<div class="eI0">
  <div class="eI1">Modell:</div>
  <div class="eI2"><h2><a href="http://dd.meteo.gc.ca/doc/LICENCE_GENERAL.txt" target="_blank">CMC</a>: "Data Source: Environment and Climate Change Canada"</h2></div>
 </div>
 <div class="eI0">
  <div class="eI1">Aktualisierung:</div>
  <div class="eI2">2 times per day, from 10:00 and 23:00 UTC</div>
 </div>
 <div class="eI0">
  <div class="eI1">Greenwich Mean Time:</div>
  <div class="eI2">12:00 UTC = 13:00 MEZ</div>
 </div>
 <div class="eI0">
  <div class="eI1">Aufl&ouml;sung:</div>
  <div class="eI2">1.0&deg; x 1.0&deg;</div>
 </div>
 <div class="eI0">
  <div class="eI1">Parameter:</div>
  <div class="eI2">Niederschlag &uuml;ber Europa in mm</div>
 </div>
 <div class="eI0">
  <div class="eI1">Beschreibung:</div>
  <div class="eI2">
Die Karte &#34;Niederschlag&#34; zeigt 6-st&uuml;ndige Modell-Niederschl&auml;ge
in mm &uuml;ber dem gesamten Nordatlantik-Europa-Ausschnitt. Es
handelt sich um eine Isoliniendarstellung. Ein Vergleich mit
den gemessenen Niederschl&auml;gen zeigt, da&szlig; die Modellwerte nur
qualitative Anhaltspunkte liefern k&ouml;nnen. Trotzdem ist die
Karte als zus&auml;tzliche Hilfe f&uuml;r den Prognostiker wichtig.
    
  </div>
 </div>
 <div class="eI0">
  <div class="eI1">Ensemble forecasting:</div>
  <div class="eI2">
is a numerical prediction method that is used to attempt to generate a representative sample of the possible future states of a dynamical system. Ensemble forecasting is a form of Monte Carlo analysis: multiple numerical predictions are conducted using slightly different initial conditions that are all plausible given the past and current set of observations, or measurements. Sometimes the ensemble of forecasts may use different forecast models for different members, or different formulations of a forecast model. The multiple simulations are conducted to account for the two sources of uncertainty in weather forecast models: (1) the errors introduced by chaos or sensitive dependence on the initial conditions; and (2) errors introduced because of imperfections in the model, such as the finite grid spacings.<br>
Considering the problem of numerical weather prediction, ensemble predictions are now commonly made at most of the major operational weather prediction facilities worldwide, including the National Centers for Environmental Prediction (US), the European Centre for Medium-Range Weather Forecasts (ECMWF), the United Kingdom Met Office, Meteo France, Environment Canada, the Japanese Meteorological Agency, the Bureau of Meteorology (Australia), the China Meteorological Administration, the Korea Meteorological Administration, and CPTEC (Brazil). Experimental ensemble forecasts are made at a number of universities, such as the University of Washington, and ensemble forecasts in the US are also generated by the US Navy and Air Force.<br>
Ideally, the relative frequency of events from the ensemble could be used directly to estimate the probability of a given weather event. For example, if 30 of 50 members indicated greater than 1 cm rainfall during the next 24 h, the probability of exceeding 1 cm could be estimated to be 60 percent. The forecast would be considered reliable if, considering all the situations in the past when a 60 percent probability was forecast, on 60 percent of those occasions did the rainfall actually exceed 1 cm. This is known as reliability or calibration. In practice, the probabilities generated from operational weather ensemble forecasts are not highly reliable, though with a set of past forecasts (reforecasts or hindcasts) and observations, the probability estimates from the ensemble can be adjusted to ensure greater reliability. Another desirable property of ensemble forecasts is sharpness. Provided that the ensemble is reliable, the more an ensemble forecast deviates from the climatological event frequency and issues 0 percent or 100 percent forecasts of an event, the more useful the forecast will be. However, sharp forecasts that are unaccompanied by high reliability will generally not be useful. Forecasts at long leads will inevitably not be particularly sharp, for the inevitable (albeit usually small) errors in the initial condition will grow with increasing forecast lead until the expected difference between two model states is as large as the difference between two random states from the forecast model's climatology.<br>
There are various ways of viewing the data such as spaghetti plots, ensemble means or Postage Stamps where a number of different results from the models run can be compared.<br>
<br>Wikipedia, Ensemble forecasting, <a href="http://en.wikipedia.org/wiki/Ensemble_forecasting" target="_blank">http://en.wikipedia.org/wiki/Ensemble_forecasting</a> (optional description here) (as of Feb. 9, 2010, 20:30 UTC).<br>
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 <div class="eI0">
  <div class="eI1">NWP:</div>
  <div class="eI2">Numerische Wettervorhersagen sind rechnergest&uuml;tzte Wettervorhersagen. Aus dem Zustand der Atmosph&auml;re zu einem gegebenen Anfangszeitpunkt wird durch numerische L&ouml;sung der relevanten Gleichungen der Zustand zu sp&auml;teren Zeiten berechnet. Diese Berechnungen umfassen teilweise mehr als 14 Tage und sind die Basis aller heutigen Wettervorhersagen.<br><br>
In einem solchen numerischen Vorhersagemodell wird das Rechengebiet mit Gitterzellen und/oder durch eine spektrale Darstellung diskretisiert, so dass die relevanten physikalischen Gr&ouml;&szlig;en, wie vor allem Temperatur, Luftdruck, Windrichtung und Windst&auml;rke, im dreidimensionalen Raum und als Funktion der Zeit dargestellt werden k&ouml;nnen. Die physikalischen Beziehungen, die den Zustand der Atmosph&auml;re und seine Ver&auml;nderung beschreiben, werden als System partieller Differentialgleichungen modelliert. Dieses dynamische System wird mit Verfahren der Numerik, welche als Computerprogramme meist in Fortran implementiert sind, n&auml;herungsweise gel&ouml;st. Aufgrund des gro&szlig;en Aufwands werden hierf&uuml;r h&auml;ufig Supercomputer eingesetzt.<br><br>
<br>Seite „Numerische Wettervorhersage“. In: Wikipedia, Die freie Enzyklop&auml;die. Bearbeitungsstand: 21. Oktober 2009, 21:11 UTC. URL: <a href="http://de.wikipedia.org/w/index.php?title=Numerische_Wettervorhersage&amp;oldid=65856709" target="_blank">http://de.wikipedia.org/w/index.php?title=Numerische_Wettervorhersage&oldid=65856709</a> (Abgerufen: 9. Februar 2010, 20:46 UTC) <br>
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