<div class="eI0">
<div class="eI1">Model:</div>
<div class="eI2"><h2>FMI (Hirlam Model from finnish meteorological institute)</h2></div>
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<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 = 07:00 EST</div>
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<div class="eI0">
<div class="eI1">Resolution:</div>
<div class="eI2">0.068025° x 0.068025°</div>
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<div class="eI0">
<div class="eI1">Parameter:</div>
<div class="eI2">Maximum wind velocity of convective wind gusts</div>
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<div class="eI0">
<div class="eI1">Description:</div>
<div class="eI2">
The method of Ivens (1987) is used by the forecasters at KNMI to predict the
maximum wind velocity associated with heavy showers or thunderstorms. The
method of Ivens is based on two multiple regression equations that were
derived using about 120 summertime cases (April to September) between 1980 and 1983.
The upper-air data were derived from the soundings at De Bilt, and
observations of
thunder by synop stations were used as an indicator of the presence of
convection.
The regression equations for the maximum wind velocity (w<sub>max</sub> ) in m/s
according
to Ivens (1987) are:<br>
<ul type="square">
<li>if T<sub>x</sub> - θ<sub>w850</sub> < 9°C
<dl>
<dd>w<sub>max</sub> = 7.66 + 0.653⋅(θ<sub>w850</sub> - θ<sub>w500</sub> ) + 0.976⋅U<sub>850</sub><br></dd>
</dl>
<li>if T<sub>x</sub> - θ<sub>w850</sub> ≥ 9° C</li>
<dl>
<dd>w<sub>max</sub> = 8.17 + 0.473⋅(θ<sub>w850</sub> - θ<sub>w500</sub> ) + (0.174⋅U<sub>850</sub> + 0.057⋅U<sub>250</sub>)⋅√(T<sub>x</sub> - θ<sub>w850</sub>)<br></dd>
</dl>
</ul>
<br>
where
<ul>
<li>T<sub>x</sub> is the maximum day-time temperature at 2 m in K
<li>θ<sub>wxxx</sub> the potential wet-bulb temperature at xxx hPa in K
<li>U<sub>xxx</sub> the wind velocity at xxx hPa in m/s.
</ul>
The amount of negative buoyancy, which is estimated in these
equations
by the difference of the potential wet-bulb temperature at 850 and at 500 hPa,
and horizontal wind velocities at one or two fixed altitudes are used to estimate
the maximum wind velocity. The effect of precipitation loading is not taken into
account by the method of Ivens.
(Source: <a href="http://www.knmi.nl/" target="_blank">KNMI</a>)
</div>
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<div class="eI0">
<div class="eI1">FMI:</div>
<a href="http://ilmatieteenlaitos.fi" target="_blank">FMI</a> <br>
<div class="eI2"> At the Finnish Meteorological Institute, results from several numerical weather prediction models are utilized. Most of all, these include products from the European Centre of Medium Range Forecasts (ECMWF), located in Reading in the United Kingdom. For shorter range forecasts, more detailed forecasts are produced in-house using a limited area models (LAMs) called HIRLAM and HARMONIE, which are being developed by FMI as an international co-operation programme with a number of European countries.<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>
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