<div class="eI0">
  <div class="eI1">模式:</div>
  <div class="eI2"><h2><a href="http://www.emc.ncep.noaa.gov/gmb/gdas/" target="_blank">GDAS</a>: "Global Data Assimilation System"</h2></div>
 </div>
 <div class="eI0">
  <div class="eI1">æ›´æ–°:</div>
  <div class="eI2">4 times per day, from 00:00, 06:00, 12:00 and 18:00 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">0.25&deg; x 0.25&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">GDAS</div>
  <div class="eI2">The Global Data Assimilation System (GDAS) is the system used by the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS) model to place observations into a gridded model space for the purpose of starting, or initializing, weather forecasts with observed data. GDAS adds the following types of observations to a gridded, 3-D, model space: surface observations, balloon data, wind profiler data, aircraft reports, buoy observations, radar observations, and satellite observations.
</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>