Model:

Updated:

1 times per day, from 00:00 UTC

Greenwich Mean Time:

12:00 UTC = 13:00 CET

Resolution:

0.125° x 0.125° (India, South Asia)

Parameter:

Maximum wind velocity of convective wind gusts

Description:

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_max ) in m/s according to Ivens (1987) are:

• if T_x - θ_w850 < 9°C

  w_max = 7.66 + 0.653⋅(θ_w850 - θ_w500 ) + 0.976⋅U₈₅₀
  

• if T_x - θ_w850 ≥ 9° C

w_max = 8.17 + 0.473⋅(θ_w850 - θ_w500 ) + (0.174⋅U₈₅₀ + 0.057⋅U₂₅₀)⋅√(T_x - θ_w850)

where

• T_x is the maximum day-time temperature at 2 m in K
• θ_wxxx the potential wet-bulb temperature at xxx hPa in K
• U_xxx the wind velocity at xxx hPa in m/s.

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: KNMI)

NCMRWF:

NCMRWF
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:
http://www.ncmrwf.gov.in/t254-model/t254_des.pdf
An brief overview of GFS is given below.
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Dynamics: Spectral, Hybrid sigma-p, Reduced Gaussian grids
Time integration: Leapfrog/Semi-implicit
Time filter: Asselin
Horizontal diffusion: 8th
order wavenumber dependent
Orography: Mean orography
Surface fluxes: Monin-obhukov Similarity
Turbulent fluxes: Non-local closure
SW Radiation; RRTM
LW Radiation: RRTM
Deep Convection: SAS
Shallow convection: Mass-flux based
Grid-scale condensation: Zhao Microphysics
Land Surface Processes: NOAH LSM
Cloud generation: Xu and Randal
Rainfall evaporation: Kessler
Air-sea interaction: Roughness length by Charnock
Gravity Wave Drag and mountain blocking: Based on Alpert
Sea-Ice model: Based on Winton
-----------------------------------------------

NWP:

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.

Wikipedia, Numerical weather prediction, http://en.wikipedia.org/wiki/Numerical_weather_prediction(as of Feb. 9, 2010, 20:50 UTC).