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Matthew E. Gropp and Casey E. Davenport

operational forecasting purposes. For example, the Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN; Dixon and Wiener 1993 ) and the Storm Cell Identification and Tracking Algorithm (SCIT; Johnson et al. 1998 ) identify and track high reflectivity deep convective cells within the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) network. These efforts have recently been extended to cell tracking within convective-allowing numerical models as a result of increased

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Malgorzata Szczodrak, Peter J. Minnett, Nicholas R. Nalli, and Wayne F. Feltz

iterative solution of the radiative transfer equation that requires initial “first guess” profiles. For the ARM observing sites, these are derived from statistics of many years of local radiosonde data. Profile retrievals over the oceans from seagoing M-AERIs, however, present several particular challenges—one being that radiosonde databases representative of marine conditions are not available. It is therefore necessary to use a different approach. Combining M-AERI retrievals with numerical forecast

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Ya-Chien Feng and Frédéric Fabry

star) and that of (a) refractivity, (b) specific humidity, and (c) temperature in the rest of the domain from the REPS ensemble model outputs. Would the impact of assimilating refractivity from the operational radar network be the same throughout the large domain? We performed a simple examination of the autocorrelations corresponding to various geographic conditions and for two different forecasting times at 0000 and 1200 UTC. Figure A2 shows the correlation of the background error in

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Volker Lehmann

. , Dibbern J. , Engelbart D. , Görsdorf U. , Lehmann V. , Neisser J. , and Neuschaefer J. W. , 1998 : Performance of the first European 482 MHz wind profiler radar with RASS under operational conditions . Meteor. Z. , NF7 , 248 – 261 . Steppeler, J. , Doms G. , Schättler U. , Bitzer H. , Gassmann A. , Damrath U. , and Gregoric G. , 2003 : Meso-gamma scale forecasts using the nonhydrostatic model LM . Meteor. Atmos. Phys. , 82 , 75 – 96 . St-James, J. S. , and Laroche S

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Yoo-Jeong Noh, John M. Forsythe, Steven D. Miller, Curtis J. Seaman, Yue Li, Andrew K. Heidinger, Daniel T. Lindsey, Matthew A. Rogers, and Philip T. Partain

1. Introduction Clouds in the atmosphere are of fundamental importance to many theoretical and applied science topics. As clouds have significant effects on radiative, chemical, and thermodynamic feedback processes, information on the complete vertical distribution structure of clouds is necessary to improve weather and climate models ( Slingo and Slingo 1988 ; Randall 1989 ). Such information may be also used as input to generate satellite operational products, such as cloud cover fraction

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Claire McCaskill, Lynn K. Shay, Jodi K. Brewster, and Patrick C. Meyers

, World Ocean Atlas 2013 , S. Levitus and A. Mishonov, Eds., NOAA Atlas NESDIS 73, 40 pp . Mainelli, M. , DeMaria M. , Shay L. K. , and Goni G. , 2008 : Application of oceanic heat estimation to operational forecasting of recent Atlantic category 5 hurricanes . Wea. Forecasting , 23 , 3 – 16 , doi: 10.1175/2007WAF2006111.1 . Mainelli-Huber, M. , 2000 : On the role of the upper ocean in tropical cyclone intensity change. M.S. thesis, Division of Meteorology and Physical Oceanography

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J. A. Pamment and B. J. Conway

is used in a fully automated operational system called Nimrod ( Golding 1995 ), which has replaced FRONTIERS. Nimrod produces very short-range forecasts of a variety of meteorological elements, including precipitation, on a more rapid cycle than FRONTIERS (every 15 min), and only by automating all parts of the system can it be made fast enough. 2. System design We have developed a scheme that combines a number of complementary methods, rather than relying on a single technique. Figure 2

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Le Van Thien, William A. Gallus Jr., Mark A. Olsen, and Nathaniel Livesey

many photochemical reactions such as its contribution to ozone depletion ( Solomon et al. 1986 ). Research interest in improved understanding of the distribution and transport processes of water vapor in the upper troposphere and lower stratosphere is great. Comparisons of water vapor observations in the upper troposphere and lower stratosphere with operational center analyses have been rare and generally limited to the European Center for Medium-Range Weather Forecast (ECMWF) analyses. By

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Emna Kamli, Cédric Chavanne, and Dany Dumont

published in the refereed literature, and the authors noted that they were unable to investigate the complete parameter space that allows HF radars to measure ocean currents within partially ice-covered waters. Fig . 2. Current maps obtained from a CODAR HFR at Sainte-Flavie (STF) (Lower St-Lawrence Estuary, Quebec, Canada) in the (right) presence and (left) absence of sea ice. Ice concentration data come from the Canadian operational ice–ocean forecasting system. With sea ice coverage and duration

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Michael H. Freilich, Hongbo Qi, and R. Scott Dunbar

Weather Forecasts (ECMWF) and U.S. National Meteorological Center [(NMC), now known as the National Centers for Environmental Prediction (NCEP)] operational surface wind analyses are interpolated to the space–time locations of ERS-1 σ o measurements in order to provide estimates of the distributions of wind speed and relative azimuth. The dependence of the antenna calibration error estimates on the model function used for the analysis is investigated in section 4 . In section 5 , the 13-month

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