• Barthold, F. E., and D. A. Kristovich, 2011: Observations of the cross-lake cloud and snow evolution in a lake-effect snow event. Mon. Wea. Rev., 139, 23862398, https://doi.org/10.1175/MWR-D-10-05001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bennartz, R., F. Fell, C. Pettersen, M. D. Shupe, and D. Schuettemeyer, 2019: Spatial and temporal variability of snowfall over Greenland from CloudSat observations. Atmos. Chem. Phys., 19, 81018121, https://doi.org/10.5194/acp-19-8101-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bintanja, R., and F. Selten, 2014: Future increases in Arctic precipitation linked to local evaporation and sea-ice retreat. Nature, 509, 479482, https://doi.org/10.1038/nature13259.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bintanja, R., and O. Andry, 2017: Towards a rain-dominated Arctic. Nat. Climate Change, 7, 263267, https://doi.org/10.1038/nclimate3240.

  • Collins, M., and et al. , 2013: Long-term climate change: Projections, commitments and irreversibility. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 1029–1136.

  • Cooper, S. J., N. B. Wood, and T. S. L’Ecuyer, 2017: A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations. Atmos. Meas. Tech., 10, 25572571, https://doi.org/10.5194/amt-10-2557-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Copernicus Climate Change Service, 2017: ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. C3S Climate Data Store, accessed 18 March 2019, https://cds.climate.copernicus.eu.

  • Durán-Alarcón, C., B. Boudevillain, J. Grazioli, N. Souverijns, N. Van Lipzig, I. Gorodetskaya, and A. Berne, 2019: The vertical structure of precipitation at two stations in East Antarctica derived from Micro Rain Radars. Cryosphere, 13, 247264, https://doi.org/10.5194/tc-13-247-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eichenlaub, V. L., 1970: Lake effect snowfall to the lee of the great lakes: Its role in Michigan. Bull. Amer. Meteor. Soc., 51, 403413, https://doi.org/10.1175/1520-0477(1970)051<0403:LESTTL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eira, I. M. G., C. Jaedicke, O. H. Magga, N. G. Maynard, D. Vikhamar-Schuler, and S. D. Mathiesen, 2013: Traditional Sámi snow terminology and physical snow classification—Two ways of knowing. Cold Reg. Sci. Technol., 85, 117130, https://doi.org/10.1016/j.coldregions.2012.09.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elder, K., J. Dozier, and J. Michaelsen, 1991: Snow accumulation and distribution in an alpine watershed. Water Resour. Res., 27, 15411552, https://doi.org/10.1029/91WR00506.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Field, P. R., 2000: Bimodal ice spectra in frontal clouds. Quart. J. Roy. Meteor. Soc., 126, 379392, https://doi.org/10.1002/qj.49712656302.

  • Field, P. R., and A. Heymsfield, 2015: Importance of snow to global precipitation. Geophys. Res. Lett., 42, 95129520, https://doi.org/10.1002/2015GL065497.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gorodetskaya, I. V., M. Tsukernik, K. Claes, M. F. Ralph, W. D. Neff, and N. P. Van Lipzig, 2014: The role of atmospheric rivers in anomalous snow accumulation in East Antarctica. Geophys. Res. Lett., 41, 61996206, https://doi.org/10.1002/2014GL060881.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, B., and D. E. Waliser, 2015: Detection of atmospheric rivers: Evaluation and application of an algorithm for global studies. J. Geophys. Res., 120, 12 51412 535, https://doi.org/10.1002/2015JD024257.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hallett, J., 1965: Field and laboratory observations of ice crystal growth from the vapor. J. Atmos. Sci., 22, 6469, https://doi.org/10.1175/1520-0469(1965)022<0064:FALOOI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoskins, B. J., and K. I. Hodges, 2002: New perspectives on the Northern Hemisphere winter storm tracks. J. Atmos. Sci., 59, 10411061, https://doi.org/10.1175/1520-0469(2002)059<1041:NPOTNH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and et al. , 2014: The Global Precipitation Measurement Mission. Bull. Amer. Meteor. Soc., 95, 701722, https://doi.org/10.1175/BAMS-D-13-00164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., Jr., and S. Medina, 2005: Turbulence as a mechanism for orographic precipitation enhancement. J. Atmos. Sci., 62, 35993623, https://doi.org/10.1175/JAS3555.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., Jr., and et al. , 2017: The Olympic Mountains Experiment (OLYMPEX). Bull. Amer. Meteor. Soc., 98, 21672188, https://doi.org/10.1175/BAMS-D-16-0182.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Instanes, A., V. Kokorev, R. Janowicz, O. Bruland, K. Sand, and T. Prowse, 2016: Changes to freshwater systems affecting Arctic infrastructure and natural resources. J. Geophys. Res. Biogeosci., 121, 567585, https://doi.org/10.1002/2015JG003125.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jeoung, H., G. Liu, K. Kim, G. Lee, and E.-K. Seo, 2020: Microphysical properties of three types of snow clouds: Implication for satellite snowfall retrievals. Atmos. Chem. Phys., 20, 14 49114 507, https://doi.org/10.5194/acp-20-14491-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J. E., T. L’Ecuyer, A. Pendergrass, H. Chepfer, R. Guzman, and V. Yettella, 2018: Scale-aware and definition-aware evaluation of modeled near-surface precipitation frequency using CloudSat observations. J. Geophys. Res., 123, 42944309, https://doi.org/10.1002/2017JD028213.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klugmann, D., K. Heinsohn, and H.-J. Kirtzel, 1996: A low cost 24 GHz FM-CW Doppler radar rain profiler. Contrib. Atmos. Phys., 61, 247253.

    • Search Google Scholar
    • Export Citation
  • Kneifel, S., and D. Moisseev, 2020: Long-term statistics of riming in nonconvective clouds derived from ground-based Doppler cloud radar observations. J. Atmos. Sci., 77, 34953508, https://doi.org/10.1175/JAS-D-20-0007.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kneifel, S., M. Maahn, G. Peters, and C. Simmer, 2011: Observation of snowfall with a low-power FM-CW K-band radar (Micro Rain Radar). Meteor. Atmos. Phys., 113, 7587, https://doi.org/10.1007/s00703-011-0142-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knowles, N., M. D. Dettinger, and D. R. Cayan, 2006: Trends in snowfall versus rainfall in the western United States. J. Climate, 19, 45454559, https://doi.org/10.1175/JCLI3850.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kristovich, D. A., and et al. , 2017: The Ontario Winter Lake-Effect Systems field campaign: Scientific and educational adventures to further our knowledge and prediction of lake-effect storms. Bull. Amer. Meteor. Soc., 98, 315332, https://doi.org/10.1175/BAMS-D-15-00034.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kulie, M. S., and R. Bennartz, 2009: Utilizing spaceborne radars to retrieve dry snowfall. J. Appl. Meteor. Climatol., 48, 25642580, https://doi.org/10.1175/2009JAMC2193.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kulie, M. S., L. Milani, N. B. Wood, S. A. Tushaus, R. Bennartz, and T. S. L’Ecuyer, 2016: A shallow cumuliform snowfall census using spaceborne radar. J. Hydrometeor., 17, 12611279, https://doi.org/10.1175/JHM-D-15-0123.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lavers, D. A., R. P. Allan, G. Villarini, B. Lloyd-Hughes, D. J. Brayshaw, and A. J. Wade, 2013: Future changes in atmospheric rivers and their implications for winter flooding in Britain. Environ. Res. Lett., 8, 034010, https://doi.org/10.1088/1748-9326/8/3/034010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lemonnier, F., and et al. , 2019: Evaluation of CloudSat snowfall rate profiles by a comparison with in situ micro-rain radar observations in East Antarctica. Cryosphere, 13, 943954, https://doi.org/10.5194/tc-13-943-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Libbrecht, K. G., 2005: The physics of snow crystals. Rep. Prog. Phys., 68, 855895, https://doi.org/10.1088/0034-4885/68/4/R03.

  • Liston, G., and M. Sturm, 2004: The role of winter sublimation in the Arctic moisture budget. Hydrol. Res., 35, 325334, https://doi.org/10.2166/nh.2004.0024.

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

  • Liu, J., J. A. Curry, H. Wang, M. Song, and R. M. Horton, 2012: Impact of declining Arctic sea ice on winter snowfall. Proc. Natl. Acad. Sci. USA, 109, 40744079, https://doi.org/10.1073/pnas.1114910109.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lund, M., C. Stiegler, J. Abermann, M. Citterio, B. U. Hansen, and D. van As, 2017: Spatiotemporal variability in surface energy balance across tundra, snow and ice in Greenland. Ambio, 46, 8193, https://doi.org/10.1007/s13280-016-0867-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maahn, M., and P. Kollias, 2012: Improved Micro Rain Radar snow measurements using Doppler spectra post-processing. Atmos. Meas. Tech., 5, 26612673, https://doi.org/10.5194/amt-5-2661-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maahn, M., C. Burgard, S. Crewell, I. V. Gorodetskaya, S. Kneifel, S. Lhermitte, K. Van Tricht, and N. P. van Lipzig, 2014: How does the spaceborne radar blind zone affect derived surface snowfall statistics in polar regions? J. Geophys. Res., 119, 13604, https://doi.org/10.1002/2014JD022079.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matrosov, S. Y., 2007: Modeling backscatter properties of snowfall at millimeter wavelengths. J. Atmos. Sci., 64, 17271736, https://doi.org/10.1175/JAS3904.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matrosov, S. Y., M. D. Shupe, and I. V. Djalalova, 2008: Snowfall retrievals using millimeter-wavelength cloud radars. J. Appl. Meteor. Climatol., 47, 769777, https://doi.org/10.1175/2007JAMC1768.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McIlhattan, E. A., C. Pettersen, N. B. Wood, and T. S. L’Ecuyer, 2020: Satellite observations of snowfall regimes over the Greenland ice sheet. Cryosphere, 14, 43794404, https://doi.org/10.5194/tc-14-4379-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., G. De Boer, G. Feingold, J. Harrington, M. D. Shupe, and K. Sulia, 2012: Resilience of persistent Arctic mixed-phase clouds. Nat. Geosci., 5, 1117, https://doi.org/10.1038/ngeo1332.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mosimann, L., 1995: An improved method for determining the degree of snow crystal riming by vertical Doppler radar. Atmos. Res., 37, 305323, https://doi.org/10.1016/0169-8095(94)00050-N.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Newman, A. J., P. A. Kucera, and L. F. Bliven, 2009: Presenting the Snowflake Video Imager (SVI). J. Atmos. Oceanic Technol., 26, 167179, https://doi.org/10.1175/2008JTECHA1148.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Norin, L., A. Devasthale, T. L’Ecuyer, N. Wood, and M. Smalley, 2015: Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground-based weather radar network over Sweden. Atmos. Meas. Tech., 8, 50095021, https://doi.org/10.5194/amt-8-5009-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Norin, L., A. Devasthale, and T. S. L’Ecuyer, 2017: The sensitivity of snowfall to weather states over Sweden. Atmos. Meas. Tech., 10, 32493263, https://doi.org/10.5194/amt-10-3249-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, H., J. E. Walsh, Y. Kim, T. Nakai, and T. Ohata, 2013: The role of declining Arctic sea ice in recent decreasing terrestrial Arctic snow depths. Polar Sci., 7, 174187, https://doi.org/10.1016/j.polar.2012.10.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pettersen, C., R. Bennartz, A. J. Merrelli, M. D. Shupe, D. D. Turner, and V. P. Walden, 2018: Precipitation regimes over central Greenland inferred from 5 years of icecaps observations. Atmos. Chem. Phys., 18, 47154735, https://doi.org/10.5194/acp-18-4715-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pettersen, C., M. S. Kulie, L. F. Bliven, A. J. Merrelli, W. A. Petersen, T. J. Wagner, D. B. Wolff, and N. B. Wood, 2020a: A composite analysis of snowfall modes from four winter seasons in Marquette, Michigan. J. Appl. Meteor. Climatol., 59, 103124, https://doi.org/10.1175/JAMC-D-19-0099.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pettersen, C., and et al. , 2020b: The precipitation imaging package: Assessment of microphysical and bulk characteristics of snow. Atmosphere, 11, 785, https://doi.org/10.3390/atmos11080785.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Putkonen, J., and G. Roe, 2003: Rain-on-snow events impact soil temperatures and affect ungulate survival. Geophys. Res. Lett., 30, 1188, https://doi.org/10.1029/2002GL016326.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, R., and et al. , 2012: How well are we measuring snow: The NOAA/FAA/NCAR winter precipitation test bed. Bull. Amer. Meteor. Soc., 93, 811829, https://doi.org/10.1175/BAMS-D-11-00052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rawlins, M. A., C. Willmott, A. Shiklomanov, E. Linder, S. Frolking, R. B. Lammers, and C. Vörösmarty, 2006: Evaluation of trends in derived snowfall and rainfall across Eurasia and linkages with discharge to the Arctic Ocean. Geophys. Res. Lett., 33, L07403, https://doi.org/10.1029/2005GL025231.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rawlins, M. A., and et al. , 2010: Analysis of the Arctic system for freshwater cycle intensification: Observations and expectations. J. Climate, 23, 57155737, https://doi.org/10.1175/2010JCLI3421.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riseth, J. Å., and et al. , 2011: Sámi traditional ecological knowledge as a guide to science: Snow, ice and reindeer pasture facing climate change. Polar Rec., 47, 202217, https://doi.org/10.1017/S0032247410000434.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodgers, C., 2000: Inverse Methods for Atmospheric Sounding. World Scientific, 238 pp.

  • Roebber, P. J., S. L. Bruening, D. M. Schultz, and J. V. Cortinas Jr., 2003: Improving snowfall forecasting by diagnosing snow density. Wea. Forecasting, 18, 264287, https://doi.org/10.1175/1520-0434(2003)018<0264:ISFBDS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rydzik, M., and A. R. Desai, 2014: Relationship between snow extent and midlatitude disturbance centers. J. Climate, 27, 29712982, https://doi.org/10.1175/JCLI-D-12-00841.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schirle, C. E., S. J. Cooper, M. A. Wolff, C. Pettersen, N. B. Wood, T. S. L’Ecuyer, T. Ilmo, and K. Nygård, 2019: Estimation of snowfall properties at a mountainous site in Norway using combined radar and in situ microphysical observations. J. Appl. Meteor. Climatol., 58, 13371352, https://doi.org/10.1175/JAMC-D-18-0281.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schneider, U., A. Becker, P. Finger, A. Meyer-Christoffer, M. Ziese, and B. Rudolf, 2014: GPCC’S new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol., 115, 1540, https://doi.org/10.1007/s00704-013-0860-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Screen, J. A., and I. Simmonds, 2012: Declining summer snowfall in the Arctic: Causes, impacts and feedbacks. Climate Dyn., 38, 22432256, https://doi.org/10.1007/s00382-011-1105-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., and R. G. Barry, 2011: Processes and impacts of Arctic amplification: A research synthesis. Global Planet. Change, 77, 8596, https://doi.org/10.1016/j.gloplacha.2011.03.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shupe, M. D., P. Kollias, P. O. G. Persson, and G. M. McFarquhar, 2008: Vertical motions in Arctic mixed-phase stratiform clouds. J. Atmos. Sci., 65, 13041322, https://doi.org/10.1175/2007JAS2479.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skofronick-Jackson, G. M., B. T. Johnson, and S. J. Munchak, 2013: Detection thresholds of falling snow from satellite-borne active and passive sensors. IEEE Trans. Geosci. Remote Sens., 51, 41774189, https://doi.org/10.1109/TGRS.2012.2227763.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skofronick-Jackson, G. M., and et al. , 2015: Global Precipitation Measurement Cold Season Precipitation Experiment (GCPEX): For measurement’s sake, let it snow. Bull. Amer. Meteor. Soc., 96, 17191741, https://doi.org/10.1175/BAMS-D-13-00262.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smalley, M., T. L’Ecuyer, M. Lebsock, and J. Haynes, 2014: A comparison of precipitation occurrence from the NCEP Stage IV QPE product and the CloudSat Cloud Profiling Radar. J. Hydrometeor., 15, 444458, https://doi.org/10.1175/JHM-D-13-048.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sodemann, H., and A. Stohl, 2013: Moisture origin and meridional transport in atmospheric rivers and their association with multiple cyclones. Mon. Wea. Rev., 141, 28502868, https://doi.org/10.1175/MWR-D-12-00256.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sokolov, A. A., N. A. Sokolova, R. A. Ims, L. Brucker, and D. Ehrich, 2016: Emergent rainy winter warm spells may promote boreal predator expansion into the Arctic. Arctic, 69, 121129, https://doi.org/10.14430/arctic4559.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Souverijns, N., A. Gossart, S. Lhermitte, I. Gorodetskaya, S. Kneifel, M. Maahn, F. L. Bliven, and N. Van Lipzig, 2017: Estimating radar reflectivity-snowfall rate relationships and their uncertainties over Antarctica by combining disdrometer and radar observations. Atmos. Res., 196, 211223, https://doi.org/10.1016/j.atmosres.2017.06.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Speirs, P., M. Gabella, and A. Berne, 2017: A comparison between the GPM Dual-Frequency Precipitation Radar and ground-based radar precipitation rate estimates in the Swiss Alps and plateau. J. Hydrometeor., 18, 12471269, https://doi.org/10.1175/JHM-D-16-0085.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stark, D., B. A. Colle, and S. E. Yuter, 2013: Observed microphysical evolution for two East Coast winter storms and the associated snow bands. Mon. Wea. Rev., 141, 20372057, https://doi.org/10.1175/MWR-D-12-00276.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and et al. , 2002: The CloudSat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation. Bull. Amer. Meteor. Soc., 83, 17711790, https://doi.org/10.1175/BAMS-83-12-1771.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tamang, S. K., A. M. Ebtehaj, A. F. Prein, and A. J. Heymsfield, 2020: Linking global changes of snowfall and wet-bulb temperature. J. Climate, 33, 3959, https://doi.org/10.1175/JCLI-D-19-0254.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tiira, J., D. N. Moisseev, A. von Lerber, D. Ori, A. Tokay, L. F. Bliven, and W. Petersen, 2016: Ensemble mean density and its connection to other microphysical properties of falling snow as observed in southern Finland. Atmos. Meas. Tech., 9, 48254841, https://doi.org/10.5194/amt-9-4825-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vavrus, S., 2007: The role of terrestrial snow cover in the climate system. Climate Dyn., 29, 7388, https://doi.org/10.1007/s00382-007-0226-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verlinde, J., and et al. , 2007: The Mixed-Phase Arctic Cloud Experiment. Bull. Amer. Meteor. Soc., 88, 205222, https://doi.org/10.1175/BAMS-88-2-205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • von Lerber, A., D. Moisseev, L. F. Bliven, W. Petersen, A.-M. Harri, and V. Chandrasekar, 2017: Microphysical properties of snow and their link to Ze –S relations during BAECC 2014. J. Appl. Meteor. Climatol., 56, 15611582, https://doi.org/10.1175/JAMC-D-16-0379.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • von Lerber, A., D. Moisseev, D. A. Marks, W. Petersen, A.-M. Harri, and V. Chandrasekar, 2018: Validation of GMI snowfall observations by using a combination of weather radar and surface measurements. J. Appl. Meteor. Climatol., 57, 797820, https://doi.org/10.1175/JAMC-D-17-0176.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., B. Geerts, and Y. Chen, 2016: Vertical structure of boundary layer convection during cold-air outbreaks at Barrow, Alaska. J. Geophys. Res., 121, 399412, https://doi.org/10.1002/2015JD023506.

    • Search Google Scholar
    • Export Citation
  • Ware, E. C., D. M. Schultz, H. E. Brooks, P. J. Roebber, and S. L. Bruening, 2006: Improving snowfall forecasting by accounting for the climatological variability of snow density. Wea. Forecasting, 21, 94103, https://doi.org/10.1175/WAF903.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wolff, M., K. Isaksen, A. Petersen-Øverleir, K. Ødemark, T. Reitan, and R. Brækkan, 2015: Derivation of a new continuous adjustment function for correcting wind-induced loss of solid precipitation: Results of a Norwegian field study. Hydrol. Earth Syst. Sci., 19, 951967, https://doi.org/10.5194/hess-19-951-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, N. B., T. S. L’Ecuyer, A. J. Heymsfield, and G. L. Stephens, 2015: Microphysical constraints on millimeter-wavelength scattering properties of snow particles. J. Appl. Meteor. Climatol., 54, 909931, https://doi.org/10.1175/JAMC-D-14-0137.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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High-Latitude Precipitation: Snowfall Regimes at Two Distinct Sites in Scandinavia

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  • 1 a Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, Wisconsin
  • | 2 b Space Science and Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin
  • | 3 c University of Utah, Salt Lake City, Utah
  • | 4 d Advanced Satellite Products Branch, NOAA/NESDIS/Center for Satellite Applications and Research, Madison, Wisconsin
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Abstract

The prevailing snowfall regimes at two Scandinavian sites, Haukeliseter, Norway, and Kiruna, Sweden, are documented using ground-based in situ and remote sensing methods. Micro Rain Radar (MRR) profiles indicate three distinct snowfall regimes occur at both sites: shallow, deep, and intermittent snowfall. The shallow snowfall regime produces the lowest mean snowfall rates and radar echo tops are confined below 1.5 km above ground level (AGL). Shallow snowfall occurs under areas of large-scale subsidence with a moist boundary layer and dry air aloft. The atmospheric ridge coinciding with shallow snowfall is highly anomalous over Haukeliseter but is more common in Kiruna where shallow snowfall was frequently observed. The shallow snowfall particle size distributions (PSDs) are broad with lower particle concentrations than other regimes, especially small particles. Deep snowfall events exhibit MRR profiles that extend above 2 km AGL and tend to be associated with weak low pressure and high relative humidity throughout the troposphere. The PSDs in deep events are narrower with high concentrations of small particles. Increasing MRR reflectivity toward the surface suggests aggregation as a possible growth process during deep snowfall events. The heaviest mean snowfall rates are associated with intermittent events that are characterized by deep MRR profiles but have variations in intensity and height. The intermittent regime is associated with anomalous, deep low pressure along the coast of Norway and enhanced relative humidity at lower levels. The PSDs reveal high concentrations of small and large particles. The analysis reveals that there are unique characteristics of shallow, deep, and intermittent snowfall regimes that are common between the sites.

© 2021 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: Julia A. Shates, shates@wisc.edu

Abstract

The prevailing snowfall regimes at two Scandinavian sites, Haukeliseter, Norway, and Kiruna, Sweden, are documented using ground-based in situ and remote sensing methods. Micro Rain Radar (MRR) profiles indicate three distinct snowfall regimes occur at both sites: shallow, deep, and intermittent snowfall. The shallow snowfall regime produces the lowest mean snowfall rates and radar echo tops are confined below 1.5 km above ground level (AGL). Shallow snowfall occurs under areas of large-scale subsidence with a moist boundary layer and dry air aloft. The atmospheric ridge coinciding with shallow snowfall is highly anomalous over Haukeliseter but is more common in Kiruna where shallow snowfall was frequently observed. The shallow snowfall particle size distributions (PSDs) are broad with lower particle concentrations than other regimes, especially small particles. Deep snowfall events exhibit MRR profiles that extend above 2 km AGL and tend to be associated with weak low pressure and high relative humidity throughout the troposphere. The PSDs in deep events are narrower with high concentrations of small particles. Increasing MRR reflectivity toward the surface suggests aggregation as a possible growth process during deep snowfall events. The heaviest mean snowfall rates are associated with intermittent events that are characterized by deep MRR profiles but have variations in intensity and height. The intermittent regime is associated with anomalous, deep low pressure along the coast of Norway and enhanced relative humidity at lower levels. The PSDs reveal high concentrations of small and large particles. The analysis reveals that there are unique characteristics of shallow, deep, and intermittent snowfall regimes that are common between the sites.

© 2021 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: Julia A. Shates, shates@wisc.edu
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