Modèle:

NCMRWF(National Centre for Medium Range Weather Forecasting from India)

Mise à jour:
1 times per day, from 00:00 UTC
Greenwich Mean Time:
12:00 UTC = 13:00 CET
Résolution:
0.125° x 0.125° (India, South Asia)
Paramètre:
Geopotential height (tens of m) at 925 hPa (solid line) and Temperature (°C) at 925 hPa (coloured, dashed line)
Description:
This chart helps to identify areas of densely packed isotherms (lines of equal temperature) indicating a front. Aside from this you can use the modeled temperature in 925 hPa (2000 ft a.s.l.) to make a rough estimate on the expected maximum temperature in 2m above the ground. However, this method does not apply to (winter) inversions.
Spaghetti plots:
are a method of viewing data from an ensemble forecast.
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.
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.

Spaghetti plot. (2009, July 7). In Wikipedia, The Free Encyclopedia. Retrieved 20:22, February 9, 2010, from http://en.wikipedia.org/w/index.php?title=Spaghetti_plot&oldid=300824682
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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NWP:
La prévision numérique du temps (PNT) est une application de la météorologie et de l'informatique. Elle repose sur le choix d'équations mathématiques offrant une proche approximation du comportement de l'atmosphère réelle. Ces équations sont ensuite résolues, à l'aide d'un ordinateur, pour obtenir une simulation accélérée des états futurs de l'atmosphère. Le logiciel mettant en œuvre cette simulation est appelé un modèle de prévision numérique du temps.


Prévision numérique du temps. (2009, décembre 12). Wikipédia, l'encyclopédie libre. Page consultée le 20:48, février 9, 2010 à partir de http://fr.wikipedia.org/w/index.php?title=Pr%C3%A9vision_num%C3%A9rique_du_temps&oldid=47652746.