COAMPS: The Naval Research Laboratory's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®)
2 times per day, from 10:00 and 23:00 UTC
Greenwich Mean Time:
12:00 UTC = 12:00 GMT
Geopotential in 500 hPa (solid, black lines) and Vorticity advection in 105/(s*6h) (colored lines)
The two types of vorticity advection are positive (PVA) and negative vorticity
The closed circles in the figure show the 500 hPa absolute vorticity
lines, the others the 500 hPa height lines. When an air parcel is moving from
an area higher vorticity to an area lower vorticity this is called: PVA
(red color). The other way around is called: NVA (blue color). PVA is
associated with upper-air divergence, i.e. upward vertical motion. NVA
is associated with down ward vertical motion. Therefore, PVA at 500
hPa is strongest above a surface low, while NVA at 500 hPa is strongest
above a surface high.
In operational meteorology Vorticity advection maps are used to identify areas
with vertical air motion to see where clouds, precipitation or clear conditions
are likely to occur. Keep in mind, however, that PVA is not the same as upward
vertical motion. Here temperature advection is important too.
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.
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).