<div class="eI0">
<div class="eI1">Model:</div>
<div class="eI2"><h2><a href="http://brams.cptec.inpe.br/" target="_blank" target="_blank">BRAMS</a>(Brazilian developments on the Regional Atmospheric Modelling System)</h2></div>
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<div class="eI0">
<div class="eI1">Ververst:</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 = 13:00 MET</div>
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<div class="eI0">
<div class="eI1">Resolutie:</div>
<div class="eI2">0.5° x 0.5°</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">Beschrijving:</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">BRAMS:</div>
<div class="eI2"><a href="http://brams.cptec.inpe.br/" target="_blank">BRAMS</a> <br>
The BRAMS Brazilian developments on the Regional Atmospheric Modelling System is a project originaly developed by ATMET, IME/USP, IAG/USP and CPTEC/INPE, funded by FINEP (Brazilian Funding Agency), aimed to produce a new version of RAMS tailored to the tropics. The main objective is to provide a single model to Brazilian Regional Weather Centers. The BRAMS/RAMS model is a multipurpose, numerical prediction model designed to simulate atmospheric circulations spanning in scale from hemispheric scales down to large eddy simulations (LES) of the planetary boundary layer. After the version 4.2 the code is developed only by CPTEC/INPE team developers. The BRAMS uses the Cathedral model, but code developed between releases is restricted to an exclusive group of software developers. The software is under CC-GNU GPL license and some parts of code may receives other restricted licenses. The BRAMS incorporate a tracer transport model and chemical model (CCATT) and becomes a unified version, BRAMS 5.x.
</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>
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