Model:

Updated:

4 times per day, from 06:00, 12:00, 18:00, and 00:00 UTC

Greenwich Mean Time:

12:00 UTC = 08:00 EDT

Resolution:

0.025° x 0.037°

Parameter:

Geopotential in 500 hPa (solid, black lines) and Temperature advection in K/6h (colored lines)

Description:

The map "T-Adv 500" shows the advection of cold or warm air at 500 hPa level. Negative values indicate cold advection, while positive values indicate warm air advection. Advection of warm or cold air causes the geopotential height to respectively rise or drop, producing vertical rising and sinking motion of air. There is, however, not a direct relationship between temperature advection and resultant vertical motion in the atmosphere since other lifting and sinking mechanisms can complicate the picture, e.g. vorticity advection (see "V-Adv maps").
In weather forecasting, temperature advection maps are often used to locate the postion of wam and cold fronts. Cold advection is common behind cold fronts, while warm advection is common behind warm fronts and ahead of cold fronts. Higher in the atmosphere temperature advection is getting less pronounced, as horizontal much more uniform in temperature and the flow is more zonal.

HARMONIE:

HARMONIE-AROME The non-hydrostatic convection-permitting HARMONIE-AROME model is developed in a code cooperation of the HIRLAM Consortium with Météo-France and ALADIN, and builds upon model components that have largely initially been developed in these two communities. The forecast model and analysis of HARMONIE-AROME are originally based on the AROME-France model from Météo-France (Seity et al, 2011, Brousseau et al, 2011) , but differ from the AROME-France configuration in various respects. A detailed description of the HARMONIE-AROME forecast model setup and its similarities and differences with respect to AROME-France can be found in (Bengtsson et al. 2017). [From: HIRLAM (2017)]

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).