Model:

WAVEWATCH III Environmental Modeling Center

Updated:
2 times per day, from 10:00 and 23:00 UTC
Greenwich Mean Time:
12:00 UTC = 01:00 NZDT
Resolution:
0.2° x 0.2° for Mediterranean
1° x 1° for Rest of World
Parameter:
Significant wave heights
Description:
The significant wave height is a commonly used statistical measure for the wave height, and closely corresponds to what a trained observer would consider to be the mean wave height. Note that the highest wave height of an individual wave will be significantly larger. The peak period is not commonly presented. The wave field generally consists of a set of individual wave fields. The peak period identifies either the locally generated "wind sea" (in cases with strong local winds) or the dominant wave system ("swell") that is generated elsewhere. Note that the peak period field shows discontinuities. These discontinuities can loosely be interpreted as swell fronts, although in reality many swell systems overlap at most locations and times (see spectra below).
Ensemble forecasting:
is a numerical prediction method that is used to attempt to generate a representative sample of the possible future states of a dynamical system. Ensemble forecasting is a form of Monte Carlo analysis: multiple numerical predictions are conducted using slightly different initial conditions that are all plausible given the past and current set of observations, or measurements. Sometimes the ensemble of forecasts may use different forecast models for different members, or different formulations of a forecast model. The multiple simulations are conducted to account for the two sources of uncertainty in weather forecast models: (1) the errors introduced by chaos or sensitive dependence on the initial conditions; and (2) errors introduced because of imperfections in the model, such as the finite grid spacings.
Considering the problem of numerical weather prediction, ensemble predictions are now commonly made at most of the major operational weather prediction facilities worldwide, including the National Centers for Environmental Prediction (US), the European Centre for Medium-Range Weather Forecasts (ECMWF), the United Kingdom Met Office, Meteo France, Environment Canada, the Japanese Meteorological Agency, the Bureau of Meteorology (Australia), the China Meteorological Administration, the Korea Meteorological Administration, and CPTEC (Brazil). Experimental ensemble forecasts are made at a number of universities, such as the University of Washington, and ensemble forecasts in the US are also generated by the US Navy and Air Force.
Ideally, the relative frequency of events from the ensemble could be used directly to estimate the probability of a given weather event. For example, if 30 of 50 members indicated greater than 1 cm rainfall during the next 24 h, the probability of exceeding 1 cm could be estimated to be 60 percent. The forecast would be considered reliable if, considering all the situations in the past when a 60 percent probability was forecast, on 60 percent of those occasions did the rainfall actually exceed 1 cm. This is known as reliability or calibration. In practice, the probabilities generated from operational weather ensemble forecasts are not highly reliable, though with a set of past forecasts (reforecasts or hindcasts) and observations, the probability estimates from the ensemble can be adjusted to ensure greater reliability. Another desirable property of ensemble forecasts is sharpness. Provided that the ensemble is reliable, the more an ensemble forecast deviates from the climatological event frequency and issues 0 percent or 100 percent forecasts of an event, the more useful the forecast will be. However, sharp forecasts that are unaccompanied by high reliability will generally not be useful. Forecasts at long leads will inevitably not be particularly sharp, for the inevitable (albeit usually small) errors in the initial condition will grow with increasing forecast lead until the expected difference between two model states is as large as the difference between two random states from the forecast model's climatology.
There are various ways of viewing the data such as spaghetti plots, ensemble means or Postage Stamps where a number of different results from the models run can be compared.

Wikipedia, Ensemble forecasting, http://en.wikipedia.org/wiki/Ensemble_forecasting (optional description here) (as of Feb. 9, 2010, 20:30 UTC).
NWW3:
The NOAA WAVEWATCH III™ operational wave model suite consists of a set of five wave models, based on version 2.22 of WAVEWATCH III™. All models use the default settings of WAVEWATCH III™ unless specified differently.
  1. The global NWW3 model
  2. The regional Alaskan Waters (AKW) model
  3. The regional Western North Atlantic (WNA) model
  4. The regional North Atlantic Hurricane (NAH) model
  5. The regional Eastern North Pacific (ENP) model
  6. The regional North Pacific Hurricane (NPH) model
All regional models obtain hourly boundary data from the global model. All models are run on the 00z, 06z, 12z and 18z model cycles, and start with a 6h hindcast to assure continuity of swell. All models provides 126 hour forecasts, with the exception of the NAH model (72 hour forecast). No wave data assimilation is performed. All models are based on shallow water physics without mean currents. Additional model information is provided in the table and bullets below. The four time steps are the global step, propagation step for longest wave, refraction step and minimum source term step.
NWP:
Numerical weather prediction uses current weather conditions as input into mathematical models of the atmosphere to predict the weather. Although the first efforts to accomplish this were done in the 1920s, it wasn't until the advent of the computer and computer simulation that it was feasible to do in real-time. Manipulating the huge datasets and performing the complex calculations necessary to do this on a resolution fine enough to make the results useful requires the use of some of the most powerful supercomputers in the world. A number of forecast models, both global and regional in scale, are run to help create forecasts for nations worldwide. Use of model ensemble forecasts helps to define the forecast uncertainty and extend weather forecasting farther into the future than would otherwise be possible.

Wikipedia, Numerical weather prediction, http://en.wikipedia.org/wiki/Numerical_weather_prediction(as of Feb. 9, 2010, 20:50 UTC).