Model:

Updated:

4 times per day, from 08:00, 14:00, 20:00, and 00:00 UTC

Greenwich Mean Time:

12:00 UTC = 08:00 EDT

Resolution:

0.01° x 0.01°

Parameter:

Geopotential height Temperature at 500 hPa

Description:

Geopotential height at 500 hPa (solid line)
Temperature at 500 hPa (colored, dashed)

The maps show the predominant tropospheric waves (trough or ridge). They virtually control the ''weather'' (dry, warm / wet, cold) and the long waves drive the smaller synoptic waves. Thus, this upper-level chart illustrates the dynamics of our atmosphere.

Spaghetti plots:

are a method of viewing data from an ensemble forecast.
A meteorological variable e.g. pressure, temperature is drawn on a chart for a number of slightly different model runs from an ensemble. The model can then be stepped forward in time and the results compared and be used to gauge the amount of uncertainty in the forecast.
If there is good agreement and the contours follow a recognisable pattern through the sequence then the confidence in the forecast can be high, conversely if the pattern is chaotic i.e resembling a plate of spaghetti then confidence will be low. Ensemble members will generally diverge over time and spaghetti plots are quick way to see when this happens.

Spaghetti plot. (2009, July 7). In Wikipedia, The Free Encyclopedia. Retrieved 20:22, February 9, 2010, from http://en.wikipedia.org/w/index.php?title=Spaghetti_plot&oldid=300824682

Arome:

Arome
The Arome forecasting system is a blend of the best components from the Méso-NH model, the Aladin model, and the IFS/Arpège data assimilation software. Its focus is on the numerical prediction of intense convective systems over mainland France by 2008. Other important weather phenomena will also begin to be reliably forecast, thanks to a high (kilometric) spatial resolution and the use of regional observing systems. The Arome software is designed to be accessible to a wide research community.

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).