Descrição:

The ThetaW map - updated every 6 hours - shows the modelled wet bulb potential temperature at the 850hPa level. The theta w (θw) areas are encircled by isotherms - lines connecting locations with equal wet bulb potential temperature. When an air parcel, starting from a certain pressure level, is lifted dry adiabatically until saturation and subsequently is brought to a level of 1000 hPa along a saturated adiabat it reaches what is called the saturated potential wet-bulb temperature: θw. As long as an air parcel undergoes an adiabatisch process, be it either dry or saturated, and in both descending and ascending motions θw does not change. Even when precipitation is evaporating adiabatically θw does not change, therefore θw is "conservative".
An air mass is defined as a quantity of air with a horizontal extent of several hundred or thousand kilometres and a thickness of several kilometres, which is homogeneous in thermal characteristics. Such an air mass may form when air has been over an extensive and homogeneous part of the Earth's surface during a considerable amount of time. This is the so-called source area. In due time, by means of radiative exchange processes and contact with the Earth's surface, an equilibrium develops which is evident from the fact that θw has approximately the same value in the entire air mass both horizontally and vertically, Hence θw can be used to characterise an air mass, with both sensible and latent heat are accounted for.
Depending on possible source areas several main air mass types can be distinguished: polar air (P), midlatitude air (ML) and (sub)tropical air (T). Also, but these are less important arctic air (A) and equatorial air (E). These five main types can be subdivided in continental air (c) and maritime air (m).

Table 1: Characteristic values for θw at 850 hPa (in °C) for various air masses.

Summer		Winter
cA < 7	mA < 9	cA < -5	mA < -7
cP 7 - 12	mP 6 - 12	CP -6 – 2	mP -3 - 5
CML 11 – 16	mML 11 - 16	CML 1 – 8	mML 3 - 9
cT 15 - 19	mT 14 - 19	CT 8 – 14	mT 8 - 16
cE > 17	mE > 18	cE > 14	mE > 16

If the θw distribution is considered on a pressure surface, preferably 850 hPa, then extensive areas with a small or no gradient can be observed. These areas of homogeneous θw values may be associated with air masses. Often various homogeneous areas are separated from one another by relatively narrow transformation zones displaying a strong gradient. Here frontal zones intersect with the pressure surface. Generally speaking a surface front is located where at 850 hPa the 'warm boundary' of the zone with the large θw gradient is present.(Source: Wageningen University)

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:

A previsão numérica do tempo usa o estado instantâneo da atmosfera como dados de entrada para modelos matemáticos da atmosfera, com vista à previsão do estado do tempo.
Apesar dos primeiros esforços para conseguir prever o tempo tivessem sido dados na década de 1920, foi apenas com o advento da era dos computadores que foi possível realizá-lo em tempo real. A manipulação de grandes conjuntos de dados e a realização de cálculos complexos para o conseguir com uma resolução suficientemente elevada para produzir resultados úteis requer o uso dos supercomputadores mais potentes do mundo. Um conjunto de modelos de previsão, quer à escala global quer à escala regional, são executados para criar previsões do tempo nacionais. O uso de previsões com modelos semelhantes ("model ensembles") ajuda a definir a incerteza da previsão e estender a previsão do tempo bastante mais no futuro, o que não seria possível conseguir de outro modo.

Contribuidores da Wikipédia, "Previsão numérica do tempo," Wikipédia, a enciclopédia livre, http://pt.wikipedia.org/w/index.php?title=Previs%C3%A3o_num%C3%A9rica_do_tempo&oldid=17351675 (accessed fevereiro 9, 2010).

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