Articles | Volume 15, issue 5
https://doi.org/10.5194/amt-15-1439-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-15-1439-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Mar 2022
Research article |
| 16 Mar 2022
| 16 Mar 2022
Snow microphysical retrieval from the NASA D3R radar during ICE-POP 2018
S. Joseph Munchak
Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
now at: The Tomorrow Companies, Inc., Boston, MA, USA
Robert S. Schrom
CORRESPONDING AUTHOR
Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Universities Space Research Association, Columbia, MD, USA
Charles N. Helms
Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Universities Space Research Association, Columbia, MD, USA
Ali Tokay
Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Catonsville, MD, USA
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Cited articles
Adams, I. S. and Bettenhausen, M. H.: The scattering properties of horizontally aligned snow crystals and crystal approximations at millimeter wavelengths, Radio Sci., 47, RS5007, https://doi.org/10.1029/2012RS005015, 2012. a
Andrić, J., Kumjian, M. R., Zrnić, D. S., Straka, J. M., and Melnikov, V. M.: Polarimetric signatures above the melting layer in winter storms: An observational and modeling study, J. Appl. Meteorol. Clim., 52, 682–700, 2013. a
Beard, K. V., Bringi, V., and Thurai, M.: A new understanding of raindrop shape, Atmos. Res., 97, 396–415, https://doi.org/10.1016/j.atmosres.2010.02.001, 2010. a
Bliven, L.: GPM Ground Validation Precipitation Imaging Package (PIP) ICE POP, NASA Global Hydrology Resource Center DAAC [data set], Huntsville, Alabama, USA, https://doi.org/10.5067/GPMGV/ICEPOP/PIP/DATA101, 2020. a
Botta, G., Aydin, K., and Verlinde, J.: Variability in millimeter wave scattering properties of dendritic ice crystals, J. Quant. Spectrosc. Ra., 131, 105–114, 2013. a
Brath, M., Ekelund, R., Eriksson, P., Lemke, O., and Buehler, S. A.: Microwave and submillimeter wave scattering of oriented ice particles, Atmos. Meas. Tech., 13, 2309–2333, https://doi.org/10.5194/amt-13-2309-2020, 2020. a, b, c
Bringi, V. N. and Chandrasekar, V.: Polarimetric Doppler Weather Radar, Cambridge University Press, 1st edn., ISBN 0521623847, 2001. a
Bukovčić, P., Ryzhkov, A., and Zrnić, D.: Polarimetric relations for snow estimation–radar verification, J. Appl. Meteorol. Clim., 59, 991–1009, 2020. a
Chandrasekar, V., Schwaller, M., Vega, M., Carswell, J., Mishra, K. V., Meneghini, R., and Nguyen, C.: Scientific and engineering overview of the NASA Dual-Frequency Dual-Polarized Doppler Radar (D3R) system for GPM Ground Validation, in: 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA, 25–30 July 2010, IEEE, 1308–1311, https://doi.org/10.1109/IGARSS.2010.5649440, 2010. a, b
Chandrasekar, V., Vega, M. A., Joshil, S., Kumar, M., Wolff, D., and Petersen, W.: Deployment and performance of the nasa d3r during the ice-pop 2018 field campaign in South Korea, in: IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018, IEEE, 8349–8351, https://doi.org/10.1109/IGARSS.2018.8517313, 2018. a
Chandrasekar, V.: GPM Ground Validation Dual-frequency Dual-polarized Doppler Radar (D3R) ICE POP, NASA Global Hydrology Resource Center DAAC [data set], Huntsville, Alabama, USA, https://doi.org/10.5067/GPMGV/ICEPOP/D3R/DATA101, 2019. a
Chase, R. J., Finlon, J. A., Borque, P., McFarquhar, G. M., Nesbitt, S. W., Tanelli, S., Sy, O. O., Durden, S. L., and Poellot, M. R.: Evaluation of triple-frequency radar retrieval of snowfall properties using coincident airborne in situ observations during OLYMPEX, Geophys. Res. Lett., 45, 5752–5760, 2018. a
Chase, R. J., Nesbitt, S. W., and McFarquhar, G. M.: A Dual-Frequency Radar Retrieval of Two Parameters of the Snowfall Particle Size Distribution Using a Neural Network, J. Appl. Meteorol. Clim., 60, 341–359, 2021. a
Chen, J. and Lamb, D.: The theoretical basis for the parametrerization of ice crystal habits: Growth by vapor deposition, J. Atmos. Sci., 51, 1206–1222, 1994. a
Connolly, P. J., Emersic, C., and Field, P. R.: A laboratory investigation into the aggregation efficiency of small ice crystals, Atmos. Chem. Phys., 12, 2055–2076, https://doi.org/10.5194/acp-12-2055-2012, 2012. a
de Boer, G., Ivey, M., Schmid, B., Lawrence, D., Dexheimer, D., Mei, F., Hubbe, J., Bendure, A., Hardesty, J., Shupe, M. D., McComiskey, A., Telg, H., Schmitt, C., Matrosov, S. Y., Brooks, I., Creamean, J., Solomon, A., Turner, D. D., Williams, C., Maahn, M., Argrow, B., Palo, S., Long, C. N., Gao, R., and Mather, J.: A bird’s eye view: Development of an operational ARM unmanned aerial capability for atmospheric research in Arctic Alaska, B. Am. Meteorol. Soc., 99, 1197–1212, 2018. a
Ekelund, R., Eriksson, P., and Kahnert, M.: Microwave single-scattering properties of non-spheroidal raindrops, Atmos. Meas. Tech., 13, 6933–6944, https://doi.org/10.5194/amt-13-6933-2020, 2020. a, b, c, d
Eriksson, P., Ekelund, R., Mendrok, J., Brath, M., Lemke, O., and Buehler, S. A.: A general database of hydrometeor single scattering properties at microwave and sub-millimetre wavelengths, Earth Syst. Sci. Data, 10, 1301–1326, https://doi.org/10.5194/essd-10-1301-2018, 2018. a
Fukuta, N. and Takahashi, T.: The growth of atmospheric ice crystals: A summary of findings in vertical supercooled cloud tunnel studies, J. Atmos. Sci., 56, 1963–1979, 1999. a
Garrett, T. J., Fallgatter, C., Shkurko, K., and Howlett, D.: Fall speed measurement and high-resolution multi-angle photography of hydrometeors in free fall, Atmos. Meas. Tech., 5, 2625–2633, https://doi.org/10.5194/amt-5-2625-2012, 2012. a
Hall, M. P. M., Goddard, J. W. F., and Cherry, S. M.: Identification of hydrometeors and other targets by dual-polarization radar, Radio Sci., 19, 132–140, 1984. a
Hari, P. and Kulmala, M.: Station for Measuring Ecosystem–Atmosphere Relations (SMEAR II), Boreal Environ. Res., 10, 315–322, 2005. a
Helms, C. N., Munchak, S. J., Tokay, A., and Pettersen, C.: A Comparative Evaluation of Snowflake Particle Size and Shape Estimation Techniques used by the Precipitation Imaging Package (PIP), Multi-Angle Snowflake Camera (MASC), and Two-Dimensional Video Disdrometer (2DVD), Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2021-427, in review, 2022. a, b
Hobbs, P. V., Chang, S., and Locatelli, J. D.: The dimensions and aggregation of ice crystals in natural clouds, J. Geophys. Res., 79, 2199–2206, 1974. a
Hosler, C. L., Jensen, D. C., and Goldshlak, L.: On the aggregation of ice crystals to form snow, Journal of Operational Meteorology, 14, 415–420, 1957. a
Jensen, A. A. and Harrington, J. Y.: Modeling ice crystal aspect ratio evolution during riming: A single-particle growth model, J. Atmos. Sci., 72, 2569–2590, 2015. a
Jiang, Z., Oue, M., Verlinde, J., Clothiaux, E. E., Aydin, K., Botta, G., and Lu, Y.: What can we conclude about the real aspect ratios of ice particle aggregates from two-dimensional images?, J. Appl. Meteorol. Clim., 56, 725–734, 2017. a
Kennedy, P. C. and Rutledge, S. A.: S-band dual-polarization radar observations of winter storms, J. Appl. Meteorol. Clim., 50, 844–858, 2011. a
Kim, K., Bang, W., Chang, E.-C., Tapiador, F. J., Tsai, C.-L., Jung, E., and Lee, G.: Impact of wind pattern and complex topography on snow microphysics during International Collaborative Experiment for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018), Atmos. Chem. Phys., 21, 11955–11978, https://doi.org/10.5194/acp-21-11955-2021, 2021. a, b
Klett, J. D.: Orientation model for particles in turbulence, J. Atmos. Sci., 52, 2276–2285, 1995. a
Kumar, M., Joshil, S. S., Chandrasekar, V., Beauchamp, R. M., Vega, M., and Zebley, J. W.: Performance trade-offs and upgrade of NASA D3R weather radar, in: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, 23–28 July 2017, 5260–5263, https://doi.org/10.1109/IGARSS.2017.8128188, 2017. a, b
Kumjian, M. R.: Principles and applications of dual-polarization weather radar. Part I: Description of the polarimetric radar variables, Journal of Operational Meteorology, 1, 226–242, 2013. a
Kumjian, M. R. and Lombardo, K. A.: Insights into the evolving microphysical and kinematic structure of northeastern U.S. winter storms from dual-polarization Doppler radar, Mon. Weather Rev., 145, 1033–1061, 2017. a
Kuo, K., Olson, W. S., Johnson, B. T., Grecu, M., Tian, L., Clune, T. L., van Aartsen, B. H., Heymsfield, A. J., Liao, L., and Meneghini, R.: The microwave radiative properties of falling snow derived from nonspherical ice particle models. Part I: An extensive database of simulated pristine crystals and aggregate particles, and their scattering properties, J. Appl. Meteorol. Clim., 56, 691–708, 2016. a
Kuroda, T. and Lacmann, R.: Growth kinetics of ice from the vapour phase and its growth forms, J. Cryst. Growth, 56, 189–205, 1982. a
L'Ecuyer, T. S. and Stephens, G. L.: An estimation-based precipitation retrieval algorithm for attenuating radars, J. Appl. Meteorol., 41, 272–285, 2002. a
Leinonen, J. and von Lerber, A.: Snowflake melting simulation using smoothed particle hydrodynamics, J. Geophys. Res.-Atmos., 123, 1811–1825, 2018. a
Leinonen, J., Lebsock, M. D., Tanelli, S., Sy, O. O., Dolan, B., Chase, R. J., Finlon, J. A., von Lerber, A., and Moisseev, D.: Retrieval of snowflake microphysical properties from multifrequency radar observations, Atmos. Meas. Tech., 11, 5471–5488, https://doi.org/10.5194/amt-11-5471-2018, 2018. a
Liao, L., Meneghini, R., Tokay, A., and Bliven, L. F.: Retrieval of snow properties for Ku-and Ka-band dual-frequency radar, J. Appl. Meteorol. Clim., 55, 1845–1858, 2016. a
Lim, K.-S. S., Chang, E.-C., Sun, R., Kim, K., Tapiador, F. J., and Lee, G.: Evaluation of simulated winter precipitation using WRF-ARW during the ICE-POP 2018 field campaign, Weather Forecast., 35, 2199–2213, 2020. a
Löhnert, U., Schween, J. H., Acquistapace, C., Ebell, K., Maahn, M., Barrera-Verdejo, M., Hirsikko, A., Bohn, B., Knaps, A., O’Connor, E., Simmer, C., Wahner, A., and Crewell, S.: JOYCE: Jülich Observatory for Cloud Evolution, B. Am. Meteorol. Soc., 96, 1157–1174, https://doi.org/10.1175/BAMS-D-14-00105.1, 2015. a
Marshall, J. S. and Gunn, K. L. S.: The microwave properties of precipitation particles, J. Atmos. Sci., 9, 322–327, 1952. a
Matrosov, S. Y., Heymsfield, A., and Wang, Z.: Dual-frequency radar ratio of nonspherical atmospheric hydrometeors, Geophys. Res. Lett., 32, L13816, https://doi.org/10.1029/2005GL023210, 2005. a
Matsuo, T. and Sasyo, Y.: Empirical formula for the melting rate of snowflakes, J. Meteorol. Soc. Jpn., Ser. II, 59, 1–9, 1981. a
Melnikov, V. and Straka, J. M.: Axis ratios and flutter angles of cloud ice particles: Retrievals from radar data, J. Atmos. Ocean. Tech., 30, 1691–1703, 2013. a
Meteomodem: Meteomodem M10 Radiosonde Information Leaflet, http://www.meteomodem.com/docs/en/Leaflet-m10.pdf (last access: 4 March 2022), 2021. a
Milewska, E. J., Vincent, L. A., Hartwell, M. M., Charlesworth, K., and Mekis, É.: Adjusting precipitation amounts from Geonor and Pluvio automated weighing gauges to preserve continuity of observations in Canada, Can. Water Resour. J., 44, 127–145, 2019. a
Mitchell, D. L., Zhang, R., and Pitter, R. L.: Mass-Dimensional Relationships for Ice Particles and the Influence of Riming on Snowfall Rates, J. Appl. Meteorol. Clim., 29, 153–163, https://doi.org/10.1175/1520-0450(1990)029<0153:MDRFIP>2.0.CO;2, 1990. a
Moisseev, D., von Lerber, A., and Tiira, J.: Quantifying the effect of riming on snowfall using ground-based observations, J. Geophys. Res.-Atmos., 122, 4019–4037, https://doi.org/10.1002/2016JD026272, 2017. a
Moisseev, D. N., Lautaportti, S., Tyynela, J., and Lim, S.: Dual-polarization radar signatures in snowstorms: Role of snowflake aggregation, J. Geophys. Res., 120, 12644–12655, 2015. a
Munchak, S. J. and Kummerow, C. D.: A modular optimal estimation method for combined radar–radiometer precipitation profiling, J. Appl. Meteorol. Clim., 50, 433–448, 2011. a
Petersen, W., Wolff, D., Zavodsky, B., and Roberts, J.: International Collaborative Experiment for PyeongChang Olympic and Paralympics (ICE-POP) Collection, NASA EOSDIS Global Hydrology Resource Center Distributed Active Archive Center [data set], Huntsville, Alabama, USA, https://doi.org/10.5067/GPMGV/ICEPOP/DATA101, 2018. a
Pettersen, C., Bliven, L. F., von Lerber, A., Wood, N. B., Kulie, M. S., Mateling, M. E., Moisseev, D. N., Munchak, S. J., Petersen, W. A., and Wolff, D. B.: The precipitation imaging package: Assessment of microphysical and bulk characteristics of snow, Atmosphere, 11, 785, https://doi.org/10.3390/atmos11080785, 2020. a, b, c
Petty, G. W. and Huang, W.: The modified gamma size distribution applied to inhomogeneous and nonspherical particles: Key relationships and conversions, J. Atmos. Sci., 68, 1460–1473, 2011. a
Rodgers, C. D.: Inverse methods for atmospheric sounding: theory and practice, vol. 2, World scientific, ISBN 981022740X, 2000. a
Ryzhkov, A. V. and Zrnić, D. S.: Discrimination between rain and snow with a polarimetric radar, J. Appl. Meteorol., 37, 1228–1240, 1998. a
Ryzhkov, A. V., Zhang, P., Reeves, H. D., Kumjian, M. R., Tschallener, T., Troemel, S., and Simmer, C.: Quasi-vertical profiles – a new way to look at polarimetric radar data, J. Atmos. Ocean. Tech., 33, 551–562, 2016. a
Schrom, R. S. and Kumjian, M. R.: Connecting microphysical processes in Colorado winter storms with vertical profiles of radar observations, J. Appl. Meteorol. Clim., 55, 1771–1787, 2016. a
Schrom, R. S., Kumjian, M. R., and Lu, Y.: Polarimetric radar observations of dendritic growth zones in Colorado winter storms, J. Appl. Meteorol. Clim., 54, 2365–2388, 2015. a
Skofronick-Jackson, G., Hudak, D., Petersen, W., Nesbitt, S. W., Chandrasekar, V., Durden, S., Gleicher, K. J., Huang, G.-J., Joe, P., Kollias, P., Reed, K. A., Schwaller, M. R., Stewart, R., Tanelli, S., Tokay, A., Wang, J. R., and Wolde, M.: Global precipitation measurement cold season precipitation experiment (GCPEX): For measurement’s sake, let it snow, B. Am.
Meteorol. Soc., 96, 1719–1741, 2015. a
Skofronick-Jackson, G., Kulie, M., Milani, L., Munchak, S. J., Wood, N. B., and Levizzani, V.: Satellite estimation of falling snow: A global precipitation measurement (GPM) core observatory perspective, J. Appl. Meteorol. Clim., 58, 1429–1448, 2019. a
Smith, A. J., Larson, V. E., Niu, J., Kankiewicz, J. A., and Carey, L. D.: Processes that generate and deplete liquid water and snow in thin midlevel mixed-phase clouds, J. Geophys. Res., 114, D12203, https://doi.org/10.1029/2008JD011531, 2009. a
Thompson, E. J., Rutledge, S. A., Dolan, B., Chandrasekar, V., and Cheong, B.: A dual-polarization radar hydrometeor classification algorithm for winter precipitation, J. Atmos. Ocean. Tech., 31, 1457–1481, 2014. a
Tiira, J., Moisseev, D. N., von Lerber, A., Ori, D., Tokay, A., Bliven, L. F., and Petersen, W.: Ensemble mean density and its connection to other microphysical properties of falling snow as observed in Southern Finland, Atmos. Meas. Tech., 9, 4825–4841, https://doi.org/10.5194/amt-9-4825-2016, 2016. a
Tridon, F., Battaglia, A., Chase, R. J., Turk, F. J., Leinonen, J., Kneifel, S., Mroz, K., Finlon, J., Bansemer, A., Tanelli, S., Heymsfield, A. J., and Nesbitt, S. W.: The microphysics of stratiform precipitation during OLYMPEX: Compatibility between triple-frequency radar and airborne in situ observations, J. Geophys. Res.-Atmos., 124, 8764–8792, 2019. a
Vega, M. A., Chandrasekar, V., Carswell, J., Beauchamp, R. M., Schwaller, M. R., and Nguyen, C.: Salient features of the dual-frequency, dual-polarized, Doppler radar for remote sensing of precipitation, Radio Sci., 49, 1087–1105, https://doi.org/10.1002/2014RS005529, 2014.
a
Vivekanandan, J., Bringi, V. N., Hagen, M., and Meischner, P.: Polarimetric radar studies of atmospheric ice particles, IEEE T. Geosci. Remote, 32, 1–10, 1994. a
Yurkin, M. A. and Hoekstra, A. G.: The discrete-dipole-approximation code ADDA: Capabilities and known limitations, J. Quant. Spectrosc. Ra., 112, 2234–2247, 2011. a
Short summary
The ability to measure snowfall with weather radar has greatly advanced with the development of techniques that utilize dual-polarization measurements, which provide information about the snow particle shape and orientation, and multi-frequency measurements, which provide information about size and density. This study combines these techniques with the NASA D3R radar, which provides dual-frequency polarimetric measurements, with data that were observed during the 2018 Winter Olympics.
The ability to measure snowfall with weather radar has greatly advanced with the development of...
