Articles | Volume 24, issue 19
https://doi.org/10.5194/acp-24-11191-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-11191-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Simulated particle evolution within a winter storm: contributions of riming to radar moments and precipitation fallout
Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA
Lynn A. McMurdie
Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA
Angela K. Rowe
Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, WI, USA
Andrew J. Heymsfield
National Center for Atmospheric Research, Boulder, CO, USA
US National Science Foundation, Alexandria, VA, USA
Related authors
Andrew DeLaFrance, Lynn A. McMurdie, Angela K. Rowe, and Andrew J. Heymsfield
Atmos. Chem. Phys., 25, 8087–8106, https://doi.org/10.5194/acp-25-8087-2025, https://doi.org/10.5194/acp-25-8087-2025, 2025
Short summary
Short summary
Numerical modeling simulations are used to investigate ice crystal growth and decay processes within a banded region of enhanced precipitation rates during a prominent winter storm. We identify robust primary ice growth in the upper portion of the cloud but decay exceeding 70 % during fallout through a subsaturated layer. The ice fall characteristics and decay rate are sensitive to the ambient cloud properties, which has implications for radar-based measurements and precipitation accumulations.
Andrew DeLaFrance, Lynn A. McMurdie, Angela K. Rowe, and Andrew J. Heymsfield
Atmos. Chem. Phys., 25, 8087–8106, https://doi.org/10.5194/acp-25-8087-2025, https://doi.org/10.5194/acp-25-8087-2025, 2025
Short summary
Short summary
Numerical modeling simulations are used to investigate ice crystal growth and decay processes within a banded region of enhanced precipitation rates during a prominent winter storm. We identify robust primary ice growth in the upper portion of the cloud but decay exceeding 70 % during fallout through a subsaturated layer. The ice fall characteristics and decay rate are sensitive to the ambient cloud properties, which has implications for radar-based measurements and precipitation accumulations.
Anthony C. Bernal Ayala, Angela K. Rowe, Lucia E. Arena, and William O. Nachlas
Atmos. Chem. Phys., 25, 7597–7617, https://doi.org/10.5194/acp-25-7597-2025, https://doi.org/10.5194/acp-25-7597-2025, 2025
Short summary
Short summary
This study analyzed particles in hailstones from Argentina to better understand hail formation and growth. A unique method was used that revealed the particles’ size, composition, and location within the hail, including a variety of particle sizes and compositions linked to local land uses, such as mountainous, agricultural, and urban areas. The findings highlight the potential impacts of natural and human-related factors on hail formation and provide a new method for studying hail globally.
Anthony C. Bernal Ayala, Angela K. Rowe, Lucia E. Arena, William O. Nachlas, and Maria L. Asar
Atmos. Meas. Tech., 17, 5561–5579, https://doi.org/10.5194/amt-17-5561-2024, https://doi.org/10.5194/amt-17-5561-2024, 2024
Short summary
Short summary
Hail is a challenging weather phenomenon to forecast due to an incomplete understanding of hailstone formation. Microscopy temperature limitations required previous studies to melt hail for analysis. This paper introduces a unique technique using a plastic cover to preserve particles in their location within the hailstone without melting. Therefore, CLSM and SEM–EDS microscopes can be used to determine individual particle sizes and their chemical composition related to hail-formation processes.
Armin Blanke, Andrew J. Heymsfield, Manuel Moser, and Silke Trömel
Atmos. Meas. Tech., 16, 2089–2106, https://doi.org/10.5194/amt-16-2089-2023, https://doi.org/10.5194/amt-16-2089-2023, 2023
Short summary
Short summary
We present an evaluation of current retrieval techniques in the ice phase applied to polarimetric radar measurements with collocated in situ observations of aircraft conducted over the Olympic Mountains, Washington State, during winter 2015. Radar estimates of ice properties agreed most with aircraft observations in regions with pronounced radar signatures, but uncertainties were identified that indicate issues of some retrievals, particularly in warmer temperature regimes.
Étienne Vignon, Lea Raillard, Christophe Genthon, Massimo Del Guasta, Andrew J. Heymsfield, Jean-Baptiste Madeleine, and Alexis Berne
Atmos. Chem. Phys., 22, 12857–12872, https://doi.org/10.5194/acp-22-12857-2022, https://doi.org/10.5194/acp-22-12857-2022, 2022
Short summary
Short summary
The near-surface atmosphere over the Antarctic Plateau is cold and pristine and resembles to a certain extent the high troposphere where cirrus clouds form. In this study, we use innovative humidity measurements at Concordia Station to study the formation of ice fogs at temperatures <−40°C. We provide observational evidence that ice fogs can form through the homogeneous freezing of solution aerosols, a common nucleation pathway for cirrus clouds.
Kamil Mroz, Alessandro Battaglia, Cuong Nguyen, Andrew Heymsfield, Alain Protat, and Mengistu Wolde
Atmos. Meas. Tech., 14, 7243–7254, https://doi.org/10.5194/amt-14-7243-2021, https://doi.org/10.5194/amt-14-7243-2021, 2021
Short summary
Short summary
A method for estimating microphysical properties of ice clouds based on radar measurements is presented. The algorithm exploits the information provided by differences in the radar response at different frequency bands in relation to changes in the snow morphology. The inversion scheme is based on a statistical relation between the radar simulations and the properties of snow calculated from in-cloud sampling.
Cited articles
Bailey, M. P. and Hallett, J.: A Comprehensive Habit Diagram for Atmospheric Ice Crystals: Confirmation from the Laboratory, AIRS II, and Other Field Studies, J. Atmos. Sci., 66, 2888–2899, https://doi.org/10.1175/2009JAS2883.1, 2009.
Bansemer, A., Delene, D., Heymsfield, A., O'Brien, J., Poellot, M., Sand, K., Sova G., Moore J., and Nairy, C.: NCAR Particle Probes IMPACTS, Dataset available online from the NASA Global Hydrometeorology Resource Center DAAC, Huntsville, Alabama, USA [data set], https://doi.org/10.5067/IMPACTS/PROBES/DATA101, 2022.
Bjerknes, J.: Extratropical Cyclones, in: Compendium of Meteorology, edited by: Malone, T. F., American Meteorological Society, Boston, MA, 577–598, https://doi.org/10.1007/978-1-940033-70-9_48, 1951.
Bohren, C. F. and Huffman, D. R.: Absorption and Scattering of Light by Small Particles, John Wiley and Sons, New York, 530 pp., ISBN 3527618163, 1983.
Brdar, S. and Seifert, A.: McSnow: A Monte-Carlo Particle Model for Riming and Aggregation of Ice Particles in a Multidimensional Microphysical Phase Space, J. Adv. Model Earth Syst., 10, 187–206, https://doi.org/10.1002/2017MS001167, 2018.
Bringi, V., Seifert, A., Wu, W., Thurai, M., Huang, G.-J., and Siewert, C.: Hurricane Dorian Outer Rain Band Observations and 1D Particle Model Simulations: A Case Study, Atmosphere, 11, 879, https://doi.org/10.3390/atmos11080879, 2020.
Brodzik, S.: Automated Surface Observing System (ASOS) IMPACTS, Dataset available online from the NASA Global Hydrometeorology Resource Center DAAC, Huntsville, Alabama, USA [data set], https://doi.org/10.5067/IMPACTS/ASOS/DATA101, 2022a.
Brodzik, S.: GOES IMPACTS, Dataset available online from the NASA Global Hydrometeorology Resource Center DAAC, Huntsville, Alabama, USA [data set], https://doi.org/10.5067/IMPACTS/GOES/DATA101, 2022b.
Brown, P. R. A. and Francis, P. N.: Improved Measurements of the Ice Water Content in Cirrus Using a Total-Water Probe, J. Atmos. Ocean. Tech., 12, 410–414, https://doi.org/10.1175/1520-0426(1995)012<0410:IMOTIW>2.0.CO;2, 1995.
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, https://doi.org/10.1175/JAMC-D-20-0177.1, 2021.
Cholette, M., Milbrandt, J. A., Morrison, H., Paquin-Ricard, D., and Jacques, D.: Combining Triple-Moment Ice with Prognostic Liquid Fraction in the P3 Microphysics Scheme: Impacts on a Simulated Squall Line, J. Adv. Model Earth Syst., 15, e2022MS003328, https://doi.org/10.1029/2022MS003328, 2023.
Colle, B. A., Garvert, M. F., Wolfe, J. B., Mass, C. F., and Woods, C. P.: The 13–14 December 2001 IMPROVE-2 Event. Part III: Simulated Microphysical Budgets and Sensitivity Studies, J. Atmos. Sci., 62, 3535–3558, https://doi.org/10.1175/JAS3552.1, 2005.
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.
DeLaFrance, A., McMurdie, L. A., Rowe, A. K., and Conrick, R.: Effects of Riming on Ice-Phase Precipitation Growth and Transport Over an Orographic Barrier, J. Adv. Model Earth Syst., 16, e2023MS003778, https://doi.org/10.1029/2023MS003778, 2024.
Field, P. R., Hogan, R. J., Brown, P. R. A., Illingworth, A. J., Choularton, T. W., and Cotton, R. J.: Parametrization of Ice-Particle Size Distributions for Mid-Latitude Stratiform Cloud, Q. J. Roy. Meteor. Soc., 131, 1997–2017, https://doi.org/10.1256/qj.04.134, 2005.
Field, P. R., Heymsfield, A. J., and Bansemer, A.: Snow Size Distribution Parameterization for Midlatitude and Tropical Ice Clouds, J. Atmos. Sci., 64, 4346–4365, https://doi.org/10.1175/2007JAS2344.1, 2007.
Grecu, M., Olson, W. S., Munchak, S. J., Ringerud, S., Liao, L., Haddad, Z., Kelley, B. L., and McLaughlin, S. F.: The GPM Combined Algorithm, J. Atmos. Ocean Tech., 33, 2225–2245, https://doi.org/10.1175/JTECH-D-16-0019.1, 2016.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., De Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 Global Reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Heymsfield, A., Bansemer, A., Heymsfield, G., Noone, D., Grecu, M., and Toohey, D.: Relationship of Multiwavelength Radar Measurements to Ice Microphysics from the IMPACTS Field Program, J. Appl. Meteorol. Clim., 62, 289–315, https://doi.org/10.1175/JAMC-D-22-0057.1, 2023.
Heymsfield, A. J.: A Comparative Study of the Rates of Development of Potential Graupel and Hail Embryos in High Plains Storms, J. Atmos. Sci., 39, 2867–2897, https://doi.org/10.1175/1520-0469(1982)039<2867:ACSOTR>2.0.CO;2, 1982.
Heymsfield, A. J., Bansemer, A., Schmitt, C., Twohy, C., and Poellot, M. R.: Effective Ice Particle Densities Derived from Aircraft Data, J. Atmos. Sci., 61, 982–1003, https://doi.org/10.1175/1520-0469(2004)061<0982:EIPDDF>2.0.CO;2, 2004.
Holton, J. R. and Hakim, G. J.: An Introduction to Dynamic Meteorology, 5th edn., Elsevier: Academic Press, Amsterdam, 532 pp., ISBN 0123848679, 2012.
Iguchi, T., Seto, S., Meneghini, R., Yoshida, N., Awaka, J., Le, M., Chandrasekhar, V., Brodzik, S., and Kubota, T.: GPM/DPR Level-2 Algorithm Theoretical Basis Document, NASA Goddard Space Flight Center, https://www.eorc.jaxa.jp/GPM/doc/algorithm/ATBD_DPR_201811_with_Appendix3b.pdf (last access: May 2024), 2018.
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, https://doi.org/10.1175/JAS-D-14-0297.1, 2015.
Jensen, A. A., Harrington, J. Y., Morrison, H., and Milbrandt, J. A.: Predicting Ice Shape Evolution in a Bulk Microphysics Model, J. Atmos. Sci., 74, 2081–2104, https://doi.org/10.1175/JAS-D-16-0350.1, 2017.
Kalesse, H., Szyrmer, W., Kneifel, S., Kollias, P., and Luke, E.: Fingerprints of a riming event on cloud radar Doppler spectra: observations and modeling, Atmos. Chem. Phys., 16, 2997–3012, https://doi.org/10.5194/acp-16-2997-2016, 2016.
Kneifel, S., Kollias, P., Battaglia, A., Leinonen, J., Maahn, M., Kalesse, H., and Tridon, F.: First Observations of Triple-Frequency Radar Doppler Spectra in Snowfall: Interpretation and Applications, Geophys. Res. Lett., 43, 2225–2233, https://doi.org/10.1002/2015GL067618, 2016.
Lawson, R. P., Stewart, R. E., Strapp, J. W., and Isaac, G. A.: Aircraft Observations of the Origin and Growth of Very Large Snowflakes, Geophys. Res. Lett., 20, 53–56, https://doi.org/10.1029/92GL02917, 1993.
Lawson, R. P., O'Connor, D., Zmarzly, P., Weaver, K., Baker, B., Mo, Q., and Jonsson, H.: The 2D-S (Stereo) Probe: Design and Preliminary Tests of a New Airborne, High-Speed, High-Resolution Particle Imaging Probe, J. Atmos. Ocean. Tech., 23, 1462–1477, https://doi.org/10.1175/JTECH1927.1, 2006.
Lawson, R. P., Gurganus, C., Woods, S., and Bruintjes, R.: Aircraft Observations of Cumulus Microphysics Ranging from the Tropics to Midlatitudes: Implications for a “New” Secondary Ice Process, J. Atmos. Sci., 74, 2899–2920, https://doi.org/10.1175/JAS-D-17-0033.1, 2017.
Leinonen, J.: High-level Interface to T-matrix Scattering Calculations: Architecture, Capabilities and Limitations, Opt. Express, 22, 1655, https://doi.org/10.1364/OE.22.001655, 2014.
Leinonen, J. and Szyrmer, W.: Radar Signatures of Snowflake Riming: A Modeling Study, Earth Space Sci., 2, 346–358, https://doi.org/10.1002/2015EA000102, 2015.
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.
Li, L., Heymsfield, G., Carswell, J., Schaubert, D. H., McLinden, M. L., Creticos, J., Perrine, M., Coon, M., Cervantes, J. I., Vega, M., Guimond, S., Tian, L., and Emory, A.: The NASA High-Altitude Imaging Wind and Rain Airborne Profiler, IEEE T. Geosci. Remote, 54, 298–310, https://doi.org/10.1109/TGRS.2015.2456501, 2016.
Lin, Y. and Colle, B. A.: A New Bulk Microphysical Scheme That Includes Riming Intensity and Temperature-Dependent Ice Characteristics, Mon. Weather Rev., 139, 1013–1035, https://doi.org/10.1175/2010MWR3293.1, 2011.
Liu, G.: Approximation of Single Scattering Properties of Ice and Snow Particles for High Microwave Frequencies, J. Atmos. Sci., 61, 2441–2456, https://doi.org/10.1175/1520-0469(2004)061<2441:AOSSPO>2.0.CO;2, 2004.
Liu, G.: A Database of Microwave Single-Scattering Properties for Nonspherical Ice Particles, B. Am. Meteorol. Soc., 89, 1563–1570, https://doi.org/10.1175/2008BAMS2486.1, 2008.
Magono, C. and Lee, C. W.: Meteorological Classification of Natural Snow Crystals, J. Fac. Sci., Hokkaido University, Series 7, Geophysics, 2, 321–335, 1966.
Mason, S. L., Chiu, C. J., Hogan, R. J., Moisseev, D., and Kneifel, S.: Retrievals of Riming and Snow Density from Vertically Pointing Doppler Radars, J. Geophys. Res.-Atmos., 123, 13807–13834, https://doi.org/10.1029/2018JD028603, 2018.
Mason, S. L., Hogan, R. J., Westbrook, C. D., Kneifel, S., Moisseev, D., and von Terzi, L.: The importance of particle size distribution and internal structure for triple-frequency radar retrievals of the morphology of snow, Atmos. Meas. Tech., 12, 4993–5018, https://doi.org/10.5194/amt-12-4993-2019, 2019.
Matrosov, S. Y.: Modeling Backscatter Properties of Snowfall at Millimeter Wavelengths, J. Atmos. Sci., 64, 1727–1736, https://doi.org/10.1175/JAS3904.1, 2007.
McLinden, M., Li, L. Heymsfield, G. M., Coon, M. and Emory, A.: The NASA GSFC 94-GHz Airborne Solid-State Cloud Radar System (CRS), J. Atmos. Ocean. Tech., 38, 1001–1017, https://doi.org/10.1175/JTECH-D-20-0127.1, 2021.
McLinden, M., Li, L., and Heymsfield, G. M.: High Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) IMPACTS, Dataset available online from the NASA Global Hydrometeorology Resource Center DAAC, Huntsville, Alabama, USA [data set], https://doi.org/10.5067/IMPACTS/HIWRAP/DATA101, 2022.
McMurdie, L. A., Heymsfield, G., Yorks, J. E., and Braun, S. A.: Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) Collection, NASA Global Hydrometeorology Resource Center DAAC, Huntsville, Alabama, USA [data set], https://doi.org/10.5067/IMPACTS/DATA101, 2019.
McMurdie, L. A., Heymsfield, G. M., Yorks, J. E., Braun, S. A., Skofronick-Jackson, G., Rauber, R. M., Yuter, S., Colle, B., McFarquhar, G. M., Poellot, M., Novak, D. R., Lang, T. J., Kroodsma, R., McLinden, M., Oue, M., Kollias, P., Kumjian, M. R., Greybush, S. J., Heymsfield, A. J., Finlon, J. A., McDonald, V. L., and Nicholls, S.: Chasing Snowstorms: The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) Campaign, B. Am. Meteorol. Soc., 103, E1243–E1269, https://doi.org/10.1175/BAMS-D-20-0246.1, 2022.
Mishchenko, M. I. and Travis, L. D.: Capabilities and Limitations of a Current FORTRAN Implementation of the T-matrix Method for Randomly Oriented, Rotationally Symmetric Scatterers, J. Quant. Spectrosc. Ra., 60, 309–324, https://doi.org/10.1016/S0022-4073(98)00008-9, 1998.
Mishchenko, M. I., Travis, L. D., and Mackowski, D. W.: T-matrix Computations of Light Scattering by Nonspherical Particles: A Review, J. Quant. Spectrosc. Ra., 55, 535–575, https://doi.org/10.1016/0022-4073(96)00002-7, 1996.
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.
Morrison, H. and Milbrandt, J.: Comparison of Two-Moment Bulk Microphysics Schemes in Idealized Supercell Thunderstorm Simulations, Mon. Weather Rev., 139, 1103–1130, https://doi.org/10.1175/2010MWR3433.1, 2011.
Morrison, H. and Milbrandt, J. A.: Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part I: Scheme Description and Idealized Tests, J. Atmos. Sci., 72, 287–311, https://doi.org/10.1175/JAS-D-14-0065.1, 2015.
Morrison, H., Curry, J. A., and Khvorostyanov, V. I.: A New Double-Moment Microphysics Parameterization for Application in Cloud and Climate Models. Part I: Description, J. Atmos. Sci., 62, 1665–1677, https://doi.org/10.1175/JAS3446.1, 2005.
Morrison, H., Van Lier-Walqui, M., Fridlind, A. M., Grabowski, W. W., Harrington, J. Y., Hoose, C., Korolev, A., Kumjian, M. R., Milbrandt, J. A., Pawlowska, H., Posselt, D. J., Prat, O. P., Reimel, K. J., Shima, S., Van Diedenhoven, B., and Xue, L.: Confronting the Challenge of Modeling Cloud and Precipitation Microphysics, J. Adv. Model Earth Syst., 12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020.
Novak, D. R., Bosart, L. F., Keyser, D., and Waldstreicher, J. S.: An Observational Study of Cold Season–Banded Precipitation in Northeast U.S. Cyclones, Weather Forecast., 19, 993–1010, https://doi.org/10.1175/815.1, 2004.
Oue, M., Kollias, P., Ryzhkov, A., and Luke, E. P.: Toward Exploring the Synergy Between Cloud Radar Polarimetry and Doppler Spectral Analysis in Deep Cold Precipitating Systems in the Arctic, J. Geophys. Res.-Atmos., 123, 2797–2815, https://doi.org/10.1002/2017JD027717, 2018.
Pruppacher, H. R. and Klett, J. D.: Microphysics of Clouds and Precipitation, 2nd rev. and enl. ed., Kluwer Academic Publishers, Dordrecht, Boston, 954 pp., https://doi.org/10.1007/978-0-306-48100-0, 1997.
Purcell, E. M. and Pennypacker, C. R.: Scattering and Absorption of Light by Nonspherical Dielectric Grains, Astrophys. J., 186, 705–714, https://doi.org/10.1086/152538, 1973.
Shima, S., Kusano, K., Kawano, A., Sugiyama, T., and Kawahara, S.: The Super-Droplet Method for the Numerical Simulation of Clouds and Precipitation: A Particle-Based and Probabilistic Microphysics Model Coupled with a Non-Hydrostatic Model, Q. J. Roy. Meteor. Soc., 135, 1307–1320, https://doi.org/10.1002/qj.441, 2009.
Skofronick-Jackson, G., Petersen, W. A., Berg, W., Kidd, C., Stocker, E. F., Kirschbaum, D. B., Kakar, R., Braun, S. A., Huffman, G. J., Iguchi, T., Kirstetter, P. E., Kummerow, C., Meneghini, R., Oki, R., Olson, W. S., Takayabu, Y. N., Furukawa, K., and Wilheit, T.: The Global Precipitation Measurement (GPM) Mission for Science and Society, B. Am. Meteorol. Soc., 98, 1679–1695, https://doi.org/10.1175/BAMS-D-15-00306.1, 2017.
Speirs, P., Gabella, M., and Berne, A.: A Comparison Between the GPM Dual-Frequency Precipitation Radar and Ground-Based Radar Precipitation Rate Estimates in the Swiss Alps and Plateau, J. Hydrometeorol., 18, 1247–1269, https://doi.org/10.1175/JHM-D-16-0085.1, 2017.
Thompson, G., Rasmussen, R. M., and Manning, K.: Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part I: Description and Sensitivity Analysis, Mon. Weather Rev., 132, 519–542, https://doi.org/10.1175/1520-0493(2004)132<0519:EFOWPU>2.0.CO;2, 2004.
Thornhill, K. L.: Turbulent Air Motion Measurement System (TAMMS) IMPACTS, Dataset available online from the NASA Global Hydrometeorology Resource Center DAAC, Huntsville, Alabama, USA [data set], https://doi.org/10.5067/IMPACTS/TAMMS/DATA101, 2022.
Thornhill, K. L., Anderson, B. E., Barrick, J. D. W., Bagwell, D. R., Friesen, R., and Lenschow, D. H.: Air Motion Intercomparison Flights During Transport and Chemical Evolution in the Pacific (TRACE-P)/ACE-ASIA, J. Geophys. Res.-Atmos., 108, 2002JD003108, https://doi.org/10.1029/2002JD003108, 2003.
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, https://doi.org/10.1029/2018JD029858, 2019.
Uccellini, L. W. and Kocin, P. J.: The Interaction of Jet Streak Circulations during Heavy Snow Events along the East Coast of the United States, Weather Forecast., 2, 289–308, https://doi.org/10.1175/1520-0434(1987)002<0289:TIOJSC>2.0.CO;2, 1987.
Van Weverberg, K., Vogelmann, A. M., Morrison, H., and Milbrandt, J. A.: Sensitivity of Idealized Squall-Line Simulations to the Level of Complexity Used in Two-Moment Bulk Microphysics Schemes, Mon. Weather Rev., 140, 1883–1907, https://doi.org/10.1175/MWR-D-11-00120.1, 2012.
Waldstreicher, J. and Brodzik, S.: NOAA Sounding IMPACTS, Dataset available online from the NASA Global Hydrometeorology Resource Center DAAC, Huntsville, Alabama, USA [data set], https://doi.org/10.5067/IMPACTS/SOUNDING/DATA201, 2022.
Williams, C. R.: How Much Attenuation Extinguishes mm-Wave Vertically Pointing Radar Return Signals?, Remote Sens., 14, 1305, https://doi.org/10.3390/rs14061305, 2022.
Zaremba, T. J., Rauber, R. M., Heimes, K., Yorks, J. E., Finlon, J. A., Nicholls, S. D., Selmer, P., McMurdie, L. A., and McFarquhar, G. M.: Cloud-Top Phase Characterization of Extratropical Cyclones over the Northeast and Midwest United States: Results from IMPACTS, J. Atmos. Sci., 81, 341–361, https://doi.org/10.1175/JAS-D-23-0123.1, 2024.
Zhang, J., Howard, K., Langston, C., Vasiloff, S., Kaney, B., Arthur, A., Van Cooten, S., Kelleher, K., Kitzmiller, D., Ding, F., Seo, D.-J., Wells, E., and Dempsey C.: National Mosaic and Multi-Sensor QPE (NMQ) System: Description, Results, and Future Plans, B. Am. Meteorol. Soc., 92, 1321–1338, https://doi.org/10.1175/2011BAMS-D-11-00047.1, 2011.
Short summary
Using a numerical model, the process whereby falling ice crystals accumulate supercooled liquid water droplets is investigated to elucidate its effects on radar-based measurements and surface precipitation. We demonstrate that this process accounted for 55% of the precipitation during a wintertime storm and is uniquely discernable from other ice crystal growth processes in Doppler velocity measurements. These results have implications for measurements from airborne and spaceborne platforms.
Using a numerical model, the process whereby falling ice crystals accumulate supercooled liquid...
Altmetrics
Final-revised paper
Preprint