<div class="eI0">
<div class="eI1">Model:</div>
<div class="eI2"><h2>ECMWF: Global Ensemble weather forecast from the "European Centre for Medium-Range Weather Forecasts"</h2></div>
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<div class="eI1">Zaktualizowano:</div>
<div class="eI2">2 times per day, from 10:00 and 23:00 UTC</div>
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<div class="eI0">
<div class="eI1">Czas uniwersalny:</div>
<div class="eI2">12:00 UTC = 13:00 CET</div>
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<div class="eI0">
<div class="eI1">Rozdzielczość:</div>
<div class="eI2">0.5° x 0.5°</div>
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<div class="eI0">
<div class="eI1">parametr:</div>
<div class="eI2">CAPE and vertical velocity at 700 hPa</div>
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<div class="eI0">
<div class="eI1">Opis:</div>
<div class="eI2">
The Convectively Available Potential Energy (CAPE) map - updated every 6 hours - shows the modelled convectively available
potential energy. CAPE represents the amount of buoyant energy (J/kg) available to accelerate a parcel vertically, or the amount of work
a parcel does on the environment. The higher the CAPE value, the more energy available to foster storm growth. The
potential energy can be converted to kinetic energy reflected in upward motion.
<BR>
It should be remembered that CAPE represents potential energy, and will only be used should a parcel be lifted to the level of free convection.
When values are above 3500 j/kg and storms do develop, they may build rapidly and quickly become severe.
Often these storms are referred to as "explosive storms" by chasers and professionals. In a high CAPE environment
storms that develop can usually be seen by the human eye as rising rapidly.
Higher CAPE typically involves stronger storms with a higher chance of large hail and other severe weather. Note that
CAPE is usually of lesser importance than the vertical shear environment for tornadoes. The probability of large hail increases
with CAPE, given at least moderate shear(values around 500-1000 J/kg are sufficient).
<BR>
CAPE is very sensitive to small differences in the moisture and temperature profiles. While the maps indicate
1000 J/kg CAPE at some location, a <a href="/cgi-bin/expertcharts?LANG=nz&MENU=0000000000&CONT=euro&MODELL=temps&MODELLTYP=4&BASE=-&VAR=temps&LKEY=UK&HH=6&ARCHIV=0&SHOW=1">skew-T thermodynamic diagram</a> at that location may indicate 500-1500 J/kg.
(Source: <a href="http://www.lightningwizard.com" target="_blank">The Lightning Wizard</a>)
<BR>
Table 1: Characteristic values for CAPE<BR>
<TABLE border=1>
<TR>
<TD><STRONG>CAPE value</STRONG></TD>
<TD><STRONG>Convective potential</STRONG></TD>
</TR>
<TR>
<TD>0 </TD>
<TD>Stable</TD>
</TR>
<TR>
<TD>0-1000</TD>
<TD>Marginally Unstable</TD>
</TR>
<TR>
<TD>1000-2500</TD>
<TD>Moderately Unstable</TD>
</TR>
<TR>
<TD>2500-3500</TD>
<TD>Very Unstable</TD>
</TR>
<TR>
<TD> 3500 + </TD>
<TD> Extremely Unstable </TD>
<TR>
</TABLE>
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<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">Numeryczna prognoza pogody - ocena stanu atmosfery w przyszłości na podstawie znajomości warunków początkowych oraz sił działających na powietrze. Numeryczna prognoza oparta jest na rozwiązaniu równań ruchu powietrza za pomocą ich dyskretyzacji i wykorzystaniu do obliczeń maszyn matematycznych.<br>
Początkowy stan atmosfery wyznacza się na podstawie jednoczesnych pomiarów na całym globie ziemskim. Równania ruchu cząstek powietrza wprowadza się zakładając, że powietrze jest cieczą. Równań tych nie można rozwiązać w prosty sposób. Kluczowym uproszczeniem, wymagającym jednak zastosowania komputerów, jest założenie, że atmosferę można w przybliżeniu opisać jako wiele dyskretnych elementów na które oddziaływają rozmaite procesy fizyczne. Komputery wykorzystywane są do obliczeń zmian w czasie temperatury, ciśnienia, wilgotności, prędkości przepływu, i innych wielkości opisujących element powietrza. Zmiany tych własności fizycznych powodowane są przez rozmaitego rodzaju procesy, takie jak wymiana ciepła i masy, opad deszczu, ruch nad górami, tarcie powietrza, konwekcję, wpÅ‚yw promieniowania słonecznego, oraz wpływ oddziaÅ‚ywania z innymi cząstkami powietrza. Komputerowe obliczenia dla wszystkich elementów atmosfery dają stan atmosfery w przyszłości czyli prognozę pogody.<br>
W dyskretyzacji równań ruchu powietrza wykorzystuje się metody numeryczne równań różniczkowych cząstkowych - stąd nazwa numeryczna prognoza pogody.<br>
<br>Zobacz Wikipedia, Numeryczna prognoza pogody, <a href="http://pl.wikipedia.org/wiki/Numeryczna_prognoza_pogody" target="_blank">http://pl.wikipedia.org/wiki/Numeryczna_prognoza_pogody</a> (dostęp lut. 9, 2010, 20:49 UTC).<br>
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