Model:

Updated:

1 times per day, from 00:00 UTC

Greenwich Mean Time:

12:00 UTC = 07:00 EST

Resolution:

0.125° x 0.125° (India, South Asia)

Parameter:

Geopotential height Temperature at 500 hPa

Description:

Geopotential height at 500 hPa (solid line)

Temperature at 500 hPa (colored, dashed)

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.

Temperature at 500 hPa (colored, dashed)

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.

Cluster of Ensemble Members:

20 members of an ensemble run are divided into different clusters which means groups with similar members according to the hierarchical "Ward method"
The average surface pressure of all members in each cluster are computed and shown as isobares.
The number of members in each cluster determines the probability of the forecast (see percentage)

Dendrogram:

A dendrogram shows the multidimensional distances between objects in a tree-like structure. Objects that are closest in a multidimensional data space are connected by a horizontal line forming a cluster. The distance between a given pair of objects (or clusters) are indicated by the height of the horizontal line.
[http://www.statistics4u.info/fundstat_germ/cc_dendrograms]. The greater the distance the bigger the differences.

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

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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).

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