Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 20, issue 23
Articles | Volume 20, issue 23
https://doi.org/10.5194/acp-20-14491-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-20-14491-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 23
Research article
 | 
30 Nov 2020
Research article |  | 30 Nov 2020

Microphysical properties of three types of snow clouds: implication for satellite snowfall retrievals

Hwayoung Jeoung, Guosheng Liu, Kwonil Kim, Gyuwon Lee, and Eun-Kyoung Seo

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Cited articles

Battaglia, A., Rustemeier, E., Tokay, A., Blahak, U., and Simmer, C.: PARSIVEL snow observations: A critical assessment, J. Atmos. Ocean. Technol., 27, 333–344, https://doi.org/10.1175/2009JTECHA1332.1, 2010. 
Bennartz, R. and Bauer, P.: Sensitivity of microwave radiances at 85-183 GHz to precipitating ice particles, Radio Sci., 38, 8075, https://doi.org/10.1029/2002rs002626, 2003. 
Casella, D., Panegrossi, G., Sanò, P., Marra, A. C., Dietrich, S., Johnson, B. T., and Kulie, M. S.: Evaluation of the GPM-DPR snowfall detection capability: Comparison with CloudSat-CPR, Atmos. Res., 197, 64–75, https://doi.org/10.1016/j.atmosres.2017.06.018, 2017. 
Chen, H., Chandrasekar, V., and Bechini, R.: An improved dual-polarization radar rainfall algorithm (DROPS2.0): Application in NASA IFloodS field campaign, J. Hydrometeorol., 18, 917–937, https://doi.org/10.1175/JHM-D-16-0124.1, 2017. 
Chen, S., Hong, Y., Kulie, M., Behrangi, A., Stepanian, P. M., Cao, Q., You, Y., Zhang, J., Hu, J., and Zhang, X.: Comparison of snowfall estimates from the NASA CloudSat Cloud Profiling Radar and NOAA/NSSL Multi-Radar Multi-Sensor System, J. Hydrol., 541, 862–872, https://doi.org/10.1016/j.jhydrol.2016.07.047, 2016. 
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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.
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