Articles | Volume 13, issue 8
https://doi.org/10.5194/amt-13-4437-2020
© 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-13-4437-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Aug 2020
Research article |
| 17 Aug 2020
| 17 Aug 2020
CLIMCAPS observing capability for temperature, moisture, and trace gases from AIRS/AMSU and CrIS/ATMS
Science and Technology Corporation, Columbia, MD 21046, USA
Christopher D. Barnet
Science and Technology Corporation, Columbia, MD 21046, USA
Related authors
Nadia Smith and Christopher D. Barnet
EGUsphere, https://doi.org/10.5194/egusphere-2024-2448, https://doi.org/10.5194/egusphere-2024-2448, 2024
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
CLIMCAPS extends the Aqua AIRS+AMSU record with retrievals from CrIS+ATMS on Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System series (JPSS-1 to JPSS-4). With “continuous” we mean a data record that is consistent in its characterization of natural variation despite changes in source instrumentation. Here we investigate how sounding continuity can improved across the full CLIMCAPS record (2002 to present day) spanning multiple instruments and satellites.
Audrey Gaudel, Ilann Bourgeois, Meng Li, Kai-Lan Chang, Jerald Ziemke, Bastien Sauvage, Ryan M. Stauffer, Anne M. Thompson, Debra E. Kollonige, Nadia Smith, Daan Hubert, Arno Keppens, Juan Cuesta, Klaus-Peter Heue, Pepijn Veefkind, Kenneth Aikin, Jeff Peischl, Chelsea R. Thompson, Thomas B. Ryerson, Gregory J. Frost, Brian C. McDonald, and Owen R. Cooper
Atmos. Chem. Phys., 24, 9975–10000, https://doi.org/10.5194/acp-24-9975-2024, https://doi.org/10.5194/acp-24-9975-2024, 2024
Short summary
Short summary
The study examines tropical tropospheric ozone changes. In situ data from 1994–2019 display increased ozone, notably over India, Southeast Asia, and Malaysia and Indonesia. Sparse in situ data limit trend detection for the 15-year period. In situ and satellite data, with limited sampling, struggle to consistently detect trends. Continuous observations are vital over the tropical Pacific Ocean, Indian Ocean, western Africa, and South Asia for accurate ozone trend estimation in these regions.
Nora Mettig, Mark Weber, Alexei Rozanov, John P. Burrows, Pepijn Veefkind, Anne M. Thompson, Ryan M. Stauffer, Thierry Leblanc, Gerard Ancellet, Michael J. Newchurch, Shi Kuang, Rigel Kivi, Matthew B. Tully, Roeland Van Malderen, Ankie Piters, Bogumil Kois, René Stübi, and Pavla Skrivankova
Atmos. Meas. Tech., 15, 2955–2978, https://doi.org/10.5194/amt-15-2955-2022, https://doi.org/10.5194/amt-15-2955-2022, 2022
Short summary
Short summary
Vertical ozone profiles from combined spectral measurements in the UV and IR spectral ranges were retrieved by using data from TROPOMI/S5P and CrIS/Suomi-NPP. The vertical resolution and accuracy of the ozone profiles are improved by combining both wavelength ranges compared to retrievals limited to UV or IR spectral data only. The advancement of our TOPAS algorithm for combined measurements is required because in the UV-only retrieval the vertical resolution in the troposphere is very limited.
Qing Yue, Eric J. Fetzer, Likun Wang, Brian H. Kahn, Nadia Smith, John M. Blaisdell, Kerry G. Meyer, Mathias Schreier, Bjorn Lambrigtsen, and Irina Tkatcheva
Atmos. Meas. Tech., 15, 2099–2123, https://doi.org/10.5194/amt-15-2099-2022, https://doi.org/10.5194/amt-15-2099-2022, 2022
Short summary
Short summary
The self-consistency and continuity of cloud retrievals from infrared sounders and imagers aboard Aqua and SNPP (Suomi National Polar-orbiting Partnership) are examined at the pixel scale. Cloud products are found to be consistent with each other. Differences between sounder products are mainly due to cloud clearing and the treatment of clouds in scenes with unsuccessful atmospheric retrievals. The impact of algorithm and instrument differences is clearly seen in the imager cloud retrievals.
Nadia Smith and Christopher D. Barnet
EGUsphere, https://doi.org/10.5194/egusphere-2024-2448, https://doi.org/10.5194/egusphere-2024-2448, 2024
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
CLIMCAPS extends the Aqua AIRS+AMSU record with retrievals from CrIS+ATMS on Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System series (JPSS-1 to JPSS-4). With “continuous” we mean a data record that is consistent in its characterization of natural variation despite changes in source instrumentation. Here we investigate how sounding continuity can improved across the full CLIMCAPS record (2002 to present day) spanning multiple instruments and satellites.
Audrey Gaudel, Ilann Bourgeois, Meng Li, Kai-Lan Chang, Jerald Ziemke, Bastien Sauvage, Ryan M. Stauffer, Anne M. Thompson, Debra E. Kollonige, Nadia Smith, Daan Hubert, Arno Keppens, Juan Cuesta, Klaus-Peter Heue, Pepijn Veefkind, Kenneth Aikin, Jeff Peischl, Chelsea R. Thompson, Thomas B. Ryerson, Gregory J. Frost, Brian C. McDonald, and Owen R. Cooper
Atmos. Chem. Phys., 24, 9975–10000, https://doi.org/10.5194/acp-24-9975-2024, https://doi.org/10.5194/acp-24-9975-2024, 2024
Short summary
Short summary
The study examines tropical tropospheric ozone changes. In situ data from 1994–2019 display increased ozone, notably over India, Southeast Asia, and Malaysia and Indonesia. Sparse in situ data limit trend detection for the 15-year period. In situ and satellite data, with limited sampling, struggle to consistently detect trends. Continuous observations are vital over the tropical Pacific Ocean, Indian Ocean, western Africa, and South Asia for accurate ozone trend estimation in these regions.
Nora Mettig, Mark Weber, Alexei Rozanov, John P. Burrows, Pepijn Veefkind, Anne M. Thompson, Ryan M. Stauffer, Thierry Leblanc, Gerard Ancellet, Michael J. Newchurch, Shi Kuang, Rigel Kivi, Matthew B. Tully, Roeland Van Malderen, Ankie Piters, Bogumil Kois, René Stübi, and Pavla Skrivankova
Atmos. Meas. Tech., 15, 2955–2978, https://doi.org/10.5194/amt-15-2955-2022, https://doi.org/10.5194/amt-15-2955-2022, 2022
Short summary
Short summary
Vertical ozone profiles from combined spectral measurements in the UV and IR spectral ranges were retrieved by using data from TROPOMI/S5P and CrIS/Suomi-NPP. The vertical resolution and accuracy of the ozone profiles are improved by combining both wavelength ranges compared to retrievals limited to UV or IR spectral data only. The advancement of our TOPAS algorithm for combined measurements is required because in the UV-only retrieval the vertical resolution in the troposphere is very limited.
Qing Yue, Eric J. Fetzer, Likun Wang, Brian H. Kahn, Nadia Smith, John M. Blaisdell, Kerry G. Meyer, Mathias Schreier, Bjorn Lambrigtsen, and Irina Tkatcheva
Atmos. Meas. Tech., 15, 2099–2123, https://doi.org/10.5194/amt-15-2099-2022, https://doi.org/10.5194/amt-15-2099-2022, 2022
Short summary
Short summary
The self-consistency and continuity of cloud retrievals from infrared sounders and imagers aboard Aqua and SNPP (Suomi National Polar-orbiting Partnership) are examined at the pixel scale. Cloud products are found to be consistent with each other. Differences between sounder products are mainly due to cloud clearing and the treatment of clouds in scenes with unsuccessful atmospheric retrievals. The impact of algorithm and instrument differences is clearly seen in the imager cloud retrievals.
Related subject area
Subject: Gases | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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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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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.
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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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.
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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.
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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.
Alistair Bell, Eric Sauvageat, Gunter Stober, Klemens Hocke, and Axel Murk
EGUsphere, https://doi.org/10.5194/egusphere-2024-2474, https://doi.org/10.5194/egusphere-2024-2474, 2024
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Hardware and software developments have been made on a 22 GHz microwave radiometer for the measurement of middle atmosphere 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 long dataset. Measurements made with new and improved instrumental hardware are used to correct previous measurements, which show better agreement than the non-corrected dataset.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-2365, https://doi.org/10.5194/egusphere-2024-2365, 2024
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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.
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.
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.
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. Discuss., https://doi.org/10.5194/amt-2024-27, https://doi.org/10.5194/amt-2024-27, 2024
Revised manuscript accepted for AMT
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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 the meteorological conditions (wind directions, speed, and boundary layer conditions) will strongly affect the agreement between the two measurements.
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.
Gregory R. McGarragh, Christopher W. O'Dell, Sean M. R. Crowell, Peter Somkuti, Eric B. Burgh, and Berrien Moore III
Atmos. Meas. Tech., 17, 1091–1121, https://doi.org/10.5194/amt-17-1091-2024, https://doi.org/10.5194/amt-17-1091-2024, 2024
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Carbon dioxide and methane are greenhouse gases that have been rapidly increasing due to human activity since the industrial revolution, leading to global warming and subsequently negative affects on the climate. It is important to measure the concentrations of these gases in order to make climate predictions that drive policy changes to mitigate climate change. GeoCarb aims to measure the concentrations of these gases from space over the Americas at unprecedented spatial and temporal scales.
Jianping Mao, James B. Abshire, S. Randy Kawa, Xiaoli Sun, and Haris Riris
Atmos. Meas. Tech., 17, 1061–1074, https://doi.org/10.5194/amt-17-1061-2024, https://doi.org/10.5194/amt-17-1061-2024, 2024
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NASA Goddard Space Flight Center has developed an integrated-path, differential absorption lidar approach to measure column-averaged atmospheric CO2 (XCO2). We demonstrated the lidar’s capability to measure XCO2 to cloud tops ,as well as to the ground, with the data from the summer 2017 airborne campaign in the US and Canada. This active remote sensing technique can provide all-sky data coverage and high-quality XCO2 measurements for future airborne science campaigns and space missions.
Jong-Uk Park, Hyun-Jae Kim, Jin-Soo Park, Jinsoo Choi, Sang Seo Park, Kangho Bae, Jong-Jae Lee, Chang-Keun Song, Soojin Park, Kyuseok Shim, Yeonsoo Cho, and Sang-Woo Kim
Atmos. Meas. Tech., 17, 197–217, https://doi.org/10.5194/amt-17-197-2024, https://doi.org/10.5194/amt-17-197-2024, 2024
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The high-spatial-resolution NO2 vertical column densities (VCDs) were measured from airborne observations using the low-cost hyperspectral imaging sensor (HIS) at three industrial areas in South Korea with the newly developed versatile NO2 VCD retrieval algorithm apt to be applied to the instruments with volatile optical and radiometric properties. The airborne HIS observations emphasized the intensifying satellite sub-grid variability in NO2 VCDs near the emission sources.
William R. Keely, Steffen Mauceri, Sean Crowell, and Christopher W. O'Dell
Atmos. Meas. Tech., 16, 5725–5748, https://doi.org/10.5194/amt-16-5725-2023, https://doi.org/10.5194/amt-16-5725-2023, 2023
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Measurement errors in satellite observations of CO2 attributed to co-estimated atmospheric variables are corrected using a linear regression on quality-filtered data. We propose a nonlinear method that improves correction against a set of ground truth proxies and allows for high throughput of well-corrected data.
Manuel López-Puertas, Maya García-Comas, Bernd Funke, Thomas von Clarmann, Norbert Glatthor, Udo Grabowski, Sylvia Kellmann, Michael Kiefer, Alexandra Laeng, Andrea Linden, and Gabriele P. Stiller
Atmos. Meas. Tech., 16, 5609–5645, https://doi.org/10.5194/amt-16-5609-2023, https://doi.org/10.5194/amt-16-5609-2023, 2023
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This paper describes a new version (V8) of ozone data from MIPAS middle-atmosphere spectra. The dataset comprises high-quality ozone profiles from 20 to 100 km, with pole-to-pole latitude coverage for the day- and nighttime, spanning 2005 until 2012. An exhaustive treatment of errors has been performed. Compared to other satellite instruments, MIPAS ozone shows a positive bias of 5 %–8 % below 70 km. In the upper mesosphere, this new version agrees much better than previous ones (within 10 %).
Minqiang Zhou, Bavo Langerock, Mahesh Kumar Sha, Christian Hermans, Nicolas Kumps, Rigel Kivi, Pauli Heikkinen, Christof Petri, Justus Notholt, Huilin Chen, and Martine De Mazière
Atmos. Meas. Tech., 16, 5593–5608, https://doi.org/10.5194/amt-16-5593-2023, https://doi.org/10.5194/amt-16-5593-2023, 2023
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Atmospheric N2O and CH4 columns are successfully retrieved from low-resolution FTIR spectra recorded by a Bruker VERTEX 70. The 1-year measurements at Sodankylä show that the N2O total columns retrieved from 125HR and VERTEX 70 spectra are −0.3 ± 0.7 % with an R value of 0.93. The relative differences between the CH4 total columns retrieved from the 125HR and VERTEX spectra are 0.0 ± 0.8 % with an R value of 0.87. Such a technique can help to fill the gap in NDACC N2O and CH4 measurements.
Simon Warnach, Holger Sihler, Christian Borger, Nicole Bobrowski, Steffen Beirle, Ulrich Platt, and Thomas Wagner
Atmos. Meas. Tech., 16, 5537–5573, https://doi.org/10.5194/amt-16-5537-2023, https://doi.org/10.5194/amt-16-5537-2023, 2023
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BrO inside volcanic gas plumes but can be used in combination with SO2 to characterize the volcanic property and its activity state. High-quality satellite observations can provide a global inventory of this important quantity. This paper investigates how to accurately detect BrO inside volcanic plumes from the satellite UV spectrum. A sophisticated novel non-volcanic background correction scheme is presented, and systematic errors including cross-interference with formaldehyde are minimized.
Vitali E. Fioletov, Chris A. McLinden, Debora Griffin, Nickolay A. Krotkov, Can Li, Joanna Joiner, Nicolas Theys, and Simon Carn
Atmos. Meas. Tech., 16, 5575–5592, https://doi.org/10.5194/amt-16-5575-2023, https://doi.org/10.5194/amt-16-5575-2023, 2023
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Snow-covered terrain, with its high reflectance in the UV, typically enhances satellite sensitivity to boundary layer pollution. However, a significant fraction of high-quality cloud-free measurements over snow is currently excluded from analyses. In this study, we investigated how satellite SO2 measurements over snow-covered surfaces can be used to improve estimations of annual SO2 emissions.
Lieven Clarisse, Bruno Franco, Martin Van Damme, Tommaso Di Gioacchino, Juliette Hadji-Lazaro, Simon Whitburn, Lara Noppen, Daniel Hurtmans, Cathy Clerbaux, and Pierre Coheur
Atmos. Meas. Tech., 16, 5009–5028, https://doi.org/10.5194/amt-16-5009-2023, https://doi.org/10.5194/amt-16-5009-2023, 2023
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Ammonia is an important atmospheric pollutant. This article presents version 4 of the algorithm which retrieves ammonia abundances from the infrared measurements of the satellite sounder IASI. A measurement operator is introduced that can emulate the measurements (so-called averaging kernels) and measurement uncertainty is better characterized. Several other changes to the product itself are also documented, most of which improve the temporal consistency of the 2007–2022 IASI NH3 dataset.
Vladimir Savastiouk, Henri Diémoz, and C. Thomas McElroy
Atmos. Meas. Tech., 16, 4785–4806, https://doi.org/10.5194/amt-16-4785-2023, https://doi.org/10.5194/amt-16-4785-2023, 2023
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This paper describes a way to significantly improve ozone measurements at low sun elevations and large ozone amounts when using the Brewer ozone spectrophotometer. The proposed algorithm will allow more uniform ozone measurements across the monitoring network. This will contribute to more reliable trend analysis and support the satellite validation. This research contributes to better understanding the physics of the instrument, and the new algorithm is based on this new knowledge.
Alexandre Danjou, Grégoire Broquet, Andrew Schuh, François-Marie Bréon, and Thomas Lauvaux
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-199, https://doi.org/10.5194/amt-2023-199, 2023
Revised manuscript accepted for AMT
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We study the capacity of XCO2 space-borne imagery to estimate urban CO2 emissions with synthetic data. We define automatic and standard methods, and objective criteria for image selection. 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 on the emission estimation of -7 % of the emissions and a spread of 56 %. Other images give a bias of -31 % and a spread of 99 %.
Yuhang Zhang, Jintai Lin, Jhoon Kim, Hanlim Lee, Junsung Park, Hyunkee Hong, Michel Van Roozendael, Francois Hendrick, Ting Wang, Pucai Wang, Qin He, Kai Qin, Yongjoo Choi, Yugo Kanaya, Jin Xu, Pinhua Xie, Xin Tian, Sanbao Zhang, Shanshan Wang, Siyang Cheng, Xinghong Cheng, Jianzhong Ma, Thomas Wagner, Robert Spurr, Lulu Chen, Hao Kong, and Mengyao Liu
Atmos. Meas. Tech., 16, 4643–4665, https://doi.org/10.5194/amt-16-4643-2023, https://doi.org/10.5194/amt-16-4643-2023, 2023
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Our tropospheric NO2 vertical column density product with high spatiotemporal resolution is based on the Geostationary Environment Monitoring Spectrometer (GEMS) and named POMINO–GEMS. Strong hotspot signals and NO2 diurnal variations are clearly seen. Validations with multiple satellite products and ground-based, mobile car and surface measurements exhibit the overall great performance of the POMINO–GEMS product, indicating its capability for application in environmental studies.
Cited articles
Aires, F., Rossow, Scott, N. A., and Chédin, A.: Remote sensing from the
infrared atmospheric sounding interferometer instrument 2. Simultaneous
retrieval of temperature, water vapor, and ozone atmospheric profiles,
J. Geophys. Res., 107, 4620, https://doi.org/10.1029/2001JD001591, 2002.
AIRS Science Team/Joao Texeira: Aqua AIRS L2 standard retrieval product
using AIRS IR and AMSU, without-HSB V6, https://doi.org/10.5067/AQUA/AIRS/DATA201,
2013.
Aumann, H. H., Chahine, M. T., Gautier, C., Goldberg, M. D., Kalnay, E.,
McMillin, L. M., Revercomb, H., Rosenkranz, P. W., Smith, W. L., Staelin, D.
H., Strow, L. L., and Susskind, J.: AIRS/AMSU/HSB on the aqua mission:
design, science objectives, data products, and processing systems, IEEE T. Geosci. Remote, 41, 253–264, https://doi.org/10.1109/TGRS.2002.808356, 2003.
Blackwell, W. J.: A neural-network technique for the retrieval of
atmospheric temperature and moisture profiles from high spectral resolution
sounding data, IEEE T. Geosci. Remote, 43, 2535–2546, https://doi.org/10.1109/TGRS.2005.855071, 2005.
Bowman, K. W., Rodgers, C. D., Kulawik, S. S., Worden, J., Sarkissian, E.,
Osterman, G., Steck, T., Ming Lou, Eldering, A., Shephard, M., Worden, H.,
Lampel, M., Clough, S., Brown, P., Rinsland, C., Gunson, M., and Beer, R.:
Tropospheric emission spectrometer: retrieval method and error analysis,
IEEE T. Geosci. Remote, 44, 1297–1307, https://doi.org/10.1109/TGRS.2006.871234, 2006.
Ceccherini, S. and Ridolfi, M.: Technical Note: Variance–covariance matrix and averaging kernels for the Levenberg–Marquardt solution of the retrieval of atmospheric vertical profiles, Atmos. Chem. Phys., 10, 3131–3139, https://doi.org/10.5194/acp-10-3131-2010, 2010.
Ceccherini, S., Carli, B., Pascale, E., Prosperi, M., Raspollini, P., and
Dinelli, B. M.: Comparison of measurements made with two different
instruments of the same atmospheric vertical profile, Appl. Optics,
42, 6465, https://doi.org/10.1364/AO.42.006465, 2003.
Ceccherini, S., Raspollini, P., and Carli, B.: Optimal use of the information
provided by indirect measurements of atmospheric vertical profiles, Opt.
Express, 17, 4944, https://doi.org/10.1364/OE.17.004944, 2009.
Chahine, M. T.: Remote sounding of cloudy atmospheres I: The single cloud
layer, J. Atmos. Sci., 31, 233–243, 1974.
Chahine, M. T.: Retemote sounding of cloudy parameters II: Multiple cloud
formations, J. Atmos. Sci., 34, 744–757, 1977.
Chahine, M. T.: Remote sensing of cloud parameters, J. Atmos. Sci., 39,
159–170, 1982.
Chahine, M. T., Pagano, T. S., Aumann, H. H., Atlas, R., Barnet, C.,
Blaisdell, J., Chen, L., Divakarla, M., Fetzer, E. J., Goldberg, M.,
Gautier, C., Granger, S., Hannon, S., Irion, F. W., Kakar, R., Kalnay, E.,
Lambrigtsen, B. H., Lee, S.-Y., Le Marshall, J., Mcmillan, W. W., Mcmillin,
L., Olsen, E. T., Revercomb, H., Rosenkranz, P., Smith, W. L., Staelin, D.,
Strow, L. L., Susskind, J., Tobin, D., Wolf, W., and Zhou, L.: AIRS:
Improving Weather Forecasting and Providing New Data on Greenhouse Gases,
B. Am. Meteorol. Soc., 87, 911–926, https://doi.org/10.1175/BAMS-87-7-911, 2006.
Chalon, G., Astruc, P., Hébert, Ph., Blumstein, D., Buil, C., Carlier,
T., Clauss, A., Siméoni, D., and Tournier, B.: IASI instrument: technical
description and measured performances, in: International Conference on Space
Optics — ICSO 2004, edited by: Costeraste, J. and Armandillo, E.,
SPIE, Toulouse, France, p. 51, 2017.
Clerbaux, C., Hadji-Lazaro, J., Hauglustaine, D., Mégie, G., Khattatov,
B., and Lamarque, J.-F.: Assimilation of carbon monoxide measured from
satellite in a three-dimensional chemistry-transport model, J.
Geophys. Res.-Atmos., 106(D14), 15385–15394,
https://doi.org/10.1029/2000JD900682, 2001.
DeSouza-Machado, S., Strow, L. L., Tangborn, A., Huang, X., Chen, X., Liu, X., Wu, W., and Yang, Q.: Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the SARTA all-sky infrared radiative transfer algorithm, Atmos. Meas. Tech., 11, 529–550, https://doi.org/10.5194/amt-11-529-2018, 2018.
Fu, D., Bowman, K. W., Worden, H. M., Natraj, V., Worden, J. R., Yu, S., Veefkind, P., Aben, I., Landgraf, J., Strow, L., and Han, Y.: High-resolution tropospheric carbon monoxide profiles retrieved from CrIS and TROPOMI, Atmos. Meas. Tech., 9, 2567–2579, https://doi.org/10.5194/amt-9-2567-2016, 2016.
Gambacorta, A. and Barnet, C. D.: Methodology and Information Content of the
NOAA NESDIS Operational Channel Selection for the Cross-Track Infrared
Sounder (CrIS), IEEE T. Geosci. Remote, 51, 3207–3216, https://doi.org/10.1109/TGRS.2012.2220369, 2013.
Gaudel, A., Cooper, O. R., Ancellet, G., Barret, B., Boynard, A., Burrows,
J. P., Clerbaux, C., Coheur, P.-F., Cuesta, J., Cuevas, E., Doniki, S.,
Dufour, G., Ebojie, F., Foret, G., Garcia, O., Granados-Muñoz, M. J.,
Hannigan, J. W., Hase, F., Hassler, B., Huang, G., Hurtmans, D., Jaffe, D.,
Jones, N., Kalabokas, P., Kerridge, B., Kulawik, S., Latter, B., Leblanc,
T., Le Flochmoën, E., Lin, W., Liu, J., Liu, X., Mahieu, E.,
McClure-Begley, A., Neu, J. L., Osman, M., Palm, M., Petetin, H.,
Petropavlovskikh, I., Querel, R., Rahpoe, N., Rozanov, A., Schultz, M. G.,
Schwab, J., Siddans, R., Smale, D., Steinbacher, M., Tanimoto, H., Tarasick,
D. W., Thouret, V., Thompson, A. M., Trickl, T., Weatherhead, E., Wespes,
C., Worden, H. M., Vigouroux, C., Xu, X., Zeng, G., Ziemke, J., Helmig, D.,
and Lewis, A.: Tropospheric Ozone Assessment Report: Present-day
distribution and trends of tropospheric ozone relevant to climate and global
atmospheric chemistry model evaluation, Elem. Sci. Anth., 6, 39, https://doi.org/10.1525/elementa.291, 2018.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan,
K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M.,
Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective
Analysis for Research and Applications, Version 2 (MERRA-2), J.
Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
GMAO: MERRA-2 inst3_3d_asm_Nv:
3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Assimilated
Meteorological Fields V5.12.4, https://doi.org/10.5067/WWQSXQ8IVFW8, 2015.
Goldberg, D. G., Qu, Y., McMillim, L. M., Wolf, W., Zhou, L., and Divakarla,
G.: AIRS near-real-time products and algorithms in support of operational
numerical weather prediction, IEEE T. Geosci. Remote, 41, 379–389, 2003.
Han, Y., Revercomb, H., Cromp, M., Gu, D., Johnson, D., Mooney, D., Scott,
D., Strow, L., Bingham, G., Borg, L., Chen, Y., DeSlover, D., Esplin, M.,
Hagan, D., Jin, X., Knuteson, R., Motteler, H., Predina, J., Suwinski, L.,
Taylor, J., Tobin, D., Tremblay, D., Wang, C., Wang, L., Wang, L., and
Zavyalov, V.: Suomi NPP CrIS measurements, sensor data record algorithm,
calibration and validation activities, and record data quality: CRIS SENSOR
DATA RECORD QUALITY, J. Geophys. Res.-Atmos., 118, 12734–12748, https://doi.org/10.1002/2013JD020344, 2013.
Hearty, T. J., Savtchenko, A., Tian, B., Fetzer, E., Yung, Y. L., Theobald,
M., Vollmer, B., Fishbein, E., and Won, Y.-I.: Estimating sampling biases and
measurement uncertainties of AIRS/AMSU-A temperature and water vapor
observations using MERRA reanalysis, J. Geophys. Res.-Atmos.,
119, 2725–2741, https://doi.org/10.1002/2013JD021205, 2014.
Hook, S.: Combined ASTER and MODIS Emissivity database over Land (CAMEL)
Coefficient Monthly Global 0.05Deg V002,
https://doi.org/10.5067/MEASURES/LSTE/CAM5K30CF.002, 2019.
Hulley, G. C., Hughes, C. G., and Hook, S. J.: Quantifying uncertainties in
land surface temperature and emissivity retrievals from ASTER and MODIS
thermal infrared data: LST&E UNCERTAINTIES, J. Geophys. Res.-Atmos., 117, D23113, https://doi.org/10.1029/2012JD018506, 2012.
Irion, F. W., Kahn, B. H., Schreier, M. M., Fetzer, E. J., Fishbein, E., Fu, D., Kalmus, P., Wilson, R. C., Wong, S., and Yue, Q.: Single-footprint retrievals of temperature, water vapor and cloud properties from AIRS, Atmos. Meas. Tech., 11, 971–995, https://doi.org/10.5194/amt-11-971-2018, 2018.
Iturbide-Sanchez, F., Liu, Q., Gambacorta, A., Barnet, C., Nalli, N. R.,
Tan, C., and Santos da Silva, S. R.: Using averaging kernels to study the
vertical resolution of nucaps temperature and water vapor, in: 2017 IEEE
International Geoscience and Remote Sensing Symposium (IGARSS),
IEEE, Fort Worth, TX, 33–35, 2017.
Levelt, P. F., Khattatov, B. V., Gille, J. C., Brasseur, G. P., Tie, X. X.,
and Waters, J. W.: Assimilation of MLS ozone measurements in the global
three-dimensional chemistry transport model ROSE, Geophys. Res. Lett., 25, 4493–4496, https://doi.org/10.1029/1998GL900152, 1998.
Liu, J., Fung, I., Kalnay, E., Kang, J.-S., Olsen, E. T., and Chen, L.:
Simultaneous assimilation of AIRS Xco2 and meteorological observations in a carbon climate model with an ensemble Kalman filter: ASSIMILATION OF AIRS XCO2, J. Geophys. Res.-Atmos., 117,
D05309, https://doi.org/10.1029/2011JD016642, 2012.
Maddy, E. S.: Investigations of the spatial and temporal resolutions of
retrievals of atmospheric CO2 from the Atmospheric InfraRed Sounder (AIRS), PhD, University of Maryland, Maryland, 2007.
Maddy, E. S. and Barnet, C. D.: Vertical Resolution Estimates in Version 5
of AIRS Operational Retrievals, IEEE T. Geosci. Remote, 46, 2375–2384, https://doi.org/10.1109/TGRS.2008.917498, 2008.
Maddy, E. S., Barnet, C. D., and Gambacorta, A.: A Computationally Efficient
Retrieval Algorithm for Hyperspectral Sounders Incorporating A Priori Information, IEEE T. Geosci. Remote, 6=, 802–806,
https://doi.org/10.1109/LGRS.2009.2025780, 2009.
Masuda, K., Takashima, T., and Takayama, Y.: Emissivity of pure and sea waters for the model sea surface in the infrared window regions, Remote
Sens. Environ., 24, 313–329, https://doi.org/10.1016/0034-4257(88)90032-6, 1988.
Merchant, C., Holl, G., Mittaz, J., and Woolliams, E.: Radiance Uncertainty
Characterisation to Facilitate Climate Data Record Creation, Remote Sens.-Basel, 11, 474, https://doi.org/10.3390/rs11050474, 2019.
Merchant, C. J., Paul, F., Popp, T., Ablain, M., Bontemps, S., Defourny, P.,
Hollmann, R., Lavergne, T., Laeng, A., de Leeuw, G., Mittaz, J., Poulsen,
C., Povey, A. C., Reuter, M., Sathyendranath, S., Sandven, S., Sofieva, V.
F., and Wagner, W.: Uncertainty information in climate data records from
Earth observation, Earth Syst. Sci. Data, 9, 511–527,
https://doi.org/10.5194/essd-9-511-2017, 2017.
Milstein, A. B. and Blackwell, W. J.: Neural network temperature and
moisture retrieval algorithm validation for AIRS/AMSU and CrIS/ATMS: NEURAL
NETWORK T AND Q VALIDATION, J. Geophys. Res.-Atmos., 121, 1414–1430, https://doi.org/10.1002/2015JD024008, 2016.
Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015.
Nalli, N. R., Gambacorta, A., Liu, Q., Barnet, C. D., Tan, C., Iturbide-Sanchez, F., Reale, T., Sun, B., Wilson, M., Borg, L., and Morris,
V. R.: Validation of Atmospheric Profile Retrievals From the SNPP
NOAA-Unique Combined Atmospheric Processing System. Part 1: Temperature and
Moisture, IEEE T. Geosci. Remote, 56, 180–190, https://doi.org/10.1109/TGRS.2017.2744558, 2018a.
Nalli, N. R., Gambacorta, A., Liu, Q., Tan, C., Iturbide-Sanchez, F.,
Barnet, C. D., Joseph, E., Morris, V. R., Oyola, M., and Smith, J. W.:
Validation of Atmospheric Profile Retrievals from the SNPP NOAA-Unique
Combined Atmospheric Processing System. Part 2: Ozone, IEEE T. Geosci. Remote, 56, 598–607, https://doi.org/10.1109/TGRS.2017.2762600, 2018b.
Pierce, R. B., Al-Saadi, J., Kittaka, C., Schaack, T., Lenzen, A., Bowman,
K., Szykman, J., Soja, A., Ryerson, T., Thompson, A. M., Bhartia, P., and
Morris, G. A.: Impacts of background ozone production on Houston and Dallas,
Texas, air quality during the Second Texas Air Quality Study field mission,
J. Geophys. Res., 114, D00F09, https://doi.org/10.1029/2008JD011337, 2009.
Pougatchev, N.: Validation of atmospheric sounders by correlative
measurements, Appl. Optics, 47, 4739, https://doi.org/10.1364/AO.47.004739, 2008.
Pougatchev, N. S., Connor, B. J., Jones, N. B., and Rinsland, C. P.:
Validation of ozone profile retrievals from infrared ground-based solar
spectra, Geophys. Res. Lett., 23, 1637–1640, https://doi.org/10.1029/96GL01501, 1996.
Ramapriyan, H. K., Behnke, J., Sofinowski, E., Lowe, D., and Esfandiari, M.
A.: Evolution of the Earth Observing System (EOS) Data and Information
System (EOSDIS), in: Standard-Based Data and Information Systems for Earth
Observation, edited by: Di, L. and Ramapriyan, H. K., Springer, Berlin, Heidelberg, 63–92, 2010.
Rodgers, C. D.: Retrieval of atmospheric temperature and composition from
remote measurements of thermal radiation, Rev. Geophys., 14, 609–624, https://doi.org/10.1029/RG014i004p00609, 1976.
Rodgers, C. D.: Information content and optimisation of high spectral
resolution remote measurements, Adv. Space Res., 21, 361–367,
https://doi.org/10.1016/S0273-1177(97)00915-0, 1998.
Rodgers, C. D.: Inverse methods for atmospheric sounding: theory and
practice, World Scientific, Singapore, Hackensack, NJ, 2000.
Rodgers, C. D. and Connor, B. J.: Intercomparison of remote sounding
instruments: Intercomparison Of Remote Sounders, J. Geophys. Res.-Atmos., 108, 4116, https://doi.org/10.1029/2002JD002299, 2003.
Segers, A. J., Eskes, H. J., van der A, R. J., van Oss, R. F., and van Velthoven, P. F. J.: Assimilation of GOME ozone profiles and a global
chemistry–transport model using a Kalman filter with anisotropic
covariance, Q. J. Roy. Meteorol. Soc., 131, 477–502, https://doi.org/10.1256/qj.04.92, 2005.
Shannon, C. E.: A mathematical theory of communication, AT&T Tech. J., 27, 379–423, 1948.
Siméoni, D., Singer, C., and Chalon, G.: Infrared atmospheric sounding
interferometer, Acta Astronaut., 40, 113–118, https://doi.org/10.1016/S0094-5765(97)00098-2, 1997.
Smith, N. and Barnet, C. D.: Uncertainty Characterization and Propagation in
the Community Long-Term Infrared Microwave Combined Atmospheric Product
System (CLIMCAPS), Remote Sens., 11, 1227, https://doi.org/10.3390/rs11101227, 2019.
Smith, N., Menzel, W. P., Weisz, E., Heidinger, A. K., and Baum, B. A.: A
Uniform Space–Time Gridding Algorithm for Comparison of Satellite Data
Products: Characterization and Sensitivity Study, J. Appl. Meteorol. Clim., 52, 255–268, https://doi.org/10.1175/JAMC-D-12-031.1, 2013.
Smith, N., Smith, W. L., Weisz, E., and Revercomb, H. E.: AIRS, IASI and CrIS
retrieval records at climate scales: An investigation into the propagation
systematic uncertainty, J. Appl. Meteorol. Clim., 54, 1465–1481, 2015.
Smith, W. L.: An improved method for calculating tropospheric temperature
and moisture from satellite radiometer measurements, Mon. Weather Rev.,
96, 387–396, https://doi.org/10.1175/1520-0493(1968)096<0387:AIMFCT>2.0.CO;2, 1968.
Smith, W. L., Weisz, E., Kireev, S. V., Zhou, D. K., Li, Z., and Borbas, E.
E.: Dual-regression retrieval algorithm for real-time processing of
satellite ultraspectral radiances, J. Appl. Meteorol. Clim., 51, 1455–1476, https://doi.org/10.1175/JAMC-D-11-0173.1, 2012.
Strow, L. L., Hannon, S. E., De Souza-Machado, S., Motteler, H. E., and
Tobin, D.: An overview of the AIRS radiative transfer model, IEEE T. Geosci. Remote, 41, 303–313, https://doi.org/10.1109/TGRS.2002.808244, 2003.
Strow, L. L., Motteler, H., Tobin, D., Revercomb, H., Hannon, S., Buijs, H.,
Predina, J., Suwinski, L., and Glumb, R.: Spectral calibration and validation
of the Cross-track Infrared Sounder on the Suomi NPP satellite: CrIS
Spectral Calibration, J. Geophys. Res.-Atmos., 118, 12486–12496, https://doi.org/10.1002/2013JD020480, 2013.
Stubenrauch, C. J., Rossow, W. B., Chéruy, F., Chédin, A., and Scott,
N. A.: Clouds as Seen by Satellite Sounders (3I) and Imagers (ISCCP). Part I: Evaluation of Cloud Parameters, J. Climate, 12, 2189–2213,
https://doi.org/10.1175/1520-0442(1999)012<2189:CASBSS>2.0.CO;2, 1999.
Susskind, J., Barnet, C. D., and Blaisdell, J. M.: Determination of atmospheric and surface parameters from simulated AIRS/AMSU sounding data:
Retrieval methodology and cloud clearing methodology, Adv. Space Res., 21,
369–384, 1998.
Susskind, J., Barnet, C. D., and Blaisdell, J. M.: Retrieval of atmospheric
and surface parameters from AIRS/AMSU/HSB data in the presence of clouds,
IEEE T. Geosci. Remote, 41, 390–409, 2003.
Susskind, J., Blaisdell, J. M., and Iredell, L.: Improved methodology for
surface and atmospheric soundings, error estimates, and quality control
procedures: the atmospheric infrared sounder science team version-6
retrieval algorithm, J. Appl. Remote Sens., 8, 084994, https://doi.org/10.1117/1.JRS.8.084994, 2014.
von Clarmann, T. and Grabowski, U.: Elimination of hidden a priori information from remotely sensed profile data, Atmos. Chem. Phys., 7, 397–408, https://doi.org/10.5194/acp-7-397-2007, 2007.
Wargan, K., Labow, G., Frith, S., Pawson, S., Livesey, N., and Partyka, G.: Evaluation of the ozone fields in NASA's MERRA-2 reanalysis, J. Climate, 30, 2961–2988, https://doi.org/10.1175/JCLI-D-16-0699.1, 2017.
Wu, X. and Smith, W. L.: Emissivity of rough sea surface for 8–13 µm: modeling and verification, Appl. Optics, 36, 2609,
https://doi.org/10.1364/AO.36.002609, 1997.
Wylie, D., Jackson, D. L., Menzel, W. P., and Bates, J. J.: Trends in Global
Cloud Cover in Two Decades of HIRS Observations, J. Climate, 18, 3021–3031, https://doi.org/10.1175/JCLI3461.1, 2005.
Xiong, X., Barnet, C., Maddy, E., Sweeney, C., Liu, X., Zhou, L., and
Goldberg, M.: Characterization and validation of methane products from the
Atmospheric Infrared Sounder (AIRS), J. Geophys. Res., 113, G00A01, https://doi.org/10.1029/2007JG000500, 2008.
Xiong, X., Barnet, C., Maddy, E. S., Gambacorta, A., King, T. S., and Wofsy, S. C.: Mid-upper tropospheric methane retrieval from IASI and its validation, Atmos. Meas. Tech., 6, 2255–2265, https://doi.org/10.5194/amt-6-2255-2013, 2013.
Yudin, V. A.: Assimilation of the 2000–2001 CO MOPITT retrievals with
optimized surface emissions, Geophys. Res. Lett., 31, L20105, https://doi.org/10.1029/2004GL021037, 2004.
Short summary
We diagnose CLIMCAPS observing capability from two different instrument suites and satellite platforms using averaging kernels that quantify information content at every retrieval scene. CLIMCAPS retrieves atmospheric state variables from infrared and microwave measurements and is designed to maintain consistency across time to support climate science and applications. We use averaging kernels to characterize the degree to which we achieved consistency in CLIMCAPS V2 observing capability.
We diagnose CLIMCAPS observing capability from two different instrument suites and satellite...
