• AMS, 2017: Atmospheric river. Glossary of Meteorology, American Meteorological Society, http://glossary.ametsoc.org/wiki/Atmospheric_river.

  • Anderson, C. J., and R. W. Arritt, 2001: Mesoscale convective systems over the United States during the 1997–98 El Niño. Mon. Wea. Rev., 129, 24432457, https://doi.org/10.1175/1520-0493(2001)129<2443:MCSOTU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Behrangi, A., B. Guan, P. J. Neiman, M. Schreier, and B. Lambrigtsen, 2016: On the quantification of atmospheric rivers precipitation from space: Composite assessments and case studies over the eastern North Pacific Ocean and the western United States. J. Hydrometeor., 17, 369382, https://doi.org/10.1175/JHM-D-15-0061.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosilovich, M., R. Lucchesi, and M. Suarez, 2016: MERRA-2: File specification. GMAO Office Note 9 (version 1.1), 73 pp., https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich785.pdf.

  • Budikova, D., J. S. M. Coleman, S. A. Strope, and A. Austin, 2010: Hydroclimatology of the 2008 midwest floods. Water Resour. Res., 46, W12524, https://doi.org/10.1029/2010WR009206.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Businger, S., D. I. Knapp, and G. F. Watson, 1990: Storm following climatology of precipitation associated with winter cyclones originating over the Gulf of Mexico. Wea. Forecasting, 5, 378403, https://doi.org/10.1175/1520-0434(1990)005<0378:SFCOPA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Corringham, T. W., F. M. Ralph, A. Gershunov, D. R. Cayan, and C. A. Talbot, 2019: Atmospheric rivers drive flood damages in the western United States. Sci. Adv., 5, eaax4631, https://doi.org/10.1126/sciadv.aax4631.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daly, C., M. Halbleib, I. Smith, W. P. Gibson, M. K. Doggett, G. H. Taylor, J. Curtis, and P. P. Pasteris, 2008: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol., 28, 20312064, https://doi.org/10.1002/joc.1688.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Debbage, N., P. Miller, S. Poore, K. Morano, T. Mote, and J. Marshall Shepherd, 2017: A climatology of atmospheric river interactions with the southeastern United States coastline. Int. J. Climatol., 37, 40774091, https://doi.org/10.1002/joc.5000.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Demaria, E. M. C., F. Dominguez, H. Hu, G. von Glinski, M. Robles, J. Skindlov, and J. Walter, 2017: Observed hydrologic impacts of landfalling atmospheric rivers in the Salt and Verde River basins of Arizona, United States. Water Resour. Res., 53, 10 02510 042, https://doi.org/10.1002/2017WR020778.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dettinger, M. D., 2011: Climate change, atmospheric rivers and floods in California—A multimodel analysis of storm frequency and magnitude changes. J. Amer. Water Resour. Assoc., 47, 514523, https://doi.org/10.1111/j.1752-1688.2011.00546.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dettinger, M. D., 2013: Atmospheric rivers as drought busters on the U.S. West Coast. J. Hydrometeor., 14, 17211732, https://doi.org/10.1175/JHM-D-13-02.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dettinger, M. D., F. M. Ralph, T. Das, P. J. Neiman, and D. R. Cayan, 2011: Atmospheric rivers, floods, and the water resources of California. Water, 3, 445478, https://doi.org/10.3390/w3020445.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dettinger, M. D., F. M. Ralph, and J. J. Rutz, 2018: Empirical return periods of the most intense vapor transports during historical atmospheric river landfalls on the U.S. West Coast. J. Hydrometeor., 19, 13631377, https://doi.org/10.1175/JHM-D-17-0247.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., and J. L. Kinter, 2009: The “Maya Express”: Floods in the U.S. Midwest. Eos, Trans. Amer. Geophys. Union, 90, 101102, https://doi.org/10.1029/2009EO120001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., and J. L. Kinter, 2010: Floods over the U.S. Midwest: A regional water cycle perspective. J. Hydrometeor., 11, 11721181, https://doi.org/10.1175/2010JHM1196.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Espinoza, V., D. E. Waliser, B. Guan, D. A. Lavers, and F. M. Ralph, 2018: Global analysis of climate change projection effects on atmospheric rivers. Geophys. Res. Lett., 45, 42994308, https://doi.org/10.1029/2017GL076968.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Florsheim, J., and M. Dettinger, 2015: Promoting atmospheric-river and snowmelt-fueled biogeomorphic processes by restoring river-floodplain connectivity in California’s Central Valley. Geomorphic Approaches to Integrated Floodplain Management of Lowland Fluvial Systems in North America and Europe, P. Hudson and H. Middelkoop, Eds., Springer, 119–141, https://doi.org/10.1007/978-1-4939-2380-9_6.

    • Crossref
    • Export Citation
  • Gao, Y., J. Lu, L. R. Leung, Q. Yang, S. Hagos, and Y. Qian, 2015: Dynamical and thermodynamical modulations on future changes of landfalling atmospheric rivers over western North America. Geophys. Res. Lett., 42, 71797186, https://doi.org/10.1002/2015GL065435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gelaro, R. W., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gershunov, A., T. Shulgina, F. M. Ralph, D. A. Lavers, and J. J. Rutz, 2017: Assessing the climate-scale variability of atmospheric rivers affecting western North America. Geophys. Res. Lett., 44, 79007908, https://doi.org/10.1002/2017GL074175.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gershunov, A., and Coauthors, 2019: Precipitation regime change in western North America: The role of atmospheric rivers. Nat. Sci. Rep., 9, 9944, https://doi.org/10.1038/s41598-019-46169-w.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, B., and D. E. Waliser, 2015: Detection of atmospheric rivers: Evaluation and application of an algorithm for global studies. J. Geophys. Res. Atmos., 120, 12 51412 535, https://doi.org/10.1002/2015JD024257.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, B., and D. E. Waliser, 2017: Atmospheric rivers in 20 year weather and climate simulations: A multimodel, global evaluation. J. Geophys. Res. Atmos., 122, 55565581, https://doi.org/10.1002/2016JD026174.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, B., and D. E. Waliser, 2019: Tracking atmospheric rivers globally: Spatial distributions and temporal evolution of life cycle characteristics. J. Geophys. Res. Atmos., 124, 12 52312 552, https://doi.org/10.1029/2019JD031205.

    • 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
  • Guan, B., D. E. Waliser, F. M. Ralph, E. J. Fetzer, and P. J. Neiman, 2016: Hydrometeorological characteristics of rain-on-snow events associated with atmospheric rivers. Geophys. Res. Lett., 43, 29642973, https://doi.org/10.1002/2016GL067978.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, B., D. E. Waliser, and F. M. Ralph, 2018: An intercomparison between reanalysis and dropsonde observations of the total water vapor transport in individual atmospheric rivers. J. Hydrometeor., 19, 321337, https://doi.org/10.1175/JHM-D-17-0114.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagos, S. M., L. R. Leung, J.-H. Yoon, J. Lu, and Y. Gao, 2016: A projection of changes in landfalling atmospheric river frequency and extreme precipitation over western North America from the large ensemble CESM simulations. Geophys. Res. Lett., 43, 13571363, https://doi.org/10.1002/2015GL067392.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Halverson, J. B., and T. D. Rabenhorst, 2010: Mega-snow in the Megalopolis: The Mid-Atlantic’s blockbuster winter of 2009–2010. Weatherwise, 63, 1623, https://doi.org/10.1080/00431672.2010.490164.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hatchett, B. J., S. Burak, J. J. Rutz, N. S. Oakley, E. H. Bair, and M. L. Kaplan, 2017: Avalanche fatalities during atmospheric river events in the western United States. J. Hydrometeor., 18, 13591374, https://doi.org/10.1175/JHM-D-16-0219.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hecht, C. W., and J. M. Cordeira, 2017: Characterizing the influence of atmospheric river orientation and intensity on precipitation distributions over north coastal California. Geophys. Res. Lett., 44, 90489058, https://doi.org/10.1002/2017GL074179.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, H., and Y. Chen, 2020: Simulation and projection of circulations associated with atmospheric rivers along the North American northeast coast. J. Climate, 33, 56735695, https://doi.org/10.1175/JCLI-D-19-0104.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hughes, M., K. M. Mahoney, P. J. Neiman, B. J. Moore, M. Alexander, and F. M. Ralph, 2014: The landfall and inland penetration of a flood-producing atmospheric river in Arizona. Part II: Sensitivity of modeled precipitation to terrain height and atmospheric river orientation. J. Hydrometeor., 15, 19541974, https://doi.org/10.1175/JHM-D-13-0176.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khouakhi, A., and G. Villarini, 2016: On the relationship between atmospheric rivers and high sea water levels along the U.S. West Coast. Geophys. Res. Lett., 43, 88158822, https://doi.org/10.1002/2016GL070086.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, J., and Coauthors, 2018: Winter precipitation characteristics in western US related to atmospheric river landfalls: Observations and model evaluations. Climate Dyn., 50, 231248, https://doi.org/10.1007/s00382-017-3601-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knippertz, P., and H. Wernli, 2010: A Lagrangian climatology of tropical moisture exports to the Northern Hemispheric extratropics. J. Climate, 23, 9871003, https://doi.org/10.1175/2009JCLI3333.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Konrad, C. P., and M. D. Dettinger, 2017: Flood runoff in relation to water vapor transport by atmospheric rivers over the western United States, 1949–2015. Geophys. Res. Lett., 44, 11 45611 462, https://doi.org/10.1002/2017GL075399.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., T. Hall, T. Knutson, K. E. Kunkel, R. J. Trapp, D. E. Waliser, and M. F. Wehner, 2017: Extreme storms. Climate Science Special Report. Vol 1, Fourth National Climate Assessment, D. J. Wuebbles et al., Eds., U.S. Global Change Research Program, 257–276, https://doi.org/10.7930/J07S7KXX.

    • Crossref
    • Export Citation
  • Lackmann, G. M., 2013: The south-central U.S. flood of May 2010: Present and future. J. Climate, 26, 46884709, https://doi.org/10.1175/JCLI-D-12-00392.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lackmann, G. M., and J. R. Gyakum, 1999: Heavy cold-season precipitation in the northwestern United States: Synoptic climatology and an analysis of the flood of 17–18 January 1986. Wea. Forecasting, 14, 687700, https://doi.org/10.1175/1520-0434(1999)014<0687:HCSPIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lamjiri, M. A., M. D. Dettinger, F. M. Ralph, and B. Guan, 2017: Hourly storm characteristics along the U.S. West Coast: Role of atmospheric rivers in extreme precipitation. Geophys. Res. Lett., 44, 70207028, https://doi.org/10.1002/2017GL074193.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lavers, D. A., and G. Villarini, 2013: Atmospheric rivers and flooding over the central United States. J. Climate, 26, 78297836, https://doi.org/10.1175/JCLI-D-13-00212.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lavers, D. A., and G. Villarini, 2015: The contribution of atmospheric rivers to precipitation in Europe and the United States. J. Hydrol., 522, 382390, https://doi.org/10.1016/j.jhydrol.2014.12.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lavers, D. A., R. P. Allan, G. Villarini, B. Lloyd-Hughes, D. J. Brayshaw, and A. J. Wade, 2013: Future changes in atmospheric rivers and their implications for winter flooding in Britain. Environ. Res. Lett., 8, 034010, https://doi.org/10.1088/1748-9326/8/3/034010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Letkewicz, C. E., and M. D. Parker, 2010: Forecasting the maintenance of mesoscale convective systems crossing the Appalachian Mountains. Wea. Forecasting, 25, 11791195, https://doi.org/10.1175/2010WAF2222379.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lora, J. M., J. L. Mitchell, C. Risi, and A. E. Tripati, 2017: North Pacific atmospheric rivers and their influence on western North America at the Last Glacial Maximum. Geophys. Res. Lett., 44, 10511059, https://doi.org/10.1002/2016GL071541.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahoney, K., and Coauthors, 2016: Understanding the role of atmospheric rivers in heavy precipitation in the southeast United States. Mon. Wea. Rev., 144, 16171632, https://doi.org/10.1175/MWR-D-15-0279.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mestas-Nuñez, A. M., D. B. Enfield, and C. Zhang, 2007: Water vapor fluxes over the Intra-Americas Sea: Seasonal and interannual variability and associations with rainfall. J. Climate, 20, 19101922, https://doi.org/10.1175/JCLI4096.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, D. K., D. Hotz, J. Winton, and L. Stewart, 2018: Investigation of atmospheric rivers impacting the Pigeon River basin of the southern Appalachian Mountains. Wea. Forecasting, 33, 283299, https://doi.org/10.1175/WAF-D-17-0060.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moore, B. J., P. J. Neiman, F. M. Ralph, and F. E. Barthold, 2012: Physical processes associated with heavy flooding rainfall in Nashville, Tennessee, and vicinity during 1–2 May 2010: The role of an atmospheric river and mesoscale convective systems. Mon. Wea. Rev., 140, 358378, https://doi.org/10.1175/MWR-D-11-00126.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mueller, M. J., K. M. Mahoney, and M. Hughes, 2017: High-resolution model-based investigation of moisture transport into the Pacific Northwest during a strong atmospheric river event. Mon. Wea. Rev., 145, 38613879, https://doi.org/10.1175/MWR-D-16-0466.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mundhenk, B. D., E. A. Barnes, E. D. Maloney, and C. F. Baggett, 2018: Skillful empirical subseasonal prediction of landfalling atmospheric river activity using the Madden-Julian oscillation and quasi-biennial oscillation. npj Climate Atmos. Sci., 1, 20177, https://doi.org/10.1038/s41612-017-0008-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakamura, J., U. Lall, Y. Kushnir, A. W. Robertson, and R. Seager, 2013: Dynamical structure of extreme floods in the U.S. Midwest and the United Kingdom. J. Hydrometeor., 14, 485504, https://doi.org/10.1175/JHM-D-12-059.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., F. M. Ralph, G. A. Wick, Y. Kuo, T. Wee, Z. Ma, G. H. Taylor, and M. D. Dettinger, 2008a: Diagnosis of an intense atmospheric river impacting the Pacific Northwest: Storm summary and offshore vertical structure observed with COSMIC satellite retrievals. Mon. Wea. Rev., 136, 43984420, https://doi.org/10.1175/2008MWR2550.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., F. M. Ralph, G. A. Wick, J. D. Lundquist, and M. D. Dettinger, 2008b: Meteorological characteristics and overland precipitation impacts of atmospheric rivers affecting the west coast of North America based on eight years of SSM/I satellite observations. J. Hydrometeor., 9, 2247, https://doi.org/10.1175/2007JHM855.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., L. J. Schick, F. M. Ralph, M. Hughes, and G. A. Wick, 2011: Flooding in western Washington: The connection to atmospheric rivers. J. Hydrometeor., 12, 13371358, https://doi.org/10.1175/2011JHM1358.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., F. M. Ralph, B. J. Moore, M. Hughes, K. M. Mahoney, J. M. Cordeira, and M. D. Dettinger, 2013: The landfall and inland penetration of a flood-producing atmospheric river in Arizona. Part I: Observed synoptic-scale, orographic, and hydrometeorological characteristics. J. Hydrometeor., 14, 460484, https://doi.org/10.1175/JHM-D-12-0101.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oakley, N. S., J. T. Lancaster, M. L. Kaplan, and F. M. Ralph, 2017: Synoptic conditions associated with cool season post-fire debris flows in the Transverse Ranges of Southern California. Nat. Hazards, 88, 327354, https://doi.org/10.1007/s11069-017-2867-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Paltan, H., D. Waliser, W. H. Lim, B. Guan, D. Yamazaki, R. Pant, and S. Dadson, 2017: Global floods and water availability driven by atmospheric rivers. Geophys. Res. Lett., 44, 10 38710 395, https://doi.org/10.1002/2017GL074882.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Payne, A. E., and G. Magnusdottir, 2015: An evaluation of atmospheric rivers over the North Pacific in CMIP5 and their response to warming under RCP 8.5. J. Geophys. Res. Atmos., 120, 11 17311 190, https://doi.org/10.1002/2015JD023586.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pfahl, S., E. Madonna, M. Boettcher, H. Joos, and H. Wernli, 2014: Warm conveyor belts in the ERA-Interim dataset (1979–2010). Part II: Moisture origin and relevance for precipitation. J. Climate, 27, 2740, https://doi.org/10.1175/JCLI-D-13-00223.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rabinowitz, J. L., A. R. Lupo, and P. E. Guinan, 2018: Evaluating linkages between atmospheric blocking patterns and heavy rainfall events across the North-Central Mississippi River valley for different ENSO phases. Adv. Meteor., 2018, 1217830, https://doi.org/10.1155/2018/1217830.

    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., and M. D. Dettinger, 2012: Historical and national perspectives on extreme West Coast precipitation associated with atmospheric rivers during December 2010. Bull. Amer. Meteor. Soc., 93, 783790, https://doi.org/10.1175/BAMS-D-11-00188.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., P. J. Neiman, D. E. Kingsmill, P. O. Persson, A. B. White, E. T. Strem, E. D. Andrews, and R. C. Antweiler, 2003: The impact of a prominent rain shadow on flooding in California’s Santa Cruz Mountains: A CALJET case study and sensitivity to the ENSO cycle. J. Hydrometeor., 4, 12431264, https://doi.org/10.1175/1525-7541(2003)004<1243:TIOAPR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., P. J. Neiman, and G. A. Wick, 2004: Satellite and CALJET aircraft observations of atmospheric rivers over the eastern North Pacific Ocean during the winter of 1997/98. Mon. Wea. Rev., 132, 17211745, https://doi.org/10.1175/1520-0493(2004)132<1721:SACAOO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., P. J. Neiman, and R. Rotunno, 2005: Dropsonde observations in low-level jets over the northeastern Pacific Ocean from CALJET-1998 and PACJET-2001: Mean vertical-profile and atmospheric-river characteristics. Mon. Wea. Rev., 133, 889910, https://doi.org/10.1175/MWR2896.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., P. J. Neiman, G. Wick, S. Gutman, M. Dettinger, D. 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., T. Coleman, P. J. Neiman, R. J. Zamora, and M. D. Dettinger, 2013: Observed impacts of duration and seasonality of atmospheric-river landfalls on soil moisture and runoff in coastal Northern California. J. Hydrometeor., 14, 443459, https://doi.org/10.1175/JHM-D-12-076.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rivera, E. R., F. Dominguez, and C. L. Castro, 2014: Atmospheric rivers and cool season extreme precipitation events in the Verde River basin of Arizona. J. Hydrometeor., 15, 813829, https://doi.org/10.1175/JHM-D-12-0189.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rutz, J. J., and W. J. Steenburgh, 2012: Quantifying the role of atmospheric rivers in the interior western United States. Atmos. Sci. Lett., 13, 257261, https://doi.org/10.1002/asl.392.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rutz, J. J., W. J. Steenburgh, and F. M. Ralph, 2014: Climatological characteristics of atmospheric rivers and their inland penetration over the western United States. Mon. Wea. Rev., 142, 905921, https://doi.org/10.1175/MWR-D-13-00168.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rutz, J. J., and Coauthors, 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 J. T. Kiehl, 2016: Atmospheric river landfall-latitude changes in future climate simulations. Geophys. Res. Lett., 43, 87758782, https://doi.org/10.1002/2016GL070470.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shields, C. A., and Coauthors, 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
  • Slinskey, E. A., P. C. Loikith, D. E. Waliser, and A. Goodman, 2019: An extreme precipitation categorization scheme and its observational uncertainty over the continental United States. J. Hydrometeor., 20, 10291052, https://doi.org/10.1175/JHM-D-18-0148.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, J. A., M. L. Baeck, G. Villarini, D. B. Wright, and W. Krajewski, 2013: Extreme flood response: The June 2008 flooding in Iowa. J. Hydrometeor., 14, 18101825, https://doi.org/10.1175/JHM-D-12-0191.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, J. A., M. L. Baeck, A. A. Ntelekos, G. Villarini, and M. Steiner, 2011: Extreme rainfall and flooding from orographic thunderstorms in the central Appalachians. Water Resour. Res., 47, W04514, https://doi.org/10.1029/2010WR010190.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Villarini, G., J. A. Smith, M. L. Baeck, R. Vitolo, D. B. Stephenson, and W. Krajewski, 2011: On the frequency of heavy rainfall for the Midwest of the United States. J. Hydrol., 400, 103120, https://doi.org/10.1016/j.jhydrol.2011.01.027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., and B. Guan, 2017: Extreme winds and precipitation during landfall of atmospheric rivers. Nat. Geosci., 10, 179183, https://doi.org/10.1038/ngeo2894.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., and J. M. Cordeira, 2020: AR modeling: Forecasts, climate simulations, and climate projections. Atmospheric Rivers, F. M. Ralph et al., Eds., Springer, 189–213.

    • Crossref
    • Export Citation
  • Warner, M. D., C. F. Mass, and E. P. Salathé, 2012: Wintertime extreme precipitation events along the Pacific Northwest coast: Climatology and synoptic evolution. Mon. Wea. Rev., 140, 20212043, https://doi.org/10.1175/MWR-D-11-00197.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Warner, M. D., C. F. Mass, and E. P. Salathé, 2015: Changes in winter atmospheric rivers along the North American West Coast in CMIP5 climate models. J. Hydrometeor., 16, 118128, https://doi.org/10.1175/JHM-D-14-0080.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, A. M., K. T. Skelly, and J. M. Cordeira, 2017: High-impact hydrologic events and atmospheric rivers in California: An investigation using the NCEI Storm Events Database. Geophys. Res. Lett., 44, 33933401, https://doi.org/10.1002/2017GL073077.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, Y., and R. E. Newell, 1994: Atmospheric rivers and bombs. Geophys. Res. Lett., 21, 19992002, https://doi.org/10.1029/94GL01710.

  • Zhu, Y., and R. E. Newell, 1998: A proposed algorithm for moisture fluxes from atmospheric rivers. Mon. Wea. Rev., 126, 725735, https://doi.org/10.1175/1520-0493(1998)126<0725:APAFMF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • View in gallery

    The seven NCA subregions and their associated abbreviations.

  • View in gallery

    AR frequency (% of days) between 1981 and 2016 at each grid cell. Results are for (a) December–February, (b) March–May, (c) June–August, and (d) September–November.

  • View in gallery

    Mean IVT magnitude (kg m−1 s−1; shading) and direction (arrows) for AR days between 1981 and 2016 at each grid cell. Results are for (a) December–February, (b) March–May, (c) June–August, and (d) September–November.

  • View in gallery

    Median AR area (×106 km2) between 1981 and 2016 at each grid cell. Results are for (a) December–February, (b) March–May, (c) June–August, and (d) September–November.

  • View in gallery

    AR composites for cities, denoted by a black “x,” in each of the seven NCA regions. Shown are composite IVT (kg m−1 s−1) and mean IVT direction (vectors) in the letter-labeled panels (AR day count per season is denoted in red in the top-right corner of each panel) and AR axis density in the adjacent unlettered panel for all AR days for 1981–2016 at each grid cell. Results are for (a)–(g) December–February and (h)–(n) September–November.

  • View in gallery

    As in Fig. 5, but for (a)–(g) June–August and (h)–(n) March–May.

  • View in gallery

    AR precipitation fraction calculated as the percent of AR-driven precipitation relative to the total precipitation between 1981 and 2016 at each grid cell. Results are for (a) December–February, (b) March–May, (c) June–August, and (d) September–November.

  • View in gallery

    AR extreme precipitation fraction (% of days) calculated as the number of linked 95th-percentile extreme precipitation AR days relative to the total number of extreme precipitation days between 1981 and 2016 at each grid cell. Results are for (a) December–February, (b) March–May, (c) June–August, and (d) September–November.

  • View in gallery

    AR fraction (% of days) calculated as the number of linked AR 95th-percentile extreme precipitation days relative to the total number of AR days between 1981 and 2016 at each grid cell. Results are for (a) December–February, (b) March–May, (c) June–August, and (d) September–November.

  • View in gallery

    Histograms of basic characteristics of ARs detected over all months between 1981 and 2016. The vertical red lines in each panel indicate the median. Results are for (a)–(g) the magnitude of mean IVT (kg m−1 s−1), (h)–(n) AR area (×107 km2), and (o)–(u) direction of mean IVT (°) for, from top to bottom, each of the seven NCA regions.

  • View in gallery

    Summarized AR characteristics for each NCA region. Shading indicates AR occurrences per unit area (number of AR days per season per 10 000 km2). Arrows represent median AR IVT direction (°), IVT magnitude (arrow size; kg m−1 s−1), and median AR area (×106 km2; arrow shading). ARs in each region are identified under the condition that at least 10% of the grid cells of the AR shape are within the region boundaries. Results are for (a) December–February, (b) March–May, (c) June–August, and (d) September–November.

  • View in gallery

    Summarized AR precipitation characteristics for each NCA region. Extreme precipitation day frequency is calculated as the spatial median of the total number of qualifying days that occurred during each season at each grid cell across the region (shading). The AR precipitation fraction, calculated as the percent of AR-driven precipitation relative to the total precipitation, is illustrated as the water level in a bucket (and labeled). Also shown is the AR extreme precipitation fraction (% of days), calculated as the number of linked 95th-percentile extreme precipitation AR days relative to the total number of extreme precipitation days, (white bar) and AR fraction (% of days), calculated as the number of linked AR 95th-percentile extreme precipitation days relative to the total number of AR days, (gray bar) between 1981 and 2016 at each grid cell. Results are for (a) December–February, (b) March–May, (c) June–August, and (d) September–November.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 30 30 30
PDF Downloads 30 30 30

A Climatology of Atmospheric Rivers and Associated Precipitation for the Seven U.S. National Climate Assessment Regions

View More View Less
  • 1 Department of Geography, Portland State University, Portland, Oregon
  • | 2 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  • | 3 Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, Los Angeles, California
Free access

Abstract

Atmospheric rivers (ARs) are long, narrow filamentary regions of enhanced vertically integrated water vapor transport (IVT) that play an important role in regional water supply and hydrometeorological extremes. Here, an AR detection algorithm is applied to global reanalysis from Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), to objectively and consistently characterize ARs regionally across the continental United States (CONUS). The characteristics of AR and associated precipitation are computed at the gridpoint scale and summarized over the seven U.S. National Climate Assessment regions. ARs are most frequent in the autumn and winter in the West, spring in the Great Plains, and autumn in the Midwest and Northeast. ARs show regional and seasonal variability in basic geometry and IVT. AR IVT composites reveal annually consistent northeastward-directed moisture transport from the Pacific Ocean in the West, whereas moisture transport patterns vary seasonally across the Southern Great Plains and Midwest. Linked AR precipitation characteristics suggest that a substantial proportion of extreme events, defined as the top 5% of 3-day precipitation totals, are associated with ARs over many parts of CONUS, including the East. Regional patterns of AR-associated precipitation highlight that seasonally varying moisture transport and lifting mechanisms differ between the East and the West where orographic lifting is key. Our study aims to contribute a comprehensive and consistent CONUS-wide, regional-scale analysis of ARs in support of ongoing NCA efforts. Given the CONUS-wide role ARs play in extreme precipitation, findings motivate continued study of associated climate change impacts.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0039.s1.

© 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: Emily A. Slinskey, slinskey@pdx.edu

Abstract

Atmospheric rivers (ARs) are long, narrow filamentary regions of enhanced vertically integrated water vapor transport (IVT) that play an important role in regional water supply and hydrometeorological extremes. Here, an AR detection algorithm is applied to global reanalysis from Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), to objectively and consistently characterize ARs regionally across the continental United States (CONUS). The characteristics of AR and associated precipitation are computed at the gridpoint scale and summarized over the seven U.S. National Climate Assessment regions. ARs are most frequent in the autumn and winter in the West, spring in the Great Plains, and autumn in the Midwest and Northeast. ARs show regional and seasonal variability in basic geometry and IVT. AR IVT composites reveal annually consistent northeastward-directed moisture transport from the Pacific Ocean in the West, whereas moisture transport patterns vary seasonally across the Southern Great Plains and Midwest. Linked AR precipitation characteristics suggest that a substantial proportion of extreme events, defined as the top 5% of 3-day precipitation totals, are associated with ARs over many parts of CONUS, including the East. Regional patterns of AR-associated precipitation highlight that seasonally varying moisture transport and lifting mechanisms differ between the East and the West where orographic lifting is key. Our study aims to contribute a comprehensive and consistent CONUS-wide, regional-scale analysis of ARs in support of ongoing NCA efforts. Given the CONUS-wide role ARs play in extreme precipitation, findings motivate continued study of associated climate change impacts.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0039.s1.

© 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: Emily A. Slinskey, slinskey@pdx.edu

1. Introduction

Atmospheric rivers (ARs) are long, narrow regions of strong horizontal water vapor transport (Zhu and Newell 1994, 1998; Ralph et al. 2004) responsible for a multitude of hydrometeorological impacts (Guan et al. 2010; Dettinger et al. 2011; Neiman et al. 2011; Moore et al. 2012; Dettinger 2013; Mahoney et al. 2016). Typically associated with a low-level jet (LLJ) ahead of the cold front in the warm sector of an extratropical cyclone (AMS 2017), ARs cover only ~10% of Earth’s zonal circumference but account for >90% of the total poleward water vapor transport in the midlatitudes (Zhu and Newell 1998; Guan and Waliser 2015). Enhanced precipitation occurs when the AR interacts with a mechanism capable of lifting it beyond saturation level. Some examples include orographic lifting (Ralph et al. 2005), convection (Letkewicz and Parker 2010), and synoptic-scale frontal systems (Businger et al. 1990), among others.

In water-stressed regions, such as parts of the southwestern United States, ARs provide a crucial source of water through replenishing reservoirs, contributing to snowpack at higher elevations, and often alleviating existing drought conditions (Guan et al. 2010; Dettinger 2013; Paltan et al. 2017). On the other hand, the extreme precipitation associated with ARs can lead to flooding (Ralph et al. 2006; Neiman et al. 2011; Konrad and Dettinger 2017), rain-on-snow events (Guan et al. 2016), levee breaks (Florsheim and Dettinger 2015), landslides (Young et al. 2017), debris flows (Oakley et al. 2017), and avalanches (Hatchett et al. 2017). Furthermore, research linking ARs with underlying patterns of damaging coastal extreme winds (Waliser and Guan 2017) and resulting storm surges (Khouakhi and Villarini 2016) suggests that ARs are associated with effects beyond their role in precipitation extremes.

The hydrometeorological extremes associated with ARs are well documented along the U.S. West Coast. Water availability concerns in California have motivated a growing number of analyses, identifying landfalling ARs as responsible for between 30% and 70% of the annual precipitation (Guan et al. 2010; Dettinger et al. 2011; Gershunov et al. 2017) as well as the majority of precipitation extremes (Ralph et al. 2004; Ralph and Dettinger 2012; Lamjiri et al. 2017) and flooding across the state (Ralph et al. 2006, 2013). Destructive flooding associated with AR conditions has also been documented over parts of Oregon and Washington (Neiman et al. 2008a, 2011; Warner et al. 2012). A study extending from the Mexico–California border northward into British Columbia, Canada, highlighted the importance of ARs in modifying the region’s climate and yielding important hydrologic consequences, including increased precipitation, river/stream flows, vapor fluxes, and changes in snow water equivalent (Neiman et al. 2008b).

Considerably less attention has focused on the role of ARs in other regions of the United States; however, a number of heavy precipitation and high-impact flood events have been linked with AR-like conditions across parts of the central/eastern United States. For example, Moore et al. (2012) and Lackmann (2013) linked AR conditions with severe flooding in Tennessee in May 2010. More recently, Rabinowitz et al. (2018) found that 15 AR events between 2010 and 2015 contributed to 67% of the total monthly precipitation across the north-central Mississippi River Valley, consistent with Lavers and Villarini (2013). Nakamura et al. (2013) further present evidence of AR conditions governed by an anomalous semistationary ridge east of the U.S. East Coast attributable to flooding in the Ohio River basin. Across the Southeast, ARs have been documented as an important contributor to annual rainfall totals and heavy precipitation event frequency (Mahoney et al. 2016; Debbage et al. 2017; Miller et al. 2018). Despite this documented importance of ARs across the continental United States (CONUS), AR climatology has not received the same level of comprehensive documentation away from the West Coast.

The importance of ARs in weather and climate has further prompted increasing interests in the behavior of ARs under global warming (e.g., Dettinger 2011; Lavers et al. 2013; Gao et al. 2015; Payne and Magnusdottir 2015; Warner et al. 2015; Hagos et al. 2016; Shields and Kiehl 2016; Espinoza et al. 2018; Gershunov et al. 2019). Existing literature suggests that many aspects of ARs may change under future warming, including frequency, geometry, integrated water vapor transport (IVT) magnitude, seasonality, and associated flood risk (Waliser and Cordeira 2020, and references therein). Furthermore, changes in the frequency or intensity of ARs could affect the occurrence and magnitude of associated precipitation and flooding, warranting continued observational analysis to benchmark historical change and provide a target for model evaluation (e.g., Guan and Waliser 2017).

ARs were highlighted for the first time in the Climate Science Special Report (CSSR) of the Fourth National Climate Assessment (NCA) as a key topic in its chapter “Extreme Storms” (Kossin et al. 2017). Key findings in the report summarized the importance of ARs along the U.S. West Coast to snowpack and annual precipitation. It also highlighted possible future increases in the frequency and severity of landfalling ARs related to increased evaporation and higher atmospheric water vapor concentrations with increasing temperature. Motivation for this work is to help provide a more comprehensive and consistent CONUS-scale analysis of ARs over the seven NCA regions (Fig. 1) as a contribution to future reports. In this study we regionally examine AR climatologies across the CONUS and investigate the associated precipitation characteristics. The hydrometeorological importance of ARs has prompted significant incentive to improve our understanding of ARs at regional scales to inform resource management, hazard resilience, and decision-making as well as provide a basis for assessing future change. Previous studies have explored AR climatology across a range of spatial scales and geographies (e.g., Dettinger et al. 2011; Moore et al. 2012; Rutz et al. 2014; Guan and Waliser 2015; Lavers and Villarini 2015; Mahoney et al. 2016; Debbage et al. 2017); however, this study is novel in the level of detail it provides in regards to AR characteristics and their relation to extreme precipitation at a relatively high spatial resolution over the CONUS. Furthermore, this study, to the authors’ knowledge, is the first to summarize AR climatology and importance as a mechanism for extreme precipitation over the seven NCA regions.

Fig. 1.
Fig. 1.

The seven NCA subregions and their associated abbreviations.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0039.1

2. Data

a. MERRA-2

ARs are identified using the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2; Gelaro et al. 2017), reanalysis internally derived IVT fields. Daily average IVT is calculated from hourly MERRA-2 IVT data provided on a 0.5° latitude × 0.625° longitude grid (Bosilovich et al. 2016) spanning 36 years over the period of 1981–2016. IVT is generated from zonal and meridional winds and specific humidity fields. The use of internally derived IVT values through the MERRA-2 system have the advantage of being calculated across all (internal) model time steps and on all model vertical coordinates, not just the standard output pressure levels, and therefore may be preferable over coasts and mountains (Dettinger et al. 2018). MERRA-2 uses the Goddard Earth Observing System Model, version 5 (GEOS-5), state-of-the-art data assimilation system and is freely available online through the Goddard Earth Sciences (GES) Data and Information Services Center (DISC) (http://disc.sci.gsfc.nasa.gov/mdisc/). While other reanalysis products could be employed to detect ARs, MERRA-2 has been utilized in the study of ARs in previous studies (e.g., Guan and Waliser 2017; Lora et al. 2017; Mundhenk et al. 2018) and is the default reference dataset for the AR Tracking Method Intercomparison Project (ARTMIP; Shields et al. 2018) facilitating the comparison of results from this study across other AR detection algorithms. AR characteristics based on MERRA/MERRA-2 and ERA-Interim are remarkably similar to each other (e.g., Guan and Waliser 2015, 2017, 2019; Guan et al. 2018), and the selection of a specific contemporary reanalysis product is not expected to change the conclusions of this paper.

b. PRISM

Spatially interpolated, ground-based precipitation measurements were obtained from the Parameter–Elevation Regressions on Independent Slopes Model (PRISM; Daly et al. 2008). This dataset incorporates observations from monitoring networks across the CONUS and uses a weighted regression to interpolate climate data based on topographic and physiographic variables using a digital elevation model. PRISM offers high-resolution precipitation measurements on a 0.04° latitude–longitude grid over the CONUS that have been used in a wide range of climatology studies (e.g., Behrangi et al. 2016; Demaria et al. 2017; Kim et al. 2018). A detailed assessment of observational uncertainty in PRISM, alongside a suite of historical precipitation measurement approaches, in capturing 3-day extreme precipitation climatology can be found in Slinskey et al. (2019). Daily precipitation estimates available from 1981 are used for this analysis. PRISM data can be obtained from the PRISM Climate Group at Oregon State University (http://prism.oregonstate.edu/).

3. Method

a. AR identification

The objective identification of ARs employed here is based on the approach introduced in Guan and Waliser (2015) and later updated and validated with in situ/dropsonde data in Guan et al. (2018). This approach applies a combination of geometry and IVT magnitude/direction criteria to identify contiguous regions (i.e., areas of connected grid cells), or “objects,” of enhanced IVT transport. Objects first retained from IVT magnitude thresholding (i.e., above the seasonally- and geographically dependent 85th percentile) are further filtered using directional and geometric requirements. In addition to having an appreciable poleward component (>50 kg m−1 s−1), more than 50% of the area of the IVT object must have IVT directions within 45° of the mean IVT direction of the object. This ensures general coherence in IVT direction within the object. Geometric requirements are then applied, and objects longer than 2000 km with length-to-width ratios >2 are retained as ARs. Multiple, sequentially higher IVT magnitude thresholds (i.e., 85th–95th percentiles at an increment of 2.5th) are applied if an IVT object fails the other criteria. For each of the 12 months, the 85th percentile IVT is shown for reference in Fig. S1 in the online supplemental material. The use of multiple IVT thresholds allows for the identification of ARs within the core region of a larger, wider object that may not meet the geometry criteria (Guan et al. 2018).

The AR detection algorithm employed here consists of a broad and generalized AR definition, as in Zhu and Newell (1998), that does not impose predetermined geographical requirements for AR identification (as noted in Guan and Waliser 2017) and does not isolate collocated mechanisms of moisture transport (e.g., North American monsoon). This method defines ARs on the basis of moisture transport and connected object characteristics only. Therefore, it does not consider spatiotemporally related phenomena (e.g., fronts and extratropical cyclones) that are part of the phenomenological understanding of ARs in the global climate. Defining ARs in this way is consistent with current literature (Shields et al. 2018), and it is beyond the scope of this study to attempt to link AR objects with any phenomena besides extreme precipitation. We will alert the reader when interpreting AR activity, characteristics, and hydrometeorological impacts if such interpretation overlaps with other well-documented phenomena of the climate system, such as tropical and extratropical cyclones, convective systems, etc.

b. Linked AR extreme precipitation days

Extreme precipitation days are defined as 3-day precipitation totals exceeding the 95th percentile of nonzero 3-day totals, calculated at each grid cell. The use of a percentile-based threshold defines extremes based on the local climatology. Three-day totals are calculated such that each day’s 3-day total includes the sum of that day and the previous two (as in Slinskey et al. 2019). While single-day totals are a common measure for precipitation, the use of multiday totals have been shown to better capture some heavy precipitation impacts while also reducing uncertainty due to temporal mismatch among data products (Ralph and Dettinger 2012). Herein we refer to qualifying 3-day totals as extreme precipitation days. An AR extreme precipitation linkage is made when at least one AR is present during the 3-day window defining the precipitation extreme.

A minimum distance-based interpolation scheme is used to link AR characteristics, defined using MERRA-2, with PRISM’s high-resolution precipitation measurements. We developed this process to assign MERRA-2’s coarser resolution grid cells to PRISM’s finer-resolution grid cells. More specifically, each PRISM grid cell is linked with the MERRA-2 grid cell that has the shortest distance from the grid cell center. All analyses are performed seasonally with winter defined as December–February (DJF), spring as March–May (MAM), summer as June–August (JJA), and autumn as September–November (SON).

4. Results

a. AR characteristics

1) AR frequency

The seasonal distribution of AR frequency, calculated at each grid cell as the percentage of days when the grid cell is within the boundary of an AR for that season, across the CONUS is shown in Fig. 2. Results show ARs are primarily a cold season phenomenon along the West Coast. Consistent with Rutz et al. (2014), maxima occur in the winter in the Southwest and in the winter and autumn in the Pacific Northwest (PNW; Figs. 2a,d). East of the Rocky Mountains, AR occurrence is notable throughout the year. A wintertime maximum is evident across the Southeast with a rate of AR occurrence of >13% of winter days (Fig. 2a). High AR occurrence over the Great Plains and Ohio River Valley in the spring (~12%; Fig. 2b) may be related to moisture transport through features like the Great Plains LLJ (Nakamura et al. 2013; Lavers and Villarini 2013) and “Maya Express” (Budikova et al. 2010; Dirmeyer and Kinter 2009; Smith et al. 2013). Over the central United States, the highest rainfall rates occur during the spring and summer coinciding with a seasonal maximum in convective activity (e.g., Dirmeyer and Kinter 2010; Villarini et al. 2011), where elevated AR frequency is also evident (Figs. 2b,c). Consistent with Nakamura et al. (2013), a springtime maximum in the Ohio River basin (Fig. 2b) supports the strong link between ARs and flooding across the region. Similarly, several studies have shown a strong connection between ARs and flooding across parts of the central United States (e.g., Lavers and Villarini 2013; Nakamura et al. 2013). An example of a particularly high-impact and persistent AR event was the 1–2 May 2010 flood in Nashville, Tennessee (Moore et al. 2012). Although given considerably less attention in the literature, ARs occur across the seasonal cycle in the Northeast with a notable maximum in the autumn, consistent with Hsu and Chen (2020) and the high AR precipitation fraction (AR contribution to total annual rainfall) noted in Lavers and Villarini (2015).

Fig. 2.
Fig. 2.

AR frequency (% of days) between 1981 and 2016 at each grid cell. Results are for (a) December–February, (b) March–May, (c) June–August, and (d) September–November.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0039.1

2) AR magnitude and direction

The seasonal distribution of mean IVT magnitude and direction at each grid cell for all AR days between 1981 and 2016 is shown in Fig. 3. An AR day is defined as any part of the identified AR object that is spatially collocated with that grid cell. Results reveal a seasonally consistent west-to-east gradient of AR IVT across the CONUS, with the exception of the immediate West Coast. Maxima in mean IVT during AR days are evident during the winter and autumn in the PNW ranging between 400 and 450 kg m−1 s−1 along the Coast Range and Cascade Mountains of Oregon and Washington. For the Southwest, and more specifically coastal California and the Sierra Nevada, the mean IVT maximum occurs during the winter between 300 and 400 kg m−1 s−1. Maxima in IVT magnitude for these regions is consistent with the seasonal distribution of AR occurrence in Figs. 3a–d. Lower IVT values across the western interior may be reflective of the influence of upwind topography, which acts to decrease the water vapor transport as an AR penetrates inland (Rutz et al. 2014). Elevation can also lead to reduced IVT magnitude since there is less atmosphere to integrate over and water vapor concentrations are much higher at lower elevations. Seasonal mean IVT direction across the West Coast is predominantly from the southwest. This follows the well-known horizontal moisture transport pathway from the subtropics to the extratropics that is sometimes referred to as the “Pineapple Express” when originating near Hawaii (Lackmann and Gyakum 1999; Dettinger 2011; Dettinger et al. 2011).

Fig. 3.
Fig. 3.

Mean IVT magnitude (kg m−1 s−1; shading) and direction (arrows) for AR days between 1981 and 2016 at each grid cell. Results are for (a) December–February, (b) March–May, (c) June–August, and (d) September–November.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0039.1

East of the Rocky Mountains, elevated IVT on AR days is extensive, revealing a pronounced line separating the eastern half of the country from the dry West. It is during the spring and summer over the Great Plains that the export of moisture from the tropics by way of the Gulf of Mexico is at a maximum (Knippertz and Wernli 2010); however, elevated mean IVT is apparent in the autumn as well. Moisture in this region is known to be transported from the Caribbean and Gulf of Mexico via the northern branch of the Caribbean LLJ, which feeds into the Great Plains LLJ (Mestas-Nuñez et al. 2007; Dirmeyer and Kinter 2010). This moisture transport pathway has been coined the “Maya Express,” exhibiting a north–south orientation. While Fig. 3 does not show moisture transport upstream of the CONUS, this feature appears to be reflected among the mean IVT direction vectors for this region. During the summer in the Midwest, across the Mississippi Valley, mean IVT values >500 kg m−1 s−1 are evident (Fig. 3c). In connection with the Great Plains LLJ, ARs have been documented as transporting moisture into regions of deep convection or mesoscale convective systems (MCSs; e.g., Anderson and Arritt 2001), with recent examples documented in May/June 2008 in the U.S. Midwest (Budikova et al. 2010; Dirmeyer and Kinter 2009; Smith et al. 2013). Similarly, the Ohio River Valley, across Tennessee and Kentucky, reveals areas of high IVT, notably during the spring and autumn at >500 kg m−1 s−1. This region is affected by extratropical cyclones that travel eastward across the United States, advecting moisture northward from the Gulf of Mexico (Lavers and Villarini 2015). The Appalachian Mountains are highlighted by decreased mean AR IVT values relative to the rest of the region, likely as a result of low-level moisture reduction through orographically enhanced precipitation (e.g., Smith et al. 2011) and higher elevation. Overall, IVT magnitude is considerably higher in the East than in the West in all seasons, with the exception of the immediate coastal zones of the PNW in the autumn and winter.

3) AR area

The seasonal distribution of AR area is shown in Fig. 4. AR area is calculated as the median area for all ARs that have overlapped a grid cell. Median, as opposed to mean, values are used to limit the influence of outliers among the often nonnormal AR area distributions. AR area has important implications for the spatial extent of associated impacts. During the winter, high values of AR area are prominent in the Northwest extending across the country from western Washington to eastern North Dakota with values >6 × 106 km2. Although less frequent, high AR area values over the interior West likely represent large features that originate in the Pacific and penetrate inland (Fig. 4a). Some examples include the January 2010 AR event that penetrated eastward across the Pacific Ranges breaking hydrometeorological records across Arizona (Neiman et al. 2013; Hughes et al. 2014) and the November 2006 events that not only severely impacted Oregon and Washington but reached Glacier National Park, Montana causing extensive flooding (Neiman et al. 2008b; Rutz et al. 2014; Mueller et al. 2017). The signal of inland-penetrating ARs over the western United States is evident in the spring and autumn as well (Figs. 4b,c). Results show that the largest ARs occur more commonly across the West compared to the East with an extensive portion of the Southwest and Southern Great Plains experiencing ARs with a smaller areal extent (<2 × 106 km2) during the summer (Fig. 4c).

Fig. 4.
Fig. 4.

Median AR area (×106 km2) between 1981 and 2016 at each grid cell. Results are for (a) December–February, (b) March–May, (c) June–August, and (d) September–November.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0039.1

b. AR composites

To synthesize the spatial characteristics of the ARs in each region, seasonal composites of AR IVT magnitude and direction, along with AR axis density plots, for a major city in each of the seven NCA regions are shown in Fig. 5 (winter/autumn) and Fig. 6 (summer/spring). Each composite represents the mean characteristics of AR IVT magnitude and direction at each grid cell for all AR days where the city was within the boundaries of an AR object (left side of Figs. 5 and 6a–n). AR axis density plots illustrate the cross-AR location of maximum IVT, showing the typical locations of the greatest AR intensity when an AR is affecting the city of interest for a given NCA region (right side of Figs. 5 and 6a–n). Following Guan and Waliser (2015), the AR axis is calculated by identifying the two grid cells on the boundary of the object to locate the maximum great-circle distance. The arc is further divided into small segments equal to the number of grid cells between the outermost points. The great-circle arc perpendicular to each segment is identified and, of the grid cells intersected by the arc, the one with maximum IVT is noted. The axis is defined by connecting the grid cells of maximum IVT.

Fig. 5.
Fig. 5.

AR composites for cities, denoted by a black “x,” in each of the seven NCA regions. Shown are composite IVT (kg m−1 s−1) and mean IVT direction (vectors) in the letter-labeled panels (AR day count per season is denoted in red in the top-right corner of each panel) and AR axis density in the adjacent unlettered panel for all AR days for 1981–2016 at each grid cell. Results are for (a)–(g) December–February and (h)–(n) September–November.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0039.1

Fig. 6.
Fig. 6.

As in Fig. 5, but for (a)–(g) June–August and (h)–(n) March–May.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0039.1

1) Winter/autumn composites

Winter/autumn composite and axis density plots are shown in Fig. 5. Regions of composite IVT magnitude and direction for Portland, Oregon; Los Angeles, California; and Rapid City, South Dakota, during the winter reveal similar patterns of predominantly northeastward-directed AR IVT from the Pacific Ocean (Figs. 5a–c), often associated with Pineapple Express–type moisture transport. In the autumn, IVT strength and direction is similar although the total number of AR days for each city is lower than that in the winter, with Los Angeles and Rapid City less than Portland (Figs. 5h–j). Axis density plots show a relatively wide north–south swath of AR axes, roughly centered on both cities (Figs. 5a–c,h–j), with high axis density over the cities themselves. For all regions, we note that in some cases AR axes appear geographically removed from the city. This occurs when the AR object touches the city on one end but the bulk of the AR extends well away from the city. Axis density plots also reveal regions of maxima that are likely associated with local topography where IVT is not being depleted from orographic uplift. For example, high values of axis density are found along the Columbia River and Snake River Valleys in the Portland composite (Fig. 5a).

The pattern of composite IVT magnitude for Rapid City (Figs. 5c,j), demonstrates the importance of inland penetrating ARs. This occurs where lower or less consistent topographical barriers allow for high water vapor transport over the interior West, common during the cool season (Rutz et al. 2014). In contrast with typical moisture transport in the western United States, eastern regions can experience corridors of strong water vapor transport that extend from multiple different moisture source regions, including the Gulf of Mexico, Caribbean Sea, and Atlantic Ocean (Pfahl et al. 2014). Composite IVT analyses for Oklahoma City, Oklahoma, show moisture transport over both the Pacific and Gulf of Mexico on AR days (Figs. 5d,k). Other cities across the eastern United States, including Columbus, Ohio; Augusta, Maine; and Washington, D.C., reveal similar patterns of composite IVT magnitude and direction as well as axis density during the cold season with a relative high occurrence of AR days (Figs. 5e–n). When compared with the western United States, cities in the East tend to show a stronger northward component in IVT direction further indicative of differing patterns of water vapor transport. According to several studies, ARs with different IVT directions are known to produce different orographic precipitation distributions and hydrological impacts (e.g., Ralph et al. 2003; Neiman et al. 2011, 2013; Hughes et al. 2014; Hecht and Cordeira 2017). ARs in the East with a stronger northward component likely run parallel to the Appalachian Mountains, rather than orthogonal like along the West Coast, thus resulting in a different impact magnitude from orographic lifting. Rainfall may also result more from frontal lifting in the East compared with the predominance of orographic lifting in the West.

2) Summer/spring composites

Summer/spring composite and axis density plots are shown in Fig. 6. ARs along the West Coast are most common during the autumn and winter, therefore AR day frequency and magnitude for Portland and Los Angeles in the spring and summer (Figs. 6a,b,h,i) is decreased relative to results in Fig. 5. In general, when ARs occur during spring/summer in these cities they continue to transport moisture directed predominantly northeastward, although with a decreased maximum in IVT magnitude relative to winter/autumn phenomena. IVT vectors directed northwest in Southern California during the summer suggest ARs may occur alongside and include contribution from other meteorological mechanisms (e.g., North American monsoon; Guan and Waliser 2017). Rapid City displays a smaller spatial area of composite IVT, less suggestive of a predominant influence from inland penetrating ARs, as well as a stronger northward-directed component in IVT direction (Figs. 6c,j). Similarly, Oklahoma City, shows seasonal maxima in the autumn and spring, along with a north–south–oriented band of high axis density extending from Texas to the Great Lakes in the spring (Fig. 6k). IVT composite results are consistent with studies that identify influence from Maya Express moisture transport which has been linked to a number of impactful flooding events across the central United States (Moore et al. 2012; Lavers and Villarini 2013; Nakamura et al. 2013). A similar pattern of water vapor transport is shown in Columbus during the spring (Fig. 6l) with a strong northward component in IVT direction. During summer/spring, ARs have been known to supply regions of deep convection across the central United States with ample low-level moisture, resulting in heavy precipitation and flooding (Lavers and Villarini 2013). For the East Coast, Augusta and Washington, D.C., continue to show broad regions of elevated composite IVT throughout the spring and summer.

c. Linked AR precipitation characteristics

1) Fraction of AR precipitation to total precipitation

The percent of climatological precipitation that falls on an AR day is shown in Fig. 7 for each season. An AR day is defined at a grid cell as any day where an AR object spatially overlaps with the grid cell and all precipitation (>1 mm) that falls on that day is considered AR precipitation. A value of 100% would indicate that all precipitation that falls at that grid cell is associated with an AR. Across the CONUS, regional and seasonal variability in AR precipitation is apparent. ARs explain ~30% of the precipitation in areas across the Northwest in the winter and autumn (Figs. 7a,d). Across California, values show ARs are responsible for over 50% of precipitation during the autumn and winter, consistent with Guan et al. (2010), Dettinger et al. (2011), and Gershunov et al. (2017). East of the Rocky Mountains, maxima in AR precipitation fractions are also apparent, notably in the Southeast and Midwest, during the winter, spring, and autumn (Figs. 7a,b,d). Notable AR fractions in the Southeast show that ARs account for between 30% and 55% of the total precipitation in the region. Several studies have demonstrated the importance of AR moisture in producing impactful precipitation across the Southeast, markedly during the winter and shoulder seasons, which is consistent with results shown here (e.g., Moore et al. 2012; Mahoney et al. 2016; Debbage et al. 2017). Summer stands out with notable low percentages suggesting that ARs are less influential in producing precipitation during these months, possibly because heavy precipitation here is often associated with localized convection and non-AR tropical disturbances (Fig. 7c). In the northeast, high AR precipitation fractions in the winter can be associated with impactful snowfall events, such as in the winter of 2009/10 (Halverson and Rabenhorst 2010). In general, ARs provide a substantial proportion of annual precipitation to many parts of the CONUS, but other mechanisms also play an important role, especially in the summer.

Fig. 7.
Fig. 7.

AR precipitation fraction calculated as the percent of AR-driven precipitation relative to the total precipitation between 1981 and 2016 at each grid cell. Results are for (a) December–February, (b) March–May, (c) June–August, and (d) September–November.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0039.1

2) ARs and extreme precipitation

The seasonal distribution of the fraction of linked AR extreme precipitation days relative to the total number of extreme precipitation days, calculated at each grid cell, is shown in Fig. 8. A value of 100% indicates that all extreme precipitation days (defined in section 3) at that grid cell were associated with an AR. ARs represent an important meteorological mechanism for generating wintertime precipitation extremes along the West Coast (Fig. 8a). They are associated with a majority of extreme precipitation days across much of California and the coastal zones of Oregon and Washington. During the winter ~8% of the Southwest had an extreme precipitation fraction > 90% (Table S1 in the online supplemental material). While ARs weaken as they propagate inland due to the precipitating out of low-level water vapor resulting from orographic lift, they also comprise a large proportion of extreme days for inland areas of the West during the winter and autumn. For example, while Arizona has a relatively low AR frequency (Fig. 2), it has values between 70% and 100%, indicating that when it does experience an extreme precipitation day it is often associated with an AR. These results are consistent with existing literature linking several impactful extreme precipitation days with AR conditions across the interior West (Rutz and Steenburgh 2012; Neiman et al. 2013; Hughes et al. 2014; Rivera et al. 2014).

Fig. 8.
Fig. 8.

AR extreme precipitation fraction (% of days) calculated as the number of linked 95th-percentile extreme precipitation AR days relative to the total number of extreme precipitation days between 1981 and 2016 at each grid cell. Results are for (a) December–February, (b) March–May, (c) June–August, and (d) September–November.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0039.1

Regions east of the Rocky Mountains also experience maxima in precipitation extremes associated with ARs. In the eastern and central United States, AR fractions are highest in the winter, spring, and autumn with several regions revealing notable maxima. For example, the Ohio River Valley, specifically across the Tennessee and Kentucky border, reveals high AR extreme precipitation fractions during the winter and spring (Figs. 8a,b), with between 75% and 85% of extreme precipitation events concurrent with an AR. These results are consistent with Lavers and Villarini (2013) which identified ARs as a major flooding agent over the central United States. The Southeast displays elevated AR extreme precipitation fractions during the nonsummer months, consistent with Mahoney et al. (2016), where winter and spring events across the western portion of the region are linked to strong synoptic weather systems transporting water vapor from the Gulf of Mexico. During the winter ~7% of the Southeast region has extreme precipitation fractions >90% (Table S1 in the online supplemental material). Although not all snowfall in the Northeast is associated with ARs, maxima in wintertime AR-driven precipitation extremes across this region may be associated with impactful snowstorms (Lavers and Villarini 2015).

The fraction of AR days with extreme precipitation relative to the total number of AR days at each grid cell is shown in Fig. 9, plotted as a percent. In other words, a value of 100% would indicate that all ARs are associated with an extreme precipitation day, as defined by the 3-day total. The highest percentages are found across the West Coast and western mountains during the winter (Fig. 9a), although few places exceed 40%. This indicates that even where ARs are common, and a high percentage of extreme precipitation days are associated with an AR, many ARs occur without there being an extreme precipitation day. This result emphasizes that ARs are not always hazardous, and can be beneficial or simply benign when it comes to precipitation impacts (Corringham et al. 2019). During the wintertime, the Rocky Mountains are visible with higher fractions on the west (windward) side of the range compared to the drier east (leeward) side, supporting the notion that this range is the second major topographic barrier encountered by landfalling ARs across the West (Fig. 9a).

Fig. 9.
Fig. 9.

AR fraction (% of days) calculated as the number of linked AR 95th-percentile extreme precipitation days relative to the total number of AR days between 1981 and 2016 at each grid cell. Results are for (a) December–February, (b) March–May, (c) June–August, and (d) September–November.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0039.1

The notably low AR fractions throughout the central United States, stretching across the Northern and Southern Great Plains, indicate that ARs are rarely associated with extreme precipitation days, despite their frequent occurrence in some portions of the region. Results show ~10%–20% of ARs are associated with extreme precipitation days across the eastern half of the United States, with greater percentages, between 25% and 35%, across the Great Lakes and Ohio River Valley in the winter (Fig. 9a). In these regions, orographic lifting of AR moisture is minimal or nonexistent, so other synoptic and mesoscale forcing (e.g., convection, frontal, isentropic lift) must play a role in AR-related precipitation intensity and duration. For example, Mahoney et al. (2016) identifies a number of precipitation triggering mechanisms working in conjunction with corridors of water vapor transport linked to heavy precipitation over the southeastern United States, including synoptic-scale frontal systems, landfalling tropical cyclones, MCSs, and orographic lifting over the Appalachian Mountains. Linked AR extreme precipitation fractions clearly demonstrate the importance of ARs as a mechanism for heavy precipitation in many portions of the CONUS, including across the East.

d. NCA region summaries

1) Seasonal and regional distribution of AR magnitude, area, and direction

Annual distributions of AR magnitude, direction, and area are shown for each of the seven NCA regions (Fig. 1) in the histograms in Fig. 10. Seasonal results are available in the online supplemental material (Figs. S2–S5). ARs in each region must have at least 10% of their grid cells within the region bounds to be included in the histogram. AR IVT magnitude reveals a distribution with a slightly longer right tail in regions across the western half of the country, including the Northwest (skewness of 0.90), Southwest (skewness of 0.80), and Northern (skewness of 0.51)/Southern Great Plains (skewness of 0.28; Figs. 10a–d), and close to normal or symmetric distributions among regions in the East (Figs. 10e–g). In general, western subregions tend to have lower median IVT magnitudes relative to the East. The Northeast has the highest median IVT magnitude at ~413 kg m−1 s−1 (Fig. 10f). The seasonal distribution of AR IVT magnitude shows western subregions with maxima during the winter (Figs. S2a–d in the online supplemental material) and the eastern regions during the summer (Figs. S4e–g in the online supplemental material). Across all seven NCA regions, AR area has a positively skewed distribution with skewness values ranging between 1.25 and 1.77 and all regions revealing a median area between 0.18 and 0.23 × 107 km2 (Figs. 10h–n). The Northwest and Northeast share the highest median area of 0.23 × 107 km2 (Figs. 10h,m). The seasonal spread of AR area continues to show positively skewed distributions with all seven regions experiencing the largest ARs during the winter months, with medians between ~0.25–0.35 × 107 km2 (Figs. S2h–n). AR IVT is consistently directed in the northeastward direction, with median IVT direction for all regions ranging between ~50°–58° (with 0° due north; Figs. 10o–u). All regions indicate that AR IVT typically has a stronger eastward than poleward component and ARs with a westward component are rare, consistent with Guan and Waliser (2015). Seasonally, western regions experience the most eastward-directed ARs during the winter (Figs. S2a–d), while eastern regions reveal a higher occurrence of north/northeastward-directed ARs, suggesting influence from southern moisture sources, such as the of Gulf of Mexico.

Fig. 10.
Fig. 10.

Histograms of basic characteristics of ARs detected over all months between 1981 and 2016. The vertical red lines in each panel indicate the median. Results are for (a)–(g) the magnitude of mean IVT (kg m−1 s−1), (h)–(n) AR area (×107 km2), and (o)–(u) direction of mean IVT (°) for, from top to bottom, each of the seven NCA regions.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0039.1

2) NCA region summary of AR characteristics

AR characteristics, as described at the grid point scale above, are summarized over the seven NCA regions in Fig. 11 and reported in Table 1. Here, region shading provides a measure of AR frequency and arrow size, direction, and color refers to median AR IVT magnitude, direction, and area, respectively. AR frequency for a given NCA region is normalized by area (i.e., number of AR days per 10 000 km2) to account for differences in region size. ARs in each region are again identified under the condition that at least 10% of the grid cells of the AR object are within the region boundaries. Results show that the largest area (arrow color) and magnitude (arrow length) ARs occur in the winter and autumn in the Northwest and Southwest, which is consistent with earlier results at the grid point scale (Figs. 3a and 4a). ARs during the winter in the Northern Great Plains also tend to have the largest area, likely related to cool season inland penetrating ARs originating over the Pacific Ocean, which must be relatively large in order to reach such an area. These three regions also experience ARs with similar median IVT directions, ~60° or northeastward, during the winter (Fig. 11a; Table 1). During the spring and summer ARs in the West tend to have a smaller areal extent, between 1 × 106 and 2.5 × 106 km2, and magnitude, between 200 and 250 kg m−1 s−1 (Figs. 11b,c). In the Southern Great Plains, AR frequency and magnitude are highest in the spring and summer. Springtime ARs in this region tend to be directed more north/northeastward, relative to other seasons, with a median IVT direction of ~46° (Table 1). The Midwest has similarly directed ARs, experiencing its highest magnitude ARs in the summer and autumn (Figs. 11c,d). The Northeast reveals notable maxima in AR frequency and magnitude, compared with the rest of the country, across the seasonal cycle. During the winter and autumn, ARs in the Southeast are relatively larger in areal extent, between 2 × 106 and 2.5 × 106 km2 (Fig. 11d), with little seasonal variation in magnitude. Although useful in summarizing AR characteristics over the NCA regions, in some cases aggregated statistics may mask subregional scale variations, for example those induced by topographic barriers.

Fig. 11.
Fig. 11.

Summarized AR characteristics for each NCA region. Shading indicates AR occurrences per unit area (number of AR days per season per 10 000 km2). Arrows represent median AR IVT direction (°), IVT magnitude (arrow size; kg m−1 s−1), and median AR area (×106 km2; arrow shading). ARs in each region are identified under the condition that at least 10% of the grid cells of the AR shape are within the region boundaries. Results are for (a) December–February, (b) March–May, (c) June–August, and (d) September–November.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0039.1

Table 1.

Aggregated statistics for seasonal AR characteristics summarized over each of the seven NCA regions including AR frequency (ARs per 10 000 km2), median IVT magnitude (kg m−1 s−1), median direction of mean AR IVT (°), and median AR area (×106 km2).

Table 1.

3) NCA region summary of AR precipitation

Seasonally and regionally summarized AR precipitation characteristics are illustrated in Fig. 12 and recorded in Table 2. Here, region shading (green) refers to extreme precipitation day frequency, calculated as the spatial median of the total number of qualifying days experienced by the region over the study period. Each region has an illustrated bucket depicted with a water level, white bar, and gray bar. The water level refers to the median fraction of AR precipitation or the amount of precipitation that fell on AR days relative to the total precipitation amount. The white bar refers to the median extreme precipitation fraction, or the number of linked AR extreme precipitation days relative to the total extreme precipitation day frequency. The gray bar refers to the median AR fraction or the number of linked AR extreme precipitation days relative to the total AR day frequency.

Fig. 12.
Fig. 12.

Summarized AR precipitation characteristics for each NCA region. Extreme precipitation day frequency is calculated as the spatial median of the total number of qualifying days that occurred during each season at each grid cell across the region (shading). The AR precipitation fraction, calculated as the percent of AR-driven precipitation relative to the total precipitation, is illustrated as the water level in a bucket (and labeled). Also shown is the AR extreme precipitation fraction (% of days), calculated as the number of linked 95th-percentile extreme precipitation AR days relative to the total number of extreme precipitation days, (white bar) and AR fraction (% of days), calculated as the number of linked AR 95th-percentile extreme precipitation days relative to the total number of AR days, (gray bar) between 1981 and 2016 at each grid cell. Results are for (a) December–February, (b) March–May, (c) June–August, and (d) September–November.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0039.1

Table 2.

Aggregated statistics for seasonal AR precipitation characteristics summarized over each of the seven NCA regions, including extreme precipitation day frequency (spatial median), fraction of AR precipitation relative to total precipitation (%), fraction of AR extreme precipitation relative to total extreme precipitation days (%), and fraction of AR extreme precipitation relative to total AR days (%).

Table 2.

Results show that the Northwest experiences the greatest number of extreme precipitation days during the winter and spring (Figs. 12a,b). In the winter, ARs are responsible for ~25% of the total precipitation received in the region with close to 75% of the extreme precipitation days related to an AR (Table 2). The Southwest also shows a maximum in the fraction of AR precipitation extremes in the winter (Fig. 12a), with 66% of all precipitation days linked to ARs and ~15% of the ARs in the region resulting in an extreme (Table 2). Only a small proportion, between 5% and 10%, of the total precipitation experienced in the Northern Great Plains is attributable to ARs across the seasonal cycle. In the Southern Great Plains, results show that ARs play an important role with the highest number of extreme precipitation days occurring in the spring and summer (Figs. 12b,c) and over 50% of the extreme precipitation days related to an AR (Table 2) in the spring. Likewise, the Midwest shares extreme precipitation day maxima in the spring and summer. During these months ARs explain between 50% and 55% of the extreme precipitation days and >20% of the total precipitation experienced by the region. The Northeast has a relatively high occurrence of extreme precipitation days across the seasonal cycle, accounting for the most precipitation during the autumn and winter (Figs. 12a,d), in some cases related to impactful snow storms experienced by the region. Consistent with Mahoney et al. (2016), ARs are more influential in the Southeast during the cool/transition season months (autumn–spring; Figs. 12a,b,d), where they are linked with between 70% and 80% of the extreme precipitation days (Table 2).

5. Summary and conclusions

In the Fourth NCA CSSR, ARs were identified as a key topic in its chapter on extreme storms, focused primarily on the U.S. West Coast. However, research has shown that ARs frequently occur and impact many regions across the CONUS. To expand our understanding and documentation of regional AR impacts, we consistently apply an objective AR detection algorithm to global reanalysis to provide a finescale pointwise and regionally aggregated annual and seasonal understanding of AR frequency, physical characteristics, and impacts across the CONUS summarized over the seven NCA regions. AR detection is based on IVT magnitude thresholds, as well as a number of geometric and directional criteria following the technique described in Guan and Waliser (2015) and updated in Guan et al. (2018).

Seasonal climatologies of AR frequency across the CONUS reveal ARs in the Northwest and Southwest are most common in the winter and autumn (Figs. 2a,d). Although considerably less widely studied, AR occurrence east of the Rocky Mountains is observable across the seasonal cycle with notable maxima across the Southeast in the winter and in the central U.S. Mississippi River basin during the summer and shoulder seasons (Figs. 2b–d). Mean IVT magnitude and direction results illustrate the influence of the mountainous coastal terrain across the West acting as a barrier reducing water vapor transport as ARs penetrate inland (Fig. 3a). Generally higher levels of background moisture and a more diverse array of precipitation triggering mechanisms in the East likely explain differences in AR occurrence and associated impacts compared to the West. Even with a generally drier background environment, the largest area ARs occur in the interior western United States (>4.5 × 106 km2) demonstrating a strong signature of large features penetrating inland from the Pacific Ocean across the interior during the winter (Fig. 4a).

Seasonal patterns of water vapor transport during AR days for major cities across the seven NCA subregions were identified based on AR axis density plots and an IVT composite analysis (Figs. 5 and 6). Western cities reveal predominantly northeastward-directed IVT influenced by moisture transported from the tropical Pacific indicative of the well-known Pineapple Express phenomenon (Figs. 5a–c). Cities in the East show seasonally varying patterns of water vapor transport. Notable north–south oriented bands of moisture were apparent among AR axis density plots for cities across the central United States in the spring (Figs. 6k,l), consistent with literature identifying Maya Express moisture transport fueling the Great Plains LLJ.

Objectively identified ARs were further linked with high-resolution precipitation measurements to examine the relationship between ARs and precipitation across the CONUS. Results show that ARs explain ~30% of the precipitation in areas across the Northwest and ~50% of the precipitation over parts of California during the autumn and winter (Figs. 7a,b). Across the Midwest and Southeast, maxima in the ratio of AR precipitation to total precipitation are evident during the winter and shoulder seasons (Figs. 7a,b,d). The seasonality of linked AR extreme precipitation days in the western and eastern United States has also been shown to starkly differ, with winter/autumn (Figs. 8a,b) days being markedly more prominent in the West and summer/spring (Figs. 8b,c) days dominant in the eastern and central United States. The fraction of linked AR extreme precipitation days relative to the total amount of ARs days revealed higher and more variable fractions west of the Rocky Mountains compared to areas to the east, likely related to the regional differences in precipitation triggering mechanisms (Fig. 9).

Regionally aggregated AR IVT and precipitation characteristics are summarized across the seven NCA regions in Figs. 1012. Histograms of the distribution of three basic AR characteristics, including IVT magnitude, direction, and area, reveal regional variations in distribution shape and median values. Higher values of median IVT magnitude are apparent in the East compared to the West (Figs. 10a–g), while both the Northwest and Northeast reveal maxima in AR area (Figs. 10h,m). All regions indicate that AR IVT typically has a stronger eastward than poleward component, with a rare occurrence of ARs with a westward component (Figs. 10o–u). Regionally aggregated statistics for AR characteristics show seasonal variability in AR size, strength, direction, and frequency (Fig. 11). Similarly, regionally summarized AR precipitation statistics highlight the importance of ARs in fueling precipitation extremes across the United States (Fig. 12).

Two caveats should be considered when interpreting results from this climatology. The first is choice of dataset and the second is choice of detection algorithm. While MERRA-2 has been used extensively for AR detection in recent literature (Guan and Waliser 2017; Lora et al. 2017; Mundhenk et al. 2018; Shields et al. 2018), results could vary slightly with use of a different reanalysis but are not expected to change the conclusions of this paper (Guan and Waliser 2015, 2017, 2019; Guan et al. 2018). The detection algorithm applied here is based on a well-documented approach; however, sensitivity of results to algorithm choice, although beyond the scope of this current study, would add robustness to this climatology (e.g., Shields et al. 2018; Rutz et al. 2019). The detection algorithm also cannot identify AR-linked phenomena, meaning that although the physical interpretation of an AR across regions may differ, for example an extratropical cyclone in a Northwest AR versus the Great Plains LLJ in Midwest ARs, the method is unable to objectively account for it.

The results of this study can be leveraged in two ways. First, the results can be used as a benchmark for considering how climate change may affect AR features and impacts. Second, observed AR characteristics can be used to evaluate the performance of climate models at simulating the seasonality and regional distribution of AR characteristics and precipitation extremes across the CONUS. Ultimately, this study yields insight into the fundamental importance of ARs in the hydroclimate of the CONUS and how that importance varies by region.

Acknowledgments

This work was carried out, in part, at the Jet Propulsion Laboratory, California Institute of Technology, and at Portland State University, under a contract from the National Aeronautics and Space Administration (NASA). Support for this project was provided by the NASA Indicators for the National Climate Assessment (NCA) Program under Award NNX16AG60G. Partial support for author E. Slinskey was provided by a JPL student summer internship. We thank Alexander Goodman for his help with data processing.

REFERENCES

  • AMS, 2017: Atmospheric river. Glossary of Meteorology, American Meteorological Society, http://glossary.ametsoc.org/wiki/Atmospheric_river.

  • Anderson, C. J., and R. W. Arritt, 2001: Mesoscale convective systems over the United States during the 1997–98 El Niño. Mon. Wea. Rev., 129, 24432457, https://doi.org/10.1175/1520-0493(2001)129<2443:MCSOTU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Behrangi, A., B. Guan, P. J. Neiman, M. Schreier, and B. Lambrigtsen, 2016: On the quantification of atmospheric rivers precipitation from space: Composite assessments and case studies over the eastern North Pacific Ocean and the western United States. J. Hydrometeor., 17, 369382, https://doi.org/10.1175/JHM-D-15-0061.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosilovich, M., R. Lucchesi, and M. Suarez, 2016: MERRA-2: File specification. GMAO Office Note 9 (version 1.1), 73 pp., https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich785.pdf.

  • Budikova, D., J. S. M. Coleman, S. A. Strope, and A. Austin, 2010: Hydroclimatology of the 2008 midwest floods. Water Resour. Res., 46, W12524, https://doi.org/10.1029/2010WR009206.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Businger, S., D. I. Knapp, and G. F. Watson, 1990: Storm following climatology of precipitation associated with winter cyclones originating over the Gulf of Mexico. Wea. Forecasting, 5, 378403, https://doi.org/10.1175/1520-0434(1990)005<0378:SFCOPA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Corringham, T. W., F. M. Ralph, A. Gershunov, D. R. Cayan, and C. A. Talbot, 2019: Atmospheric rivers drive flood damages in the western United States. Sci. Adv., 5, eaax4631, https://doi.org/10.1126/sciadv.aax4631.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daly, C., M. Halbleib, I. Smith, W. P. Gibson, M. K. Doggett, G. H. Taylor, J. Curtis, and P. P. Pasteris, 2008: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol., 28, 20312064, https://doi.org/10.1002/joc.1688.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Debbage, N., P. Miller, S. Poore, K. Morano, T. Mote, and J. Marshall Shepherd, 2017: A climatology of atmospheric river interactions with the southeastern United States coastline. Int. J. Climatol., 37, 40774091, https://doi.org/10.1002/joc.5000.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Demaria, E. M. C., F. Dominguez, H. Hu, G. von Glinski, M. Robles, J. Skindlov, and J. Walter, 2017: Observed hydrologic impacts of landfalling atmospheric rivers in the Salt and Verde River basins of Arizona, United States. Water Resour. Res., 53, 10 02510 042, https://doi.org/10.1002/2017WR020778.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dettinger, M. D., 2011: Climate change, atmospheric rivers and floods in California—A multimodel analysis of storm frequency and magnitude changes. J. Amer. Water Resour. Assoc., 47, 514523, https://doi.org/10.1111/j.1752-1688.2011.00546.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dettinger, M. D., 2013: Atmospheric rivers as drought busters on the U.S. West Coast. J. Hydrometeor., 14, 17211732, https://doi.org/10.1175/JHM-D-13-02.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dettinger, M. D., F. M. Ralph, T. Das, P. J. Neiman, and D. R. Cayan, 2011: Atmospheric rivers, floods, and the water resources of California. Water, 3, 445478, https://doi.org/10.3390/w3020445.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dettinger, M. D., F. M. Ralph, and J. J. Rutz, 2018: Empirical return periods of the most intense vapor transports during historical atmospheric river landfalls on the U.S. West Coast. J. Hydrometeor., 19, 13631377, https://doi.org/10.1175/JHM-D-17-0247.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., and J. L. Kinter, 2009: The “Maya Express”: Floods in the U.S. Midwest. Eos, Trans. Amer. Geophys. Union, 90, 101102, https://doi.org/10.1029/2009EO120001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., and J. L. Kinter, 2010: Floods over the U.S. Midwest: A regional water cycle perspective. J. Hydrometeor., 11, 11721181, https://doi.org/10.1175/2010JHM1196.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Espinoza, V., D. E. Waliser, B. Guan, D. A. Lavers, and F. M. Ralph, 2018: Global analysis of climate change projection effects on atmospheric rivers. Geophys. Res. Lett., 45, 42994308, https://doi.org/10.1029/2017GL076968.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Florsheim, J., and M. Dettinger, 2015: Promoting atmospheric-river and snowmelt-fueled biogeomorphic processes by restoring river-floodplain connectivity in California’s Central Valley. Geomorphic Approaches to Integrated Floodplain Management of Lowland Fluvial Systems in North America and Europe, P. Hudson and H. Middelkoop, Eds., Springer, 119–141, https://doi.org/10.1007/978-1-4939-2380-9_6.

    • Crossref
    • Export Citation
  • Gao, Y., J. Lu, L. R. Leung, Q. Yang, S. Hagos, and Y. Qian, 2015: Dynamical and thermodynamical modulations on future changes of landfalling atmospheric rivers over western North America. Geophys. Res. Lett., 42, 71797186, https://doi.org/10.1002/2015GL065435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gelaro, R. W., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gershunov, A., T. Shulgina, F. M. Ralph, D. A. Lavers, and J. J. Rutz, 2017: Assessing the climate-scale variability of atmospheric rivers affecting western North America. Geophys. Res. Lett., 44, 79007908, https://doi.org/10.1002/2017GL074175.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gershunov, A., and Coauthors, 2019: Precipitation regime change in western North America: The role of atmospheric rivers. Nat. Sci. Rep., 9, 9944, https://doi.org/10.1038/s41598-019-46169-w.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, B., and D. E. Waliser, 2015: Detection of atmospheric rivers: Evaluation and application of an algorithm for global studies. J. Geophys. Res. Atmos., 120, 12 51412 535, https://doi.org/10.1002/2015JD024257.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, B., and D. E. Waliser, 2017: Atmospheric rivers in 20 year weather and climate simulations: A multimodel, global evaluation. J. Geophys. Res. Atmos., 122, 55565581, https://doi.org/10.1002/2016JD026174.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, B., and D. E. Waliser, 2019: Tracking atmospheric rivers globally: Spatial distributions and temporal evolution of life cycle characteristics. J. Geophys. Res. Atmos., 124, 12 52312 552, https://doi.org/10.1029/2019JD031205.

    • 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
  • Guan, B., D. E. Waliser, F. M. Ralph, E. J. Fetzer, and P. J. Neiman, 2016: Hydrometeorological characteristics of rain-on-snow events associated with atmospheric rivers. Geophys. Res. Lett., 43, 29642973, https://doi.org/10.1002/2016GL067978.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, B., D. E. Waliser, and F. M. Ralph, 2018: An intercomparison between reanalysis and dropsonde observations of the total water vapor transport in individual atmospheric rivers. J. Hydrometeor., 19, 321337, https://doi.org/10.1175/JHM-D-17-0114.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagos, S. M., L. R. Leung, J.-H. Yoon, J. Lu, and Y. Gao, 2016: A projection of changes in landfalling atmospheric river frequency and extreme precipitation over western North America from the large ensemble CESM simulations. Geophys. Res. Lett., 43, 13571363, https://doi.org/10.1002/2015GL067392.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Halverson, J. B., and T. D. Rabenhorst, 2010: Mega-snow in the Megalopolis: The Mid-Atlantic’s blockbuster winter of 2009–2010. Weatherwise, 63, 1623, https://doi.org/10.1080/00431672.2010.490164.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hatchett, B. J., S. Burak, J. J. Rutz, N. S. Oakley, E. H. Bair, and M. L. Kaplan, 2017: Avalanche fatalities during atmospheric river events in the western United States. J. Hydrometeor., 18, 13591374, https://doi.org/10.1175/JHM-D-16-0219.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hecht, C. W., and J. M. Cordeira, 2017: Characterizing the influence of atmospheric river orientation and intensity on precipitation distributions over north coastal California. Geophys. Res. Lett., 44, 90489058, https://doi.org/10.1002/2017GL074179.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, H., and Y. Chen, 2020: Simulation and projection of circulations associated with atmospheric rivers along the North American northeast coast. J. Climate, 33, 56735695, https://doi.org/10.1175/JCLI-D-19-0104.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hughes, M., K. M. Mahoney, P. J. Neiman, B. J. Moore, M. Alexander, and F. M. Ralph, 2014: The landfall and inland penetration of a flood-producing atmospheric river in Arizona. Part II: Sensitivity of modeled precipitation to terrain height and atmospheric river orientation. J. Hydrometeor., 15, 19541974, https://doi.org/10.1175/JHM-D-13-0176.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khouakhi, A., and G. Villarini, 2016: On the relationship between atmospheric rivers and high sea water levels along the U.S. West Coast. Geophys. Res. Lett., 43, 88158822, https://doi.org/10.1002/2016GL070086.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, J., and Coauthors, 2018: Winter precipitation characteristics in western US related to atmospheric river landfalls: Observations and model evaluations. Climate Dyn., 50, 231248, https://doi.org/10.1007/s00382-017-3601-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knippertz, P., and H. Wernli, 2010: A Lagrangian climatology of tropical moisture exports to the Northern Hemispheric extratropics. J. Climate, 23, 9871003, https://doi.org/10.1175/2009JCLI3333.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Konrad, C. P., and M. D. Dettinger, 2017: Flood runoff in relation to water vapor transport by atmospheric rivers over the western United States, 1949–2015. Geophys. Res. Lett., 44, 11 45611 462, https://doi.org/10.1002/2017GL075399.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., T. Hall, T. Knutson, K. E. Kunkel, R. J. Trapp, D. E. Waliser, and M. F. Wehner, 2017: Extreme storms. Climate Science Special Report. Vol 1, Fourth National Climate Assessment, D. J. Wuebbles et al., Eds., U.S. Global Change Research Program, 257–276, https://doi.org/10.7930/J07S7KXX.

    • Crossref
    • Export Citation
  • Lackmann, G. M., 2013: The south-central U.S. flood of May 2010: Present and future. J. Climate, 26, 46884709, https://doi.org/10.1175/JCLI-D-12-00392.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lackmann, G. M., and J. R. Gyakum, 1999: Heavy cold-season precipitation in the northwestern United States: Synoptic climatology and an analysis of the flood of 17–18 January 1986. Wea. Forecasting, 14, 687700, https://doi.org/10.1175/1520-0434(1999)014<0687:HCSPIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lamjiri, M. A., M. D. Dettinger, F. M. Ralph, and B. Guan, 2017: Hourly storm characteristics along the U.S. West Coast: Role of atmospheric rivers in extreme precipitation. Geophys. Res. Lett., 44, 70207028, https://doi.org/10.1002/2017GL074193.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lavers, D. A., and G. Villarini, 2013: Atmospheric rivers and flooding over the central United States. J. Climate, 26, 78297836, https://doi.org/10.1175/JCLI-D-13-00212.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lavers, D. A., and G. Villarini, 2015: The contribution of atmospheric rivers to precipitation in Europe and the United States. J. Hydrol., 522, 382390, https://doi.org/10.1016/j.jhydrol.2014.12.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lavers, D. A., R. P. Allan, G. Villarini, B. Lloyd-Hughes, D. J. Brayshaw, and A. J. Wade, 2013: Future changes in atmospheric rivers and their implications for winter flooding in Britain. Environ. Res. Lett., 8, 034010, https://doi.org/10.1088/1748-9326/8/3/034010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Letkewicz, C. E., and M. D. Parker, 2010: Forecasting the maintenance of mesoscale convective systems crossing the Appalachian Mountains. Wea. Forecasting, 25, 11791195, https://doi.org/10.1175/2010WAF2222379.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lora, J. M., J. L. Mitchell, C. Risi, and A. E. Tripati, 2017: North Pacific atmospheric rivers and their influence on western North America at the Last Glacial Maximum. Geophys. Res. Lett., 44, 10511059, https://doi.org/10.1002/2016GL071541.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahoney, K., and Coauthors, 2016: Understanding the role of atmospheric rivers in heavy precipitation in the southeast United States. Mon. Wea. Rev., 144, 16171632, https://doi.org/10.1175/MWR-D-15-0279.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mestas-Nuñez, A. M., D. B. Enfield, and C. Zhang, 2007: Water vapor fluxes over the Intra-Americas Sea: Seasonal and interannual variability and associations with rainfall. J. Climate, 20, 19101922, https://doi.org/10.1175/JCLI4096.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, D. K., D. Hotz, J. Winton, and L. Stewart, 2018: Investigation of atmospheric rivers impacting the Pigeon River basin of the southern Appalachian Mountains. Wea. Forecasting, 33, 283299, https://doi.org/10.1175/WAF-D-17-0060.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moore, B. J., P. J. Neiman, F. M. Ralph, and F. E. Barthold, 2012: Physical processes associated with heavy flooding rainfall in Nashville, Tennessee, and vicinity during 1–2 May 2010: The role of an atmospheric river and mesoscale convective systems. Mon. Wea. Rev., 140, 358378, https://doi.org/10.1175/MWR-D-11-00126.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mueller, M. J., K. M. Mahoney, and M. Hughes, 2017: High-resolution model-based investigation of moisture transport into the Pacific Northwest during a strong atmospheric river event. Mon. Wea. Rev., 145, 38613879, https://doi.org/10.1175/MWR-D-16-0466.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mundhenk, B. D., E. A. Barnes, E. D. Maloney, and C. F. Baggett, 2018: Skillful empirical subseasonal prediction of landfalling atmospheric river activity using the Madden-Julian oscillation and quasi-biennial oscillation. npj Climate Atmos. Sci., 1, 20177, https://doi.org/10.1038/s41612-017-0008-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakamura, J., U. Lall, Y. Kushnir, A. W. Robertson, and R. Seager, 2013: Dynamical structure of extreme floods in the U.S. Midwest and the United Kingdom. J. Hydrometeor., 14, 485504, https://doi.org/10.1175/JHM-D-12-059.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., F. M. Ralph, G. A. Wick, Y. Kuo, T. Wee, Z. Ma, G. H. Taylor, and M. D. Dettinger, 2008a: Diagnosis of an intense atmospheric river impacting the Pacific Northwest: Storm summary and offshore vertical structure observed with COSMIC satellite retrievals. Mon. Wea. Rev., 136, 43984420, https://doi.org/10.1175/2008MWR2550.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., F. M. Ralph, G. A. Wick, J. D. Lundquist, and M. D. Dettinger, 2008b: Meteorological characteristics and overland precipitation impacts of atmospheric rivers affecting the west coast of North America based on eight years of SSM/I satellite observations. J. Hydrometeor., 9, 2247, https://doi.org/10.1175/2007JHM855.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., L. J. Schick, F. M. Ralph, M. Hughes, and G. A. Wick, 2011: Flooding in western Washington: The connection to atmospheric rivers. J. Hydrometeor., 12, 13371358, https://doi.org/10.1175/2011JHM1358.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., F. M. Ralph, B. J. Moore, M. Hughes, K. M. Mahoney, J. M. Cordeira, and M. D. Dettinger, 2013: The landfall and inland penetration of a flood-producing atmospheric river in Arizona. Part I: Observed synoptic-scale, orographic, and hydrometeorological characteristics. J. Hydrometeor., 14, 460484, https://doi.org/10.1175/JHM-D-12-0101.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oakley, N. S., J. T. Lancaster, M. L. Kaplan, and F. M. Ralph, 2017: Synoptic conditions associated with cool season post-fire debris flows in the Transverse Ranges of Southern California. Nat. Hazards, 88, 327354, https://doi.org/10.1007/s11069-017-2867-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Paltan, H., D. Waliser, W. H. Lim, B. Guan, D. Yamazaki, R. Pant, and S. Dadson, 2017: Global floods and water availability driven by atmospheric rivers. Geophys. Res. Lett., 44, 10 38710 395, https://doi.org/10.1002/2017GL074882.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Payne, A. E., and G. Magnusdottir, 2015: An evaluation of atmospheric rivers over the North Pacific in CMIP5 and their response to warming under RCP 8.5. J. Geophys. Res. Atmos., 120, 11 17311 190, https://doi.org/10.1002/2015JD023586.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pfahl, S., E. Madonna, M. Boettcher, H. Joos, and H. Wernli, 2014: Warm conveyor belts in the ERA-Interim dataset (1979–2010). Part II: Moisture origin and relevance for precipitation. J. Climate, 27, 2740, https://doi.org/10.1175/JCLI-D-13-00223.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rabinowitz, J. L., A. R. Lupo, and P. E. Guinan, 2018: Evaluating linkages between atmospheric blocking patterns and heavy rainfall events across the North-Central Mississippi River valley for different ENSO phases. Adv. Meteor., 2018, 1217830, https://doi.org/10.1155/2018/1217830.

    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., and M. D. Dettinger, 2012: Historical and national perspectives on extreme West Coast precipitation associated with atmospheric rivers during December 2010. Bull. Amer. Meteor. Soc., 93, 783790, https://doi.org/10.1175/BAMS-D-11-00188.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., P. J. Neiman, D. E. Kingsmill, P. O. Persson, A. B. White, E. T. Strem, E. D. Andrews, and R. C. Antweiler, 2003: The impact of a prominent rain shadow on flooding in California’s Santa Cruz Mountains: A CALJET case study and sensitivity to the ENSO cycle. J. Hydrometeor., 4, 12431264, https://doi.org/10.1175/1525-7541(2003)004<1243:TIOAPR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., P. J. Neiman, and G. A. Wick, 2004: Satellite and CALJET aircraft observations of atmospheric rivers over the eastern North Pacific Ocean during the winter of 1997/98. Mon. Wea. Rev., 132, 17211745, https://doi.org/10.1175/1520-0493(2004)132<1721:SACAOO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., P. J. Neiman, and R. Rotunno, 2005: Dropsonde observations in low-level jets over the northeastern Pacific Ocean from CALJET-1998 and PACJET-2001: Mean vertical-profile and atmospheric-river characteristics. Mon. Wea. Rev., 133, 889910, https://doi.org/10.1175/MWR2896.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., P. J. Neiman, G. Wick, S. Gutman, M. Dettinger, D. 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., T. Coleman, P. J. Neiman, R. J. Zamora, and M. D. Dettinger, 2013: Observed impacts of duration and seasonality of atmospheric-river landfalls on soil moisture and runoff in coastal Northern California. J. Hydrometeor., 14, 443459, https://doi.org/10.1175/JHM-D-12-076.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rivera, E. R., F. Dominguez, and C. L. Castro, 2014: Atmospheric rivers and cool season extreme precipitation events in the Verde River basin of Arizona. J. Hydrometeor., 15, 813829, https://doi.org/10.1175/JHM-D-12-0189.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rutz, J. J., and W. J. Steenburgh, 2012: Quantifying the role of atmospheric rivers in the interior western United States. Atmos. Sci. Lett., 13, 257261, https://doi.org/10.1002/asl.392.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rutz, J. J., W. J. Steenburgh, and F. M. Ralph, 2014: Climatological characteristics of atmospheric rivers and their inland penetration over the western United States. Mon. Wea. Rev., 142, 905921, https://doi.org/10.1175/MWR-D-13-00168.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rutz, J. J., and Coauthors, 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 J. T. Kiehl, 2016: Atmospheric river landfall-latitude changes in future climate simulations. Geophys. Res. Lett., 43, 87758782, https://doi.org/10.1002/2016GL070470.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shields, C. A., and Coauthors, 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
  • Slinskey, E. A., P. C. Loikith, D. E. Waliser, and A. Goodman, 2019: An extreme precipitation categorization scheme and its observational uncertainty over the continental United States. J. Hydrometeor., 20, 10291052, https://doi.org/10.1175/JHM-D-18-0148.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, J. A., M. L. Baeck, G. Villarini, D. B. Wright, and W. Krajewski, 2013: Extreme flood response: The June 2008 flooding in Iowa. J. Hydrometeor., 14, 18101825, https://doi.org/10.1175/JHM-D-12-0191.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, J. A., M. L. Baeck, A. A. Ntelekos, G. Villarini, and M. Steiner, 2011: Extreme rainfall and flooding from orographic thunderstorms in the central Appalachians. Water Resour. Res., 47, W04514, https://doi.org/10.1029/2010WR010190.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Villarini, G., J. A. Smith, M. L. Baeck, R. Vitolo, D. B. Stephenson, and W. Krajewski, 2011: On the frequency of heavy rainfall for the Midwest of the United States. J. Hydrol., 400, 103120, https://doi.org/10.1016/j.jhydrol.2011.01.027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., and B. Guan, 2017: Extreme winds and precipitation during landfall of atmospheric rivers. Nat. Geosci., 10, 179183, https://doi.org/10.1038/ngeo2894.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., and J. M. Cordeira, 2020: AR modeling: Forecasts, climate simulations, and climate projections. Atmospheric Rivers, F. M. Ralph et al., Eds., Springer, 189–213.

    • Crossref
    • Export Citation
  • Warner, M. D., C. F. Mass, and E. P. Salathé, 2012: Wintertime extreme precipitation events along the Pacific Northwest coast: Climatology and synoptic evolution. Mon. Wea. Rev., 140, 20212043, https://doi.org/10.1175/MWR-D-11-00197.1.

    • Crossref
    • Search Google Scholar