Model:

Osvježeno:

1 times per day, from 00:00 UTC

Greenwich Mean Time:

12:00 UTC = 13:00 GMT

Razlučivost:

0.125° x 0.125° (India, South Asia)

Parametar:

Precipitation in mm (or litres per square metres)

Opis:

The precipitation map - updated every 6 hours - shows the modeled precipitation in mm. The precipitation areas are encircled by isohyets - lines with equal amounts of precipitation. However, modeling precipitation is still not very reliable. If you compare the modeled results with observed values you will realize that the model is nothing better than a first order approach. Yet this chart is of some use for forecasters.
Note: Based on international convention meteorologists use the metric system. 100 mm of precipitation is equivalent to roughly 4 inches.

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
-----------------------------------------------

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).