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Jili Dong, Ricardo Domingues, Gustavo Goni, George Halliwell, Hyun-Sook Kim, Sang-Ki Lee, Michael Mehari, Francis Bringas, Julio Morell, and Luis Pomales

. 2000 ; Lin et al. 2008 ; Mainelli et al. 2008 ; Goni et al. 2016 ) can efficiently reduce storm-induced sea surface temperature (SST) cooling. Barrier layers are usually linked with low-salinity waters near the surface, associated with the heavy precipitation that accompanies a storm or freshwater discharge from the Amazon and Orinoco Rivers (e.g., Kelly et al. 2000 ; Corredor et al. 2003 ; Balaguru et al. 2012a ; Johns et al. 2014 ). The low salinity values near the surface define strong

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John G. W. Kelley, David W. Behringer, H. Jean Thiebaux, and Bhavani Balasubramaniyan

extrapolating SST information into the mixed layer of a daily ocean model, 3) no operational access to sea surface height anomalies derived from the Ocean Topography Experiment / Poseidon ( TOPEX/Poseidon ) or European Remote Sensing Satellite-2 ( ERS-2 ) satellite altimeter data, and 4) uncertainty in the appropriate method for translating sea surface height changes into subsurface temperature and salinity changes in a daily, real-time coastal ocean forecast model. A new, two-cycle configuration of

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L. C. Breaker, L. D. Burroughs, Y. Y. Chao, J. F. Culp, N. L. Guinasso Jr., R. L. Teboulle, and C. R. Wong

observations from three National DataBuoy Center (NDBC) buoys and three Coastal-Marine Automated Network stations close to the storm track;2) water levels and storm surge at 15 locations in the Bahamas, around the coast of Florida, and along thenorthern coast of the Gulf of Mexico; 3) currents, temperatures, and salinities at a depth of I 1 m in the northernGulf; and 4) spatial analyses of sea surface temperature (SST) before and after the passage of Andrew. Sea level pressure, wind direction, wind

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Karthik Balaguru, Gregory R. Foltz, L. Ruby Leung, Samson M. Hagos, and David R. Judi

profiles only to compute seawater density, TCHP, and T dy , setting the salinity to a constant 36 psu, which is the mean value for the Atlantic. Note that we are performing hindcasts of intensity using the observed storm tracks. We use the Monte Carlo approach of repeated random sampling to estimate the uncertainty in model performance and to evaluate the significance of replacing SST with T dy in the model. First, we randomly select two-thirds of the input data and train the linear regression model

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Lasse Makkonen, Ross D. Brown, and Paul T. Mitten

thedifferent response (Fig. 1 ). Overland (1990) goes togreat pains to justify this response by mentioning a"supercooling hypothesis" and noting "observationalsupport for the existence of extreme icing at low seatemperatures." However, there is more convincing observational evidence (see Shellard 1974) that icing isrelatively insensitive to sea-surface temperature. Thisinsensitivity was a feature of the time-dependent icingmodel of Horjen and Vefsnmo (1987), and pulsedspray, saline-water wind tunnel

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R. M. Clancy and LCDR W. D. Sadler

-scaleversion. For example, it uses OI decorrelation scalesand noise-to-signal ratios, which are keyed to individualwater masses, with the decorrelation functions becoming elongated parallel to the fronts that separate thewater masses near the fronts. Furthermore, and mostimportant, OTIS 3.0 relies heavily on water-massclimatologies, synthetic salinity-temperature-depth(STD) algorithms and "ocean feature models" (Tunnicliffe and Cummings 1991 ), in conjunction with theOI formalism and ocean-bogus database, to

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Kosuke Ito, Tohru Kuroda, Kazuo Saito, and Akiyoshi Wada

horizontal grid spacing is 5 km on a Lambert conformal projection plane with a vertical representation of 50 layers up to 22 km. The time step is 24 s. We refer to this nonhydrostatic model of the atmosphere as AMSM. CMSM is very similar to AMSM except that a vertically one-dimensional upper-ocean model developed by Price et al. (1986) is coupled with a grid of the lowest atmospheric layer over the ocean. The diagnostic variables of the upper-ocean model are ocean temperature, salinity, and horizontal

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Michelle Mainelli, Mark DeMaria, Lynn K. Shay, and Gustavo Goni

field. The Naval Oceanographic Office (NAVOCEANO) Generalized Digital Environmental Model (GDEM), version 2.1, is the monthly climatological database used for this study ( Teague et al. 1990 ). GDEM is a database of temperature and salinity profiles for 39 standard levels of the ocean at 0.5° latitude and longitude intervals. Since the GDEM, version 2.1, database did not cover the Atlantic basin in areas of shallow waters, monthly climatological temperature and salinity fields (objectively analyzed

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James E. Overland

around a vessel is quite complex. The freezingprocess itself is complicated because of the influenceof ocean salinity, which affects the freezing temperatureand contributes to forming a low density "spongy" icewith air and brine pockets (Makkonen 1987). Thevolume of water that flows back offthe vessel is muchgreater than that which remains accreted as icing, yetcooling applies to the total mass flux. A result of thesefactors is large variation of icing accumulation withrespect to location on the

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Andrew Cottrill, Harry H. Hendon, Eun-Pa Lim, Sally Langford, Kay Shelton, Andrew Charles, David McClymont, David Jones, and Yuriy Kuleshov

atmosphere and the ocean are coupled by the Ocean Atmosphere Sea Ice Soil model (OASIS), described by Valcke et al. (2000) . POAMA-2 uses a new advanced ocean initialization scheme provided by the POAMA Ensemble Ocean Data Assimilation System (PEODAS), which is based on an ensemble Kalman filter ( Yin et al. 2011 ). The implementation of PEODAS is an improvement over the previous system, POAMA-1.5b, as it assimilates both ocean temperature and salinity observations into the model every 3 days. The

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