Model:

ECMWF: Global weather forecast model from the "European Centre for Medium-Range Weather Forecasts". ECMWF is now running its own Artificial Intelligence/Integrated Forecasting System (AIFS) as part of its experiment suite. These machine-learning-based models are very fast, and they produce a 10-day forecast with 6-hourly time steps in approximately one minute.

Osvježeno:
4 times per day, from 3:30, 09:30, 15:30 and 21:30 UTC
Greenwich Mean Time:
12:00 UTC = 14:00 BST
Razlučivost:
0.25° x 0.25°
Parametar:
Sea Level Pressure in hPa
Opis:
The surface chart (also known as surface synoptic chart) presents the distribution of the atmospheric pressure observed at any given station on the earth's surface reduced to sea level. You can read the positions of the controlling weather features (highs, lows, ridges or troughs) from the distribution of the isobars (lines of equal sea level pressure). The isobars define the pressure field. The pressure field is the dominating player in the weather system. Additionally, this map helps you to identify synoptic-scale waves and gives you a first estimate on meso-scale fronts.
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.
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).