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Inger-Lise Frogner, Ulf Andrae, Jelena Bojarova, Alfons Callado, Pau Escribà, Henrik Feddersen, Alan Hally, Janne Kauhanen, Roger Randriamampianina, Andrew Singleton, Geert Smet, Sibbo van der Veen, and Ole Vignes

clustering [e.g., mean sea level pressure (MSLP)] due to the increasing bias with forecast length in the ECMWF HRES forecasts used. One of the main drawbacks of the SLAF method is that the number of perturbations—and hence ensemble members—is limited by the length of the coarse-resolution deterministic forecast. In practice the ensemble size is limited to 10–12 perturbed members. The advantage of SLAF is that it offers an easy operational implementation with presumably already available deterministic

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Peter Caplan, John Derber, William Gemmill, Song-You Hong, Hua-Lu Pan, and David Parrish

improved the selection of directions. In September 1994, scatterometer ocean surface wind vectors started to be operationally produced by NCEP. Table 1 shows error statistics based on 1 yr of satellite data and buoy data matchups with a time window of ±3 h and a space window of 50 km from the buoy. Error statistics for wind direction for both NCEP and ESA are given. Extensive testing in the model at resolution T62 revealed little impact either on forecast skill scores or large-scale atmospheric

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Yves Bouteloup

and Derome (1995) . The first one is the method of Lorenz ( Lorenz 1965 ), which is based on the singular modes of the tangent linear model. This method has been adopted operationally at the European Centre for Medium-Range Weather Forecasts (ECMWF) and has been described by Mureau et al. (1993) and Palmer et al. (1993) . They use a Lanczos algorithm to compute, with a T42 primitive equation model, the first eigenvalues of the MM * operator, where M is the tangent linear model and M * its

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Udai Shimada, Hiromi Owada, Munehiko Yamaguchi, Takeshi Iriguchi, Masahiro Sawada, Kazumasa Aonashi, Mark DeMaria, and Kate D. Musgrave

–dynamical models used for intensity forecasts. Since its first implementation for the Atlantic basin in the early 1990s, the skill of SHIPS forecasts has steadily improved. SHIPS is widely used in operational centers around the world and is one of the most accurate guidance models for TC intensity forecasts (e.g., Rappaport et al. 2012 ; Sampson and Knaff 2014 ). SHIPS is a multiple regression model that predicts changes in TC intensity. Explanatory variables (hereafter predictors) used in the regression

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Kyoung-Ho Cho, Yan Li, Hui Wang, Kwang-Soon Park, Jin-Yong Choi, Kwang-Il Shin, and Jae-Il Kwon

1. Introduction Operational oceanographic systems have been developed to accurately predict both present and continuous future conditions for the marginal seas of the northwestern Pacific Ocean, including the Yellow Sea, the East and South China Seas, and the East/Japan Sea ( Kagimoto et al. 2008 ; Miyazawa et al. 2008 ; You 2010 ; Dombrowsky 2011 ; Lim et al. 2011 ; Zhu 2011 ). Ocean forecast information obtained from operational forecasting systems is provided to marine industries

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Steven E. Peckham, Tatiana G. Smirnova, Stanley G. Benjamin, John M. Brown, and Jaymes S. Kenyon

-frequency perturbations have been removed and the initial model state is balanced. Another challenge is determining the optimal time filtering interval. A longer time interval results in a sharper filter, but also usurps the computational time otherwise available for the operational forecast. A backward–forward two-pass DFI application was developed for the NOAA Rapid Update Cycle (RUC) model/assimilation system ( Benjamin et al. 2004 ) where it was found to be essential to reduce accumulating analysis imbalances for

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Siebren de Haan and Siebe H. van der Veen

was notoriously difficult. Various attempts were made to use cloud information in NWP models. Changing the upper-air cloud water mixing ratio in order to match Geostationary Operational Environmental Satellite (GOES) cloud observations resulted in a positive impact on cloud and precipitation forecasts ( Bayler et al. 2000 ). Other studies showed an impact on cloud cover forecasts in the first 3–4 h ( Yucel et al. 2002 , 2003 ) when observed cloud fields were introduced. Experiments with direct

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C. Bryan Young, A. Allen Bradley, Witold F. Krajewski, Anton Kruger, and Mark L. Morrissey

1. Introduction In recent years, the National Weather Service (NWS) has installed the Next-Generation Weather Radar (NEXRAD) system at forecast offices across the country. The NEXRAD system consists of a network of Weather Surveillance Radar-1988 Doppler (WSR-88D) radars ( Crum et al. 1993 ). Reflectivity observations from each WSR-88D are used to generate many operational products, including estimates of precipitation developed with the NEXRAD precipitation processing system ( Klazura and Imy

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Nam-Young Kang, Myeong-Soon Lim, James B. Elsner, and Dong-Hyun Shin

1. Introduction Tropical cyclone (TC) activity is a major concern to a large number of people worldwide where lives and property are at risk ( Mendelsohn et al. 2012 ; World Bank 2010 ). A forecast of where a TC will go is the single most important piece of information for disaster preparedness. Since forecasts are less than perfect, error distances are computed between the operational track-forecast position and the position observed as the best estimate of the TC center location. Forecast

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Steven E. Koch and Christopher O’Handley

waves can exert important controls upon convection and mesoscale precipitation patterns, but in general, the operational community mistakenly perceives gravity waves as being too inconsequential, or occurring too infrequently, or being too difficult to forecast and diagnose, to be worthy of consideration in a daily forecast environment. Issues that immediately arise in this weather forecasting context include the following: 1) What kinds of gravity waves are important to the weather? 2) How

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