A machine learning based 24-h-technique for an area-wide rainfall retrieval using MSG SEVIRI data over Central Europe

A machine learning based 24-h-technique for an area-wide rainfall retrieval using MSG SEVIRI data over Central Europe

The aim of the present study was to develop a 24-h-technique for the process-related and quantitative estimation of precipitation in connection with extra-tropical cyclones in the mid-latitudes based on MSG SEVIRI data using the machine learning algorithm random forest. The algorithms and approach...

Täydet tiedot

Tallennettuna:
Bibliografiset tiedot
Päätekijä: Kühnlein, Meike
Muut tekijät: Nauss, Thomas (Prof.) (BetreuerIn (Doktorarbeit))
Aineistotyyppi: Dissertation
Kieli:englanti
Julkaistu: Philipps-Universität Marburg 2014
Aiheet:
Wolkeneigenschaften
Geowissenschaften
Satellite remote sensing
Wolken
Random forests
Cloud properties
Precipitation
Angewandte Geographie
Meteosat-8 SEVIRI
Satellitenfernerkundung
Maschinelles Lernverfahren
Maschine learning
Clouds
Niederschlag
Earth sciences
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