Articles | Volume 16, issue 23
https://doi.org/10.5194/amt-16-5827-2023
© Author(s) 2023. 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-16-5827-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
Andrew M. Vogelmann
Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
Edward Luke
Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
Pavlos Kollias
Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA
Zhien Wang
College of Arts and Sciences, University of Colorado Boulder, Boulder, CO, USA
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
William I. Gustafson Jr.
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
Susanne Glienke
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
Jason Tomlinson
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
Neel Desai
Department of Meteorology and Climate Science, San Jose State University, San Jose, CA, USA
Related authors
Damao Zhang, Jennifer Comstock, Chitra Sivaraman, Kefei Mo, Raghavendra Krishnamurthy, Jingjing Tian, Tianning Su, Zhanqing Li, and Natalia Roldán-Henao
Atmos. Meas. Tech., 18, 3453–3475, https://doi.org/10.5194/amt-18-3453-2025, https://doi.org/10.5194/amt-18-3453-2025, 2025
Short summary
Short summary
Planetary boundary layer height (PBLHT) is an important parameter in atmospheric process studies and numerical model simulations. We use machine learning methods to produce a best-estimate planetary boundary layer height (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements. We demonstrated that PBLHT-BE-ML greatly improved the comparisons against sounding-derived PBLHT.
Dan Lubin, Xun Zou, Johannes Mülmenstädt, Andrew Vogelmann, Maria Cadeddu, and Damao Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2768, https://doi.org/10.5194/egusphere-2025-2768, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
The US Department of Energy Atmospheric Radiation Measurement (ARM) North Slope of Alaska Facility has measured solar and atmospheric infrared radiation, and cloud properties, for the past 25 years. Statistically significant trends are emerging, including increasing infrared radiation due to a warming atmosphere, and decreasing solar radiation due to increasing liquid water content in clouds. These trends are influenced by large-scale atmospheric circulation patterns and by atmospheric rivers.
Andrew M. Sayer, Brian Cairns, Kirk D. Knobelspiesse, Luca Lelli, Chamara Rajapakshe, Scott E. Giangrande, Gareth E. Thomas, and Damao Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2005, https://doi.org/10.5194/egusphere-2025-2005, 2025
Short summary
Short summary
Satellites can estimate cloud height in several ways: two include a thermal technique (colder clouds being higher up), and another looking at colours of light that oxygen in the atmosphere absorbs (darker clouds being lower down). It can also be measured (from ground or space) by radar and lidar. We compare satellite data we developed using the oxygen method with other estimates to help us refine our technique.
Lexie Goldberger, Maxwell Levin, Carlandra Harris, Andrew Geiss, Matthew D. Shupe, and Damao Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1501, https://doi.org/10.5194/egusphere-2025-1501, 2025
Short summary
Short summary
This study leverages machine learning models to classify cloud thermodynamic phases using multi-sensor remote sensing data collected at the Department of Energy Atmospheric Radiation Measurement North Slope of Alaska observatory. We evaluate model performance, feature importance, application of the model to another observatory, and quantify how the models respond to instrument outages.
Fan Mei, Qi Zhang, Damao Zhang, Jerome D. Fast, Gourihar Kulkarni, Mikhail S. Pekour, Christopher R. Niedek, Susanne Glienke, Israel Silber, Beat Schmid, Jason M. Tomlinson, Hardeep S. Mehta, Xena Mansoura, Zezhen Cheng, Gregory W. Vandergrift, Nurun Nahar Lata, Swarup China, and Zihua Zhu
Atmos. Chem. Phys., 25, 3425–3444, https://doi.org/10.5194/acp-25-3425-2025, https://doi.org/10.5194/acp-25-3425-2025, 2025
Short summary
Short summary
This study highlights the unique capability of the ArcticShark, an uncrewed aerial system, in measuring vertically resolved atmospheric properties. Data from 32 research flights in 2023 reveal seasonal patterns and correlations with conventional measurements. The consistency and complementarity of in situ and remote sensing methods are highlighted. The study demonstrates the ArcticShark’s versatility in bridging data gaps and improving the understanding of vertical atmospheric structures.
Fan Mei, Jennifer M. Comstock, Mikhail S. Pekour, Jerome D. Fast, Krista L. Gaustad, Beat Schmid, Shuaiqi Tang, Damao Zhang, John E. Shilling, Jason M. Tomlinson, Adam C. Varble, Jian Wang, L. Ruby Leung, Lawrence Kleinman, Scot Martin, Sebastien C. Biraud, Brian D. Ermold, and Kenneth W. Burk
Earth Syst. Sci. Data, 16, 5429–5448, https://doi.org/10.5194/essd-16-5429-2024, https://doi.org/10.5194/essd-16-5429-2024, 2024
Short summary
Short summary
Our study explores a comprehensive dataset from airborne field studies (2013–2018) conducted using the US Department of Energy's Gulfstream 1 (G-1). The 236 flights span diverse regions, including the Arctic, US Southern Great Plains, US West Coast, eastern North Atlantic, Amazon Basin in Brazil, and Sierras de Córdoba range in Argentina. This dataset provides unique insights into atmospheric dynamics, aerosols, and clouds and makes data available in a more accessible format.
Damao Zhang, Jennifer Comstock, and Victor Morris
Atmos. Meas. Tech., 15, 4735–4749, https://doi.org/10.5194/amt-15-4735-2022, https://doi.org/10.5194/amt-15-4735-2022, 2022
Short summary
Short summary
The planetary boundary layer is the lowest part of the atmosphere. Its structure and depth (PBLHT) significantly impact air quality, global climate, land–atmosphere interactions, and a wide range of atmospheric processes. To test the robustness of the ceilometer-estimated PBLHT under different atmospheric conditions, we compared ceilometer- and radiosonde-estimated PBLHTs using multiple years of U.S. DOE ARM measurements at various ARM observatories located around the world.
Annakaisa von Lerber, Mario Mech, Annette Rinke, Damao Zhang, Melanie Lauer, Ana Radovan, Irina Gorodetskaya, and Susanne Crewell
Atmos. Chem. Phys., 22, 7287–7317, https://doi.org/10.5194/acp-22-7287-2022, https://doi.org/10.5194/acp-22-7287-2022, 2022
Short summary
Short summary
Snowfall is an important climate indicator. However, microphysical snowfall processes are challenging for atmospheric models. In this study, the performance of a regional climate model is evaluated in modeling the spatial and temporal distribution of Arctic snowfall when compared to CloudSat satellite observations. Excellent agreement in averaged annual snowfall rates is found, and the shown methodology offers a promising diagnostic tool to investigate the shown differences further.
Zeen Zhu, Fan Yang, Steven Krueger, and Yangang Liu
EGUsphere, https://doi.org/10.5194/egusphere-2025-3489, https://doi.org/10.5194/egusphere-2025-3489, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
To better understand cloud behavior, we used computer simulations to study how the air mix in clouds. Our results show that the pattern of mixing seen from aircraft measurements may not reflect the true mixing process happening inside clouds. This result suggests that care is needed when using aircraft data to study the cloud mixing process and that new ways of observing clouds could offer clearer insights.
Shaoyue Qiu, Xue Zheng, Peng Wu, Hsiang-He Lee, and Xiaoli Zhou
EGUsphere, https://doi.org/10.5194/egusphere-2025-3465, https://doi.org/10.5194/egusphere-2025-3465, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Model liquid water path (LWP) responses match satellite observations, showing decreased (increased) LWP for non-precipitating thin (precipitating) clouds, due to realistic simulations of non-precipitating and drizzling regimes. However, LWP increases for non-precipitating thick clouds, opposite to satellite-based decreases, due to excessive precipitation, moist bias in cloud-top humidity, and cloud droplet number–LWP relations from internal cloud processes, rather than aerosol-cloud interaction.
Damao Zhang, Jennifer Comstock, Chitra Sivaraman, Kefei Mo, Raghavendra Krishnamurthy, Jingjing Tian, Tianning Su, Zhanqing Li, and Natalia Roldán-Henao
Atmos. Meas. Tech., 18, 3453–3475, https://doi.org/10.5194/amt-18-3453-2025, https://doi.org/10.5194/amt-18-3453-2025, 2025
Short summary
Short summary
Planetary boundary layer height (PBLHT) is an important parameter in atmospheric process studies and numerical model simulations. We use machine learning methods to produce a best-estimate planetary boundary layer height (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements. We demonstrated that PBLHT-BE-ML greatly improved the comparisons against sounding-derived PBLHT.
Dan Lubin, Xun Zou, Johannes Mülmenstädt, Andrew Vogelmann, Maria Cadeddu, and Damao Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2768, https://doi.org/10.5194/egusphere-2025-2768, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
The US Department of Energy Atmospheric Radiation Measurement (ARM) North Slope of Alaska Facility has measured solar and atmospheric infrared radiation, and cloud properties, for the past 25 years. Statistically significant trends are emerging, including increasing infrared radiation due to a warming atmosphere, and decreasing solar radiation due to increasing liquid water content in clouds. These trends are influenced by large-scale atmospheric circulation patterns and by atmospheric rivers.
Andrew M. Sayer, Brian Cairns, Kirk D. Knobelspiesse, Luca Lelli, Chamara Rajapakshe, Scott E. Giangrande, Gareth E. Thomas, and Damao Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2005, https://doi.org/10.5194/egusphere-2025-2005, 2025
Short summary
Short summary
Satellites can estimate cloud height in several ways: two include a thermal technique (colder clouds being higher up), and another looking at colours of light that oxygen in the atmosphere absorbs (darker clouds being lower down). It can also be measured (from ground or space) by radar and lidar. We compare satellite data we developed using the oxygen method with other estimates to help us refine our technique.
Jialin Yan, Mariko Oue, Pavlos Kollias, Edward Luke, and Fan Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2149, https://doi.org/10.5194/egusphere-2025-2149, 2025
Short summary
Short summary
In this study, we analyzed over six years of ground-based radar and weather balloon data collected in northern Alaska. We found that ice particle changes depend strongly on temperature, humidity conditions and turbulence. We also found that turbulence and the presence of supercooled liquid water often occur together, and when they do, ice particle growth is especially strong. These findings help scientists to improve weather models.
Lexie Goldberger, Maxwell Levin, Carlandra Harris, Andrew Geiss, Matthew D. Shupe, and Damao Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1501, https://doi.org/10.5194/egusphere-2025-1501, 2025
Short summary
Short summary
This study leverages machine learning models to classify cloud thermodynamic phases using multi-sensor remote sensing data collected at the Department of Energy Atmospheric Radiation Measurement North Slope of Alaska observatory. We evaluate model performance, feature importance, application of the model to another observatory, and quantify how the models respond to instrument outages.
Min Deng, Scott E. Giangrande, Michael P. Jensen, Karen Johnson, Christopher R. Williams, Jennifer M. Comstock, Ya-Chien Feng, Alyssa Matthews, Iosif A. Lindenmaier, Timothy G. Wendler, Marquette Rocque, Aifang Zhou, Zeen Zhu, Edward Luke, and Die Wang
Atmos. Meas. Tech., 18, 1641–1657, https://doi.org/10.5194/amt-18-1641-2025, https://doi.org/10.5194/amt-18-1641-2025, 2025
Short summary
Short summary
A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation log-linear behavior as a function of rainfall rates in light and moderate rain conditions. This resulting reflectivity offset during the recent field campaign is compared favorably with the traditional disdrometer comparison near the rain onset, while it also demonstrates similar trends with respect to collocated and independently calibrated reference radars.
Fan Yang, Hamed Fahandezh Sadi, Raymond A. Shaw, Fabian Hoffmann, Pei Hou, Aaron Wang, and Mikhail Ovchinnikov
Atmos. Chem. Phys., 25, 3785–3806, https://doi.org/10.5194/acp-25-3785-2025, https://doi.org/10.5194/acp-25-3785-2025, 2025
Short summary
Short summary
Large-eddy simulations of a convection cloud chamber show two new microphysics regimes, cloud oscillation and cloud collapse, due to haze–cloud interactions. Our results suggest that haze particles and their interactions with cloud droplets should be considered especially in polluted conditions. To properly simulate haze–cloud interactions, we need to resolve droplet activation and deactivation processes, instead of using Twomey-type activation parameterization.
Fan Mei, Qi Zhang, Damao Zhang, Jerome D. Fast, Gourihar Kulkarni, Mikhail S. Pekour, Christopher R. Niedek, Susanne Glienke, Israel Silber, Beat Schmid, Jason M. Tomlinson, Hardeep S. Mehta, Xena Mansoura, Zezhen Cheng, Gregory W. Vandergrift, Nurun Nahar Lata, Swarup China, and Zihua Zhu
Atmos. Chem. Phys., 25, 3425–3444, https://doi.org/10.5194/acp-25-3425-2025, https://doi.org/10.5194/acp-25-3425-2025, 2025
Short summary
Short summary
This study highlights the unique capability of the ArcticShark, an uncrewed aerial system, in measuring vertically resolved atmospheric properties. Data from 32 research flights in 2023 reveal seasonal patterns and correlations with conventional measurements. The consistency and complementarity of in situ and remote sensing methods are highlighted. The study demonstrates the ArcticShark’s versatility in bridging data gaps and improving the understanding of vertical atmospheric structures.
Jerome D. Fast, Adam C. Varble, Fan Mei, Mikhail Pekour, Jason Tomlinson, Alla Zelenyuk, Art J. Sedlacek III, Maria Zawadowicz, and Louisa Emmons
Atmos. Chem. Phys., 24, 13477–13502, https://doi.org/10.5194/acp-24-13477-2024, https://doi.org/10.5194/acp-24-13477-2024, 2024
Short summary
Short summary
Aerosol property measurements recently collected on the ground and by a research aircraft in central Argentina during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign exhibit large spatial and temporal variability. These measurements coupled with coincident meteorological information provide a valuable data set needed to evaluate and improve model predictions of aerosols in a traditionally data-sparse region of South America.
Fan Mei, Jennifer M. Comstock, Mikhail S. Pekour, Jerome D. Fast, Krista L. Gaustad, Beat Schmid, Shuaiqi Tang, Damao Zhang, John E. Shilling, Jason M. Tomlinson, Adam C. Varble, Jian Wang, L. Ruby Leung, Lawrence Kleinman, Scot Martin, Sebastien C. Biraud, Brian D. Ermold, and Kenneth W. Burk
Earth Syst. Sci. Data, 16, 5429–5448, https://doi.org/10.5194/essd-16-5429-2024, https://doi.org/10.5194/essd-16-5429-2024, 2024
Short summary
Short summary
Our study explores a comprehensive dataset from airborne field studies (2013–2018) conducted using the US Department of Energy's Gulfstream 1 (G-1). The 236 flights span diverse regions, including the Arctic, US Southern Great Plains, US West Coast, eastern North Atlantic, Amazon Basin in Brazil, and Sierras de Córdoba range in Argentina. This dataset provides unique insights into atmospheric dynamics, aerosols, and clouds and makes data available in a more accessible format.
Kristopher Scarci, Ryan C. Scott, Madison L. Ghiz, Andrew M. Vogelmann, and Dan Lubin
Atmos. Chem. Phys., 24, 6681–6697, https://doi.org/10.5194/acp-24-6681-2024, https://doi.org/10.5194/acp-24-6681-2024, 2024
Short summary
Short summary
We demonstrate what can be learned about an Antarctic region's climate from basic atmospheric irradiance measurements made by broadband and filter radiometers, instruments suitable for deployment at very remote sites, assisted by meteorological reanalysis and satellite remote sensing. Analysis of shortwave and longwave irradiance reveals subtle contrasts between meteorological regimes favoring cloud ice versus liquid water, relevant to onset versus inhibition of surface melt over ice shelves.
Matthew W. Christensen, Peng Wu, Adam C. Varble, Heng Xiao, and Jerome D. Fast
Atmos. Chem. Phys., 24, 6455–6476, https://doi.org/10.5194/acp-24-6455-2024, https://doi.org/10.5194/acp-24-6455-2024, 2024
Short summary
Short summary
Clouds are essential to keep Earth cooler by reflecting sunlight back to space. We show that an increase in aerosol concentration suppresses precipitation in clouds, causing them to accumulate water and expand in a polluted environment with stronger turbulence and radiative cooling. This process enhances their reflectance by 51 %. It is therefore prudent to account for cloud fraction changes in assessments of aerosol–cloud interactions to improve predictions of climate change.
Lindsay M. Sheridan, Raghavendra Krishnamurthy, William I. Gustafson Jr., Ye Liu, Brian J. Gaudet, Nicola Bodini, Rob K. Newsom, and Mikhail Pekour
Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024, https://doi.org/10.5194/wes-9-741-2024, 2024
Short summary
Short summary
In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas and provided valuable year-long analyses of offshore low-level jet (LLJ) characteristics at heights relevant to wind turbines. In addition to the LLJ climatology, this work provides validation of LLJ representation in atmospheric models that are essential for assessing the potential energy yield of offshore wind farms.
Zeen Zhu, Fan Yang, Pavlos Kollias, Raymond A. Shaw, Alex B. Kostinski, Steve Krueger, Katia Lamer, Nithin Allwayin, and Mariko Oue
Atmos. Meas. Tech., 17, 1133–1143, https://doi.org/10.5194/amt-17-1133-2024, https://doi.org/10.5194/amt-17-1133-2024, 2024
Short summary
Short summary
In this article, we demonstrate the feasibility of applying advanced radar technology to detect liquid droplets generated in the cloud chamber. Specifically, we show that using radar with centimeter-scale resolution, single drizzle drops with a diameter larger than 40 µm can be detected. This study demonstrates the applicability of remote sensing instruments in laboratory experiments and suggests new applications of ultrahigh-resolution radar for atmospheric sensing.
Yang Wang, Chanakya Bagya Ramesh, Scott E. Giangrande, Jerome Fast, Xianda Gong, Jiaoshi Zhang, Ahmet Tolga Odabasi, Marcus Vinicius Batista Oliveira, Alyssa Matthews, Fan Mei, John E. Shilling, Jason Tomlinson, Die Wang, and Jian Wang
Atmos. Chem. Phys., 23, 15671–15691, https://doi.org/10.5194/acp-23-15671-2023, https://doi.org/10.5194/acp-23-15671-2023, 2023
Short summary
Short summary
We report the vertical profiles of aerosol properties over the Southern Great Plains (SGP), a region influenced by shallow convective clouds, land–atmosphere interactions, boundary layer turbulence, and the aerosol life cycle. We examined the processes that drive the aerosol population and distribution in the lower troposphere over the SGP. This study helps improve our understanding of aerosol–cloud interactions and the model representation of aerosol processes.
Raghavendra Krishnamurthy, Gabriel García Medina, Brian Gaudet, William I. Gustafson Jr., Evgueni I. Kassianov, Jinliang Liu, Rob K. Newsom, Lindsay M. Sheridan, and Alicia M. Mahon
Earth Syst. Sci. Data, 15, 5667–5699, https://doi.org/10.5194/essd-15-5667-2023, https://doi.org/10.5194/essd-15-5667-2023, 2023
Short summary
Short summary
Our understanding and ability to observe and model air–sea processes has been identified as a principal limitation to our ability to predict future weather. Few observations exist offshore along the coast of California. To improve our understanding of the air–sea transition zone and support the wind energy industry, two buoys with state-of-the-art equipment were deployed for 1 year. In this article, we present details of the post-processing, algorithms, and analyses.
Philipp Gasch, James Kasic, Oliver Maas, and Zhien Wang
Atmos. Meas. Tech., 16, 5495–5523, https://doi.org/10.5194/amt-16-5495-2023, https://doi.org/10.5194/amt-16-5495-2023, 2023
Short summary
Short summary
This paper rethinks airborne wind measurements and investigates a new design for airborne Doppler lidar systems. Recent advances in lidar technology allow the use of multiple lidar systems with fixed viewing directions instead of a single lidar attached to a scanner. Our simulation results show that the proposed new design offers great potential for both higher accuracy and higher-resolution airborne wind measurements.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
Short summary
Short summary
To assess the ability of Earth system model (ESM) predictions, we developed a tool called ESMAC Diags to understand how aerosols, clouds, and aerosol–cloud interactions are represented in ESMs. This paper describes its version 2 functionality. We compared the model predictions with measurements taken by planes, ships, satellites, and ground instruments over four regions across the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Weixing Hao, Fan Mei, Susanne Hering, Steven Spielman, Beat Schmid, Jason Tomlinson, and Yang Wang
Atmos. Meas. Tech., 16, 3973–3986, https://doi.org/10.5194/amt-16-3973-2023, https://doi.org/10.5194/amt-16-3973-2023, 2023
Short summary
Short summary
Airborne aerosol instrumentation plays a crucial role in understanding the spatial distribution of ambient aerosol particles. This study investigates a versatile water-based condensation particle counter through simulations and experiments. It provides valuable insights to improve versatile water-based condensation particle counter (vWCPC) aerosol measurement and operation for the community.
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
Short summary
Short summary
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.
Francesca Gallo, Janek Uin, Kevin J. Sanchez, Richard H. Moore, Jian Wang, Robert Wood, Fan Mei, Connor Flynn, Stephen Springston, Eduardo B. Azevedo, Chongai Kuang, and Allison C. Aiken
Atmos. Chem. Phys., 23, 4221–4246, https://doi.org/10.5194/acp-23-4221-2023, https://doi.org/10.5194/acp-23-4221-2023, 2023
Short summary
Short summary
This study provides a summary statistic of multiday aerosol plume transport event influences on aerosol physical properties and the cloud condensation nuclei budget at the U.S. Department of Energy Atmospheric Radiation Measurement Facility in the eastern North Atlantic (ENA). An algorithm that integrates aerosol properties is developed and applied to identify multiday aerosol transport events. The influence of the aerosol plumes on aerosol populations at the ENA is successively assessed.
Zackary Mages, Pavlos Kollias, Zeen Zhu, and Edward P. Luke
Atmos. Chem. Phys., 23, 3561–3574, https://doi.org/10.5194/acp-23-3561-2023, https://doi.org/10.5194/acp-23-3561-2023, 2023
Short summary
Short summary
Cold-air outbreaks (when cold air is advected over warm water and creates low-level convection) are a dominant cloud regime in the Arctic, and we capitalized on ground-based observations, which did not previously exist, from the COMBLE field campaign to study them. We characterized the extent and strength of the convection and turbulence and found evidence of secondary ice production. This information is useful for model intercomparison studies that will represent cold-air outbreak processes.
Matthew W. Christensen, Po-Lun Ma, Peng Wu, Adam C. Varble, Johannes Mülmenstädt, and Jerome D. Fast
Atmos. Chem. Phys., 23, 2789–2812, https://doi.org/10.5194/acp-23-2789-2023, https://doi.org/10.5194/acp-23-2789-2023, 2023
Short summary
Short summary
An increase in aerosol concentration (tiny airborne particles) is shown to suppress rainfall and increase the abundance of droplets in clouds passing over Graciosa Island in the Azores. Cloud drops remain affected by aerosol for several days across thousands of kilometers in satellite data. Simulations from an Earth system model show good agreement, but differences in the amount of cloud water and its extent remain despite modifications to model parameters that control the warm-rain process.
Christopher R. Niedek, Fan Mei, Maria A. Zawadowicz, Zihua Zhu, Beat Schmid, and Qi Zhang
Atmos. Meas. Tech., 16, 955–968, https://doi.org/10.5194/amt-16-955-2023, https://doi.org/10.5194/amt-16-955-2023, 2023
Short summary
Short summary
This novel micronebulization aerosol mass spectrometry (MS) technique requires a low sample volume (10 μL) and can quantify nanogram levels of organic and inorganic particulate matter (PM) components when used with 34SO4. This technique was successfully applied to PM samples collected from uncrewed atmospheric measurement platforms and provided chemical information that agrees well with real-time data from a co-located aerosol chemical speciation monitor and offline data from secondary ion MS.
Lindsay M. Sheridan, Raghu Krishnamurthy, Gabriel García Medina, Brian J. Gaudet, William I. Gustafson Jr., Alicia M. Mahon, William J. Shaw, Rob K. Newsom, Mikhail Pekour, and Zhaoqing Yang
Wind Energ. Sci., 7, 2059–2084, https://doi.org/10.5194/wes-7-2059-2022, https://doi.org/10.5194/wes-7-2059-2022, 2022
Short summary
Short summary
Using observations from lidar buoys, five reanalysis and analysis models that support the wind energy community are validated offshore and at rotor-level heights along the California Pacific coast. The models are found to underestimate the observed wind resource. Occasions of large model error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production, and mischaracterization of the diurnal wind speed cycle in summer months.
Jerome D. Fast, David M. Bell, Gourihar Kulkarni, Jiumeng Liu, Fan Mei, Georges Saliba, John E. Shilling, Kaitlyn Suski, Jason Tomlinson, Jian Wang, Rahul Zaveri, and Alla Zelenyuk
Atmos. Chem. Phys., 22, 11217–11238, https://doi.org/10.5194/acp-22-11217-2022, https://doi.org/10.5194/acp-22-11217-2022, 2022
Short summary
Short summary
Recent aircraft measurements from the HI-SCALE campaign conducted over the Southern Great Plains (SGP) site in Oklahoma are used to quantify spatial variability of aerosol properties in terms of grid spacings typically used by weather and climate models. Surprisingly large horizontal gradients in aerosol properties were frequently observed in this rural area. This spatial variability can be used as an uncertainty range when comparing surface point measurements with model predictions.
Damao Zhang, Jennifer Comstock, and Victor Morris
Atmos. Meas. Tech., 15, 4735–4749, https://doi.org/10.5194/amt-15-4735-2022, https://doi.org/10.5194/amt-15-4735-2022, 2022
Short summary
Short summary
The planetary boundary layer is the lowest part of the atmosphere. Its structure and depth (PBLHT) significantly impact air quality, global climate, land–atmosphere interactions, and a wide range of atmospheric processes. To test the robustness of the ceilometer-estimated PBLHT under different atmospheric conditions, we compared ceilometer- and radiosonde-estimated PBLHTs using multiple years of U.S. DOE ARM measurements at various ARM observatories located around the world.
Fan Mei, Mikhail S. Pekour, Darielle Dexheimer, Gijs de Boer, RaeAnn Cook, Jason Tomlinson, Beat Schmid, Lexie A. Goldberger, Rob Newsom, and Jerome D. Fast
Earth Syst. Sci. Data, 14, 3423–3438, https://doi.org/10.5194/essd-14-3423-2022, https://doi.org/10.5194/essd-14-3423-2022, 2022
Short summary
Short summary
This work focuses on an expanding number of data sets observed using ARM TBS (133 flights) and UAS (seven flights) platforms by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research, such as the aerosol–cloud interaction in the boundary layer.
Sagar P. Parajuli, Georgiy L. Stenchikov, Alexander Ukhov, Suleiman Mostamandi, Paul A. Kucera, Duncan Axisa, William I. Gustafson Jr., and Yannian Zhu
Atmos. Chem. Phys., 22, 8659–8682, https://doi.org/10.5194/acp-22-8659-2022, https://doi.org/10.5194/acp-22-8659-2022, 2022
Short summary
Short summary
Rainfall affects the distribution of surface- and groundwater resources, which are constantly declining over the Middle East and North Africa (MENA) due to overexploitation. Here, we explored the effects of dust on rainfall using WRF-Chem model simulations. Although dust is considered a nuisance from an air quality perspective, our results highlight the positive fundamental role of dust particles in modulating rainfall formation and distribution, which has implications for cloud seeding.
Baike Xi, Xiquan Dong, Xiaojian Zheng, and Peng Wu
Atmos. Meas. Tech., 15, 3761–3777, https://doi.org/10.5194/amt-15-3761-2022, https://doi.org/10.5194/amt-15-3761-2022, 2022
Short summary
Short summary
This study develops an innovative method to determine the cloud phases over the Southern Ocean (SO) using the combination of radar and lidar measurements during the ship-based field campaign of MARCUS. Results from our study show that the low-level, deep, and shallow cumuli are dominant, and the mixed-phase clouds occur more than single phases over the SO. The mixed-phase cloud properties are similar to liquid-phase (ice-phase) clouds in the midlatitudes (polar) region of the SO.
Zeen Zhu, Pavlos Kollias, Edward Luke, and Fan Yang
Atmos. Chem. Phys., 22, 7405–7416, https://doi.org/10.5194/acp-22-7405-2022, https://doi.org/10.5194/acp-22-7405-2022, 2022
Short summary
Short summary
Drizzle (small rain droplets) is an important component of warm clouds; however, its existence is poorly understood. In this study, we capitalized on a machine-learning algorithm to develop a drizzle detection method. We applied this algorithm to investigate drizzle occurrence and found out that drizzle is far more ubiquitous than previously thought. This study demonstrates the ubiquitous nature of drizzle in clouds and will improve understanding of the associated microphysical process.
Annakaisa von Lerber, Mario Mech, Annette Rinke, Damao Zhang, Melanie Lauer, Ana Radovan, Irina Gorodetskaya, and Susanne Crewell
Atmos. Chem. Phys., 22, 7287–7317, https://doi.org/10.5194/acp-22-7287-2022, https://doi.org/10.5194/acp-22-7287-2022, 2022
Short summary
Short summary
Snowfall is an important climate indicator. However, microphysical snowfall processes are challenging for atmospheric models. In this study, the performance of a regional climate model is evaluated in modeling the spatial and temporal distribution of Arctic snowfall when compared to CloudSat satellite observations. Excellent agreement in averaged annual snowfall rates is found, and the shown methodology offers a promising diagnostic tool to investigate the shown differences further.
Shuaiqi Tang, Jerome D. Fast, Kai Zhang, Joseph C. Hardin, Adam C. Varble, John E. Shilling, Fan Mei, Maria A. Zawadowicz, and Po-Lun Ma
Geosci. Model Dev., 15, 4055–4076, https://doi.org/10.5194/gmd-15-4055-2022, https://doi.org/10.5194/gmd-15-4055-2022, 2022
Short summary
Short summary
We developed an Earth system model (ESM) diagnostics package to compare various types of aerosol properties simulated in ESMs with aircraft, ship, and surface measurements from six field campaigns across spatial scales. The diagnostics package is coded and organized to be flexible and modular for future extension to other field campaign datasets and adapted to higher-resolution model simulations. Future releases will include comprehensive cloud and aerosol–cloud interaction diagnostics.
Yun Lin, Jiwen Fan, Pengfei Li, Lai-yung Ruby Leung, Paul J. DeMott, Lexie Goldberger, Jennifer Comstock, Ying Liu, Jong-Hoon Jeong, and Jason Tomlinson
Atmos. Chem. Phys., 22, 6749–6771, https://doi.org/10.5194/acp-22-6749-2022, https://doi.org/10.5194/acp-22-6749-2022, 2022
Short summary
Short summary
How sea spray aerosols may affect cloud and precipitation over the region by acting as ice-nucleating particles (INPs) is unknown. We explored the effects of INPs from marine aerosols on orographic cloud and precipitation for an atmospheric river event observed during the 2015 ACAPEX field campaign. The marine INPs enhance the formation of ice and snow, leading to less shallow warm clouds but more mixed-phase and deep clouds. This work suggests models need to consider the impacts of marine INPs.
Heike Kalesse-Los, Willi Schimmel, Edward Luke, and Patric Seifert
Atmos. Meas. Tech., 15, 279–295, https://doi.org/10.5194/amt-15-279-2022, https://doi.org/10.5194/amt-15-279-2022, 2022
Short summary
Short summary
It is important to detect the vertical distribution of cloud droplets and ice in mixed-phase clouds. Here, an artificial neural network (ANN) previously developed for Arctic clouds is applied to a mid-latitudinal cloud radar data set. The performance of this technique is contrasted to the Cloudnet target classification. For thick/multi-layer clouds, the machine learning technique is better at detecting liquid than Cloudnet, but if lidar data are available Cloudnet is at least as good as the ANN.
Xiaojian Zheng, Baike Xi, Xiquan Dong, Peng Wu, Timothy Logan, and Yuan Wang
Atmos. Chem. Phys., 22, 335–354, https://doi.org/10.5194/acp-22-335-2022, https://doi.org/10.5194/acp-22-335-2022, 2022
Short summary
Short summary
This study uses ground-based observations to investigate the physical processes in the aerosol–cloud interactions in non-precipitating marine boundary layer clouds, over the eastern North Atlantic Ocean. Results show that the cloud responses to the aerosols are diminished with limited water vapor supply, while they are enhanced with increasing water vapor availability. The clouds are found to be most sensitive to the aerosols under sufficient water vapor and strong boundary layer turbulence.
Fan Mei, Steven Spielman, Susanne Hering, Jian Wang, Mikhail S. Pekour, Gregory Lewis, Beat Schmid, Jason Tomlinson, and Maynard Havlicek
Atmos. Meas. Tech., 14, 7329–7340, https://doi.org/10.5194/amt-14-7329-2021, https://doi.org/10.5194/amt-14-7329-2021, 2021
Short summary
Short summary
This study focuses on understanding a versatile water-based condensation particle counter (vWCPC 3789) performance under various ambient pressure conditions (500–1000 hPa). A vWCPC has the advantage of avoiding health and safety concerns. However, its performance characterization under low pressure is rare but crucial for ensuring successful airborne deployment. This paper provides advanced knowledge of operating a vWCPC 3789 to capture the spatial variations of atmospheric aerosols.
Fan Mei, Jian Wang, Shan Zhou, Qi Zhang, Sonya Collier, and Jianzhong Xu
Atmos. Chem. Phys., 21, 13019–13029, https://doi.org/10.5194/acp-21-13019-2021, https://doi.org/10.5194/acp-21-13019-2021, 2021
Short summary
Short summary
This work focuses on understanding aerosol's ability to act as cloud condensation nuclei (CCN) and its variations with organic oxidation level and volatility using measurements at a rural site. Aerosol properties were examined from four air mass sources. The results help improve the accurate representation of aerosol from different ambient aerosol emissions, transformation pathways, and atmospheric processes in a climate model.
Sinan Gao, Chunsong Lu, Yangang Liu, Seong Soo Yum, Jiashan Zhu, Lei Zhu, Neel Desai, Yongfeng Ma, and Shang Wu
Atmos. Chem. Phys., 21, 11225–11241, https://doi.org/10.5194/acp-21-11225-2021, https://doi.org/10.5194/acp-21-11225-2021, 2021
Short summary
Short summary
Only a few studies have been focused on the vertical variation of entrainment mixing with low resolutions which are crucial to cloud-related processes. A sawtooth pattern allows for an examination of mixing with high vertical resolution. A new measure is introduced to estimate entrainment mixing to overcome difficulties in existing measures, where vertical profile indicates that entrainment mixing becomes more homogeneous with decreasing altitudes, consistent with the dynamical measures.
Madison L. Ghiz, Ryan C. Scott, Andrew M. Vogelmann, Jan T. M. Lenaerts, Matthew Lazzara, and Dan Lubin
The Cryosphere, 15, 3459–3494, https://doi.org/10.5194/tc-15-3459-2021, https://doi.org/10.5194/tc-15-3459-2021, 2021
Short summary
Short summary
We investigate how melt occurs over the vulnerable ice shelves of West Antarctica and determine that the three primary mechanisms can be evaluated using archived numerical weather prediction model data and satellite imagery. We find examples of each mechanism: thermal blanketing by a warm atmosphere, radiative heating by thin clouds, and downslope winds. Our results signify the potential to make a multi-decadal assessment of atmospheric stress on West Antarctic ice shelves in a warming climate.
Yang Wang, Guangjie Zheng, Michael P. Jensen, Daniel A. Knopf, Alexander Laskin, Alyssa A. Matthews, David Mechem, Fan Mei, Ryan Moffet, Arthur J. Sedlacek, John E. Shilling, Stephen Springston, Amy Sullivan, Jason Tomlinson, Daniel Veghte, Rodney Weber, Robert Wood, Maria A. Zawadowicz, and Jian Wang
Atmos. Chem. Phys., 21, 11079–11098, https://doi.org/10.5194/acp-21-11079-2021, https://doi.org/10.5194/acp-21-11079-2021, 2021
Short summary
Short summary
This paper reports the vertical profiles of trace gas and aerosol properties over the eastern North Atlantic, a region of persistent but diverse subtropical marine boundary layer (MBL) clouds. We examined the key processes that drive the cloud condensation nuclei (CCN) population and how it varies with season and synoptic conditions. This study helps improve the model representation of the aerosol processes in the remote MBL, reducing the simulated aerosol indirect effects.
Maria A. Zawadowicz, Kaitlyn Suski, Jiumeng Liu, Mikhail Pekour, Jerome Fast, Fan Mei, Arthur J. Sedlacek, Stephen Springston, Yang Wang, Rahul A. Zaveri, Robert Wood, Jian Wang, and John E. Shilling
Atmos. Chem. Phys., 21, 7983–8002, https://doi.org/10.5194/acp-21-7983-2021, https://doi.org/10.5194/acp-21-7983-2021, 2021
Short summary
Short summary
This paper describes the results of a recent field campaign in the eastern North Atlantic, where two mass spectrometers were deployed aboard a research aircraft to measure the chemistry of aerosols and trace gases. Very clean conditions were found, dominated by local sulfate-rich acidic aerosol and very aged organics. Evidence of
long-range transport of aerosols from the continents was also identified.
Zhibo Zhang, Qianqian Song, David B. Mechem, Vincent E. Larson, Jian Wang, Yangang Liu, Mikael K. Witte, Xiquan Dong, and Peng Wu
Atmos. Chem. Phys., 21, 3103–3121, https://doi.org/10.5194/acp-21-3103-2021, https://doi.org/10.5194/acp-21-3103-2021, 2021
Short summary
Short summary
This study investigates the small-scale variations and covariations of cloud microphysical properties, namely, cloud liquid water content and cloud droplet number concentration, in marine boundary layer clouds based on in situ observation from the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) campaign. We discuss the dependence of cloud variations on vertical location in cloud and the implications for warm-rain simulations in the global climate models.
Jessie M. Creamean, Gijs de Boer, Hagen Telg, Fan Mei, Darielle Dexheimer, Matthew D. Shupe, Amy Solomon, and Allison McComiskey
Atmos. Chem. Phys., 21, 1737–1757, https://doi.org/10.5194/acp-21-1737-2021, https://doi.org/10.5194/acp-21-1737-2021, 2021
Short summary
Short summary
Arctic clouds play a role in modulating sea ice extent. Importantly, aerosols facilitate cloud formation, and thus it is crucial to understand the interactions between aerosols and clouds. Vertical measurements of aerosols and clouds are needed to tackle this issue. We present results from balloon-borne measurements of aerosols and clouds over the course of 2 years in northern Alaska. These data shed light onto the vertical distributions of aerosols relative to clouds spanning multiple seasons.
Yuan Wang, Xiaojian Zheng, Xiquan Dong, Baike Xi, Peng Wu, Timothy Logan, and Yuk L. Yung
Atmos. Chem. Phys., 20, 14741–14755, https://doi.org/10.5194/acp-20-14741-2020, https://doi.org/10.5194/acp-20-14741-2020, 2020
Short summary
Short summary
A recent aircraft field campaign near the Azores in the summer of 2017 provides ample observations of aerosols and clouds with detailed vertical information. This study utilizes those observational data in combination with the aerosol-aware large-eddy simulations and aerosol reanalysis data to examine the significance of the long-range-transported aerosol effect on marine-boundary-layer clouds. It is the first time that the ACE-ENA aircraft campaign data are used for this topic.
Mingxuan Wu, Xiaohong Liu, Hongbin Yu, Hailong Wang, Yang Shi, Kang Yang, Anton Darmenov, Chenglai Wu, Zhien Wang, Tao Luo, Yan Feng, and Ziming Ke
Atmos. Chem. Phys., 20, 13835–13855, https://doi.org/10.5194/acp-20-13835-2020, https://doi.org/10.5194/acp-20-13835-2020, 2020
Short summary
Short summary
The spatiotemporal distributions of dust aerosol simulated by global climate models (GCMs) are highly uncertain. In this study, we evaluate dust extinction profiles, optical depth, and surface concentrations simulated in three GCMs and one reanalysis against multiple satellite retrievals and surface observations to gain process-level understanding. Our results highlight the importance of correctly representing dust emission, dry/wet deposition, and size distribution in GCMs.
Dale M. Ward, Xiquan Dong, Baike Xi, Peng Wu, Xiaojian Zheng, and Yuan Wang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-817, https://doi.org/10.5194/acp-2020-817, 2020
Preprint withdrawn
Short summary
Short summary
Marine boundary layer clouds in subtropical regions strongly impact global energy balance, but complete understanding of the processes that control their development remain elusive. We analyze aircraft in-situ measurements of clouds collected in a field campaign for cases that contain organized structures tens of kilometres in extent embedded within a larger overcast cloud field. Failure to account for these structures can lead to misrepresentation in models and satellite retrievals.
Cited articles
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989.
Baumgardner, D., Abel, S. J., Axisa, D., Cotton, R., Crosier, J., Field, P., Gurganus, C., Heymsfield, A., Korolev, A., Krämer, M., Lawson, P., McFarquhar, G., Ulanowski, Z., and Um, J.: Cloud Ice Properties: In Situ Measurement Challenges, Chap. 9, in: Ice Formation and Evolution in Clouds and Precipitation: Measurement and Modeling Challenges, Meteor. Mon., 58, 9.1–9.23, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0011.1, 2017.
Bennartz, R. and Rausch, J.: Global and regional estimates of warm cloud droplet number concentration based on 13 years of AQUA-MODIS observations, Atmos. Chem. Phys., 17, 9815–9836, https://doi.org/10.5194/acp-17-9815-2017, 2017.
Boers, R., Acarreta, J. R., and Gras, J. L.: Satellite monitoring of the first indirect aerosol effect: Retrieval of the droplet concentration of water clouds, J. Geophys. Res.-Atmos., 111, D22208, https://doi.org/10.1029/2005JD006838, 2006.
Brenguier, J.-L., Burnet, F., and Geoffroy, O.: Cloud optical thickness and liquid water path – does the k coefficient vary with droplet concentration?, Atmos. Chem. Phys., 11, 9771–9786, https://doi.org/10.5194/acp-11-9771-2011, 2011.
Cadeddu, M.: Microwave Radiometer – 3-Channel (MWR3C) Instrument Handbook, ARM Technical Report DOE/SC-ARM-TR-108, DOE Office of Science Atmospheric Radiation Measurement (ARM) Program, https://doi.org/10.2172/1039668, 2021.
Cadeddu, M. P., Liljegren, J. C., and Turner, D. D.: The Atmospheric radiation measurement (ARM) program network of microwave radiometers: instrumentation, data, and retrievals, Atmos. Meas. Tech., 6, 2359–2372, https://doi.org/10.5194/amt-6-2359-2013, 2013.
Cadeddu, M. P., Ghate, V. P., and Mech, M.: Ground-based observations of cloud and drizzle liquid water path in stratocumulus clouds, Atmos. Meas. Tech., 13, 1485–1499, https://doi.org/10.5194/amt-13-1485-2020, 2020.
Chen, J., Liu, Y., Zhang, M., and Peng, Y.: New understanding and quantification of the regime dependence of aerosol-cloud interaction for studying aerosol indirect effects, Geophys. Res. Lett., 43, 1780–1787, https://doi.org/10.1002/2016GL067683, 2016.
Chiu, J. C., Marshak, A., Huang, C.-H., Várnai, T., Hogan, R. J., Giles, D. M., Holben, B. N., O'Connor, E. J., Knyazikhin, Y., and Wiscombe, W. J.: Cloud droplet size and liquid water path retrievals from zenith radiance measurements: examples from the Atmospheric Radiation Measurement Program and the Aerosol Robotic Network, Atmos. Chem. Phys., 12, 10313–10329, https://doi.org/10.5194/acp-12-10313-2012, 2012.
Cromwell, E., Tomlinson, J., and Pekour, M.: Cloud and Aerosol Spectrometer aboard aircraft (AAFCAS), Atmospheric Radiation Measurement (ARM) User Facility [data set], https://doi.org/10.5439/1438488, 2023.
Dong, X., Ackerman, T. P., and Clothiaux, E. E. 1998: Parameterizations of the microphysical and shortwave radiative properties of boundary layer stratus from ground-based measurements, J. Geophys. Res.-Atmos., 103, 31681–31693, https://doi.org/10.1029/1998JD200047, 1998.
Donovan, D. P., Klein Baltink, H., Henzing, J. S., de Roode, S. R., and Siebesma, A. P.: A depolarisation lidar-based method for the determination of liquid-cloud microphysical properties, Atmos. Meas. Tech., 8, 237–266, https://doi.org/10.5194/amt-8-237-2015, 2015.
Fairless, T., Jensen, M., Zhou, A, and Giangrande, S. E: Interpolated Sounding and Gridded Sounding Value-Added Products, ARM Technical Report DOE/SC-ARM-TR-183, DOE Office of Science Atmospheric Radiation Measurement (ARM) Program, https://doi.org/10.2172/1248938, 2021.
Fan, J., Wang, Y., Rosenfeld, D., and Liu, X.: Review of Aerosol–Cloud Interactions: Mechanisms, Significance, and Challenges, J. Atmos. Sci., 73, 4221–4252, https://doi.org/10.1175/JAS-D-16-0037.1, 2016.
Fernald, F. G.: Analysis of atmospheric lidar observations: some comments, Appl. Optics, 23, 652–653, 1984.
Grosvenor, D. P., Sourdeval, O., Zuidema, P., Ackerman, A., Alexandrov, M. D., Bennartz, R., Boers, R., Cairns, B., Chiu, C., Christensen, M., Deneke, H., Diamond, M., Feingold, G., Fridlind, A., Hünerbein, A., Knist, C., Kollias, P., Marshak, A., McCoy, D., Merk, D., Painemal, D., Rausch, J., Rosenfeld, D., Russchenberg, H., Seifert, P., Sinclair, K., Stier, P., B. van D., Wendisch, M., Werner, F., Wood, R., Zhang, Z., and Quaas, J.: Remote sensing of cloud droplet number concentration in warm clouds: A review of the current state of knowledge and perspectives, Rev. Geophys., 56, 409–453, https://doi.org/10.1029/2017RG000593, 2018.
Gryspeerdt, E., Quaas, J., Ferrachat, S., Gettelman, A., Ghan, S., Lohmann, U., Morrison, H., Neubauer, D., Partridge, D. G., Stier, P., Takemura, T., Wang, H., Wang, M., and Zhang, K.: Constraining the instantaneous aerosol influence on cloud albedo, P. Natl. Acad. Sci. USA, 114, 4899–4904, https://doi.org/10.1073/pnas.1617765114, 2017.
Hodges, G. B. and Michalsky, J. J.: Multifilter Rotating Shadowband Radiometer (MFRSR) Handbook With subsections for derivative instruments: Multifilter Radiometer (MFR) Normal Incidence Multifilter Radiometer (NIMFR), ARM Technical Report DOE/SC-ARM-TR-144, DOE Office of Science Atmospheric Radiation Measurement (ARM) Program, https://doi.org/10.2172/1251387, 2016.
Hogan, R. J.: Fast lidar and radar multiple-scattering models. Part I: Small-angle scattering using the photon variance–covariance method, J. Atmos. Sci., 65, 3621–3635, https://doi.org/10.1175/2008JAS2642.1, 2008.
Holdridge, D.: Balloon-Borne Sounding System (SONDE) Instrument Handbook, ARM Technical Report DOE/SC-ARM-TR-029, DOE Office of Science Atmospheric Radiation Measurement (ARM) Program, https://doi.org/10.2172/1020712, 2020.
Illingworth, A. J., Hogan, R. J., O'Connor, E. J., Bouniol, D., Brooks, M. E., Delanoe, J., Donovan, D. P., Eastment, J. D., Gaussiat, N., Goddard, J. W. F., Haeffelin, M., Klein Baltink, H., Krasnov, O. A., Pelon, J., Piriou, J.-M., Protat, A., Russchenberg, H. W. J., Seifert, A., Tompkins, A. M., van Zadelhoff, G.-J., Vinit, F., Willen, U., Wilson, D. R., and Wrench, C. L.: Cloudnet – continuous evaluation of cloud profiles in seven operational models using ground-based observations, B. Am. Meteorol. Soc., 88, 883–898, 2007.
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2391 pp., https://doi.org/10.1017/9781009157896, 2021.
Johnson, K. L., Giangrande, S. E., and Zhou, A.: Ka-Band ARM Zenith Radar (KAZR) Active Remote Sensing of Clouds (ARSCL) CloudSat Calibration (KAZRARSCL- CLOUDSAT) (Value-Added Product Report), ARM Technical Report DOE/SC-ARM-TR-279, DOE Office of Science Atmospheric Radiation Measurement (ARM) Program, https://doi.org/10.2172/1847644, 2022.
Klett, J. D.: Stable analytical inversion solution for processing lidar returns, Appl. Optics, 20, 211–220, https://doi.org/10.1364/AO.20.000211, 1981.
Kollias, P., Rémillard, J., Luke, E., and Szyrmer, W.: Cloud radar Doppler spectra in drizzling stratiform clouds: 1. Forward modeling and remote sensing applications, J. Geophys. Res., 116, D13201, https://doi.org/10.1029/2010JD015237, 2011.
Lim, K.-S. S., Riihimaki, L., Comstock, J. M., Schmid, B., Sivaraman, C., Shi, Y., and McFarquhar, G. M.: Evaluation of long-term surface-retrieved cloud droplet number concentration with in situ aircraft observations, J. Geophys. Res.-Atmos., 121, 2318–2331, https://doi.org/10.1002/2015JD024082, 2016.
Luke, E. and Kollias, P.: Separating Cloud and Drizzle Radar Moments during Precipitation Onset Using Doppler Spectra, J. Atmos. Ocean. Tech., 26, 167–179, 1656–1671, https://doi.org/10.1175/JTECH-D-11-00195.1, 2013.
Mace, G. G. and Sassen, K.: A constrained algorithm for retrieval of stratocumulus cloud properties using solar radiation, microwave radiometer, and millimeter cloud radar data, J. Geophys. Res., 105, 29099– 29108, https://doi.org/10.1029/2000JD900403, 2000.
Marais, W. J., Holz, R. E., Hu, Y. H., Kuehn, R. E., Eloranta, E. E., and Willett, R. M.: Approach to simultaneously denoise and invert backscatter and extinction from photon-limited atmospheric lidar observations, Appl. Optics, 55, 8316–8334, https://doi.org/10.1364/AO.55.008316, 2016.
Martin, G. M., Johnson, D. W., and Spice, A.: The Measurement and Parameterization of Effective Radius of Droplets in Warm Stratocumulus Clouds, J. Atmos. Sci., 51, 1823–1842, https://doi.org/10.1175/1520-0469(1994)051<1823:TMAPOE>2.0.CO;2., 1994.
Martucci, G. and O'Dowd, C. D.: Ground-based retrieval of continental and marine warm cloud microphysics, Atmos. Meas. Tech., 4, 2749–2765, https://doi.org/10.5194/amt-4-2749-2011, 2011.
McComiskey, A., Feingold, G., Frisch, A. S., Turner, D. D., Miller, M. A., Chiu, J. C., Min, Q., and Ogren, J. A.: An assessment of aerosol-cloud interactions in marine stratus clouds based on surface remote sensing, J. Geophys. Res.-Atmos., 114, D09203, https://doi.org/10.1029/2008JD011006, 2009.
McCoy, I. L., McCoy, D. T., Wood, R., Regayre, L., Watson-Parris, D., Grosvenor, D. P., Mulcahy, J. P., Hu, Y., Bender, F. A.-M., Field, P. R., Carslaw, K. S., and Gordon, H.: The hemispheric contrast in cloud microphysical properties constrains aerosol forcing, P. Natl. Acad. Sci. USA, 117, 18998–19006, https://doi.org/10.1073/pnas.1922502117,2020.
Mei, F., Burk, K., Ermold, B., and Zhang, D.: Fast Cloud Droplet Probe aboard aircraft (AAFFCDP), Atmospheric Radiation Measurement (ARM) User Facility [data set], https://doi.org/10.5439/1417472, 2023.
Merk, D., Deneke, H., Pospichal, B., and Seifert, P.: Investigation of the adiabatic assumption for estimating cloud micro- and macrophysical properties from satellite and ground observations, Atmos. Chem. Phys., 16, 933–952, https://doi.org/10.5194/acp-16-933-2016, 2016.
Miles, N. L., Verlinde, J., and Clothiaux, E. E.: Cloud Droplet Size Distributions in Low-Level Stratiform Clouds, J. Atmos. Sci., 57, 295–311, https://doi.org/10.1175/1520-0469(2000)057<0295:CDSDIL>2.0.CO;2, 2000.
Min, Q. and Harrison, L. C.: Cloud properties derived from surface MFRSR measurements and comparison with GOES results at the ARM SGP site, Geophys. Res. Lett., 23, 1641–1644, https://doi.org/10.1029/96GL01488, 1996.
Moore, R. H., Karydis, V. A., Capps, S. L., Lathem, T. L., and Nenes, A.: Droplet number uncertainties associated with CCN: an assessment using observations and a global model adjoint, Atmos. Chem. Phys., 13, 4235–4251, https://doi.org/10.5194/acp-13-4235-2013, 2013.
Muradyan, P. and Coulter, R.: Micropulse Lidar (MPL) Handbook, ARM Technical Report DOE/SC-ARM-TR-019, DOE Office of Science Atmospheric Radiation Measurement (ARM) Program, https://doi.org/10.2172/1020714, 2020.
Newsom, R. K., Bambha, R., and Chand, D.: Raman Lidar (RL) Instrument Handbook, ARM Technical Report DOE/SC-ARM-TR-038, DOE Office of Science Atmospheric Radiation Measurement (ARM) Program, https://doi.org/10.2172/1020561, 2022.
O'Connor, E. J., Illingworth, A. J., and Hogan, R. J.: A Technique for Autocalibration of Cloud Lidar, J. Atmos. Ocean. Tech., 21, 777–786, https://doi.org/10.1175/1520-0426(2004)021<0777:ATFAOC>2.0.CO;2, 2004.
Painemal, D. and Zuidema, P.: Microphysical variability in southeast Pacific Stratocumulus clouds: synoptic conditions and radiative response, Atmos. Chem. Phys., 10, 6255–6269, https://doi.org/10.5194/acp-10-6255-2010, 2010.
Pawlowska, H., Grabowski, W. W., and Brenguier, J.-L.: Observations of the width of cloud droplet spectra in stratocumulus, Geophys. Res. Lett., 33, L19810, https://doi.org/10.1029/2006GL026841, 2006.
Regayre, L. A., Pringle, K. J., B. Booth, B. B., Lee, L. A., Mann, G. W., Browse, J., Woodhouse, M. T., Rap, A., Reddington, C. L., and Carslaw, K. S.: Uncertainty in the magnitude of aerosol-cloud radiative forcing over recent decades, Geophys. Res. Lett., 41, 9040–9049, https://doi.org/10.1002/2014GL062029, 2014.
Riihimaki, L., McFarlane, S., and Sivaraman, C.: Droplet Number Concentration Value-Added Product, ARM Technical Report DOE/SC-ARM-TR-140, DOE Office of Science Atmospheric Radiation Measurement (ARM) Program, https://doi.org/10.2172/1237963, 2021.
Riihimaki, L., McFarlane, S., Sivaraman, C., and Zhang, D.: Droplet number concentration (NDROPMFRSR), Atmospheric Radiation Measurement (ARM) User Facility [data set], https://doi.org/10.5439/1131339, 2023.
Rosenfeld, D., Zhu, Y., Wang, M., Zheng, Y., Goren, T., and Yu, S.: Aerosol-driven droplet concentrations dominate coverage and water of oceanic low-level clouds, Science, 363, eaav0566, https://doi.org/10.1126/science.aav0566, 2019.
Sarna, K., Donovan, D. P., and Russchenberg, H. W. J.: Estimating the optical extinction of liquid water clouds in the cloud base region, Atmos. Meas. Tech., 14, 4959–4970, https://doi.org/10.5194/amt-14-4959-2021, 2021.
Schmidt, J., Wandinger, U., and Malinka, A.: Dual-field-of-view Raman lidar measurements for the retrieval of cloud microphysical properties, Appl. Optics, 52, 2235, https://doi.org/10.1364/AO.52.002235, 2013.
Snider, J. R., Leon, D., and Wang, Z.: Droplet Concentration and Spectral Broadening in Southeast Pacific Stratocumulus Clouds, J. Atmos. Sci., 74, 719–749, https://doi.org/10.1175/JAS-D-16-0043.1, 2017.
Stephens, G. L., Li, J., Wild, M., Clayson, C. A., Loeb, N., Kato, S., L'Ecuyer, T., Stackhouse Jr., P. W., Lebsock, M., and Andrews, T.: An update on Earth's energy balance in light of the latest global observations, Nat. Geosci., 5, 691–696, https://doi.org/10.1038/ngeo1580, 2012.
Storelvmo, T., Kristjánsson, J. E., Ghan, S. J., Kirkevåg, A., Seland, Ø., and Iversen, T.: Predicting cloud droplet number concentration in Community Atmosphere Model (CAM)-Oslo, J. Geophys. Res., 111, D24208, https://doi.org/10.1029/2005JD006300, 2006.
Thorsen, T. J. and Fu, Q.: Automated Retrieval of Cloud and Aerosol Properties from the ARM Raman Lidar. Part II: Extinction, J. Atmos. Ocean. Tech., 32, 1999–2023, https://doi.org/10.1175/JTECH-D-14-00178.1, 2015.
Thorsen, T. J., Fu, Q., Newsom, R. K., Turner, D. D., and Comstock, J. M.: Automated Retrieval of Cloud and Aerosol Properties from the ARM Raman Lidar. Part I: Feature Detection, J. Atmos. Ocean. Tech., 32, 1977–1998, https://doi.org/10.1175/JTECH-D-14-00150.1, 2015.
Turner, D. D., Clough, S. A., Liljegren, J. C., Clothiaux, E. E., Cady-Pereira, K. E., and Gaustad, K. L.: Retrieving liquid water path and precipitable water vapor from the Atmospheric Radiation Measurement (ARM) microwave radiometers, IEEE T. Geosci. Remote, 45, 3680–3690, 2007.
Turner, D. D., McFarlane, S. A., Riihimaki, L., Shi, Y., Lo, C., and Min, Q.: Cloud Optical Properties from the Multifilter Shadowband Radiometer (MFRSRCLDOD): An ARM Value- Added Product, ARM Technical Report DOE/SC-ARM-TR-139, DOE Office of Science Atmospheric Radiation Measurement (ARM) Program, https://doi.org/10.2172/1237958, 2014.
Twomey, S.: Influence of pollution on shortwave albedo of clouds, J. Atmos. Sci., 34, 1149–1152, https://doi.org/10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2, 1977.
Vogelmann, A. M., McFarquhar, G. M., Ogren, J. A., Turner, D. D., Comstock, J. M., Feingold, G., Long, C. N., Jonsson, H. H., Bucholtz, A., Collins, D. R., Diskin, G. S., Gerber, H., Lawson, R. P., Woods, R. K., Andrews, E., Yang, H.-J., Chiu, J. C., Hartsock, D., Hubbe, J. M., Lo, C., Marshak, A., Monroe, J. W., McFarlane, S. A., Schmid, B., Tomlinson, J. M., and Toto, T.: Racoro Extended-Term Aircraft Observations of Boundary Layer Clouds, B. Am. Meteorol. Soc., 93, 861–878, https://doi.org/10.1175/BAMS-D-11-00189.1, 2012.
Wang, J., Wood, R., Jensen, M. P., Chiu, J. C., Liu, Y., Lamer, K., Desai, N., Giangrande, S. E., Knopf, D. A., Kollias, P., Laskin, A., Liu, X., Lu, C., Mechem, D., Mei, F., Starzec, M., Tomlinson, J., Wang, Y., Yum, S. S., Zheng, G., Aiken, A. C., Azevedo, E. B., Blanchard, Y., China, S., Dong, X., Gallo, F., Gao, S., Ghate, V. P., Glienke, S., Goldberger, L., Hardin, J. C., Kuang, C., Luke, E. P., Matthews, A. A., Miller, M. A., Moffet, R., Pekour, M., Schmid, B., Sedlacek, A. J., Shaw, R. A., Shilling, J. E., Sullivan, A., Suski, K., Veghte, D. P., Weber, R., Wyant, M., Yeom, J., Zawadowicz, M., and Zhang, Z.: Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA), B. Am. Meteorol. Soc, 103, E619–E641, https://doi.org/10.1175/BAMS-D-19-0220.1, 2022.
Welton, E. J., Campbell, J. R., Spinhirne, J. D., and Scott, V. S.: Global monitoring of clouds and aerosols using a network of micro-pulse lidar systems, in: Lidar Remote Sensing for Industry and Environmental Monitoring, edited by: Singh, U. N., Itabe, T., and Sugimoto, N., Proc. SPIE, 4153, 151–158, https://doi.org/10.1117/12.417040, 2001.
Wood, R., Mechoso, C. R., Bretherton, C. S., Weller, R. A., Huebert, B., Straneo, F., Albrecht, B. A., Coe, H., Allen, G., Vaughan, G., Daum, P., Fairall, C., Chand, D., Gallardo Klenner, L., Garreaud, R., Grados, C., Covert, D. S., Bates, T. S., Krejci, R., Russell, L. M., de Szoeke, S., Brewer, A., Yuter, S. E., Springston, S. R., Chaigneau, A., Toniazzo, T., Minnis, P., Palikonda, R., Abel, S. J., Brown, W. O. J., Williams, S., Fochesatto, J., Brioude, J., and Bower, K. N.: The VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx): goals, platforms, and field operations, Atmos. Chem. Phys., 11, 627–654, https://doi.org/10.5194/acp-11-627-2011, 2011.
Wood, R., Wyant, M., Bretherton, C. S., Rémillard, J., Kollias, P., Fletcher, J., Stemmler, J., De Szoeke, S., Yuter, S., Miller, M., Mechem, D., Tselioudis, G., Chiu, J. C., Mann, J. A. L., O'Connor, E. J., Hogan, R. J., Dong, X., Miller, M., Ghate, V., Jefferson, A., Min, Q., Minnis, P., Palikonda, R., Albrecht, B., Luke, E., Hannay, C., and Lin, Y.: Clouds, aerosols, and precipitation in the marine boundary layer: An arm mobile facility deployment, B. Am. Meteorol. Soc., 96, 419–440, https://doi.org/10.1175/BAMS-D-13-00180.1, 2015.
Wu, P., Dong, X., Xi, B., Tian, J., and Ward, D. M.: Profiles of MBL Cloud and Drizzle Microphysical Properties Retrieved From Ground-Based Observations and Validated by Aircraft In Situ Measurements Over the Azores, J. Geophys. Res.-Atmos., 125, e2019JD032205, https://doi.org/10.1029/2019JD032205, 2020a.
Wu, P., Dong, X., and Xi, B.: A climatology of marine boundary layer cloud and drizzle properties derived from groundbased observations over the azores, J. Climate, 33, 10133–10148, https://doi.org/10.1175/JCLI-D-20-0272.1, 2020b.
Yeom, J. M., Yum, S. S., Shaw, R. A., La, I., Wang, J., Lu, C., Liu, Y., Mei, F., Schmid, B., and Matthews, A.: Vertical Variations of Cloud Microphysical Relationships in Marine Stratocumulus Clouds Observed During the ACE-ENA Campaign, J. Geophys. Res.-Atmos., 126, e2021JD034700, https://doi.org/10.1029/2021JD034700, 2021.
Zelinka, M. D., Randall, D. A., Webb, M. J., and Klein, S. A.: Clearing Clouds of Uncertainty, Nat. Clim. Change, 7, 674–678, https://doi.org/10.1038/nclimate3402, 2017.
Zhang, D.: Lidar-based cloud droplet number concentration retrievals at the ARM ENA observatory, Atmospheric Radiation Measurement (ARM) User Facility [data set], https://adc.arm.gov/discovery/#/results/s::droplet%20number%20concentration, last access: 5 December 2023.
Zhang, D., Vogelmann, A., Kollias, P., Luke, E., Yang, F., Lubin, D., and Wang, Z.: Comparison of Antarctic and Arctic single-layer stratiform mixed-phase cloud properties using ground-based remote sensing measurements. J. Geophys. Res.-Atmos., 124, 10186–10204, https://doi.org/10.1029/2019JD030673, 2019.
Zhang, Z., Song, Q., Mechem, D. B., Larson, V. E., Wang, J., Liu, Y., Witte, M. K., Dong, X., and Wu, P.: Vertical dependence of horizontal variation of cloud microphysics: observations from the ACE-ENA field campaign and implications for warm-rain simulation in climate models, Atmos. Chem. Phys., 21, 3103–3121, https://doi.org/10.5194/acp-21-3103-2021, 2021.
Zhao, C., Xie, S., Klein, S. A., Protat, A., Shupe, M. D., McFarlane, S. A., Comstock, J. M., Delanoë, J., Deng, M., Dunn, M., Hogan, R. J., Huang, D., Jensen, M. P., Mace, G. G., McCoy, R., O'Connor, E. J., Turner, D. D., and Wang, Z.: Toward understanding of differences in current cloud retrievals of ARM ground-based measurements, J. Geophys. Res.-Atmos., 117, D10206, https://doi.org/10.1029/2011JD016792, 2012.
Zhu, Z., Kollias, P., Luke, E., and Yang, F.: New insights on the prevalence of drizzle in marine stratocumulus clouds based on a machine learning algorithm applied to radar Doppler spectra, Atmos. Chem. Phys., 22, 7405–7416, https://doi.org/10.5194/acp-22-7405-2022, 2022.
Short summary
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.
Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft...