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Hongwen Kang, Chung-Kyu Park, Saji N. Hameed, and Karumuri Ashok

1. Introduction Korea is located in East Asia and its climate is under the influence of the East Asia monsoon (EAM). In summer (June–August), the major rain-producing system is the monsoon subtropical front known as changma in Korea. More than 60% of the annual precipitation is concentrated during the changma period. The EAM often leads to severe disasters such as flooding or drought, which makes the forecast of the interannual variation of EAM important for the management of water resources

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Jing Lei and Peter Bickel

localization. Traditional covariance tapering techniques for the EnKF are not applicable for the particle filter, where the forecast ensemble is updated directly from the likelihood function and the covariance matrix is not used. The sliding window localization method used in Brusdal et al. (2003) and Ott et al. (2004) seems feasible but the resampling/reweighting step of the ordinary particle filter breaks the connection between overlapping local windows. For an introduction to particle filters in

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Altuğ Aksoy, Fuqing Zhang, and John W. Nielsen-Gammon

. Wea. Rev. , 127 , 1835 – 1849 . Derber , J. C. , 1989 : A variational continuous assimilation technique. Mon. Wea. Rev. , 117 , 2437 – 2446 . Etherton , B. J. , and C. H. Bishop , 2004 : Resilience of hybrid ensemble/3DVAR analysis schemes to model error and ensemble covariance error. Mon. Wea. Rev. , 132 , 1065 – 1080 . Evensen , G. , 1994 : Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J

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James P. Kossin and Matthew Sitkowski

Naval Research Laboratory in Monterey, California). Microwave instruments are able to see through the upper-level cirrus cloud that masks the presence of secondary convective rings in infrared images of hurricanes. The microwave imagery was used to examine nearly 175 tropical cyclones. Whenever possible, additional information from official forecast discussions, aircraft reconnaissance data and “vortex messages,” and both airborne and land-based radar data were also utilized. There is presently no

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Thomas M. Hamill

ensemble transform (ET) technique in the NCEP global operational forecast system . Tellus , 60A , 62 – 79 . Weigel , A. P. , M. A. Liniger , and C. Appenzeller , 2008 : Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts? Quart. J. Roy. Meteor. Soc. , 134 , 241 – 260 . Whitaker , J. S. , X. Wei , and F. Vitart , 2006 : Improving week-2 forecasts with multimodel reforecast ensembles . Mon. Wea. Rev. , 134 , 2279 – 2284 . Wilks

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Paul J. Roebber

dependence of degree day forecast technique, skill and value . Wea. Forecasting , 13 , 783 – 794 , doi: 10.1175/1520-0434(1998)013<0783:TRDODD>2.0.CO;2 . Roebber , P. J. , 2010 : Seeking consensus: A new approach . Mon. Wea. Rev. , 138 , 4402 – 4415 , doi: 10.1175/2010MWR3508.1 . Roebber , P. J. , 2013 : Using evolutionary programming to generate skillful extreme value probabilistic forecasts . Mon. Wea. Rev. , 141 , 3170 – 3185 , doi: 10.1175/MWR-D-12-00285.1 . Roebber , P. J. , 2015

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Jerry M. Davis and Paul N. Rappoport

176 MONTHLY WEATHER REVIEW VOLUME102The Use of Time Series Analysis Techniques in Forecasting Meteorological Drought JERRY M. DAVIS1NOAA Climatologist for Ohio, and Depariment of Geography, The Ohio State Uni,ersily, Columbus 43210AKD PAUL N. P~APPOPORT Department of Economics, Ths Ohio State University, Columbus 43210 (Manuscript received 27

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Christine Marais and Luc Musson-Genon

et al. 1988). These scores, compared with themean absolute error of PERIDOT forecasts for thesame stations (2 K, 0.0009 kg kg-~, 2.5 m s-i), showthat roughly one-half of the forecast error can be assigned to local inhomogeneity. The purpose of this paper is to reduce this portion of the error. For that, two different but complementary approaches can be envisaged. First, a statistical approachis attempted: this technique consists in determiningthe appropriate predictors on a learning file and

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Michael A. Hollan and Brian C. Ancell

; Weisman et al. 2008 ). Another very important advancement in NWP is the use of ensembles. Ensemble forecasting is a probabilistic technique designed to account for unavoidable errors encountered in NWP (e.g., in the initial conditions, or model physics), aiming to quantify the inherent uncertainties involved with atmospheric prediction ( Leutbecher and Palmer 2008 ). By using an ensemble of forecasts that are all slightly altered by perturbed initial conditions, for example, one can simulate a wide

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Donald R. Wiesnet and Michael Matson

828 MONTHLY WEATHER REVIEW VOLV~E 104A Possible Forecasting Technique for Winter Snow Cover in the Northern Hemisphere and l~urasia DONALD R. V~IESNET AND MICHAEL MATSONNOAA /Na~ional Emgronm,nt~ Sa~llil~ S~, Waslgnglon, D. C. 20233 (Manuscript received 5 December 1975, in revised form 29 March 1976)ABSTRACT Winter season suow and ice charts of the Northern Hemisphere based on satellite

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