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  <div class="eI1">Modelo:</div>
  <div class="eI2"><h2>CFS: The NCEP Climate Forecast System (CFS)</h2></div>
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  <div class="eI1">Actualiza&ccedil;&atilde;o:</div>
  <div class="eI2">1 times per day, at 17:00 UTC</div>
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  <div class="eI1">Greenwich Mean Time:</div>
  <div class="eI2">12:00 UTC = 12:00 WET</div>
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  <div class="eI1">Resolution:</div>
  <div class="eI2">1.0&deg; x 1.0&deg;</div>
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  <div class="eI1">par&acirc;metro:</div>
  <div class="eI2">Sea Level Pressure in hPa </div>
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  <div class="eI1">Descri&ccedil;&atilde;o:</div>
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The surface chart (also known as surface synoptic chart) presents the distribution of 
the atmospheric pressure observed at any given station on the earth's surface 
reduced to sea level.
You can read the positions of the controlling weather features (highs, lows, ridges or 
troughs) from the distribution of the isobars (lines of equal sea level pressure).
The isobars define the pressure field. The pressure field is the dominating player in 
the weather system.
Additionally, this map helps you to identify synoptic-scale waves and gives you a first 
estimate on meso-scale fronts.
    
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  <div class="eI1">Cluster of Ensemble Members:</div>
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20 members of an ensemble run are divided into different clusters which means groups with similar members according to the hierarchical "Ward method"
The average surface pressure of all members in each cluster are computed and shown as isobares.
The number of members in each cluster determines the probability of the forecast (see percentage)
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  <div class="eI1">Dendrograma:</div>
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A dendrogram shows the multidimensional distances between objects in a tree-like structure.  Objects that are closest in a multidimensional data space are connected by a horizontal line forming a cluster. The distance between a given pair of objects (or clusters) are indicated by the height of the horizontal line.
[http://www.statistics4u.info/fundstat_germ/cc_dendrograms]. The greater the distance the bigger the differences.
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  <div class="eI1">CFS:</div>
  <div class="eI2">The CFS model is different to any other operational weather forecasting model you will see on Weatheronline.
<br>
Developed at the Environmental Modelling Center at NCEP (National Centers for Environment Prediction) in the USA, 
the CFS became operational in August 2004.
<br>
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.
<br>
Four runs of the model are then made, each with slightly differing starting conditions, and from 
these a prediction is made.
<br>
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.
<br>
A description of the CFS is given in the following manuscript.<br>
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.<br>
<a href="http://cfs.ncep.noaa.gov/" target="_blank">http://cfs.ncep.noaa.gov/</a><br>
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  <div class="eI1">NWP:</div>
  <div class="eI2">A previs&atilde;o num&eacute;rica do tempo usa o estado instant&acirc;neo da atmosfera como dados de entrada para modelos matem&aacute;ticos da atmosfera, com vista &agrave; previs&atilde;o do estado do tempo.<br>
Apesar dos primeiros esforços para conseguir prever o tempo tivessem sido dados na d&eacute;cada de 1920, foi apenas com o advento da era dos computadores que foi possível realiz&aacute;-lo em tempo real. A manipulaç&atilde;o de grandes conjuntos de dados e a realizaç&atilde;o de c&aacute;lculos complexos para o conseguir com uma resoluç&atilde;o suficientemente elevada para produzir resultados úteis requer o uso dos supercomputadores mais potentes do mundo. Um conjunto de modelos de previs&atilde;o, quer &agrave; escala global quer &agrave; escala regional, s&atilde;o executados para criar previsões do tempo nacionais. O uso de previsões com modelos semelhantes ("model ensembles") ajuda a definir a incerteza da previs&atilde;o e estender a previs&atilde;o do tempo bastante mais no futuro, o que n&atilde;o seria possível conseguir de outro modo.<br>
<br>Contribuidores da Wikip&eacute;dia, "Previs&atilde;o num&eacute;rica do tempo," Wikip&eacute;dia, a enciclop&eacute;dia livre, <a href="http://pt.wikipedia.org/w/index.php?title=Previs%C3%A3o_num%C3%A9rica_do_tempo&amp;oldid=17351675" target="_blank">http://pt.wikipedia.org/w/index.php?title=Previs%C3%A3o_num%C3%A9rica_do_tempo&oldid=17351675</a> (accessed fevereiro 9, 2010). <br>
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