Editorial Type:
Article
Article Type:
Research Article

High-Elevation Precipitation Patterns: Using Snow Measurements to Assess Daily Gridded Datasets across the Sierra Nevada, California*

Jessica D. Lundquist +Civil and Environmental Engineering, University of Washington, Seattle, Washington

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Mimi R. Abel #Cooperative Institute for Research in Environmental Science, University of Colorado Boulder, Boulder, Colorado
@NOAA/Earth System Research Laboratory/Physical Sciences Division, Boulder, Colorado

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Brian Henn +Civil and Environmental Engineering, University of Washington, Seattle, Washington

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Ethan D. Gutmann &National Center for Atmospheric Research, Boulder, Colorado

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Ben Livneh #Cooperative Institute for Research in Environmental Science, University of Colorado Boulder, Boulder, Colorado
@NOAA/Earth System Research Laboratory/Physical Sciences Division, Boulder, Colorado

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Jeff Dozier ** Bren School of Environmental Science and Management, University of California, Santa Barbara, Santa Barbara, California

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Paul Neiman #Cooperative Institute for Research in Environmental Science, University of Colorado Boulder, Boulder, Colorado

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Print Publication:
01 Aug 2015
DOI:
https://doi.org/10.1175/JHM-D-15-0019.1
Page(s):
1773–1792
Restricted access

Abstract

Gridded spatiotemporal maps of precipitation are essential for hydrometeorological and ecological analyses. In the United States, most of these datasets are developed using the Cooperative Observer (COOP) network of ground-based precipitation measurements, interpolation, and the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) to map these measurements to places where data are not available. Here, we evaluate two daily datasets gridded at ° resolution against independent daily observations from over 100 snow pillows in California’s Sierra Nevada from 1990 to 2010. Over the entire period, the gridded datasets performed reasonably well, with median total water-year errors generally falling within ±10%. However, errors in individual storm events sometimes exceeded 50% for the median difference across all stations, and in many cases, the same underpredicted storms appear in both datasets. Synoptic analysis reveals that these underpredicted storms coincide with 700-hPa winds from the west or northwest, which are associated with post-cold-frontal flow and disproportionately small precipitation rates in low-elevation valley locations, where the COOP stations are primarily located. This atmospheric circulation leads to a stronger than normal valley-to-mountain precipitation gradient and underestimation of actual mountain precipitation. Because of the small average number of storms (<10) reaching California each year, these individual storm misses can lead to large biases (~20%) in total water-year precipitation and thereby significantly affect estimates of statewide water resources.

Joint Institute for the Study of the Atmosphere and Ocean Contribution Number 2405.

Corresponding author address: Jessica D. Lundquist, Civil and Environmental Engineering, University of Washington, Box 352700, Seattle, WA 98195. E-mail: jdlund@u.washington.edu

Abstract

Gridded spatiotemporal maps of precipitation are essential for hydrometeorological and ecological analyses. In the United States, most of these datasets are developed using the Cooperative Observer (COOP) network of ground-based precipitation measurements, interpolation, and the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) to map these measurements to places where data are not available. Here, we evaluate two daily datasets gridded at ° resolution against independent daily observations from over 100 snow pillows in California’s Sierra Nevada from 1990 to 2010. Over the entire period, the gridded datasets performed reasonably well, with median total water-year errors generally falling within ±10%. However, errors in individual storm events sometimes exceeded 50% for the median difference across all stations, and in many cases, the same underpredicted storms appear in both datasets. Synoptic analysis reveals that these underpredicted storms coincide with 700-hPa winds from the west or northwest, which are associated with post-cold-frontal flow and disproportionately small precipitation rates in low-elevation valley locations, where the COOP stations are primarily located. This atmospheric circulation leads to a stronger than normal valley-to-mountain precipitation gradient and underestimation of actual mountain precipitation. Because of the small average number of storms (<10) reaching California each year, these individual storm misses can lead to large biases (~20%) in total water-year precipitation and thereby significantly affect estimates of statewide water resources.

Joint Institute for the Study of the Atmosphere and Ocean Contribution Number 2405.

Corresponding author address: Jessica D. Lundquist, Civil and Environmental Engineering, University of Washington, Box 352700, Seattle, WA 98195. E-mail: jdlund@u.washington.edu
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