<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> </div> <div class="eI0"> <div class="eI1">БнбнЭщуз:</div> <div class="eI2">1 times per day, from 00:00 UTC</div> </div> <div class="eI0"> <div class="eI1">МЭупт чсьнпт ГксЯнпхйфт:</div> <div class="eI2">12:00 UTC = 14:00 EET</div> </div> <div class="eI0"> <div class="eI1">Resolution:</div> <div class="eI2">0.125° x 0.125° (India, South Asia)</div> </div> <div class="eI0"> <div class="eI1">РбсЬмефспт:</div> <div class="eI2">Geopotential height Temperature at 500 hPa </div> </div> <div class="eI0"> <div class="eI1">Description:</div> <div class="eI2"> Geopotential height at 500 hPa (solid line)<br> Temperature at 500 hPa (colored, dashed)<br><br> The maps show the predominant tropospheric waves (trough or ridge). They virtually control the ''weather'' (dry, warm / wet, cold) and the long waves drive the smaller synoptic waves. Thus, this upper-level chart illustrates the dynamics of our atmosphere. </div> </div> <div class="eI0"> <div class="eI1">Spaghetti plots:</div> <div class="eI2"> are a method of viewing data from an ensemble forecast.<br> A meteorological variable e.g. pressure, temperature is drawn on a chart for a number of slightly different model runs from an ensemble. The model can then be stepped forward in time and the results compared and be used to gauge the amount of uncertainty in the forecast.<br> If there is good agreement and the contours follow a recognisable pattern through the sequence then the confidence in the forecast can be high, conversely if the pattern is chaotic i.e resembling a plate of spaghetti then confidence will be low. Ensemble members will generally diverge over time and spaghetti plots are quick way to see when this happens.<br> <br>Spaghetti plot. (2009, July 7). In Wikipedia, The Free Encyclopedia. Retrieved 20:22, February 9, 2010, from <a href="http://en.wikipedia.org/w/index.php?title=Spaghetti_plot&oldid=300824682" target="_blank">http://en.wikipedia.org/w/index.php?title=Spaghetti_plot&oldid=300824682</a> </div> </div> <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> ----------------------------------------------- <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> </div>