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 925 hPa (2000 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.
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
COAMPS:®
The Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) has been developed by the Marine Meteorology Division (MMD) of the Naval Research Laboratory (NRL). The atmospheric components of COAMPS®, described below, are used operationally by the U.S. Navy for short-term numerical weather prediction for various regions around the world.
The atmospheric portion of COAMPS® represents a complete three-dimensional data assimilation system comprised of data quality control, analysis, initialization, and forecast model components. Features include a globally relocatable grid, user-defined grid resolutions and dimensions, nested grids, an option for idealized or real-time simulations, and code that allows for portability between mainframes and workstations. The nonhydrostatic atmospheric model includes predictive equations for the momentum, the non-dimensional pressure perturbation, the potential temperature, the turbulent kinetic energy, and the mixing ratios of water vapor, clouds, rain, ice, grauple, and snow, and contains advanced parameterizations for boundary layer processes, precipitation, and radiation.
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).