Model:

ECMWF: Global weather forecast model from the "European Centre for Medium-Range Weather Forecasts". ECMWF is now running its own Artificial Intelligence/Integrated Forecasting System (AIFS) as part of its experiment suite. These machine-learning-based models are very fast, and they produce a 10-day forecast with 6-hourly time steps in approximately one minute.

Updated:
4 times per day, from 3:30, 09:30, 15:30 and 21:30 UTC
Greenwich Mean Time:
12:00 UTC = 08:00 EDT
Resolution:
0.25° x 0.25°
Parameter:
Geopotential height (tens of m) at 850 hPa (solid line) and Temperature (°C) at 850 hPa (coloured, dashed line)
Description:
This chart helps to identify areas of densely packed isotherms (lines of equal temperature) indicating a front. Aside from this you can use the modeled temperature in 850 hPa (5000 ft a.s.l.) to make a rough estimate on the expected maximum temperature in 2m above the ground. However, this method does not apply to (winter) inversions.
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).