Articles | Volume 14, issue 11
https://doi.org/10.5194/amt-14-7277-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-7277-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Nov 2021
Research article |
| 18 Nov 2021
| 18 Nov 2021
Neural-network-based estimation of regional-scale anthropogenic CO2 emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia
Farhan Mustafa
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Key Laboratory of Meteorological Disasters, Ministry of Education, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
Lingbing Bu
CORRESPONDING AUTHOR
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Key Laboratory of Meteorological Disasters, Ministry of Education, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
Qin Wang
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Key Laboratory of Meteorological Disasters, Ministry of Education, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
Na Yao
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Key Laboratory of Meteorological Disasters, Ministry of Education, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
Muhammad Shahzaman
School of Atmospheric Sciences (SAS), Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
Muhammad Bilal
School of Marine Sciences (SMS), Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
Rana Waqar Aslam
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China
Rashid Iqbal
Department of Agronomy, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
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Qin Wang, Farhan Mustafa, Lingbing Bu, Shouzheng Zhu, Jiqiao Liu, and Weibiao Chen
Atmos. Meas. Tech., 14, 6601–6617, https://doi.org/10.5194/amt-14-6601-2021, https://doi.org/10.5194/amt-14-6601-2021, 2021
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In this work, an airborne experiment was carried out to validate a newly developed CO2 monitoring IPDA lidar against the in situ measurements obtained from a commercial CO2 monitoring instrument installed on an aircraft. The XCO2 values calculated with the IPDA lidar measurements were compared with the dry-air CO2 mole fraction measurements obtained from the in situ instruments, and the results showed a good agreement between the two datasets.
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EGUsphere, https://doi.org/10.5194/egusphere-2024-3152, https://doi.org/10.5194/egusphere-2024-3152, 2024
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Atmos. Meas. Tech., 17, 4425–4443, https://doi.org/10.5194/amt-17-4425-2024, https://doi.org/10.5194/amt-17-4425-2024, 2024
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China has launched the atmospheric environment monitoring satellite DQ-1, which consists of an advanced lidar system. Our research presents a retrieval algorithm of the DQ-1 lidar system, and the retrieval results are consistent with other datasets. We also use the DQ-1 dataset to investigate dust and volcanic aerosols. This research shows that the DQ-1 lidar system can accurately measure the Earth's atmosphere and has potential for scientific applications.
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Atmos. Meas. Tech., 14, 6601–6617, https://doi.org/10.5194/amt-14-6601-2021, https://doi.org/10.5194/amt-14-6601-2021, 2021
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In this work, an airborne experiment was carried out to validate a newly developed CO2 monitoring IPDA lidar against the in situ measurements obtained from a commercial CO2 monitoring instrument installed on an aircraft. The XCO2 values calculated with the IPDA lidar measurements were compared with the dry-air CO2 mole fraction measurements obtained from the in situ instruments, and the results showed a good agreement between the two datasets.
Related subject area
Subject: Gases | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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Sanjeevani Panditharatne, Helen Brindley, Caroline Cox, Richard Siddans, Jonathan Murray, Laura Warwick, and Stuart Fox
Atmos. Meas. Tech., 18, 717–735, https://doi.org/10.5194/amt-18-717-2025, https://doi.org/10.5194/amt-18-717-2025, 2025
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Observations from the upcoming European Space Agency’s Far-Infrared Outgoing Radiation Understanding and Monitoring (FORUM) satellite are theorised to be highly sensitive to distributions of water vapour within Earth’s atmosphere. We exploit this sensitivity and extend the Infrared Microwave Sounding retrieval scheme for use on observations from FORUM. This scheme is evaluated on both simulated and observed measurements and shows good agreement with references of the atmospheric state.
Kelley C. Wells, Dylan B. Millet, Jared F. Brewer, Vivienne H. Payne, Karen E. Cady-Pereira, Rick Pernak, Susan Kulawik, Corinne Vigouroux, Nicholas Jones, Emmanuel Mahieu, Maria Makarova, Tomoo Nagahama, Ivan Ortega, Mathias Palm, Kimberly Strong, Matthias Schneider, Dan Smale, Ralf Sussmann, and Minqiang Zhou
Atmos. Meas. Tech., 18, 695–716, https://doi.org/10.5194/amt-18-695-2025, https://doi.org/10.5194/amt-18-695-2025, 2025
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Atmospheric volatile organic compounds (VOCs) affect both air quality and climate. Satellite measurements can help us to assess and predict their global impacts. We present new decadal (2012–2023) measurements of four key VOCs – methanol, ethene, ethyne, and hydrogen cyanide (HCN) – from the Cross-track Infrared Sounder. The measurements reflect emissions from major forests, wildfires, and industry and provide new information to advance understanding of these sources and their changes over time.
Otto Lamminpää, Jouni Susiluoto, Jonathan Hobbs, James McDuffie, Amy Braverman, and Houman Owhadi
Atmos. Meas. Tech., 18, 673–694, https://doi.org/10.5194/amt-18-673-2025, https://doi.org/10.5194/amt-18-673-2025, 2025
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We develop and demonstrate a fast forward function emulator for remote sensing of greenhouse gases. These forward functions are computationally expensive to evaluate, and as such the key challenge for many satellite missions in their data processing is the time used in these evaluations. Our method is fast and accurate enough, less than 1 % relative error, so that it could be safely used in operational processing.
Alistair Bell, Eric Sauvageat, Gunter Stober, Klemens Hocke, and Axel Murk
Atmos. Meas. Tech., 18, 555–567, https://doi.org/10.5194/amt-18-555-2025, https://doi.org/10.5194/amt-18-555-2025, 2025
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Hardware and software developments have been made on a 22 GHz microwave radiometer for the measurement of middle-atmospheric water vapour near Bern, Switzerland. Previous measurements dating back to 2010 have been re-calibrated and an improved optimal estimation retrieval performed on these measurements, giving a 13-year dataset. Measurements made with new and improved instrumental hardware are used to correct previous measurements, which show better agreement than the non-corrected dataset.
Alexandre Danjou, Grégoire Broquet, Andrew Schuh, François-Marie Bréon, and Thomas Lauvaux
Atmos. Meas. Tech., 18, 533–554, https://doi.org/10.5194/amt-18-533-2025, https://doi.org/10.5194/amt-18-533-2025, 2025
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We study the capacity of XCO2 spaceborne imagery to estimate urban CO2 emissions with synthetic data. We define automatic and standard methods and objective criteria for image selection. The wind variability and urban emission budget guide the emission estimation error. Images with low wind variability and high urban emissions account for 47 % of images and give a bias in the emission estimation of −7 % and a spread of 56 %. Other images give a bias of −31 % and a spread of 99 %.
Yiguo Pang, Longfei Tian, Denghui Hu, Shuang Gao, and Guohua Liu
Atmos. Meas. Tech., 18, 455–470, https://doi.org/10.5194/amt-18-455-2025, https://doi.org/10.5194/amt-18-455-2025, 2025
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The spatial adjacency of methane point sources can result in plume overlapping, presenting challenges for quantification from space. A separation and quantification method combining the Gaussian plume model and the integrated mass enhancement method is proposed. A modern parameter estimation technique is introduced to separate the overlapping plumes from satellite observations. The proposed method is evaluated with synthesized observations and real satellite observations.
Maximilian Reuter, Michael Hilker, Stefan Noël, Antonio Di Noia, Michael Weimer, Oliver Schneising, Michael Buchwitz, Heinrich Bovensmann, John P. Burrows, Hartmut Bösch, and Ruediger Lang
Atmos. Meas. Tech., 18, 241–264, https://doi.org/10.5194/amt-18-241-2025, https://doi.org/10.5194/amt-18-241-2025, 2025
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Carbon dioxide (CO2) and methane (CH4) are the main anthropogenic greenhouse gases. The European Copernicus CO2 monitoring satellite mission CO2M will provide measurements of their atmospheric concentrations, but the accuracy requirements are demanding and conventional retrieval methods computationally expensive. We present a new retrieval algorithm based on artificial neural networks that has the potential to meet the stringent requirements of the CO2M mission with minimal computational effort.
Xiaoyi Zhao, Vitali Fioletov, Debora Griffin, Chris McLinden, Ralf Staebler, Cristian Mihele, Kevin Strawbridge, Jonathan Davies, Ihab Abboud, Sum Chi Lee, Alexander Cede, Martin Tiefengraber, and Robert Swap
Atmos. Meas. Tech., 17, 6889–6912, https://doi.org/10.5194/amt-17-6889-2024, https://doi.org/10.5194/amt-17-6889-2024, 2024
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This study explores differences between remote sensing and in situ instruments in terms of their vertical, horizontal, and temporal sampling differences. Understanding and resolving these differences are critical for future analyses linking satellite, ground-based remote sensing, and in situ observations in air quality monitoring. It shows that the meteorological conditions (wind directions, speed, and boundary layer conditions) will strongly affect the agreement between the two measurements.
Leon Kuhn, Steffen Beirle, Sergey Osipov, Andrea Pozzer, and Thomas Wagner
Atmos. Meas. Tech., 17, 6485–6516, https://doi.org/10.5194/amt-17-6485-2024, https://doi.org/10.5194/amt-17-6485-2024, 2024
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This paper presents a new machine learning model that allows us to compute NO2 concentration profiles from satellite observations. A neural network was trained on synthetic data from the regional chemistry and transport model WRF-Chem. This is the first model of its kind. We present a thorough model validation study, covering various seasons and regions of the world.
Swathi Maratt Satheesan, Kai-Uwe Eichmann, John P. Burrows, Mark Weber, Ryan Stauffer, Anne M. Thompson, and Debra Kollonige
Atmos. Meas. Tech., 17, 6459–6484, https://doi.org/10.5194/amt-17-6459-2024, https://doi.org/10.5194/amt-17-6459-2024, 2024
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CHORA, an advanced cloud convective differential technique, enhances the accuracy of tropospheric-ozone retrievals. Unlike the traditional Pacific cloud reference sector scheme, CHORA introduces a local-cloud reference sector and an alternative approach (CLCT) for precision. Analysing monthly averaged TROPOMI data from 2018 to 2022 and validating with SHADOZ ozonesonde data, CLCT outperforms other methods and so is the preferred choice, especially in future geostationary satellite missions.
Yuhang Zhang, Huan Yu, Isabelle De Smedt, Jintai Lin, Nicolas Theys, Michel Van Roozendael, Gaia Pinardi, Steven Compernolle, Ruijing Ni, Fangxuan Ren, Sijie Wang, Lulu Chen, Jos Van Geffen, Mengyao Liu, Alexander Cede, Alexis Merlaud, Martina Friedrich, Andreas Richter, Ankie Piters, Vinod Kumar, Vinayak Sinha, Thomas Wagner, Yongjoo Choi, Hisahiro Takashima, Yugo Kanaya, Hitoshi Irie, Robert Spurr, Wenfu Sun, and Lorenzo Fabris
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-182, https://doi.org/10.5194/amt-2024-182, 2024
Revised manuscript accepted for AMT
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We developed an advanced POMINO algorithm for global retrieval of TROPOMI HCHO and NO2 VCDs with much improved consistency. Sensitivity tests demonstrate the complexity and non-linear interactions of auxiliary parameters in the AMF calculation. An improved agreement is found with measurements from a global ground-based instrument network. The POMINO retrieval provides a useful source of information for studies combining HCHO and NO2.
Minqiang Zhou, Pucai Wang, Bart Dils, Bavo Langerock, Geoff Toon, Christian Hermans, Weidong Nan, Qun Cheng, and Martine De Mazière
Atmos. Meas. Tech., 17, 6385–6396, https://doi.org/10.5194/amt-17-6385-2024, https://doi.org/10.5194/amt-17-6385-2024, 2024
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Solar absorption spectra near 2967 cm−1 recorded by a ground-based FTIR with a high spectral resolution of 0.0035 cm-1 are applied to retrieve C3H8 columns for the first time in Xianghe, China, within the NDACC-IRWG. The mean and standard deviation of the C3H8 columns are 1.80 ± 0.81 (1σ) × 1015 molec. cm-2. Good correlations are found between C3H8 and other non-methane hydrocarbons, such as C2H6 (R = 0.84) and C2H2 (R = 0.79), as well as between C3H8 and CO (R = 0.72).
Farrer Owsley-Brown, Martin J. Wooster, Mark J. Grosvenor, and Yanan Liu
Atmos. Meas. Tech., 17, 6247–6264, https://doi.org/10.5194/amt-17-6247-2024, https://doi.org/10.5194/amt-17-6247-2024, 2024
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Landscape fires produce vast amounts of smoke, affecting the atmosphere locally and globally. Whether a fire is flaming or smouldering strongly impacts the rate at which smoke is produced as well as its composition. This study tested two methods to determine these combustion phases in laboratory fires and compared them to the smoke emitted. One of these methods improved estimates of smoke emission significantly. This suggests potential for improvement in global emission estimates.
Sora Seo, Pieter Valks, Ronny Lutz, Klaus-Peter Heue, Pascal Hedelt, Víctor Molina García, Diego Loyola, Hanlim Lee, and Jhoon Kim
Atmos. Meas. Tech., 17, 6163–6191, https://doi.org/10.5194/amt-17-6163-2024, https://doi.org/10.5194/amt-17-6163-2024, 2024
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In this study, we developed an advanced retrieval algorithm for tropospheric NO2 columns from geostationary satellite spectrometers and applied it to GEMS measurements. The DLR GEMS NO2 retrieval algorithm follows the heritage from previous and existing algorithms, but improved approaches are applied to reflect the specific features of geostationary satellites. The DLR GEMS NO2 retrievals demonstrate a good capability for monitoring diurnal variability with a high spatial resolution.
Theodore K. Koenig, François Hendrick, Douglas Kinnison, Christopher F. Lee, Michel Van Roozendael, and Rainer Volkamer
Atmos. Meas. Tech., 17, 5911–5934, https://doi.org/10.5194/amt-17-5911-2024, https://doi.org/10.5194/amt-17-5911-2024, 2024
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Atmospheric bromine destroys ozone, impacts oxidation capacity, and oxidizes mercury into its toxic form. We constrain bromine by remote sensing of BrO from a mountaintop. Previous measurements retrieved two to three pieces of information vertically; we apply new methods to get five and a half vertically and two more in time. We compare with aircraft measurements to validate the methods and look at variations in BrO over the Pacific.
Kavitha Mottungan, Chayan Roychoudhury, Vanessa Brocchi, Benjamin Gaubert, Wenfu Tang, Mohammad Amin Mirrezaei, John McKinnon, Yafang Guo, David W. T. Griffith, Dietrich G. Feist, Isamu Morino, Mahesh K. Sha, Manvendra K. Dubey, Martine De Mazière, Nicholas M. Deutscher, Paul O. Wennberg, Ralf Sussmann, Rigel Kivi, Tae-Young Goo, Voltaire A. Velazco, Wei Wang, and Avelino F. Arellano Jr.
Atmos. Meas. Tech., 17, 5861–5885, https://doi.org/10.5194/amt-17-5861-2024, https://doi.org/10.5194/amt-17-5861-2024, 2024
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A combination of data analysis techniques is introduced to separate local and regional influences on observed levels of carbon dioxide, carbon monoxide, and methane from an established ground-based remote sensing network. We take advantage of the covariations in these trace gases to identify the dominant type of sources driving these levels. Applying these methods in conjunction with existing approaches to other datasets can better address uncertainties in identifying sources and sinks.
Frank Hase, Paolo Castracane, Angelika Dehn, Omaira Elena García, David W. T. Griffith, Lukas Heizmann, Nicholas B. Jones, Tomi Karppinen, Rigel Kivi, Martine de Mazière, Justus Notholt, and Mahesh Kumar Sha
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-140, https://doi.org/10.5194/amt-2024-140, 2024
Revised manuscript accepted for AMT
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The primary measurement result delivered by a Fourier Transform spectrometer is an interferogram, and the spectrum required for further analysis needs to be calculated from the interferogram by a Fourier analysis. The paper deals with technical aspects of this process and shows how the reconstruction of the spectrum can be optimized.
Edward Malina, Jure Brence, Jennifer Adams, Jovan Tanevski, Sašo Džeroski, Valentin Kantchev, and Kevin W. Bowman
EGUsphere, https://doi.org/10.5194/egusphere-2024-2392, https://doi.org/10.5194/egusphere-2024-2392, 2024
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The large fleet of Earth Observation satellites in orbit currently generate huge volumes of data, requiring significant computational resources to process in a timely manner. We present a method for predicting poor quality measurements using machine learning. We find that machine learning methods can accurately predict poor quality measurements, and remove them from the processing change, saving time and computational resources.
Chia-Pang Kuo and Christian Kummerow
Atmos. Meas. Tech., 17, 5637–5653, https://doi.org/10.5194/amt-17-5637-2024, https://doi.org/10.5194/amt-17-5637-2024, 2024
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A small satellite about the size of a shoe box, named TEMPEST, carries only a microwave sensor and is designed to measure the water cycle of the Earth from space in an economical way compared with traditional satellites, which have additional infrared sensors. To overcome the limitation, extra infrared signals from GOES-R ABI are combined with TEMPEST microwave measurements. Compared with ground observations, improved humidity information is extracted from the merged TEMPEST and ABI signals.
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.
Matthieu Dogniaux and Cyril Crevoisier
Atmos. Meas. Tech., 17, 5373–5396, https://doi.org/10.5194/amt-17-5373-2024, https://doi.org/10.5194/amt-17-5373-2024, 2024
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Many CO2-observing satellite concepts, with very different design choices and trade-offs, are expected to be put into orbit during the upcoming decade. This work uses numerical simulations to explore the impact of critical design parameters on the performance of upcoming CO2-observing satellite concepts.
Francesca Vittorioso, Vincent Guidard, and Nadia Fourrié
Atmos. Meas. Tech., 17, 5279–5299, https://doi.org/10.5194/amt-17-5279-2024, https://doi.org/10.5194/amt-17-5279-2024, 2024
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The future Meteosat Third Generation Infrared Sounder (MTG-IRS) will represent a major innovation for the monitoring of the chemical state of the atmosphere. MTG-IRS will have the advantage of being based on a geostationary platform and acquiring data with a high temporal frequency. This work aims to evaluate its potential impact over Europe within a chemical transport model (MOCAGE). The results indicate that the assimilation of these data always has a positive impact on ozone analysis.
Matthias Schneider, Kinya Toride, Farahnaz Khosrawi, Frank Hase, Benjamin Ertl, Christopher J. Diekmann, and Kei Yoshimura
Atmos. Meas. Tech., 17, 5243–5259, https://doi.org/10.5194/amt-17-5243-2024, https://doi.org/10.5194/amt-17-5243-2024, 2024
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Despite its importance for extreme weather and climate feedbacks, atmospheric convection is not well constrained. This study assesses the potential of novel tropospheric water vapour isotopologue satellite observations for improving the analyses of convective events. We find that the impact of the isotopologues is small for stable atmospheric conditions but significant for unstable conditions, which have the strongest societal impacts (e.g. storms and flooding).
Mengyao Liu, Ronald van der A, Michiel van Weele, Lotte Bryan, Henk Eskes, Pepijn Veefkind, Yongxue Liu, Xiaojuan Lin, Jos de Laat, and Jieying Ding
Atmos. Meas. Tech., 17, 5261–5277, https://doi.org/10.5194/amt-17-5261-2024, https://doi.org/10.5194/amt-17-5261-2024, 2024
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A new divergence method was developed and applied to estimate methane emissions from TROPOMI observations over the Middle East, where it is typically challenging for a satellite to measure methane due to its complicated orography and surface albedo. Our results show the potential of TROPOMI to quantify methane emissions from various sources rather than big emitters from space after objectively excluding the artifacts in the retrieval.
Yujin J. Oak, Daniel J. Jacob, Nicholas Balasus, Laura H. Yang, Heesung Chong, Junsung Park, Hanlim Lee, Gitaek T. Lee, Eunjo S. Ha, Rokjin J. Park, Hyeong-Ahn Kwon, and Jhoon Kim
Atmos. Meas. Tech., 17, 5147–5159, https://doi.org/10.5194/amt-17-5147-2024, https://doi.org/10.5194/amt-17-5147-2024, 2024
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We present an improved NO2 product from GEMS by calibrating it to TROPOMI using machine learning and by reprocessing both satellite products to adopt common NO2 profiles. Our corrected GEMS product combines the high data density of GEMS with the accuracy of TROPOMI, supporting the combined use for analyses of East Asia air quality including emissions and chemistry. This method can be extended to other species and geostationary satellites including TEMPO and Sentinel-4.
Alexander C. Bradley, Barbara Dix, Fergus Mackenzie, J. Pepijn Veefkind, and Joost A. de Gouw
EGUsphere, https://doi.org/10.5194/egusphere-2024-2352, https://doi.org/10.5194/egusphere-2024-2352, 2024
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Currently measurement of methane from the TROPOMI satellite is biased with respect to surface reflectance. This study demonstrates a new method of correcting for this bias on a seasonal timescale to allow for differences in surface reflectance in areas of intense agriculture where growing seasons may introduce a reflectance bias. We have successfully implemented this technique in the Denver-Julesburg basin where agriculture and methane extraction infrastructure is often co-located.
Tannaz H. Mohammadloo, Matthew Jones, Bas van de Kerkhof, Kyle Dawson, Brendan James Smith, Stephen Conley, Abigail Corbett, and Rutger IJzermans
EGUsphere, https://doi.org/10.5194/egusphere-2024-1175, https://doi.org/10.5194/egusphere-2024-1175, 2024
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Methane is a potent greenhouse gas. Trustable detection and quantification of methane emissions at facility level is critical to identify the largest sources, and to prioritize them for repair. We provide a systematic analysis of the uncertainty in drone-based methane emission surveys, based on theoretical considerations and historical data sets. We provide guidelines to industry on how to avoid or minimize potential errors in drone-based measurements for methane emission quantification.
Xianzhong Duan, Ming Chang, Guotong Wu, Suping Situ, Shengjie Zhu, Qi Zhang, Yibo Huangfu, Weiwen Wang, Weihua Chen, Bin Yuan, and Xuemei Wang
Atmos. Meas. Tech., 17, 4065–4079, https://doi.org/10.5194/amt-17-4065-2024, https://doi.org/10.5194/amt-17-4065-2024, 2024
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Accurately estimating biogenic volatile organic compound (BVOC) emissions in forest ecosystems has been challenging. This research presents a framework that utilizes drone-based lidar, photogrammetry, and image recognition technologies to identify plant species and estimate BVOC emissions. The largest cumulative isoprene emissions were found in the Myrtaceae family, while those of monoterpenes were from the Rubiaceae family.
Fengxin Xie, Tao Ren, Changying Zhao, Yuan Wen, Yilei Gu, Minqiang Zhou, Pucai Wang, Kei Shiomi, and Isamu Morino
Atmos. Meas. Tech., 17, 3949–3967, https://doi.org/10.5194/amt-17-3949-2024, https://doi.org/10.5194/amt-17-3949-2024, 2024
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This study demonstrates a new machine learning approach to efficiently and accurately estimate atmospheric carbon dioxide levels from satellite data. Rather than using traditional complex physics-based retrieval methods, neural network models are trained on simulated data to rapidly predict CO2 concentrations directly from satellite spectral measurements.
Steffen Beirle and Thomas Wagner
Atmos. Meas. Tech., 17, 3439–3453, https://doi.org/10.5194/amt-17-3439-2024, https://doi.org/10.5194/amt-17-3439-2024, 2024
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We present a new method for estimating emissions and lifetimes for nitrogen oxides emitted from large cities by using satellite NO2 observations combined with wind fields. The estimate is based on the simultaneous evaluation of the downwind plumes for opposing wind directions. This allows us to derive seasonal mean emissions and lifetimes for 100 cities around the globe.
Qiurun Yu, Dylan Jervis, and Yi Huang
Atmos. Meas. Tech., 17, 3347–3366, https://doi.org/10.5194/amt-17-3347-2024, https://doi.org/10.5194/amt-17-3347-2024, 2024
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This study estimated the effects of aerosols on GHGSat satellite methane retrieval and investigated the performance of simultaneously retrieving aerosol and methane information using a multi-angle viewing method. Results suggested that the performance of GHGSat methane retrieval improved when aerosols were considered, and the multi-angle viewing method is insensitive to the satellite angle setting. This performance assessment is useful for improving future GHGSat-like instruments.
Zhonghua He, Ling Gao, Miao Liang, and Zhao-Cheng Zeng
Atmos. Meas. Tech., 17, 2937–2956, https://doi.org/10.5194/amt-17-2937-2024, https://doi.org/10.5194/amt-17-2937-2024, 2024
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Using Gaofen-5B satellite data, this study detected 93 methane plume events from 32 coal mines in Shanxi, China, with emission rates spanning from 761.78 ± 185.00 to 12729.12 ± 4658.13 kg h-1, showing significant variability among sources. This study highlights Gaofen-5B’s capacity for monitoring large methane point sources, offering valuable support in reducing greenhouse gas emissions.
Heesung Chong, Gonzalo González Abad, Caroline R. Nowlan, Christopher Chan Miller, Alfonso Saiz-Lopez, Rafael P. Fernandez, Hyeong-Ahn Kwon, Zolal Ayazpour, Huiqun Wang, Amir H. Souri, Xiong Liu, Kelly Chance, Ewan O'Sullivan, Jhoon Kim, Ja-Ho Koo, William R. Simpson, François Hendrick, Richard Querel, Glen Jaross, Colin Seftor, and Raid M. Suleiman
Atmos. Meas. Tech., 17, 2873–2916, https://doi.org/10.5194/amt-17-2873-2024, https://doi.org/10.5194/amt-17-2873-2024, 2024
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We present a new bromine monoxide (BrO) product derived using radiances measured from OMPS-NM on board the Suomi-NPP satellite. This product provides nearly a decade of global stratospheric and tropospheric column retrievals, a feature that is currently rare in publicly accessible datasets. Both stratospheric and tropospheric columns from OMPS-NM demonstrate robust performance, exhibiting good agreement with ground-based observations collected at three stations (Lauder, Utqiagvik, and Harestua).
Norbert Glatthor, Thomas von Clarmann, Bernd Funke, Maya García-Comas, Udo Grabowski, Michael Höpfner, Sylvia Kellmann, Michael Kiefer, Alexandra Laeng, Andrea Linden, Manuel López-Puertas, and Gabriele P. Stiller
Atmos. Meas. Tech., 17, 2849–2871, https://doi.org/10.5194/amt-17-2849-2024, https://doi.org/10.5194/amt-17-2849-2024, 2024
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We present global atmospheric methane (CH4) and nitrous oxide (N2O) distributions retrieved from measurements of the MIPAS instrument on board the Environmental Satellite (Envisat) during 2002 to 2012. Monitoring of these gases is of scientific interest because both of them are strong greenhouse gases. We analyze the latest, improved version of calibrated MIPAS measurements. Further, we apply a new retrieval scheme leading to an improved CH4 and N2O data product .
Matthieu Dogniaux, Joannes D. Maasakkers, Daniel J. Varon, and Ilse Aben
Atmos. Meas. Tech., 17, 2777–2787, https://doi.org/10.5194/amt-17-2777-2024, https://doi.org/10.5194/amt-17-2777-2024, 2024
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We analyze Landsat 8 (L8) and Sentinel-2B (S-2B) observations of the 2022 Nord Stream 2 methane leak and show how challenging this case is for usual data analysis methods. We provide customized calibrations for this Nord Stream 2 case and assess that no firm conclusion can be drawn from L8 or S-2B single overpasses. However, if we opportunistically assume that L8 and S-2B results are independent, we find an averaged L8 and S-2B combined methane leak rate of 502 ± 464 t h−1.
Jack H. Bruno, Dylan Jervis, Daniel J. Varon, and Daniel J. Jacob
Atmos. Meas. Tech., 17, 2625–2636, https://doi.org/10.5194/amt-17-2625-2024, https://doi.org/10.5194/amt-17-2625-2024, 2024
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Methane is a potent greenhouse gas and a current high-priority target for short- to mid-term climate change mitigation. Detection of individual methane emitters from space has become possible in recent years, and the volume of data for this task has been rapidly growing, outpacing processing capabilities. We introduce an automated approach, U-Plume, which can detect and quantify emissions from individual methane sources in high-spatial-resolution satellite data.
Anna Vaughan, Gonzalo Mateo-García, Luis Gómez-Chova, Vít Růžička, Luis Guanter, and Itziar Irakulis-Loitxate
Atmos. Meas. Tech., 17, 2583–2593, https://doi.org/10.5194/amt-17-2583-2024, https://doi.org/10.5194/amt-17-2583-2024, 2024
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Methane is a potent greenhouse gas that has been responsible for around 25 % of global warming since the industrial revolution. Consequently identifying and mitigating methane emissions comprise an important step in combating the climate crisis. We develop a new deep learning model to automatically detect methane plumes from satellite images and demonstrate that this can be applied to monitor large methane emissions resulting from the oil and gas industry.
Stefan Noël, Michael Buchwitz, Michael Hilker, Maximilian Reuter, Michael Weimer, Heinrich Bovensmann, John P. Burrows, Hartmut Bösch, and Ruediger Lang
Atmos. Meas. Tech., 17, 2317–2334, https://doi.org/10.5194/amt-17-2317-2024, https://doi.org/10.5194/amt-17-2317-2024, 2024
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FOCAL-CO2M is one of the three operational retrieval algorithms which will be used to derive XCO2 and XCH4 from measurements of the forthcoming European CO2M mission. We present results of applications of FOCAL-CO2M to simulated spectra, from which confidence is gained that the algorithm is able to fulfil the challenging requirements on systematic errors for the CO2M mission (spatio-temporal bias ≤ 0.5 ppm for XCO2 and ≤ 5 ppb for XCH4).
Marvin Knapp, Ralph Kleinschek, Sanam N. Vardag, Felix Külheim, Helge Haveresch, Moritz Sindram, Tim Siegel, Bruno Burger, and André Butz
Atmos. Meas. Tech., 17, 2257–2275, https://doi.org/10.5194/amt-17-2257-2024, https://doi.org/10.5194/amt-17-2257-2024, 2024
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Imaging carbon dioxide (CO2) plumes of anthropogenic sources from planes and satellites has proven valuable for detecting emitters and monitoring climate mitigation efforts. We present the first images of CO2 plumes taken with a ground-based spectral camera, observing a coal-fired power plant as a validation target. We develop a technique to find the source emission strength with an hourly resolution, which reasonably agrees with the expected emissions under favorable conditions.
Karl Voglmeier, Voltaire A. Velazco, Luca Egli, Julian Gröbner, Alberto Redondas, and Wolfgang Steinbrecht
Atmos. Meas. Tech., 17, 2277–2294, https://doi.org/10.5194/amt-17-2277-2024, https://doi.org/10.5194/amt-17-2277-2024, 2024
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Comparison between total ozone column (TOC) measurements from ground-based Dobson and Brewer spectrophotometers generally reveals seasonally varying differences of a few percent. This study recommends a new TOC retrieval approach, which effectively eliminates these seasonally varying differences by applying new ozone absorption cross sections, appropriate slit functions for the Dobson instrument, and climatological values for the effective ozone temperature.
Jihanne El Haouari, Jean-Michel Gaucel, Christelle Pittet, Jean-Yves Tourneret, and Herwig Wendt
EGUsphere, https://doi.org/10.48550/arXiv.2404.05298, https://doi.org/10.48550/arXiv.2404.05298, 2024
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This paper explores new techniques based on sparse representations for estimating the spectral response functions of high-resolution spectrometers. The method is highly competitive with commonly used parametric models yielding more accurate estimates while accounting for wavelength dependence. The resulting normalized estimation errors of the spectrometer spectral responses are less than 1 %, which will allow better quantification of trace gas concentrations at the Earth surface.
Richard Eastes, J. Scott Evans, Quan Gan, Bill McClintock, and Jerry Lumpe
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-52, https://doi.org/10.5194/amt-2024-52, 2024
Revised manuscript accepted for AMT
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The temperature is essential to understanding the thermosphere. Most temperature measurements have indirect or had large uncertainties, especially in the lower-middle thermosphere where data are rarely available. Since October 2018 NASA’s GOLD mission has produced disk images of neutral temperatures near 160 km at locations over the Americas and Atlantic Ocean. This paper discusses both temperature retrieval techniques and issues in interpreting GOLD’s images of temperatures.
Wenfu Tang, Benjamin Gaubert, Louisa Emmons, Daniel Ziskin, Debbie Mao, David Edwards, Avelino Arellano, Kevin Raeder, Jeffrey Anderson, and Helen Worden
Atmos. Meas. Tech., 17, 1941–1963, https://doi.org/10.5194/amt-17-1941-2024, https://doi.org/10.5194/amt-17-1941-2024, 2024
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We assimilate different MOPITT CO products to understand the impact of (1) assimilating multispectral and joint retrievals versus single spectral products, (2) assimilating satellite profile products versus column products, and (3) assimilating multispectral and joint retrievals versus assimilating individual products separately.
Juseon Bak, Xiong Liu, Kai Yang, Gonzalo Gonzalez Abad, Ewan O'Sullivan, Kelly Chance, and Cheol-Hee Kim
Atmos. Meas. Tech., 17, 1891–1911, https://doi.org/10.5194/amt-17-1891-2024, https://doi.org/10.5194/amt-17-1891-2024, 2024
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The new version (V2) of the OMI ozone profile product is introduced to improve retrieval quality and long-term consistency of tropospheric ozone by incorporating the recent collection 4 OMI L1b spectral products and refining radiometric correction, forward model calculation, and a priori ozone data.
Andrea Orfanoz-Cheuquelaf, Carlo Arosio, Alexei Rozanov, Mark Weber, Annette Ladstätter-Weißenmayer, John P. Burrows, Anne M. Thompson, Ryan M. Stauffer, and Debra E. Kollonige
Atmos. Meas. Tech., 17, 1791–1809, https://doi.org/10.5194/amt-17-1791-2024, https://doi.org/10.5194/amt-17-1791-2024, 2024
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Valuable information on the tropospheric ozone column (TrOC) can be obtained globally by combining space-borne limb and nadir measurements (limb–nadir matching, LNM). This study describes the retrieval of TrOC from the OMPS instrument (since 2012) using the LNM technique. The OMPS-LNM TrOC was compared with ozonesondes and other satellite measurements, showing a good agreement with a negative bias within 1 to 4 DU. This new dataset is suitable for pollution studies.
Gabriele P. Stiller, Thomas von Clarmann, Norbert Glatthor, Udo Grabowski, Sylvia Kellmann, Michael Kiefer, Alexandra Laeng, Andrea Linden, Bernd Funke, Maya García-Comas, and Manuel López-Puertas
Atmos. Meas. Tech., 17, 1759–1789, https://doi.org/10.5194/amt-17-1759-2024, https://doi.org/10.5194/amt-17-1759-2024, 2024
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CFC-11, CFC-12, and HCFC-22 contribute to the depletion of ozone and are potent greenhouse gases. They have been banned by the Montreal protocol. With MIPAS on Envisat the atmospheric composition could be observed between 2002 and 2012. We present here the retrieval of their atmospheric distributions for the final data version 8. We characterise the derived data by their error budget and their spatial resolution. An additional representation for direct comparison to models is also provided.
Nicole Jacobs, Christopher W. O'Dell, Thomas E. Taylor, Thomas L. Logan, Brendan Byrne, Matthäus Kiel, Rigel Kivi, Pauli Heikkinen, Aronne Merrelli, Vivienne H. Payne, and Abhishek Chatterjee
Atmos. Meas. Tech., 17, 1375–1401, https://doi.org/10.5194/amt-17-1375-2024, https://doi.org/10.5194/amt-17-1375-2024, 2024
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The accuracy of trace gas retrievals from spaceborne observations, like those from the Orbiting Carbon Observatory 2 (OCO-2), are sensitive to the referenced digital elevation model (DEM). Therefore, we evaluate several global DEMs, used in versions 10 and 11 of the OCO-2 retrieval along with the Copernicus DEM. We explore the impacts of changing the DEM on biases in OCO-2-retrieved XCO2 and inferred CO2 fluxes. Our findings led to an update to OCO-2 v11.1 using the Copernicus DEM globally.
Eamon K. Conway, Amir H. Souri, Joshua Benmergui, Kang Sun, Xiong Liu, Carly Staebell, Christopher Chan Miller, Jonathan Franklin, Jenna Samra, Jonas Wilzewski, Sebastien Roche, Bingkun Luo, Apisada Chulakadabba, Maryann Sargent, Jacob Hohl, Bruce Daube, Iouli Gordon, Kelly Chance, and Steven Wofsy
Atmos. Meas. Tech., 17, 1347–1362, https://doi.org/10.5194/amt-17-1347-2024, https://doi.org/10.5194/amt-17-1347-2024, 2024
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The work presented here describes the processes required to convert raw sensor data for the MethaneAIR instrument to geometrically calibrated data. Each algorithm is described in detail. MethaneAIR is the airborne simulator for MethaneSAT, a new satellite under development by MethaneSAT LLC, a subsidiary of the EDF. MethaneSAT's goals are to precisely map over 80 % of the production sources of methane emissions from oil and gas fields across the globe to a high degree of accuracy.
Javier Roger, Luis Guanter, Javier Gorroño, and Itziar Irakulis-Loitxate
Atmos. Meas. Tech., 17, 1333–1346, https://doi.org/10.5194/amt-17-1333-2024, https://doi.org/10.5194/amt-17-1333-2024, 2024
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Methane emissions can be identified using remote sensing, but surface-related structures disturb detection. In this work, a variation of the matched filter method that exploits a large fraction of the near-infrared range (1000–2500 nm) is applied. In comparison to the raw matched filter, it reduces background noise and strongly attenuates the surface-related artifacts, which leads to a greater detection capability. We propose this variation as a standard methodology for methane detection.
Blanca Fuentes Andrade, Michael Buchwitz, Maximilian Reuter, Heinrich Bovensmann, Andreas Richter, Hartmut Boesch, and John P. Burrows
Atmos. Meas. Tech., 17, 1145–1173, https://doi.org/10.5194/amt-17-1145-2024, https://doi.org/10.5194/amt-17-1145-2024, 2024
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We developed a method to estimate CO2 emissions from localized sources, such as power plants, using satellite data and applied it to estimate CO2 emissions from the Bełchatów Power Station (Poland). As the detection of CO2 emission plumes from satellite data is difficult, we used observations of co-emitted NO2 to constrain the emission plume region. Our results agree with CO2 emission estimations based on the power-plant-generated power and emission factors.
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Short summary
A neural-network-based approach was suggested to estimate CO2 emissions using satellite-based net primary productivity (NPP) and XCO2 retrievals. XCO2 anomalies were calculated for each year using OCO-2 retrievals. A Generalized Regression Neural Network (GRNN) model was then built; NPP, XCO2 anomalies, and ODIAC CO2 emissions from 2015 to 2018 were used as a training dataset; and, finally, CO2 emissions were predicted for 2019 based on the NPP and XCO2 anomalies calculated for the same year.
A neural-network-based approach was suggested to estimate CO2 emissions using satellite-based...
