<div class="eI0">
  <div class="eI1">Model:</div>
  <div class="eI2"><h2><a href="http://www.ncmrwf.gov.in/" target="_blank" target="_blank">NCMRWF</a>(National  Centre  for  Medium  Range  Weather  Forecasting from India)</h2></div>
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  <div class="eI1">Updated:</div>
  <div class="eI2">1 times per day, from 00:00 UTC</div>
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  <div class="eI1">Greenwich Mean Time:</div>
  <div class="eI2">12:00 UTC = 12:00 GMT</div>
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  <div class="eI1">Resolution:</div>
  <div class="eI2">0.125&deg; x 0.125&deg; (India, South Asia)</div>
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  <div class="eI1">Parameter:</div>
  <div class="eI2">Wet bulb freezing level</div>
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  <div class="eI1">Description:</div>
  <div class="eI2">

(Abbrev. WBZ) - the height where the wet-bulb temperature goes below 0&deg;C. It is important because WBZ heights between 7000 ft and 10,500 ft (above ground level) correlate well with large hail at the surface when storms develop in an airmass primed for strong convection. Higher values infer mid and upper level stability and also indicate a large melting area for falling hail. Lower WBZ heights indicate that the low level atmosphere is often too cool and stable to support large hail.
(Source: <a href="http://www.weather.gov/" title="National Weather Service" target="_blank">National Weather Service</a>)

    
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  <div class="eI1">NCMRWF:</div>
  <div class="eI2"><a href="http://www.ncmrwf.gov.in/" target="_blank">NCMRWF</a> <br>
This modeling system is an up-graded version of NCEP GFS (as per 28 July 2010). A general description of the modeling system can be found in the following link:<br>
http://www.ncmrwf.gov.in/t254-model/t254_des.pdf<br>
An brief overview of GFS is given below. <br>
------------------------------------------------------ <br>
Dynamics: Spectral, Hybrid sigma-p, Reduced Gaussian grids  <br>
Time integration: Leapfrog/Semi-implicit <br>
Time filter: Asselin <br>
Horizontal diffusion: 8th<br>
 order wavenumber dependent <br>
Orography: Mean orography <br>
Surface fluxes: Monin-obhukov Similarity <br>
Turbulent fluxes: Non-local closure <br>
SW Radiation; RRTM <br>
LW Radiation: RRTM <br>
Deep Convection: SAS <br>
Shallow convection: Mass-flux based <br>
Grid-scale condensation: Zhao Microphysics <br>
Land Surface Processes: NOAH LSM <br>
Cloud generation: Xu and Randal <br>
Rainfall evaporation: Kessler <br>
Air-sea interaction: Roughness length by Charnock <br>
Gravity Wave Drag and mountain blocking: Based on Alpert <br>
Sea-Ice model: Based on Winton <br>
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 <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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