Model:

Güncelleme:

2 times per day, from 10:00 and 23:00 UTC

Greenwich Mean Time:

12:00 UTC = 14:00 EET

Resolution:

0.25° x 0.25°

Parametre:

Soaring Index

Tarife:

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

Table 2: Critical values for the Soaring Index

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 |

GME:

GME is the first operational weather forecast model which uses an icosahedral-hexagonal grid covering the globe. In comparison to traditional grid structures like latitude-longitude grids the icosahedral-hexagonal grid offers the advantage of a rather small variability of the area of the grid elements. Moreover, the notorious "pole-problem" of the latitude-longitude grid does not exist in the GME grid.

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).