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Aleksandr Falkovich, Isaac Ginis, and Stephen Lord

position and 3D structure of the major fronts in the Atlantic basin: the Gulf Stream (GS) and Loop Current (LC). These fronts are important for hurricane forecasting, as they may influence the SST response in the coupled model and thus the track and intensity of hurricanes. The ocean data assimilation and initialization procedure in the 2001 operational GFDL/URI coupled model is described in Bender and Ginis (2000) . Some additional modifications were made in 2002 and will be referred to hereafter as

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Yu-Tai Hou, Kenneth A. Campana, Kenneth E. Mitchell, Shi-Keng Yang, and Larry L. Stowe

channels in daytime and three at nightfrom AVHRR data. That gives CLAVR a greater ability to detect certain cloud types, such as thin cirrus andlow stratus. Designed to be an operational product, CLAVR is also compared with total cloud forecasts fromthe National Meteorological Center (NMC) Medium Range Forecast (MRF) Model. The datasets are mappedto the orbits of NOAA polar satellites, such that errors from temporal sampling are minimized. A set of statisticalscores, histograms, and maps are used to

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Hans Hersbach

European Centre for Medium-Range Weather Forecasts (ECMWF) scatterometer winds have been assimilated in the operational integrated forecast and assimilation system (IFS) from 30 January 1996 onward. The four-dimensional variational assimilation system at ECMWF allows for a dynamically consistent use of observations. In this way, information of scatterometer surface winds is propagated to the entire troposphere ( Isaksen and Janssen 2004 ). Currently (December 2009), data are used from the Active

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Shihe Ren, Xueming Zhu, Marie Drevillon, Hui Wang, Yunfei Zhang, Ziqing Zu, and Ang Li

( Nieto et al. 2012 ). Synoptic satellite observations and ocean forecast/reanalysis products can be used jointly to enable front detection as applications of operational oceanography. To this aim, a number of automated frontal lines detection algorithms have been developed to describe the frontal activities precisely. There are two primary approaches to objective detection: the gradient-based method ( Belkin and O’Reilly 2009 ; Canny 1986 ; Castelao et al. 2006 ; Kazmin and Rienecker 1996 ; Oram

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Miriam M. De Las Heras, Gerrit Burgers, and Peter A. E. M. Janssen

1350JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGYVOLUME 11Variational Wave Data Assimilation in a Third-Generation WaveMIRIAM M. DE LAS HERAS,* GERRIT BURGERS, AND PETER A. E. M. JANSSEN ?Department of Oceanography, KNMI, De Bilt, the Netherlands(Manuscript received l ~eptember 1993, in final form 22 February 1994) The adjoint of the wave model WAM, which runs operationally performing global wave forecast at theEuropean Centre for Medium-Range Weather Forecasts, has been constructed. In this

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Adam L. Houston and Jason M. Keeler

” soundings], the presence of sufficiently strong CIN will prevent deep convection initiation (DCI). In prior decades, operational assessment of capping inversion strength was limited to rawinsonde launches, thus requiring forecasters to estimate inversion strength both spatially (in between sounding sites) and temporally, based on thermal advection, large-scale ascent/descent, with specific methods detailed in Johns and Doswell (1992) . One such method utilizes the forecast 700 mb temperature field

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Steven R. Chiswell, Steven Businger, Michael Bevis, Fredrick Solheim, Christian Rocken, and Randolph Ware

output.those obtained from radiosonde soundings over theentire operational sounding network (Fig. 10), providing an rmse of 2.58 K (~ 1%). In contrast to theprofile method above, we find that the values derivedfrom the model output do fall along the line of oneto-one correspondence. Figure 11 displays the resultsof a 2-month operational use of NGM data to provide12-h forecasts at individual sounding sites. Three locations: Nassau, Bahamas; Midland, Texas; and Caribou, Maine, representative of

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Quanhua Liu, Alexander Ignatov, Fuzhong Weng, and XingMing Liang

1. Introduction Sea surface temperature (SST) is critically important to characterize air–sea interaction, global water cycle, oceanic and atmospheric circulation and forecasting, climate, fisheries, tropical cyclogenesis, sea fog and sea-breeze formation, and for many other applications ( Reynolds et al. 2007 ). Because of its extreme importance, SST was identified as one of the two priority environmental data records (EDRs), derived from the Visible Infrared Imager Radiometer Suite (VIIRS) on

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Edmund Keith Stone and Gary Pearce

between the observation and the background value is then found (o−b value). The model fields used were T + 2-, 3-, 4-h forecasts retrieved from the operational archive. The wind vector was split into u and υ components prior to processing. The o−b values for every observation have been found and then placed into 250-m high-altitude bins. The average (mean, black lines) and root-mean-square (RMS, gray lines) o−b values for all of the observations in each bin were then calculated and plotted in

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Ying-Hwa Kuo and Richard A. Anthes

the interior region represent the error due to thespecified temperature errors; the differences over the interior region illustrate theeffect of the erroneous boundary conditions.the ,existence of random errors in geopotential heightat the boundary. In an operational system, the boundary conditionsin geopotential height could be obtained from a largescale model's forecast temperatures. The forecasttemperature errors are likely to be more highly correlated in the vertical than are the random

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