Articles | Volume 12, issue 2
https://doi.org/10.5194/gmd-12-613-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-613-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
07 Feb 2019
Methods for assessment of models |
| 07 Feb 2019
| 07 Feb 2019
Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets
Grzegorz Muszynski
CORRESPONDING AUTHOR
Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, UK
National Energy Research Scientific Computing Center (NERSC), Lawrence Berkeley National Laboratory, CA 94720, USA
Karthik Kashinath
National Energy Research Scientific Computing Center (NERSC), Lawrence Berkeley National Laboratory, CA 94720, USA
Vitaliy Kurlin
Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, UK
Michael Wehner
National Energy Research Scientific Computing Center (NERSC), Lawrence Berkeley National Laboratory, CA 94720, USA
Computational Research Division, Lawrence Berkeley National Laboratory, CA 94720, USA
Prabhat
National Energy Research Scientific Computing Center (NERSC), Lawrence Berkeley National Laboratory, CA 94720, USA
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- Persistent homology of coarse-grained state-space networks A. Myers et al. 10.1103/PhysRevE.107.034303
- Automatic detection, classification, and long‐term investigation of temporal–spatial changes of atmospheric rivers in the Middle East N. Esfandiari & M. Rezaei 10.1002/joc.7674
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- Atmospheric rivers and Australian precipitation: Impact of detection algorithm choice S. Pradhan et al. 10.1016/j.jhydrol.2024.132586
- From Trees to Barcodes and Back Again: Theoretical and Statistical Perspectives L. Kanari et al. 10.3390/a13120335
- EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 1: Development of deep learning model J. Quinting & C. Grams 10.5194/gmd-15-715-2022
- The synergy of topological data analysis and machine learning for low-cost e-nose systems R. Shylaja et al. 10.1007/s00542-025-05859-4
- Predicting next day direction of stock price movement using machine learning methods with persistent homology: Evidence from Kuala Lumpur Stock Exchange M. Ismail et al. 10.1016/j.asoc.2020.106422
- Stratifying the space of barcodes using Coxeter complexes B. Brück & A. Garin 10.1007/s41468-022-00104-7
- Comparative analysis of Ball Mapper and conventional Mapper in investigating air pollutants’ behavior V. Madukpe et al. 10.1007/s10661-024-13477-2
- Increased amplitude of atmospheric rivers and associated extreme precipitation in ultra-high-resolution greenhouse warming simulations A. Nellikkattil et al. 10.1038/s43247-023-00963-7
- Bow echo alarm system using Topological Data Analysis H. Canot et al. 10.3934/ammc.2025002
- A topological perspective on weather regimes K. Strommen et al. 10.1007/s00382-022-06395-x
- TriCCo v1.1.0 – a cubulation-based method for computing connected components on triangular grids A. Voigt et al. 10.5194/gmd-15-7489-2022
- Meridional Heat Transport During Atmospheric Rivers in High‐Resolution CESM Climate Projections C. Shields et al. 10.1029/2019GL085565
- Using machine learning to identify novel hydroclimate states K. Marvel & B. Cook 10.1098/rsta.2021.0287
- Pushing the frontiers in climate modelling and analysis with machine learning V. Eyring et al. 10.1038/s41558-024-02095-y
- Topological data analysis via unsupervised machine learning for recognizing atmospheric river patterns on flood detection F. Ohanuba et al. 10.1016/j.sciaf.2021.e00968
- Application of topological data analysis to flood disaster management in Nigeria F. Ohanuba et al. 10.4491/eer.2022.411
- Deciphering Active Wildfires in the Southwestern USA Using Topological Data Analysis H. Kim & C. Vogel 10.3390/cli7120135
- Application of Topological Data Analysis to Multi-Resolution Matching of Aerosol Optical Depth Maps D. Ofori-Boateng et al. 10.3389/fenvs.2021.684716
- A Topological Data Analysis approach for retrieving Local Climate Zones patterns in satellite data C. Sena et al. 10.1016/j.envc.2021.100359
- SAR-UNet: A Model of Atmospheric River Recognition Network Based on Spatial Attention Mechanism 月. 罗 10.12677/CSA.2023.134081
- A Deep‐Learning Ensemble Method to Detect Atmospheric Rivers and Its Application to Projected Changes in Precipitation Regime Y. Tian et al. 10.1029/2022JD037041
- The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology J. Rutz et al. 10.1029/2019JD030936
- Objective identification of tropical cyclone‐induced remote moisture transport using digraphs S. Xiao et al. 10.1002/qj.4612
Latest update: 02 Apr 2025
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 a 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.,...
