Articles | Volume 15, issue 16
https://doi.org/10.5194/amt-15-4735-2022
© Author(s) 2022. 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-15-4735-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Aug 2022
Research article |
| 22 Aug 2022
| 22 Aug 2022
Comparison of planetary boundary layer height from ceilometer with ARM radiosonde data
Pacific Northwest National Laboratory, Richland, WA, USA
Jennifer Comstock
Pacific Northwest National Laboratory, Richland, WA, USA
Victor Morris
Pacific Northwest National Laboratory, Richland, WA, USA
Related authors
Fan Mei, Jian Wang, Israel Silber, Nurun Nahar Lata, Gregory W. Vandergrift, Jing Li, Bo Chen, Sarah Brooks, Michael P. Jensen, Min Deng, Damao Zhang, Darielle Dexheimer, Beat Schmid, Zezhen Cheng, and Swarup China
EGUsphere, https://doi.org/10.5194/egusphere-2026-2245, https://doi.org/10.5194/egusphere-2026-2245, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Tethered balloon measurements from 149 flights during DOE ARM TRACER over Houston characterize vertical aerosol and CCN structure. Back-trajectory clustering identifies three air mass types—marine, mixed, and urban—with distinct profiles shaped by boundary-layer depth and coastal circulations. A case study shows mesoscale advection simultaneously transforms thermodynamic and aerosol conditions, underscoring the need to constrain meteorology before attributing cloud changes to aerosol forcing.
Jingjing Tian, Gourihar Kulkarni, Jennifer M. Comstock, John E. Shilling, Damao Zhang, Peng Wu, and Fan Mei
EGUsphere, https://doi.org/10.5194/egusphere-2026-1432, https://doi.org/10.5194/egusphere-2026-1432, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Cloud condensation nuclei are tiny particles that attract water vapor and help form clouds. A ground-based lidar method can estimate their number at different heights, but assumes the aerosol type does not change with height. Comparisons with aircraft data show good agreement of this method when aerosols are well mixed, but larger errors when stacked layers occur. We also developed an index to flag these complex conditions and indicate when this estimation method is more reliable.
Dan Lubin, Xun Zou, Johannes Mülmenstädt, Andrew Vogelmann, Maria Cadeddu, and Damao Zhang
Atmos. Chem. Phys., 26, 295–311, https://doi.org/10.5194/acp-26-295-2026, https://doi.org/10.5194/acp-26-295-2026, 2026
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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
Atmos. Meas. Tech., 18, 6681–6703, https://doi.org/10.5194/amt-18-6681-2025, https://doi.org/10.5194/amt-18-6681-2025, 2025
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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
Atmos. Meas. Tech., 18, 5393–5414, https://doi.org/10.5194/amt-18-5393-2025, https://doi.org/10.5194/amt-18-5393-2025, 2025
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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, and application of the model to another observatory and quantify how the models respond to instrument outages.
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
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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.
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
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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
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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, 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.
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
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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.
Fan Mei, Jian Wang, Israel Silber, Nurun Nahar Lata, Gregory W. Vandergrift, Jing Li, Bo Chen, Sarah Brooks, Michael P. Jensen, Min Deng, Damao Zhang, Darielle Dexheimer, Beat Schmid, Zezhen Cheng, and Swarup China
EGUsphere, https://doi.org/10.5194/egusphere-2026-2245, https://doi.org/10.5194/egusphere-2026-2245, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
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Tethered balloon measurements from 149 flights during DOE ARM TRACER over Houston characterize vertical aerosol and CCN structure. Back-trajectory clustering identifies three air mass types—marine, mixed, and urban—with distinct profiles shaped by boundary-layer depth and coastal circulations. A case study shows mesoscale advection simultaneously transforms thermodynamic and aerosol conditions, underscoring the need to constrain meteorology before attributing cloud changes to aerosol forcing.
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This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Cloud condensation nuclei are tiny particles that attract water vapor and help form clouds. A ground-based lidar method can estimate their number at different heights, but assumes the aerosol type does not change with height. Comparisons with aircraft data show good agreement of this method when aerosols are well mixed, but larger errors when stacked layers occur. We also developed an index to flag these complex conditions and indicate when this estimation method is more reliable.
Israel Silber, Donna M. Flynn, Jennifer M. Comstock, Erol L. Cromwell, and Brian D. Ermold
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We describe ASISKYCOVER, a new machine learning algorithm for pixel segmentation of all-sky imager (ASI-16) data used by the Atmospheric Radiation Measurement (ARM) User Facility. ASISKYCOVER provides cloud cover and thickness estimates, detects artifacts, and reports uncertainties. Using one year of data from the ARM Southern Great Plains site and comparisons with other ARM datasets, we demonstrate its use and robustness, which will improve cloud cover analyses and data evaluation efforts.
Israel Silber, Jennifer M. Comstock, Adam K. Theisen, Michael R. Kieburtz, Zeen Zhu, and Jenni Kyrouac
Atmos. Meas. Tech., 19, 485–506, https://doi.org/10.5194/amt-19-485-2026, https://doi.org/10.5194/amt-19-485-2026, 2026
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We present PrecipBE, a multi-instrument precipitation event best-estimate data product developed at the Atmospheric Radiation Measurement (ARM) User Facility, providing time series and tabular statistics of events, which could help advance model evaluation and cloud-process studies. We demonstrate PrecipBE's utilization with a brief 30-year trend analysis of ARM Southern Great Plains (SGP) site data, suggesting shorter, less intense events, but rising annual rainfall, driven by rare extremes.
Dan Lubin, Xun Zou, Johannes Mülmenstädt, Andrew Vogelmann, Maria Cadeddu, and Damao Zhang
Atmos. Chem. Phys., 26, 295–311, https://doi.org/10.5194/acp-26-295-2026, https://doi.org/10.5194/acp-26-295-2026, 2026
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
Atmos. Meas. Tech., 18, 6681–6703, https://doi.org/10.5194/amt-18-6681-2025, https://doi.org/10.5194/amt-18-6681-2025, 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
Atmos. Meas. Tech., 18, 5393–5414, https://doi.org/10.5194/amt-18-5393-2025, https://doi.org/10.5194/amt-18-5393-2025, 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, and application of the model to another observatory and quantify how the models respond to instrument outages.
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.
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 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.
Israel Silber, Jennifer M. Comstock, Michael R. Kieburtz, and Lynn M. Russell
Earth Syst. Sci. Data, 17, 29–42, https://doi.org/10.5194/essd-17-29-2025, https://doi.org/10.5194/essd-17-29-2025, 2025
Short summary
Short summary
We present ARMTRAJ, a set of multipurpose trajectory datasets, which augments cloud, aerosol, and boundary layer studies utilizing the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility data. ARMTRAJ data include ensemble run statistics that enhance consistency and serve as uncertainty metrics for air mass coordinates and state variables. ARMTRAJ will soon become a near real-time product that will accompany past, ongoing, and future ARM deployments.
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.
Evgueni Kassianov, Connor J. Flynn, James C. Barnard, Brian D. Ermold, and Jennifer M. Comstock
Atmos. Meas. Tech., 17, 4997–5013, https://doi.org/10.5194/amt-17-4997-2024, https://doi.org/10.5194/amt-17-4997-2024, 2024
Short summary
Short summary
Conventional ground-based radiometers commonly measure solar radiation at a few wavelengths within a narrow spectral range. These limitations prevent improved retrievals of aerosol, cloud, and surface characteristics. To address these limitations, an advanced ground-based radiometer with expanded spectral coverage and hyperspectral capability is introduced. Its good performance is demonstrated using reference data collected over three coastal regions with diverse types of aerosols and clouds.
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
Short summary
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.
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.
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.
Cited articles
Bopape, M.-J. M., Plant, R. S., and Coceal, O.: Resolution Dependence of Turbulent
Structures in Convective Boundary Layer Simulations, Atmosphere, 11, 986, https://doi.org/10.3390/atmos11090986, 2020.
Bradley, R. S., Keimig, F. T., and Diaz, H. F.: Recent changes in the North
American Arctic boundary layer in winter, J. Geophys. Res., 98, 8851–8858,
https://doi.org/10.1029/93JD00311, 1993.
Brooks, I. M.: Finding Boundary Layer Top: Application of a Wavelet
Covariance Transform to Lidar Backscatter Profiles, J. Atmos. Oceanic Technol., 20,
1092–1105, https://doi.org/10.1175/1520-0426(2003)020<1092:FBLTAO>2.0.CO;2, 2003.
Caicedo, V., Rappenglück, B., Lefer, B., Morris, G., Toledo, D., and Delgado, R.: Comparison of aerosol lidar retrieval methods for boundary layer height detection using ceilometer aerosol backscatter data, Atmos. Meas. Tech., 10, 1609–1622, https://doi.org/10.5194/amt-10-1609-2017, 2017.
Dang, R., Yang, Y., Hu, X.-M., Wang, Z., and Zhang, S.: A Review of Techniques
for Diagnosing the Atmospheric Boundary Layer Height (ABLH) Using Aerosol
Lidar Data, Remote Sens, 11, 1590, https://doi.org/10.3390/rs11131590, 2019.
Davis, K. J., Gamage, N., Hagelberg, C. R., Kiemle, C., Lenschow, D. H., and
Sullivan, P. P.: An objective method for deriving atmospheric structure from
airborne lidar observations, J. Atmos. Ocean. Technol., 17, 1455–1468, 2000.
Frioud, M., Mitev, V., Matthey, R., Häberli, C., Richner, H., Werner,
R., and Vogt, S.: Elevated aerosol stratification above Rhine Valley under
strong anticyclonic conditions, Atmos. Environ., 37, 1785–1797, https://doi.org/10.1016/S1352-2310(03)00049-9, 2003.
Fritz, A. M., Lapo, K., Freundorfer, A., Linhardt, T., and Thomas, C. K.:
Revealing the morning transition in the mountain boundary layer using
fiber-optic distributed temperature sensing,
Geophys. Res. Lett., 48, e2020GL092238,
https://doi.org/10.1029/2020GL092238, 2021.
Haeffelin, M., Angelini, F., Morille, Y., Martucci, G., Frey, S., Gobbi, G. P., Lolli, S., O’Dowd, C. D., Sauvage, L., Xueref Rémy, I., Wastine, B., and Feist, D. G.: Evaluation of Mixing-Height Retrievals from Automatic Profiling Lidars and Ceilometers in View of Future Integrated Networks in Europe, Bound-Lay. Meteorol., 143, 49–75, https://doi.org/10.1007/s10546-011-9643-z, 2012.
Haman, C. L., Lefer, B., and Morris, G. A.: Seasonal Variability in the
Diurnal Evolution of the Boundary Layer in a Near-Coastal Urban
Environment, J. Atmos. Ocean. Tech., 29, 697–710, https://doi.org/10.1175/JTECH-D-11-00114.1, 2012.
Heffter, J. L.: Transport Layer Depth Calculations, Second Joint Conference on
Applications of Air Pollution Meteorology, 24–27 March 1980, New Orleans, Louisiana, https://doi.org/10.1175/1520-0477-61.1.65, 1980.
Holtslag, A. A. M., De Bruijn, E. I. F., and Pan, H.: A High Resolution Air
Mass Transformation Model for Short-Range Weather Forecasting,
Mon. Weather Rev., 118,
1561–1575, https://doi.org/10.1175/1520-0493(1990)118<1561:AHRAMT>2.0.CO;2, 1990.
Kepert, J. D., Schwendike, J., and Ramsay, H.: Why is the Tropical
Cyclone Boundary Layer Not “Well Mixed”?, J. Atmos. Sci., 73, 957–973, 2016.
Konor, C. S., Boezio, G. C., Mechoso, C. R., and Arakawa, A.:
Parameterization of PBL Processes in an Atmospheric General Circulation
Model: Description and Preliminary Assessment, Mon. Weather Rev., 137, 1061–1082, https://doi.org/10.1175/2008MWR2464.1, 2009.
Kotthaus, S. and Grimmond, C. S. B.: Atmospheric boundary-layer characteristics
from ceilometer measurements. Part 1: A new method to track mixed layer
height and classify clouds, Q. J. Roy. Meteorol. Soc., 144, 1525–1538,
https://doi.org/10.1002/qj.3299, 2018.
Kurowski, M. J., Malinowski, P. S., and Grabowski, W. W.: A numerical
investigation of entrainment and transport within a stratocumulus-topped
boundary layer, Q. J. Roy. Meteorol. Soc., 135, 77–92, https://doi.org/10.1002/qj.354,
2009.
LeMone, M. A., Angevine, W. M., Bretherton, C. S., Chen, F., Dudhia, J., Fedorovich, E., Katsaros, K. B., Lenschow, D. H., Mahrt, L., Patton, E. G., Sun, J., Tjernström, M., and Weil, J.: 100 Years of Progress in Boundary Layer
Meteorology, Meteor. Monogr., 59, 9.1–9.85, https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0013.1, 2019.
Lewis, E. R.: Marine ARM GPCI Investigation of Clouds (MAGIC) field campaign
report, edited by: Stafford, R., DOE ARM Climate Research Facility,
DOE/SC-ARM-16-057, https://doi.org/10.2172/1343577, 2016.
Liu, S. and Liang, X.: Observed Diurnal Cycle Climatology of Planetary
Boundary Layer Height, J. Climate, 23, 5790–5809, https://doi.org/10.1175/2010JCLI3552.1, 2010.
Luo, T., Yuan, R., and Wang, Z.: Lidar-based remote sensing of atmospheric boundary layer height over land and ocean, Atmos. Meas. Tech., 7, 173–182, https://doi.org/10.5194/amt-7-173-2014, 2014.
Luo, T., Wang, Z., Zhang, D., and Chen, B.: Marine boundary layer structure as observed by A-train satellites, Atmos. Chem. Phys., 16, 5891–5903, https://doi.org/10.5194/acp-16-5891-2016, 2016.
Mahrt, L.: Modelling the depth of the stable boundary-layer, Bound.-Lay. Meteorol., 21, 3–19,
https://doi.org/10.1007/BF00119363, 1981.
Mahrt, L.: Stratified Atmospheric Boundary Layers, Bound.-Lay. Meteorol., 90, 375–396, https://doi.org/10.1023/A:1001765727956, 1999.
Martucci, G., Milroy, C., and O'Dowd, C. D.: Detection of Cloud-Base Height
Using Jenoptik CHM15K and Vaisala CL31 Ceilometers, J. Atmos. Ocean. Tech., 27, 305–318,
https://doi.org/10.1175/2009JTECHA1326.1, 2010.
Mather, J. H. and Voyles, J. W.: The Arm Climate Research Facility: A
Review of Structure and Capabilities, B. Am. Meteorol. Soc., 94, 377–392, https://doi.org/10.1175/BAMS-D-11-00218.1, 2013.
Morris, V. and Brian, E.: Boundary-Layer Height Data with CEIL (CEILPBLHT), Atmospheric Radiation Measurement (ARM) user facility [data set], https://doi.org/10.5439/1095593, 2022.
Münkel, C. and Roininen, R.: Automatic monitoring of boundary layer structures with ceilometers, Vaisala News, No. 184, 7–9, https://www.vaisala.com/sites/default/files/documents/vn184_07_AutomaticMonitoringofBoundaryLayerStructureswithCeilometers.pdf (last access: 9 August 2022), 2010.
Münkel, C. and Räsänen, J.: New optical concept for commercial
lidar ceilometers scanning the boundary layer, Proc. SPIE 5571, Remote
Sensing of Clouds and the Atmosphere IX,
https://doi.org/10.1117/12.565540, 2004.
Münkel, C., Eresmaa, N., Räsänen, J., and Karppinen, A.: Retrieval of mixing height
and dust concentration with lidar ceilometer, Bound.-Lay. Meteorol. 124, 117–128,
https://doi.org/10.1007/s10546-006-9103-3, 2007.
Noh, Y., Cheon, W. G., Hong, S. Y., and Raasch, S.: Improvement of the K-profile Model for the
Planetary Boundary Layer based on Large Eddy Simulation
Data, Bound.-Lay. Meteorol., 107, 401–427, https://doi.org/10.1023/A:1022146015946,
2003.
Riihimaki, L. and Zhang, D.: Planetary Boundary Layer Height (PBLHTSONDE1MCFARL), Atmospheric Radiation Measurement (ARM) user facility [data set], https://doi.org/10.5439/1150253, 2022.
Sawyer, V. and Li, Z.: Detection, variations and intercomparison of the
planetary boundary layer depth from radiosonde, lidar and infrared
spectrometer, Atmos. Environ., 79, 518–528,
https://doi.org/10.1016/j.atmosenv.2013.07.019, 2013.
Seibert, P., Beyrich, F., Gryning, S. E., Joffre, S., Rasmussen, A., and
Tercier, P.: Review and intercomparison of operational methods for the
determination of the mixing height, Atmos. Environ., 34, 1001–1027, https://doi.org/10.1016/S1352-2310(99)00349-0, 2000.
Seidel, D. J., Ao, C. O., and Li, K.: Estimating climatological planetary
boundary layer heights from radiosonde observations: Comparison of methods
and uncertainty analysis, J. Geophys. Res., 115, D16113, https://doi.org/10.1029/2009JD013680, 2010.
Seidel, D. J., Zhang, Y., Beljaars, A., Golaz, J.-C., Jacobson, A. R.,
and Medeiros, B.: Climatology of the planetary boundary layer over the
continental United States and Europe, J. Geophys. Res., 117, D17106, https://doi.org/10.1029/2012JD018143, 2012.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics: From Air
Pollution to Climate Change, second ed. WileyBlackwell, Hoboken, NJ, ISBN-13 978-0471720188,
ISBN-10 0471720186,
2006.
Sicard, M., Pérez, C., Rocadenbosch, F., Baldasano, J. M., and García-Vizcaino, D.: Mixed-Layer Depth Determination
in the Barcelona Coastal Area From Regular Lidar Measurements: Methods,
Results and Limitations, Bound.-Lay. Meteorol., 119, 135–157,
https://doi.org/10.1007/s10546-005-9005-9, 2006.
Sivaraman, C., McFarlane, S., Chapman, E., Jensen, M., Toto, T., Liu, S.,
and Fischer, M.: Planetary boundary layer (PBL) height value added product
(VAP): Radiosonde retrievals, U.S. Department of Energy Rep.
DOE/SC-ARM-TR-132, 36 pp., https://www.arm.gov/publications/tech_reports/doe-sc-arm-tr-132.pdf (last access: 9 August 2022), 2013.
Sørensen, J. H., Rasmussen, A., Ellermann, T., and Lyck, E.: Mesoscale
Influence on Long-range Transport – Evidence From ETEX Modeling and
Observations, Atmos. Environ., 32, 4207–4217, https://doi.org/10.1007/978-1-4615-4153-0_27, 1998.
Su, T., Li, Z., and Kahn, R.: A new method to retrieve the diurnal
variability of planetary boundary layer height from lidar under different
thermodynamic stability conditions, Remote Sens. Environ., 237, 111519,
https://doi.org/10.1016/j.rse.2019.111519, 2020.
Steyn, D. G., Baldi, M., and Hoff, R. M.: The Detection of Mixed Layer Depth
and Entrainment Zone Thickness from Lidar Backscatter Profiles, J. Atmos. Oceanic Technol., 16,
953–959, https://doi.org/10.1175/1520-0426(1999)016<0953:TDOMLD>2.0.CO;2, 1999.
Stull, R. B.: An Introduction to Boundary Layer Meteorology, Kluwer
Academic, ISBN-13 978-9027727695,
ISBN-10 9027727694,
1988.
von Engeln, A. and Teixeira, J.: A Planetary Boundary Layer Height
Climatology Derived from ECMWF Reanalysis Data, J. Climate, 26, 6575–6590,
https://doi.org/10.1175/JCLI-D-12-00385.1, 2013.
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.
The planetary boundary layer is the lowest part of the atmosphere. Its structure and depth...
