Modelo:

Actualizado:

1 times per day, from 00:00 UTC

Tiempo medio de Greenwich:

12:00 UTC = 13:00 CET

Resolutión:

0.125° x 0.125° (India, South Asia)

Parámetro:

Soaring Index

Descripción:

The Soaring Index map - updated every 6 hours - shows the modelled lift rate by thermals (convective clouds). The index is based on weather information between 5 000 feet (1 524 metres) and 20 000 feet (6 096 metres) and is expressed in Kelvin.
Table 1: Characteristic values for Soaring Index for soaring

Soaring Index	Soaring Conditions
Below -10   -10 to 5   5 to 20   Above 20	Poor   Moderate   Good   Excellent*

Table 2: Critical values for the Soaring Index

Soaring Index	Convective potential
15-20	Isolated showers, 20% risk for thunderstorms
20-25	Occasionally showers, 20-40% risk for thunderstorms
25-30	Frequent showers, 40-60% risk for thunderstorms.
30-35	60-80% risk for thunderstorms.
35 +	>80% risk for thunderstorms

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.
------------------------------------------------------
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).