Editorial Type:
Article
Article Type:
Research Article

Downscaling of ERA-Interim Temperature in the Contiguous United States and Its Implications for Rain–Snow Partitioning

Guoqiang Tang State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China, and Joint Institute for Regional Earth System Science and Engineering, and Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California

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Ali Behrangi Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, Arizona, and Jet Propulsion Laboratory, California Institute of Technology, Pasadena, and Joint Institute for Regional Earth System Science and Engineering, and Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California

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Ziqiang Ma Institute of Remote Sensing and Geographic Information System, Peking University, Beijing, China

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Di Long State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China

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Yang Hong State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China, and School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, Oklahoma

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Print Publication:
01 Jul 2018
DOI:
https://doi.org/10.1175/JHM-D-18-0041.1
Page(s):
1215–1233
Restricted access

Abstract

Precipitation phase has an important influence on hydrological processes. The Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) uses temperature data from reanalysis products to implement rain–snow classification. However, the coarse resolution of reanalysis data may not reveal the spatiotemporal variabilities of temperature, necessitating appropriate downscaling methods. This study compares the performance of eight air temperature Ta downscaling methods in the contiguous United States and six mountain ranges using temperature from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) as the benchmark. ERA-Interim Ta is downscaled from the original 0.75° to 0.1°. The results suggest that the two purely statistical downscaling methods [nearest neighbor (NN) and bilinear interpolation (BI)] show similar performance with each other. The five downscaling methods based on the free-air temperature lapse rate (TLR), which is calculated using temperature and geopotential heights at different pressure levels, notably improves the accuracy of Ta. The improvement is particularly obvious in mountainous regions. We further calculated wet-bulb temperature Tw, for rain–snow classification, using Ta and dewpoint temperature from ERA-Interim and PRISM. TLR-based downscaling methods result in more accurate Tw compared to NN and BI in the western United States, whereas the improvement is limited in the eastern United States. Rain–snow partitioning is conducted using a critical threshold of Tw with Snow Data Assimilation System (SNODAS) snowfall data serving as the benchmark. ERA-Interim-based Tw using TLR downscaling methods is better than that using NN/BI and IMERG precipitation phase. In conclusion, TLR-based downscaling methods show promising prospects in acquiring high-quality Ta and Tw with high resolution and improving rain–snow partitioning, particularly in mountainous regions.

© 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 authors: Ali Behrangi, behrangi@email.arizona.edu; Yang Hong, hongyang@tsinghua.edu.cn

Abstract

Precipitation phase has an important influence on hydrological processes. The Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) uses temperature data from reanalysis products to implement rain–snow classification. However, the coarse resolution of reanalysis data may not reveal the spatiotemporal variabilities of temperature, necessitating appropriate downscaling methods. This study compares the performance of eight air temperature Ta downscaling methods in the contiguous United States and six mountain ranges using temperature from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) as the benchmark. ERA-Interim Ta is downscaled from the original 0.75° to 0.1°. The results suggest that the two purely statistical downscaling methods [nearest neighbor (NN) and bilinear interpolation (BI)] show similar performance with each other. The five downscaling methods based on the free-air temperature lapse rate (TLR), which is calculated using temperature and geopotential heights at different pressure levels, notably improves the accuracy of Ta. The improvement is particularly obvious in mountainous regions. We further calculated wet-bulb temperature Tw, for rain–snow classification, using Ta and dewpoint temperature from ERA-Interim and PRISM. TLR-based downscaling methods result in more accurate Tw compared to NN and BI in the western United States, whereas the improvement is limited in the eastern United States. Rain–snow partitioning is conducted using a critical threshold of Tw with Snow Data Assimilation System (SNODAS) snowfall data serving as the benchmark. ERA-Interim-based Tw using TLR downscaling methods is better than that using NN/BI and IMERG precipitation phase. In conclusion, TLR-based downscaling methods show promising prospects in acquiring high-quality Ta and Tw with high resolution and improving rain–snow partitioning, particularly in mountainous regions.

© 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 authors: Ali Behrangi, behrangi@email.arizona.edu; Yang Hong, hongyang@tsinghua.edu.cn
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  • Alduchov, O. A., and R. E. Eskridge, 1996: Improved Magnus form approximation of saturation vapor pressure. J. Appl. Meteor., 35, 601–609, https://doi.org/10.1175/1520-0450(1996)035<0601:IMFAOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Barrett, A. P., 2003: National Operational Hydrologic Remote Sensing Center Snow Data Assimilation System (SNODAS) products at NSIDC. NSIDC Special Rep. 11, 19 pp., https://nsidc.org/sites/nsidc.org/files/files/nsidc_special_report_11.pdf.

  • Behrangi, A., A. S. Gardner, J. T. Reager, and J. B. Fisher, 2017: Using GRACE to constrain precipitation amount over cold mountainous basins. Geophys. Res. Lett., 44, 219–227, https://doi.org/10.1002/2016GL071832.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Behrangi, A., X. Yin, S. Rajagopal, D. Stampoulis, and H. Ye, 2018: On distinguishing snowfall from rainfall using near-surface atmospheric information: Comparative analysis, uncertainties, and hydrologic importance. Quart. J. Roy. Meteor. Soc., https://doi.org/10.1002/qj.3240, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Betts, A. K., and A. C. M. Beljaars, 2017: Analysis of near-surface biases in ERA-Interim over the Canadian Prairies. J. Adv. Model. Earth Syst., 9, 2158–2173, https://doi.org/10.1002/2017MS001025.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bieniek, P. A., U. S. Bhatt, J. E. Walsh, T. S. Rupp, J. Zhang, J. R. Krieger, and R. Lader, 2016: Dynamical downscaling of ERA-Interim temperature and precipitation for Alaska. J. Appl. Meteor. Climatol., 55, 635–654, https://doi.org/10.1175/JAMC-D-15-0153.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Daly, C., M. Halbleib, J. 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, 2031–2064, https://doi.org/10.1002/joc.1688.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140–158, https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ding, B., K. Yang, J. Qin, L. Wang, Y. Chen, and X. He, 2014: The dependence of precipitation types on surface elevation and meteorological conditions and its parameterization. J. Hydrol., 513, 154–163, https://doi.org/10.1016/j.jhydrol.2014.03.038.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Froidurot, S., I. Zin, B. Hingray, and A. Gautheron, 2014: Sensitivity of precipitation phase over the Swiss Alps to different meteorological variables. J. Hydrometeor., 15, 685–696, https://doi.org/10.1175/JHM-D-13-073.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gao, L., M. Bernhardt, and K. Schulz, 2012: Elevation correction of ERA-Interim temperature data in complex terrain. Hydrol. Earth Syst. Sci., 16, 4661–4673, https://doi.org/10.5194/hess-16-4661-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gao, L., M. Bernhardt, K. Schulz, and X. Chen, 2017: Elevation correction of ERA-Interim temperature data in the Tibetan Plateau. Int. J. Climatol., 37, 3540–3552, https://doi.org/10.1002/joc.4935.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gardner, A. S., M. J. Sharp, R. M. Koerner, C. Labine, S. Boon, S. J. Marshall, D. O. Burgess, and D. Lewis, 2009: Near-surface temperature lapse rates over Arctic glaciers and their implications for temperature downscaling. J. Climate, 22, 4281–4298, https://doi.org/10.1175/2009JCLI2845.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gerlitz, L., O. Conrad, A. Thomas, and J. Böhner, 2014: Warming patterns over the Tibetan Plateau and adjacent lowlands derived from elevation- and bias-corrected ERA-Interim data. Climate Res., 58, 235–246, https://doi.org/10.3354/cr01193.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gruber, S., 2012: Derivation and analysis of a high-resolution estimate of global permafrost zonation. Cryosphere, 6, 221–233, https://doi.org/10.5194/tc-6-221-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hamlet, A. F., and D. P. Lettenmaier, 2005: Production of temporally consistent gridded precipitation and temperature fields for the continental United States. J. Hydrometeor., 6, 330–336, https://doi.org/10.1175/JHM420.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Harder, P., and J. Pomeroy, 2013: Estimating precipitation phase using a psychrometric energy balance method. Hydrol. Processes, 27, 1901–1914, https://doi.org/10.1002/hyp.9799.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Harpold, A. A., M. L. Kaplan, P. Z. Klos, T. Link, J. P. McNamara, S. Rajagopal, R. Schumer, and C. M. Steele, 2017: Rain or snow: Hydrologic processes, observations, prediction, and research needs. Hydrol. Earth Syst. Sci., 21, 1–22, https://doi.org/10.5194/hess-21-1-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Huffman, G. J., and Coauthors, 2017: NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG). Algorithm Theoretical Basis Doc., version 5.1, 34 pp., https://pmm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V5.1b.pdf.

  • Liu, G., 2008: Deriving snow cloud characteristics from CloudSat observations. J. Geophys. Res., 113, D00A09, https://doi.org/10.1029/2007JD009766.

    • Search Google Scholar
    • Export Citation

  • Lundquist, J. D., and D. R. Cayan, 2007: Surface temperature patterns in complex terrain: Daily variations and long-term change in the central Sierra Nevada, California. J. Geophys. Res., 112, D11124, https://doi.org/10.1029/2006JD007561.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ma, Z., Z. Shi, Y. Zhou, J. Xu, W. Yu, and Y. Yang, 2017: A spatial data mining algorithm for downscaling TMPA 3B43 V7 data over the Qinghai–Tibet Plateau with the effects of systematic anomalies removed. Remote Sens. Environ., 200, 378–395, https://doi.org/10.1016/j.rse.2017.08.023.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Maurer, E. P., A. W. Wood, J. C. Adam, D. P. Lettenmaier, and B. Nijssen, 2002: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. J. Climate, 15, 3237–3251, https://doi.org/10.1175/1520-0442(2002)015<3237:ALTHBD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Minder, J. R., P. W. Mote, and J. D. Lundquist, 2010: Surface temperature lapse rates over complex terrain: Lessons from the Cascade Mountains. J. Geophys. Res., 115, D14122, https://doi.org/10.1029/2009JD013493.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mokhov, I. I., and M. G. Akperov, 2006: Tropospheric lapse rate and its relation to surface temperature from reanalysis data. Izv., Atmos. Ocean. Phys., 42, 430–438, https://doi.org/10.1134/S0001433806040037.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rajagopal, S., and A. A. Harpold, 2016: Testing and improving temperature thresholds for snow and rain prediction in the western United States. J. Amer. Water Resour. Assoc., 52, 1142–1154, https://doi.org/10.1111/1752-1688.12443.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Safeeq, M., G. S. Mauger, G. E. Grant, I. Arismendi, A. F. Hamlet, and S.-Y. Lee, 2014: Comparing large-scale hydrological model predictions with observed streamflow in the Pacific Northwest: Effects of climate and groundwater. J. Hydrometeor., 15, 2501–2521, https://doi.org/10.1175/JHM-D-13-0198.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sims, E. M., and G. Liu, 2015: A parameterization of the probability of snow–rain transition. J. Hydrometeor., 16, 1466–1477, https://doi.org/10.1175/JHM-D-14-0211.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stull, R., 2011: Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteor. Climatol., 50, 2267–2269, https://doi.org/10.1175/JAMC-D-11-0143.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tang, G., Z. Zeng, D. Long, X. Guo, B. Yong, W. Zhang, and Y. Hong, 2016: Statistical and hydrological comparisons between TRMM and GPM level-3 products over a midlatitude basin: Is day-1 IMERG a good successor for TMPA 3B42V7? J. Hydrometeor., 17, 121–137, https://doi.org/10.1175/JHM-D-15-0059.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tang, G., Z. Zeng, M. Ma, R. Liu, Y. Wen, and Y. Hong, 2017a: Can near-real-time satellite precipitation products capture rainstorms and guide flood warning for the 2016 summer in South China? IEEE Geosci. Remote Sens. Lett., 14, 1208–1212, https://doi.org/10.1109/LGRS.2017.2702137.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tang, G., Y. X. Wen, J. Y. Gao, D. Long, Y. Z. Ma, W. Wan, and Y. Hong, 2017b: Similarities and differences between three coexisting spaceborne radars in global rainfall and snowfall estimation. Water Resour. Res., 53, 3835–3853, https://doi.org/10.1002/2016WR019961.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tang, G., D. Long, Y. Hong, J. Gao, and W. Wan, 2018: Documentation of multifactorial relationships between precipitation and topography of the Tibetan Plateau using spaceborne precipitation radars. Remote Sens. Environ., 208, 82–96, https://doi.org/10.1016/j.rse.2018.02.007.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wang, R., M. Kumar, and T. E. Link, 2016: Potential trends in snowmelt-generated peak streamflows in a warming climate. Geophys. Res. Lett., 43, 5052–5059, https://doi.org/10.1002/2016GL068935.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wen, Y., P. Kirstetter, J. J. Gourley, Y. Hong, A. Behrangi, and Z. Flamig, 2017: Evaluation of MRMS snowfall products over the western United States. J. Hydrometeor., 18, 1707–1713, https://doi.org/10.1175/JHM-D-16-0266.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ye, H., J. Cohen, and M. Rawlins, 2013: Discrimination of solid from liquid precipitation over northern Eurasia using surface atmospheric conditions. J. Hydrometeor., 14, 1345–1355, https://doi.org/10.1175/JHM-D-12-0164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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