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Mathew M. Gunshor, Timothy J. Schmit, and W. Paul Menzel

satellite data. In addition, all numerical forecast models assimilate either satellite radiances or products derived from satellite radiances. Absolute validation is difficult because there currently does not exist a method to measure earth-emitted radiances precisely. Differences between various operational satellite-measured radiances can be determined by continual intercalibration. Some details for each of the satellite instruments used in this study can be found in Table 1 . On Geostationary

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Harry T. Ochs III and Stanley Q. Kidder

has access to electricalpower and standard telephone lines. The use of new technology with on-site computer capabilities allows rapidgeneration of products specifically tailored to meet the requirements of individual field projects, both for forecastingthe 9perations and noweasting during operations.1. Introduction Meteorological field programs generally require thefrequent acquisition and synthesis of vast quantities ofweather data for forecasting and operational purposes.In the past

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Ken Dixon, Clifford F. Mass, Gregory J. Hakim, and Robert H. Holzworth

1. Introduction Advances in computing power have made it possible for operational regional forecast systems to reach convection-permitting horizontal grid spacing (Δ x ≤ 4 km), alleviating the need for a cumulus parameterization scheme (CPS) and creating the opportunity to assimilate convective-scale observations to improve forecasts ( Weisman et al. 1997 ; Kain et al. 2008 ). Today, radar reflectivity and lightning-flash-rate observations are assimilated through latent heating into the

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J. D. Stark, C. Donlon, A. O’Carroll, and G. Corlett

1. Introduction Satellite sea surface temperature (SST) measurements are used in many applications because they provide a synoptic view of the dynamic thermal character of the ocean’s surface. SST measurements are fundamentally important to agencies and institutions tasked with the study of operational weather and ocean forecasting, climate variability, and military operations. Accurate maps of SST are arguably one of the best climate indicators in their own right. Statistical seasonal

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Tony Reale, Bomin Sun, Franklin H. Tilley, and Michael Pettey

NPROVS collocations containing COSMIC data ( Sun et al. 2011 ) are helping to narrow down uncertainties in long-term upper-air temperature change ( Sherwood et al. 2005 ) and to improve the radiation correction schemes used in NWP data assimilation and weather forecasting. Acknowledgments This work was supported by the JPSS Program in conjunction with the EDR calibration and validation of operational weather products from the NPP polar-orbiting satellite. We specially acknowledge Dr. Mitch Goldberg

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W. R. Moninger

could be any one of a number of possible meteorological archetypes (i.e., stereotyped phenomena such as convective cells or gust fronts) are determined bycomparing current evidence with stored meteorological knowledge.1. Introduction Over the next several years, the amount of dataavailable to operational weather forecasters will increase by several orders of magnitude. Doppler radars,atmospheric profilers, and other new instruments willprovide temporally and spatially dense datasets, revealing

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Sandy Dance, Elizabeth Ebert, and David Scurrah

grids from each individual nowcast system were then averaged to create consensus strike probability nowcasts. In principle the input nowcasts could be weighted according to their recent accuracy; however, this was not done during B08FDP. These consensus strike probability grids formed the basis for integrated objective thunderstorm guidance used by operational forecasters at the Beijing Meteorological Bureau (BMB) during the Olympic Games ( Bally et al. 2009 ). A sample strike probability chart is

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J. Scott Greene, W. Ethan Cook, David Knapp, and Patrick Haines

accuracy of surface wind forecasts within the 8 m s −1 limit given the error magnitudes shown in Figs. 1–4 , demonstrating the superiority of one model over another within this range becomes very difficult. In order to show significant differences between models, the confidence intervals bracketing their error magnitudes cannot overlap. This requires that the uncertainty in verification data not exceed one-third of the magnitude of allowable operational error. Therefore, as illustrated in Fig. 6

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Iwan Holleman, Hans van Gasteren, and Willem Bouten

. R. , 2006 : Using radar wind observations to improve mesoscale numerical weather prediction. Wea. Forecasting , 21 , 502 – 522 . 10.1175/WAF936.1 Fig . 1. Map of the Netherlands showing the locations of the operational weather radar in De Bilt and the X-band bird tracking radar at the Air Force Base De Peel. Fig . 2. Two examples of VAD data from the operational weather radar in De Bilt are shown. (top) VAD during the passage of a cold front (1409 UTC 8 Mar 2003) and (bottom) intense bird

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A. Gangopadhyay and A. R. Robinson

first week to have a better forecast at the end of the second week. The reinitialization methodology is very common in weather forecasting and was adopted in the operational gulfcasting by Robinson et al. (1989) in a quasigeostrophic dynamical system, which had a reasonable forecast skill over a 1-week period ( Glenn and Robinson 1994 ). We have shown that the GS meandering and ring–stream interactions are well reproduced via a primitive equation model and a novel initialization methodology. The

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