<div class="eI0"> <div class="eI1">Model:</div> <div class="eI2"><h2><a href="http://www.meteofrance.fr/" target="_blank" target="_blank">Arome</a> from Meteo France</h2></div> </div> <div class="eI0"> <div class="eI1">Updated:</div> <div class="eI2">4 times per day, from 08:00, 14:00, 20:00, and 00:00 UTC</div> </div> <div class="eI0"> <div class="eI1">Greenwich Mean Time:</div> <div class="eI2">12:00 UTC = 08:00 EDT</div> </div> <div class="eI0"> <div class="eI1">Resolution:</div> <div class="eI2">0.025° x 0.025°</div> </div> <div class="eI0"> <div class="eI1">Parameter:</div> <div class="eI2">Isotachs 10m</div> </div> <div class="eI0"> <div class="eI1">Description:</div> <div class="eI2"> This map shows isotachs - lines on a given surface connecting points with equal wind speed - together with isobars - the line of equal atmospheric pressure at 10m above the ground. The unit used is kph (kilometers per hour). (<a href="javascript:NeuFenster()">wind-converter</a>)<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&oldid=300824682" target="_blank">http://en.wikipedia.org/w/index.php?title=Spaghetti_plot&oldid=300824682</a> </div> </div> <div class="eI0"> <div class="eI1">Arome:</div> <div class="eI2"><a href="http://www.cnrm.meteo.fr/spip.php" target="_blank">Arome</a> <br> The Arome forecasting system is a blend of the best components from the Méso-NH model, the Aladin model, and the IFS/Arpège data assimilation software. Its focus is on the numerical prediction of intense convective systems over mainland France by 2008. Other important weather phenomena will also begin to be reliably forecast, thanks to a high (kilometric) spatial resolution and the use of regional observing systems. The Arome software is designed to be accessible to a wide research community.</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://en.wikipedia.org/wiki/Numerical_weather_prediction" target="_blank">http://en.wikipedia.org/wiki/Numerical_weather_prediction</a>(as of Feb. 9, 2010, 20:50 UTC).<br> </div></div> </div>