Descrição:

This chart shows the relative humidity at Pa. In the forefield of a trough line as well as at and near fronts (Jets), warmer less dense air is forced to ascend. As the ascending air cooles, the relative humidity increases, eventually resulting in condensation and the formation of clouds.This process is known as frontal lifting.
High relative humidity at 700 hPa - equivalent to ca. 10000 ft a.s.l. - indicates the areas of frontal lifting and thus the active zones of the current weather.

Spaghetti plots:

are a method of viewing data from an ensemble forecast.
A meteorological variable e.g. pressure, temperature is drawn on a chart for a number of slightly different model runs from an ensemble. The model can then be stepped forward in time and the results compared and be used to gauge the amount of uncertainty in the forecast.
If there is good agreement and the contours follow a recognisable pattern through the sequence then the confidence in the forecast can be high, conversely if the pattern is chaotic i.e resembling a plate of spaghetti then confidence will be low. Ensemble members will generally diverge over time and spaghetti plots are quick way to see when this happens.

Spaghetti plot. (2009, July 7). In Wikipedia, The Free Encyclopedia. Retrieved 20:22, February 9, 2010, from http://en.wikipedia.org/w/index.php?title=Spaghetti_plot&oldid=300824682

CFS:

The CFS model is different to any other operational weather forecasting model you will see on Weatheronline.
Developed at the Environmental Modelling Center at NCEP (National Centers for Environment Prediction) in the USA, the CFS became operational in August 2004.
The systems works by taking reanalysis data (NCEP Reanalysis 2) and ocean conditions from GODAS (Global Ocean data Assimilation). Both of these data sets are for the previous day, and so you should be aware that before initialisation the data is already one day old.
Four runs of the model are then made, each with slightly differing starting conditions, and from these a prediction is made.
Caution should be employed when using the forecasts made by the CFS. However, it is useful when monitored daily in assessing forecasts for the coming months, the confidence levels in these forecasts and in an assessment of how such long range models perform.
A description of the CFS is given in the following manuscript.
S. Saha, S. Nadiga, C. Thiaw, J. Wang, W. Wang, Q. Zhang, H. M. van den Dool, H.-L. Pan, S. Moorthi, D. Behringer, D. Stokes, M. Pena, S. Lord, G. White, W. Ebisuzaki, P. Peng, P. Xie , 2006 : The NCEP Climate Forecast System. Journal of Climate, Vol. 19, No. 15, pages 3483.3517.
http://cfs.ncep.noaa.gov/

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).

CFS: The NCEP Climate Forecast System (CFS)