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, Wehner, Michael and Prabhat,
(2019) Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets. GEOSCIENTIFIC MODEL DEVELOPMENT, 12 (2). pp. 613-628.

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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 version 5.1 of the Community Atmosphere Model version 5.1 (CAM5.1) and the reanalysis product of the second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2). An advantage of the proposed method is that it is threshold-free – there is no need to determine any threshold criteria for the detection method – when the spatial resolution of the climate model changes. 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: 25 Feb 2019 16:30
Last Modified: 14 Mar 2024 17:32
DOI: 10.5194/gmd-12-613-2019
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3033267