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

humidity during descent. Surface winds from AMSU and ASCAT satellite sensors near 0000 UTC 25 June, about 3 h prior to the sonde deployments (not shown), indicate that surface winds along the DC-8 flight track were between 7 and 10 m s −1 . This was, once again, a strong enough wind to produce some limited whitecapping from breaking waves, mixing the ocean upper layer and minimizing differences between skin and bulk SST. The fast-fall sondes traveled only 3.5–4.0 km to the north and northeast

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Hyun Mee Kim and Byoung-Joo Jung

for around 70% of the cases. Moreover, for recurving TCs that are influenced more by the environmental flow than the straight-moving TCs ( Peng and Reynolds 2006 ), TESVs usually indicate a midlatitude trough upstream of the TC motion as sensitive ( Peng and Reynolds 2006 ), but the ETKF identifies regions of large ensemble variance that are usually located over the ocean (i.e., subtropical ridge) as sensitive ( Majumdar et al. 2006 ). Meanwhile, Reynolds et al. (2007) demonstrated that SVs with

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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

1. Introduction The tropical cyclone (TC) is one of the most threatening natural phenomena that cause great human and economic losses. The lack of observations over the ocean regions where TCs spend most of their lifetime seriously degrades the accuracy of forecasts ( Wu 2006 ). Therefore, it is worthwhile to assimilate the special data obtained from both aircraft (with dropwindsondes deployed) and satellites in areas that may have the maximum influence on numerical model predictions of TCs. To

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

. Walsh , 2007 : The CBLAST-Hurricane Program and the next-generation fully coupled atmosphere–wave–ocean models for hurricane research and prediction. Bull. Amer. Meteor. Soc. , 88 , 311 – 317 . Chessa , P. A. , G. Ficca , M. Marrocu , and R. Buizza , 2004 : Application of a limited-area short-range ensemble forecast system to a case of heavy rainfall in the Mediterranean region. Wea. Forecasting , 19 , 566 – 581 . Davis , C. A. , and Coauthors , 2008 : Prediction of

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

1. Introduction In 1997, the Japan Meteorological Agency (JMA) started to provide public users with 3-day track forecasts of tropical cyclones (TCs) in the western North Pacific Ocean and South China Sea based on numerical weather prediction [NWP; the (Regional Specialized Meteorological Center) RSMC Tokyo-Typhoon Center 1997 ]. Since then, the remarkable progress of the JMA NWP system has brought considerable improvement of the track forecasts. According to the verification of the JMA global

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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

correlations for the second and third SVs shown in Table 3 , which implies that the linearity is degraded for the second and third SVs. 6. Summary and discussion In this study, the horizontal structures, vertical distributions, and growth rates of SVs for Typhoon Usagi were investigated using different moist physics and norms. The MM5, its tangent linear, and adjoint models with a Lanczos algorithm were used to calculate SVs, which maximize the tropospheric TE over a region including land and ocean near

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

covariances of background error between different time levels. In the current EnKF system (and En-4D-Var approach), the same spatial localization is applied to all temporal cross covariances as for the covariances of the background error at each individual time level. This may not be appropriate in situations where the background error may have a maximum temporal cross covariance at distant locations, due to rapid advection or wave propagation over the assimilation time window. An alternative localization

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