Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets

Journal Article · 06 February 2019 · Geoscientific Model Development (Online)

DOI: https://doi.org/10.5194/gmd-12-613-2019 · OSTI ID:1529262

Muszynski, Grzegorz ^[1]; Kashinath, Karthik ^[2]; Kurlin, Vitaliy ^[3]; Wehner, Michael  ^[4]

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1. Univ. of Liverpool (United Kingdom). Dept .of Computer Science; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
3. Univ. of Liverpool (United Kingdom). Dept .of Computer Science
4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Div.

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.

• Research Organization: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
• Contributing Organization: Prabhat
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1529262
• Journal Information: Geoscientific Model Development (Online), Journal Name: Geoscientific Model Development (Online) Journal Issue: 2 Vol. 12; ISSN 1991-9603
• Publisher: European Geosciences UnionCopyright Statement
• Country of Publication: United States
• Language: English