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

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.
