Tarife:

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.

Ensemble forecasting:

is a numerical prediction method that is used to attempt to generate a representative sample of the possible future states of a dynamical system. Ensemble forecasting is a form of Monte Carlo analysis: multiple numerical predictions are conducted using slightly different initial conditions that are all plausible given the past and current set of observations, or measurements. Sometimes the ensemble of forecasts may use different forecast models for different members, or different formulations of a forecast model. The multiple simulations are conducted to account for the two sources of uncertainty in weather forecast models: (1) the errors introduced by chaos or sensitive dependence on the initial conditions; and (2) errors introduced because of imperfections in the model, such as the finite grid spacings.
Considering the problem of numerical weather prediction, ensemble predictions are now commonly made at most of the major operational weather prediction facilities worldwide, including the National Centers for Environmental Prediction (US), the European Centre for Medium-Range Weather Forecasts (ECMWF), the United Kingdom Met Office, Meteo France, Environment Canada, the Japanese Meteorological Agency, the Bureau of Meteorology (Australia), the China Meteorological Administration, the Korea Meteorological Administration, and CPTEC (Brazil). Experimental ensemble forecasts are made at a number of universities, such as the University of Washington, and ensemble forecasts in the US are also generated by the US Navy and Air Force.
Ideally, the relative frequency of events from the ensemble could be used directly to estimate the probability of a given weather event. For example, if 30 of 50 members indicated greater than 1 cm rainfall during the next 24 h, the probability of exceeding 1 cm could be estimated to be 60 percent. The forecast would be considered reliable if, considering all the situations in the past when a 60 percent probability was forecast, on 60 percent of those occasions did the rainfall actually exceed 1 cm. This is known as reliability or calibration. In practice, the probabilities generated from operational weather ensemble forecasts are not highly reliable, though with a set of past forecasts (reforecasts or hindcasts) and observations, the probability estimates from the ensemble can be adjusted to ensure greater reliability. Another desirable property of ensemble forecasts is sharpness. Provided that the ensemble is reliable, the more an ensemble forecast deviates from the climatological event frequency and issues 0 percent or 100 percent forecasts of an event, the more useful the forecast will be. However, sharp forecasts that are unaccompanied by high reliability will generally not be useful. Forecasts at long leads will inevitably not be particularly sharp, for the inevitable (albeit usually small) errors in the initial condition will grow with increasing forecast lead until the expected difference between two model states is as large as the difference between two random states from the forecast model's climatology.
There are various ways of viewing the data such as spaghetti plots, ensemble means or Postage Stamps where a number of different results from the models run can be compared.

Wikipedia, Ensemble forecasting, http://en.wikipedia.org/wiki/Ensemble_forecasting (optional description here) (as of Feb. 9, 2010, 20:30 UTC).

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).

GEFS: Global Ensemble weather forecast from the "American Weatherservice "