Extreme Surface Winds during Landfalling Atmospheric Rivers: The Modulating Role of Near-Surface Stability

Terence J. Pagano aUniversity of Wisconsin–Madison, Madison, Wisconsin
bCollege of Natural and Social Sciences, California State University, Los Angeles, Los Angeles, California
cJet Propulsion Laboratory, California Institute of Technology, Pasadena, California

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Duane E. Waliser cJet Propulsion Laboratory, California Institute of Technology, Pasadena, California
dJoint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, Los Angeles, California

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Bin Guan cJet Propulsion Laboratory, California Institute of Technology, Pasadena, California
dJoint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, Los Angeles, California

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Hengchun Ye bCollege of Natural and Social Sciences, California State University, Los Angeles, Los Angeles, California

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F. Martin Ralph eCenter for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California

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Jinwon Kim fNational Institute of Meteorological Sciences, Korea Meteorological Administration, Seogwipo-si, Jeju-do, South Korea

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Abstract

Atmospheric rivers (ARs) are long and narrow regions of strong horizontal water vapor transport. Upon landfall, ARs are typically associated with heavy precipitation and strong surface winds. A quantitative understanding of the atmospheric conditions that favor extreme surface winds during ARs has implications for anticipating and managing various impacts associated with these potentially hazardous events. Here, a global AR database (1999–2014) with relevant information from MERRA-2 reanalysis, QuikSCAT, and AIRS satellite observations is used to better understand and quantify the role of near-surface static stability in modulating surface winds during landfalling ARs. The temperature difference between the surface and 1 km MSL (ΔT; used here as a proxy for near-surface static stability), along with integrated water vapor transport (IVT), is analyzed to quantify their relationships to surface winds using bivariate linear regression. In four regions where AR landfalls are common, the MERRA-2-based results indicate that IVT accounts for 22%–38% of the variance in surface wind speed. Combining ΔT with IVT increases the explained variance to 36%–52%. Substitution of QuikSCAT surface winds and AIRS ΔT in place of the MERRA-2 data largely preserves this relationship (e.g., 44% as compared with 52% explained variance for U.S. West Coast). Use of an alternate static stability measure—the bulk Richardson number—yields a similar explained variance (47%). Last, AR cases within the top and bottom 25% of near-surface static stability indicate that extreme surface winds (gale or higher) are more likely to occur in unstable conditions (5.3% and 14.7% during weak and strong IVT, respectively) than in stable conditions (0.58% and 6.15%).

Corresponding author: Terence Pagano, tpagano@wisc.edu

Abstract

Atmospheric rivers (ARs) are long and narrow regions of strong horizontal water vapor transport. Upon landfall, ARs are typically associated with heavy precipitation and strong surface winds. A quantitative understanding of the atmospheric conditions that favor extreme surface winds during ARs has implications for anticipating and managing various impacts associated with these potentially hazardous events. Here, a global AR database (1999–2014) with relevant information from MERRA-2 reanalysis, QuikSCAT, and AIRS satellite observations is used to better understand and quantify the role of near-surface static stability in modulating surface winds during landfalling ARs. The temperature difference between the surface and 1 km MSL (ΔT; used here as a proxy for near-surface static stability), along with integrated water vapor transport (IVT), is analyzed to quantify their relationships to surface winds using bivariate linear regression. In four regions where AR landfalls are common, the MERRA-2-based results indicate that IVT accounts for 22%–38% of the variance in surface wind speed. Combining ΔT with IVT increases the explained variance to 36%–52%. Substitution of QuikSCAT surface winds and AIRS ΔT in place of the MERRA-2 data largely preserves this relationship (e.g., 44% as compared with 52% explained variance for U.S. West Coast). Use of an alternate static stability measure—the bulk Richardson number—yields a similar explained variance (47%). Last, AR cases within the top and bottom 25% of near-surface static stability indicate that extreme surface winds (gale or higher) are more likely to occur in unstable conditions (5.3% and 14.7% during weak and strong IVT, respectively) than in stable conditions (0.58% and 6.15%).

Corresponding author: Terence Pagano, tpagano@wisc.edu

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