Articles | Volume 5, issue 11
https://doi.org/10.5194/amt-5-2635-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-5-2635-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
06 Nov 2012
Research article |
| 06 Nov 2012
Corrections for OMI SO2 BRD retrievals influenced by row anomalies
H. Yan
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, China
Graduate University of the Chinese Academy of Sciences, Beijing, China
L. Chen
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, China
J. Tao
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, China
L. Su
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, China
J. Huang
Key Laboratory of Digital Earth Science, Center for Earth Observation and Digital Earth Chinese Academy of Sciences, Beijing, China
D. Han
Normal College, Qingdao University, Qingdao, China
C. Yu
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, China
Graduate University of the Chinese Academy of Sciences, Beijing, China
Related subject area
Subject: Gases | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Highly resolved mapping of NO2 vertical column densities from GeoTASO measurements over a megacity and industrial area during the KORUS-AQ campaign
Advances in retrieving XCH4 and XCO from Sentinel-5 Precursor: improvements in the scientific TROPOMI/WFMD algorithm
Use of machine learning and principal component analysis to retrieve nitrogen dioxide (NO2) with hyperspectral imagers and reduce noise in spectral fitting
Understanding the variations and sources of CO, C2H2, C2H6, H2CO, and HCN columns based on 3 years of new ground-based Fourier transform infrared measurements at Xianghe, China
Detecting and quantifying methane emissions from oil and gas production: algorithm development with ground-truth calibration based on Sentinel-2 satellite imagery
An improved formula for the complete data fusion
TUNER-compliant error estimation for MIPAS: methodology
Using portable low-resolution spectrometers to evaluate TCCON biases in North America
Synergistic retrieval and complete data fusion methods applied to simulated FORUM and IASI-NG measurements
Retrieval of atmospheric CFC-11 and CFC-12 from high-resolution FTIR observations at Hefei and comparisons with other independent datasets
Evaluation of the methane full-physics retrieval applied to TROPOMI ocean sun glint measurements
Harmonized retrieval of middle atmospheric ozone from two microwave radiometers in Switzerland
Update on the GOSAT TANSO–FTS SWIR Level 2 retrieval algorithm
New plume comparison metrics for the inversion of passive gases emissions
Assessment of the error budget for stratospheric ozone profiles retrieved from OMPS limb scatter measurements
Algorithm theoretical basis for ozone and sulfur dioxide retrievals from DSCOVR EPIC
Impact of 3D cloud structures on the atmospheric trace gas products from UV–Vis sounders – Part 2: Impact on NO2 retrieval and mitigation strategies
A new algorithm to generate a priori trace gas profiles for the GGG2020 retrieval algorithm
Tropospheric ozone retrieval by a combination of TROPOMI/S5P measurements with BASCOE assimilated data
Version 8 IMK/IAA MIPAS ozone profiles: nominal observation mode
A new machine-learning-based analysis for improving satellite-retrieved atmospheric composition data: OMI SO2 as an example
Complementing XCO2 imagery with ground-based CO2 and 14CO2 measurements to monitor CO2 emissions from fossil fuels on a regional to local scale
Accounting for surface reflectance spectral features in TROPOMI methane retrievals
On the potential of a neural-network-based approach for estimating XCO2 from OCO-2 measurements
The Space Carbon Observatory (SCARBO) concept: assessment of XCO2 and XCH4 retrieval performance
Improved retrieval of SO2 plume height from TROPOMI using an iterative Covariance-Based Retrieval Algorithm
Impact of instrumental line shape characterization on ozone monitoring by FTIR spectrometry
Synergetic use of IASI profile and TROPOMI total-column level 2 methane retrieval products
Comment on “Synergetic use of IASI profile and TROPOMI total-column level 2 methane retrieval products” by Schneider et al. (2022)
Correcting 3D cloud effects in XCO2 retrievals from OCO-2
Retrievals of Precipitable Water Vapor and Aerosol Optical Depth from direct sun measurements with EKO MS711 and MS712 Spectroradiometers
An optimal estimation-based retrieval of upper atmospheric oxygen airglow and temperature from SCIAMACHY limb observations
Ozone Monitoring Instrument (OMI) collection 4: establishing a 17-year-long series of detrended level-1b data
Impact of 3D cloud structures on the atmospheric trace gas products from UV–Vis sounders – Part 3: Bias estimate using synthetic and observational data
Retrieval of greenhouse gases from GOSAT and GOSAT-2 using the FOCAL algorithm
Synergy of Using Nadir and Limb Instruments for Tropospheric Ozone Monitoring (SUNLIT)
DARCLOS: a cloud shadow detection algorithm for TROPOMI
Combined UV and IR ozone profile retrieval from TROPOMI and CrIS measurements
Improved ozone monitoring by ground-based FTIR spectrometry
On the consistency of methane retrievals using the Total Carbon Column Observing Network (TCCON) and multiple spectroscopic databases
The MOPITT Version 9 CO product: sampling enhancements and validation
Retrieving H2O/HDO columns over cloudy and clear-sky scenes from the Tropospheric Monitoring Instrument (TROPOMI)
Sentinel-5P TROPOMI NO2 retrieval: impact of version v2.2 improvements and comparisons with OMI and ground-based data
Level 2 processor and auxiliary data for ESA Version 8 final full mission analysis of MIPAS measurements on ENVISAT
Optimized Umkehr profile algorithm for ozone trend analyses
Mapping methane plumes at very high spatial resolution with the WorldView-3 satellite
Mapping the spatial distribution of NO2 with in situ and remote sensing instruments during the Munich NO2 imaging campaign
Improved monitoring of shipping NO2 with TROPOMI: decreasing NOx emissions in European seas during the COVID-19 pandemic
Simulated multispectral temperature and atmospheric composition retrievals for the JPL GEO-IR Sounder
Truth and uncertainty. A critical discussion of the error concept versus the uncertainty concept
Gyo-Hwang Choo, Kyunghwa Lee, Hyunkee Hong, Ukkyo Jeong, Wonei Choi, and Scott J. Janz
Atmos. Meas. Tech., 16, 625–644, https://doi.org/10.5194/amt-16-625-2023, https://doi.org/10.5194/amt-16-625-2023, 2023
Short summary
Short summary
This study discusses the morning and afternoon distribution of NO2 emissions in large cities and industrial areas in South Korea, one of the largest NO2 emitters around the world, using GeoTASO, an airborne remote sensing instrument developed to support geostationary satellite missions. NO2 measurements from GeoTASO were compared with those from ground-based remote sensing instruments including Pandora and in situ sensors.
Oliver Schneising, Michael Buchwitz, Jonas Hachmeister, Steffen Vanselow, Maximilian Reuter, Matthias Buschmann, Heinrich Bovensmann, and John P. Burrows
Atmos. Meas. Tech., 16, 669–694, https://doi.org/10.5194/amt-16-669-2023, https://doi.org/10.5194/amt-16-669-2023, 2023
Short summary
Short summary
Methane and carbon monoxide are important constituents of the atmosphere in the context of climate change and air pollution. We present the latest advances in the TROPOMI/WFMD algorithm to simultaneously retrieve atmospheric methane and carbon monoxide abundances from space. The changes in the latest product version are described in detail, and the resulting improvements are demonstrated. An overview of the products is provided including a discussion of annual increases and validation results.
Joanna Joiner, Sergey Marchenko, Zachary Fasnacht, Lok Lamsal, Can Li, Alexander Vasilkov, and Nickolay Krotkov
Atmos. Meas. Tech., 16, 481–500, https://doi.org/10.5194/amt-16-481-2023, https://doi.org/10.5194/amt-16-481-2023, 2023
Short summary
Short summary
Nitrogen dioxide (NO2) is an important trace gas for both air quality and climate. NO2 affects satellite ocean color products. A new ocean color instrument – OCI (Ocean Color Instrument) – will be launched in 2024 on a NASA satellite. We show that it will be possible to measure NO2 from OCI even though it was not designed for this. The techniques developed here, based on machine learning, can also be applied to instruments already in space to speed up algorithms and reduce the effects of noise.
Minqiang Zhou, Bavo Langerock, Pucai Wang, Corinne Vigouroux, Qichen Ni, Christian Hermans, Bart Dils, Nicolas Kumps, Weidong Nan, and Martine De Mazière
Atmos. Meas. Tech., 16, 273–293, https://doi.org/10.5194/amt-16-273-2023, https://doi.org/10.5194/amt-16-273-2023, 2023
Short summary
Short summary
The ground-based FTIR measurements at Xianghe provide carbon monoxide (CO), acetylene (C2H2), ethane (C2H6), formaldehyde (H2CO), and hydrogen cyanide (HCN) total columns between June 2018 and November 2021. The retrieval strategies, information, and uncertainties of these five important trace gases are presented and discussed. This study provides insight into the time series, variations, and correlations of these five species in northern China.
Zhan Zhang, Evan D. Sherwin, Daniel J. Varon, and Adam R. Brandt
Atmos. Meas. Tech., 15, 7155–7169, https://doi.org/10.5194/amt-15-7155-2022, https://doi.org/10.5194/amt-15-7155-2022, 2022
Short summary
Short summary
This work developed a multi-band–multi-pass–multi-comparison-date Sentinel-2 methane retrieval algorithm, and the method was calibrated by data from a controlled release test. To our knowledge, this is the first study that validates the performance of a Sentinel-2 methane detection algorithm by calibration with a ground-truth testing. It illustrates the potential for additional validation with systematic future experiments wherein algorithms can be tuned to meet different detection expectations.
Simone Ceccherini, Nicola Zoppetti, and Bruno Carli
Atmos. Meas. Tech., 15, 7039–7048, https://doi.org/10.5194/amt-15-7039-2022, https://doi.org/10.5194/amt-15-7039-2022, 2022
Short summary
Short summary
A new formula of the complete data fusion that, differently from the original one, does not contain matrices that can be singular is discussed. We show that the new formula is a generalization of the original one and analytically and numerically, using a real IASI ozone measurement, derive the errors made with the old formula when the generalized inverse of singular matrices is used. An operational version of the new formula that includes interpolation and coincidence errors is also provided.
Thomas von Clarmann, Norbert Glatthor, Udo Grabowski, Bernd Funke, Michael Kiefer, Anne Kleinert, Gabriele P. Stiller, Andrea Linden, and Sylvia Kellmann
Atmos. Meas. Tech., 15, 6991–7018, https://doi.org/10.5194/amt-15-6991-2022, https://doi.org/10.5194/amt-15-6991-2022, 2022
Short summary
Short summary
Errors of profiles of temperature and mixing ratios retrieved from spectra recorded with the Michelson Interferometer for Passive Atmospheric Sounding are estimated. All known and quantified sources of uncertainty are considered. Some ongoing uncertaities contribute to both the random and to the systematic errors. In some cases, one source of uncertainty propagates onto the error budget via multiple pathways. Problems arise when the correlations of errors to be propagated are unknown.
Nasrin Mostafavi Pak, Jacob Hedelius, Sebastien Roche, Liz Cunningham, Bianca Baier, Colm Sweeney, Coleen Roehl, Joshua Laughner, Geoffrey Toon, Paul Wennberg, Harrison Parker, Colin Arrowsmith, Joseph Mendonca, Pierre Fogal, Tyler Wizenberg, Beatriz Herrera, Kimberly Strong, Kaley A. Walker, Felix Vogel, and Debra Wunch
EGUsphere, https://doi.org/10.5194/egusphere-2022-1331, https://doi.org/10.5194/egusphere-2022-1331, 2022
Short summary
Short summary
The TCCON is a network of ground based remote sensing instruments that measure greenhouses in the atmosphere. The consistency between the TCCON measurements is crucial to accurately infer changes in atmospheric composition. In this work, we use portable remote sensing instruments (EM27/SUNs) as traveling standards to evaluate biases between TCCON stations in North America. We also improve the retrievals of EM27/SUNs and evaluate the previous (GGG2014) and newest (GGG2020) retrieval algorithms.
Marco Ridolfi, Cecilia Tirelli, Simone Ceccherini, Claudio Belotti, Ugo Cortesi, and Luca Palchetti
Atmos. Meas. Tech., 15, 6723–6737, https://doi.org/10.5194/amt-15-6723-2022, https://doi.org/10.5194/amt-15-6723-2022, 2022
Short summary
Short summary
Synergistic retrieval (SR) and complete data fusion (CDF) methods exploit the complementarity of coinciding remote-sensing measurements. We assess the performance of the SR and CDF methods on the basis of synthetic measurements of the FORUM and IASI-NG missions. In the case of perfectly matching measurements, SR and CDF results differ by less than 1 / 10 of the error due to measurement noise. In the case of a realistic mismatch, the two methods show differences in the order of their error bars.
Xiangyu Zeng, Wei Wang, Cheng Liu, Changgong Shan, Yu Xie, Peng Wu, Qianqian Zhu, Minqiang Zhou, Martine De Mazière, Emmanuel Mahieu, Irene Pardo Cantos, Jamal Makkor, and Alexander Polyakov
Atmos. Meas. Tech., 15, 6739–6754, https://doi.org/10.5194/amt-15-6739-2022, https://doi.org/10.5194/amt-15-6739-2022, 2022
Short summary
Short summary
CFC-11 and CFC-12, which are classified as ozone-depleting substances, also have high global warming potentials. This paper describes obtaining the CFC-11 and CFC-12 total columns from the solar spectra based on ground-based Fourier transform infrared spectroscopy at Hefei, China. The seasonal variation and annual trend of the two gases are analyzed, and then the data are compared with other independent datasets.
Alba Lorente, Tobias Borsdorff, Mari C. Martinez-Velarte, Andre Butz, Otto P. Hasekamp, Lianghai Wu, and Jochen Landgraf
Atmos. Meas. Tech., 15, 6585–6603, https://doi.org/10.5194/amt-15-6585-2022, https://doi.org/10.5194/amt-15-6585-2022, 2022
Short summary
Short summary
The TROPOspheric Monitoring Instrument (TROPOMI) performs observations over ocean in every orbit, enhancing the monitoring capabilities of methane from space. In the sun glint geometry the mirror-like reflection at the water surface provides a signal that is high enough to retrieve methane with high accuracy and precision. We present 4 years of methane concentrations over the ocean, and we assess its quality. We also show the importance of ocean observations to quantify total CH4 emissions.
Eric Sauvageat, Eliane Maillard Barras, Klemens Hocke, Alexander Haefele, and Axel Murk
Atmos. Meas. Tech., 15, 6395–6417, https://doi.org/10.5194/amt-15-6395-2022, https://doi.org/10.5194/amt-15-6395-2022, 2022
Short summary
Short summary
We present new harmonized ozone time series from two ground-based microwave radiometers in Switzerland. The new series consist of hourly ozone profiles in the middle atmosphere (~ 20–70 km) from 2009 until 2021. Cross-validation of the new data series shows the benefit of the harmonization process compared to the previous versions. Comparisons with collocated satellite observations is used to further validate these time series for long-term ozone monitoring over central Europe.
Yu Someya, Yukio Yoshida, Hirofumi Ohyama, Shohei Nomura, Akihide Kamei, Isamu Morino, Hitoshi Mukai, Tsuneo Matsunaga, Joshua L. Laughner, Voltaire A. Velazco, Benedikt Herkommer, Yao Té, Mahesh Kumar Sha, Rigel Kivi, Minqiang Zhou, Young Suk Oh, Nicholas M. Deutscher, and David W. T. Griffith
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-281, https://doi.org/10.5194/amt-2022-281, 2022
Revised manuscript accepted for AMT
Short summary
Short summary
This study presents an update of the NIES retrieval algorithm for the SWIR Level 2 product of GOSAT. The main changes in the algorithm from the previous version are the treatment of cirrus clouds, the degradation model of the sensor, solar irradiance, and gas absorption coefficient tables. The retrieval results showed improvements in fitting accuracy and an increase in the data amount over land. On the other hand, there are still large biases of XCO2 which should be corrected over the ocean.
Pierre J. Vanderbecken, Joffrey Dumont Le Brazidec, Alban Farchi, Marc Bocquet, Yelva Roustan, Élise Potier, and Grégoire Broquet
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-226, https://doi.org/10.5194/amt-2022-226, 2022
Revised manuscript accepted for AMT
Short summary
Short summary
Instruments dedicated to monitoring atmospheric gaseous compounds from space will provide images of urban-scale plumes. We discuss here the use of new metrics to compare observed plumes with model predictions that will be less sensitive to meteorology uncertainties to limit their impact on the corrections made to emissions. We have evaluated our metrics on diverse plumes and shown that by eliminating some aspects of the discrepancies, they are indeed less sensitive to meteorological variations.
Carlo Arosio, Alexei Rozanov, Victor Gorshelev, Alexandra Laeng, and John P. Burrows
Atmos. Meas. Tech., 15, 5949–5967, https://doi.org/10.5194/amt-15-5949-2022, https://doi.org/10.5194/amt-15-5949-2022, 2022
Short summary
Short summary
This paper characterizes the uncertainties affecting the ozone profiles retrieved at the University of Bremen through OMPS limb satellite observations. An accurate knowledge of the uncertainties is relevant for the validation of the product and to correctly interpret the retrieval results. We investigate several sources of uncertainties, estimate a total random and systematic component, and verify the consistency of the combined OMPS-MLS total uncertainty.
Xinzhou Huang and Kai Yang
Atmos. Meas. Tech., 15, 5877–5915, https://doi.org/10.5194/amt-15-5877-2022, https://doi.org/10.5194/amt-15-5877-2022, 2022
Short summary
Short summary
This paper describes the algorithm for O3 and SO2 retrievals from DSCOVR EPIC. Algorithm advances, including the improved O3 profile representation and the regulated direct fitting inversion technique, improve the accuracy of O3 and SO2 from the multi-channel measurements of DSCOVR EPIC. A thorough error analysis is provided to quantify O3 and SO2 retrieval uncertainties due to various error sources and simplified algorithm physics treatments.
Huan Yu, Claudia Emde, Arve Kylling, Ben Veihelmann, Bernhard Mayer, Kerstin Stebel, and Michel Van Roozendael
Atmos. Meas. Tech., 15, 5743–5768, https://doi.org/10.5194/amt-15-5743-2022, https://doi.org/10.5194/amt-15-5743-2022, 2022
Short summary
Short summary
In this study, we have investigated the impact of 3D clouds on the tropospheric NO2 retrieval from UV–visible sensors. We applied standard NO2 retrieval methods including cloud corrections to synthetic data generated by the 3D radiative transfer model. A sensitivity study was done for synthetic data, and dependencies on various parameters were investigated. Possible mitigation strategies were investigated and compared based on 3D simulations and observed data.
Joshua L. Laughner, Sébastien Roche, Matthäus Kiel, Geoffrey C. Toon, Debra Wunch, Bianca C. Baier, Sébastien Biraud, Huilin Chen, Rigel Kivi, Thomas Laemmel, Kathryn McKain, Pierre-Yves Quéhé, Constantina Rousogenous, Britton B. Stephens, Kaley Walker, and Paul O. Wennberg
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-267, https://doi.org/10.5194/amt-2022-267, 2022
Revised manuscript accepted for AMT
Short summary
Short summary
Observations using sunlight to measure surface-to-space total column of greenhouse gases in the atmosphere need an initial guess of the vertical distribution of those gases to start from. We have developed an approach to provide those initial guess profiles that uses readily available meteorological data as input. This lets us make these guesses without simulating them with a global model. The profiles generated this way match independent observations well.
Klaus-Peter Heue, Diego Loyola, Fabian Romahn, Walter Zimmer, Simon Chabrillat, Quentin Errera, Jerry Ziemke, and Natalya Kramarova
Atmos. Meas. Tech., 15, 5563–5579, https://doi.org/10.5194/amt-15-5563-2022, https://doi.org/10.5194/amt-15-5563-2022, 2022
Short summary
Short summary
To retrieve tropospheric ozone column information, we subtract stratospheric column data of BASCOE from TROPOMI/S5P total ozone columns.
The new S5P-BASCOE data agree well with existing tropospheric data like OMPS-MERRA-2. The data are also compared to ozone soundings.
The tropospheric ozone columns show the expected temporal and spatial patterns. We will also apply the algorithm to future UV nadir missions like Sentinel 4 or 5 or to recent and ongoing missions like GOME_2 or OMI.
Michael Kiefer, Thomas von Clarmann, Bernd Funke, Maya Garcia-Comas, Norbert Glatthor, Udo Grabowski, Michael Höpfner, Sylvia Kellmann, Alexandra Laeng, Andrea Linden, Manuel Lopez-Puertas, and Gabriele Stiller
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-257, https://doi.org/10.5194/amt-2022-257, 2022
Revised manuscript accepted for AMT
Short summary
Short summary
A new ozone data set, derived from radiation measurements of the space-borne instrument MIPAS is presented. It consists of more than 2 million single ozone profiles from 2002–2012, covering virtually all latitudes, and altitudes between 5 and 70 km. Progress in data calibration and processing methods allowed a significant improvement of the data quality, compared to previous data versions. Hence, the data set will help to better understand, e.g., the time evolution of ozone in the Stratosphere.
Can Li, Joanna Joiner, Fei Liu, Nickolay A. Krotkov, Vitali Fioletov, and Chris McLinden
Atmos. Meas. Tech., 15, 5497–5514, https://doi.org/10.5194/amt-15-5497-2022, https://doi.org/10.5194/amt-15-5497-2022, 2022
Short summary
Short summary
Satellite observations provide information on the sources of SO2, an important pollutant that affects both air quality and climate. However, these observations suffer from relatively poor data quality due to weak signals of SO2. Here, we use a machine learning technique to analyze satellite SO2 observations in order to reduce the noise and artifacts over relatively clean areas while keeping the signals near pollution sources. This leads to significant improvement in satellite SO2 data.
Elise Potier, Grégoire Broquet, Yilong Wang, Diego Santaren, Antoine Berchet, Isabelle Pison, Julia Marshall, Philippe Ciais, François-Marie Bréon, and Frédéric Chevallier
Atmos. Meas. Tech., 15, 5261–5288, https://doi.org/10.5194/amt-15-5261-2022, https://doi.org/10.5194/amt-15-5261-2022, 2022
Short summary
Short summary
Atmospheric inversion at local–regional scales over Europe and pseudo-data assimilation are used to evaluate how CO2 and 14CO2 ground-based measurement networks could complement satellite CO2 imagers to monitor fossil fuel (FF) CO2 emissions. This combination significantly improves precision in the FF emission estimates in areas with a dense network but does not strongly support the separation of the FF from the biogenic signals or the spatio-temporal extrapolation of the satellite information.
Alba Lorente, Tobias Borsdorff, Mari C. Martinez-Velarte, and Jochen Landgraf
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-255, https://doi.org/10.5194/amt-2022-255, 2022
Revised manuscript accepted for AMT
Short summary
Short summary
In the TROPOMI methane data, there are few false methane anomalies that can be misinterpreted as enhancements caused by strong emission sources. These artefacts are caused by features of the underlying surfaces, that are not well characterised in the retrieval algorithm. Here we improve the representation of the surface reflectance dependency with wavelength in the forward model, removing the artificial localized CH4 enhancements found in several locations like Siberia, Australia, and Algeria.
François-Marie Bréon, Leslie David, Pierre Chatelanaz, and Frédéric Chevallier
Atmos. Meas. Tech., 15, 5219–5234, https://doi.org/10.5194/amt-15-5219-2022, https://doi.org/10.5194/amt-15-5219-2022, 2022
Short summary
Short summary
The estimate of atmospheric CO2 from space measurement is difficult. Current methods are based on a detailed description of the atmospheric radiative transfer. These are affected by significant biases and errors and are very computer intensive. Instead we have proposed using a neural network approach. A first attempt led to confusing results. Here we provide an interpretation for these results and describe a new version that leads to high-quality estimates.
Matthieu Dogniaux, Cyril Crevoisier, Silvère Gousset, Étienne Le Coarer, Yann Ferrec, Laurence Croizé, Lianghai Wu, Otto Hasekamp, Bojan Sic, and Laure Brooker
Atmos. Meas. Tech., 15, 4835–4858, https://doi.org/10.5194/amt-15-4835-2022, https://doi.org/10.5194/amt-15-4835-2022, 2022
Short summary
Short summary
The Space Carbon Observatory (SCARBO) concept proposes a constellation of small satellites that would carry a miniaturized Fabry–Pérot imaging interferometer named NanoCarb and an aerosol instrument named SPEXone. In this work, we assess the performance of this concept for the retrieval of the total weighted columns of CO2 and CH4 and show the interest of adding the SPEXone aerosol instrument to improve the CO2 and CH4 column retrieval.
Nicolas Theys, Christophe Lerot, Hugues Brenot, Jeroen van Gent, Isabelle De Smedt, Lieven Clarisse, Mike Burton, Matthew Varnam, Catherine Hayer, Benjamin Esse, and Michel Van Roozendael
Atmos. Meas. Tech., 15, 4801–4817, https://doi.org/10.5194/amt-15-4801-2022, https://doi.org/10.5194/amt-15-4801-2022, 2022
Short summary
Short summary
Sulfur dioxide plume height after a volcanic eruption is an important piece of information for many different scientific studies and applications. Satellite UV retrievals are useful in this respect, but available algorithms have shown so far limited sensitivity to SO2 height. Here we present a new technique to improve the retrieval of SO2 plume height for SO2 columns as low as 5 DU. We demonstrate the algorithm using TROPOMI measurements and compare with other height estimates.
Omaira E. García, Esther Sanromá, Frank Hase, Matthias Schneider, Sergio Fabián León-Luis, Thomas Blumenstock, Eliezer Sepúlveda, Carlos Torres, Natalia Prats, Alberto Redondas, and Virgilio Carreño
Atmos. Meas. Tech., 15, 4547–4567, https://doi.org/10.5194/amt-15-4547-2022, https://doi.org/10.5194/amt-15-4547-2022, 2022
Short summary
Short summary
Retrieving high-precision concentrations of atmospheric trace gases from FTIR (Fourier transform infrared) spectrometry requires a precise knowledge of the instrumental performance. In this context, this paper examines the impact on the ozone (O3) retrievals of several approaches used to characterise the instrumental line shape (ILS) function of ground-based FTIR spectrometers within NDACC (Network for the Detection of Atmospheric Composition Change).
Matthias Schneider, Benjamin Ertl, Qiansi Tu, Christopher J. Diekmann, Farahnaz Khosrawi, Amelie N. Röhling, Frank Hase, Darko Dubravica, Omaira E. García, Eliezer Sepúlveda, Tobias Borsdorff, Jochen Landgraf, Alba Lorente, André Butz, Huilin Chen, Rigel Kivi, Thomas Laemmel, Michel Ramonet, Cyril Crevoisier, Jérome Pernin, Martin Steinbacher, Frank Meinhardt, Kimberly Strong, Debra Wunch, Thorsten Warneke, Coleen Roehl, Paul O. Wennberg, Isamu Morino, Laura T. Iraci, Kei Shiomi, Nicholas M. Deutscher, David W. T. Griffith, Voltaire A. Velazco, and David F. Pollard
Atmos. Meas. Tech., 15, 4339–4371, https://doi.org/10.5194/amt-15-4339-2022, https://doi.org/10.5194/amt-15-4339-2022, 2022
Short summary
Short summary
We present a computationally very efficient method for the synergetic use of level 2 remote-sensing data products. We apply the method to IASI vertical profile and TROPOMI total column space-borne methane observations and thus gain sensitivity for the tropospheric methane partial columns, which is not achievable by the individual use of TROPOMI and IASI. These synergetic effects are evaluated theoretically and empirically by inter-comparisons to independent references of TCCON, AirCore, and GAW.
Simone Ceccherini
Atmos. Meas. Tech., 15, 4407–4410, https://doi.org/10.5194/amt-15-4407-2022, https://doi.org/10.5194/amt-15-4407-2022, 2022
Short summary
Short summary
The equivalence between the data fusion performed using the Kalman filter and the Complete Data Fusion has been proved, and a generalization of the Complete Data Fusion formula, that is valid also in the case that the noise error covariance matrices of the fused products are singular, is derived. The two methods are also equivalent to the measurement–space–solution data fusion method, and for moderately nonlinear problems, the three methods are all equivalent to the simultaneous retrieval.
Steffen Mauceri, Steven Massie, and Sebastian Schmidt
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-202, https://doi.org/10.5194/amt-2022-202, 2022
Revised manuscript accepted for AMT
Short summary
Short summary
The Orbiting Carbon Observatory-2 makes space-based measurements of reflected sun light. Using a retrieval algorithm these measurements are converted to CO2 concentrations in the atmosphere. However, the converted CO2 concentrations contain errors for observations close to clouds. Using a simple machine learning approach, we developed a model to correct these remaining errors. The model is able to reduce errors over land and ocean by 31 % and 55 %, respectively.
Congcong Qiao, Song Liu, Juan Huo, Xihan Mu, Ping Wang, Shengjie Jia, Xuehua Fan, and Minzheng Duan
EGUsphere, https://doi.org/10.5194/egusphere-2022-315, https://doi.org/10.5194/egusphere-2022-315, 2022
Short summary
Short summary
We established a spectral-fitting method to derive precipitable water vapor and aerosol optical depth based on strict radiative transfer theory by the spectral measurements of direct sun from EKO MS711 and MS712 spectroradiometers. The retrievals were compared with that of collocated CE-318 Photometer, the results showed a high degree of consistency. Besides the water vapor absorption bands near 940 nm, that near 1370 nm is demonstrated more suitable for water vapor retrieval of drier atmosphere.
Kang Sun, Mahdi Yousefi, Christopher Chan Miller, Kelly Chance, Gonzalo González Abad, Iouli E. Gordon, Xiong Liu, Ewan O'Sullivan, Christopher E. Sioris, and Steven C. Wofsy
Atmos. Meas. Tech., 15, 3721–3745, https://doi.org/10.5194/amt-15-3721-2022, https://doi.org/10.5194/amt-15-3721-2022, 2022
Short summary
Short summary
This study of upper atmospheric airglow from oxygen is motivated by the need to measure oxygen simultaneously with methane and CO2 in satellite remote sensing. We provide an accurate understanding of the spatial, temporal, and spectral distribution of airglow emissions, which will help in the satellite remote sensing of greenhouse gases and constraining the chemical and physical processes in the upper atmosphere.
Quintus Kleipool, Nico Rozemeijer, Mirna van Hoek, Jonatan Leloux, Erwin Loots, Antje Ludewig, Emiel van der Plas, Daley Adrichem, Raoul Harel, Simon Spronk, Mark ter Linden, Glen Jaross, David Haffner, Pepijn Veefkind, and Pieternel F. Levelt
Atmos. Meas. Tech., 15, 3527–3553, https://doi.org/10.5194/amt-15-3527-2022, https://doi.org/10.5194/amt-15-3527-2022, 2022
Short summary
Short summary
A new collection-4 dataset for the Ozone Monitoring Instrument (OMI) mission has been established to supersede the current collection-3 level-1b (L1b) data, produced with a newly developed L01b data processor based on the TROPOspheric Monitoring Instrument (TROPOMI) L01b processor. The collection-4 L1b data have a similar output format to the TROPOMI L1b data for easy connection of the data series. Many insights from the TROPOMI algorithms, as well as from OMI collection-3 usage, were included.
Arve Kylling, Claudia Emde, Huan Yu, Michel van Roozendael, Kerstin Stebel, Ben Veihelmann, and Bernhard Mayer
Atmos. Meas. Tech., 15, 3481–3495, https://doi.org/10.5194/amt-15-3481-2022, https://doi.org/10.5194/amt-15-3481-2022, 2022
Short summary
Short summary
Atmospheric trace gases such as nitrogen dioxide (NO2) may be measured by satellite instruments sensitive to solar ultraviolet–visible radiation reflected from Earth and its atmosphere. For a single pixel, clouds in neighbouring pixels may affect the radiation and hence the retrieved trace gas amount. We found that for a solar zenith angle less than about 40° this cloud-related NO2 bias is typically below 10 %, while for larger solar zenith angles the NO2 bias is on the order of tens of percent.
Stefan Noël, Maximilian Reuter, Michael Buchwitz, Jakob Borchardt, Michael Hilker, Oliver Schneising, Heinrich Bovensmann, John P. Burrows, Antonio Di Noia, Robert J. Parker, Hiroshi Suto, Yukio Yoshida, Matthias Buschmann, Nicholas M. Deutscher, Dietrich G. Feist, David W. T. Griffith, Frank Hase, Rigel Kivi, Cheng Liu, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, Christof Petri, David F. Pollard, Markus Rettinger, Coleen Roehl, Constantina Rousogenous, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Mihalis Vrekoussis, and Thorsten Warneke
Atmos. Meas. Tech., 15, 3401–3437, https://doi.org/10.5194/amt-15-3401-2022, https://doi.org/10.5194/amt-15-3401-2022, 2022
Short summary
Short summary
We present a new version (v3) of the GOSAT and GOSAT-2 FOCAL products.
In addition to an increased number of XCO2 data, v3 also includes products for XCH4 (full-physics and proxy), XH2O and the relative ratio of HDO to H2O (δD). For GOSAT-2, we also present first XCO and XN2O results. All FOCAL data products show reasonable spatial distribution and temporal variations and agree well with TCCON. Global XN2O maps show a gradient from the tropics to higher latitudes on the order of 15 ppb.
Viktoria F. Sofieva, Risto Hänninen, Mikhail Sofiev, Monika Szeląg, Hei Shing Lee, Johanna Tamminen, and Christian Retscher
Atmos. Meas. Tech., 15, 3193–3212, https://doi.org/10.5194/amt-15-3193-2022, https://doi.org/10.5194/amt-15-3193-2022, 2022
Short summary
Short summary
We present tropospheric ozone column datasets that have been created using combinations of total ozone column from OMI and TROPOMI with stratospheric ozone column datasets from several available limb-viewing instruments (MLS, OSIRIS, MIPAS, SCIAMACHY, OMPS-LP, GOMOS). The main results are (i) several methodological developments, (ii) new tropospheric ozone column datasets from OMI and TROPOMI, and (iii) a new high-resolution dataset of ozone profiles from limb satellite instruments.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 15, 3121–3140, https://doi.org/10.5194/amt-15-3121-2022, https://doi.org/10.5194/amt-15-3121-2022, 2022
Short summary
Short summary
Cloud shadows are observed by the TROPOMI satellite instrument as a result of its high spatial resolution. These shadows contaminate TROPOMI's air quality measurements, because shadows are generally not taken into account in the models that are used for aerosol and trace gas retrievals. We present the Detection AlgoRithm for CLOud Shadows (DARCLOS) for TROPOMI, which is the first cloud shadow detection algorithm for a satellite spectrometer.
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.
Omaira Elena García, Esther Sanromá, Matthias Schneider, Frank Hase, Sergio Fabián León-Luis, Thomas Blumenstock, Eliezer Sepúlveda, Alberto Redondas, Virgilio Carreño, Carlos Torres, and Natalia Prats
Atmos. Meas. Tech., 15, 2557–2577, https://doi.org/10.5194/amt-15-2557-2022, https://doi.org/10.5194/amt-15-2557-2022, 2022
Short summary
Short summary
Accurate observations of atmospheric ozone (O3) are essential to monitor in detail its key role in atmospheric chemistry. In this context, this paper has assessed the effect of using different retrieval strategies on the quality of O3 products from ground-based NDACC FTIR (Fourier transform infrared) spectrometry, with the aim of providing an improved O3 retrieval that could be applied at any NDACC FTIR station.
Edward Malina, Ben Veihelmann, Matthias Buschmann, Nicholas M. Deutscher, Dietrich G. Feist, and Isamu Morino
Atmos. Meas. Tech., 15, 2377–2406, https://doi.org/10.5194/amt-15-2377-2022, https://doi.org/10.5194/amt-15-2377-2022, 2022
Short summary
Short summary
Methane retrievals from remote sensing instruments are fundamentally based on spectroscopic parameters, which indicate spectral-line positions, and their characteristics. These parameters are stored in several databases that vary in their make-up. Here we assess how concentrations of methane isotopologues measured from the same Total Carbon Column Observing Network (TCCON) instruments vary across a range of spectral windows using different spectroscopic databases and comment on the implications.
Merritt Deeter, Gene Francis, John Gille, Debbie Mao, Sara Martínez-Alonso, Helen Worden, Dan Ziskin, James Drummond, Róisín Commane, Glenn Diskin, and Kathryn McKain
Atmos. Meas. Tech., 15, 2325–2344, https://doi.org/10.5194/amt-15-2325-2022, https://doi.org/10.5194/amt-15-2325-2022, 2022
Short summary
Short summary
The MOPITT (Measurements of Pollution in the Troposphere) satellite instrument uses remote sensing to obtain retrievals (measurements) of carbon monoxide (CO) in the atmosphere. This paper describes the latest MOPITT data product, Version 9. Globally, the number of daytime MOPITT retrievals over land has increased by 30 %–40 % compared to the previous product. The reported improvements in the MOPITT product should benefit a wide variety of applications including studies of pollution sources.
Andreas Schneider, Tobias Borsdorff, Joost aan de Brugh, Alba Lorente, Franziska Aemisegger, David Noone, Dean Henze, Rigel Kivi, and Jochen Landgraf
Atmos. Meas. Tech., 15, 2251–2275, https://doi.org/10.5194/amt-15-2251-2022, https://doi.org/10.5194/amt-15-2251-2022, 2022
Short summary
Short summary
This paper presents an extended H₂O/HDO total column dataset from short-wave infrared measurements by TROPOMI including cloudy and clear-sky scenes. Coverage is tremendously increased compared to previous TROPOMI HDO datasets. The new dataset is validated against recent ground-based FTIR measurements from TCCON and against aircraft measurements over the ocean. The use of the new dataset is demonstrated with a case study of a cold air outbreak in January 2020.
Jos van Geffen, Henk Eskes, Steven Compernolle, Gaia Pinardi, Tijl Verhoelst, Jean-Christopher Lambert, Maarten Sneep, Mark ter Linden, Antje Ludewig, K. Folkert Boersma, and J. Pepijn Veefkind
Atmos. Meas. Tech., 15, 2037–2060, https://doi.org/10.5194/amt-15-2037-2022, https://doi.org/10.5194/amt-15-2037-2022, 2022
Short summary
Short summary
Nitrogen dioxide (NO2) is one of the main data products measured by the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite. This study describes improvements in the TROPOMI NO2 retrieval leading to version v2.2, operational since 1 July 2021. It compares results with previous versions v1.2–v1.4 and with Ozone Monitoring Instrument (OMI) and ground-based measurements.
Piera Raspollini, Enrico Arnone, Flavio Barbara, Massimo Bianchini, Bruno Carli, Simone Ceccherini, Martyn P. Chipperfield, Angelika Dehn, Stefano Della Fera, Bianca Maria Dinelli, Anu Dudhia, Jean-Marie Flaud, Marco Gai, Michael Kiefer, Manuel López-Puertas, David P. Moore, Alessandro Piro, John J. Remedios, Marco Ridolfi, Harjinder Sembhi, Luca Sgheri, and Nicola Zoppetti
Atmos. Meas. Tech., 15, 1871–1901, https://doi.org/10.5194/amt-15-1871-2022, https://doi.org/10.5194/amt-15-1871-2022, 2022
Short summary
Short summary
The MIPAS instrument onboard the ENVISAT satellite provided 10 years of measurements of the atmospheric emission al limb that allow for the retrieval of latitude- and altitude-resolved atmospheric composition. We describe the improvements implemented in the retrieval algorithm used for the full mission reanalysis, which allows for the generation of the global distributions of 21 atmospheric constituents plus temperature with increased accuracy with respect to previously generated data.
Irina Petropavlovskikh, Koji Miyagawa, Audra McClure-Beegle, Bryan Johnson, Jeannette Wild, Susan Strahan, Krzysztof Wargan, Richard Querel, Lawrence Flynn, Eric Beach, Gerard Ancellet, and Sophie Godin-Beekmann
Atmos. Meas. Tech., 15, 1849–1870, https://doi.org/10.5194/amt-15-1849-2022, https://doi.org/10.5194/amt-15-1849-2022, 2022
Short summary
Short summary
The Montreal Protocol and its amendments assure the recovery of the stratospheric ozone layer that protects the Earth from harmful ultraviolet radiation. To monitor ozone recovery, multiple satellites and ground-based observational platforms collect ozone data. The changes in instruments can influence the continuation of the ozone data. We discuss a method to remove instrumental artifacts from ozone records to improve the internal consistency among multiple observational records.
Elena Sánchez-García, Javier Gorroño, Itziar Irakulis-Loitxate, Daniel J. Varon, and Luis Guanter
Atmos. Meas. Tech., 15, 1657–1674, https://doi.org/10.5194/amt-15-1657-2022, https://doi.org/10.5194/amt-15-1657-2022, 2022
Short summary
Short summary
This study seeks to present the as-yet-unknown potential use of WorldView-3 for the mapping of methane point source emissions. The proposed retrieval methodology is based on the idea that the spectral channels not affected by methane can be used to predict the methane-affected band through regression analysis. The results show the precise location of 26 independent point emissions over different methane hotspot regions worldwide, which prove the game-changing potential that this mission entails.
Gerrit Kuhlmann, Ka Lok Chan, Sebastian Donner, Ying Zhu, Marc Schwaerzel, Steffen Dörner, Jia Chen, Andreas Hueni, Duc Hai Nguyen, Alexander Damm, Annette Schütt, Florian Dietrich, Dominik Brunner, Cheng Liu, Brigitte Buchmann, Thomas Wagner, and Mark Wenig
Atmos. Meas. Tech., 15, 1609–1629, https://doi.org/10.5194/amt-15-1609-2022, https://doi.org/10.5194/amt-15-1609-2022, 2022
Short summary
Short summary
Nitrogen dioxide (NO2) is an air pollutant whose concentration often exceeds air quality guideline values, especially in urban areas. To map the spatial distribution of NO2 in Munich, we conducted the Munich NO2 Imaging Campaign (MuNIC), where NO2 was measured with stationary, mobile, and airborne in situ and remote sensing instruments. The campaign provides a unique dataset that has been used to compare the different instruments and to study the spatial variability of NO2 and its sources.
Tobias Christoph Valentin Werner Riess, Klaas Folkert Boersma, Jasper van Vliet, Wouter Peters, Maarten Sneep, Henk Eskes, and Jos van Geffen
Atmos. Meas. Tech., 15, 1415–1438, https://doi.org/10.5194/amt-15-1415-2022, https://doi.org/10.5194/amt-15-1415-2022, 2022
Short summary
Short summary
This paper reports on improved monitoring of ship nitrogen oxide emissions by TROPOMI. With its fantastic resolution we can identify lanes of ship nitrogen dioxide (NO2) pollution not detected from space before. The quality of TROPOMI NO2 data over sea is improved further by recent upgrades in cloud retrievals and the use of sun glint scenes. Lastly, we study the impact of COVID-19 on ship NO2 in European seas and compare the found reductions to emission estimates gained from ship-specific data.
Vijay Natraj, Ming Luo, Jean-Francois Blavier, Vivienne H. Payne, Derek J. Posselt, Stanley P. Sander, Zhao-Cheng Zeng, Jessica L. Neu, Denis Tremblay, Longtao Wu, Jacola A. Roman, Yen-Hung Wu, and Leonard I. Dorsky
Atmos. Meas. Tech., 15, 1251–1267, https://doi.org/10.5194/amt-15-1251-2022, https://doi.org/10.5194/amt-15-1251-2022, 2022
Short summary
Short summary
High-fidelity monitoring and forecast of air quality and the hydrological cycle require understanding the vertical distribution of temperature, humidity, and trace gases at high spatiotemporal resolution. We describe a new instrument concept, called the JPL GEO-IR Sounder, that would provide this information for the first time from a single instrument platform. Simulations demonstrate the benefits of combining measurements from multiple wavelengths for this purpose from geostationary orbit.
Thomas von Clarmann, Steven Compernolle, and Frank Hase
Atmos. Meas. Tech., 15, 1145–1157, https://doi.org/10.5194/amt-15-1145-2022, https://doi.org/10.5194/amt-15-1145-2022, 2022
Short summary
Short summary
Contrary to the claims put forward in
Evaluation of measurement data – Guide to the expression of uncertainty in measurementissued by the JCGM, the error concept and the uncertainty concept are the same. Arguments in favor of the contrary were found not to be compelling. Neither was any evidence presented that
errorsand
uncertaintiesdefine a different relation between the measured and true values, nor is a Bayesian concept beyond the mere subjective probability referred to.
Cited articles
Bhartia, P. K. and Wellemeyer, C. W.: TOMS-V8 total ozone algorithm, in: OMI Algorithm Theoretical Basis Document, OMI Ozone Products, edited by: Bhartia, P. K., Greenbelt, MD: NASA/Goddard Space Flight Center, 2002.
Bogumil, K., Orphal, J., Homann, T., Voigt, S., Spietz, P., Fleischmann, O. C., Vogel, A., Hartmann, M., Kromminga, H., Bovensmann, H., Frerick, J., and Burrows, J. P.: Measurements of molecular absorption spectra with the SCIAMACHY pre-flight model: instrument characterization and reference data for atmospheric remote-sensing in the 230–2380 nm region, J. Photoch. Photobio. A, 157, 167–184, https://doi.org/10.1016/S1010-6030(03)00062-5, 2003.
Carn, S. A., Krueger, A. J., Krotkov, N. A., Yang, K., and Levelt, P. F.: Sulfur dioxide emissions from Peruvian copper smelters detected by the Ozone Monitoring Instrument, Geophys. Res. Lett., 34, L09801, https://doi.org/10.1029/2006GL029020, 2007.
Chin, M., Savoie, D. L., Huebert, B. J., Bandy, A. R., Thornton, D. C., Bates, T. S., Quinn, P. K., Saltzman, E. S., and De Bruyn, W. J.: Atmospheric sulfur cycle simulated in the global model GOCART: Comparison with field observations and regional budgets, J. Geophys. Res., 105, 24689–24712, https://doi.org/10.1029/2000JD900385, 2000.
Corradini, S., Merucci, L., and Prata, A. J.: Retrieval of SO2 from thermal infrared satellite measurements: correction procedures for the effects of volcanic ash, Atmos. Meas. Tech., 2, 177–191, https://doi.org/10.5194/amt-2-177-2009, 2009.
Cullis, C. F. and Hirschler, M. M.: Atmospheric Sulfur- Natural and Man-made sources, Atmos. Environ., 14, 1263–1278, 1980.
Dave, J. V.: Meaning of successive iteration of the auxiliary equation of radiative tranfer, Astrophys. J., 140, 1292–1303, 1964.
Dobber, M. R. and Braak, R.: Known instrumental effects that affect the OML1B product of the Ozone Monitoring Instrument on EOS Aura, last update: 17 December 2010.
Dobber, M. R., Dirksen, R. J., Levelt, P. F., Van den Oord, G. H. J., Voors, R. H. M., Kleipool, Q., Jaross, G., Kowalewski, M., Hilsenrath, E., Leppelmeier, G. W., de Vries, J., Dierssen, W., and Rozemeijer, N. C.: Ozone Monitoring Instrument Calibration, IEEE T. Geosci. Remote, 44, 1209–1238, https://doi.org/10.1109/TGRS.2006.869987, 2006.
Dobber, M. R., Kleipool, Q., Dirksen, R. J., Levelt, P. F., Jaross, G., Taylor, S. L., Kelly, T. J., Flynn, L. E., Leppelmeier, G. W., and Rozemeijer, N. C.: Validation of ozone monitoring instrument level 1b data products, J. Geophys. Res., 113, D15S06, https://doi.org/10.1029/2007JD008665, 2008.
Finlayson-Pitts, B. J. and Pitts, J. N.: Chemistry of the Upper and Lower Atmosphere, Academic Press, San Diego, USA, 2000.
IPCC: Climate Change 2001: The Scientific Basis, Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), edited by: Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., van der Linden, P. J., Dai, X., Maskell, K., and Johnson, C. A., Cambridge University Press, Cambridge, UK and New York, NY, USA, 881 pp., 2001.
Jaross, G. and Warner, J.: Use of Antarctica for validating reflected solar radiation measured by satellite sensors, J. Geophys. Res., 113, D16S34, https://doi.org/10.1029/2007JD008835, 2008.
Khokhar, M. F., Frankenberg, C., Van Roozendael, M., Beirle, S., Kühl, S., Richter, A., Platt, U., and Wagner, T.: Satellite observations of atmospheric SO2 from volcanic eruptions during the time-period of 1996–2002, Adv. Space Res., 36, 879–887, https://doi.org/10.1016/j.asr.2005.04.114, 2005.
Krotkov, N. A., Carn, S. A., Krueger, A. J., Bhartia, P. K., and Yang, K.: Band Residual Difference Algorithm for Retrieval of SO2 From the Aura Ozone Monitoring Instrument (OMI), IEEE T. Geosci. Remote, 44, 1259–1266, 2006.
Krotkov, N. A., McClure, B., Dickerson, R. R., Carn, S. A., Li, C., Bhartia, P. K., Yang, K., Krueger, A. J., Li, Z. Q., Levelt, P. F., Chen, H. B., Wang, P. C., and Lu, D.: Validation of SO2 retrievals from the Ozone Monitoring Instrument over NE China, J. Geophys. Res., 113, D16S40, https://doi.org/10.1029/2007JD008818, 2008.
Krueger, A. J.: Sighting of El Chichón Sulfur Dioxide Clouds with the Nimbus 7 Total Ozone Mapping Spectrometer, Science, 220, 1377–1379, 1983.
Krueger, A. J., Krotkov, N. A., Datta, S., Flittner, D., and Dubovik, O.: SO2, in: OMI Algorithm Theoretical Basis Document, OMI Trace Gas Algorithms, 2, edited by: Chance, K., Smithsonian Astrophysical Observatory, Cambridge, MA, 49–59, 2002.
Lee, C., Richter, A., Weber, M., and Burrows, J. P.: SO2 Retrieval from SCIAMACHY using the Weighting Function DOAS (WFDOAS) technique: comparison with Standard DOAS retrieval, Atmos. Chem. Phys., 8, 6137–6145, https://doi.org/10.5194/acp-8-6137-2008, 2008.
Levelt, P. F., Van der Oord, G. H. J., Dobber, M. R., Malkki, A., Visser, H., de Vries, J., Stammes, P., Lundell, J. O. V., and Saari, H.: The Ozone Monitoring Instrument, IEEE T. Geosci. Remote, 44, 1093–1101, 2006.
Li, C., Zhang, Q., Krotkov, N. A., Streets, D. G., He, K. B., Tsay, S. C., and Gleason, J. F.: Recent large reduction in sulfur dioxide emissions from Chinese power plants observed by the Ozone Monitoring Instrument, Geophys. Res. Lett., 37, L08807, https://doi.org/10.1029/2010GL042594, 2010.
McPeters, R. D., Labow, G. J., and Logan, J. A.: Ozone climatological profiles for satellite retrieval algorithms, J. Geophys. Res., 112, D05308, https://doi.org/10.1029/2005JD006823, 2007.
Platt, U.: Differential optical absorption spectroscopy (DOAS), in: Air Monitoring by Spectroscopic Techniques, Chemical Analysis Series, edited by: Sigrist, M. W., John Wiley & Sons, Inc., 127, 27–84, 1994.
Platt, U. and Stutz, J.: Differential Optical Absorption Spectroscopy: Principles and Applications, Physics of Earth and Space Environments, Hardcover, Springer, Verlag Berlin Heidelberg, 593 pp., 2008.
Realmuto, V. J., Abrams, M. J., Buongiorno, M. F., and Pieri, D. C.: The use of multispectral thermal infrared image data to estimate the sulfur dioxide flux from volcanoes: A case study from Mount Etna, Sicily, 29 July 1986, J. Geophys. Res., 99, 481–488, https://doi.org/10.1029/93JB02062, 1994.
Richter, A., Wittrock, F., and Burrows, J. P.: SO2 measurements with SCIAMACHY, Atmospheric Science Conference, Eur.Space Agency Cent. for Earth Obs., Frascati, Italy, 8–12 May 2006.
Rix, M., Valks, P., Van Gent, J., Van Roozendael, M., Spurr, R., Hao, N., Emmadi, S., and Zimmer, W.: Monitoring of volcanic SO2 emissions using the GOME-2 satellite instrument, Geophys. Res. Abstr., EGU2010-4754, EGU General Assembly 2010, Vienna, Austria, 2010.
Seinfeld, J. H. and Pandis, S.: Atmospheric Chemistry and Physics, John Wiley & Sons, New York, USA, 1998.
Smith, R. C.: Ozone, Middle Ultraviolet-Radiation and the Aquatic Environment, Photochem. Photobiol., 50, 459–468, 1989.
Van den Oord, G. H. J., Voors, R. H. M., and De Vries, J.: The Level 0 to Level 1B processor for OMI radiance, irradiance and calibration data, in: OMI Algorithm Theoretical Basis Document, OMI instrument, Level 0–1b processor, Calibration & Operations, edited by: Levelt, P. F., 2002.
Van den Oord, G. H. J., Rozemeijer, N. C., Schenkelaars, V., Levelt, P. F., Dobber, M. R., Voors, R. H. M., Claas, J., De Vries, J., Ter Linden, M., De Haan, C., and de Berg, T. V.: OMI level 0 to 1b processing and operational aspects, IEEE T. Geosci. Remote, 44, 1380–1397, https://doi.org/10.1109/TGRS.2006.872935, 2006.
Veihelmann, B. and Kleipool, Q.: Reducing Along-Track Stripes in OMI-Level 2 Products, TN-OMIE-KNMI-785, 24 pp., 2006.
Voors, R.: OMI GDPS Algorithm to correct for wavelength shifts due to inhomogeneous slit illumination, TN-OMIE-KNMI-680, Issue 1, 2005.
Wellemeyer, C. G., Taylor, S. L., Seftor, C. J., McPeters, R. D., and Bhartia, P. K.: A correction for total ozone mapping spectrometer profile shape errors at high latitude, J. Geophys. Res., 102, 9029–9038, 1997.
Yan, H. H., Chen, L. F., Tao, J. H., Han, D., Su, L., and Yu, C.: SO2 long-term monitoring by satellite in the Pearl River Delta, Journal of Remote Sensing, 16, 390–404, 2012.
Yang, K., Krotkov, N. A., Krueger, A. J., Carn, S. A., Bhartia, P. K., and Levelt, P. F.: Retrieval of large volcanic SO2 columns from the Aura Ozone Monitoring Instrument: comparison and limitations, J. Geophys. Res., 112, D24S43, https://doi.org/10.1029/2007JD008825, 2007.
