• Chen, X., L. R. Leung, M. Wigmosta, and M. Richmond, 2019: Impact of atmospheric rivers on surface hydrological processes in western U.S. watersheds. J. Geophys. Res. Atmos., 124, 88968916, https://doi.org/10.1029/2019JD030468.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, Y., J. Lu, and L. R. Leung, 2016: Uncertainties in projecting future changes in atmospheric rivers and their impacts on heavy precipitation over Europe. J. Climate, 29, 67116726, https://doi.org/10.1175/JCLI-D-16-0088.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gorodetskaya, I. V., M. Tsukernik, K. Claes, M. F. Ralph, W. D. Neff, and N. P. M. Van Lipzig, 2014: The role of atmospheric rivers in anomalous snow accumulation in East Antarctica. Geophys. Res. Lett., 41, 61996206, https://doi.org/10.1002/2014GL060881.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, B., N. P. Molotch, D. E. Waliser, E. J. Fetzer, and P. J. Neiman, 2010: Extreme snowfall events linked to atmospheric rivers and surface air temperature via satellite measurements. Geophys. Res. Lett., 37, L20401, https://doi.org/10.1029/2010GL044696.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kurth, T., and et al. , 2018: Exascale deep learning for climate analytics. Proc. Int. Conf. for High Performance Computing, Networking, Storage, and Analysis, Piscataway, NJ, IEEE, 51, https://dl.acm.org/doi/10.5555/3291656.3291724.

    • Search Google Scholar
    • Export Citation
  • Mudigonda, M., and et al. , 2017: Segmenting and tracking extreme climate events using neural networks. 31st Conf. on Neural Information Processing System, Long Beach, CA, NIPS, https://dl4physicalsciences.github.io/files/nips_dlps_2017_20.pdf.

    • Search Google Scholar
    • Export Citation
  • Muszynski, G., K. Kashinath, V. Kurlin, and M. Wehner, 2019: Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets. Geosci. Model Dev., 12, 613628, https://doi.org/10.5194/gmd-12-613-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., P. J. Neiman, G. A. Wick, S. I. Gutman, M. D. Dettinger, D. R. Cayan, and A. B. White, 2006: Flooding on California’s Russian River: Role of atmospheric rivers. Geophys. Res. Lett., 33, L13801, https://doi.org/10.1029/2006GL026689.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., J. J. Rutz, J. M. Cordeira, M. Dettinger, M. Anderson, D. Reynolds, L. J. Schick, and C. Smallcomb, 2019a: A scale to characterize the strength and impacts of atmospheric rivers. Bull. Amer. Meteor. Soc., 100, 269289, https://doi.org/10.1175/BAMS-D-18-0023.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., and et al. , 2019b: ARTMIP-early start comparison of atmospheric river detection tools: How many atmospheric rivers hit northern California’s Russian River watershed? Climate Dyn ., 52, 49734994, https://doi.org/10.1007/s00382-018-4427-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rutz, J. J., and et al. , 2019: The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying uncertainties in atmospheric river climatology. J. Geophys. Res. Atmos., 124, 13 77713 802, https://doi.org/10.1029/2019JD030936.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shields, C. A., and et al. , 2018: Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Project goals and experimental design. Geosci. Model Dev., 11, 24552474, https://doi.org/10.5194/gmd-11-2455-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shields, C. A., J. J. Rutz, L. R. Leung, F. M. Ralph, M. Wehner, T. O’Brien, and R. Pierce, 2019: Defining uncertainties through comparison of atmospheric river tracking methods. Bull. Amer. Meteor. Soc., 100, ES93ES96, https://doi.org/10.1175/BAMS-D-18-0200.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 284 284 67
PDF Downloads 292 292 84

Detection Uncertainty Matters for Understanding Atmospheric Rivers

View More View Less
  • 1 Lawrence Berkeley National Laboratory, Berkeley, California
  • | 2 University of Michigan, Ann Arbor, Michigan
  • | 3 National Center for Atmospheric Research, Boulder, Colorado
  • | 4 National Weather Service/Western Region Headquarters/Science and Technology Infusion Division, Salt Lake City, Utah
  • | 5 MeteoGalicia, Galicia, Spain
  • | 6 Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California
  • | 7 University of California, Berkeley, Berkeley, California
  • | 8 Purdue University, West Lafayette, Indiana
  • | 9 Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California
  • | 10 University of California, Los Angeles, Los Angeles, California
  • | 11 Center for Environmental and Marine Studies (CESAM), Department of Physics, University of Aveiro, Aveiro, Portugal
  • | 12 University of California, Davis, Davis, California
  • | 13 Lawrence Berkeley National Laboratory, Berkeley, California
  • | 14 Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California
  • | 15 University of California, Berkeley, Berkeley, California
  • | 16 Shirshov Institute of Oceanology, Russian Academy of Sciences, Moscow, Russia
  • | 17 Yale University, New Haven, Connecticut
  • | 18 University of California, Davis, Davis, California
  • | 19 Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California
  • | 20 University of California, Santa Cruz, Santa Cruz, California
  • | 21 Lawrence Berkeley National Laboratory, Berkeley, California
  • | 22 Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
  • | 23 Center for Hydrometeorology and Remote Sensing, University of California, Irvine, Irvine, California
  • | 24 Purdue University, West Lafayette, Indiana
  • | 25 University of California, Davis, Davis, California
  • | 26 Lawrence Berkeley National Laboratory, Berkeley, California
  • | 27 University of California, Berkeley, Berkeley, California
  • | 28 Pacific Northwest National Laboratory, Richland, Washington
  • | 29 Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California
  • | 30 Lawrence Berkeley National Laboratory, Berkeley, California
© Get Permissions
Restricted access

CURRENT AFFILIATIONS: T. A. O’Brien—Earth and Atmospheric Sciences, Indiana University, Bloomington, Indiana; Patricola—Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa; J. O’Brien—National Center for Atmospheric Research, Boulder, Colorado

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Travis A. O’Brien, obrienta@iu.edu

CURRENT AFFILIATIONS: T. A. O’Brien—Earth and Atmospheric Sciences, Indiana University, Bloomington, Indiana; Patricola—Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa; J. O’Brien—National Center for Atmospheric Research, Boulder, Colorado

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Travis A. O’Brien, obrienta@iu.edu
Save