Articles | Volume 14, issue 2
https://doi.org/10.5194/amt-14-1743-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-1743-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Mar 2021
Research article |
| 03 Mar 2021
| 03 Mar 2021
A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar
Xiaoyu Hu
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou,
730000, China
Jinming Ge
CORRESPONDING AUTHOR
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou,
730000, China
Jiajing Du
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou,
730000, China
Qinghao Li
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou,
730000, China
Jianping Huang
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou,
730000, China
Qiang Fu
Department of Atmospheric Sciences, University of Washington, Seattle, WA 98105, USA
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Yuxin Zhao, Jiming Li, Deyu Wen, Yarong Li, Yuan Wang, and Jianping Huang
Atmos. Chem. Phys., 24, 9435–9457, https://doi.org/10.5194/acp-24-9435-2024, https://doi.org/10.5194/acp-24-9435-2024, 2024
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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
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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
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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
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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
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Bida Jian, Jiming Li, Guoyin Wang, Yuxin Zhao, Yarong Li, Jing Wang, Min Zhang, and Jianping Huang
Atmos. Chem. Phys., 21, 9809–9828, https://doi.org/10.5194/acp-21-9809-2021, https://doi.org/10.5194/acp-21-9809-2021, 2021
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Zhiyuan Hu, Jianping Huang, Chun Zhao, Qinjian Jin, Yuanyuan Ma, and Ben Yang
Atmos. Chem. Phys., 20, 1507–1529, https://doi.org/10.5194/acp-20-1507-2020, https://doi.org/10.5194/acp-20-1507-2020, 2020
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Zhiyuan Hu, Jianping Huang, Chun Zhao, Yuanyuan Ma, Qinjian Jin, Yun Qian, L. Ruby Leung, Jianrong Bi, and Jianmin Ma
Atmos. Chem. Phys., 19, 12709–12730, https://doi.org/10.5194/acp-19-12709-2019, https://doi.org/10.5194/acp-19-12709-2019, 2019
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Xiaoyue Liu, Jianping Huang, Jiping Huang, Changyu Li, and Lei Ding
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-36, https://doi.org/10.5194/essd-2019-36, 2019
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Atmospheric oxygen is crucial to life on earth. In this paper, we quantify oxygen consumption and production processes under the impact of human activities to build a dynamic global oxygen budget on a grid scale. Our result shows that the oxygen consumption related to human activities has risen significantly in recent decades while the oxygen production only displays a faint increase. Regionally, boreal forest and Tibetan plateau become the most important sources of atmospheric oxygen.
Jiming Li, Qiaoyi Lv, Bida Jian, Min Zhang, Chuanfeng Zhao, Qiang Fu, Kazuaki Kawamoto, and Hua Zhang
Atmos. Chem. Phys., 18, 7329–7343, https://doi.org/10.5194/acp-18-7329-2018, https://doi.org/10.5194/acp-18-7329-2018, 2018
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Kai Tang, Zhongwei Huang, Jianping Huang, Teruya Maki, Shuang Zhang, Atsushi Shimizu, Xiaojun Ma, Jinsen Shi, Jianrong Bi, Tian Zhou, Guoyin Wang, and Lei Zhang
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Atmos. Chem. Phys., 17, 9035–9047, https://doi.org/10.5194/acp-17-9035-2017, https://doi.org/10.5194/acp-17-9035-2017, 2017
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Ling Qi, Qinbin Li, Cenlin He, Xin Wang, and Jianping Huang
Atmos. Chem. Phys., 17, 7459–7479, https://doi.org/10.5194/acp-17-7459-2017, https://doi.org/10.5194/acp-17-7459-2017, 2017
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Siyu Chen, Jianping Huang, Litai Kang, Hao Wang, Xiaojun Ma, Yongli He, Tiangang Yuan, Ben Yang, Zhongwei Huang, and Guolong Zhang
Atmos. Chem. Phys., 17, 2401–2421, https://doi.org/10.5194/acp-17-2401-2017, https://doi.org/10.5194/acp-17-2401-2017, 2017
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Jianrong Bi, Jianping Huang, Brent Holben, and Guolong Zhang
Atmos. Chem. Phys., 16, 15501–15516, https://doi.org/10.5194/acp-16-15501-2016, https://doi.org/10.5194/acp-16-15501-2016, 2016
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Jianzhong Xu, Jinsen Shi, Qi Zhang, Xinlei Ge, Francesco Canonaco, André S. H. Prévôt, Matthias Vonwiller, Sönke Szidat, Jinming Ge, Jianmin Ma, Yanqing An, Shichang Kang, and Dahe Qin
Atmos. Chem. Phys., 16, 14937–14957, https://doi.org/10.5194/acp-16-14937-2016, https://doi.org/10.5194/acp-16-14937-2016, 2016
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Jin Ming Ge, Huayue Liu, Jianping Huang, and Qiang Fu
Atmos. Chem. Phys., 16, 7773–7783, https://doi.org/10.5194/acp-16-7773-2016, https://doi.org/10.5194/acp-16-7773-2016, 2016
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Nocturnal low-level jet (NLLJ), which refers to a narrow zone of strong winds, occurs frequently over the Taklimakan Desert. It is found that the NLLJ contains more momentum than without NLLJ, and the downward momentum transfer process is more intense and rapid in the warm season. The coincidence of the larger surface winds during NLLJ days with an enhancement of aerosol optical depth indicates that the NLLJ is an important mechanism for dust emission and transport over this region.
Zhiyuan Hu, Chun Zhao, Jianping Huang, L. Ruby Leung, Yun Qian, Hongbin Yu, Lei Huang, and Olga V. Kalashnikova
Geosci. Model Dev., 9, 1725–1746, https://doi.org/10.5194/gmd-9-1725-2016, https://doi.org/10.5194/gmd-9-1725-2016, 2016
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This study conducts the simulation of WRF-Chem with the quasi-global configuration for 2010–2014, and evaluates the simulation with multiple observation datasets for the first time. This study demonstrates that the WRF-Chem quasi-global simulation can be used for investigating trans-Pacific transport of aerosols and providing reasonable inflow chemical boundaries for the western USA to further understand the impact of transported pollutants on the regional air quality and climate.
Xiaodan Guan, Jianping Huang, Yanting Zhang, Yongkun Xie, and Jingjing Liu
Atmos. Chem. Phys., 16, 5159–5169, https://doi.org/10.5194/acp-16-5159-2016, https://doi.org/10.5194/acp-16-5159-2016, 2016
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An obvious peak in the total anthropogenic dust column, with much higher magnitude than those of wet regions, was observed in semi-arid regions. The anthropogenic dust column burden of semi-arid takes a positively correlated with the population and population change, indicating the production of anthropogenic dust in semi-arid regions is partly induced by human activities.
X. Guan, J. Huang, R. Guo, H. Yu, P. Lin, and Y. Zhang
Atmos. Chem. Phys., 15, 13777–13786, https://doi.org/10.5194/acp-15-13777-2015, https://doi.org/10.5194/acp-15-13777-2015, 2015
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Dynamical adjustment methodology has been applied to the raw surface air temperature and has successfully identified and separated the contribution of dynamically induced temperature (DIT) and radiatively forced temperature (RFT). It found that regional anthropogenic radiative forcing caused the enhanced warming in the semi-arid region, which may be closely associated with local human activities.
R. Zhang, H. Wang, D. A. Hegg, Y. Qian, S. J. Doherty, C. Dang, P.-L. Ma, P. J. Rasch, and Q. Fu
Atmos. Chem. Phys., 15, 12805–12822, https://doi.org/10.5194/acp-15-12805-2015, https://doi.org/10.5194/acp-15-12805-2015, 2015
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We use a global climate model with an explicit source tagging technique to quantify contributions of emissions from various geographical regions and sectors to BC in North America. Model results are evaluated against measurements of near-surface and in-snow BC. We found strong spatial variations of BC and its radiative forcing that can be quantitatively attributed to the various source origins, and also identified a significant source of BC in snow that is likely missing in most climate models.
Y. Liu, Y. Sato, R. Jia, Y. Xie, J. Huang, and T. Nakajima
Atmos. Chem. Phys., 15, 12581–12594, https://doi.org/10.5194/acp-15-12581-2015, https://doi.org/10.5194/acp-15-12581-2015, 2015
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We firstly evaluated the Spectral Radiation-Transport Model for Aerosol Species combined with a non-hydrostatic regional model through comparing the simulation results and satellite observations, both in horizontal and vertical. The dust and anthropogenic aerosols in summer over the Tibetan Plateau are evaluated, and their distributions over the TP are presented. The transport of these aerosols over the Tibetan Plateau is also explored via combining the simulation results and reanalysis data.
W. Sun, R. R. Baize, G. Videen, Y. Hu, and Q. Fu
Atmos. Chem. Phys., 15, 11909–11918, https://doi.org/10.5194/acp-15-11909-2015, https://doi.org/10.5194/acp-15-11909-2015, 2015
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A method is reported for retrieving super-thin cloud optical depth with polarized light. It is found that near-backscatter p-polarized light is sensitive to clouds, but not to ocean conditions. Near-backscatter p-polarized intensity linearly relates to super-thin cloud optical depth. Based on these findings, super-thin cloud optical depth can be retrieved with little effect from surface reflection.
J. P. Huang, J. J. Liu, B. Chen, and S. L. Nasiri
Atmos. Chem. Phys., 15, 11653–11665, https://doi.org/10.5194/acp-15-11653-2015, https://doi.org/10.5194/acp-15-11653-2015, 2015
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To understand the contribution of anthropogenic dust to the total global dust load, a new technique for distinguishing anthropogenic dust from natural dust is proposed by using CALIPSO dust measurements and PBL height retrievals along with a land use data set. Results reveal that local anthropogenic dust aerosol accounts for about 25% of the global continental dust load.
Q. Jin, J. Wei, Z.-L. Yang, B. Pu, and J. Huang
Atmos. Chem. Phys., 15, 9897–9915, https://doi.org/10.5194/acp-15-9897-2015, https://doi.org/10.5194/acp-15-9897-2015, 2015
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Satellite data show that Indian summer monsoon (ISM) rainfall is closely associated with Middle East dust aerosols. Numerical modeling shows that the increased ISM rainfall is related to the enhanced southwesterly flow and moisture transport from the Arabian Sea to the Indian subcontinent, associated with the development of an anomalous low-pressure system over the Iranian Plateau and the Arabian Sea due to dust-induced atmospheric heating.
R. Zhang, H. Wang, Y. Qian, P. J. Rasch, R. C. Easter, P.-L. Ma, B. Singh, J. Huang, and Q. Fu
Atmos. Chem. Phys., 15, 6205–6223, https://doi.org/10.5194/acp-15-6205-2015, https://doi.org/10.5194/acp-15-6205-2015, 2015
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We use the CAM5 model with a novel source-tagging technique to characterize the fate of BC particles emitted from various geographical regions and sectors and their transport pathways to the Himalayas and Tibetan Plateau (HTP). We show a comprehensive picture of the seasonal and regional dependence of BC source attributions, and find strong seasonal and spatial variations in BC-in-snow radiative forcing in the HTP that can be quantitatively attributed to the various regional/sectoral sources.
J. Li, J. Huang, K. Stamnes, T. Wang, Q. Lv, and H. Jin
Atmos. Chem. Phys., 15, 519–536, https://doi.org/10.5194/acp-15-519-2015, https://doi.org/10.5194/acp-15-519-2015, 2015
L. Geng, J. Cole-Dai, B. Alexander, J. Erbland, J. Savarino, A. J. Schauer, E. J. Steig, P. Lin, Q. Fu, and M. C. Zatko
Atmos. Chem. Phys., 14, 13361–13376, https://doi.org/10.5194/acp-14-13361-2014, https://doi.org/10.5194/acp-14-13361-2014, 2014
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Examinations on snowpit and firn core results from Summit, Greenland suggest that there are two mechanisms leading to the observed double nitrate peaks in some years in the industrial era: 1) long-rang transport of nitrate and 2) enhanced local photochemical production of nitrate. Both of these mechanisms are related to pollution transport, as the additional nitrate from either direct transport or enhanced local photochemistry requires enhanced nitrogen sources from anthropogenic emissions.
C. Zhao, Z. Hu, Y. Qian, L. Ruby Leung, J. Huang, M. Huang, J. Jin, M. G. Flanner, R. Zhang, H. Wang, H. Yan, Z. Lu, and D. G. Streets
Atmos. Chem. Phys., 14, 11475–11491, https://doi.org/10.5194/acp-14-11475-2014, https://doi.org/10.5194/acp-14-11475-2014, 2014
Hongru Yan, Zhanqing Li, Jianping Huang, Maureen Cribb, and Jianjun Liu
Atmos. Chem. Phys., 14, 7113–7124, https://doi.org/10.5194/acp-14-7113-2014, https://doi.org/10.5194/acp-14-7113-2014, 2014
C. Zhao, S. Chen, L. R. Leung, Y. Qian, J. F. Kok, R. A. Zaveri, and J. Huang
Atmos. Chem. Phys., 13, 10733–10753, https://doi.org/10.5194/acp-13-10733-2013, https://doi.org/10.5194/acp-13-10733-2013, 2013
S. Feng and Q. Fu
Atmos. Chem. Phys., 13, 10081–10094, https://doi.org/10.5194/acp-13-10081-2013, https://doi.org/10.5194/acp-13-10081-2013, 2013
R. Zhang, D. A. Hegg, J. Huang, and Q. Fu
Atmos. Chem. Phys., 13, 6091–6099, https://doi.org/10.5194/acp-13-6091-2013, https://doi.org/10.5194/acp-13-6091-2013, 2013
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Cloud mask algorithm from the EarthCARE Multi-Spectral Imager: the M-CM products
Across-track extension of retrieved cloud and aerosol properties for the EarthCARE mission: the ACMB-3D product
Insights into 3D cloud radiative transfer effects for the Orbiting Carbon Observatory
Evaluation of polarimetric ice microphysical retrievals with OLYMPEX campaign data
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
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Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
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Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
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This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
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ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
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Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
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The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
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This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
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ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
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Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
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
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The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
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
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This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
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
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Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
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
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This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloe David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
EGUsphere, https://doi.org/10.5194/egusphere-2024-1336, https://doi.org/10.5194/egusphere-2024-1336, 2024
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This study demonstrates the potential for enhancing severe hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
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
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We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R.H. Barrett
EGUsphere, https://doi.org/10.5194/egusphere-2024-961, https://doi.org/10.5194/egusphere-2024-961, 2024
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Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
EGUsphere, https://doi.org/10.5194/egusphere-2024-1180, https://doi.org/10.5194/egusphere-2024-1180, 2024
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We introduce an innovative method to retrieve cloud fraction and optical thickness based on polarimetry, well-suited for satellite observations providing multi-angle polarization measurements. The cloud fraction and the cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and a second in the sun-glint region or at a scattering angle of approximately 90°.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
EGUsphere, https://doi.org/10.5194/egusphere-2024-829, https://doi.org/10.5194/egusphere-2024-829, 2024
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The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
EGUsphere, https://doi.org/10.5194/egusphere-2024-837, https://doi.org/10.5194/egusphere-2024-837, 2024
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In this study, we present a toolkit of two Python algorithms to extract information about the cloud and precipitation particles present in clouds from data measured by ground-based radar instruments. The data consist of Doppler spectra, in which several peaks are formed by hydrometeor populations with different fall velocities. The detection of the specific peaks makes it possible to assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-28, https://doi.org/10.5194/amt-2024-28, 2024
Revised manuscript accepted for AMT
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This manuscript describes a new treatment of the spatial mis-registration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
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We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
EGUsphere, https://doi.org/10.5194/egusphere-2024-101, https://doi.org/10.5194/egusphere-2024-101, 2024
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Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observations experiments.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
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In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
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The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, https://doi.org/10.5194/amt-17-961-2024, 2024
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We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, https://doi.org/10.5194/amt-17-839-2024, 2024
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The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is an ESA–JAXA satellite mission to be launched in 2024. We presented an overview of the EarthCARE processors' development, with processors developed by teams in Europe, Japan, and Canada. EarthCARE will allow scientists to evaluate the representation of cloud, aerosol, precipitation, and radiative flux in weather forecast and climate models, with the objective to better understand cloud processes and improve weather and climate models.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
EGUsphere, https://doi.org/10.5194/egusphere-2023-2392, https://doi.org/10.5194/egusphere-2023-2392, 2024
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Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles, and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
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The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
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The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Patrick Chazette and Jean-Christophe Raut
Atmos. Meas. Tech., 16, 5847–5861, https://doi.org/10.5194/amt-16-5847-2023, https://doi.org/10.5194/amt-16-5847-2023, 2023
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The vertical profiles of the effective radii of ice crystals and ice water content in Arctic semi-transparent stratiform clouds were assessed using quantitative ground-based lidar measurements. The field campaign was part of the Pollution in the ARCtic System (PARCS) project which took place from 13 to 26 May 2016 in Hammerfest (70° 39′ 48″ N, 23° 41′ 00″ E). We show that under certain cloud conditions, lidar measurement combined with a dedicated algorithmic approach is an efficient tool.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
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Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, https://doi.org/10.5194/amt-16-5387-2023, 2023
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A new database is constructed from over 20 years of satellite records that comprises millions of deep convective clouds and spans the global tropics and subtropics. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast
Atmos. Meas. Tech., 16, 5305–5326, https://doi.org/10.5194/amt-16-5305-2023, https://doi.org/10.5194/amt-16-5305-2023, 2023
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Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
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The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
Atmos. Meas. Tech., 16, 4571–4588, https://doi.org/10.5194/amt-16-4571-2023, https://doi.org/10.5194/amt-16-4571-2023, 2023
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We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Hadrien Verbois, Yves-Marie Saint-Drenan, Vadim Becquet, Benoit Gschwind, and Philippe Blanc
Atmos. Meas. Tech., 16, 4165–4181, https://doi.org/10.5194/amt-16-4165-2023, https://doi.org/10.5194/amt-16-4165-2023, 2023
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Solar surface irradiance (SSI) estimations inferred from satellite images are essential to gain a comprehensive understanding of the solar resource, which is crucial in many fields. This study examines the recent data-driven methods for inferring SSI from satellite images and explores their strengths and weaknesses. The results suggest that while these methods show great promise, they sometimes dramatically underperform and should probably be used in conjunction with physical approaches.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, https://doi.org/10.5194/amt-16-3931-2023, 2023
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We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Zeen Zhu, Pavlos Kollias, and Fan Yang
Atmos. Meas. Tech., 16, 3727–3737, https://doi.org/10.5194/amt-16-3727-2023, https://doi.org/10.5194/amt-16-3727-2023, 2023
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We show that large rain droplets, with large inertia, are unable to follow the rapid change of velocity field in a turbulent environment. A lack of consideration for this inertial effect leads to an artificial broadening of the Doppler spectrum from the conventional simulator. Based on the physics-based simulation, we propose a new approach to generate the radar Doppler spectra. This simulator provides a valuable tool to decode cloud microphysical and dynamical properties from radar observation.
Gerd-Jan van Zadelhoff, David P. Donovan, and Ping Wang
Atmos. Meas. Tech., 16, 3631–3651, https://doi.org/10.5194/amt-16-3631-2023, https://doi.org/10.5194/amt-16-3631-2023, 2023
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The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission features the UV lidar ATLID. The ATLID FeatureMask algorithm provides a high-resolution detection probability mask which is used to guide smoothing strategies within the ATLID profile retrieval algorithm, one step further in the EarthCARE level-2 processing chain, in which the microphysical retrievals and target classification are performed.
Shannon L. Mason, Robin J. Hogan, Alessio Bozzo, and Nicola L. Pounder
Atmos. Meas. Tech., 16, 3459–3486, https://doi.org/10.5194/amt-16-3459-2023, https://doi.org/10.5194/amt-16-3459-2023, 2023
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We present a method for accurately estimating the contents and properties of clouds, snow, rain, and aerosols through the atmosphere, using the combined measurements of the radar, lidar, and radiometer instruments aboard the upcoming EarthCARE satellite, and evaluate the performance of the retrieval, using test scenes simulated from a numerical forecast model. When EarthCARE is in operation, these quantities and their estimated uncertainties will be distributed in a data product called ACM-CAP.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
Atmos. Meas. Tech., 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, https://doi.org/10.5194/amt-16-3363-2023, 2023
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The response of clouds to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. We consider cloud retrievals from spaceborne observations, the existing CALIOP lidar and future ATLID lidar; show how they compare for the same scenes; and discuss the advantage of adding a new lidar for detecting cloud changes in the long run. We show that ATLID's advanced technology should allow for better detecting thinner clouds during daytime than before.
Woosub Roh, Masaki Satoh, Tempei Hashino, Shuhei Matsugishi, Tomoe Nasuno, and Takuji Kubota
Atmos. Meas. Tech., 16, 3331–3344, https://doi.org/10.5194/amt-16-3331-2023, https://doi.org/10.5194/amt-16-3331-2023, 2023
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JAXA EarthCARE synthetic data (JAXA L1 data) were compiled using the global storm-resolving model (GSRM) NICAM (Nonhydrostatic ICosahedral
Atmospheric Model) simulation with 3.5 km horizontal resolution and the Joint-Simulator. JAXA L1 data are intended to support the development of JAXA retrieval algorithms for the EarthCARE sensor before launch of the satellite. The expected orbit of EarthCARE and horizontal sampling of each sensor were used to simulate the signals.
Philipp Gregor, Tobias Zinner, Fabian Jakub, and Bernhard Mayer
Atmos. Meas. Tech., 16, 3257–3271, https://doi.org/10.5194/amt-16-3257-2023, https://doi.org/10.5194/amt-16-3257-2023, 2023
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This work introduces MACIN, a model for short-term forecasting of direct irradiance for solar energy applications. MACIN exploits cloud images of multiple cameras to predict irradiance. The model is applied to artificial images of clouds from a weather model. The artificial cloud data allow for a more in-depth evaluation and attribution of errors compared with real data. Good performance of derived cloud information and significant forecast improvements over a baseline forecast were found.
Christian Matar, Céline Cornet, Frédéric Parol, Laurent C.-Labonnote, Frédérique Auriol, and Marc Nicolas
Atmos. Meas. Tech., 16, 3221–3243, https://doi.org/10.5194/amt-16-3221-2023, https://doi.org/10.5194/amt-16-3221-2023, 2023
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The optimal estimation formalism is applied to OSIRIS airborne high-resolution multi-angular measurements to retrieve COT and Reff. The corresponding uncertainties related to measurement errors, which are up to 6 and 12 %, the non-retrieved parameters, which are less than 0.5 %, and the cloud model assumptions show that the heterogeneous vertical profiles and the 3D radiative transfer effects lead to average uncertainties of 5 and 4 % for COT and 13 and 9 % for Reff.
Yuichiro Hagihara, Yuichi Ohno, Hiroaki Horie, Woosub Roh, Masaki Satoh, and Takuji Kubota
Atmos. Meas. Tech., 16, 3211–3219, https://doi.org/10.5194/amt-16-3211-2023, https://doi.org/10.5194/amt-16-3211-2023, 2023
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The CPR on the EarthCARE satellite is the first satellite-borne Doppler radar. We evaluated the effectiveness of horizontal integration and the unfolding method for the reduction of the Doppler error (the standard deviation of the random error) in the CPR_ECO product. The error was higher in the tropics than in the other latitudes due to frequent rain echo occurrence and limitation of its unfolding correction. If we use low-mode operation (high PRF), the errors become small enough.
Kamil Mroz, Bernat Puidgomènech Treserras, Alessandro Battaglia, Pavlos Kollias, Aleksandra Tatarevic, and Frederic Tridon
Atmos. Meas. Tech., 16, 2865–2888, https://doi.org/10.5194/amt-16-2865-2023, https://doi.org/10.5194/amt-16-2865-2023, 2023
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We present the theoretical basis of the algorithm that estimates the amount of water and size of particles in clouds and precipitation. The algorithm uses data collected by the Cloud Profiling Radar that was developed for the upcoming Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite mission. After the satellite launch, the vertical distribution of cloud and precipitation properties will be delivered as the C-CLD product.
Anja Hünerbein, Sebastian Bley, Stefan Horn, Hartwig Deneke, and Andi Walther
Atmos. Meas. Tech., 16, 2821–2836, https://doi.org/10.5194/amt-16-2821-2023, https://doi.org/10.5194/amt-16-2821-2023, 2023
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The Multi-Spectral Imager (MSI) on board the EarthCARE satellite will provide the information needed for describing the cloud and aerosol properties in the cross-track direction, complementing the measurements from the Cloud Profiling Radar, Atmospheric Lidar and Broad-Band Radiometer. The accurate discrimination between clear and cloudy pixels is an essential first step. Therefore, the cloud mask algorithm provides a cloud flag, cloud phase and cloud type product for the MSI observations.
Zhipeng Qu, Howard W. Barker, Jason N. S. Cole, and Mark W. Shephard
Atmos. Meas. Tech., 16, 2319–2331, https://doi.org/10.5194/amt-16-2319-2023, https://doi.org/10.5194/amt-16-2319-2023, 2023
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This paper describes EarthCARE’s L2 product ACM-3D. It includes the scene construction algorithm (SCA) used to produce the indexes for reconstructing 3D atmospheric scene based on satellite nadir retrievals. It also provides the information about the buffer zone sizes of 3D assessment domains and the ranking scores for selecting the best 3D assessment domains. These output variables are needed to run 3D radiative transfer models for the radiative closure assessment of EarthCARE’s L2 retrievals.
Steven T. Massie, Heather Cronk, Aronne Merrelli, Sebastian Schmidt, and Steffen Mauceri
Atmos. Meas. Tech., 16, 2145–2166, https://doi.org/10.5194/amt-16-2145-2023, https://doi.org/10.5194/amt-16-2145-2023, 2023
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This paper provides insights into the effects of clouds on Orbiting Carbon Observatory (OCO-2) measurements of CO2. Calculations are carried out that indicate the extent to which this satellite experiment underestimates CO2, due to these cloud effects, as a function of the distance between the surface observation footprint and the nearest cloud. The paper discusses how to lessen the influence of these cloud effects.
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
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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.
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.
Bony, S., Stevens, B., Frierson, D. M. W., Jakob, C., Kageyama, M., Pincus, R., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H., Watanabe, M., and Webb, M. J.: Clouds, circulation and climate sensitivity, Nat. Geosci., 8, 261–268, https://doi.org/10.1038/ngeo2398, 2015.
Brandes, E. A. and Ikeda, K.: Freezing-Level Estimation with Polarimetric Radar, J. Appl. Meteorol., 43, 1541–1553, https://doi.org/10.1175/jam2155.1, 2004.
Brient, F. and Bony, S.: Interpretation of the positive low-cloud feedback predicted by a climate model under global warming, Clim. Dynam., 40, 2415–2431, https://doi.org/10.1007/s00382-011-1279-7, 2012.
Ceppi, P., Hartmann, D. L., and Webb, M. J.: Mechanisms of the Negative Shortwave Cloud Feedback in Middle to High Latitudes, J. Climate, 29, 139–157, https://doi.org/10.1175/jcli-d-15-0327.1, 2016.
Chandra, A., Zhang, C., Kollias, P., Matrosov, S., and Szyrmer, W.: Automated rain rate estimates using the Ka-band ARM zenith radar (KAZR), Atmos. Meas. Tech., 8, 3685–3699, https://doi.org/10.5194/amt-8-3685-2015, 2015.
Chapman, J. W., Reynolds, D. R., Wilson, K., and Holyoak, M.: Long-range seasonal migration in insects: mechanisms, evolutionary drivers and ecological consequences, Ecol. Lett., 18, 287–302, https://doi.org/10.1111/ele.12407, 2015.
Chernykh, I. V., Alduchov, O. A., and Eskridge, R. E.: Trends in Low and High Cloud Boundaries and Errors in Height Determination of Cloud Boundaries, B. Am. Meteorol. Soc., 82, 1941–1947, https://doi.org/10.1175/1520-0477(2001)082<1941:Tilahc>2.3.Co;2, 2001.
Clothiaux, E. E., Ackerman, T. P., Mace, G. G., Moran, K. P., Marchand, R. T., Miller, M. A., and Martner, B. E.: Objective Determination of Cloud Heights and Radar Reflectivities Using a Combination of Active Remote Sensors at the ARM CART Sites, J. Appl. Meteorol., 39, 645–665, https://doi.org/10.1175/1520-0450(2000)039<0645:odocha>2.0.co;2, 2000.
Devisetty, H. K., Jha, A. K., Das, S. K., Deshpande, S. M., Krishna, U. V. M., Kalekar, P. M., and Pandithurai, G.: A case study on bright band transition from very light to heavy rain using simultaneous observations of collocated X- and Ka-band radars, J. Earth Syst. Sci., 128, 136, https://doi.org/10.1007/s12040-019-1171-0, 2019.
Fu, Q. and Feng, S.: Responses of terrestrial aridity to global warming, J. Geophys. Res.-Atmos., 119, 7863–7875, https://doi.org/10.1002/2014jd021608, 2014.
Fu, Q., Carlin, B., and Mace, G.: Cirrus horizontal inhomogeneity and OLR bias, Geophys. Res. Lett., 27, 3341–3344, https://doi.org/10.1029/2000gl011944, 2000.
Fu, Q., Baker, M., and Hartmann, D. L.: Tropical cirrus and water vapor: an effective Earth infrared iris feedback?, Atmos. Chem. Phys., 2, 31–37, https://doi.org/10.5194/acp-2-31-2002, 2002.
Fu, Q., Smith, M., and Yang, Q.: The Impact of Cloud Radiative Effects on the Tropical Tropopause Layer Temperatures, Atmosphere-Basel, 9, 377, https://doi.org/10.3390/atmos9100377, 2018.
Garrett, T. J. and Zhao, C.: Ground-based remote sensing of thin clouds in the Arctic, Atmos. Meas. Tech., 6, 1227–1243, https://doi.org/10.5194/amt-6-1227-2013, 2013.
Ge, J., Zhu, Z., Zheng, C., Xie, H., Zhou, T., Huang, J., and Fu, Q.: An improved hydrometeor detection method for millimeter-wavelength cloud radar, Atmos. Chem. Phys., 17, 9035–9047, https://doi.org/10.5194/acp-17-9035-2017, 2017.
Ge, J., Zheng, C., Xie, H., Xin, Y., Huang, J., and Fu, Q.: Midlatitude Cirrus Clouds at the SACOL Site: Macrophysical Properties and Large-Scale Atmospheric States, J. Geophys. Res.-Atmos., 123, 2256–2271, https://doi.org/10.1002/2017jd027724, 2018.
Ge, J., Wang, Z., Liu, Y., Su, J., Wang, C., and Dong, Z.: Linkages between mid-latitude cirrus cloud properties and large-scale meteorology at the SACOL site, Clim. Dynam., 53, 5035–5046, https://doi.org/10.1007/s00382-019-04843-9, 2019.
Ge, J. M., Huang, J. P., Xu, C. P., Qi, Y. L., and Liu, H. Y.: Characteristics of Taklimakan dust emission and distribution: A satellite and reanalysis field perspective, J. Geophys. Res.-Atmos., 119, 11772–11783, https://doi.org/10.1002/2014jd022280, 2014.
Geerts, B. and Miao, Q.: The Use of Millimeter Doppler Radar Echoes to Estimate Vertical Air Velocities in the Fair-Weather Convective Boundary Layer, J. Atmos. Ocean. Tech., 22, 225–246, https://doi.org/10.1175/jtech1699.1, 2005.
Givati, A. and Rosenfeld, D.: Quantifying Precipitation Suppression Due to Air Pollution, J. Appl. Meteorol., 43, 1038–1056, https://doi.org/10.1175/1520-0450(2004)043<1038:Qpsdta>2.0.Co;2, 2004.
Golbon-Haghighi, M.-H., Zhang, G., Li, Y., and Doviak, R.: Detection of Ground Clutter from Weather Radar Using a Dual-Polarization and Dual-Scan Method, Atmosphere, 7, 83, https://doi.org/10.3390/atmos7060083, 2016.
Görsdorf, U., Lehmann, V., Bauer-Pfundstein, M., Peters, G., Vavriv, D., Vinogradov, V., and Volkov, V.: A 35-GHz Polarimetric Doppler Radar for Long-Term Observations of Cloud Parameters – Description of System and Data Processing, J. Atmos. Ocean. Tech., 32, 675–690, https://doi.org/10.1175/jtech-d-14-00066.1, 2015.
Harrison, R. G., Nicoll, K. A., and Aplin, K. L.: Evaluating stratiform cloud base charge remotely, Geophys. Res. Lett., 44, 6407–6412, https://doi.org/10.1002/2017gl073128, 2017.
Hu, X., Ge, J., Li, Y., Marchand, R., Huang, J., and Fu, Q.: Improved Hydrometeor Detection Method: An Application to CloudSat, Earth Space Sci., 7, e2019EA000900, https://doi.org/10.1029/2019ea000900, 2020.
Huang, J., Ge, J., and Weng, F.: Detection of Asia dust storms using multisensor satellite measurements, Remote Sens. Environ., 110, 186–191, https://doi.org/10.1016/j.rse.2007.02.022, 2007.
Huang, J., Huang, Z., Bi, J., Zhang, W., and Zhang, L.: Micro-Pulse Lidar Measurements of Aerosol Vertical Structure over the Loess Plateau, Atmos. Ocean. Sci. Lett., 1, 8–11, https://doi.org/10.1080/16742834.2008.11446756, 2008a.
Huang, J., Zhang, W., Zuo, J., Bi, J., Shi, J., Wang, X., Chang, Z., Huang, Z., Yang, S., Zhang, B., Wang, G., Feng, G., Yuan, J., Zhang, L., Zuo, H., Wang, S., Fu, C., and Chou, J.: An Overview of the Semi-arid Climate and Environment Research Observatory over the Loess Plateau, Adv. Atmos. Sci., 25, 906–921, https://doi.org/10.1007/s00376-008-0906-7, 2008b.
Huang, J., Yu, H., Dai, A., Wei, Y., and Kang, L.: Drylands face potential threat under 2 ∘C global warming target, Nat. Clim. Change, 7, 417–422, https://doi.org/10.1038/nclimate3275, 2017.
Huang, J., Huang, J., Liu, X., Li, C., Ding, L., and Yu, H.: The global oxygen budget and its future projection, Sci. Bull., 63, 1180–1186, https://doi.org/10.1016/j.scib.2018.07.023, 2018.
Huang, J., Zhang, G., Zhang, Y., Guan, X., Wei, Y., and Guo, R.: Global
desertification vulnerability to climate change and human activities, Land
Degrad. Dev., 31, 1380–1391, https://doi.org/10.1002/ldr.3556, 2020.
Huang, J. P., Lin, B., Minnis, P., Wang, T., Wang, X., Hu, Y., Yi, Y., and Ayers, J. K.: Satellite-based assessment of possible dust aerosols semi-direct effect on cloud water path over East Asia, Geophys. Res. Lett., 33, L19802, https://doi.org/10.1029/2006gl026561, 2006.
Huang, J. P., Wang, T., Wang, W., Li, Z., and Yan, H.: Climate effects of dust aerosols over East Asian arid and semiarid regions, J. Geophys. Res.-Atmos., 119, 11398–11416, https://doi.org/10.1002/2014jd021796, 2014.
Hubbert, J. C., Wilson, J. W., Weckwerth, T. M., Ellis, S. M., Dixon, M., and Loew, E.: S-Pol's Polarimetric Data Reveal Detailed Storm Features (and Insect Behavior), B. Am. Meteorol. Soc., 99, 2045–2060, https://doi.org/10.1175/bams-d-17-0317.1, 2018.
Huo, J., Lu, D., Duan, S., Bi, Y., and Liu, B.: Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar, Atmos. Meas. Tech., 13, 1–11, https://doi.org/10.5194/amt-13-1-2020, 2020.
Islam, T., Rico-Ramirez, M. A., Han, D., Bray, M., and Srivastava, P. K.: Fuzzy logic based melting layer recognition from 3 GHz dual polarization radar: appraisal with NWP model and radio sounding observations, Theor. Appl. Climatol., 112, 317–338, https://doi.org/10.1007/s00704-012-0721-z, 2012.
Jing Su, Jianping Huang, Qiang Fu, Minnis, P., Jinming Ge, and Jianrong Bi: Estimation of Asian dust aerosol effect on cloud radiation forcing using Fu-Liou radiative model and CERES measurements, Atmos. Chem. Phys., 8, 2763–2771, https://doi.org/10.5194/acp-8-2763-2008, 2008.
Johnson, C. A., Coutinho, R. M., Berlin, E., Dolphin, K. E., Heyer, J., Kim, B., Leung, A., Sabellon, J. L., Amarasekare, P., and Carroll, S.: Effects of temperature and resource variation on insect population dynamics: the bordered plant bug as a case study, Funct. Ecol., 30, 1122–1131, https://doi.org/10.1111/1365-2435.12583, 2016.
Johnson, K., Toto, T., and Giangrande, S.: Ka-Band ARM Zenith Radar
Corrections Value-Added Product, U.S. Department of Energy, Washington, DC, 2017.
Kalapureddy, M. C. R., Sukanya, P., Das, S. K., Deshpande, S. M., Pandithurai, G., Pazamany, A. L., Ambuj K., J., Chakravarty, K., Kalekar, P., Devisetty, H. K., and Annam, S.: A simple biota removal algorithm for 35 GHz cloud radar measurements, Atmos. Meas. Tech., 11, 1417–1436, https://doi.org/10.5194/amt-11-1417-2018, 2018.
Khanal, A. K., Delrieu, G., Cazenave, F., and Boudevillain, B.: Radar Remote Sensing of Precipitation in High Mountains: Detection and Characterization of Melting Layer in the Grenoble Valley, French Alps, Atmosphere-Basel, 10, 784, https://doi.org/10.3390/atmos10120784, 2019.
Kim, S.-W., Chung, E.-S., Yoon, S.-C., Sohn, B.-J., and Sugimoto, N.: Intercomparisons of cloud-top and cloud-base heights from ground-based Lidar, CloudSat and CALIPSO measurements, Int. J. Remote Sens., 32, 1179–1197, https://doi.org/10.1080/01431160903527439, 2011.
Kollias, P., Clothiaux, E. E., Miller, M. A., Albrecht, B. A., Stephens, G. L., and Ackerman, T. P.: Millimeter-Wavelength Radars: New Frontier in Atmospheric Cloud and Precipitation Research, B. Am. Meteorol. Soc., 88, 1608–1624, https://doi.org/10.1175/bams-88-10-1608, 2007a.
Kollias, P., Clothiaux, E. E., Miller, M. A., Luke, E. P., Johnson, K. L., Moran, K. P., Widener, K. B., and Albrecht, B. A.: The Atmospheric Radiation Measurement Program Cloud Profiling Radars: Second-Generation Sampling Strategies, Processing, and Cloud Data Products, J. Atmos. Ocean. Tech., 24, 1199–1214, https://doi.org/10.1175/jtech2033.1, 2007b.
Kollias, P., Remillard, J., Luke, E., and Szyrmer, W.: Cloud radar Doppler
spectra in drizzling stratiform clouds: 1. Forward modeling and remote sensing
applications, J. Geophys. Res.-Atmos., 116, D13201, https://doi.org/10.1029/2010jd015237, 2011.
Kollias, P., Puigdomènech Treserras, B., and Protat, A.: Calibration of the 2007–2017 record of Atmospheric Radiation Measurements cloud radar observations using CloudSat, Atmos. Meas. Tech., 12, 4949–4964, https://doi.org/10.5194/amt-12-4949-2019, 2019.
Kowalewski, S. and Peters, G.: Analysis of Z–R Relations Based on LDR Signatures within the Melting Layer, J. Atmos. Ocean. Tech., 27, 1555–1561, https://doi.org/10.1175/2010jtecha1363.1, 2010.
Linero, A. R. and Daniels, M. J.: Bayesian Approaches for Missing Not at Random Outcome Data: The Role of Identifying Restrictions, Stat. Sci., 33, 198–213, https://doi.org/10.1214/17-sts630, 2018.
Liu, Z., Vaughan, M. A., Winker, D. M., Hostetler, C. A., Poole, L. R., Hlavka, D., Hart, W., and McGill, M.: Use of probability distribution functions for discriminating between cloud and aerosol in lidar backscatter data, J. Geophys. Res., 109, D15202, https://doi.org/10.1029/2004jd004732, 2004.
Liu, Z., Vaughan, M., Winker, D., Kittaka, C., Getzewich, B., Kuehn, R., Omar, A., Powell, K., Trepte, C., and Hostetler, C.: The CALIPSO Lidar Cloud and Aerosol Discrimination: Version 2 Algorithm and Initial Assessment of Performance, J. Atmos. Ocean. Tech., 26, 1198–1213, https://doi.org/10.1175/2009jtecha1229.1, 2009.
Liu, Z., Kar, J., Zeng, S., Tackett, J., Vaughan, M., Avery, M., Pelon, J., Getzewich, B., Lee, K.-P., Magill, B., Omar, A., Lucker, P., Trepte, C., and Winker, D.: Discriminating between clouds and aerosols in the CALIOP version 4.1 data products, Atmos. Meas. Tech., 12, 703–734, https://doi.org/10.5194/amt-12-703-2019, 2019.
Luke, E. P., Kollias, P., Johnson, K. L., and Clothiaux, E. E.: A technique for the automatic detection of insect clutter in cloud radar returns, J. Atmos. Ocean. Tech., 25, 1498–1513, https://doi.org/10.1175/2007jtecha953.1, 2008.
Ma, J., Hu, Z., Yang, M., and Li, S.: Improvement of X-Band Polarization Radar
Melting Layer Recognition by the Bayesian Method and Its Impact on Hydrometeor
Classification, Adv. Atmos. Sci., 37, 105–116, https://doi.org/10.1007/s00376-019-9007-z, 2019.
Mace, G. G. and Berry, E.: Using Active Remote Sensing to Evaluate
Cloud-Climate Feedbacks: a Review and a Look to the Future, Current Climate
Change Reports, 3, 185–192, https://doi.org/10.1007/s40641-017-0067-9,
2017.
Marchand, R., Mace, G. G., Ackerman, T., and Stephens, G.: Hydrometeor Detection UsingCloudsat – An Earth-Orbiting 94-GHz Cloud Radar, J. Atmos. Ocean. Tech., 25, 519–533, https://doi.org/10.1175/2007jtecha1006.1, 2008.
Martner, B. E. and Moran, K. P.: Using cloud radar polarization measurements to evaluate stratus cloud and insect echoes, J. Geophys. Res.-Atmos., 106, 4891–4897, https://doi.org/10.1029/2000jd900623, 2001.
Matrosov, S. Y., Clark, K. A., and Kingsmill, D. E.: A Polarimetric Radar Approach to Identify Rain, Melting-Layer, and Snow Regions for Applying Corrections to Vertical Profiles of Reflectivity, J. Appl. Meteorol. Clim., 46, 154–166, https://doi.org/10.1175/jam2508.1, 2007.
Nuijens, L., Emanuel, K., Masunaga, H., and L'Ecuyer, T.: Implications of Warm Rain in Shallow Cumulus and Congestus Clouds for Large-Scale Circulations, Surv. Geophys., 38, 1257–1282, https://doi.org/10.1007/s10712-017-9429-z, 2017.
O'Connor, E. J., Hogan, R. J., and Illingworth, A. J.: Retrieving Stratocumulus Drizzle Parameters Using Doppler Radar and Lidar, J. Appl. Meteorol., 44, 14–27, https://doi.org/10.1175/jam-2181.1, 2005.
Oh, S.-B., Lee, Y. H., Jeong, J.-H., Kim, Y.-H., and Joo, S.: Estimation of the liquid water content and Z-LWC relationship using Ka-band cloud radar and a microwave radiometer, Meteorol. Appl., 25, 423–434, https://doi.org/10.1002/met.1710, 2018.
Perry, L. B., Seimon, A., Andrade-Flores, M. F., Endries, J. L., Yuter, S. E., Velarde, F., Arias, S., Bonshoms, M., Burton, E. J., Winkelmann, I. R., Cooper, C. M., Mamani, G., Rado, M., Montoya, N., and Quispe, N.: Characteristics of Precipitating Storms in Glacierized Tropical Andean Cordilleras of Peru and Bolivia, Ann. Am. Assoc. Geogr., 107, 309–322, https://doi.org/10.1080/24694452.2016.1260439, 2017.
Pinsky, M. and Khain, A.: Theoretical Analysis of the Entrainment–Mixing Process at Cloud Boundaries. Part II: Motion of Cloud Interface, J. Atmos. Sci., 76, 2599–2616, https://doi.org/10.1175/jas-d-18-0314.1, 2019.
Quaas, J., Quaas, M. F., Boucher, O., and Rickels, W.: Regional climate engineering by radiation management: Prerequisites and prospects, Earths Future, 4, 618–625, https://doi.org/10.1002/2016ef000440, 2016.
Rico-Ramirez, M. A. and Cluckie, I. D.: Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar, IEEE T. Geosci. Remote, 46, 1892–1904, https://doi.org/10.1109/tgrs.2008.916979, 2008.
SACOL: Homepage, available at: http://climate.lzu.edu.cn, last access: 23 February 2021.
Shupe, M. D.: A ground-based multisensor cloud phase classifier, Geophys. Res. Lett., 34, L22809, https://doi.org/10.1029/2007gl031008, 2007.
Sokol, Z., Minářová, J., and Novák, P.: Classification of Hydrometeors Using Measurements of the Ka-Band Cloud Radar Installed at the Milešovka Mountain (Central Europe), Remote Sens.-Basel, 10, 1674, https://doi.org/10.3390/rs10111674, 2018.
Terai, C. R., Klein, S. A., and Zelinka, M. D.: Constraining the low-cloud optical depth feedback at middle and high latitudes using satellite observations, J. Geophys. Res.-Atmos., 121, 9696–9716, https://doi.org/10.1002/2016jd025233, 2016.
Thomas, C. F. G., Brain, P., and Jepson, P. C.: Aerial activity of linyphiid spiders: modelling dispersal distances from meteorology and behaviour, J. Appl. Ecol., 40, 912–927, https://doi.org/10.1046/j.1365-2664.2003.00844.x, 2003.
Toll, V., Christensen, M., Quaas, J., and Bellouin, N.: Weak average liquid-cloud-water response to anthropogenic aerosols, Nature, 572, 51–55, https://doi.org/10.1038/s41586-019-1423-9, 2019.
Turner, D. D., Vogelmann, A. M., Austin, R. T., Barnard, J. C., Cady-Pereira, K., Chiu, J. C., Clough, S. A., Flynn, C., Khaiyer, M. M., Liljegren, J., Johnson, K., Lin, B., Long, C., Marshak, A., Matrosov, S. Y., McFarlane, S. A., Miller, M., Min, Q., Minimis, P., O'Hirok, W., Wang, Z., and Wiscombe, W.: Thin Liquid Water Clouds: Their Importance and Our Challenge, B. Am. Meteorol. Soc., 88, 177–190, https://doi.org/10.1175/bams-88-2-177, 2007.
van der Linden, R., Fink, A. H., and Redl, R.: Satellite-based climatology of low-level continental clouds in southern West Africa during the summer monsoon season, J. Geophys. Res.-Atmos., 120, 1186–1201, https://doi.org/10.1002/2014jd022614, 2015.
Watanabe, M., Kamae, Y., Shiogama, H., DeAngelis, A. M., and Suzuki, K.: Low clouds link equilibrium climate sensitivity to hydrological sensitivity, Nat. Clim. Change, 8, 901–906, https://doi.org/10.1038/s41558-018-0272-0, 2018.
Williams, C. R., Maahn, M., Hardin, J. C., and de Boer, G.: Clutter mitigation, multiple peaks, and high-order spectral moments in 35 GHz vertically pointing radar velocity spectra, Atmos. Meas. Tech., 11, 4963–4980, https://doi.org/10.5194/amt-11-4963-2018, 2018.
Wood, C. R., O'Connor, E. J., Hurley, R. A., Reynolds, D. R., and Illingworth, A. J.: Cloud-radar observations of insects in the UK convective boundary layer, Meteorol. Appl., 16, 491–500, https://doi.org/10.1002/met.146, 2009.
Wu, P., Dong, X., and Xi, B.: Marine boundary layer drizzle properties and their impact on cloud property retrieval, Atmos. Meas. Tech., 8, 3555–3562, https://doi.org/10.5194/amt-8-3555-2015, 2015.
Xie, H., Zhou, T., Fu, Q., Huang, J., Huang, Z., Bi, J., Shi, J., Zhang, B., and Ge, J.: Automated detection of cloud and aerosol features with SACOL micro-pulse lidar in northwest China, Opt. Express, 25, 30732–30753, https://doi.org/10.1364/oe.25.030732, 2017.
Xin, Y., Su, J., Li, X., Hu, X., Ge, J., and Fu, Q.: Retrieval of ice cloud microphysical properties at the SACOL, Chinese Sci. Bull., 64, 2728–2740, https://doi.org/10.1360/n972019-00104, 2019.
Xue, H., Feingold, G., and Stevens, B.: Aerosol Effects on Clouds, Precipitation, and the Organization of Shallow Cumulus Convection, J. Atmos. Sci., 65, 392–406, https://doi.org/10.1175/2007jas2428.1, 2008.
Yang, F., Luke, E. P., Kollias, P., Kostinski, A. B., and Vogelmann, A. M.: Scaling of Drizzle Virga Depth With Cloud Thickness for Marine Stratocumulus Clouds, Geophys. Res. Lett., 45, 3746–3753, https://doi.org/10.1029/2018gl077145, 2018.
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of Higher Climate Sensitivity in CMIP6 Models, Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019gl085782, 2020.
Zhang, H., Wang, M., Guo, Z., Zhou, C., Zhou, T., Qian, Y., Larson, V. E., Ghan, S., Ovchinnikov, M., Bogenschutz, P. A., and Gettelman, A.: Low-Cloud Feedback in CAM5-CLUBB: Physical Mechanisms and Parameter Sensitivity Analysis, J. Adv. Model. Earth Sy., 10, 2844–2864, https://doi.org/10.1029/2018ms001423, 2018.
Zheng, J., Liu, L., Zhu, K., Wu, J., and Wang, B.: A Method for Retrieving Vertical Air Velocities in Convective Clouds over the Tibetan Plateau from TIPEX-III Cloud Radar Doppler Spectra, Remote Sens.-Basel, 9, 964, https://doi.org/10.3390/rs9090964, 2017.
Zhu, Z., Zheng, C., Ge, J., Huang, J., and Fu, Q.: Cloud macrophysical properties from KAZR at the SACOL, Chinese Sci. Bull., 62, 824–835, https://doi.org/10.1360/n972016-00857, 2017.
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.
Cloud radars are powerful instruments that can probe detailed cloud structures. However, radar...
