Topological Data Analysis and Machine Learning for Recognizing Atmospheric River Patterns in Large Climate Datasets

Muszynski, Grzegorz, Kashinath, Karthik, Kurlin, Vitaliy ORCID: 0000-0001-5328-5351 and Wehner, Michael ORCID: 0000-0001-5991-0082 (2018) Topological Data Analysis and Machine Learning for Recognizing Atmospheric River Patterns in Large Climate Datasets. Geoscientific Model Development. pp. 1-24.

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Abstract

<jats:p>Abstract. Identifying weather patterns that frequently lead to extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Here we propose an automated method for recognizing atmospheric rivers (ARs) in climate data using topological data analysis and machine learning. The method provides useful information about topological features (shape characteristics) and statistics of ARs. We illustrate this method by applying it to outputs of 5 version 5.1 of the Community Atmosphere Model (CAM5.1) and reanalysis product of the second Modern-Era Retrospective Analysis for Research &amp;amp; Applications (MERRA-2). An advantage of the proposed method is that it is threshold-free. Hence this method may be useful in evaluating model biases in calculating AR statistics. Further, the method can be applied to different climate scenarios without tuning since it does not rely on threshold conditions. We show that the method is suitable for rapidly analyzing large amounts of climate model and reanalysis output data. </jats:p>

Item Type:	Article
Uncontrolled Keywords:	13 Climate Action
Depositing User:	Symplectic Admin
Date Deposited:	17 Jan 2019 12:06
Last Modified:	15 Mar 2024 20:55
DOI:	10.5194/gmd-2018-53
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3031139