Global Distribution of Snow Precipitation Features and Their Properties from 3 Years of GPM Observations

Abishek Adhikari Department of Physical and Environmental Sciences, Texas A&M University–Corpus Christi, Corpus Christi, Texas

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Chuntao Liu Department of Physical and Environmental Sciences, Texas A&M University–Corpus Christi, Corpus Christi, Texas

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Mark S. Kulie Department of Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, Michigan

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Abstract

Using a 3-yr Global Precipitation Mission (GPM) Ku-band Precipitation Radar (KuPR) dataset, snow features (SFs) are defined by grouping the contiguous area of nonzero solid precipitation. The near-surface wet bulb temperatures calculated from ERA-Interim reanalysis data are used to verify that SFs are colder than 1°C to omit snowfall that melts before reaching the surface. The properties of SFs are summarized to understand the global distribution and characteristics of snow systems. The seasonal and diurnal variations of SFs and their properties are analyzed over Northern and Southern Hemispheric land and ocean separately.

To quantify the amount of snow missed by the GPM KuPR and the amount of snow underestimated by the CloudSat Cloud Profiling (CPR), 3-yr KuPR pixel-level data are compared with 4-yr CloudSat CPR observations. The overall underestimation of snowfall during heavy snow events by CPR is less than 3% compared to the combined CPR and KuPR estimates. KuPR underestimates about 52% of weak snow. Only a small percentage of SFs have sizes greater than 10 000 km2 (0.35%), maximum near-surface reflectivity above 30 dBZ (5.1%), or echo top above 5 km (1.6%); however, they contribute 40%, 49.5%, or 30.4% of the global volumetric snow detected by KuPR. Snow in the Northern Hemisphere has stronger diurnal and seasonal variation compared to the Southern Hemisphere. Most of the SFs over the ocean are found with relatively smaller, less intense, and shallower echo tops than over land.

© 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: Abishek Adhikari, aadhikari1@islander.tamucc.edu

Abstract

Using a 3-yr Global Precipitation Mission (GPM) Ku-band Precipitation Radar (KuPR) dataset, snow features (SFs) are defined by grouping the contiguous area of nonzero solid precipitation. The near-surface wet bulb temperatures calculated from ERA-Interim reanalysis data are used to verify that SFs are colder than 1°C to omit snowfall that melts before reaching the surface. The properties of SFs are summarized to understand the global distribution and characteristics of snow systems. The seasonal and diurnal variations of SFs and their properties are analyzed over Northern and Southern Hemispheric land and ocean separately.

To quantify the amount of snow missed by the GPM KuPR and the amount of snow underestimated by the CloudSat Cloud Profiling (CPR), 3-yr KuPR pixel-level data are compared with 4-yr CloudSat CPR observations. The overall underestimation of snowfall during heavy snow events by CPR is less than 3% compared to the combined CPR and KuPR estimates. KuPR underestimates about 52% of weak snow. Only a small percentage of SFs have sizes greater than 10 000 km2 (0.35%), maximum near-surface reflectivity above 30 dBZ (5.1%), or echo top above 5 km (1.6%); however, they contribute 40%, 49.5%, or 30.4% of the global volumetric snow detected by KuPR. Snow in the Northern Hemisphere has stronger diurnal and seasonal variation compared to the Southern Hemisphere. Most of the SFs over the ocean are found with relatively smaller, less intense, and shallower echo tops than over land.

© 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: Abishek Adhikari, aadhikari1@islander.tamucc.edu
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  • Behrangi, A., M. Lebsock, S. Wong, and B. Lambrigtsen, 2012: On the quantification of oceanic rainfall using spaceborne sensors. J. Geophys. Res., 117, D20105, https://doi.org/10.1029/2012JD017979.

    • Search Google Scholar
    • Export Citation
  • Bolton, D., 1980: The computation of equivalent potential temperature. Mon. Wea. Rev., 108, 10461053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boucher, R. J., and J. G. Wieler, 1985: Radar determination of snowfall rate and accumulation. J. Climate Appl. Meteor., 24, 6873, https://doi.org/10.1175/1520-0450(1985)024<0068:RDOSRA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, E. K. M., S. Lee, and K. L. Swanson, 2002: Storm track dynamics. J. Climate, 15, 21632183, https://doi.org/10.1175/1520-0442(2002)015<02163:STD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davies-Jones, R., 2008: An efficient and accurate method for computing the wet-bulb temperature along pseudoadiabats. Mon. Wea. Rev., 136, 27642785, https://doi.org/10.1175/2007MWR2224.1.

    • 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, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ellis, T. D., T. L’Ecuyer, J. M. Haynes, and G. L. Stephens, 2009: How often does it rain over the global oceans? The perspective from CloudSat. Geophys. Res. Lett., 36, L03815, https://doi.org/10.1029/2008GL036728.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferraro, R. R., F. Weng, N. C. Grody, and A. Basist, 1996: An eight-year (1987–1994) time series of rainfall, clouds, water vapor, snow cover, and sea ice derived from SSM/I measurements. Bull. Amer. Meteor. Soc., 77, 891905, https://doi.org/10.1175/1520-0477(1996)077<0891:AEYTSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Field, P. R., and A. J. 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
  • Frankoski, N. J., and A. T. Degaetano, 2011: An East Coast winter storm precipitation climatology. Int. J. Climatol., 31, 802814, https://doi.org/10.1002/joc.2121.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grim, J. A., N. F. Laird, and D. R. Kristovich, 2004: Mesoscale vortices embedded within a lake-effect shoreline band. Mon. Wea. Rev., 132, 22692274, https://doi.org/10.1175/1520-0493(2004)132<2269:MVEWAL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamada, A., and Y. N. Takayabu, 2016: Improvements in detection of light precipitation with the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM DPR). J. Atmos. Oceanic Technol., 33, 653667, https://doi.org/10.1175/JTECH-D-15-0097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, Y., and E. R. Westwater, 2000: Analysis and improvement of tipping calibration for ground-based microwave radiometers. IEEE Trans. Geosci. Remote Sens., 38, 12601276, https://doi.org/10.1109/36.843018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hiley, M. J., M. S. Kulie, and R. Bennartz, 2011: Uncertainty analysis for CloudSat snowfall retrievals. J. Appl. Meteor. Climatol., 50, 399418, https://doi.org/10.1175/2010JAMC2505.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and Coauthors, 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
  • Iguchi, T., S. Seto, R. Meneghini, N. Yoshida, J. Awaka, and T. Kubota, 2010: GPM/DPR level-2. Algorithm Theoretical Basis Doc., 72 pp., https://pmm.nasa.gov/sites/default/files/document_files/ATBD_GPM_DPR_n3_dec15.pdf.

  • Iguchi, T., S. Seto, R. Meneghini, N. Yoshida, J. Awaka, M. Le, V. Chandrasekar, and T. Kubota, 2015: GPM/DPR level-2. Algorithm Theoretical Basis Doc., 68 pp., http://www.eorc.jaxa.jp/GPM/doc/algorithm/ATBD_DPR_2015_whole_2a.pdf.

  • Katsumata, M., H. Uyeda, K. Iwanami, and G. Liu, 2000: The response of 36- and 89-GHz microwave channels to convective snow clouds over ocean: Observation and modeling. J. Appl. Meteor., 39, 23222335, https://doi.org/10.1175/1520-0450(2000)039<2322:TROAGM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidd, C., A. Becker, G. J. Huffman, C. L. Muller, P. Joe, G. Skofronick-Jackson, and D. B. Kirschbaum, 2017: So, how much of the Earth’s surface is covered by rain gauges? Bull. Amer. Meteor. Soc., 98, 6978, https://doi.org/10.1175/BAMS-D-14-00283.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kneifel, S., U. Löhnert, A. Battaglia, S. Crewell, and D. Siebler, 2010: Snow scattering signals in ground-based passive microwave radiometer measurements. J. Geophys. Res., 115, D16214, https://doi.org/10.1029/2010JD013856.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kongoli, C., 2003: A new snowfall detection algorithm over land using measurements from the Advanced Microwave Sounding Unit (AMSU). Geophys. Res. Lett., 30, 1756, https://doi.org/10.1029/2003GL017177.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kristovich, D. R., and M. L. Spinar, 2005: Diurnal variations in lake-effect precipitation near the western Great Lakes. J. Hydrometeor., 6, 210218, https://doi.org/10.1175/JHM403.1.

    • 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., R. Bennartz, T. J. Greenwald, Y. Chen, and F. Weng, 2010: Uncertainties in microwave properties of frozen precipitation: Implications for remote sensing and data assimilation. J. Atmos. Sci., 67, 34713487, https://doi.org/10.1175/2010JAS3520.1.

    • 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
  • Kummerow, C., and Coauthors, 2000: The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J. Appl. Meteor., 39, 19651982, https://doi.org/10.1175/1520-0450(2001)040<1965:TSOTTR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Levizzani, V., S. Laviola, and E. Cattani, 2011: Detection and measurement of snowfall from space. Remote Sens., 3, 145166, https://doi.org/10.3390/rs3010145.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., 2016: GPM precipitation feature database. Tech. Doc., 15 pp., http://atmos.tamucc.edu/trmm/data/document/GPM_database_description_1.0_201601.pdf.

  • Liu, C., and E. J. Zipser, 2009: “Warm rain” in the tropics: Seasonal and regional distributions based on 9 yr of TRMM data. J. Climate, 22, 767779, https://doi.org/10.1175/2008JCLI2641.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., and E. J. Zipser, 2015: The global distribution of largest, deepest, and most intense precipitation systems. Geophys. Res. Lett., 42, 35913595, https://doi.org/10.1002/2015GL063776.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., E. J. Zipser, D. J. Cecil, S. W. Nesbitt, and S. Sherwood, 2008: A cloud and precipitation feature database from nine years of TRMM observations. J. Appl. Meteor. Climatol., 47, 27122728, https://doi.org/10.1175/2008JAMC1890.1.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Liu, G., and J. A. Curry, 1997: Precipitation characteristics in Greenland-Iceland-Norwegian Seas determined by using satellite microwave data. J. Geophys. Res., 102, 13 98713 997, https://doi.org/10.1029/96JD03090.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, G., and E. K. Seo, 2013: Detecting snowfall over land by satellite high-frequency microwave observations: The lack of scattering signature and a statistical approach. J. Geophys. Res. Atmos., 118, 13761387, https://doi.org/10.1002/jgrd.50172.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, N., and C. Liu, 2016: Global distribution of deep convection reaching tropopause in 1 year GPM observations. J. Geophys. Res. Atmos., 121, 38243842, https://doi.org/10.1002/2015JD024430.

    • 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
  • Mülmenstädt, J., O. Sourdeval, J. Delanoë, and J. Quaas, 2015: Frequency of occurrence of rain from liquid-, mixed-, and ice-phase clouds derived from A-Train satellite retrievals. Geophys. Res. Lett., 42, 65026509, https://doi.org/10.1002/2015GL064604.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nesbitt, S. W., and E. J. Zipser, 2003: The diurnal cycle of rainfall and convective intensity according to three years of TRMM measurements. J. Climate, 16, 14561475, https://doi.org/10.1175/1520-0442-16.10.1456.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Noh, Y. J., G. Liu, E. K. Seo, J. R. Wang, and K. Aonashi, 2006: Development of a snowfall retrieval algorithm at high microwave frequencies. J. Geophys. Res., 111, D22216, https://doi.org/10.1029/2005JD006826.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Noh, Y. J., G. Liu, A. S. Jones, and T. H. V. Haar, 2009: Toward snowfall retrieval over land by combining satellite and in situ measurements. J. Geophys. Res., 114, D24205, https://doi.org/10.1029/2009JD012307.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Precipitation Processing System Team, 2014: Global Precipitation Measurement file specification for GPM products, version 1.08. NASA Doc., 1131 pp., https://storm.pps.eosdis.nasa.gov/storm/filespec.GPM.V1.pdf.

  • Seto, S., T. Iguchi, and T. Oki, 2013: The basic performance of a precipitation retrieval algorithm for the global precipitation measurement mission’s single/dual-frequency radar measurements. IEEE Trans. Geosci. Remote Sens., 51, 52395251, https://doi.org/10.1109/TGRS.2012.2231686.

    • 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, 14661477, https://doi.org/10.1175/JHM-D-14-0211.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skofronick-Jackson, G., and B. T. Johnson, 2011: Surface and atmospheric contributions to passive microwave brightness temperatures for falling snow events. J. Geophys. Res., 116, D02213, https://doi.org/10.1029/2010JD014438.

    • Search Google Scholar
    • Export Citation
  • Skofronick-Jackson, G., J. A. Weinman, M. J. Kim, and D. E. Chang, 2004: A physical model to determine snowfall over land by microwave radiometry. IEEE Trans. Geosci. Remote Sens., 42, 10471058, https://doi.org/10.1109/TGRS.2004.825585.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skofronick-Jackson, G., and Coauthors, 2017: The Global Precipitation Measurement (GPM) mission for science and society. Bull. Amer. Meteor. Soc., 98, 16791695, https://doi.org/10.1175/BAMS-D-15-00306.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spencer, R., H. Goodman, and R. Hood, 1989: Precipitation retrieval over land and ocean with the SSM/I: Identification and characteristics of the scattering signal. J. Atmos. Oceanic Technol., 6, 254273, https://doi.org/10.1175/1520-0426(1989)006<0254:PROLAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Staelin, D. H., and F. W. Chen, 2000: Precipitation observations near 54 and 183 GHz using the NOAA-15 satellite. IEEE Trans. Geosci. Remote Sens., 38, 23222332, https://doi.org/10.1109/36.868889.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and Coauthors, 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
  • Stephens, G. L., and Coauthors, 2010: Dreary state of precipitation in global models. J. Geophys. Res., 115, D24211, https://doi.org/10.1029/2010JD014532.

    • Search Google Scholar
    • Export Citation
  • Sun, Y., S. Solomon, A. Dai, and R. W. Portmann, 2006: How often does it rain? J. Climate, 19, 916934, https://doi.org/10.1175/JCLI3672.1.

  • Vivekanandan, J., J. Turk, and V. N. Bringi, 1991: Ice water path estimation and characterization using passive microwave radiometry. J. Appl. Meteor., 30, 14071421, https://doi.org/10.1175/1520-0450(1991)030<1407:IWPEAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weng, F., and N. C. Grody, 1994: Retrieval of cloud liquid water using the Special Sensor Microwave Imager (SSM/I). J. Geophys. Res., 99, 25 53525 551, https://doi.org/10.1029/94JD02304.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, N. B., T. S. L’Ecuyer, D. G. Vane, G. L. Stephens, and P. Partain, 2013: Level 2C snow profile process description and interface control document. JPL Doc., 21 pp., http://www.cloudsat.cira.colostate.edu/sites/default/files/products/files/2C-SNOW-PROFILE_PDICD.P_R04.20130210.pdf.

  • Xie, X., U. Lhnert, S. Kneifel, and S. Crewell, 2012: Snow particle orientation observed by ground-based microwave radiometry. J. Geophys. Res., 117, D02206, https://doi.org/10.1029/2011JD016369.

    • Search Google Scholar
    • Export Citation
  • You, Y., N. Y. Wang, and R. Ferraro, 2015: A prototype precipitation retrieval algorithm over land using passive microwave observations stratified by surface condition and precipitation vertical structure. J. Geophys. Res. Atmos., 120, 52955315, https://doi.org/10.1002/2014JD022534.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • You, Y., N.-Y. Wang, R. Ferraro, and P. Meyers, 2016: A prototype precipitation retrieval algorithm over land for ATMS. J. Hydrometeor., 17, 16011621, https://doi.org/10.1175/JHM-D-15-0163.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • You, Y., N.-Y. Wang, R. Ferraro, and S. Rudlosky, 2017: Quantifying the snowfall detection performance of the GPM Microwave Imager channels over land. J. Hydrometeor., 18, 729751, https://doi.org/10.1175/JHM-D-16-0190.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zipser, E., C. Liu, D. Cecil, S. W. Nesbitt, and S. Yorty, 2006: Where are the most intense thunderstorms on Earth? Bull. Amer. Meteor. Soc., 87, 10571071, https://doi.org/10.1175/BAMS-87-8-1057.

    • Crossref
    • Search Google Scholar
    • Export Citation
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