Details

Autor(en) / Beteiligte

• Bartok, Juraj
• Habala, Ondrej
• Bednar, Peter
• Gazak, Martin
• Hluchý, Ladislav

Titel

Data mining and integration for predicting significant meteorological phenomena

Ist Teil von

• Procedia computer science, 2010-05, Vol.1 (1), p.37-46

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2010

Quelle

Elsevier ScienceDirect Journals

Beschreibungen/Notizen

• This paper describes the planned contribution of the project Data Mining Meteo (DMM) to the research of parametrized models and methods for detection and prediction of significant meteorological phenomena, especially fog and low cloud cover. The project is expected to cover methods for integration of distributed meteorological data necessary for running the prediction models, training models and then mining the data in order to be able to efficiently and quickly predict even randomly occurring phenomena. We present the methods and technologies we will use for integration of the input data, distributed on different vendors’ servers. The meteorological detection and prediction methods are based on statistical and climatological methods combined with knowledge discovery — data mining of meteorological data (SYNOP, METAR messages, weather radar imagery, “raw” meteorological data from stations, satellite imagery and results of common meteorological prediction models).