4.7 Article

Characterizing the Spatial Scales of Extreme Daily Precipitation in the United States

Journal

JOURNAL OF CLIMATE
Volume 31, Issue 19, Pages 8023-8037

Publisher

AMER METEOROLOGICAL SOC
DOI: 10.1175/JCLI-D-18-0019.1

Keywords

North America; Extreme events; Precipitation

Funding

  1. Stanford Research Computing Center
  2. Stanford's Center for Computational Earth and Environmental Science
  3. U.S. Department of Energy
  4. Stanford University
  5. UCLA Sustainable LA Grand Challenge
  6. The Nature Conservancy
  7. University of California, Los Angeles

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The spatial extent of an extreme precipitation event can be important for a basin's hydrologic response and subsequent flood risk, and may yield insights into underlying atmospheric processes. Using a relaxed moving-neighborhood approach, we develop indicator semivariograms based on precipitation records from the Global Historical Climatology Network-Daily (GHCN-D) station network to directly quantify the climatological length scales of extreme daily precipitation over the United States during 1965-2014. We find that the length scales of extreme (90th percentile) daily precipitation events vary both regionally and seasonally. Over the eastern half of the United States, daily extreme precipitation length scales reach 400 km during the winter months, but are approximately half as large during the summer months. The Northwest region, on the other hand, exhibits little seasonal variation, with extreme precipitation length scales of approximately 150 km throughout the year. By leveraging in situ station measurements, our study avoids some of the uncertainties associated with satellite or interpolated precipitation data, and provides the longest climatological assessment of length scales of extreme daily precipitation over the United States to date. Although the length scales that we calculate can be sensitive to station density, neighborhood size, and neighborhood relaxation, we find that the interregional and interseasonal differences in length scales are relatively robust. Our method could be extended to quantify changes in the spatial extent of extreme daily precipitation in the recent past, and to investigate the underlying causes of any changes that are detected.

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