<div class="eI0">
  <div class="eI1">模式:</div>
  <div class="eI2"><h2>CFS: The NCEP Climate Forecast System (CFS)</h2></div>
 </div>
 <div class="eI0">
  <div class="eI1">æ›´æ–°:</div>
  <div class="eI2">1 times per day, at 17:00 UTC</div>
 </div>
 <div class="eI0">
  <div class="eI1">格林尼治平时:</div>
  <div class="eI2">12:00 UTC = 20:00 北京时间</div>
 </div>
 <div class="eI0">
  <div class="eI1">Resolution:</div>
  <div class="eI2">1.0&deg; x 1.0&deg;</div>
 </div>
 <div class="eI0">
  <div class="eI1">参量:</div>
  <div class="eI2">Soaring Index</div>
 </div>
 <div class="eI0">
  <div class="eI1">描述:</div>
  <div class="eI2">

The Soaring Index map - updated every 6 hours - shows the modelled lift rate by thermals (convective clouds).
The index is based on weather information between 5 000 feet (1 524 metres) and 20 000 feet (6 096 metres)
and is expressed in Kelvin. 
<BR>
Table 1: Characteristic values for Soaring Index for soaring<BR>
<TABLE border=1>
<TBODY>
  <TR>
    <TD align=middle><B>Soaring Index</B></TD>
    <TD align=middle><B>Soaring Conditions</B></TD>
  </TR>
<TR>
  <TD align=middle>Below -10<BR>&nbsp;<BR>-10 to 5<BR>&nbsp;<BR>5 to 20<BR>&nbsp;<BR>Above 20</TD>
  <TD align=middle>Poor<BR>&nbsp;<BR>Moderate<BR>&nbsp;<BR>Good<BR>&nbsp;<BR>Excellent<SUP>*</SUP></TD>
</TR>
</TBODY>
</TABLE>

<BR>
Table 2: Critical values for the Soaring Index<BR>
<TABLE border=1>
<TR>
   <TD><STRONG>Soaring Index</STRONG></TD>

   <TD><STRONG>Convective potential</STRONG></TD>
</TR>
<TR>
   <TD>15-20</TD>
   <TD>Isolated showers, 20% risk for thunderstorms</TD>
</TR>
<TR>
   <TD>20-25</TD>
   <TD>Occasionally showers, 20-40% risk for thunderstorms</TD>

</TR>
<TR>
   <TD>25-30</TD>
   <TD>Frequent showers, 40-60% risk for thunderstorms.</TD>
</TR>
<TR>
   <TD>30-35</TD>
   <TD>60-80% risk for thunderstorms.</TD>
</TR>

<TR>
	<TD>35 + </TD>
	<TD>>80% risk for thunderstorms </TD>
<TR>
</TABLE> 

    
  </div>
 </div>
 <div class="eI0">
  <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>
</div></div>
 <div class="eI0">
  <div class="eI1">NWP:</div>
  <div class="eI2">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.<br>
<br>Wikipedia, Numerical weather prediction, <a href="http://zh.wikipedia.org/wiki/數值天氣預報" target="_blank">http://zh.wikipedia.org/wiki/數值天氣預報</a>(as of Feb. 9, 2010, 20:50 UTC).<br>
</div></div>
</div>