Model:

Güncelleme:

4 times per day, from 08:00, 14:00, 20:00, and 00:00 UTC

Greenwich Mean Time:

12:00 UTC = 14:00 EET

Resolution:

0.1° x 0.1° (Avrupa)
0.5° x 0.5°

Parametre:

Deniz Seviyesi Basıncı (hPa)

Tarife:

Yüzey haritası (Ayrıca sinoptik harita olarak da bilinir.) herhangi bir istasyonda ölçülen, deniz seviyesine indirgenmiş atmosfer basıncını göstermektedir. Verilen pozisyonlari, izobarlardan (deniz seviyesine indirgenen) kontrollendirilmiş, meteorolojik özellikleri(yüksekler,alçaklar, girintiler veya çıkıntılar) okuyabilirsiniz.

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

Arpège:

Arpège
ARPEGE uses a set of primitive equations with a triangular spectral truncation on the horizontal, with a variable horizontal resolution, with a finite elements representation on the vertical and a “sigma-pressure” hybrid vertical coordinate. It also utilizes a temporal two time level semi-implicit semi-lagrangian scheme. The horizontal resolution of the ARPEGE model is around 7.5km over France and 37km over the Antipodes. It has 105 vertical levels, with the first level at 10m above the surface and an upper level at around 70km. Its time step is of 360 seconds.

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).