Articles | Volume 14, issue 2
https://doi.org/10.5194/amt-14-1743-2021
https://doi.org/10.5194/amt-14-1743-2021
Research article
 | 
03 Mar 2021
Research article |  | 03 Mar 2021

A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar

Xiaoyu Hu, Jinming Ge, Jiajing Du, Qinghao Li, Jianping Huang, and Qiang Fu

Related authors

The Tibetan Plateau space-based tropospheric aerosol climatology: 2007–2020
Honglin Pan, Jianping Huang, Jiming Li, Zhongwei Huang, Minzhong Wang, Ali Mamtimin, Wen Huo, Fan Yang, Tian Zhou, and Kanike Raghavendra Kumar
Earth Syst. Sci. Data, 16, 1185–1207, https://doi.org/10.5194/essd-16-1185-2024,https://doi.org/10.5194/essd-16-1185-2024, 2024
Short summary
Unique structure, radiative effects and precipitation characteristics of deep convection systems in the Tibetan Plateau compared to tropical oceans
Yuxin Zhao, Jiming Li, Deyu Wen, Yarong Li, Yuan Wang, and Jianping Huang
EGUsphere, https://doi.org/10.5194/egusphere-2024-480,https://doi.org/10.5194/egusphere-2024-480, 2024
Short summary
A comprehensive reappraisal of long-term aerosol characteristics, trends, and variability in Asia
Shikuan Jin, Yingying Ma, Zhongwei Huang, Jianping Huang, Wei Gong, Boming Liu, Weiyan Wang, Ruonan Fan, and Hui Li
Atmos. Chem. Phys., 23, 8187–8210, https://doi.org/10.5194/acp-23-8187-2023,https://doi.org/10.5194/acp-23-8187-2023, 2023
Short summary
Diurnal cycles of cloud cover and its vertical distribution over the Tibetan Plateau revealed by satellite observations, reanalysis datasets, and CMIP6 outputs
Yuxin Zhao, Jiming Li, Lijie Zhang, Cong Deng, Yarong Li, Bida Jian, and Jianping Huang
Atmos. Chem. Phys., 23, 743–769, https://doi.org/10.5194/acp-23-743-2023,https://doi.org/10.5194/acp-23-743-2023, 2023
Short summary
Technical note: Uncertainties in eddy covariance CO2 fluxes in a semiarid sagebrush ecosystem caused by gap-filling approaches
Jingyu Yao, Zhongming Gao, Jianping Huang, Heping Liu, and Guoyin Wang
Atmos. Chem. Phys., 21, 15589–15603, https://doi.org/10.5194/acp-21-15589-2021,https://doi.org/10.5194/acp-21-15589-2021, 2021
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024,https://doi.org/10.5194/amt-17-3679-2024, 2024
Short summary
A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024,https://doi.org/10.5194/amt-17-3583-2024, 2024
Short summary
Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024,https://doi.org/10.5194/amt-17-3323-2024, 2024
Short summary
A new approach to crystal habit retrieval from far-infrared spectral radiance measurements
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024,https://doi.org/10.5194/amt-17-3171-2024, 2024
Short summary
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024,https://doi.org/10.5194/amt-17-3011-2024, 2024
Short summary

Cited articles

Abrol, D. P.: Diversity of pollinating insects visiting litchi flowers (Litchi chinensis Sonn.) and path analysis of environmental factors influencing foraging behaviour of four honeybee species, J. Apicult. Res., 45, 180–187, https://doi.org/10.1080/00218839.2006.11101345, 2015. 
Arulraj, M. and Barros, A. P.: Shallow Precipitation Detection and Classification Using Multifrequency Radar Observations and Model Simulations, J. Atmos. Ocean. Tech., 34, 1963–1983, https://doi.org/10.1175/jtech-d-17-0060.1, 2017. 
Bala, G., Caldeira, K., Nemani, R., Cao, L., Ban-Weiss, G., and Shin, H.-J.: Albedo enhancement of marine clouds to counteract global warming: impacts on the hydrological cycle, Clim. Dynam., 37, 915–931, https://doi.org/10.1007/s00382-010-0868-1, 2010. 
Baldini, L. and Gorgucci, E.: Identification of the Melting Layer through Dual-Polarization Radar Measurements at Vertical Incidence, J. Atmos. Ocean. Tech., 23, 829–839, https://doi.org/10.1175/jtech1884.1, 2006. 
Download
Short summary
Cloud radars are powerful instruments that can probe detailed cloud structures. However, radar echoes in the lower atmosphere are always contaminated by clutter. We proposed a multi-dimensional probability distribution function that can effectively discriminate low-level clouds from clutter by considering their different features in several variables. We applied this method to the radar observations at the SACOL site and found the results have good agreement with lidar detection.