Description:

The Convectively Available Potential Energy (CAPE) map - updated every 6 hours - shows the modelled convectively available potential energy. CAPE represents the amount of buoyant energy (J/kg) available to accelerate a parcel vertically, or the amount of work a parcel does on the environment. The higher the CAPE value, the more energy available to foster storm growth. The potential energy can be converted to kinetic energy reflected in upward motion.
It should be remembered that CAPE represents potential energy, and will only be used should a parcel be lifted to the level of free convection. When values are above 3500 j/kg and storms do develop, they may build rapidly and quickly become severe. Often these storms are referred to as "explosive storms" by chasers and professionals. In a high CAPE environment storms that develop can usually be seen by the human eye as rising rapidly. Higher CAPE typically involves stronger storms with a higher chance of large hail and other severe weather. Note that CAPE is usually of lesser importance than the vertical shear environment for tornadoes. The probability of large hail increases with CAPE, given at least moderate shear(values around 500-1000 J/kg are sufficient).
CAPE is very sensitive to small differences in the moisture and temperature profiles. While the maps indicate 1000 J/kg CAPE at some location, a skew-T thermodynamic diagram at that location may indicate 500-1500 J/kg. (Source: The Lightning Wizard)
Table 1: Characteristic values for CAPE

CAPE value	Convective potential
0	Stable
0-1000	Marginally Unstable
1000-2500	Moderately Unstable
2500-3500	Very Unstable
3500 +	Extremely Unstable

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"