Description:

The equivalent potential temperature map - updated every 6 hours - shows the modelled equivalent potential temperature at the 850hPa level. The equivalent potential temperature is commonly referred to as Theta-e (θe). θe is the temperature of a parcel of air after it was lifted until it became saturated with water vapour (adibatically). When this parcel becomes saturated and condensation begins, the process of condensation releases latent heat into the surrounding air. This latent heat further warms the air making the air even more buoyant. We refer to this as a moist adiabatic or saturated adiabatic process. Moist adiabatic expansion increases the instability of the parcel. If this process of moist adiabatic expansion continues, all of the water may condense out of the rising parcel and precipitate out, yielding a dry parcel, and is dropped adiabatically to an atmospheric pressure of 1000 hPa. The potential temperature of that new dry parcel is called the equivalent potential temperature (θe) of the original moist parcel
In meteorology θe is used to indicate areas with unstable and thus positively buoyant air. The θe of an air parcel increases with increasing temperature and increasing dewpoint as for the latter more latent heat that can be released. Therefore, in a region with adequate instability, areas of relatively high θe (called θe ridges) are often the burst points for thermodynamically induced thunderstorms and MCS's. θe ridges can often be found in those areas experiencing the greatest warm air advection and moisture advection. (source: the weather prediction Keep in mind that if a strong cap is in place, convective storms will not occur even if θe is high.
As different origins of airmasses largely determine their own θe, one can use this parameter as a marker. Fronts are easily seen as steep gradients in θe. The boundary layer θe shows where fronts are located near the surface, while 700 hPa θe shows where they are near the 3000 m level. In winter it occurs often that warm fronts do not penetrate into the heavy, cold airmass near the surface.

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).

CMC: "Data Source: Environment and Climate Change Canada"