<div class="eI0">
  <div class="eI1">模式:</div>
  <div class="eI2"><h2>Times Series from the ECMWF Ensemble</h2></div>
 </div>
 <div class="eI0">
  <div class="eI1">æ›´æ–°:</div>
  <div class="eI2">2 times per day, from 10:00 and 23:00 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">0.5&deg; x 0.5&deg;</div>
 </div>
 <div class="eI0">
  <div class="eI1">参量:</div>
  <div class="eI2"><font face="夹发砰" size="2"> z T 925 hPa </div>
 </div>
 <div class="eI0">
  <div class="eI1">描述:</div>
  <div class="eI2">
850百帕位势高度(位势什米，实线)。<br>
850百帕温度(&degC，彩色虚线)。<br><br>
这幅图帮您识别用于确定锋面的等温线密集区。 此外，您还能根据
模式计算出的850百帕温度粗略地估计地面以上2米的最高温度。
不过，当出现(冬季)逆温时，这种方法不适用。<br><br>
    
  </div>
 </div>
 <div class="eI0">
  <div class="eI1">Spaghetti plots:</div>
  <div class="eI2">
are a method of viewing data from an ensemble forecast.<br>
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.<br>
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.<br>
<br>Spaghetti plot. (2009, July 7). In Wikipedia, The Free Encyclopedia. Retrieved 20:22, February 9, 2010, from <a href="http://en.wikipedia.org/w/index.php?title=Spaghetti_plot&amp;oldid=300824682" target="_blank">http://en.wikipedia.org/w/index.php?title=Spaghetti_plot&amp;oldid=300824682</a>
   </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 class="eI0">
  <div class="eI1">模式:</div>
  <div class="eI2"><h2>Times Series from the ECMWF Ensemble</h2></div>
 </div>
 <div class="eI0">
  <div class="eI1">æ›´æ–°:</div>
  <div class="eI2">2 times per day, from 10:00 and 23:00 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">0.5&deg; x 0.5&deg;</div>
 </div>
 <div class="eI0">
  <div class="eI1">参量:</div>
  <div class="eI2">Geopotential height (tens of m) at 925 hPa (solid line) and Temperature (&deg;C) at 925 hPa (coloured, dashed line) </div>
 </div>
 <div class="eI0">
  <div class="eI1">描述:</div>
  <div class="eI2">
This chart helps to identify areas of densely packed isotherms (lines of equal temperature) 
indicating a front. Aside from this you can use the modeled temperature in 925 hPa (2000 ft a.s.l.)
to make a rough estimate on the expected maximum temperature in 2m above the ground.
However, this method does not apply to (winter) inversions. 
    
  </div>
 </div>
 <div class="eI0">
  <div class="eI1">Spaghetti plots:</div>
  <div class="eI2">
are a method of viewing data from an ensemble forecast.<br>
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.<br>
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.<br>
<br>Spaghetti plot. (2009, July 7). In Wikipedia, The Free Encyclopedia. Retrieved 20:22, February 9, 2010, from <a href="http://en.wikipedia.org/w/index.php?title=Spaghetti_plot&amp;oldid=300824682" target="_blank">http://en.wikipedia.org/w/index.php?title=Spaghetti_plot&amp;oldid=300824682</a>
   </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>