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Jean-Jacques Morcrette, George Mozdzynski, and Martin Leutbecher

checked with only very small differences between R 159/ R 95 and R 95/ R 63. The EPS, operational since 5 June 2007, is therefore run at T L 399 L 62 R 95 then at T L 255 L 62 R 63. 5. Impact on high-resolution T L 799L91 10-day forecasts Results in terms of objective scores (i.e., anomaly correlation at different geopotential heights, rmse and mean errors in temperature and winds) when the radiation resolution is reduced, are not shown for the T L 799 L 91 forecasts as they are as or more

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S. G. Gopalakrishnan, David P. Bacon, Nash'at N. Ahmad, Zafer Boybeyi, Thomas J. Dunn, Mary S. Hall, Yi Jin, Pius C. S. Lee, Douglas E. Mays, Rangarao V. Madala, Ananthakrishna Sarma, Mark D. Turner, and Timothy R. Wait

(1977) ; Anthes (1972 , 1977 , 1982) ; Kurihara (1973) ; Emanuel (1988) ; Bender et al. (1993) ; Kurihara et al. (1993 , 1995) ; Krishnamurti et al. (1995) ; Liu et al. (1997 , 1999) have all led to a better understanding of the structure and evolution of hurricanes (see, for instance, Liu et al. 1997 for a brief review on hurricane research). Yet, to date there is no operational model that can forecast both hurricane track and intensity reasonably well ( Emanuel 1999 ; Willoughby

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Israel L. Jirak and William R. Cotton

modeling and data assimilation improvements, as well as the incorporation of more data (e.g., precipitation, profiler winds, and surface winds and moisture) ( Mesinger et al. 2006 ). The NARR provides a long-term, consistent, high-resolution dataset to examine the environment prior to the development of MCSs over the United States. During BAMEX, analyses and forecasts generated by the operational Eta Model and EDAS were used to examine the environmental conditions. Numerous basic (e.g., height, wind

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Craig S. Schwartz, Glen S. Romine, Ryan A. Sobash, Kathryn R. Fossell, and Morris L. Weisman

-France (e.g., Bouttier et al. 2012 ), and NOAA’s National Severe Storms Laboratory. Thus, NCAR’s use of a continuously cycling EnKF to initialize a pseudo-operational convection-allowing EPS for CONUS-scale prediction is unique, 1 and there are several reasons for this choice. First, EnKFs allow a seamless integration of DA and ensemble forecasting that provides dynamically consistent initial ensembles, meaning the role of external models can be relegated solely to providing boundary conditions. This

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Dennis S. Walts and Larry O. Pochop

VOL. 105, NO. 1 MONTHLY WEATHER REVIEW JANUARY 1977Operational Objective Temperature Forecasts at Non-MOS~ Stations DENNIS S. WALTS National Weather Service Forecast Offce, Cheyenne, Wyo. 82001 Laxly O. I)OCHOPAgricultural Engineering Division, University of Wyomlng2, Laramie 82071 (Manuscript received 6 July 1976, in revised form 15 October 1976)ABSTRACT A method is presented

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W. S. Harley

VOL. 4, NO. 3 JOURNAL OF APPLIED METEOROLOGY JUNE 1965An Operational Method for Quantitative l~recipitation Forecasting W. S.Meteorological Service of Canada, Toronto, Ontario(Manuscript received 9 October 1964, in revised form 10 March 1965)ABSTRACT A complete operational method for quantitative precipitation forecasting (Q.P.F.), is developed bycombining the technique for determining large scale vertical

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Clark Evans, Donald F. Van Dyke, and Todd Lericos

make a forecast in an operational setting has not increased, nor has the amount of data that an individual forecaster or researcher can mentally process in a meaningful fashion when interrogating any given weather event, how ensemble guidance is best utilized remains uncertain. Ongoing experiments on the global scale [e.g., The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble, TIGGE; Bougeault et al. (2010) ] and on the regional scale [e.g., the

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Baiquan Zhou, Panmao Zhai, and Ruoyun Niu

that were triggered by clusters of PEPEs ( Galarneau et al. 2012 ; Lau and Kim 2012 ; Lu 2000 ). However, accurately predicting PEPEs remains a challenge for forecasters in operational meteorology ( Ebert et al. 2003 ). The problem they face is primarily the insufficient skill of operational global numerical weather prediction (NWP) models to correctly predict the location, intensity, and duration of PEPEs ( Ralph et al. 2010 ; Sukovich et al. 2014 ). Numerous evaluation studies have been

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Ming Cai, Chul-Su Shin, H. M. van den Dool, Wanqiu Wang, S. Saha, and A. Kumar

1. Introduction Traditionally, operational weather forecast centers have not paid much attention to changing greenhouse gases (GHG). In a 2-week forecast this may seem acceptable at first thought. Indeed, the National Centers for Environmental Prediction (NCEP) Global Forecast System as of 2008 still employed a 1988 GHG concentration, so the need for updating GHGS in the model apparently must have seemed low (an update is planned for 2009). In most cases short-term climate prediction (or

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Barbara G. Brown and Allan H. Murphy

190 WEATHER AND FORECASTING VOLUME2Quantification of Uncertainty in Fire-Weather Forecasts: Some Results of Operational and Experimental Forecasting Programs BARBARA G. BROWN* AND ALLAN H. MURPHYDepartment of Atmospheric Sciences, Oregon State University, Corvallis, OR 97331(Manuscript received 19 May 1986, in final form 16 March 1987) ABSTRACT Fire

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