Articles | Volume 18, issue 12
https://doi.org/10.5194/gmd-18-3559-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-3559-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
18 Jun 2025
Model evaluation paper |
| 18 Jun 2025
| 18 Jun 2025
Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data
Wonbae Bang
BK21 Weather Extremes Education & Research Team, Department of Atmospheric Sciences, Center for Atmospheric REmote sensing (CARE), Kyungpook National University, Daegu, Republic of Korea
Jacob T. Carlin
NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma, USA
Kwonil Kim
School of Marine and Atmospheric Sciences, Stony Brook University, New York, USA
Alexander V. Ryzhkov
Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma, USA
NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma, USA
Guosheng Liu
Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, Florida, USA
GyuWon Lee
CORRESPONDING AUTHOR
BK21 Weather Extremes Education & Research Team, Department of Atmospheric Sciences, Center for Atmospheric REmote sensing (CARE), Kyungpook National University, Daegu, Republic of Korea
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Chia-Lun Tsai, Kwonil Kim, Yu-Chieng Liou, Jung-Hoon Kim, YongHee Lee, and GyuWon Lee
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Hwayoung Jeoung, Guosheng Liu, Kwonil Kim, Gyuwon Lee, and Eun-Kyoung Seo
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Radar and radiometer observations were used to study cloud liquid and snowfall in three types of snow clouds. While near-surface and shallow clouds have an area fraction of 90 %, deep clouds contribute half of the total snowfall volume. Deeper clouds have heavier snowfall, although cloud liquid is equally abundant in all three cloud types. The skills of a GMI Bayesian algorithm are examined. Snowfall in deep clouds may be reasonably retrieved, but it is challenging for near-surface clouds.
Cited articles
Allabakash, S., Lim, S., and Jang, B. J.: Melting layer detection and characterization based on range height indicator–quasi vertical profiles, Remote Sens., 11, 2848, https://doi.org/10.3390/rs11232848, 2019.
Amemiya, Y.: Generalization of the TLS approach in the errors-in-variables problem, Recent Advances in Total Least Squares Techniques and Errors-in-Variables Modeling, edited by: Van Huffel, S., SIAM, ISBN 978-0-89871-396-6, 1997.
Atlas, D., Srivastava, R. C., and Sekhon, R. S.: Doppler radar characteristics of precipitation at vertical incidence, Rev. Geophys., 11, 1–35, https://doi.org/10.1029/RG011i001p00001, 1973.
Baldwin, M., Treadon, R., and Contorno, S.: Precipitation type prediction using a decision tree approach with NMC's mesoscale eta model, in: the 10th Conf. on Numerical Weather Prediction, 30–31, American Meteorological Society, 1994.
Bang, W. and Kim, K.: Plot program for MRR data of ICE-POP 2018, Zenodo [code], https://doi.org/10.5281/zenodo.14352684, 2024.
Bang, W. and Lee, G.: 1DSBM-19M outputs for ICE-POP 2018 events, Zenodo [data set], https://doi.org/10.5281/zenodo.14353025, 2024.
Bang, W., Kim, K., Yeom, D., Cho, S. J., Lee, C. L., Lee, D., Ye, B. Y., and Lee, G.: Characteristics Analysis of Snow Particle Size Distribution in Gangwon Region according to Topography, J. Korean Earth Sci. Soc., 40, 227–239, https://doi.org/10.5467/JKESS.2019.40.3.227, 2019.
Bang, W., Lee, G., Ryzhkov, A., Schuur, T., and Sunny Lim, K. S.: Comparison of Microphysical Characteristics between the Southern Korean Peninsula and Oklahoma Using Two-Dimensional Video Disdrometer Data, J. Hydrometeorol., 21, 2675–2690, https://doi.org/10.1175/JHM-D-20-0087.1, 2020.
Bang, W., Kim, K., and Lee, G.: ICEPOP 2018 observation data for evaluation of Spectral Bin Model, Zenodo [data set], https://doi.org/10.5281/zenodo.14351937, 2024a.
Bang, W., Kim, K., and Lee, G.: New decision algorithm of surface precipitation type for PARSIVEL data and decision result of 131 precipitation events in 5 ICE-POP 2018 sites, Zenodo [data set], https://doi.org/10.5281/zenodo.14353519, 2024b.
Bang, W., Liu, G., and Lee, G.: Calculation & plot program of 5 different methods for evaluation of Spectral Bin Model, Zenodo [code], https://doi.org/10.5281/zenodo.14354011, 2024c.
Barthazy, E. and Schefold, R.: Fall velocity of snowflakes of different riming degree and crystal types, Atmos. Res., 82, 391–398, https://doi.org/10.1016/j.atmosres.2005.12.009, 2006.
Benjamin, S. G., Brown, J. M., and Smirnova, T. G.: Explicit precipitation-type diagnosis from a model using a mixed-phase bulk cloud–precipitation microphysics parameterization, Weather Forecast., 31, 609–619, https://doi.org/10.1175/WAF-D-15-0136.1, 2016.
Bluestein, H. B.: Synoptic-Dynamic Meteorology in Midlatitudes: Volume II: Observations and Theory of Weather Systems, 1st Edn., Oxford University Press, 594 pp., ISBN 978-0-19-506265-9, 1993.
Bourgouin, P.: A method to determine precipitation type, Weather Forecast., 15, 583–592, https://doi.org/10.1175/1520-0434(2000)015<0583:AMTDPT>2.0.CO;2, 2000.
Brandes, E. A., Ikeda, K., Zhang, G., Schönhuber, M., and Rasmussen, R. M.: A statistical and physical description of hydrometeor distributions in Colorado snowstorms using a video disdrometer, J. Appl. Meteorol. Climatol., 46, 634–650, https://doi.org/10.1175/JAM2489.1, 2007.
Carlin, J. T. and Ryzhkov, A. V.: Estimation of melting-layer cooling rate from dual-polarization radar: Spectral bin model simulations, J. Appl. Meteorol. Clim., 58, 1485–1508, https://doi.org/10.1175/JAMC-D-18-0343.1, 2019.
Carlin, J., Bang, W., Ryzhkov, A., and Lee, G.: Spectral Bin Model (version: 1DSBM-19M), Zenodo [code], https://doi.org/10.5281/zenodo.14350651, 2024.
Chae, Y. J., Kim, B. G., Choi, Y. G., Jung, J. H., Kim, J. Y., Lim, B. H., and Chang, K. H.: Observation of Ice Pellets and its Association with Meteorological Conditions in the Yeongdong Region of Korea, Asia Pac. J. Atmos. Sci., 60, 329–343, https://doi.org/10.1007/s13143-024-00361-9, 2024.
Dedekind, Z., Grazioli, J., Austin, P. H., and Lohmann, U.: Heavy snowfall event over the Swiss Alps: did wind shear impact secondary ice production?, Atmos. Chem. Phys., 23, 2345–2364, https://doi.org/10.5194/acp-23-2345-2023, 2023.
Gehring, J., Oertel, A., Vignon, É., Jullien, N., Besic, N., and Berne, A.: Microphysics and dynamics of snowfall associated with a warm conveyor belt over Korea, Atmos. Chem. Phys., 20, 7373–7392, https://doi.org/10.5194/acp-20-7373-2020, 2020.
Gong, J., Zeng, X., Wu, D. L., Munchak, S. J., Li, X., Kneifel, S., Ori, D., Liao, L., and Barahona, D.: Linkage among ice crystal microphysics, mesoscale dynamics, and cloud and precipitation structures revealed by collocated microwave radiometer and multifrequency radar observations, Atmos. Chem. Phys., 20, 12633–12653, https://doi.org/10.5194/acp-20-12633-2020, 2020.
Häggmark, L. and Ivarsson, K.-I.: MESAN Mesoskalig analys, SMHI RMK Nr. 75, 21–28, 1997.
Häggmark, L., Ivarsson, K. I., Gollvik, S., and Olofsson, P. O.: Mesan, an operational mesoscale analysis system, Tellus A, 52, 2–20, https://doi.org/10.3402/tellusa.v52i1.12250, 2000.
Heymsfield, A.: Ice crystal terminal velocities, J. Atmos. Sci., 29, 1348–1357, https://doi.org/10.1175/1520-0469(1972)029<1348:ICTV>2.0.CO;2, 1972.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C. E. J., Nelkin, Sorooshian, S., Tan, J., and Xie P.: NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG). GPM NASA Algorithm Theoretical Basis Document (ATBD) Version 06, 39 pp., https://gpm.nasa.gov/sites/default/files/2020-05/IMERG_ATBD_V06.3.pdf (last access: 1 March 2025), 2020.
In, S. R., Nam, H. G., Lee, J. H., Park, C. G., Shim, J. K., and Kim, B. J.: Verification of planetary boundary layer Height for local data assimilation and prediction system (LDAPS) using the winter season intensive observation data during ICE-POP 2018, Atmosphere, 28, 369–382, https://doi.org/10.14191/Atmos.2018.28.4.369, 2018.
Kim, J., Yoon, D., Cha, D. H., Choi, Y., Kim, J., and Son, S. W.: Impacts of the East Asian winter monsoon and local sea surface temperature on heavy snowfall over the Yeongdong region, J. Climate, 32, 6783–6802, https://doi.org/10.1175/JCLI-D-18-0411.1, 2019.
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.
Koolwine, T.: Freezing rain, MS thesis, Dept. of Physics, University of Toronto, Canada, 92 pp., 1975.
Kumjian, M. R., Ganson, S. M., and Ryzhkov, A. V.: Freezing of raindrops in deep convective updrafts: A microphysical and polarimetric model, J. Atmos. Sci., 69, 3471–3490, https://doi.org/10.1175/JAS-D-12-067.1, 2012.
Kwon, S., Jung, S. H., and Lee, G.: A Case Study on Microphysical Characteristics of Mesoscale Convective System Using Generalized DSD Parameters Retrieved from Dual-Polarimetric Radar Observations, Remote Sens., 12, 1812, https://doi.org/10.3390/rs12111812, 2020.
Lee, G. and Kim, K.: International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic winter games (ICE-POP 2018), in: AGU Fall Meeting, A52B-06, American Geophysical Union, 2019.
Lee, J. E., Jung, S. H., Park, H. M., Kwon, S., Lin, P. L., and Lee, G.: Classification of precipitation types using fall velocity-diameter relationships from 2D-video distrometer measurements, Adv. Atmos. Sci., 32, 1277–1290, https://doi.org/10.1007/s00376-015-4234-4, 2015.
Lee, S. M., Han, S. U., Won, H. Y., Ha, J. C., Lee, Y. H., Lee, J. H., and Park, J. C.: A method for the discrimination of precipitation type using thickness and improved matsuo's scheme over South Korea, Atmosphere, 24, 151–158, https://doi.org/10.14191/Atmos.2014.24.2.151, 2014.
Libbrecht, K. G.: Morphogenesis on ice: The physics of snow crystals, Engineering and Science, 64, 10–19, https://resolver.caltech.edu/CaltechES:64.1.Snow (last access: 1 March 2025), 2001.
Lin, J.: A Python library of standard atmospheric and oceanic sciences calculation routines, GitHub, https://github.com/PyAOS/aoslib (last access: 1 March 2025), 2016.
Löffler-Mang, M. and Joss, J.: An optical disdrometer for measuring size and velocity of hydrometeors, J. Atmos. Ocean. Tech., 17, 130–139, https://doi.org/10.1175/1520-0426(2000)017<0130:AODFMS>2.0.CO;2, 2000.
Maahn, M. and Kollias, P.: Improved Micro Rain Radar snow measurements using Doppler spectra post-processing, Atmos. Meas. Tech., 5, 2661–2673, https://doi.org/10.5194/amt-5-2661-2012, 2012.
Matsuo, T., Sasyo, Y., and Sato, Y.: Relationship between types of precipitation on the ground and surface meteorological elements, Meteorol. Soc. Jpn., 59, 462–476, https://doi.org/10.2151/jmsj1965.59.4_462, 1981.
Nagumo, N. and Fujiyoshi, Y.: Microphysical properties of slow-falling and fast-falling ice pellets formed by freezing associated with evaporative cooling, Mon. Weather Rev., 143, 4376–4392, https://doi.org/10.1175/MWR-D-15-0054.1, 2015.
Nam, H.-G., Kim, B.-G., Han, S.-O., Lee, C., and Lee, S.-S.: Characteristics of easterly-induced snowfall in Yeongdong and its relationship to air-sea temperature difference, Asia Pac. J. Atmos. Sci., 50, 541–552, https://doi.org/10.1007/s13143-014-0044-3, 2014.
Pruppacher, R. H. and Klett, D. J.: Microphysics of Clouds and Precipitation, Kluwer Academic Publishers, ISBN 978-0-7923-4211-3, 1997.
Ramer, J.: An empirical technique for diagnosing precipitation type from model output, in: the Fifth Int. Conf. on Aviation Weather Systems, 227–230, American Meteorological Society, 1993.
Reeves, H. D., Ryzhkov, A. V., and Krause, J.: Discrimination between winter precipitation types based on spectral-bin microphysical modelling, J. Appl. Meteorol. Clim., 55, 1747–1761, https://doi.org/10.1175/JAMC-D-16-0044.1, 2016.
Rogers, R. R. and Yau, M. K.: A Short Course in Cloud Physics, 3rd edn., Elsevier Press, ISBN 978-0-08-057094-5, 1989.
Schuur, T. J., Park, H.-S., Ryzhkov, A. V., and Reeves, H. D.: Classification of precipitation types during transitional winter weather using the RUC model and polarimetric radar retrievals, J. Appl. Meteorol. Clim., 51, 763–779, https://doi.org/10.1175/JAMC-D-11-091.1, 2012.
Shin, K., Kim, K., Song, J. J., and Lee, G.: Classification of precipitation types based on machine learning using dual-polarization radar measurements and thermodynamic fields, Remote Sens., 14, 3820, https://doi.org/10.3390/rs14153820, 2022.
Sims, E. M. and Liu, G.: A parameterization of the probability of snow–rain transition, J. Hydrometeorol., 16, 1466–1477, https://doi.org/10.1175/JHM-D-14-0211.1, 2015.
Stewart, R. E. and King, P.: Freezing precipitation in winter storms, Mon. Weather Rev., 115, 1270–1280, https://doi.org/10.1175/1520-0493(1987)115<1270:FPIWS>2.0.CO;2, 1987.
Stull, R.: Wet-bulb temperature from relative humidity and air temperature, J. Appl. Meteorol. Clim., 50, 2267–2269, https://doi.org/10.1175/JAMC-D-11-0143.1, 2011.
Vázquez-Martín, S., Kuhn, T., and Eliasson, S.: Shape dependence of snow crystal fall speed, Atmos. Chem. Phys., 21, 7545–7565, https://doi.org/10.5194/acp-21-7545-2021, 2021.
Yuter, S. E., Kingsmill, D. E., Nance, L. B., and Löffler-Mang, M.: Observations of precipitation size and fall speed characteristics within coexisting rain and wet snow, J. Appl. Meteorol. Clim., 45, 1450–1464, https://doi.org/10.1175/JAM2406.1, 2006.
Short summary
Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM has a relatively higher accuracy for dry-snow and wet-snow events, whereas it has lower accuracy for rain events. When the microphysics scheme in the SBM was optimized for the corresponding region, the accuracy for rain events improved.
Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been...
