<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="eI0">
  <div class="eI1">Updated:</div>
  <div class="eI2">1 times per day, from 00:00 UTC</div>
 </div>
 <div class="eI0">
  <div class="eI1">Greenwich Mean Time:</div>
  <div class="eI2">12:00 UTC = 17:00 IST</div>
 </div>
 <div class="eI0">
  <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="eI0">
  <div class="eI1">Parameter:</div>
  <div class="eI2">Geopotential in 500 hPa (solid, black lines) and Vorticity advection in 10<sup>5</sup>/(s*6h) (colored lines)</div>
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 <div class="eI0">
  <div class="eI1">Description:</div>
  <div class="eI2">
The two types of vorticity advection are positive (PVA) and negative vorticity
advection (NVA). <img border="0" src="//www.weatheronline.in/daten/expertgifs/v_adv_en.jpg" align="left">
The closed circles in the figure show the 500 hPa absolute vorticity
lines, the others the 500 hPa height lines. When an air parcel is moving from
an area higher vorticity to an area lower vorticity this is called: PVA
(red color). The other way around is called: NVA (blue color). PVA is
associated with upper-air divergence, i.e. upward vertical motion. NVA
is associated with down ward vertical motion. Therefore, PVA&nbsp; at 500
hPa is strongest above a surface low, while NVA at 500 hPa is strongest
above a surface high. <br>
In operational meteorology Vorticity advection maps are used to identify areas 
with vertical air motion to see where clouds, precipitation or clear conditions 
are likely to occur. Keep in mind, however, that PVA is not the same as upward
vertical motion. Here temperature advection is important too.<br>

    
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 <div class="eI0">
  <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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