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Discussion papers | Copyright
https://doi.org/10.5194/gmd-2018-53
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 07 May 2018

Methods for assessment of models | 07 May 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

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

Grzegorz Muszynski1,2, Karthik Kashinath2, Vitaliy Kurlin1, Michael Wehner2, and Prabhat2 Grzegorz Muszynski et al.
  • 1Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, United Kingdom
  • 2Lawrence Berkeley National Laboratory, Berkeley, California, 94720, United States

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 & 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.

Grzegorz Muszynski et al.
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Latest update: 18 Oct 2018
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Short summary
We present the automated method for recognizing Atmospheric Rivers in climate data, i.e. climate model output and reanalysis product. The method is based on Topological Data Analysis and Machine Learning, both of which are powerful tools that the climate science community often does not use. An advantage of the proposed method is that it is free of selection of subjective threshold conditions on some physical variable. This method is also suitable for rapidly analyzing large amounts of data.
We present the automated method for recognizing Atmospheric Rivers in climate data, i.e. climate...
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