average 48-h forecast position is as accurate as a 24-h track forecast was 10 yr ago ( Franklin 2004 ). However, there is virtually no improvement in our ability to predict hurricane intensity in terms of minimum sea level pressure, maximum wind speed, or amount of precipitation

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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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Sim D. Aberson

1. Introduction The need for additional data acquisition over data-sparse tropical oceans to improve tropical cyclone analysis and forecasting has long been known (e.g., Riehl et al. 1956 ). Between 1982 and 1996, the National Oceanographic and Atmospheric Administration (NOAA) Hurricane Research Division (HRD) conducted 20 “synoptic flow” experiments to gather observations in the tropical cyclone core and environment in the North Atlantic basin ( Burpee et al. 1996 ). The NOAA

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