Revealing Multimodality in Ensemble Weather Prediction

Galmiche, Natacha; Hauser, Helwig; Spengler, Thomas; Spensberger, Clemens; Brun, Morten; Blaser, Nello

Revealing Multimodality in Ensemble Weather Prediction

Files

007-011.pdf (2.18 MB)

1004-file1.zip (12.51 MB)

Date

2021

Authors

Galmiche, Natacha
Hauser, Helwig
Spengler, Thomas
Spensberger, Clemens
Brun, Morten
Blaser, Nello

Publisher

The Eurographics Association

Abstract

Ensemble methods are widely used to simulate complex non-linear systems and to estimate forecast uncertainty. However, visualizing and analyzing ensemble data is challenging, in particular when multimodality arises, i.e., distinct likely outcomes. We propose a graph-based approach that explores multimodality in univariate ensemble data from weather prediction. Our solution utilizes clustering and a novel concept of life span associated with each cluster. We applied our method to historical predictions of extreme weather events and illustrate that our method aids the understanding of the respective ensemble forecasts.

        @inproceedings{10.2312:mlvis.20211073
,
booktitle = {Machine Learning Methods in Visualisation for Big Data
},
editor = {Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
},
title = {{Revealing Multimodality in Ensemble Weather Prediction
}},
author = {Galmiche, Natacha and 
Hauser, Helwig and 
Spengler, Thomas and 
Spensberger, Clemens and 
Brun, Morten and 
Blaser, Nello
},
year = {2021
},
publisher = {The Eurographics Association
},
ISBN = {978-3-03868-146-5
},
DOI = {10.2312/mlvis.20211073
}
}