Articles | Volume 13, issue 12
https://doi.org/10.5194/amt-13-6901-2020
© Author(s) 2020. 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-13-6901-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Dec 2020
Research article |
| 18 Dec 2020
| 18 Dec 2020
Determining cloud thermodynamic phase from the polarized Micro Pulse Lidar
Jasper R. Lewis
CORRESPONDING AUTHOR
Joint Center for Earth Systems Technology, University of Maryland
Baltimore County, Baltimore, Maryland, USA
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
James R. Campbell
Naval Research Laboratory, Monterey, California, USA
Sebastian A. Stewart
Science Systems and Applications, Inc., Lanham, Maryland, USA
Ivy Tan
McGill University, Montreal, Quebec, Canada
Ellsworth J. Welton
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Simone Lolli
CNR-IMAA, Istituto di Metodologie per l'Analisi Ambientale, Tito
Scalo, Italy
Related authors
Simone Lolli, Erica K. Dolinar, Jasper R. Lewis, Andreu Salcedo-Bosch, James R. Campbell, and Ellsworth J. Welton
EGUsphere, https://doi.org/10.5194/egusphere-2025-1237, https://doi.org/10.5194/egusphere-2025-1237, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Clouds strongly influence Earth's climate by changing how sunlight is reflected or absorbed. We studied thin, high-altitude clouds using radar-laser measurements collected over twenty years at NASA GSFC. Our findings show these clouds increasingly trap heat, partly because of shrinking snow and ice cover. This trend could further accelerate warming locally, underlining the need for accurate cloud observations to improve climate forecasts and strategies to respond to climate change.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
Short summary
Short summary
In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
David M. Giles, Alexander Sinyuk, Mikhail G. Sorokin, Joel S. Schafer, Alexander Smirnov, Ilya Slutsker, Thomas F. Eck, Brent N. Holben, Jasper R. Lewis, James R. Campbell, Ellsworth J. Welton, Sergey V. Korkin, and Alexei I. Lyapustin
Atmos. Meas. Tech., 12, 169–209, https://doi.org/10.5194/amt-12-169-2019, https://doi.org/10.5194/amt-12-169-2019, 2019
Short summary
Short summary
Clouds or instrumental anomalies may perturb ground-based solar measurements used to calculate aerosol optical depth (AOD). This study presents a new algorithm of automated near-real-time (NRT) quality controls with improved cloud screening for AERONET AOD measurements. Results from the new and old algorithms have excellent agreement for the highest-quality AOD level, while the new algorithm provides higher-quality NRT AOD for applications such as data assimilation and satellite evaluation.
Simone Lolli, Fabio Madonna, Marco Rosoldi, James R. Campbell, Ellsworth J. Welton, Jasper R. Lewis, Yu Gu, and Gelsomina Pappalardo
Atmos. Meas. Tech., 11, 1639–1651, https://doi.org/10.5194/amt-11-1639-2018, https://doi.org/10.5194/amt-11-1639-2018, 2018
Short summary
Short summary
We evaluate the comparability of aerosol and cloud vertically resolved optical properties obtained with varying lidar profiling techniques and/or data processing methodologies. The discrepancies are assessed by evaluating climate-sensitive direct radiative effects, computed by radiative transfer code means. Results show important discrepancies up to 0.8 W m−2 due to lidar data smoothing in cirrus clouds and a 0.05 W m−2 difference between Raman and elastic lidar technique on a dust layer aloft.
Simone Lolli, James R. Campbell, Jasper R. Lewis, Yu Gu, and Ellsworth J. Welton
Atmos. Chem. Phys., 17, 7025–7034, https://doi.org/10.5194/acp-17-7025-2017, https://doi.org/10.5194/acp-17-7025-2017, 2017
Short summary
Short summary
We compare net TOA radiative forcing between the simplified Corti–Peter (CP) and relatively complex Fu–Liou–Gu models for cirrus clouds observed by NASA MPLNET at Singapore in 2010–11 and Greenbelt, Maryland, in 2012. We find daytime forcing discrepancies up to 65 % between the two, which is greater than previous studies. In some cases, the sign of net TOA daytime forcing also differs. We attribute model differences to numerical simplifications in CP via regression that are not valid globally.
J. R. Campbell, M. A. Vaughan, M. Oo, R. E. Holz, J. R. Lewis, and E. J. Welton
Atmos. Meas. Tech., 8, 435–449, https://doi.org/10.5194/amt-8-435-2015, https://doi.org/10.5194/amt-8-435-2015, 2015
Short summary
Short summary
Digital thresholds based on 2012 CALIOP satellite lidar measurements are investigated for distinguishing cirrus cloud presence, including cloud top temperatures and heights combined with layer depolarization and phase and optical depths. A cloud top temperature of -37 C is found to exhibit the most stable performance, owing to it being the point of homogeneous liquid-water freezing. Depolarization and phase help but are mostly ambiguous at warmer temperatures where mixed-phase clouds propagate.
Simone Lolli, Erica K. Dolinar, Jasper R. Lewis, Andreu Salcedo-Bosch, James R. Campbell, and Ellsworth J. Welton
EGUsphere, https://doi.org/10.5194/egusphere-2025-1237, https://doi.org/10.5194/egusphere-2025-1237, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Clouds strongly influence Earth's climate by changing how sunlight is reflected or absorbed. We studied thin, high-altitude clouds using radar-laser measurements collected over twenty years at NASA GSFC. Our findings show these clouds increasingly trap heat, partly because of shrinking snow and ice cover. This trend could further accelerate warming locally, underlining the need for accurate cloud observations to improve climate forecasts and strategies to respond to climate change.
Fengjiao Chen, Yuanjian Yang, Lu Yu, Yang Li, Weiguang Liu, Yan Liu, and Simone Lolli
Atmos. Chem. Phys., 25, 1587–1601, https://doi.org/10.5194/acp-25-1587-2025, https://doi.org/10.5194/acp-25-1587-2025, 2025
Short summary
Short summary
The microphysical mechanisms of precipitation responsible for the varied impacts of aerosol particles on shallow precipitation remain unclear. This study reveals that coarse aerosol particles invigorate shallow rainfall through enhanced coalescence processes, whereas fine aerosol particles suppress shallow rainfall through intensified microphysical breaks. These impacts are independent of thermodynamic environments but are more significant in low-humidity conditions.
Tao Shi, Yuanjian Yang, Ping Qi, and Simone Lolli
Atmos. Chem. Phys., 24, 12807–12822, https://doi.org/10.5194/acp-24-12807-2024, https://doi.org/10.5194/acp-24-12807-2024, 2024
Short summary
Short summary
This paper explored the formation mechanisms of the amplified canopy urban heat island intensity (ΔCUHII) during heat wave (HW) periods in the megacity of Beijing from the perspectives of mountain–valley breeze and urban morphology. During the mountain breeze phase, high-rise buildings with lower sky view factors (SVFs) had a pronounced effect on the ΔCUHII. During the valley breeze phase, high-rise buildings exerted a dual influence on the ΔCUHII.
Yenny González, María F. Sánchez-Barrero, Ioana Popovici, África Barreto, Stephane Victori, Ellsworth J. Welton, Rosa D. García, Pablo G. Sicilia, Fernando A. Almansa, Carlos Torres, and Philippe Goloub
EGUsphere, https://doi.org/10.5194/egusphere-2024-2727, https://doi.org/10.5194/egusphere-2024-2727, 2024
Short summary
Short summary
We characterize the optical properties of various aerosols using a compact dual-wavelength depolarization lidar (CIMEL CE376) at 532 and 808 nm. Through a modified two-wavelength Klett inversion method, we assess the vertical distribution and temporal evolution of Saharan dust, volcanic aerosols, and wildfire smoke in the subtropical North Atlantic from August 2021 to August 2023. The study confirms the CE376 lidar's effectiveness in monitoring and characterizing atmospheric aerosols over time.
Tianwen Wei, Mengya Wang, Kenan Wu, Jinlong Yuan, Haiyun Xia, and Simone Lolli
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-178, https://doi.org/10.5194/amt-2024-178, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
This study uses three years of wind lidar measurements to investigate the dynamics of the urban PBL in Hefei, China. Results show that nocturnal low-level jets occur most frequently in spring and intensify in summer, significantly increasing turbulence and shear intensity near the ground level, especially during the night. Cloud cover raises the MLH by around 100 m during the night due to the greenhouse effect and decreases it by up to 200 m in the afternoon by obstructing solar radiation.
Tao Shi, Yuanjian Yang, Lian Zong, Min Guo, Ping Qi, and Simone Lolli
EGUsphere, https://doi.org/10.5194/egusphere-2024-3111, https://doi.org/10.5194/egusphere-2024-3111, 2024
Short summary
Short summary
Our study explored the daily temperature patterns in urban areas of the Yangtze River Delta, focusing on how weather and human activities impact these patterns. We found that temperatures were higher at night, and weather patterns had a bigger impact during the day, while human activities mattered more at night. This helps us understand and address urban overheating.
Anton Lopatin, Oleg Dubovik, Georgiy Stenchikov, Ellsworth J. Welton, Illia Shevchenko, David Fuertes, Marcos Herreras-Giralda, Tatsiana Lapyonok, and Alexander Smirnov
Atmos. Meas. Tech., 17, 4445–4470, https://doi.org/10.5194/amt-17-4445-2024, https://doi.org/10.5194/amt-17-4445-2024, 2024
Short summary
Short summary
We compare aerosol properties over the King Abdullah University of Science and Technology campus using Generalized Retrieval of Aerosol and Surface Properties (GRASP) and the Micro-Pulse Lidar Network (MPLNET). We focus on the impact of different aerosol retrieval assumptions on daytime and nighttime retrievals and analyze seasonal variability in aerosol properties, aiding in understanding aerosol behavior and improving retrieval. Our work has implications for climate and public health.
Tao Shi, Yuanjian Yang, Gaopeng Lu, Zuofang Zheng, Yucheng Zi, Ye Tian, Lei Liu, and Simone Lolli
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2024-3, https://doi.org/10.5194/acp-2024-3, 2024
Revised manuscript under review for ACP
Short summary
Short summary
This study found that CG lightning tends to cluster around the outer boundaries of large cities, but gathers within small cities. The urban underlying surface can contribute to the separation of cold pools, weakening vertical airflow, and triggering thunderstorm bifurcation. The density of buildings also influences the barrier effect. This research provides a foundation for predicting and assessing urban CG lightning risks.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
Short summary
Short summary
In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Xiaoxia Shang, Antti Lipponen, Maria Filioglou, Anu-Maija Sundström, Mark Parrington, Virginie Buchard, Anton S. Darmenov, Ellsworth J. Welton, Eleni Marinou, Vassilis Amiridis, Michael Sicard, Alejandro Rodríguez-Gómez, Mika Komppula, and Tero Mielonen
Atmos. Chem. Phys., 24, 1329–1344, https://doi.org/10.5194/acp-24-1329-2024, https://doi.org/10.5194/acp-24-1329-2024, 2024
Short summary
Short summary
In June 2019, smoke particles from a Canadian wildfire event were transported to Europe. The long-range-transported smoke plumes were monitored with a spaceborne lidar and reanalysis models. Based on the aerosol mass concentrations estimated from the observations, the reanalysis models had difficulties in reproducing the amount and location of the smoke aerosols during the transport event. Consequently, more spaceborne lidar missions are needed for reliable monitoring of aerosol plumes.
Simone Lolli, Michaël Sicard, Francesco Amato, Adolfo Comeron, Cristina Gíl-Diaz, Tony C. Landi, Constantino Munoz-Porcar, Daniel Oliveira, Federico Dios Otin, Francesc Rocadenbosch, Alejandro Rodriguez-Gomez, Andrés Alastuey, Xavier Querol, and Cristina Reche
Atmos. Chem. Phys., 23, 12887–12906, https://doi.org/10.5194/acp-23-12887-2023, https://doi.org/10.5194/acp-23-12887-2023, 2023
Short summary
Short summary
We evaluated the long-term trends and seasonal variability of the vertically resolved aerosol properties over the past 17 years in Barcelona. Results shows that air quality is improved, with a consistent drop in PM concentrations at the surface, as well as the column aerosol optical depth. The results also show that natural dust outbreaks are more likely in summer, with aerosols reaching an altitude of 5 km, while in winter, aerosols decay as an exponential with a scale height of 600 m.
Peng Xian, Jianglong Zhang, Norm T. O'Neill, Travis D. Toth, Blake Sorenson, Peter R. Colarco, Zak Kipling, Edward J. Hyer, James R. Campbell, Jeffrey S. Reid, and Keyvan Ranjbar
Atmos. Chem. Phys., 22, 9915–9947, https://doi.org/10.5194/acp-22-9915-2022, https://doi.org/10.5194/acp-22-9915-2022, 2022
Short summary
Short summary
The study provides baseline Arctic spring and summertime aerosol optical depth climatology, trend, and extreme event statistics from 2003 to 2019 using a combination of aerosol reanalyses, remote sensing, and ground observations. Biomass burning smoke has an overwhelming contribution to black carbon (an efficient climate forcer) compared to anthropogenic sources. Burning's large interannual variability and increasing summer trend have important implications for the Arctic climate.
Peng Xian, Jianglong Zhang, Norm T. O'Neill, Jeffrey S. Reid, Travis D. Toth, Blake Sorenson, Edward J. Hyer, James R. Campbell, and Keyvan Ranjbar
Atmos. Chem. Phys., 22, 9949–9967, https://doi.org/10.5194/acp-22-9949-2022, https://doi.org/10.5194/acp-22-9949-2022, 2022
Short summary
Short summary
The study provides a baseline Arctic spring and summertime aerosol optical depth climatology, trend, and extreme event statistics from 2003 to 2019 using a combination of aerosol reanalyses, remote sensing, and ground observations. Biomass burning smoke has an overwhelming contribution to black carbon (an efficient climate forcer) compared to anthropogenic sources. Burning's large interannual variability and increasing summer trend have important implications for the Arctic climate.
Lian Zong, Yuanjian Yang, Haiyun Xia, Meng Gao, Zhaobin Sun, Zuofang Zheng, Xianxiang Li, Guicai Ning, Yubin Li, and Simone Lolli
Atmos. Chem. Phys., 22, 6523–6538, https://doi.org/10.5194/acp-22-6523-2022, https://doi.org/10.5194/acp-22-6523-2022, 2022
Short summary
Short summary
Heatwaves (HWs) paired with higher ozone (O3) concentration at surface level pose a serious threat to human health. Taking Beijing as an example, three unfavorable synoptic weather patterns were identified to dominate the compound HW and O3 pollution events. Under the synergistic stress of HWs and O3 pollution, public mortality risk increased, and synoptic patterns and urbanization enhanced the compound risk of events in Beijing by 33.09 % and 18.95 %, respectively.
Martin J. Osborne, Johannes de Leeuw, Claire Witham, Anja Schmidt, Frances Beckett, Nina Kristiansen, Joelle Buxmann, Cameron Saint, Ellsworth J. Welton, Javier Fochesatto, Ana R. Gomes, Ulrich Bundke, Andreas Petzold, Franco Marenco, and Jim Haywood
Atmos. Chem. Phys., 22, 2975–2997, https://doi.org/10.5194/acp-22-2975-2022, https://doi.org/10.5194/acp-22-2975-2022, 2022
Short summary
Short summary
Using the Met Office NAME dispersion model, supported by satellite- and ground-based remote-sensing observations, we describe the dispersion of aerosols from the 2019 Raikoke eruption and the concurrent wildfires in Alberta Canada. We show how the synergy of dispersion modelling and multiple observation sources allowed observers in the London VAAC to arrive at a more complete picture of the aerosol loading at altitudes commonly used by aviation.
Gemine Vivone, Giuseppe D'Amico, Donato Summa, Simone Lolli, Aldo Amodeo, Daniele Bortoli, and Gelsomina Pappalardo
Atmos. Chem. Phys., 21, 4249–4265, https://doi.org/10.5194/acp-21-4249-2021, https://doi.org/10.5194/acp-21-4249-2021, 2021
Short summary
Short summary
We developed a methodology to retrieve the atmospheric boundary layer height from elastic and multi-wavelength lidar observations that uses a new approach based on morphological image processing techniques. The intercomparison with other state-of-the-art algorithms shows on average 30 % improved performance. The algorithm also shows excellent performance with respect to the running time, i.e., just few seconds to execute the whole signal processing chain over 72 h of continuous measurements.
Yan Yu, Olga V. Kalashnikova, Michael J. Garay, Huikyo Lee, Myungje Choi, Gregory S. Okin, John E. Yorks, James R. Campbell, and Jared Marquis
Atmos. Chem. Phys., 21, 1427–1447, https://doi.org/10.5194/acp-21-1427-2021, https://doi.org/10.5194/acp-21-1427-2021, 2021
Short summary
Short summary
Given the current uncertainties in the simulated diurnal variability of global dust mobilization and concentration, observational characterization of the variations in dust mobilization and concentration will provide a valuable benchmark for evaluating and constraining such model simulations. The current study investigates the diurnal cycle of dust loading across the global tropics, subtropics, and mid-latitudes by analyzing aerosol observations from the International Space Station.
Jianglong Zhang, Robert J. D. Spurr, Jeffrey S. Reid, Peng Xian, Peter R. Colarco, James R. Campbell, Edward J. Hyer, and Nancy L. Baker
Geosci. Model Dev., 14, 27–42, https://doi.org/10.5194/gmd-14-27-2021, https://doi.org/10.5194/gmd-14-27-2021, 2021
Short summary
Short summary
A first-of-its-kind scheme has been developed for assimilating Ozone Monitoring Instrument (OMI) aerosol index (AI) measurements into the Naval Aerosol Analysis and Predictive System. Improvements in model simulations demonstrate the utility of OMI AI data assimilation for improving the accuracy of aerosol model analysis over cloudy regions and bright surfaces. This study can be considered one of the first attempts at direct radiance assimilation in the UV spectrum for aerosol analyses.
Yan Yu, Olga V. Kalashnikova, Michael J. Garay, Huikyo Lee, Myungje Choi, Gregory S. Okin, John E. Yorks, and James R. Campbell
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-975, https://doi.org/10.5194/acp-2019-975, 2019
Preprint withdrawn
Short summary
Short summary
Given the current uncertainties in the simulated diurnal variability of global dust mobilization and concentration, observational characterization of the variations in dust mobilization and concentration will provide a valuable benchmark for evaluating and constraining such model simulations. The current study investigates the diurnal cycle of dust loading across the global tropics, sub-tropics, and mid-latitudes by analyzing aerosol observations from the International Space Station.
David M. Giles, Alexander Sinyuk, Mikhail G. Sorokin, Joel S. Schafer, Alexander Smirnov, Ilya Slutsker, Thomas F. Eck, Brent N. Holben, Jasper R. Lewis, James R. Campbell, Ellsworth J. Welton, Sergey V. Korkin, and Alexei I. Lyapustin
Atmos. Meas. Tech., 12, 169–209, https://doi.org/10.5194/amt-12-169-2019, https://doi.org/10.5194/amt-12-169-2019, 2019
Short summary
Short summary
Clouds or instrumental anomalies may perturb ground-based solar measurements used to calculate aerosol optical depth (AOD). This study presents a new algorithm of automated near-real-time (NRT) quality controls with improved cloud screening for AERONET AOD measurements. Results from the new and old algorithms have excellent agreement for the highest-quality AOD level, while the new algorithm provides higher-quality NRT AOD for applications such as data assimilation and satellite evaluation.
Mayra I. Oyola, James R. Campbell, Peng Xian, Anthony Bucholtz, Richard A. Ferrare, Sharon P. Burton, Olga Kalashnikova, Benjamin C. Ruston, and Simone Lolli
Atmos. Chem. Phys., 19, 205–218, https://doi.org/10.5194/acp-19-205-2019, https://doi.org/10.5194/acp-19-205-2019, 2019
Short summary
Short summary
We conceptualized the aerosol radiative impact of an inline aerosol analysis field coupled with a global meteorological forecast system utilizing NAAPS and NAVGEM analysis and surface albedo fields. Model simulations were compared with in situ validation data collected during the NASA 2013 SEAC4RS experiment. Instantaneous heating rates peaked around 7 K day-1 in the lower part of the troposphere, while the HSRL profiles resulted in values of up to 18 K day-1 in the in the mid-troposphere.
Fabio Madonna, Marco Rosoldi, Simone Lolli, Francesco Amato, Joshua Vande Hey, Ranvir Dhillon, Yunhui Zheng, Mike Brettle, and Gelsomina Pappalardo
Atmos. Meas. Tech., 11, 2459–2475, https://doi.org/10.5194/amt-11-2459-2018, https://doi.org/10.5194/amt-11-2459-2018, 2018
Short summary
Short summary
The accurate monitoring of climate based on the use of low-cost and low-maintenance automatic system represents one of the challenges for the scientific community and instrument manufacturers for the next decade. In the frame of two experiments, INTERACT and INTERACT-II, taking place at CIAO (CNR-IMAA Atmospheric Observatory) in Tito Scalo, Potenza, Italy, commercial low-cost lidars have been compared with advanced lidar systems to assess their performances.
Simone Lolli, Fabio Madonna, Marco Rosoldi, James R. Campbell, Ellsworth J. Welton, Jasper R. Lewis, Yu Gu, and Gelsomina Pappalardo
Atmos. Meas. Tech., 11, 1639–1651, https://doi.org/10.5194/amt-11-1639-2018, https://doi.org/10.5194/amt-11-1639-2018, 2018
Short summary
Short summary
We evaluate the comparability of aerosol and cloud vertically resolved optical properties obtained with varying lidar profiling techniques and/or data processing methodologies. The discrepancies are assessed by evaluating climate-sensitive direct radiative effects, computed by radiative transfer code means. Results show important discrepancies up to 0.8 W m−2 due to lidar data smoothing in cirrus clouds and a 0.05 W m−2 difference between Raman and elastic lidar technique on a dust layer aloft.
Travis D. Toth, James R. Campbell, Jeffrey S. Reid, Jason L. Tackett, Mark A. Vaughan, Jianglong Zhang, and Jared W. Marquis
Atmos. Meas. Tech., 11, 499–514, https://doi.org/10.5194/amt-11-499-2018, https://doi.org/10.5194/amt-11-499-2018, 2018
Longtao Wu, Hui Su, Olga V. Kalashnikova, Jonathan H. Jiang, Chun Zhao, Michael J. Garay, James R. Campbell, and Nanpeng Yu
Atmos. Chem. Phys., 17, 7291–7309, https://doi.org/10.5194/acp-17-7291-2017, https://doi.org/10.5194/acp-17-7291-2017, 2017
Short summary
Short summary
The WRF-Chem simulation successfully captures aerosol variations in the cold season in the San Joaquin Valley (SJV) but has poor performance in the warm season. High-resolution model simulation can better resolve nonhomogeneous distribution of anthropogenic emissions in urban areas, resulting in better simulation of aerosols in the cold season in the SJV. Poor performance of the WRF-Chem model in the warm season in the SJV is mainly due to misrepresentation of dust emission and vertical mixing.
Simone Lolli, James R. Campbell, Jasper R. Lewis, Yu Gu, and Ellsworth J. Welton
Atmos. Chem. Phys., 17, 7025–7034, https://doi.org/10.5194/acp-17-7025-2017, https://doi.org/10.5194/acp-17-7025-2017, 2017
Short summary
Short summary
We compare net TOA radiative forcing between the simplified Corti–Peter (CP) and relatively complex Fu–Liou–Gu models for cirrus clouds observed by NASA MPLNET at Singapore in 2010–11 and Greenbelt, Maryland, in 2012. We find daytime forcing discrepancies up to 65 % between the two, which is greater than previous studies. In some cases, the sign of net TOA daytime forcing also differs. We attribute model differences to numerical simplifications in CP via regression that are not valid globally.
Jeffrey S. Reid, Peng Xian, Brent N. Holben, Edward J. Hyer, Elizabeth A. Reid, Santo V. Salinas, Jianglong Zhang, James R. Campbell, Boon Ning Chew, Robert E. Holz, Arunas P. Kuciauskas, Nofel Lagrosas, Derek J. Posselt, Charles R. Sampson, Annette L. Walker, E. Judd Welton, and Chidong Zhang
Atmos. Chem. Phys., 16, 14041–14056, https://doi.org/10.5194/acp-16-14041-2016, https://doi.org/10.5194/acp-16-14041-2016, 2016
Short summary
Short summary
This paper describes aspects of the 2012 7 Southeast Asian Studies (7SEAS) operations period, the largest within the Maritime Continent. Included were an enhanced deployment of Aerosol Robotic Network (AERONET) sun photometers, multiple lidars, and a Singapore supersite. Simultaneously, a ship was dispatched to the Palawan Archipelago and Sulu Sea of the Philippines for September 2012 to observe transported smoke and pollution as it entered the southwest monsoon trough.
Jeffrey S. Reid, Nofel D. Lagrosas, Haflidi H. Jonsson, Elizabeth A. Reid, Samuel A. Atwood, Thomas J. Boyd, Virendra P. Ghate, Peng Xian, Derek J. Posselt, James B. Simpas, Sherdon N. Uy, Kimo Zaiger, Donald R. Blake, Anthony Bucholtz, James R. Campbell, Boon Ning Chew, Steven S. Cliff, Brent N. Holben, Robert E. Holz, Edward J. Hyer, Sonia M. Kreidenweis, Arunas P. Kuciauskas, Simone Lolli, Min Oo, Kevin D. Perry, Santo V. Salinas, Walter R. Sessions, Alexander Smirnov, Annette L. Walker, Qing Wang, Liya Yu, Jianglong Zhang, and Yongjing Zhao
Atmos. Chem. Phys., 16, 14057–14078, https://doi.org/10.5194/acp-16-14057-2016, https://doi.org/10.5194/acp-16-14057-2016, 2016
Short summary
Short summary
This paper describes aspects of the 2012 7 Southeast Asian Studies (7SEAS) operations period, the largest within the Maritime Continent. Included were an enhanced deployment of Aerosol Robotic Network (AERONET) sun photometers, multiple lidars, and a Singapore supersite. Simultaneously, a ship was dispatched to the Palawan Archipelago and Sulu Sea of the Philippines for September 2012 to observe transported smoke and pollution as it entered the southwest monsoon trough.
Peng Lynch, Jeffrey S. Reid, Douglas L. Westphal, Jianglong Zhang, Timothy F. Hogan, Edward J. Hyer, Cynthia A. Curtis, Dean A. Hegg, Yingxi Shi, James R. Campbell, Juli I. Rubin, Walter R. Sessions, F. Joseph Turk, and Annette L. Walker
Geosci. Model Dev., 9, 1489–1522, https://doi.org/10.5194/gmd-9-1489-2016, https://doi.org/10.5194/gmd-9-1489-2016, 2016
Short summary
Short summary
An 11-year, 1-degree aerosol reanalysis is presented for use in studies of aerosol effects on climate and atmospheric processes. The reanalysis uses the Navy Aerosol Analysis and Prediction System, constrained by aerosol optical thickness (AOT) data from NASA sensors. Fine and coarse mode AOT at 550 nm agrees well with ground-based measurements, and reproduces the decadal AOT trends found using standalone satellite products. This dataset is a resource for basic and applied science research.
R. Alfaro-Contreras, J. Zhang, J. R. Campbell, and J. S. Reid
Atmos. Chem. Phys., 16, 47–69, https://doi.org/10.5194/acp-16-47-2016, https://doi.org/10.5194/acp-16-47-2016, 2016
Short summary
Short summary
The spatial distributions and trends of above-cloud aerosol (ACA) events are studied using seven and a half years of MODIS, OMI, and CALIOP data. The active- (CALIOP) and passive-based (MODIS-OMI) methods have their advantages and caveats, and thus both are used to get a thorough and robust comparison of ACA distribution and climatology. For the first time, baseline above-cloud CALIOP aerosol optical depth and OMI aerosol index thresholds are derived and examined for each sensor.
E. P. Nowottnick, P. R. Colarco, E. J. Welton, and A. da Silva
Atmos. Meas. Tech., 8, 3647–3669, https://doi.org/10.5194/amt-8-3647-2015, https://doi.org/10.5194/amt-8-3647-2015, 2015
V. Buchard, A. M. da Silva, P. R. Colarco, A. Darmenov, C. A. Randles, R. Govindaraju, O. Torres, J. Campbell, and R. Spurr
Atmos. Chem. Phys., 15, 5743–5760, https://doi.org/10.5194/acp-15-5743-2015, https://doi.org/10.5194/acp-15-5743-2015, 2015
Short summary
Short summary
MERRAero is an aerosol reanalysis based on the GEOS-5 earth system model that incorporates an online aerosol module and assimilation of AOD from MODIS sensors. This study assesses the quality of MERRAero absorption using independent OMI observations. In addition to comparisons to OMI absorption AOD, we have developed a radiative transfer interface to simulate the UV aerosol index from assimilated aerosol fields at OMI footprint. Also, we fully diagnose the model using MISR, AERONET and CALIPSO.
J. S. Reid, N. D. Lagrosas, H. H. Jonsson, E. A. Reid, W. R. Sessions, J. B. Simpas, S. N. Uy, T. J. Boyd, S. A. Atwood, D. R. Blake, J. R. Campbell, S. S. Cliff, B. N. Holben, R. E. Holz, E. J. Hyer, P. Lynch, S. Meinardi, D. J. Posselt, K. A. Richardson, S. V. Salinas, A. Smirnov, Q. Wang, L. Yu, and J. Zhang
Atmos. Chem. Phys., 15, 1745–1768, https://doi.org/10.5194/acp-15-1745-2015, https://doi.org/10.5194/acp-15-1745-2015, 2015
Short summary
Short summary
This paper reports on the first measurements of aerosol particles embedded in the convectively active southwest monsoonal flow of the South China Sea. The paper describes the research cruise and discusses how variability in aerosol characteristics relates to regional meteorological phenomena such as and the Madden Julian Oscillation, tropical cyclones, squall lines and the monsoonal flow itself. Of special interest is how aerosol transport relates to meteorological drivers of convective activity.
J. R. Campbell, M. A. Vaughan, M. Oo, R. E. Holz, J. R. Lewis, and E. J. Welton
Atmos. Meas. Tech., 8, 435–449, https://doi.org/10.5194/amt-8-435-2015, https://doi.org/10.5194/amt-8-435-2015, 2015
Short summary
Short summary
Digital thresholds based on 2012 CALIOP satellite lidar measurements are investigated for distinguishing cirrus cloud presence, including cloud top temperatures and heights combined with layer depolarization and phase and optical depths. A cloud top temperature of -37 C is found to exhibit the most stable performance, owing to it being the point of homogeneous liquid-water freezing. Depolarization and phase help but are mostly ambiguous at warmer temperatures where mixed-phase clouds propagate.
T. F. Eck, B. N. Holben, J. S. Reid, A. Arola, R. A. Ferrare, C. A. Hostetler, S. N. Crumeyrolle, T. A. Berkoff, E. J. Welton, S. Lolli, A. Lyapustin, Y. Wang, J. S. Schafer, D. M. Giles, B. E. Anderson, K. L. Thornhill, P. Minnis, K. E. Pickering, C. P. Loughner, A. Smirnov, and A. Sinyuk
Atmos. Chem. Phys., 14, 11633–11656, https://doi.org/10.5194/acp-14-11633-2014, https://doi.org/10.5194/acp-14-11633-2014, 2014
R. P. Aryal, K. J. Voss, P. A. Terman, W. C. Keene, J. L. Moody, E. J. Welton, and B. N. Holben
Atmos. Chem. Phys., 14, 7617–7629, https://doi.org/10.5194/acp-14-7617-2014, https://doi.org/10.5194/acp-14-7617-2014, 2014
T. D. Toth, J. Zhang, J. R. Campbell, E. J. Hyer, J. S. Reid, Y. Shi, and D. L. Westphal
Atmos. Chem. Phys., 14, 6049–6062, https://doi.org/10.5194/acp-14-6049-2014, https://doi.org/10.5194/acp-14-6049-2014, 2014
S. Lolli, A. Delaval, C. Loth, A. Garnier, and P. H. Flamant
Atmos. Meas. Tech., 6, 3349–3358, https://doi.org/10.5194/amt-6-3349-2013, https://doi.org/10.5194/amt-6-3349-2013, 2013
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE radar, lidar, and imager: the CPR_CLP, AC_CLP, and ACM_CLP products
Peering into the heart of thunderstorm clouds: insights from cloud radar and spectral polarimetry
Retrieving cloud-base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI
Discriminating between “drizzle or rain” and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation
3D cloud masking across a broad swath using multi-angle polarimetry and deep learning
Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds
Retrieval of cloud fraction and optical thickness of liquid water clouds over the ocean from multi-angle polarization observations
Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks
Retrieval of cloud fraction using machine learning algorithms based on FY-4A AGRI observations
Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer observations
PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations
An advanced spatial coregistration of cloud properties for the atmospheric Sentinel missions: application to TROPOMI
Contrail altitude estimation using GOES-16 ABI data and deep learning
The Ice Cloud Imager: retrieval of frozen water column properties
Supercooled liquid water cloud classification using lidar backscatter peak properties
Marine cloud base height retrieval from MODIS cloud properties using machine learning
Empirical model for backscattering polarimetric variables in rain at W-band: motivation and implications
Wet-Radome Attenuation in ARM Cloud Radars and Its Utilization in Radar Calibration Using Disdrometer Measurements
How well can brightness temperature differences of spaceborne imagers help to detect cloud phase? A sensitivity analysis regarding cloud phase and related cloud properties
ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data
Simulation and detection efficiency analysis for measurements of polar mesospheric clouds using a spaceborne wide-field-of-view ultraviolet imager
The Chalmers Cloud Ice Climatology: retrieval implementation and validation
The algorithm of microphysical-parameter profiles of aerosol and small cloud droplets based on the dual-wavelength lidar data
Bayesian cloud-top phase determination for Meteosat Second Generation
Mitigation of satellite OCO-2 CO2 biases in the vicinity of clouds with 3D calculations using the Education and Research 3D Radiative Transfer Toolbox (EaR3T)
Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties
Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements
A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations
Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network
A new approach to crystal habit retrieval from far-infrared spectral radiance measurements
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
Algorithm for continual monitoring of fog life cycles based on geostationary satellite imagery as a basis for solar energy forecasting
A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations
Infrared Radiometric Image Classification and Segmentation of Cloud Structure Using Deep-learning Framework for Ground-based Infrared Thermal Camera Observations
Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements
Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar
Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data
The EarthCARE mission: science data processing chain overview
Cloud optical and physical properties retrieval from EarthCARE multi-spectral imager: the M-COP products
Cloud top heights and aerosol columnar properties from combined EarthCARE lidar and imager observations: the AM-CTH and AM-ACD products
Raman lidar-derived optical and microphysical properties of ice crystals within thin Arctic clouds during PARCS campaign
Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements
Deep convective cloud system size and structure across the global tropics and subtropics
A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images
Numerical model generation of test frames for pre-launch studies of EarthCARE's retrieval algorithms and data management system
Segmentation of polarimetric radar imagery using statistical texture
Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: Local optimization
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Y. Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech., 18, 1325–1338, https://doi.org/10.5194/amt-18-1325-2025, https://doi.org/10.5194/amt-18-1325-2025, 2025
Short summary
Short summary
This study introduces the JAXA EarthCARE Level 2 (L2) cloud product using satellite observations and simulated EarthCARE data. The outputs from the product feature a 3D global view of the dominant ice habit categories and corresponding microphysics. Habit and size distribution transitions from cloud to precipitation are quantified by the L2 cloud algorithms. With Doppler data, the products can be beneficial for further understanding of the coupling of cloud microphysics, radiation, and dynamics.
Ho Yi Lydia Mak and Christine Unal
Atmos. Meas. Tech., 18, 1209–1242, https://doi.org/10.5194/amt-18-1209-2025, https://doi.org/10.5194/amt-18-1209-2025, 2025
Short summary
Short summary
The dynamics of thunderclouds are studied using cloud radar. Supercooled liquid water and conical graupel are likely present, while chain-like ice crystals may occur at cloud top. Ice crystals are vertically aligned seconds before lightning and resume their usual horizontal alignment afterwards in some cases. Updrafts and downdrafts are found near cloud core and edges respectively. Turbulence is strong. Radar measurement modes that are more suited for investigating thunderstorms are recommended.
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech., 18, 773–792, https://doi.org/10.5194/amt-18-773-2025, https://doi.org/10.5194/amt-18-773-2025, 2025
Short summary
Short summary
In satellite remote sensing, estimating cloud-base height (CBH) is more challenging than estimating cloud-top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI on JAXA’s GCOM-C satellite to estimate CBHs together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
Atmos. Meas. Tech., 18, 487–508, https://doi.org/10.5194/amt-18-487-2025, https://doi.org/10.5194/amt-18-487-2025, 2025
Short summary
Short summary
We present a technique to discriminate between the Cloudnet target classification of "drizzle or rain" and sea salt aerosols that is applicable to marine Cloudnet sites. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for measurements over the Barbados Cloud Observatory (BCO). A first-ever analysis of the Cloudnet product including the new "haze echo" target over 2 years at the BCO is presented.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 18, 73–91, https://doi.org/10.5194/amt-18-73-2025, https://doi.org/10.5194/amt-18-73-2025, 2025
Short summary
Short summary
Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we found that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024, https://doi.org/10.5194/amt-17-7129-2024, 2024
Short summary
Short summary
This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine learning method. Retrievals from a machine learning algorithm are used to provide a priori states, and a radiative transfer model is used to create lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and it is applicable to both daytime and nighttime conditions.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024, https://doi.org/10.5194/amt-17-7027-2024, 2024
Short summary
Short summary
Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024, https://doi.org/10.5194/amt-17-6965-2024, 2024
Short summary
Short summary
This article presents a novel technique to estimate liquid water content (LWC) profiles in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows retrieving the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6769–6789, https://doi.org/10.5194/amt-17-6769-2024, https://doi.org/10.5194/amt-17-6769-2024, 2024
Short summary
Short summary
We introduce an innovative method to retrieve the cloud fraction and optical thickness of liquid water clouds over the ocean based on polarimetry. This is well suited for satellite observations providing multi-angle polarization measurements. Cloud fraction and cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and one in the sun glint region.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloé David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
Atmos. Meas. Tech., 17, 6707–6734, https://doi.org/10.5194/amt-17-6707-2024, https://doi.org/10.5194/amt-17-6707-2024, 2024
Short summary
Short summary
This study demonstrates the potential of enhancing severe-hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe-hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
Jinyi Xia and Li Guan
Atmos. Meas. Tech., 17, 6697–6706, https://doi.org/10.5194/amt-17-6697-2024, https://doi.org/10.5194/amt-17-6697-2024, 2024
Short summary
Short summary
This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP) algorithms. The results demonstrate excellent performance in distinguishing clear-sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Yuli Liu and Ian S. Adams
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-188, https://doi.org/10.5194/amt-2024-188, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
This paper presents our latest development in tomographic cloud reconstruction algorithms that use multi-angle TB observations to reconstruct the spatial distribution of ice clouds. Compared to nadir-only retrievals, the tomographic technique provides a detailed reconstruction of ice clouds’ inner structure with high spatial resolution and significantly improves retrieval accuracy. Also, the tomography technique effectively increases detection sensitivity for small ice cloud particles.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
Short summary
Short summary
In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
Short summary
Short summary
This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024, https://doi.org/10.5194/amt-17-6145-2024, 2024
Short summary
Short summary
Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
Short summary
Short summary
The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
Short summary
Short summary
Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
Short summary
Short summary
Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Alexander Myagkov, Tatiana Nomokonova, and Michael Frech
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-143, https://doi.org/10.5194/amt-2024-143, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
The study examines the use of the spheroidal shape approximation for calculating cloud radar observables in rain and identifies some limitations. To address these, it introduces the empirical scattering model (EMS) based on high-quality Doppler spectra from a 94 GHz radar. The ESM offers improved accuracy and directly incorporates natural rain's microphysical effects. This new model can enhance retrieval and calibration methods, benefiting cloud radar polarimetry experts and scattering modelers.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-2615, https://doi.org/10.5194/egusphere-2024-2615, 2024
Short summary
Short summary
A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation (WRA) logarithmic behavior as a function of rainfall rates in light and moderate rain conditions. This WRA technique is applied to the measurements during the ARM TRACER campaign and reports Ze offsets that compare favorably with results from other traditional calibration methods.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
Short summary
Short summary
This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
Short summary
Short summary
ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
Short summary
Short summary
Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
Short summary
Short summary
The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
Short summary
Short summary
This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
Short summary
Short summary
ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Yu-Wen Chen, K. Sebastian Schmidt, Hong Chen, Steven T. Massie, Susan S. Kulawik, and Hironobu Iwabuchi
EGUsphere, https://doi.org/10.5194/egusphere-2024-1936, https://doi.org/10.5194/egusphere-2024-1936, 2024
Short summary
Short summary
Retrievals of CO2 column-averaged dry air mole fractions from space can be done with spaceborne spectrometers such as OCO-2. Clouds in the vicinity of a footprint lead to spectral perturbations that bias those retrievals well beyond the required accuracy for global assessments of CO2 sources and sinks. This paper presents two physics-based mitigation techniques for these biases based on accompanying imagery, which can be used operationally.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
Short summary
Short summary
Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
Short summary
Short summary
The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
Short summary
Short summary
This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
Short summary
Short summary
Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
Short summary
Short summary
This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
Short summary
Short summary
We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak
EGUsphere, https://doi.org/10.5194/egusphere-2023-2885, https://doi.org/10.5194/egusphere-2023-2885, 2024
Short summary
Short summary
Fog and low stratus (FLS) are both persistent clouds close to the Earth’s surface. In the context of photovoltaic power production, FLS is particularly important, as FLS, impact large regions simultaneously, making regional power grid balancing hard. This study introduces a new machine leanring based algorithm developed for the MSG geostationary satellites that can provide a coherent and detailed view of FLS development over large areas over the 24 H day cycle.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
Short summary
Short summary
We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
EGUsphere, https://doi.org/10.5194/egusphere-2024-101, https://doi.org/10.5194/egusphere-2024-101, 2024
Short summary
Short summary
Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observations experiments.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
Short summary
Short summary
In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
Short summary
Short summary
The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, https://doi.org/10.5194/amt-17-961-2024, 2024
Short summary
Short summary
We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, https://doi.org/10.5194/amt-17-839-2024, 2024
Short summary
Short summary
The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is an ESA–JAXA satellite mission to be launched in 2024. We presented an overview of the EarthCARE processors' development, with processors developed by teams in Europe, Japan, and Canada. EarthCARE will allow scientists to evaluate the representation of cloud, aerosol, precipitation, and radiative flux in weather forecast and climate models, with the objective to better understand cloud processes and improve weather and climate models.
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
Short summary
Short summary
The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
Short summary
Short summary
The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Patrick Chazette and Jean-Christophe Raut
Atmos. Meas. Tech., 16, 5847–5861, https://doi.org/10.5194/amt-16-5847-2023, https://doi.org/10.5194/amt-16-5847-2023, 2023
Short summary
Short summary
The vertical profiles of the effective radii of ice crystals and ice water content in Arctic semi-transparent stratiform clouds were assessed using quantitative ground-based lidar measurements. The field campaign was part of the Pollution in the ARCtic System (PARCS) project which took place from 13 to 26 May 2016 in Hammerfest (70° 39′ 48″ N, 23° 41′ 00″ E). We show that under certain cloud conditions, lidar measurement combined with a dedicated algorithmic approach is an efficient tool.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
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.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, https://doi.org/10.5194/amt-16-5387-2023, 2023
Short summary
Short summary
A new database is constructed from over 20 years of satellite records that comprises millions of deep convective clouds and spans the global tropics and subtropics. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast
Atmos. Meas. Tech., 16, 5305–5326, https://doi.org/10.5194/amt-16-5305-2023, https://doi.org/10.5194/amt-16-5305-2023, 2023
Short summary
Short summary
Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
Short summary
Short summary
The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
Atmos. Meas. Tech., 16, 4571–4588, https://doi.org/10.5194/amt-16-4571-2023, https://doi.org/10.5194/amt-16-4571-2023, 2023
Short summary
Short summary
We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Hadrien Verbois, Yves-Marie Saint-Drenan, Vadim Becquet, Benoit Gschwind, and Philippe Blanc
Atmos. Meas. Tech., 16, 4165–4181, https://doi.org/10.5194/amt-16-4165-2023, https://doi.org/10.5194/amt-16-4165-2023, 2023
Short summary
Short summary
Solar surface irradiance (SSI) estimations inferred from satellite images are essential to gain a comprehensive understanding of the solar resource, which is crucial in many fields. This study examines the recent data-driven methods for inferring SSI from satellite images and explores their strengths and weaknesses. The results suggest that while these methods show great promise, they sometimes dramatically underperform and should probably be used in conjunction with physical approaches.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, https://doi.org/10.5194/amt-16-3931-2023, 2023
Short summary
Short summary
We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Cited articles
Campbell, J. R. and Sassen, K.: Polar stratospheric clouds at the South
Pole from 5 years of continuous lidar data: Macrophysical, optical and
thermodynamic properties, J. Geophys. Res., 113, D20204,
https://doi.org/10.1029/2007JD009680, 2008.
Campbell, J. R., Hlavka, D. L., Welton, E. J., Flynn, C. J., Turner, D. D.,
Spinhirne, J. D., Scott, V. S., and Hwang, I. H.: Full-time, eye-safe cloud
and aerosol lidar observation at Atmosphere Radiation Measurement program
sites: Instrument and data processing, J. Atmos. Ocean. Tech., 19,
431–442, https://doi.org/10.1175/1520-0426(2002)019<0431:FTESCA>2.0.CO;2, 2002.
Campbell, J. R., Welton, E. J., Spinhirne, J. D., Ji, Q., Tsay, S.-C.,
Piketh, S. J., Barenbrug, M., and Holben, B. N.: Micropulse lidar
observations of tropospheric aerosols over northeastern South Africa during
the ARREX and SAFARI 2000 dry season experiments, J. Geophys. Res.,
108, 8497, https://doi.org/10.1029/2002JD002563, 2003.
Campbell, J. R., Vaughan, M. A., Oo, M., Holz, R. E., Lewis, J. R., and Welton, E. J.: Distinguishing cirrus cloud presence in autonomous lidar measurements, Atmos. Meas. Tech., 8, 435–449, https://doi.org/10.5194/amt-8-435-2015, 2015.
Campbell, J. R., Lolli, S., Lewis, J. R., Gu, Y., and Welton, E. J.: Daytime
cirrus cloud top-of-atmosphere radiative forcing properties at a midlatitude
site and their global consequence, J. Appl. Meteorol. Clim., 55,
1667–1679, https://doi.org/10.1175/JAMC-D-15-0217.1, 2016.
Campbell, J. R., Dolinar, E. K., Lolli, S., Fochesatto, G. J., Gu, Y.,
Lewis, J. R., Marquis, J. W., McHardy, T. M., Ryglicki, D. R., and Welton,
E. J.: Cirrus cloud top-of-the-atmosphere net daytime forcing in the Alaskan
subarctic from ground-based MPLNET monitoring, J. Appl. Meteorol. Clim., 99, 27–32,
https://doi.org/10.1175/JAMC-D-20-0077.1, 2020.
Choi, Y.-S., Lindzen, R. S., Ho, C.-H., and Kim, J.: Space observations of
cold-cloud phase change, P. Natl. Acad. Sci. USA, 107, 11211–11216,
https://doi.org/10.1073/pnas.1006241107, 2010.
Cohen, A., Neumann, J., and Low, W.: An Experimental Determination of the
Depolarization of Scattered Laser Light by Atmospheric Air, J. Appl.
Meteorol., 8, 952–954, https://doi.org/10.1175/1520-0450(1969)008<0952:AEDOTD>2.0.CO;2, 1969.
Coopman, Q., Riedi, J., Zeng, S., and Garrett, T. J.: Space-Based Analysis
of the Cloud Thermodynamic Phase Transition for Varying Microphysical and
Meteorological Regimes, Geophys. Res. Lett., 47, e2020GL087122,
https://doi.org/10.1029/2020GL087122, 2020.
Costa, A., Meyer, J., Afchine, A., Luebke, A., Günther, G., Dorsey, J. R., Gallagher, M. W., Ehrlich, A., Wendisch, M., Baumgardner, D., Wex, H., and Krämer, M.: Classification of Arctic, midlatitude and tropical clouds in the mixed-phase temperature regime, Atmos. Chem. Phys., 17, 12219–12238, https://doi.org/10.5194/acp-17-12219-2017, 2017.
Flynn, C. J., Mendoza, A., Zheng, Y., and Mathur, S.: Novel
polarization-sensitive micropulse lidar measurement technique, Opt. Express,
15, 2785–2790, https://doi.org/10.1364/OE.15.002785, 2007.
Furtado, K., Field, P. R., Boutle, I. A., Morcrette, C. J., and Wilkinson,
J. M.: A physically based subgrid parameterization for the production and maintenance of mixed-phase clouds in a general circulation model, J. Atmos. Sci., 73, 279–291, https://doi.org/10.1175/JAS-D-15-0021.1, 2016.
Hogan, R. J., Illingworth, A. J., O'Connor, E. J., and Poiares Baptista, J.
P. V.: Characteristics of mixed-phase clouds. II: A climatology from
ground-based lidar, Q. J. Roy. Meteor. Soc., 129, 2117–2134,
https://doi.org/10.1256/qj.01.209, 2003.
Holben, B. N., Eck, T. F., Slutsker, I., Tanré, D., Buis, J. P., Setzer,
A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F.,
Jankowiak, I., and Smirnov, A.: AERONET – A federated instrument network and
data archive for aerosol characterization, Remote Sens. Environ., 66, 1–16,
1998.
Hu, Y., Liu, Z., Winker, D., Vaughan, M., Noel, V., Bissonnette, L., Roy,
G., and McGill, M.: Simple relationship between lidar multiple scattering
and depolarization for water clouds, Opt. Lett., 31, 1809–1811, 2006.
Hu, Y., Rodier, S., Xu, K., Sun, W., Huang, J., Lin, B., Zhai, P., and
Josset, D.: Occurrence, liquid water content, and fraction of supercooled water clouds from combined CALIOP/IIR/MODIS measurements, J. Geophys. Res., 115, D00H34, https://doi.org/10.1029/2009JD012384, 2010.
IPCC: Climate Change 2013: The Physical Science Basis, Contribution of
Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, 1535 pp., 2013.
Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.-K., Hnilo, J., Fiorino,
M., and Potter, G. L.: NCEP-DOE AMIP-II Reanalysis (R-2), B. Am.
Meteorol. Soc., 83, 1631–1643, https://doi.org/10.1175/BAMS-83-11-1631, 2002.
Kirin, O. A., Lupyan, E. A., Uvarov, I. A., and Kramareva, L. S.: The
eruption of the volcano Raikoke June 21, 2019, Modern problems of remote
sensing of the Earth from space, 16, 303–307,
https://doi.org/10.21046/2070-7401-2019-16-3-303-307, 2019.
Korolev, A., McFarquhar, G., Field, P. R., Franklin, C., Lawson, P., Wang,
Z., Williams, E., Abel, S. J., Axisa, D., Borrmann, S., Crosier, J., Fugal,
J., Krämer, M., Lohmann, U., Schlenczek, O., Schnaiter, M., and
Wendisch, M.: Mixed-Phase Clouds: Progress and Challenges, Meteor.
Mon., 58, 5.1–5.50,
https://doi.org/10.1175/AMSMONOGRAPHS-D-17-0001.1, 2017.
Lewis, J. R., Campbell, J. R., Welton, E. J., Stewart, S. A., and Haftings, P. C.: Overview of MPLNET Version 3 Cloud Detection, J. Atmos. Ocean. Tech.,
33, 2113–2134, https://doi.org/10.1175/JTECH-D-15-0190.1, 2016.
Lewis, J. R., Campbell, J. R., Welton, E. J., and Haftings, P. C.: MPLNET L15_CLD, available at: https://mplnet.gsfc.nasa.gov/out/data, last access: 15 December 2020.
Lolli, S., Welton, E. J., and Campbell, J. R.: Evaluating Light Rain Drop
Size Estimates from Multiwavelength Micropulse Lidar Network Profiling, J.
Atmos. Ocean. Tech., 30, 2798–2807,
https://doi.org/10.1175/JTECH-D-13-00062.1, 2013.
Lolli, S., Campbell, J. R., Lewis, J. R., Gu, Y., Marquis, J. W., Chew, B.
N., Liew, S., Salinas, S. V., and Welton, E. J.: Daytime
Top-of-the-Atmosphere Cirrus Cloud Radiative Forcing Properties at
Singapore, J. Appl. Meteorol. Clim., 56, 1249–1257,
https://doi.org/10.1175/JAMC-D-16-0262.1, 2017.
Lolli, S., Vivone, G., Lewis, J. R., Sicard, M., Welton, E. J., Campbell, J.
R., Comerón, A., D'Adderio, L. P., Tokay, A., Giunta, A., and Pappalardo,
G.: Overview of the New Version 3 NASA Micro-Pulse Lidar Network (MPLNET)
Automatic Precipitation Detection Algorithm, Remote Sens., 12, 71,
https://doi.org/10.3390/rs12010071, 2020.
Matus, A. V. and L'Ecuyer, T. S.: The role of cloud phase in Earth's
radiation budget, J. Geophys. Res.-Atmos., 122, 2559–2578,
https://doi.org/10.1002/2016JD025951, 2017.
Molod, A., Takacs, L., Suarez, M., Bacmeister, J., Song, I.-S., and
Eichmann, A.: The GEOS-5 atmospheric general circulation model: Mean climate
and development from MERRA to Fortuna, NASA Tech. Rep. Series on Global
Modeling and Data Assimilation, NASA/TM-2012-104606, Vol. 28, NASA Goddard
Space Flight Center, 117 pp., 2012.
Pal, S. R. and Carswell, A. I.: Polarization properties of lidar
backscattering from clouds, Appl. Opt., 12, 1530–1535, 1973.
Pal, S. R., Steinbrecht, W., and Carswell, A. I.: Automated method for lidar
determination of cloud base height and vertical extent, Appl. Opt., 31, 1488–1494, https://doi.org/10.1364/AO.31.001488, 1992.
Peterson, D. A., Fromm, M. D., Solbrig, J. E., Hyer, E. J., Surratt, M. L., and Campbell, J. R.: Detection and inventory of intense pyroconvection in
western North America using GOES-15 daytime infrared data, J. Appl. Meteorol.
Clim., 56, 471–493, https://doi.org/10.1175/JAMC-D-16-0226.1, 2017.
Peterson, D. A., Campbell, J. R., Hyer, E. J., Fromm, M. D., Kablick III, G.
P., Cossuth, J. H., and DeLand, M. T.: Wildfire-driven thunderstorms cause a
volcano-like stratospheric injection of smoke, npj Clim. Atmos. Sci., 1, 30,
https://doi.org/10.1038/s41612-018-0039-3, 2018.
Platt, C. M., Young, S. A., Carswell, A. I., Pal, S. R., McCormick, M. P.,
Winker, D. M., DelGuasta, M., Stefanutti, L., Eberhard, W. L., Hardesty, M.,
Flamant, P. H., Valentin, R., Forgan, B., Gimmestad, G. G., Jäger, H.,
Khmelevtsov, S. S., Kolev, I., Kaprieolev, B., Lu, D., Sassen, K.,
Shamanaev, V. S., Uchino, O., Mizuno, Y., Wandinger, U., Weitkamp, C.,
Ansmann, A., and Wooldridge, C.: The Experimental Cloud Lidar Pilot Study
(ECLIPS) for cloud–radiation research, B. Am. Meteorol. Soc., 75,
1635–1654, https://doi.org/10.1175/1520-0477(1994)075<1635:TECLPS>2.0.CO;2, 1994.
Ramanathan, V., Cess, R. D., Harrison, E. F., Minnis, P., Barkstrom, B. R.,
Ahmad, E., and Hartmann, D.: Cloud-radiative forcing and climate: Results from the Earth Radiation Budget Experiment, Science, 243, 57–63, https://doi.org/10.1126/science.243.4887.57, 1989.
Rienecker, M. M., Suarez, M. J., Todling, R., Bacemeister, J., Takacs, L.,
Liu, H.-C., Gu, W., Sienkiewicz, M., Koster, R. D., Gelaro, R., Stanjer, I., and Nielsen, J. E.: The GEOS-5 data assimilation system – Documentation of
versions 5.0.1, 5.1.0, and 5.2.0, NASA Tech. Rep. Series on Global Modeling and Data Assimilation,
NASA/TM-2008-104606, Vol. 27, NASA Goddard Space Flight Center, 101 pp., 2008.
Ringer, M. A., McAvaney, B. J., Andronova, N., Buja, L. E., Esch, M.,
Ingram, W. J., Li, B., Quaas, J., Roeckner, E., Senior, C. A., Soden, B. J.,
Volodin, E. M., Webb, M. J., and Williams, K. D.: Global mean cloud
feedbacks in idealized climate change experiments, Geophys. Res. Lett., 33,
L07718, https://doi.org/10.1029/2005GL025370, 2006.
Sassen, K.: The polarization lidar technique of cloud research: A review and
current assessment, B. Am. Meteorol. Soc., 72, 1848–1866, 1991.
Sassen, K.: Polarization in lidar, in: Lidar, Springer
Series in Optical Sciences, edited by: Weitkamp, C., Vol. 102, Springer, New York, NY, USA, https://doi.org/10.1007/0-387-25101-4_2, 2005.
Sassen, K. and Campbell, J. R.: A midlatitude cirrus cloud climatology from
the Facility for Atmospheric Remote Sensing. Part I: Macrophysical and
synoptic properties, J. Atmos. Sci., 58, 481–496,
https://doi.org/10.1175/1520-0469(2001)058<0481:AMCCCF>2.0.CO;2, 2001.
Sassen, K. and Petrilla, R. L.: Lidar depolarization from multiple
scattering in marine stratus clouds, Appl. Opt., 25, 1450–1459, 1986.
Schotland, R. M., Sassen, K., and Stone, R. J.: Observations by lidar of linear depolarization ratios of hydrometeors, J. Appl. Meteorol., 10, 1011–1017, 1971.
Shupe, M. D., Matrosov, S. Y., and Uttal, T.: Arctic mixed-phase cloud
properties derived from surface-based sensors at SHEBA, J. Atmos. Sci., 63,
697–711, https://doi.org/10.1175/JAS3659.1, 2006.
Stephens, G. L.: Cloud feedbacks in the climate system: A critical review,
J. Climate, 18, 237–273, 2005.
Sun, Z. and Shine, K. P.: Studies of radiative properties of ice and mixed phase clouds, Q. J. Roy. Meteor. Soc., 120, 111–137, 1994.
Tan, I. and Storelvmo, T.: Evidence of strong contributions from mixed-phase
clouds to Arctic climate change, Geophys. Res. Lett., 46, 2894–2902,
https://doi.org/10.1029/2018GL081871, 2019.
Tan, I., Storelvmo, T., and Choi, Y.-S.: Spaceborne lidar observations of
the ice-nucleating potential of dust, polluted dust and smoke aerosols in
mixed-phase clouds, J. Geophys. Res.-Atmos., 119, 6653–6665,
https://doi.org/10.1002/2013JD021333, 2014.
Tan, I., Storelvmo, T., and Zelinka, M. D.: Observational constraints on
mixed-phase clouds imply higher climate sensitivity, Science, 352, 224–227, https://doi.org/10.1126/science.aad5300, 2016.
Torres, O., Bhartia, P. K., Taha, G., Jethva, H., Das, S., Colarco, P.,
Krotkov, N., Omar, A., and Ahn, C.: Stratospheric injection of massive smoke
plume from Canadian boreal fires in 2017 as seen by DSCOVR-EPIC, CALIOP and
OMPS-LP observations, J. Geophys. Res., 125, e2020JD032579,
https://doi.org/10.1029/2020JD032579, 2020.
Wang, Z., Letu, H., Shang, H., Zhao, C., Li, J., and Ma, R.: A supercooled
water cloud detection algorithm using Himawari-8 satellite measurements, J.
Geophys. Res.-Atmos., 124, 2724–2738, https://doi.org/10.1029/2018JD029784, 2019.
Weitkamp, C.: Lidar: Range-Resolved Optical Remote Sensing of the
Atmosphere, Springer, New York, USA, 2005.
Welton, E. J. and Campbell, J. R.: Micropulse lidar signals: Uncertainty
analysis, J. Atmos. Ocean. Tech., 19, 2089–2094, https://doi.org/10.1175/1520-0426(2002)019,2089:MLSUA.2.0.CO;2, 2002.
Welton, E. J., Campbell, J. R., Spinhirne, J. D., and Scott III, V. S.: Global
monitoring of clouds and aerosols using a network of micro-pulse lidar
systems, Lidar Remote Sensing for Industry and Environmental Monitoring, edited by: Singh, U. N., Itabe, T., and Sugimoto, N., International Society for Optical Engineering, SPIE P., 4153, 151–158, 2001.
Welton, E. J., Voss, K. J., Quinn, P. K., Flatau, P. J., Markowicz, K.,
Campbell, J. R., Spinhirne, J. D., Gordon, H. R., and Johnson, J. E.: Measurements of aerosol vertical profiles and optical
properties during INDOEX 1999 using micropulse lidars, J. Geophys. Res.,
107, 8019, https://doi.org/10.1029/2000JD000038, 2002.
Welton, E. J., Stewart, S. A., Lewis, J. R., Belcher, L. R., Campbell, J.
R., and Lolli, S.: Status of the Micro Pulse Lidar Network (MPLNET): Overview of the network and future plans, new version 3 data products, and the polarized MPL, EPJ Web Conf., 176, 09003,
https://doi.org/10.1051/epjconf/201817609003, 2018.
Wielicki, B. A., Cess, R. D., King, M. D., Randall, D. A., and Harrison, E.
F.: Mission to planet Earth: Role of clouds and radiation in climate, B.
Am. Meteorol. Soc., 76, 2125–2153,
https://doi.org/10.1175/1520-0477(1995)076<2125:MTPERO>2.0.CO;2, 1995.
Winker, D. M., Pelon, J., Coakley, J. A., Ackermann, S. A., Charlson, R. J.,
Colarco, P. R., Flamant, P., Fu, Q., Hoff, R. M., Kittaka, C., Kubar, T. L.,
Treut, H. L., McCormick, M. P., Mégie, G., Poole, L., Powell, K.,
Trepte, C., Vaughan, M. A., and Wielicki, B. A.: The CALIPSO mission: A
global 3D view of aerosols and clouds, B. Am. Meteorol. Soc., 91,
1211–1229, https://doi.org/10.1175/2010BAMS3009.1, 2010.
Yi, B., Rapp, A. D., Yang, P., Baum, B. A., and King, M. D.: A comparison of
Aqua MODIS ice and liquid water cloud physical and optical properties between collection 6 and collection 5.1: Cloud radiative effects, J. Geophys. Res.-Atmos., 122, 4550–4564, https://doi.org/10.1002/2016JD025654, 2017.
Yorks, J. E., Hlavka, D. L., Hart, W. D., and McGill, M. J.: Statistics of cloud optical properties from airborne lidar measurements, J. Atmos. Ocean. Tech., 28, 869–883,
https://doi.org/10.1175/2011JTECHA1507.1, 2011.
Zhang, D., Wang, Z., and Liu, D.: A global view of mid-level liquid-layer
topped stratiform cloud distribution and phase partition from CALIPSO and CloudSat measurements, J. Geophys. Res., 115,
D00H13, https://doi.org/10.1029/2010JD014030, 2010.
Short summary
In this work, the authors describe a process to determine the thermodynamic cloud phase using the Micro Pulse Lidar Network volume depolarization ratio measurements and temperature profiles from the Global Modeling and Assimilation Office GEOS-5 model. A multi-year analysis and comparisons to supercooled liquid water fractions derived from CALIPSO satellite measurements are used to demonstrate the efficacy of the method.
In this work, the authors describe a process to determine the thermodynamic cloud phase using...
