Articles | Volume 20, issue 23
https://doi.org/10.5194/acp-20-14491-2020
https://doi.org/10.5194/acp-20-14491-2020
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

Related authors

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, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025,https://doi.org/10.5194/gmd-18-3559-2025, 2025
Short summary
Advantages of using multiple Doppler radars with different wavelengths for three dimensional wind retrieval
Chia-Lun Tsai, Kwonil Kim, Yu-Chieng Liou, and GyuWon Lee
EGUsphere, https://doi.org/10.5194/egusphere-2025-1908,https://doi.org/10.5194/egusphere-2025-1908, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Estimating the snow density using collocated Parsivel and Micro-Rain Radar measurements: a preliminary study from ICE-POP 2017/2018
Wei-Yu Chang, Yung-Chuan Yang, Chen-Yu Hung, Kwonil Kim, Gyuwon Lee, and Ali Tokay
Atmos. Chem. Phys., 24, 11955–11979, https://doi.org/10.5194/acp-24-11955-2024,https://doi.org/10.5194/acp-24-11955-2024, 2024
Short summary
Introducing graupel density prediction in Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) microphysics and evaluation of the modified scheme during the ICE-POP field campaign
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024,https://doi.org/10.5194/gmd-17-7199-2024, 2024
Short summary
High-resolution 3D winds derived from a modified WISSDOM synthesis scheme using multiple Doppler lidars and observations
Chia-Lun Tsai, Kwonil Kim, Yu-Chieng Liou, and GyuWon Lee
Atmos. Meas. Tech., 16, 845–869, https://doi.org/10.5194/amt-16-845-2023,https://doi.org/10.5194/amt-16-845-2023, 2023
Short summary

Related subject area

Subject: Clouds and Precipitation | Research Activity: Remote Sensing | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Relationship between latent and radiative heating fields of tropical cloud systems using synergistic satellite observations
Xiaoting Chen, Claudia J. Stubenrauch, and Giulio Mandorli
Atmos. Chem. Phys., 25, 6857–6880, https://doi.org/10.5194/acp-25-6857-2025,https://doi.org/10.5194/acp-25-6857-2025, 2025
Short summary
Shallow cloud variability in Houston, Texas, during the ESCAPE and TRACER field experiments
Zackary Mages, Pavlos Kollias, Bernat Puigdomènech Treserras, Paloma Borque, and Mariko Oue
Atmos. Chem. Phys., 25, 6025–6045, https://doi.org/10.5194/acp-25-6025-2025,https://doi.org/10.5194/acp-25-6025-2025, 2025
Short summary
How does the lifetime of detrained cirrus impact the high-cloud radiative effect in the tropics?
George Horner and Edward Gryspeerdt
Atmos. Chem. Phys., 25, 5617–5631, https://doi.org/10.5194/acp-25-5617-2025,https://doi.org/10.5194/acp-25-5617-2025, 2025
Short summary
Anvil–radiation diurnal interaction: shortwave radiative-heating destabilization driving the diurnal variation of convective anvil outflow and its modulation on the radiative cancellation
Zhenquan Wang
Atmos. Chem. Phys., 25, 5021–5039, https://doi.org/10.5194/acp-25-5021-2025,https://doi.org/10.5194/acp-25-5021-2025, 2025
Short summary
Impact of wildfire smoke on Arctic cirrus formation – Part 1: Analysis of MOSAiC 2019–2020 observations
Albert Ansmann, Cristofer Jimenez, Johanna Roschke, Johannes Bühl, Kevin Ohneiser, Ronny Engelmann, Martin Radenz, Hannes Griesche, Julian Hofer, Dietrich Althausen, Daniel A. Knopf, Sandro Dahlke, Tom Gaudek, Patric Seifert, and Ulla Wandinger
Atmos. Chem. Phys., 25, 4847–4866, https://doi.org/10.5194/acp-25-4847-2025,https://doi.org/10.5194/acp-25-4847-2025, 2025
Short summary

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. 
Download
Short summary
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.
Share
Altmetrics
Final-revised paper
Preprint