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

Kühnlein, Meike

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...

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Opis bibliograficzny
1. autor:	Kühnlein, Meike
Kolejni autorzy:	Nauss, Thomas (Prof.) (Promotor doktoranta)
Format:	Dissertation
Język:	angielski
Wydane:	Philipps-Universität Marburg 2014
Hasła przedmiotowe:	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
Dostęp online:	PDF pełnotekstowe
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A machine learning based 24-h-technique for an area-wide rainfall retrieval using MSG SEVIRI data over Central Europe

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