AbstractsBusiness Management & Administration

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

by Meike Kühnlein




Institution: Philipps-Universität Marburg
Department: Fachbereich Geographie
Degree: PhD
Year: 2014
Record ID: 1098900
Full text PDF: http://archiv.ub.uni-marburg.de/diss/z2014/0475


Abstract

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 approaches needed were successfully developed and implemented within three working packages: (WP1) The cloud property retrieval SLALOM, first developed for Terra MODIS, was successfully transferred and adapted to the specific requirements of the SEVIRI system and an extensive validation study was carried out. The cloud optical properties retrieved by SLALOM, namely cloud effective radius and cloud optical thickness that were needed for satellitebased rainfall estimation in WP2 and WP3, were compared against the well known and validated NASA MODIS cloud property product (MODIS 06) as well as the cloud optical depth product (2B-TAU) of CloudSat. The suitability of SLALOM has been shown over the North Atlantic and over the European continent (chapter 3). (WP2) A new 24-h-technique for rainfall rate assignment was developed for MSG SEVIRI using the machine learning algorithm random forest as fundamental prediction algorithm. Based on the precipitation processes in connection with extra-tropical cyclones, rainfall rates were assigned to advectivestratiform and convective precipitating areas by means of individual RF models. As predictor variables for the RF models satellite-based information on cloud top height, cloud top temperature, cloud phase and cloud water path were chosen. The different illumination conditions (daytime, twilight and night-time) were taken into account with a proper SEVIRI spectral channel selection as surrogates for theses cloud physical parameters. The development was realised in three steps: First, an extensive tuning study was carried out to customise each of the RF models. Secondly, the RF models were trained using the optimum model parameter values found in the tuning study. Finally, the final RF models were used to predict rainfall rates using an independent validation data set and the results were validated against co-located rainfall rates observed by the RADOLAN RW product of the DWD. The outstanding validation results during all times of the day confirmed the ability of RF as tool for the rainfall rate assignment technique from MSG SEVIRI data (chapter 4). (WP3) A new coherent daytime, twilight and night-time rainfall retrieval was developed for MSG SEVIRI. The technique aims to retrieve rainfall rates for precipitation events in connection with extra-tropical cyclones in the midlatitudes in a continuous manner resulting in a 24 hour prediction. Based on the dominant precipitation processes, the proposed rainfall…