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Steven J. Lambert

, varioustransformations were applied to place the data on acommon grid. From January 1946 to December 1952the data were derived from manually digitized valuesproduced by the US Navy Applied Research Operational Weather Analysis (AROWA) Project. Point values were extracted from hand analyses at the cornersand the centers of 10- x 10- latitude-longitude quadrangles forming a diamond grid with a grid spacing ofabout 7- (Berry et al. 1953). These point values werelater objectively analyzed by the Fleet NumericalWeather

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Kevin P. Gallo, David R. Easterling, and Thomas C. Peterson

,and Ken Weathers at the National Climatic Data Center, who where responsible for the land use/land coversurvey of weather observation stations. We also wishto thank David Bowman for his assistance with dataextraction. This research was partially funded by theU.S. Department of Energy through InteragencyAgreement DE-AI05-90ER60952.REFERENCESChangnon, S. A,, 1992: Inadvertent weather modification in urban areas: Lessons for global climate change. Bull. Amer. Meteor. Soc., 73, 619-627.Gallo, K. P., A

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J. M. Gutiérrez, D. San-Martín, S. Brands, R. Manzanas, and S. Herrera

classification or weather typing techniques (M2a–M2c) based on the k -means clustering algorithm, which was applied to the atmospheric state vector formed by all the considered predictors standardized at a gridbox level to avoid biased results due to different scales ( Gutiérrez et al. 2004 ). M2a and M2b are modifications of the abovementioned analog method, with the search space being quantized into weather types (WTs). Weather types are first calculated applying the k -means method (obtaining their

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Eun-Soon Im, Rebecca L. Gianotti, and Elfatih A. B. Eltahir

1. Introduction Over the past decade, we have worked on improving the skill of the Regional Climate Model, version 3 (RegCM3; Pal et al. 2007 ), in simulating the climate over different regions through the incorporation of new physical schemes or modification of existing schemes. The version of RegCM3 that includes all of the Massachusetts Institute of Technology (MIT)-based upgrades will be referred to here as the MIT regional climate model (MRCM). The most significant difference in MRCM

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Traute Crueger, Bjorn Stevens, and Renate Brokopf

the original Tiedtke scheme, which was used by ECHAM3 ( Tiedtke 1989 , 1993 ), and also by the European Centre for Medium-Range Weather Forecasts (ECMWF), with minor modifications, for a number of years. Nordeng modified Tiedtke’s representation of deep convection by relating organized entrainment and detrainment to convective activity itself. Deep convective organized entrainment takes place as an inflow of environmental air into the cumulus updraft when the cloud parcels accelerate upward (i

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Laurence Picon and Michel Desbois

, statistical relationships betweenradiances and chosen meteorological parameters areinvestigated. Because of the scarcity of upper-air datain the African-Atlantic area, these parameters are derived from analyses of the European Centre for Medium Range Weather Forecasts (ECMWF), whichmerges all available data through a sophisticated assimilation system. Keeping in mind that these analysesare not perfect, we nevertheless consider them as thebest approximation presently available for describinglarge

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Seth E. Snell, Sucharita Gopal, and Robert K. Kaufmann

. The ANNs used in this analysis are multilayer feedforward backpropagation networks. a. Data sources and preparation Maximum daily temperature for a 63-yr period from 1931 through 1993 are used as input and output vectors for the ANNs. These data are obtained from National Oceanic and Atmospheric Association (NOAA) ground weather stations ( NCDC 1994 ). The output vector consists of 11 stations in the midcontinental portion of the United States ( Fig. 1 ). The output vector is estimated using two

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Christopher D. Roller, Jian-Hua Qian, Laurie Agel, Mathew Barlow, and Vincent Moron

1. Introduction Weather type (WT) analysis is a way to objectively and compactly describe the climatology of main weather patterns for a region. It can be used as a basis for understanding a broad scale of relationships such as flow patterns and the influence of teleconnections on those patterns, as well as the progression from one weather pattern to another. A common method of isolating weather patterns involves the k -means clustering technique ( Diday and Simon 1976 ; Ghil and Robertson

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Andrew W. Robertson, Sergey Kirshner, and Padhraic Smyth

1. Introduction One of the major challenges in tailoring seasonal climate forecasts to meet societal needs, is that the potential users of climate information are often concerned with the characteristics of high-frequency weather at a particular location. Unfortunately, the statistics of local weather are generally poorly represented in the coarse-resolution general circulation models (GCMs) that are typically used to make seasonal climate forecasts. Moreover, the seasonal predictability of

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Laurence S. Kalkstein, Paul C. Dunne, and Russell S. Vose

studies of this type. Anautomated synoptic index was constructed for the winter months in four western North American Arctic locationsto determine if the frequency of occurrence of the coldest and mildest air masses has changed and if the physicalcharacter of these air masses has shown signs of modification over the past 40 years. It appears that the frequenciesof the majority of the coldest air masses have tended to decrease, while those of the warmest air masses haveincreased. In addition, the very

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