• Carroll, T., D. Cline, G. Fall, A. Nilsson, L. Li, and A. Rost, 2001: NOHRSC operations and the simulation of snow cover properties for the conterminous U.S. Proc. 69th Annual Meeting of the Western Snow Conf., Sun Valley, ID, Western Snow Conference, 14 pp., www.westernsnowconference.org/sites/westernsnowconference.org/PDFs/2001Carroll.pdf.

  • Clow, D. W., L. Nanus, K. L. Verdin, and J. Schmidt, 2012: Evaluation of SNODAS snow depth and snow water equivalent estimates for the Colorado Rocky Mountains, USA. Hydrol. Processes, 26, 25832591, https://doi.org/10.1002/hyp.9385.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dozier, J., E. H. Bair, and R. E. Davis, 2016: Estimating the spatial distribution of snow water equivalent in the world’s mountains. Wiley Interdiscip. Rev.:Water, 3, 461474, https://doi.org/10.1002/wat2.1140.

    • 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., N. P. Molotch, D. E. Waliser, S. M. Jepsen, T. H. Painter, and J. Dozier, 2013: Snow water equivalent in the Sierra Nevada: Blending snow sensor observations with snowmelt model simulations. Water Resour. Res., 49, 50295046, https://doi.org/10.1002/wrcr.20387.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hedrick, A., H.-P. Marshall, A. Winstral, K. Elder, S. Yueh, and D. Cline, 2015: Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements. Cryosphere, 9, 1323, https://doi.org/10.5194/tc-9-13-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, S. V., and Coauthors, 2016: Assimilation of gridded GRACE terrestrial water storage estimates in the North American Land Data Assimilation System. J. Hydrometeor., 17, 19511972, https://doi.org/10.1175/JHM-D-15-0157.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Swenson, S. C., and D. M. Lawrence, 2012: A new fractional snow-covered area parameterization for the Community Land Model and its effect on the surface energy balance. J. Geophys. Res., 117, D21107, https://doi.org/10.1029/2012JD018178.

    • Search Google Scholar
    • Export Citation
  • Tedesco, M., and P. S. Narvekar, 2010: Assessment of the NASA AMSR-E SWE product. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 3, 141159, https://doi.org/10.1109/JSTARS.2010.2040462.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vuyovich, C. M., J. M. Jacobs, and S. F. Daly, 2014: Comparison of passive microwave and modeled estimates of total watershed SWE in the continental United States. Water Resour. Res., 50, 90889102, https://doi.org/10.1002/2013WR014734.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wrzesien, M. L., M. T. Durand, T. M. Pavelsky, I. M. Howat, S. A. Margulis, and L. S. Huning, 2017: Comparison of methods to estimate snow water equivalent at the mountain range scale: A case study of the California Sierra Nevada. J. Hydrometeor., 18, 11011119, https://doi.org/10.1175/JHM-D-16-0246.1.

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    • Search Google Scholar
    • Export Citation
  • View in gallery

    Frequency histograms for the area of individual SNODAS grid cells for the (left) Sierra Nevada study domain and (right) entire SNODAS dataset.

  • View in gallery

    Updated SNODAS time series of daily SWE. Daily values for all other datasets remain the same.

  • View in gallery

    Updated SNODAS values and reference dataset averages. The solid black horizontal line indicates the reference average and the dashed lines are ±50% of the mean.

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Corrigendum

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  • 1 School of Earth Sciences, and Byrd Polar and Climate Research Center, The Ohio State University, Columbus, Ohio
  • | 2 Department of Geological Sciences, University of North Carolina, Chapel Hill, North Carolina
  • | 3 School of Earth Sciences, and Byrd Polar and Climate Research Center, The Ohio State University, Columbus, Ohio
  • | 4 Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, California
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© 2018 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: Melissa L. Wrzesien, wrzesien.1@osu.edu

© 2018 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: Melissa L. Wrzesien, wrzesien.1@osu.edu

Because of an incorrect assumption about the spatial resolution of the National Weather Service’s Snow Data Assimilation System (SNODAS), the authors have identified an important error in our recent paper comparing various estimates of SWE in the Sierra Nevada (Wrzesien et al. 2017). The assumed SNODAS spatial resolution of 1 km (gridcell area of 1 km2) should have been 30 arc s. At the latitudes where SNODAS is available, this assumption leads to gridcell areas considerably smaller than 1 km2 (Fig. 1). Though Carroll et al. (2001) and much of the literature erroneously identify the resolution of SNODAS as 1 km or 1 km2 (see literature list below), we acknowledge that this is an error we should have caught. Generally, the new SWE values for SNODAS are ~67% of the previous estimates, since actual areas of SNODAS grid cells are ~67% of our previous assumption (see Fig. 1).

Fig. 1.
Fig. 1.

Frequency histograms for the area of individual SNODAS grid cells for the (left) Sierra Nevada study domain and (right) entire SNODAS dataset.

Citation: Journal of Hydrometeorology 19, 1; 10.1175/JHM-D-17-0175.1

We summarize the changes to the SNODAS SWE values for both peak SWE and 1 April SWE (Table 1). We have also included the peak SWE and 1 April SWE values for the reference mean, since those values are also affected by the new SNODAS calculations. In Table 2, we show the updated percent difference values between all three WRF simulations and the reference mean. These are also depicted in the updated Fig. 7 from the original paper.

Table 1.

Updated SWE values (km3) from SNODAS and the reference dataset mean.

Table 1.
Table 2.

Updated comparison between WRF and the reference dataset mean. Italics indicate a closer match now between the WRF estimate and the reference mean.

Table 2.

Despite the changes, the overall conclusion of the paper remains well supported. The three reference datasets together still provide the best guess of actual snow conditions for the Sierra Nevada. With the updated SNODAS values, SNODAS and the Sierra Nevada Snow Reanalysis (SNSR) are in closer agreement with one another. WRF estimates are still within ±50% of the reference mean (except for WRF 27 km in 2014, which was not within ±50% in the previous estimate, either). Our results still show that regional climate models, such as WRF, provide estimates that are more reasonable than global/continental United States plus southern Canada (CONUS+) products, as can be seen from the updated versions of both Fig. 3 and Fig. 7

Fig. 3.
Fig. 3.

Updated SNODAS time series of daily SWE. Daily values for all other datasets remain the same.

Citation: Journal of Hydrometeorology 19, 1; 10.1175/JHM-D-17-0175.1

Fig. 7.
Fig. 7.

Updated SNODAS values and reference dataset averages. The solid black horizontal line indicates the reference average and the dashed lines are ±50% of the mean.

Citation: Journal of Hydrometeorology 19, 1; 10.1175/JHM-D-17-0175.1

Though the principal conclusions of the original manuscript are unchanged, we regret any inconvenience our incorrect SNODAS assumption may have caused.

Acknowledgments

The authors acknowledge Nataniel Holtzman for identifying our incorrect assumption of SNODAS spatial resolution.

REFERENCES

  • Carroll, T., D. Cline, G. Fall, A. Nilsson, L. Li, and A. Rost, 2001: NOHRSC operations and the simulation of snow cover properties for the conterminous U.S. Proc. 69th Annual Meeting of the Western Snow Conf., Sun Valley, ID, Western Snow Conference, 14 pp., www.westernsnowconference.org/sites/westernsnowconference.org/PDFs/2001Carroll.pdf.

  • Clow, D. W., L. Nanus, K. L. Verdin, and J. Schmidt, 2012: Evaluation of SNODAS snow depth and snow water equivalent estimates for the Colorado Rocky Mountains, USA. Hydrol. Processes, 26, 25832591, https://doi.org/10.1002/hyp.9385.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dozier, J., E. H. Bair, and R. E. Davis, 2016: Estimating the spatial distribution of snow water equivalent in the world’s mountains. Wiley Interdiscip. Rev.:Water, 3, 461474, https://doi.org/10.1002/wat2.1140.

    • 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., N. P. Molotch, D. E. Waliser, S. M. Jepsen, T. H. Painter, and J. Dozier, 2013: Snow water equivalent in the Sierra Nevada: Blending snow sensor observations with snowmelt model simulations. Water Resour. Res., 49, 50295046, https://doi.org/10.1002/wrcr.20387.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hedrick, A., H.-P. Marshall, A. Winstral, K. Elder, S. Yueh, and D. Cline, 2015: Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements. Cryosphere, 9, 1323, https://doi.org/10.5194/tc-9-13-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, S. V., and Coauthors, 2016: Assimilation of gridded GRACE terrestrial water storage estimates in the North American Land Data Assimilation System. J. Hydrometeor., 17, 19511972, https://doi.org/10.1175/JHM-D-15-0157.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Swenson, S. C., and D. M. Lawrence, 2012: A new fractional snow-covered area parameterization for the Community Land Model and its effect on the surface energy balance. J. Geophys. Res., 117, D21107, https://doi.org/10.1029/2012JD018178.

    • Search Google Scholar
    • Export Citation
  • Tedesco, M., and P. S. Narvekar, 2010: Assessment of the NASA AMSR-E SWE product. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 3, 141159, https://doi.org/10.1109/JSTARS.2010.2040462.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vuyovich, C. M., J. M. Jacobs, and S. F. Daly, 2014: Comparison of passive microwave and modeled estimates of total watershed SWE in the continental United States. Water Resour. Res., 50, 90889102, https://doi.org/10.1002/2013WR014734.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wrzesien, M. L., M. T. Durand, T. M. Pavelsky, I. M. Howat, S. A. Margulis, and L. S. Huning, 2017: Comparison of methods to estimate snow water equivalent at the mountain range scale: A case study of the California Sierra Nevada. J. Hydrometeor., 18, 11011119, https://doi.org/10.1175/JHM-D-16-0246.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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