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Yung Y. Chao, Jose-Henrique G. M. Alves, and Hendrik L. Tolman

basis: the North Atlantic and North Pacific hurricane wave models, NAH and NPH. The NAH and NPH models are part of the National Oceanic and Atmospheric Administration’s (NOAA) WAVEWATCH III (NWW3) wave forecasting system, which also includes a global model, and three other regional models covering the following domains: Alaskan waters (AKW), western North Atlantic (WNA), and eastern North Pacific (ENP). All models in the NWW3 suite are implementations of the third-generation spectral ocean wave

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Andrew W. Colman, Erika J. Palin, Michael G. Sanderson, Robert T. Harrison, and Ian M. Leggett

National Oceanic and Atmospheric Administration (NOAA), uses WAVEWATCH III 1 ( Tolman 1992 , 2009 ) to routinely forecast wave heights in the Atlantic Ocean up to 180 h ahead ( Cao et al. 2007 ). The Met Office forecasts wave height and direction out to 5 days ahead for the United Kingdom using WAVEWATCH III, and an ensemble wave height forecasting system is currently being tested ( Bocquet 2010 ). The European Centre for Medium-Range Weather Forecasts (ECMWF) uses the Wave Action Model (WAM) for

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Arun Chawla, Hendrik L. Tolman, Vera Gerald, Deanna Spindler, Todd Spindler, Jose-Henrique G. M. Alves, Degui Cao, Jeffrey L. Hanson, and Eve-Marie Devaliere

public release of WW3 also accounts for depth-limited wave breaking, which was absent in the earlier versions of WW3. The multigrid version of WW3 has been used to develop a new operational forecast system for NCEP. The National Oceanic and Atmospheric Administration (NOAA) Multigrid WaveWatch 3 system (hereafter referred to as NMWW3) has been operational since December 2007 and has replaced the older modeling suite (NWW3, AKW, WNA and ENP). NMWW3 was developed to take advantage of the new features

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Juanjuan Wang, Benxia Li, Zhiyi Gao, and Jiuke Wang

, and bottom-induced wave breaking dissipation and nonlinear wave interactions. The operational wave forecasts at ECMWF include the ocean wave stand-alone model (HRES-SAW), the ocean wave coupled to the high-resolution atmospheric model (HRES-WAM), the ensemble wave forecast (ENS-WAM), and the seasonal wave forecast (SEAS-WAM). Data from the HRES-WAM system were used in this study. (Key characteristics of operational wave forecasts are introduced at

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Bing Fu, Tim Li, Melinda S. Peng, and Fuzhong Weng

Warning Center (JTWC). The TC minimum pressure data are from the Hong Kong Observatory. b. Analysis methods To examine the synoptic-scale atmospheric signatures prior to TC formation, a time-filtering method is used to obtain 3–8-day disturbances for both the QuikSCAT and TMI data. The selection of such a 3–8-day bandwidth is based on the results of Lau and Lau (1990 , 1992 ), who pointed out that the most significant synoptic-scale wave spectrum appears in this band in the WNP. The filtering scheme

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Steven E. Koch and Stephen Saleeby

1. Introduction The various systems composing the National Weather Service (NWS) modernization ( Friday 1994 ) have now been fully implemented: the Weather Surveillance Radar-1988 Doppler network, the National Oceanic and Atmospheric Administration (NOAA) Profiler Network, the Automated Surface Observing System (ASOS), and the Geostationary Operational Environmental Satellite. Among these systems, the one that has undeniably had the least positive impact so far upon the ability of forecasters

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W. Erick Rogers, Paul A. Wittmann, David W. C. Wang, R. Michael Clancy, and Y. Larry Hsu

the periods of January 2001 and January–February 2002) was very likely caused by the inaccuracy of the forcing fields from the operational global atmospheric model NOGAPS, in particular a negative bias in predictions of high wind speed ( U 10 > 15 m s −1 ) events by that model. Bias associated with the wave model itself (internal error) was believed to be only secondary. b. The operational meteorological product For wind forcing, both of the Navy’s global wave models use wind vectors from the

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Stephen D. Eckermann, Andreas Dörnbrack, Harald Flentje, Simon B. Vosper, M. J. Mahoney, T. Paul Bui, and Kenneth S. Carslaw

temperature amplitudes and wave-induced turbulence potential. The fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) provided detailed forecasts of long-wavelength mountain wave–induced stratospheric temperature variability over Scandinavia, as well as streamlines for planning quasi-Lagrangian flights along forecast particle trajectories (e.g., Wirth et al. 1999 ). The Vosper Orographic Model (3DVOM) provided high-resolution forecasts of shorter

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Julio T. Bacmeister, Paul A. Newman, Bruce L. Gary, and K. Roland Chan

background atmospheric density profile, which we assumeis simply proportional to pressure. The parameter a isa dimensionless factor that depends on ridge shape,and L is horizontal length representing the extent ofthe wave disturbance. As will be seen below, these lasttwo parameters drop out in the calculation of the wavedisplacement profile. We assume further that wave momentum flux remains constant with height until wave breaking occurs.This will be the case for hydrostatic waves in a slowlyvarying

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Yung Y. Chao and Hendrik L. Tolman

grid resolution is 0.25° × 0.25° in latitude and longitude. Both models obtain boundary data from NCEP’s global wave model, which has a resolution of 1.00° × 1.25° in latitude and longitude. The model physics consist of the default model settings of WAVEWATCH III version 2.22, as described in detail in Tolman (2002b) . The difference between the two models lies in their input winds. The WNA model is driven solely with wind obtained from the NCEP Global Forecast System (GFS) atmospheric model

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