Articles | Volume 24, issue 5
https://doi.org/10.5194/acp-24-2861-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-2861-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Mar 2024
Research article |
| 05 Mar 2024
| 05 Mar 2024
A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations
Piyushkumar N. Patel
CORRESPONDING AUTHOR
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Oak Ridge Associated Universities, Oak Ridge, TN, USA
Jonathan H. Jiang
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Ritesh Gautam
Environmental Defense Fund, Washington, DC, USA
Harish Gadhavi
Space and Atmospheric Sciences Division, Physical Research Laboratory, Ahmedabad, India
Olga Kalashnikova
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Michael J. Garay
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
School of Meteorology, University of Oklahoma, OK, USA
Feng Xu
School of Meteorology, University of Oklahoma, OK, USA
Ali Omar
Science Directorate, NASA Langley Research Center, Hampton, VA, USA
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Jacob B. H. Bushey, Michael Bertolacci, Joshua Benmergui, Julian Kostinek, Maryann Sargent, Marvin Knapp, Ethan Kyzivat, Sasha Ayvazov, Marcus Russi, Zhan Zhang, Apisada Chulakadabba, Jia Chen, Christopher Chan Miller, Sébastien Roche, Lucas A. Estrada, James D. East, Luna Yin, James P. Williams, Anthony Himmelberger, Ritesh Gautam, Mark Omara, and Steven C. Wofsy
EGUsphere, https://doi.org/10.5194/egusphere-2026-4693, https://doi.org/10.5194/egusphere-2026-4693, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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MethaneSAT was a designed to measure the concentration of methane in the atmosphere over oil and gas producing regions. Our modeling framework uses observations from MethaneSAT to estimate emissions from the Permian Basin. We show that the model accurately captures spatial patterns in emissions and compares well to other models that rely on different atmospheric transport. This data set is valuable for emissions monitoring and mitigation at high spatial resolution and with high precision.
Hua Zhang, Shuxiao Wang, Xi Zhao, Zhijun Wu, Yan Yin, Siyu Chen, Dantong Liu, Jingchuan Chen, Hui Jiang, Jiewen Shen, Da Gao, Dejia Yin, Yicong He, Zeqi Li, Shengyue Li, Zhaoxin Dong, Manish Shrivastava, Jonathan H. Jiang, Muyan Peng, and Bin Zhao
Atmos. Chem. Phys., 26, 12329–12354, https://doi.org/10.5194/acp-26-12329-2026, https://doi.org/10.5194/acp-26-12329-2026, 2026
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Mineral dust can initiate ice formation in clouds, affecting cloud properties and regional climate. Using an improved model, we examined their contrasting impacts in mixed-phase and ice clouds over East Asia. In mixed-phase clouds, dust promotes liquid-to-ice conversion and reduces cloud reflectivity, causing moderate warming, whereas in ice clouds, it greatly enhances ice formation and leads to stronger warming. This study advances understanding of aerosol–cloud–climate interactions.
Emily D. Lenhardt, Lan Gao, Siddhant Gupta, Greg M. McFarquhar, Feng Xu, Richard A. Ferrare, Chris A. Hostetler, and Jens Redemann
Atmos. Chem. Phys., 26, 11709–11732, https://doi.org/10.5194/acp-26-11709-2026, https://doi.org/10.5194/acp-26-11709-2026, 2026
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Interactions between clouds and small particles in the atmosphere can cause changes to cloud properties, such as how much of the sun's energy they reflect and whether precipitation is likely to occur. Here we use a new machine learning dataset to investigate these interactions using observations from the Southeast Atlantic. We find that smoke particles above cloud tops have a strong impact on cloud properties in stable environments. This method can also be applied to satellite data.
Zhaoyan Liu, Mark A. Vaughan, Pengwang Zhai, Anne Emilie Garnier, Shan Zeng, Sharon D. Rodier, Yongxiang Hu, Ali H. Omar, and Charles R. Trepte
Atmos. Meas. Tech., 19, 5325–5335, https://doi.org/10.5194/amt-19-5325-2026, https://doi.org/10.5194/amt-19-5325-2026, 2026
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Accurate polarization calibration is vital for spaceborne lidars. Optically Thick Ice Cloud (OTIC)-based calibration enables semi-continuous daytime calibration, but molecular scattering at shorter wavelengths biases the signal. We introduce a vector radiative transfer-based Molecular Scattering Correction (MSC) and validate it using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), eliminating ~ 1% daytime bias at 532 nm. MSC is essential for the Atmospheric Lidar (ATLID) at 355 nm.
Ray Nassar, Matthew Boland, Alexander P. Trishchenko, Chris E. Sioris, and Ali H. Omar
EGUsphere, https://doi.org/10.5194/egusphere-2026-3463, https://doi.org/10.5194/egusphere-2026-3463, 2026
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Satellites using different orbits have led to advances in weather prediction and environmental science. We introduce the Inclined Geosynchronous Orbit (IGO) as a novel orbit for Earth observation and contrast it with other orbits. Key advantages are presented here for observing atmospheric composition, but extend to other fields in which the area of interest reaches to the high latitudes of both hemispheres, such as surface imaging.
Patrick C. Taylor, Armin Sorooshian, Rei Ueyama, Sebastian Schmidt, Ihab Abboud, Quincy Allison, Kevin Barry, Sebastian Becker, Holly A. Bender, Joseph R. Bennett, James B. Blair, Niklas Bohn, Linette Boisvert, Matthew D. Brown, Roelof Bruintjes, Anthony Bucholtz, Megan Buzanowicz, Brian Cairns, Filippo Calì Quaglia, Eduard Chemyakin, Bo Chen, Gao Chen, Hong Chen, Yu-Wen Chen, Zezhen Cheng, Swarup China, Dan Chirica, Yonghoon Choi, Peter Colarco, Brian Collister, Ewan Crosbie, Maurice J. Cross, Janet Daniels, Paul DeMott, Joshua P. DiGangi, Alcide Giorgio di Sarra, Glenn S. Diskin, Erica K. Dolinar, Eva-Lou Edwards, Samuel Ephraim, Nikolaos Evangeliou, Romanos Foskinis, Francesca Gallo, Lan Gao, José Luis Gómez-Amo, Daisy Gonzalez, Christine Groot Zwaaftink, Pawan Gupta, Ivan Heckman, Michael Hendrickson, Miguel Ricardo A. Hilario, Ken Hirata, Michelle Hofton, Andrew L. Holen, Ulas Im, Alia L. Khan, Ralph Kahn, Alexei V. Korolev, Sonia Kreidenweis, Thomas Krumpen, Nathan Kurtz, Leslie Lait, Bradley Lamkin, Jack Landy, Nurun Nahar Lata, Paul Lawson, Samuel LeBlanc, Sean Leavor, Jing Li, Thorsten Markus, Hal Maring, Andreas H. Massling, Camille Mavis, Flynn McGinnity, Kerry Meyer, Gabriel Mojica, Richard H. Moore, Parker Morris, Giovanni Muscari, Vikas Nataraja, Amin R. Nehrir, Athanasios Nenes, Edward P. Nowottnick, Matteo Ottaviani, Chelsea Parker, Ryan Patnaude, Michael Perez, Russell J. Perkins, Colten Peterson, Alek Petty, Stevie Phothisane, Chris Polashenski, Kerri A. Pratt, Kayla M. Preisler, John Prytherch, David Rabine, Jens Redemann, Ju-Mee Ryoo, Joseph S. Schlosser, Vanessa Selimovic, Michael A. Shook, Morgan Silverman, Henrik Skov, Alexander Smirnov, Cassidy Soloff, Amy Solomon, Snorre Stamnes, Azusa Takeishi, David R. Thompson, K. Lee Thornhill, Rachel Tilling, Michael Tjernström, Monica Tosco, Pedro C. Valdelomar, David Van Gilst, Jian Wang, Zhien Wang, Andrzej Wasilewski, Manfred Wendisch, Brent Wilder, Edward L. Winstead, Albert Wu, Peng Xian, Lauren M. Zamora, Jiaoshi Zhang, Lei Zhang, Lu Zhang, Luke Ziemba, and Paquita Zuidema
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-241, https://doi.org/10.5194/essd-2026-241, 2026
Preprint under review for ESSD
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This article summarizes the data sets collected during the Arctic Radiation Cloud aerosol Sea ice Interaction eXperiment (ARCSIX), NASA's most comprehensive Arctic field campaign to date. The overarching goal was to quantify the contributions of surface, clouds, aerosol particles, and precipitation to summer sea ice melt. This paper describes the ARCSIX implementation including the flight strategy, instruments, complementary data sets, access, and usage details.
Zhan Zhang, Maryann Sargent, Ethan Manninen, Jack D. Warren, Apisada Chulakadabba, Marcus Russi, Sasha Ayvazov, Joshua Benmergui, Marvin Knapp, Ethan Kyzivat, Christopher C. Miller, Sébastien Roche, Bingkun Luo, David J. Miller, Maya Nasr, Manuel Perez-Carrasco, Kang Sun, James P. Williams, Katlyn MacKay, Mark Omara, Jia Chen, Luis Guanter, Ritesh Gautam, Jonathan Franklin, Xiong Liu, and Steven C. Wofsy
Atmos. Meas. Tech., 19, 4637–4651, https://doi.org/10.5194/amt-19-4637-2026, https://doi.org/10.5194/amt-19-4637-2026, 2026
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Methane released into the atmosphere is often difficult to detect in satellite imagery because the signals can be weak and hidden by background noise. We developed an automated method that improves the visibility of methane plumes while reducing false detections, decreasing the need for time-consuming manual inspection. The method identifies more small emission sources across different instruments, helping build a more complete understanding of methane emissions and their impacts.
Christopher Chan Miller, Sébastien Roche, Jonas Wilzweski, Xiong Liu, Kelly V. Chance, Bingkun Luo, Kang Sun, Jonathan E. Franklin, Joshua S. Benmergui, Maya Nasr, David J. Miller, Sean Crowell, Nathan Leisso, Eleanor Walker, Ritesh Gautam, Nicholas LoFaso, Sasha Ayvazov, David Wells, Carlos Cervantes, Christopher Hairfield, and Steven Wofsy
EGUsphere, https://doi.org/10.5194/egusphere-2026-3365, https://doi.org/10.5194/egusphere-2026-3365, 2026
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Reducing oil and gas methane emissions is among the fastest ways to slow near-term climate change, and satellites offer a transparent means of verifying progress. MethaneSAT fills a gap between existing instruments, which either measure methane accurately but at coarse scales, or detect large leaks at fine resolution without the accuracy to account for all sources. We present the first measurements and show they meet the accuracy requirements for regional emissions mapping at the 2–4 km2 scale.
David J. Miller, Kang Sun, Jonathan E. Franklin, Nathan Leisso, Sébastien Roche, Bingkun Luo, Christopher Chan Miller, Sasha Ayvazov, Sean Crowell, Nick LoFaso, Tom Kampe, Peter Spuhler, Betsy Farris, Eleanor Walker, Tom Melendez, Ritesh Gautam, Xiong Liu, and Steven C. Wofsy
EGUsphere, https://doi.org/10.5194/egusphere-2026-2675, https://doi.org/10.5194/egusphere-2026-2675, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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With a focus on mitigating climate change, the MethaneSAT satellite mission aims to catalyze methane emission reductions. MethaneSAT has provided high-resolution emissions products across global oil and gas basins. We present novel calibration methods of MethaneSAT's instrumentation that demonstrate stable on-orbit performance compared with ground-based calibration. Our results are important for similar satellite missions focused on monitoring climate-relevant emissions.
James P. Williams, Joshua Benmergui, Marvin Knapp, Mark Omara, Anthony Himmelberger, Ethan Kyzivat, Kaiya Weatherby, Ben Lyke, Jack Warren, Katlyn MacKay, Sasha Ayvazov, Marcus Russi, Nicholas LoFaso, Tom Melendez, Christopher C. Miller, Sebastien Roche, Maryann Sargent, Jonathan Franklin, Maya Nasr, Zhan Zhang, David J. Miller, Bingkun Luo, Luis Guanter, Steven P. Hamburg, Steven C. Wofsy, and Ritesh Gautam
Atmos. Chem. Phys., 26, 5961–5981, https://doi.org/10.5194/acp-26-5961-2026, https://doi.org/10.5194/acp-26-5961-2026, 2026
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Methane is a potent greenhouse gas, and satellite observations are critical for reducing emissions. Using 2024–2025 MethaneSAT data, we quantify methane emissions across six major oil and gas basins at high resolution, revealing substantial underestimation at basin, subbasin, and local levels. Our results underscore the need for new, spatially resolved measurements and the continued advancement of remote sensing capabilities to provide more detailed, actionable insights for methane mitigation.
Luis Guanter, Javier Roger, Jack Warren, Maryann Sargent, Zhan Zhang, Sébastien Roche, Christopher Chan Miller, Michael Steiner, Harvey Hadfield, Mark Omara, James Williams, Katlyn MacKay, Jonathan E. Franklin, Bingkun Luo, Steven C. Wofsy, Steven P. Hamburg, and Ritesh Gautam
Atmos. Chem. Phys., 26, 2941–2963, https://doi.org/10.5194/acp-26-2941-2026, https://doi.org/10.5194/acp-26-2941-2026, 2026
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We evaluate the potential of the MethaneSAT satellite mission to detect and quantify methane plumes from high emitting point sources and use the existing MethaneSAT data archive to evaluate super-emissions from the most important oil and gas basins in the world.
Katlyn MacKay, Joshua Benmergui, James P. Williams, Mark Omara, Anthony Himmelberger, Maryann Sargent, Jack D. Warren, Christopher C. Miller, Sébastien Roche, Zhan Zhang, Jonathan Franklin, Luis Guanter, Steven Wofsy, and Ritesh Gautam
Atmos. Chem. Phys., 26, 1179–1192, https://doi.org/10.5194/acp-26-1179-2026, https://doi.org/10.5194/acp-26-1179-2026, 2026
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Reducing methane emissions from the oil/gas sector can mitigate near-term climate warming and the losses of a valuable energy resource. We analyze data collected using MethaneAIR to assess methane emissions in regions accounting for 70 % of United States onshore oil/gas production in 2023. We estimate total oil/gas methane emissions across all measured regions to be ~8 Tg/yr, equivalent to 1.6 % of produced gas, which is five times higher than reported by the US Environmental Protection Agency.
Jack H. Bruno, Daniel J. Jacob, Xiaolin Wang, Melissa P. Sulprizio, Lucas A. Estrada, Daniel J. Varon, Steven C. Wofsy, Mark Omara, and Ritesh Gautam
EGUsphere, https://doi.org/10.5194/egusphere-2025-4626, https://doi.org/10.5194/egusphere-2025-4626, 2025
Preprint archived
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We use data from both aircraft (MethaneAIR) and satellite (TROPOMI) based measurements of methane in the atmosphere to better understand methane emissions from fossil fuel extraction. Data from these instruments are combined with a computer model of the atmosphere to improve estimates of methane emissions. We find that combining data from multiple sources provides more information than either source on its own. The tools and data we use are freely available.
Emily Lenhardt, Lan Gao, Chris A. Hostetler, Richard A. Ferrare, Sharon P. Burton, Richard H. Moore, Luke D. Ziemba, Ewan Crosbie, Armin Sorooshian, Cassidy Soloff, and Jens Redemann
Atmos. Chem. Phys., 25, 13747–13768, https://doi.org/10.5194/acp-25-13747-2025, https://doi.org/10.5194/acp-25-13747-2025, 2025
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Small particles that form cloud droplets greatly impact Earth's climate but are very difficult to measure. If we can measure them using satellite-based instruments, we greatly increase the amount of available data on their concentrations. In this study, we find that including information about particle size is most important to measure them accurately from such satellite-based instruments. This can inform future studies on how to obtain more accurate information about small particles.
Sina Hasheminassab, David M. Tratt, Olga V. Kalashnikova, Clement S. Chang, Morad Alvarez, Kerry N. Buckland, Michael J. Garay, Francesca M. Hopkins, Eric R. Keim, Le Kuai, Yaning Miao, Payam Pakbin, William C. Porter, and Mohammad H. Sowlat
Atmos. Chem. Phys., 25, 11935–11950, https://doi.org/10.5194/acp-25-11935-2025, https://doi.org/10.5194/acp-25-11935-2025, 2025
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Ammonia (NH3) is a key air pollutant linked to fine particle pollution, yet its sources remain poorly understood. Using airborne infrared imaging and ground sensors, we mapped NH3 emissions in California’s Salton Sea region with unprecedented detail. We found high emissions from farms, geothermal plants, and waste sites, including sources missing from inventories. These findings highlight the need for better NH3 monitoring to improve air quality models and guide pollution reduction strategies.
Jack D. Warren, Maryann Sargent, James P. Williams, Mark Omara, Christopher C. Miller, Sebastien Roche, Katlyn MacKay, Ethan Manninen, Apisada Chulakadabba, Anthony Himmelberger, Joshua Benmergui, Zhan Zhang, Luis Guanter, Steve Wofsy, and Ritesh Gautam
Atmos. Chem. Phys., 25, 10661–10675, https://doi.org/10.5194/acp-25-10661-2025, https://doi.org/10.5194/acp-25-10661-2025, 2025
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Mitigating anthropogenic methane emissions requires a detailed understanding of emitting facilities. We use observations of methane point sources from the MethaneAIR instrument from 2021–2023 that covered ~80 % of US onshore oil and gas production regions. We attribute these observations to facility types to explore how emissions vary by industrial sectors. Oil and gas facilities make up most point source emissions nationally, but in certain basins other sectors can make up the majority.
Harish Shivraj Gadhavi, Akanksha Arora, Chaithanya Jain, Mahesh Kumar Sha, Frank Hase, Matthias Max Frey, Srikanthan Ramachandran, and Achuthan Jayaraman
Atmos. Meas. Tech., 18, 4497–4514, https://doi.org/10.5194/amt-18-4497-2025, https://doi.org/10.5194/amt-18-4497-2025, 2025
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We measured carbon dioxide (CO₂) and methane (CH₄) over South India using a ground-based spectrometer and compared the results with satellite data from GOSAT (Greenhouse gases Observing SATellite; Japan) and OCO-2 (Orbiting Carbon Observatory-2; NASA, USA). The satellite data have good accuracy and precision for estimating emissions. A computer model showed that changes in methane levels are influenced more by background levels than by local sources.
Luis Guanter, Jack Warren, Mark Omara, Apisada Chulakadabba, Javier Roger, Maryann Sargent, Jonathan E. Franklin, Steven C. Wofsy, and Ritesh Gautam
Atmos. Meas. Tech., 18, 3857–3872, https://doi.org/10.5194/amt-18-3857-2025, https://doi.org/10.5194/amt-18-3857-2025, 2025
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This study presents a data processing scheme for the detection and quantification of methane emissions using the MethaneAIR airborne spectrometer. We show that the proposed methods enable the detection of smaller plumes (compared with those detectable using other existing methods) and improve the potential of MethaneAIR to survey methane point sources across large regions.
Abdulamid A. Fakoya, Jens Redemann, Pablo E. Saide, Lan Gao, Logan T. Mitchell, Calvin Howes, Amie Dobracki, Ian Chang, Gonzalo A. Ferrada, Kristina Pistone, Samuel E. Leblanc, Michal Segal-Rozenhaimer, Arthur J. Sedlacek III, Thomas Eck, Brent Holben, Pawan Gupta, Elena Lind, Paquita Zuidema, Gregory Carmichael, and Connor J. Flynn
Atmos. Chem. Phys., 25, 7879–7902, https://doi.org/10.5194/acp-25-7879-2025, https://doi.org/10.5194/acp-25-7879-2025, 2025
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Tiny atmospheric particles from wildfire smoke impact the climate by interacting with sunlight and clouds, the extent of which is uncertain due to gaps in understanding how smoke changes over time. We developed a new method using remote sensing instruments to track how these particles evolve during atmospheric transport. Our results show that the ability of these particles to absorb sunlight increases as they travel. This discovery could help improve predictions of future climate scenarios.
James P. Williams, Mark Omara, Anthony Himmelberger, Daniel Zavala-Araiza, Katlyn MacKay, Joshua Benmergui, Maryann Sargent, Steven C. Wofsy, Steven P. Hamburg, and Ritesh Gautam
Atmos. Chem. Phys., 25, 1513–1532, https://doi.org/10.5194/acp-25-1513-2025, https://doi.org/10.5194/acp-25-1513-2025, 2025
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We utilize peer-reviewed facility-level oil and gas methane emission rate data gathered in prior work to estimate the relative contributions of methane sources emitting at different emission rates in the United States. We find that the majority of total methane emissions in the US oil and gas sector stem from a large number of small sources emitting in aggregate, corroborating findings from several other studies.
Myungje Choi, Alexei Lyapustin, Gregory L. Schuster, Sujung Go, Yujie Wang, Sergey Korkin, Ralph Kahn, Jeffrey S. Reid, Edward J. Hyer, Thomas F. Eck, Mian Chin, David J. Diner, Olga Kalashnikova, Oleg Dubovik, Jhoon Kim, and Hans Moosmüller
Atmos. Chem. Phys., 24, 10543–10565, https://doi.org/10.5194/acp-24-10543-2024, https://doi.org/10.5194/acp-24-10543-2024, 2024
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This paper introduces a retrieval algorithm to estimate two key absorbing components in smoke (black carbon and brown carbon) using DSCOVR EPIC measurements. Our analysis reveals distinct smoke properties, including spectral absorption, layer height, and black carbon and brown carbon, over North America and central Africa. The retrieved smoke properties offer valuable observational constraints for modeling radiative forcing and informing health-related studies.
Christopher Chan Miller, Sébastien Roche, Jonas S. Wilzewski, Xiong Liu, Kelly Chance, Amir H. Souri, Eamon Conway, Bingkun Luo, Jenna Samra, Jacob Hawthorne, Kang Sun, Carly Staebell, Apisada Chulakadabba, Maryann Sargent, Joshua S. Benmergui, Jonathan E. Franklin, Bruce C. Daube, Yang Li, Joshua L. Laughner, Bianca C. Baier, Ritesh Gautam, Mark Omara, and Steven C. Wofsy
Atmos. Meas. Tech., 17, 5429–5454, https://doi.org/10.5194/amt-17-5429-2024, https://doi.org/10.5194/amt-17-5429-2024, 2024
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MethaneSAT is an upcoming satellite mission designed to monitor methane emissions from the oil and gas (O&G) industry globally. Here, we present observations from the first flight campaign of MethaneAIR, a MethaneSAT-like instrument mounted on an aircraft. MethaneAIR can map methane with high precision and accuracy over a typically sized oil and gas basin (~200 km2) in a single flight. This paper demonstrates the capability of the upcoming satellite to routinely track global O&G emissions.
Mark Omara, Anthony Himmelberger, Katlyn MacKay, James P. Williams, Joshua Benmergui, Maryann Sargent, Steven C. Wofsy, and Ritesh Gautam
Earth Syst. Sci. Data, 16, 3973–3991, https://doi.org/10.5194/essd-16-3973-2024, https://doi.org/10.5194/essd-16-3973-2024, 2024
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We review, analyze, and synthesize previous peer-reviewed measurement-based data on facility-level oil and gas methane emissions and use these data to develop a high-resolution spatially explicit inventory of US basin-level and national methane emissions. This work provides an improved assessment of national methane emissions relative to government inventories in support of accurate and comprehensive methane emissions assessment, attribution, and mitigation.
Brian Nathan, Joannes D. Maasakkers, Stijn Naus, Ritesh Gautam, Mark Omara, Daniel J. Varon, Melissa P. Sulprizio, Lucas A. Estrada, Alba Lorente, Tobias Borsdorff, Robert J. Parker, and Ilse Aben
Atmos. Chem. Phys., 24, 6845–6863, https://doi.org/10.5194/acp-24-6845-2024, https://doi.org/10.5194/acp-24-6845-2024, 2024
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Venezuela's Lake Maracaibo region is notoriously hard to observe from space and features intensive oil exploitation, although production has strongly decreased in recent years. We estimate methane emissions using 2018–2020 TROPOMI satellite observations with national and regional transport models. Despite the production decrease, we find relatively constant emissions from Lake Maracaibo between 2018 and 2020, indicating that there could be large emissions from abandoned infrastructure.
Apisada Chulakadabba, Maryann Sargent, Thomas Lauvaux, Joshua S. Benmergui, Jonathan E. Franklin, Christopher Chan Miller, Jonas S. Wilzewski, Sébastien Roche, Eamon Conway, Amir H. Souri, Kang Sun, Bingkun Luo, Jacob Hawthrone, Jenna Samra, Bruce C. Daube, Xiong Liu, Kelly Chance, Yang Li, Ritesh Gautam, Mark Omara, Jeff S. Rutherford, Evan D. Sherwin, Adam Brandt, and Steven C. Wofsy
Atmos. Meas. Tech., 16, 5771–5785, https://doi.org/10.5194/amt-16-5771-2023, https://doi.org/10.5194/amt-16-5771-2023, 2023
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We show that MethaneAIR, a precursor to the MethaneSAT satellite, demonstrates accurate point source quantification during controlled release experiments and regional observations in 2021 and 2022. Results from our two independent quantification methods suggest the accuracy of our sensor and algorithms is better than 25 % for sources emitting 200 kg h−1 or more. Insights from these measurements help establish the capabilities of MethaneSAT and MethaneAIR.
Yun Lin, Yuan Wang, Jen-Shan Hsieh, Jonathan H. Jiang, Qiong Su, Lijun Zhao, Michael Lavallee, and Renyi Zhang
Atmos. Chem. Phys., 23, 13835–13852, https://doi.org/10.5194/acp-23-13835-2023, https://doi.org/10.5194/acp-23-13835-2023, 2023
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Tropical cyclones (TCs) can cause catastrophic damage to coastal regions. We used a numerical model that explicitly simulates aerosol–cloud interaction and atmosphere–ocean coupling. We show that aerosols and ocean coupling work together to make TC storms bigger but weaker. Moreover, TCs in polluted air have more rainfall and higher sea levels, leading to more severe storm surges and flooding. Our research highlights the roles of aerosols and ocean-coupling feedbacks in TC hazard assessment.
Robert R. Nelson, Marcin L. Witek, Michael J. Garay, Michael A. Bull, James A. Limbacher, Ralph A. Kahn, and David J. Diner
Atmos. Meas. Tech., 16, 4947–4960, https://doi.org/10.5194/amt-16-4947-2023, https://doi.org/10.5194/amt-16-4947-2023, 2023
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Shallow and coastal waters are nutrient-rich and turbid due to runoff. They are also located in areas where the atmosphere has more aerosols than open-ocean waters. NASA's Multi-angle Imaging SpectroRadiometer (MISR) has been monitoring aerosols for over 23 years but does not report results over shallow waters. We developed a new algorithm that uses all four of MISR’s bands and considers light leaving water surfaces. This algorithm performs well and increases over-water measurements by over 7 %.
Mark Omara, Ritesh Gautam, Madeleine A. O'Brien, Anthony Himmelberger, Alex Franco, Kelsey Meisenhelder, Grace Hauser, David R. Lyon, Apisada Chulakadabba, Christopher Chan Miller, Jonathan Franklin, Steven C. Wofsy, and Steven P. Hamburg
Earth Syst. Sci. Data, 15, 3761–3790, https://doi.org/10.5194/essd-15-3761-2023, https://doi.org/10.5194/essd-15-3761-2023, 2023
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We acquire, integrate, and analyze ~ 6 million geospatial oil and gas infrastructure data records based on information available in the public domain and develop an open-access global database including all the major oil and gas facility types that are important sources of methane emissions. This work helps fulfill a crucial geospatial data need, in support of the assessment, attribution, and mitigation of global oil and gas methane emissions at high resolution.
Daniel J. Varon, Daniel J. Jacob, Benjamin Hmiel, Ritesh Gautam, David R. Lyon, Mark Omara, Melissa Sulprizio, Lu Shen, Drew Pendergrass, Hannah Nesser, Zhen Qu, Zachary R. Barkley, Natasha L. Miles, Scott J. Richardson, Kenneth J. Davis, Sudhanshu Pandey, Xiao Lu, Alba Lorente, Tobias Borsdorff, Joannes D. Maasakkers, and Ilse Aben
Atmos. Chem. Phys., 23, 7503–7520, https://doi.org/10.5194/acp-23-7503-2023, https://doi.org/10.5194/acp-23-7503-2023, 2023
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We use TROPOMI satellite observations to quantify weekly methane emissions from the US Permian oil and gas basin from May 2018 to October 2020. We find that Permian emissions are highly variable, with diverse economic and activity drivers. The most important drivers during our study period were new well development and natural gas price. Permian methane intensity averaged 4.6 % and decreased by 1 % per year.
Zichong Chen, Daniel J. Jacob, Ritesh Gautam, Mark Omara, Robert N. Stavins, Robert C. Stowe, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Drew C. Pendergrass, and Sarah Hancock
Atmos. Chem. Phys., 23, 5945–5967, https://doi.org/10.5194/acp-23-5945-2023, https://doi.org/10.5194/acp-23-5945-2023, 2023
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We quantify methane emissions from individual countries in the Middle East and North Africa by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. We show that the ability to simply relate oil/gas emissions to activity metrics is compromised by stochastic nature of local infrastructure and management practices. We find that the industry target for oil/gas methane intensity is achievable through associated gas capture, modern infrastructure, and centralized operations.
Emily D. Lenhardt, Lan Gao, Jens Redemann, Feng Xu, Sharon P. Burton, Brian Cairns, Ian Chang, Richard A. Ferrare, Chris A. Hostetler, Pablo E. Saide, Calvin Howes, Yohei Shinozuka, Snorre Stamnes, Mary Kacarab, Amie Dobracki, Jenny Wong, Steffen Freitag, and Athanasios Nenes
Atmos. Meas. Tech., 16, 2037–2054, https://doi.org/10.5194/amt-16-2037-2023, https://doi.org/10.5194/amt-16-2037-2023, 2023
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Small atmospheric particles, such as smoke from wildfires or pollutants from human activities, impact cloud properties, and clouds have a strong influence on climate. To better understand the distributions of these particles, we develop relationships to derive their concentrations from remote sensing measurements from an instrument called a lidar. Our method is reliable for smoke particles, and similar steps can be taken to develop relationships for other particle types.
Ian Chang, Lan Gao, Connor J. Flynn, Yohei Shinozuka, Sarah J. Doherty, Michael S. Diamond, Karla M. Longo, Gonzalo A. Ferrada, Gregory R. Carmichael, Patricia Castellanos, Arlindo M. da Silva, Pablo E. Saide, Calvin Howes, Zhixin Xue, Marc Mallet, Ravi Govindaraju, Qiaoqiao Wang, Yafang Cheng, Yan Feng, Sharon P. Burton, Richard A. Ferrare, Samuel E. LeBlanc, Meloë S. Kacenelenbogen, Kristina Pistone, Michal Segal-Rozenhaimer, Kerry G. Meyer, Ju-Mee Ryoo, Leonhard Pfister, Adeyemi A. Adebiyi, Robert Wood, Paquita Zuidema, Sundar A. Christopher, and Jens Redemann
Atmos. Chem. Phys., 23, 4283–4309, https://doi.org/10.5194/acp-23-4283-2023, https://doi.org/10.5194/acp-23-4283-2023, 2023
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Abundant aerosols are present above low-level liquid clouds over the southeastern Atlantic during late austral spring. The model simulation differences in the proportion of aerosol residing in the planetary boundary layer and in the free troposphere can greatly affect the regional aerosol radiative effects. This study examines the aerosol loading and fractional aerosol loading in the free troposphere among various models and evaluates them against measurements from the NASA ORACLES campaign.
Jason L. Tackett, Jayanta Kar, Mark A. Vaughan, Brian J. Getzewich, Man-Hae Kim, Jean-Paul Vernier, Ali H. Omar, Brian E. Magill, Michael C. Pitts, and David M. Winker
Atmos. Meas. Tech., 16, 745–768, https://doi.org/10.5194/amt-16-745-2023, https://doi.org/10.5194/amt-16-745-2023, 2023
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The accurate identification of aerosol types in the stratosphere is important to characterize their impacts on the Earth climate system. The space-borne lidar on board CALIPSO is well-posed to identify aerosols in the stratosphere from volcanic eruptions and major wildfire events. This paper describes improvements implemented in the version 4.5 CALIPSO data release to more accurately discriminate between volcanic ash, sulfate, and smoke within the stratosphere.
Sudipta Ghosh, Sagnik Dey, Sushant Das, Nicole Riemer, Graziano Giuliani, Dilip Ganguly, Chandra Venkataraman, Filippo Giorgi, Sachchida Nand Tripathi, Srikanthan Ramachandran, Thazhathakal Ayyappen Rajesh, Harish Gadhavi, and Atul Kumar Srivastava
Geosci. Model Dev., 16, 1–15, https://doi.org/10.5194/gmd-16-1-2023, https://doi.org/10.5194/gmd-16-1-2023, 2023
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Accurate representation of aerosols in climate models is critical for minimizing the uncertainty in climate projections. Here, we implement region-specific emission fluxes and a more accurate scheme for carbonaceous aerosol ageing processes in a regional climate model (RegCM4) and show that it improves model performance significantly against in situ, reanalysis, and satellite data over the Indian subcontinent. We recommend improving the model performance before using them for climate studies.
Siddhant Gupta, Greg M. McFarquhar, Joseph R. O'Brien, Michael R. Poellot, David J. Delene, Ian Chang, Lan Gao, Feng Xu, and Jens Redemann
Atmos. Chem. Phys., 22, 12923–12943, https://doi.org/10.5194/acp-22-12923-2022, https://doi.org/10.5194/acp-22-12923-2022, 2022
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The ability of NASA’s Terra and Aqua satellites to retrieve cloud properties and estimate the changes in cloud properties due to aerosol–cloud interactions (ACI) was examined. There was good agreement between satellite retrievals and in situ measurements over the southeast Atlantic Ocean. This suggests that, combined with information on aerosol properties, satellite retrievals of cloud properties can be used to study ACI over larger domains and longer timescales in the absence of in situ data.
Hazel Vernier, Neeraj Rastogi, Hongyu Liu, Amit Kumar Pandit, Kris Bedka, Anil Patel, Madineni Venkat Ratnam, Buduru Suneel Kumar, Bo Zhang, Harish Gadhavi, Frank Wienhold, Gwenael Berthet, and Jean-Paul Vernier
Atmos. Chem. Phys., 22, 12675–12694, https://doi.org/10.5194/acp-22-12675-2022, https://doi.org/10.5194/acp-22-12675-2022, 2022
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The chemical composition of the stratospheric aerosols collected aboard high-altitude balloons above the summer Asian monsoon reveals the presence of nitrate/nitrite. Using numerical simulations and satellite observations, we found that pollution as well as lightning could explain some of our observations.
Lu Shen, Ritesh Gautam, Mark Omara, Daniel Zavala-Araiza, Joannes D. Maasakkers, Tia R. Scarpelli, Alba Lorente, David Lyon, Jianxiong Sheng, Daniel J. Varon, Hannah Nesser, Zhen Qu, Xiao Lu, Melissa P. Sulprizio, Steven P. Hamburg, and Daniel J. Jacob
Atmos. Chem. Phys., 22, 11203–11215, https://doi.org/10.5194/acp-22-11203-2022, https://doi.org/10.5194/acp-22-11203-2022, 2022
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We use 22 months of TROPOMI satellite observations to quantity methane emissions from the oil (O) and natural gas (G) sector in the US and Canada at the scale of both individual basins as well as country-wide aggregates. We find that O/G-related methane emissions are underestimated in these inventories by 80 % for the US and 40 % for Canada, and 70 % of the underestimate in the US is from five O/G basins, including Permian, Haynesville, Anadarko, Eagle Ford, and Barnett.
Daniel J. Jacob, Daniel J. Varon, Daniel H. Cusworth, Philip E. Dennison, Christian Frankenberg, Ritesh Gautam, Luis Guanter, John Kelley, Jason McKeever, Lesley E. Ott, Benjamin Poulter, Zhen Qu, Andrew K. Thorpe, John R. Worden, and Riley M. Duren
Atmos. Chem. Phys., 22, 9617–9646, https://doi.org/10.5194/acp-22-9617-2022, https://doi.org/10.5194/acp-22-9617-2022, 2022
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We review the capability of satellite observations of atmospheric methane to quantify methane emissions on all scales. We cover retrieval methods, precision requirements, inverse methods for inferring emissions, source detection thresholds, and observations of system completeness. We show that current instruments already enable quantification of regional and national emissions including contributions from large point sources. Coverage and resolution will increase significantly in coming years.
Sujung Go, Alexei Lyapustin, Gregory L. Schuster, Myungje Choi, Paul Ginoux, Mian Chin, Olga Kalashnikova, Oleg Dubovik, Jhoon Kim, Arlindo da Silva, Brent Holben, and Jeffrey S. Reid
Atmos. Chem. Phys., 22, 1395–1423, https://doi.org/10.5194/acp-22-1395-2022, https://doi.org/10.5194/acp-22-1395-2022, 2022
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This paper presents a retrieval algorithm of iron-oxide species (hematite, goethite) content in the atmosphere from DSCOVR EPIC observations. Our results display variations within the published range of hematite and goethite over the main dust-source regions but show significant seasonal and spatial variability. This implies a single-viewing satellite instrument with UV–visible channels may provide essential information on shortwave dust direct radiative effects for climate modeling.
Xiao Lu, Daniel J. Jacob, Haolin Wang, Joannes D. Maasakkers, Yuzhong Zhang, Tia R. Scarpelli, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Hannah Nesser, A. Anthony Bloom, Shuang Ma, John R. Worden, Shaojia Fan, Robert J. Parker, Hartmut Boesch, Ritesh Gautam, Deborah Gordon, Michael D. Moran, Frances Reuland, Claudia A. Octaviano Villasana, and Arlyn Andrews
Atmos. Chem. Phys., 22, 395–418, https://doi.org/10.5194/acp-22-395-2022, https://doi.org/10.5194/acp-22-395-2022, 2022
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We evaluate methane emissions and trends for 2010–2017 in the gridded national emission inventories for the United States, Canada, and Mexico by inversion of in situ and satellite methane observations. We find that anthropogenic methane emissions for all three countries are underestimated in the national inventories, largely driven by oil emissions. Anthropogenic methane emissions in the US peak in 2014, in contrast to the report of a steadily decreasing trend over 2010–2017 from the US EPA.
Sarah J. Doherty, Pablo E. Saide, Paquita Zuidema, Yohei Shinozuka, Gonzalo A. Ferrada, Hamish Gordon, Marc Mallet, Kerry Meyer, David Painemal, Steven G. Howell, Steffen Freitag, Amie Dobracki, James R. Podolske, Sharon P. Burton, Richard A. Ferrare, Calvin Howes, Pierre Nabat, Gregory R. Carmichael, Arlindo da Silva, Kristina Pistone, Ian Chang, Lan Gao, Robert Wood, and Jens Redemann
Atmos. Chem. Phys., 22, 1–46, https://doi.org/10.5194/acp-22-1-2022, https://doi.org/10.5194/acp-22-1-2022, 2022
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Between July and October, biomass burning smoke is advected over the southeastern Atlantic Ocean, leading to climate forcing. Model calculations of forcing by this plume vary significantly in both magnitude and sign. This paper compares aerosol and cloud properties observed during three NASA ORACLES field campaigns to the same in four models. It quantifies modeled biases in properties key to aerosol direct radiative forcing and evaluates how these biases propagate to biases in forcing.
Zhao-Cheng Zeng, Vijay Natraj, Feng Xu, Sihe Chen, Fang-Ying Gong, Thomas J. Pongetti, Keeyoon Sung, Geoffrey Toon, Stanley P. Sander, and Yuk L. Yung
Atmos. Meas. Tech., 14, 6483–6507, https://doi.org/10.5194/amt-14-6483-2021, https://doi.org/10.5194/amt-14-6483-2021, 2021
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Large carbon source regions such as megacities are also typically associated with heavy aerosol loading, which introduces uncertainties in the retrieval of greenhouse gases from reflected and scattered sunlight measurements. In this study, we developed a full physics algorithm to retrieve greenhouse gases in the presence of aerosols and demonstrated its performance by retrieving CO2 and CH4 columns from remote sensing measurements in the Los Angeles megacity.
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Short summary
Global measurements of cloud condensation nuclei (CCN) are essential for understanding aerosol–cloud interactions and predicting climate change. To address this gap, we introduced a remote sensing algorithm that retrieves vertically resolved CCN number concentrations from airborne and spaceborne lidar systems. This innovation offers a global distribution of CCN concentrations from space, facilitating model evaluation and precise quantification of aerosol climate forcing.
Global measurements of cloud condensation nuclei (CCN) are essential for understanding...
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