Ver. 01

Abstract Collection
The 18th International Workshop on
Greenhouse Gas Measurements from Space(IWGGMS-18)

July 12th – 14th, 2022
OnlineJapan Aerospace Exploration Agency (JAXA)
National Institute for Environmental Studies (NIES)

Contents
Session 1 “Results from current missions”
1-1. The OCO-2 and OCO-3 Missions: Status, results and plans (Vivienne Payne, JPL, US)
1-2. Progress in understanding the natural carbon cycle with remote sensing CO2 observations(Junjie Liu, JPL, US)
1-3. Gridded Level 3 TROPOMI Methane Data Products (Olga Khaliukova, Colorado School ofMines, US)
1-4. Results from GHGSat Constellation’s First Full Year of Operation and Latest Phase of
Expansion (Dylan Jervis, GHGSat, Canada)
1-5. More than a decade of GOSAT and GOSAT-2 operations and data products using their uniquecapabilities of FTS multiplex advantage and target observations (Akihiko Kuze, JAXA, Japan)
1-6. Current Status and Recent Topics on GOSAT and GOSAT-2 Standard Products and Other Data
(Tsuneo Matsunaga, NIES, Japan)
1-7. Towards near-real-time XCO2 retrieval from OCO-2 observations using Neural Network
techniques (Cédric Bacour, LSCE, France)
1-8. On-orbit characterization of the TanSat instrument line shape using observed solar spectra(Zhaonan Cai, CAS, China)
Session 2 “Status of / Results expected from future missions”
2-1. Assessment of the optimal operating point for a high resolution XCO2 imager dedicated to themonitoring of 0.1 to 15.0 MtC.yr-1 sources. (Francesco Graziosi, LSCE, France)
2-2. The MicroCarb CO2 mission : status and technical insight (Denis Jouglet, CNES, France)
2-3. Quantifying localized carbon dioxide emissions from space: the CO2Image demonstrator (Julia
Marshall, DLR, Germany)
2-4. The Copernicus Anthropogenic CO2 Monitoring (CO2M) Mission - Recent Progress (Yasjka
Meijer, ESA & Ruediger Lang, EUMETSAT, Germany)
2-5. The GeoCarb Mission: Progress and Future Plans (Sean Crowell, Univ. Oklahoma, US)
2-6. The GOSAT-GW greenhouse gases observing mission: Updates (Hiroshi Tanimoto, NIES,
Japan)
2-7. The future China’s greenhouse gas monitoring satellite missions (Yi Liu, CAS, China)
Session 3 “Retrieval algorithms and methods for inter-instrument and product
Cal/Val”
3-1. Radiance Comparison from OCO-3 and OCO-2 Simultaneous Nadir Observations (thomas pkurosu, JPL, US)- i -3-2. Using Clear Ocean Scenes to Constrain Changes in Instrument Response in OCO-3 (Graziela
R. Keller, JPL, US)
3-3. UPlume: Automated algorithm for methane plume detection and source quantification bysatellite point source imagers (Jack H. Bruno, Harvard Univ., US)
3-4. Understanding causes of differences between the GGG2014 and GGG2020 data products(Joshua L. Laughner, JPL, US)
3-5. Characterization of TANSO-FTS-2 onboard GOSAT-2 and the Level-1 algorithm updates(Junko Kasuya, JAXA, Japan)
3-6. Towards Arctic and boreal methane flux estimates: systematic evaluation of TROPOMI XCH4
observations at high latitudes (Hannakaisa Lindqvist, FMI, Finland)
3-7. Comparison of IASI CH4 retrievals based on ASIMUT and RTTOV (Charles Robert, IASBBIRA, Belgium)
Session 4 “Uncertainty quantification and bias correction techniques, including insitu measurements”
4-1. Commercial ship and aircraft-based evaluation of satellite derived XCO2 and XCH4 over oceans- updates and prospective (Astrid Müller, NIES, Japan)
4-2. Analysis of Biases in XCO2 measured by a Pulsed Multiwavelength Airborne Lidar during the2017 ASCENDS/ABoVE Campaign (James B. Abshire, NASA, US)
Session 5 “Observations to quantify hot spots and local/urban emissions”
5-1. Monitoring facility-scale CO2 emission changes from space (Ray Nassar, ECCC, Canada)
5-2. Monitoring anthropogenic emissions from space: insights from OCO-3’s Snapshot Area
Mapping (SAM) mode (Abhishek Chatterjee, JPL, US)
5-3. Satellite monitoring of urban CO2 emissions: optimizing the selection of cities andmeteorological conditions (Danjou Alexandre, LSCE, France)
5-4. Carbon Mapper: on-orbit performance predictions and airborne prototyping (Riley Duren,
Carbon Mapper, US)
5-5. Estimating urban methane emissions from space (Eric A. Kort, Univ. Michigan, US)
5-6. Strong methane point sources contribute a disproportionate fraction of total emissions acrossmultiple basins in the U.S. (Daniel H. Cusworth, Univ. Arizona, US)
5-7. Detectable Weekly Cycle of Urban CO2 Emissions from Satellite Observations: A Case Studyin Los Angeles (Zhao-Cheng Zeng, Peking Univ. China)
5-8. The impact of viewing geometry on the interpretation of high-resolution measurements of CO2
plumes (Nicolas Tufel, DLR, Germany)
5-9. Urban XCO2 gradients from space: results from OCO-3 SAM’s and a dense network of
EM27/SUN over Mexico City (Ke Che, CAS, China)- ii -

5-10. The Greenhouse gas Observations of Biospheric and Local Emissions from the Upper sky(GOBLEU): New multi-species observations from passenger aircrafts in support of monitoringof Japan’s anthropogenic carbon emissions (Hiroshi Suto, JAXA, Japan)
Session 6 “Flux estimates and atmospheric inversions from space-based GHG
measurements and solar-induced chlorophyll fluorescence (SIF)”
6-1. Using OCO-2 column CO2 retrievals to rapidly detect and estimate extreme terrestrial biospherecarbon anomalies (Andrew F. Feldman, NASA, US)
6-2. Is GOSAT XCO2 useful in the OCO era? (Christopher O’Dell, CSU, US)
6-3. MethaneAIR Point Source Quantification: Method Exploration and Control Release
Experiments (Apisada Chulakadabba, Harvard Univ., US)
6-4. Predictability of fossil fuel CO2 from air quality emissions (Kazuyuki Miyazaki, JPL, US)
6-5. GOSAT and GOSAT-2 Level 4 global surface flux estimates (Makoto Saito, NIES, Japan)
6-6. Estimating the trend, seasonality, and variability of global and regional CO2 fluxes with a
Bayesian flux-inversion framework (Michael Bertolacci, Univ. Wollongong, Australia)
6-7. Interannual variability of anthropogenic methane emission estimated by an inverse model using
GOSAT and surface observations (Rajesh Janardanan, NIES, Japan)
6-8. Towards assimilation of GOSAT partial column retrievals in CTE-CH4 atmospheric inverse
model for estimation of global and regional CH4 budgets (Aki Tsuruta, FMI, Finland)
6-9. Increasing Natural Gas Demand Threatens China’s Carbon Reduction (Fenjuan Wang, NIES,
Japan)
6-10. Evaluation of GOSAT-2 SIF with ground-based SIF in three common vegetation types (Tomoki
Morozumi, NIES, Japan)
Session 7 “Towards an international space-based GHG emission monitoring
system”
7-1. The US Plan for Measuring, Monitoring, Reporting, and Verifying (MMRV) GHG emissions(Ken Jucks, NASA, US)
7-2. Mapping Emissions Over Offshore Oil and Gas Platforms Using Sun Glint with Carbon Mapper(Alana K Ayasse, Univ. Arizona, US)
7-3. Building a validation support service for CO2M and other GHG satellite missions (Mahesh
Kumar Sha, BIRA-IASB, Belgium)
7-4. Non-local metrics applied to the comparison of CO2 plumes and their sensitivities to mesoscalemeteorology (Pierre J. Vanderbecken, CEREA, France)
7-5. CO2 plume detection using deep neural networks: application tosynthetic images of the XCO2
field over the Paris area (Joffrey Dumont Le Brazidec, CEREA, France)- iii -

Session 8 “Stakeholder needs and engagement for the Global Stocktake”
8-1. CO2 Budget (2015-2020) constrained by space-based and ground-based atmospheric CO2 datain support of the Global Stocktake (Brendan Byrne, JPL, US)
8-2. Using Remote Sensing to Detect and Mitigate Methane Plumes in California (Jorn Herner,
CARB, US)
8-3. Linking regional to global greenhouse gas budgets in RECCAP2 (Prabir K. Patra, JAMSTEC,Japan)
Invited Talk
A Role for Space-based CO2 and CH4 observations in Greenhouse Gas Emissions Monitoring(David Crisp, Crisp Spectra LLC, US)- iv -

Poster Sessions

PosterNum.

SpeakerPresentation title

Session 1. Results from current missions
1-P01
1-P02
1-P03
1-P04

Abhishek Chatterjee(JPL, US)
Christopher Chan
Miller(Harvard CFA, US)
Dmitry Belikov(Chiba Univ., Japan)
Luis Guanter(IIAMA, Spain)
Robert Rosenberg(JPL, US)Advancing Arctic-Boreal carbon cycle science using OCO-2
retrievals
XCH4 retrieval from MethaneAIR using the CO2-Proxy Approach:First results over major US oil and gas basins
An analysis of GOSAT-TIR observation sensitivity to the nearsurface CH4
Satellite-based detection of methane plumes from offshore platforms

1-P06

Lu, Yao(CAS, China)

1-P07

Matthäus Kiel
(JPL, US)

Evaluating OCO-2 & OCO-3 Retrospective Calibration Using Dark,
Lamp, and Science Spectra
Retrieval of solar-induced chlorophyll fluorescence (SIF) fromsatellite measurements: comparison of SIF between TanSat andOCO-2
An overview of the new and improved OCO-3 ACOS v10 XCO2 data
product

1-P08

Sébastien Roche(Harvard Univ., US)

Using the O2 column from the O2 singlet delta band to derive proxy
XCH4 from MethaneAIR observations

1-P05

1-P10
1-P11

Susan S. Kulawik(BAER Inst., US)
Thomas E. Taylor(CSU, US)

Validation of TROPOMI Methane in the GeoCarb Domain
Evaluation of XCO2 estimates from OCO-2 and OCO-3 observations
using the NASA ACOS version 10 retrieval algorithm

Session 2. Status of / Results expected from future missions
2-P01
2-P02
2-P04
2-P05
2-P06
2-P07
2-P08

Hisashi Yashiro(NIES, Japan)
Jonathan E. Franklin(Harvard Univ. US)
Ruediger Lang
(EUMETSAT,
Germany)
Ray Nassar(ECCC, Canada)
Amir H. Souri(Harvard CFA, US)
Dongxu Yang
(CAS, China)
Jonas Wilzewski(Harvard Univ., US)

Overview of GOSAT-GW TANSO-3 data processing and productdistribution in NIES
MethaneSAT: A new approach to quantifying area and point source
methane emissions
Operational product processing developments for the Copernicus
anthropogenic CO2 Monitoring (CO2M) mission
Overview of the proposed Arctic Observing Mission (AOM)
MethaneSAT OSSE Simulation to Assess Errors in XCH4 Derived
From the CO2 and O2 Proxy Methods
IAPCAS supports TanSat (-2) missions
Development of the MethaneSAT cloud and aerosol filter- v -Session 3. Retrieval algorithms and methods for inter-instrument and product Cal/Val
3-P01
3-P02
3-P03
3-P04
3-P05
3-P06
3-P07
3-P08
3-P09
3-P10
3-P11
3-P12
3-P13
3-P14
3-P15
3-P16
3-P17

Antti Mikkonen(FMI, Finland)
Christopher O’Dell(CSU, US)
Mukunda M. Gogoi(ISRO, India)
Peter Somkuti(Univ. Oklahoma,
US)
Bingkun Luo(Harvard CFA, US)
Fabiano Oyafuso(JPL, US)Joseph Mendonca(ECCC, Canada)
Kei Shiomi(JAXA, Japan)
Le Kuai(JPL, US)
Makiko Hashimoto(JAXA, Japan)
Nicole Jacobs(CSU, US)
Rigel Kivi
(FMI, Finland)
Robert R. Nelson(JPL, US)Yu Someya(NIES, Japan)
Yu Someya(NIES, Japan)
Yukio Yoshida(NIES, Japan)
Kang Sun(Univ. Buffalo, US)

The potential and feasibility of improved glint observations oversnow for CO2M
Solving scene inhomogeneity-induced errors without a slit
homogenizer: application to GeoCarb
Satellite (GOSAT-2 CAI-2) and surface (ARFINET) observations of
Aerosol Black Carbon over India: Regional and Global Inferences
Biases in XCH4 retrievals from the 2.3 μm spectral band
Development of the MethaneSAT L0-1B processor
Updates to the Spectroscopic Model for the OCO-2 Retrieval
Algorithm: ABSCO v5.2 for Level 2 v11
A Neural Network Approach to Filtering OCO-2 Retrievals OverSnow
Recent status of GOSAT calibration and validation activities in 2022
An overview of the tests and evaluations of the OCO-2 version 11
retrieval algorithm
Comparison of aerosol properties between GOSAT-2/TANSO-CAI-2
and ground-based observations and other satellites
Exploring the sensitivity of northern high latitude retrievals from
OCO-2 to the referenced digital elevation model
Carbon dioxide and methane measurements at Sodankylä, Finlandand comparisons with satellite borne observations
Water and Temperature SVD Estimates to Improve OCO-2 XCO2
ErrorsUpdate on the retrieval algorithm for the NIES TANSO-FTS/GOSATSWIR L2 product
Design of the retrieval algorithm and level 2 product for greenhousegases from GOSAT-GWGOSAT-2 TANSO-FTS-2 SWIR L2 Version 02 Products
Oxygen Airglow Studies in Support of the MethaneSAT Mission

Session 4. Uncertainty quantification and bias correction techniques, including in situ
measurements
4-P01
4-P02
4-P03

Ryoichi Imasu(Univ. Tokyo, Japan)
Jonas Hachmeister(Univ. Bremen,
Germany)
Takashi Maki
(JMA/MRI, Japan)

XCO2 observation network for verification of Kanto intensive
observations and sun-glint observations by GOSAT and GOSAT-2
Improvements of S5P methane retrievals by using updated digital
elevation models
Comparison of GOSAT-2 XCO2 and an independent inversion
analysis- vi -

4-P04

Yukitomo Tsutsumi(NIES, Japan)

Validation of recent results of the GOSAT-2 FTS-2 SWIR L2
products

Session 5. Observations to quantify hot spots and local/urban emissions
5-P01

Dustin Roten(Univ. Utah, US)

5-P02

Jianping Mao(Univ. Maryland)

5-P03
5-P04
5-P05

5-P06

5-P07

5-P08

Matthias M. Frey
(NIES, Japan)
Tonghui Zhao
(CAS, China)
B. Fuentes Andrade(Univ. Bremen,
Germany)
Omid Moeini(ECCC, Canada)
Raquel SerranoCalvo(TU Delft,
Netherlands)
Steffen Vanselow(Univ. Bremen,
Germany)

The Information Content of Dense Carbon Dioxide Measurementsfrom Space: A Case Study with OCO-3
Accurately Measuring Atmospheric CO2 Enhancements andQuantifying CO2 Emissions from Wildfires – an Airborne
Demonstration
Urban Greenhouse Gas Emission Monitoring in Seoul and Tokyo
Development of an integrated lightweight multi-rotor UAV payload
for atmospheric carbon dioxide mole fraction measurements
Deriving CO2 emissions of localized sources from OCO-3 XCO2 andTROPOMI NO2 data
Quantification of CO2 emissions over the Greater Toronto Area:
Comparison of model simulations with OCO and EM27/SUN
measurements
Analysis of methane mitigation actions in the Permian basin using
TROPOMI: drastic reduction of oil and gas activity scenario
Detection of locally elevated methane concentrations by analyzing
Sentinel-5 Precursor satellite data

Session 6. Flux estimates and atmospheric inversions from space-based GHG
measurements and solar-induced chlorophyll fluorescence (SIF)
6-P01

Audrey FortemsCheiney(LSCE, France)

6-P02

Aki Tsuruta(FMI, Finland)

6-P03
6-P04
6-P05
6-P06
6-P07
6-P08

Brendan Byrne(JPL, US)
Dien Wu(Caltech, US)
Eva-Marie Schömann
(Heidelberg Univ.,
Germany)
Masataka Watanabe(Chuo Univ., Japan)
Pascal Prunet(SPASCIA, France)
Shamil Maksyutov(NIES, Japan)

NOx and CO inversions for the derivation of FFCO2 emissions in
EuropeSpatial distribution and Seasonal cycle of northern high latitudes CH4
fluxes inferred from an atmospheric inverse model using TROPOMI
XCH4How does the ability to recover CO2 flux anomalies scale withobservational coverage?
Informing sector characteristics of urban CO2 emissions using coemitted trace gases
Seasonal and Interannual Variability of Australian Carbon Fluxes
Seen by GOSAT
Estimation of GHG emission/absorption using GOSAT satellite data
in Mongolia
Plume detection and characterization from XCO2 imagery: Evaluationof Gaussian methods for quantifying plant and city fluxes
Regional land carbon sink estimates with NIES inverse model using
ground-based, GOSAT and OCO-2 data

- vii -

6-P09

Zhiqiang Liu(CAS, China)

Global (2°×2.5°) and regional (0.5°×0.625°) daily surface CO2 flux
inferred from in-situ and OCO-2 B10 observations based on the
Carbon in Ocean-Land-Atmosphere (COLA) system

6-P10

Jin Ma(Utrecht Univ.,
Netherlands)

Global carbonyl sulfide budgets constrained by NOAA surface
network and MIPAS satellite

Session 7. Towards an international space-based GHG emission monitoring system
7-P01

Benedikt,
Herkommer(KIT, Germany)

7-P02

John R. Worden(JPL, US)

7-P03

Rajesh Janardanan(NIES, Japan)

Using a portable FTIR spectrometer for intercalibration of TCCON
stations: The COCCON travel standard
The 2019 Methane Budget and Uncertainties at 1 Degree Resolution
and Each Country Through Bayesian Integration of GOSAT Total
Column Methane Data And A Priori Inventory Estimates
Observations of atmospheric CH4 enhancements due to anthropogenic
emissions by the GOSAT-2 satellite- viii -           

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2

Gridded Level 3 TROPOMI Methane Data Products
Olga Khaliukova*1, William Daniels1, Lewis Blake2, and Dorit Hammerling1
1

2

Department of Applied Mathematics and Statistics, Colorado School of Mines, USA
Division for Climate Modeling and Air Pollution, Norwegian Meteorological Institute, Norway

The Global Methane Pledge, an initiative introduced at the 2021 UN Climate Change Conference, is a majorcommitment signed by over 100 countries to reduce methane emissions by 30% by 2030. As of 2020, the oiland gas industry accounts for 32% of anthropogenic methane emissions in the US, making it a promisingavenue for reducing anthropogenic emissions. Satellite data is a useful tool for monitoring methane emissions
due to its global coverage and, in many cases, open access. The TROPOspheric Monitoring Instrument(TROPOMI) onboard the Copernicus Sentinel-5 Precursor satellite measures methane at a 7x5.5 km2
resolution and provides a geolocated Level 2 product. The Level 2 products can be challenging to use for nonexperts and to the best of our knowledge, there is no official Level 3 product publicly available. We processed
TROPOMI Level 2 orbit files to create two versions of a gridded 0.5°x0.5° Level 3 methane product. The first
product is created by taking averages of the Level 2 data that fall with latitude- and longitude-based grid cells.
We create this product for two major US oil and gas producing basins, the Bakken and Permian, at daily andmonthly temporal resolutions. Data are available starting from May 1, 2018. We give an example use-case forthe average-based Level 3 product in economic analysis for the management of flaring and methane emissions.
The second product is based on spatial modeling and attempts to leverage the spatial correlation structure of
the Level 2 methane observations. We use the multi-resolution approximation (M-RA) spatial model to makepredictions of the methane field and provide an alternative Level 3 product on a regular grid along withuncertainties. We show initial results from this more sophisticated product and compare it to the simple
averaged product.
Keywords: TROPOMI, satellite data, methane, Level 3 product, M-RA
3

Results from GHGSat Constellation’s First Full Year of Operationand Latest Phase of Expansion
Dylan Jervis*1, Marianne Girard1, Jean-Philippe MacLean1, Jason McKeever1, Antoine Ramier1,
Mathias Strupler1, Ewan Tarrant1, and David Young1
1. GHGSat Inc., Canada

It has been more than a year since the inception of GHGSat’s commercial constellation for high spatial
resolution (~25 m) methane sensing and quantification. We report on the emissions that we have detected
and quantified with GHGSat-C1 and C2 - broken down by geographical location, source sector, as well as
temporal trends in the emission rates that we have measured.
We also present an analysis of GHGSat methane measurement performance. In particular, we quantify how
the column density precision compares to both the shot-noise prediction and estimated error, as well as
correlations with environmental variables such as the solar zenith angle, albedo, and albedo heterogeneity.
We show results from an independently organized single-blind controlled release study where we
successfully detected and quantified plumes over a wide range of emission rates.
Methane emissions may be mitigated in a cost-effective way if emission sources can be readily identified.
With the launch of GHGSat-C3, C4 and C5 in May 2022, we anticipate successful commissioning of all
three systems – including controlled release validation – that will more than double the coverage of theexisting GHGSat constellation, all with emission rate detection sensitivities down to 100 kg/hr. The
GHGSat constellation successfully bridges the spatial and detection sensitivity scales between aircraft andglobal-mapper satellite measurements and has been shown to be an effective tool for detecting andattributing methane emissions.
Keywords: Remote Sensing, Methane, High-resolution

4

Figure 1: An example of a single GHGSat measurement of underground coal mining facilities in Europe
showing the retrieved methane enhancement above background (colour scale) overlaid on 3rd-party
reflectance imagery.

5More than a decade of GOSAT and GOSAT-2 operations and data
products using their unique capabilities of FTS multiplex advantage
and target observations
Akihiko Kuze*1, Hiroshi Suto1, Kei Shiomi1,
JAXA GOSAT and GOSAT-2 team, CEOS WGCV, EO-Dashboard team
1. Japan Aerospace Exploration Agency, Tsukuba-city, Ibaraki, Japan
JAXA has been operating the GOSAT and GOSAT-2 satellites, which have been measuring two greenhousegases for more than a decade. Over this period, we have accumulated data on radiance spectra of reflectedsunlight with two linear polarizations and thermal emissions, thank to updated Level 1 products with radiancedegradation correction and the new solar irradiance data set of the Total and Spectral Solar Irradiance Sensor1 (TSIS-1) Hybrid Solar Reference Spectrum (HSRS). The release plans for the next versions of GOSAT V300
and GOSAT-2 V220 follow.
Utilizing the multiplex advantages of the FTS, the EORC L2 algorithm retrieves the partial column densities
of lower- and upper- tropospheric carbon dioxide (CO2) and methane (CH4), at roughly 0- 4 km and 4- 12 km,
respectively. Thirteen years of total and partial columns data on 2, 2, and 11 vertical layers of CO2, CH4 andwater vapor (H2O), respectively, and solar-induced chlorophyll fluorescence (SIF) are available athttps://www.eorc.jaxa.jp/GOSAT/GPCG/download_v2/. We have examined the lower-tropospheric CO2
products from GOSAT target observations to estimate emissions over global megacities.
We started the joint ESA-NASA-JAXA #2 dashboard program to relate the stories about the air we breatheand greenhouse gases as a product of combustion. We apply CO2 data from multiple instruments on GOSAT,GOSAT-2, and OCO-3, using nitrogen dioxide (NO2) as a proxy for anthropogenic CO2 emissions and SIF
from TROPOMI over target megacities such as Cairo, Shanghai, New York City and Tokyo. The Committee
on Earth Observation Satellites (CEOS) working group calibration and validation (WGCV) has opened
the Vicarious Calibration Portal for Space-borne GHGs Sensors at
https://www.eorc.jaxa.jp/GOSAT/GHGs_Vical/index.html. Using a seamless dataset from multiple sensors,
we provide satellite data and calibration and validation dataset of the ground, radiosondes, airplanes,
methodology, and analysis results to contribute to flux estimation, and data assimilation.
Key words: GOSAT, GOSAT-2, CEOS WGCV, TSIS, EO-Dashboard

6

Current Status and Recent Topics on
GOSAT and GOSAT-2 Standard Products and Other Data
Tsuneo Matsunaga *1, Isamu Morino1, Yukio Yoshida1, Makoto Saito1, Hibiki Noda1, Hirofumi
Ohyama1, Hisashi Yashiro1, Yu Someya1, Tazu Saeki1, Yosuke Niwa1, Akihide Kamei1, Fumie
Kawazoe1, Jiye Zeng1, Ryoichi Imasu2, Teruyuki Nakajima2, Takashi Nakajima3, Naoko Saitoh4,
and Makiko Hashimoto5
1. National Institute for Environmental Studies, Japan
2. The University of Tokyo, Japan
3. Tokai University
4. Chiba University
5. Japan Aerospace Exploration Agency, JapanGOSAT Series is a Japanese Earth observation satellite project jointly promoted by the Ministry of
the Environment, the Japan Aerospace Exploration Agency (JAXA), and the National Institute forEnvironmental Studies (NIES) to monitor global distribution of greenhouse gases from space. It
consists of three satellites, GOSAT launched in 2009, GOSAT-2 launched in 2018, and GOSAT-GW
to be launched in FY2023. GOSAT data for 13 years since April 2009 and GOSAT-2 data for 3
years since March 2019 are being distributed from respective NIES websites.
In this presentation, the current status of various standard products derived from GOSAT and
GOSAT-2 observations, such as column-average concentrations and regional fluxes of carbon
dioxide and methane, will be introduced. In addition, recent topics such as contributions to UN's 1st
Global Stocktake and the rapid increase of methane concentration in recent years (Figure 1) will be
discussed.

7

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8

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9

On-orbit characterization of the TanSat instrument line
shape using observed solar spectra
Zhaonan Cai*1,2, Kang Sun3,4, Dongxu Yang1,2, Yi Liu1,2,5, Lu Yao1,2, Chao Lin6, andXiong Liu7
1. Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese
Academy of Science, Beijing, China
2. LAGEO, Institute of Atmospheric Physics, Chinese
Academy of Sciences, Beijing 100029, China
3. Department of Civil, Structural and Environmental Engineering, University at
Buffalo, Buffalo, NY, USA
4. Research and Education in Energy, Environment and Water Institute, University at
Buffalo, Buffalo, NY, USA
5. University of Chinese Academy of Science, Beijing, 100049, China
6. Changchun Institute of Optics, Fine Mechanics and Physics, China
7. Center for Astrophysics | Harvard & Smithsonian, Cambridge, MA, USA

The Chinese carbon dioxide measurement satellite (TanSat) had collected a large number ofmeasurements in the solar calibration mode. In order to improve the accuracy of XCO2 retrieval,the Instrument Line Shape (ILS, also known as the slit function) must be determined accurately.
In this study, we characterize on-orbit ILS of TanSat by fitting measured solar irradiance from2017 to 2018 with a well-calibrated high spectral resolution solar reference spectrum. Various
advanced analytical functions and stretch/sharpen of the tabulated preflight ILS are used torepresent the ILS for each wavelength window, footprint, and band. Using supper Gaussian+P7
and stretch/sharpen functions significantly reduces the fitting residual in O2 A-band and weakCO2 band comparing to using the preflight ILS. Difference between the derived ILS width andon-ground preflight ILS can be up to -3.5% in weak CO2 band, depending on footprint andwavelength. Large amplitude of the ILS wings depending on the wavelength, footprint andbands indicates possible uncorrected stray light. Broadening ILS wings will cause filling-in ofdeep absorption lines of the spectra, which is confirmed using offline bias correction of thesolar-induced fluorescence retrieval. Errors due to the imperfect ILS are estimated using
simulated TanSat spectra. Simulations shows that XCO2 retrieval is sensitive to errors in the
ILS, and 4% uncertainty in the full width of half maximum (FWHM) or 20% uncertainty in the
ILS wings could induce an error of up to 1 ppm in the XCO2 retrieval.

10Assessment of the optimal operating point for a high resolution
XCO2 imager dedicated to the monitoring of 0.1 to 15.0 MtC.yr-1
sources.
F. Graziosi 1,2, G. Broquet *1, P. Kumar 1, P. Ciais 1, D. Simeoni 3
1

Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRSUVSQ, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
2

European Commission Joint Research Centre, Ispra (Va), Italy
3

Thales Alenia Space, 06150 La Bocca, France

The CO2M constellation planned for launch in 2025 or 2026 should become the main mission for thesatellite monitoring of the CO2 anthropogenic emissions on the short-term. Recent studies indicate thatit could support the detection and inversion of plumes from cities and plants emitting more than 1-2
MtC/year. However, the spatial resolution and precision of its XCO2 images should not be fine enoughto support the monitoring of smaller size sources. Cities and plants emitting more than 1 MtC/year
represent ~40 % of the global emissions. High resolution satellite imagery could thus play an importantrole by opening the opportunity to monitor smaller sources. Indeed, new instrumental concepts, like
Compact Gas Imager acquiring signal with very high XCO2 gain in one mega pixels image of XCO2,
could support the imagery of XCO2 at sub-kilometric resolutions, with similar levels of precision but
with swaths narrower than 100 km (while CO2M will have a swath > 200 km). In that regard, we use
academic inversions with pseudo-images to study the coverage of sources in terms of extent andemission rate and the corresponding share of the global emissions that could be ensured by new highresolution XCO2 imagery concepts, depending on the spatial resolution, precision and swath of the
instrumental scenarios. Our study assesses the different satellite configurations in terms ofcomplementarity with the coverage by CO2M. Our results indicate that the 50 km swaths and 100-200m spatial resolution scenarios can provide < 20% precision estimates of instant emissions for a set ofsources representing a larger percentage of global emissions compared to the scenarios with larger
swaths (up to 240 km) but coarser sampling resolutions (up to 2 km). The narrow swath scenarios canprovide good instant estimates for sources not covered by CO2M configuration that represent more than
30 % of the global emissions.

11

The MicroCarb CO2 mission : status and technical insight
D. Jouglet1 (*), P. Landiech1, F.M. Breon2, C. Deniel1, D. Pradines1, E. Cansot1, C. Pittet1, P.
Lafrique1, C. Revel1, C. Luitot1, B. Vidal1, P. Prieur1, L. Pistre1, S. Prady3, V. Lever4, F.
Duruisseau4, I. Souffer4, P. Rieu3(1) Centre National d’Etude Spatiales (CNES)(2) Laboratoire des Sciences du Climat et de l’Environnement (LSCE)(3) Magellium – Toulouse – France(4) CapGemini – Toulouse – France

Decision by the French government of the MicroCarb program development by CNES occurred in December
2015, in the frame of the COP-21. Since then, partnerships for major contributions in the program have beendeveloped with UKSA, EUMETSAT and EU through H2020 IOD-IOV program operated by ESA. MicroCarb
will be the first European mission dedicated to CO2 fluxes monitoring from space, with a target launch forearly 2024.
MicroCarb will make accurate measurements of the atmospheric CO2 column integrated concentrations(random error <1ppm, regional bias <0.2ppm) from an affordable micro satellite .The main objective is thestudy of natural fluxes for a better understanding of their mechanisms. An imagery mode is also implemented
as a demonstrator for local emission estimations. The MicroCarb instrument is a grating spectrometer that
acquires high-resolution spectra in four spectral bands: CO2 at 1.61 and 2.03 μm, O2 at 0.76 and 1.27 μm, thislatter being specific to the MicroCarb mission. An imager is also embedded for cloud detection.
The presentation will introduce the mission, its objectives and the main technical characteristics andpartnership organization. The current status of the program development will be highlighted.
Then, a technical insight of the mission will be given, with the following items : scientific algorithms developed
to retrieve the CO2 contents from the raw measurements; current estimated performances of MicroCarb L1 &L2 orbital simulated products (including the impact of the instrument, satellite and algorithms); application of
the L2 processor on real OCO-2 L1b data and comparison to TCCON and EM27; quick overview of the cal/val
planned activities.
Key words: MicroCarb, CO2, retrieval, performances, cal/val

12

Quantifying localized carbon dioxide emissions from space:
the CO2Image demonstrator
Julia Marshall*1, David Krutz1, Dietrich Feist1, Friedemann Reum1,
Klaus-Dirk Gottschaldt1, Bastian Kern1, Patrick Joeckel1, Andreas Baumgartner1,
Claas Koehler1, Guenter Lichtenberg1, Sander Slijkhuis1, Carsten Paproth1, Ilse Sebastian1,
Leon Scheidweiler2, Christian Frankenberg3, Andre Butz2, Anke Roiger1
1.

Deutsches Zentrum für Luft- und Raumfahrt, Institute für Physik der Atmosphäre,
Oberpfaffenhofen, Germany

2.

Institut für Umweltphysik, Heidelberg University, Heidelberg, Germany

3.

California Institute of Technology, Pasadena, California

Space-based measurements of carbon dioxide (CO2) are the backbone of the global and national-scale carbon
monitoring systems currently being developed to support and verify greenhouse gas emission reduction
measures. Current and planned satellite missions such as JAXA’s GOSAT and NASA’s OCO series and theupcoming Copernicus Carbon Dioxide Monitoring (CO2M) mission aim to constrain national and regionalscale emissions down to scales of urban agglomerations and large point sources with emissions in excess of~10 MtCO2/year.
We report on the demonstrator mission “CO2Image”, now in Phase B, which is planned for launch in 2026.
The mission will complement the suite of planned CO2 sensors by zooming in on facility-scale emissions,detecting and quantifying emissions from point sources as small as 1 MtCO2/year. A fleet of CO2Image sensors
would be able to monitor almost 90% of the CO2 emissions from coal-fired power plants worldwide. The key
feature of the mission is a target region approach, able to observe up to five scenes of 50 km x 50 km per orbit,
with a spatial resolution of 50 m x 50 m. This will enable CO2Image to resolve plumes from individual
localized sources, essentially providing super-resolution nests for survey missions such as CO2M.
We present the instrument concept, which is based on a spaceborne push-broom imaging grating spectrometermeasuring spectra of reflected solar radiation in the SWIR-2 spectral window. It relies on a comparatively
simple spectral setup, with a single spectral window and a moderate spectral resolution of approximately ~ 1.3
nm. The instrument is designed to fly in a sun-synchronous orbit at an altitude of 500 to 600 km. We report on
ongoing work in developing the sampling strategy and robust methods to estimate emission strengths based on
retrieved scenes.

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14



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15

The GOSAT-GW greenhouse gases observing mission: Updates
Hiroshi Tanimoto*1, Tsuneo Matsunaga1, Takafumi Sugita1, Hisashi Yashiro1, Isamu Morino1,
Makoto Saito1, Hirofumi Ohyama1, Satoshi Inomata1, Kohei Ikeda1, Yu Someya1, Tamaki
Fujinawa1, Yukio Yoshida1, Yousuke Yamashita1, Astrid Mueller1, Matthias Frey1, Tazu Saeki1,
Nobuko Saigusa1, Yugo Kanaya2, Takashi Sekiya2, Prabir Patra2, Masayuki Takigawa2, Yasuko
Kasai3, and Tomohiro Sato3
1. National Institute for Environmental Studies, Japan
2. Japan Agency for Marine-Earth Science and Technology, Japan
3. National Institute of Information and Communications Technology, Japan
Emissions inventories are one of key research elements in tackling the climate change issues. The bottom-up
inventories provide, for example, accurate estimates of greenhouse gases (GHG) emissions from fossil fueluse, but can have large uncertainties in other sectors and are restricted to managed lands. Inventories can also
be derived by the top-down approach using atmospheric inverse models, complementing the bottom-up
methods by providing an integrated constraint on surface fluxes from all sectors/processes on spatial scales.
For these models a variety of observations are used, including those from ground-based, ship, aircraft andsatellite platforms. In particular, recent improvements in the capability of satellite observations of atmosphericcomposition are driving great advances in the modeling. There are several plans to launch additional GHG and
air quality (AQ) observing satellites in near future. In Japan a plan is in progress to launch the third satellite in
the GOSAT series, named “Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW)”,
that will make observations of carbon dioxide (CO2), methane (CH4), and nitrogen dioxide (NO2) at ahorizontal resolution of 3 km or less. The missions of GOSAT-GW include (1) monitoring of wholeatmosphere-mean concentrations of GHGs, (2) validation of nationwide anthropogenic emissions of GHGs,
and (3) detection of GHGs emissions from large sources, such as megacities and power plants. In parallel, theNational Institute for Environmental Studies (NIES) has been operating a long-term program for monitoring
trace gases of atmospheric importance in Asia and the Pacific. Both long-lived GHGs and short-lived climate
forcers (SLCFs) are observed with commercial cargo vessels and passenger aircraft that are in regular operationin constant routes for long periods over a wide area between various ports/airports. These routine platforms
offer the advantage of systematic measurements of trace gases and aerosols, providing long-term datasets.
Hence, the challenge imposed to us is how to combine these multiple observing platforms into one integrated
system that can be best used for better understanding of the changing emissions and resulting impacts on theatmospheric composition. We will provide an overview of the projects and some recent highlights and discuss
future perspectives in supporting the Global Stocktake (GST) mechanism, a key element in the Paris
Agreement.
Keywords: greenhouse gas, air pollutant, carbon dioxide, nitrogen oxides, Global Stocktake

16

The future China’s greenhouse gas monitoring satellite missions
Yi Liu*1
1. Institute of Atmospheric Physics, Chinese Academy of Sciences, China

The greenhouse gas mainly includes Carbon dioxide (CO2) and Methane (CH4) play a very importantrole in global warming. Our understanding of global sources and sinks of CO2 and CH4 and the coupling withclimate change is still insufficient due to limitations in the surface monitoring network. Satellite remote sensing
can provide global and densely sampled observations of CO2 and CH4 which fill the gaps in surface networks,
and which can help to develop reliable estimates of regional surface fluxes.
Since the first Chinese greenhouse gas monitoring satellite mission (TanSat) launched in 2016, global
XCO2 and optimized CO2 surface flux over land has been provide from TanSat monitoring. The FY-3 and GF5(02) tried different way on CO2 and CH4 measurement implementation, based on Fourier transform technics
comparing to grading spectrum that onboard TanSat.
After TanSat, the next generation of GHGs satellite, DQ-1, DQ-2, TanSat-2 and future FY series satellite
mission that aiming to Global Stocktake service. The TanSat-2 mission will be a 3-6 satellites constellation. In
order to improve the measurement coverage and repeat frequency, the satellite has a option to be built as a
middle earth orbit, and hence each satellite observe earth in a large than 800 km width across track with 2x2
km pixel. The NIR/SWIR hyperspectral measurement on backscattered sunlight from spectrometer onboard
TanSat-2 includes 0.76 um (O2A), 1.61 um (CO2), 2.06 um (CO2) and 2.3 um (CH4 and CO) band, the O2Bband and O2 1.27 um band is also in consideration. There will be air pollution instruments onboard TanSat-2
to monitoring NO2 and aerosol optical properties. TanSat-2 aims to measured atmospheric CO2, as well as CH4
in global coverage, at least twice per day, which will be helpful to investigate the daily variation of greenhousegas and the emission that related to anthropogenic activities.
To improve the nighttime measurement, the DQ-1 and DQ-2 will carry greenhouse gas lidar, andcoordinate measurement with passive instrument will provide more accurate result in aerosol highly polluted
area. DQ-1 had been launched on April 2022.

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18



19

Using Clear Ocean Scenes to Constrain Changes in Instrument
Response in OCO-3
Graziela R. Keller*1, Robert A. Rosenberg1, Aronne Merrelli2, Christopher W. O'Dell3, Gary D.
Spiers1, Shanshan Yu1, Richard Lee1, Annmarie Eldering1, and Abhishek Chatterjee1
1. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
2. University of Michigan, Ann Arbor, USA.
3. Colorado State University, Fort Collins, CO, USA

NASA’s OCO-3 instrument, launched in 2019, measures atmospheric carbon dioxide and solar induced
fluorescence (SIF) from the International Space Station (ISS). It consists of three long slit imaging
spectrometers observing reflected solar light in three infrared spectral channels centered at 0.765 (ABO2), 1.61
(WCO2), and 2.06 μm (SCO2). As the band names suggest, the shortest wavelength channel is characterized
by the presence of absorption lines from molecular oxygen and the other two channels show numerous carbon
dioxide absorption lines. Due to its physical position onboard the ISS, OCO-3 cannot perform solar calibrations
and relies on three on-board calibration lamps to track changes in instrument response. Throughout the mission,
OCO-3 has experienced both gradual and abrupt changes in instrument response, which show wavelength
dependence within the bands. These changes have two separate causes, depending on the band. The ABO2 isaffected by gradual accumulation of contaminants and by their abrupt removal during planned decontamination
events. The CO2 bands, while not as impacted by contaminants, show sudden changes in overall instrument
response related to instrument resets. In order to mitigate the changes in instrument response, OCO-3’sradiometric calibration is frequently updated inflight. Analysis of thousands of clear ocean soundings has
shown that during periods of high contamination, more accentuated starting in June 2020 and reaching amaximum in January 2021, the inflight correction for the ABO2, in data version 10, was less than ideal, withrelative in-band calibration errors reaching 3 percent. This is caused by contaminants impacting the data fromeach lamp and from science observations differently. Here, we present the results obtained using clear ocean
scenes to track the quality of the inflight relative calibration within the ABO2 and WCO2 and to further
constrain the spectral shape of gain degradation in the ABO2 as viewed on Earth scenes.

Keywords: OCO-3, Radiometric Calibration, Lamps

20

UPlume: Automated algorithm for methane plume detection andsource quantification by satellite point source imagers
Jack H. Bruno*1,2, David Gains2, Daniel J. Varon1,2, Daniel J. Jacob1
1. Harvard University, United States
2. GHGSat, Canada
Methane is a potent greenhouse gas. Identification and quantification of methane point sources isa crucial step in understanding primary contributors to regional emissions and identifying targets for highimpact mitigation. Point source imaging satellites like GHGSat-C1 are powerful tools for detection ofpoint sources globally. Current methods for plume identification and source rate estimation are often error
prone and labor intensive. They are not scalable to the quantity of high spatial resolution methane datathat is presently becoming available from point source imagers. We have developed a hybridized method,
UPlume, combining machine learning with a U-Net architecture for plume detection with physics-basedsource rate estimation methods. The UPlume method is successful in producing accurate plume masks in
GHGSat-C1 imagery and separating out artifacts. Source rate estimations from the UPlume method
support the viability of this method for automated detection and quantification over large datasets. The
separation of masking and source rate estimation within UPlume allows visualization of the plume mask,
identification of the point source, and quality control on the accuracy of image segmentation.
Keywords: Methane, Machine Learning, Image Segmentation

21

Understanding causes of differences between the GGG2014 and
GGG2020 data products
Joshua L. Laughner*1, Geoffrey C. Toon1, Debra Wunch2, Coleen M. Roehl3, Paul O. Wennberg3,4,Erin McGee2, Sebastíen Roche5, Kimberly Strong2, Pascal Jeseck6,7, Yao Té6,7
1. Jet Propulsion Laboratory, California Institute of Technology, USA
2. Department of Physics, University of Toronto, Canada
3. Division of Geological and Planetary Sciences, California Institute of Technology, USA
4. Division of Engineering and Applied Science, California Institute of Technology, USA
5. School of Engineering and Applied Sciences, Harvard University, USA
6. Laboratoire d'Etudes du Rayonnement et de la Matière en Astrophysique et Atmosphères(LERMA-IPSL), Sorbonne Université, France
7. CNRS, Observatoire de Paris, PSL Université, France

The Total Carbon Column Observing Network (TCCON) provides column-average measurements ofgreenhouse gases from sites around the world. A new version of the TCCON data products, produced using
the latest version of GGG (GGG2020) was released at the end of April 2022. In this presentation, we willsummarize the overall changes to column average CO2 and CH4 products in the new GGG2020 release, andexamine several sites’ data in detail to understand the factors driving the differences between the previous
GGG2014 and new GGG2020 data product.

Keywords: TCCON, CO2, CH4, GGG2020

22

Characterization of TANSO-FTS-2 onboard GOSAT-2 and the Level-1
algorithm updates
Junko Kasuya*1, Hiroshi Suto1, Kei Shiomi1, Mayumi Shigetoh1, Akihiko Kuze1
1

Japan Aerospace Exploration Agency

Greenhouse gases Observing SATellite-2 (GOSAT-2) launched on 29 October 2018, as the follow-up to
Japanese Greenhouse gases Observing SATellite (GOSAT), aiming to improve CO2 and CH4 flux estimation
globally and locally by adding CO channel and optimizing sampling patterns with wider pointing angles andimproved target observation capability.
TANSO-FTS-2, a high-performance thermal near-infrared sensor for carbon observation, is installed in
GOSAT-2. It uses Fourier-transform spectrometer technology inherited from GOSAT to acquire solarreflected lights with orthogonal linear polarizations and thermal radiation simultaneously. A key feature of
TANSO-FTS-2 is that it allows to set a large AT/CT angle, which enables fully customized observation
patterns to observe emission sources such as large cities, power plants, and waste disposal plants. TANSOFTS-2 is equipped with multiple calibration sources and modes for instrument calibration: solar diffuser
target observation, monochromatic laser observation, blackbody observation, deep space observation, andLunar observation. The Level 1 products are observation results corrected for the effects of instrument
characteristics.
The latest version is V210, which applies an algorithm that corrects for changes in sensitivity that depend on
the orbit time and matches the NADIR radiance with that of other satellites. We are in the process ofanalyzing the V210 products in detail and preparing it for the next update. In particular, the laser calibration
was performed at two wavelengths in GOSAT-2, enabling a more detailed evaluation. We obtained laser
calibration continuously for 90 minutes (one orbit around the Earth) and confirmed that the SWIR sensitivity
variation with orbital position. The SWIR brightness accuracy can be improved by updating to optimized
brightness conversion coefficients that minimize the wavelength-dependent difference from TSIS. For thecalibration of the high AT angles data, we plan to perform a calibration to minimize the difference by matchup IASI and GOSAT-2 observations with off-NADIR observations over Antarctica. In addition, AT angledependent characteristics are calibrated by observing the satellite calibration site RRV, US under multiple AT
angle conditions.
Keywords: GOSAT-2, TANSO-FTS-2, Level-1 algorithm

23

Towards Arctic and boreal methane flux estimates: systematic
evaluation of TROPOMI XCH4 observations at high latitudes
Hannakaisa Lindqvist*1, Ella Kivimäki1, Aki Tsuruta1, Rigel Kivi2, Tomi Karppinen2, Tuula Aalto1,
Leif Backman1, Alba Lorente3, Oliver Schneising4, Michael Buchwitz4, Debra Wunch5, Kimberly
Strong5, Matthias Buschmann4, Huilin Chen6, and Johanna Tamminen1
1. Finnish Meteorological Institute, Helsinki, Finland
2. Finnish Meteorological Institute, Sodankylä, Finland
3. SRON Netherlands Institute for Space Research, Netherlands
4. Institute of Environmental Physics (IUP), University of Bremen, Germany
5. Department of Physics, University of Toronto, Canada
6. University of Groningen, Netherlands
The Arctic and boreal regions are experiencing a rapid increase in temperature, resulting in a changing
cryosphere, increasing human activity and a potential increase in the high-latitude methane emissions. Sentinel
5P TROPOMI observations provide an unprecedented coverage of XCH4 in this region, compared to previous
missions or in situ measurements. We present a systematic comparison of three TROPOMI methane products– operational and the scientific SRON and WFMD products – focusing exclusively on high Northern latitudes.
We evaluate the seasonal coverage and regional XCH4 features over the continuous and discontinuous
permafrost regions, reflecting the potential of TROPOMI to inform inversion models on changing emissions
in these regions. We also evaluate biases by carrying out comparisons to high-latitude TCCON, using thenewest GGG2020 retrievals. TCCON analysis is further complemented by comparisons to atmospheric profile
measurements with AirCore carried out in Northern Finland.
We find that TROPOMI observations enable seasonal analyses at high latitudes, even over permafrost regions.
The operational, scientific SRON and WFMD products show a generally good agreement but also systematic
seasonal and latitudinal differences. As presented by Tsuruta et al. (IWGGMS-18), seasonal biases may havea significant impact on the fluxes solved using inverse modelling (up to 0.5 TgCH4/month in the fall over highNorthern latitudes). All products have biases smaller than 27 ppb against the TCCON. TCCON/GGG2020
does not systematically improve the agreement. We make an effort to distinguish and analyse the albedo effects
from snow cover and the changes in the CH4 profile shape caused by high-altitude depletion of methane in thepolar vortex in order to identify potential, collective improvements. Collaboration in high-latitude algorithm,
validation, and flux analysis developments will continue in the new ESA MethaneCAMP project thatparticipates the ESA-NASA Arctic Methane and Permafrost Challenge (AMPAC).
Keywords: Arctic methane, TROPOMI, validation, permafrost, AMPAC

24

Comparison of IASI CH4 retrievals based on ASIMUT and
RTTOVCharles Robert*1, Sophie Vandenbussche1, Justin Erwin1, Ann Carine Vandaele1, andMartine De Mazière1
eMail : charles.robert@aeronomie.be
1. The Royal Belgian Institute for Space Aeronomy (BIRA-IASB)
Avenue Circulaire 3, 1180 Brussels, Belgium
Atmospheric methane (CH4) is measured continuously from space, providing valuable information at
global scales for atmospheric monitoring. Although most CH4 measurements from space are based on
observations in the shortwave infrared (SWIR) leading to a more uniform sensitivity to the atmosphericcolumn, thermal infrared observations (TIR) also provide valuable information about the CH4
atmospheric content, especially in the upper troposphere and lower stratosphere.
Among the various instruments performing measurements in the TIR, the Infrared AtmosphericSounding Interferometer (IASI) instruments onboard the METOP satellites have been observing the
Earth’s atmosphere for nearly 15 years. Its low radiometric noise, large spectral coverage and good
spectral resolution have led to the retrieval of several atmospheric species of interest. The Royal Belgian
Institute for Space Aeronomy (BIRA-IASB) has been involved in the retrieval of several species basedon IASI observations such as mineral dust, volcanic ash, CH4 and N2O.
In this work, we present recent developments of the IASI-derived CH4 dataset from BIRA-IASB. The
official BIRA-IASB CH4 product is retrieved based on the ASIMUT radiative transfer software, whichmodels the radiance using a full line-by-line calculation of the absorption cross-section of atmosphericspecies and performs a retrieval based on the Optimal Estimation Method (OEM). It allows great
flexibility, for instance in terms of spectroscopic databases and species included in the modelling, at thecost of computational efficiency.
RTTOV is another RTM which is less flexible since it is based on fixed spectroscopic data and allows
only a subset of species to be explicitly modelled and varied for the purpose atmospheric retrievals, but
is very fast and shows good accuracy. RTTOV has recently been used for the retrieval of the N2O productat BIRA-IASB and has been adapted to also retrieve CH4 with IASI as an alternative product.
In this work, we compare the results of using these two RTM on IASI observations in the TIR andhighlight the differences in the CH4 retrievals and spectral residuals. We will also look at the impact ofusing different spectroscopic databases and the implementation of the Tikhonov regularization.
Keywords: methane, IASI, ASIMUT, RTTOV, RTM

25

Commercial ship and aircraft-based evaluation of satellite derived
XCO2 and XCH4 over oceans updates and prospective
Astrid Müller*1, Hiroshi Tanimoto1, Takafumi Sugita1, Toshinobu Machida1, Shin-ichiro Nakaoka1,
Matthias Max Frey1, Ralph Kleinschek2, Ken von Buenau2, Isamu Morino1, André Butz2,
Prabir K. Patra3, Joshua Laughner4
1. National Institute for Environmental Studies, Japan
2. Institute of Environmental Physics, Heidelberg University, Germany
3. Japan Agency for Marine-Earth Science and Technology, Japan
4. Jet Propulsion Laboratory, California Institute of Technology, USA
Carbon dioxide (CO2) and methane (CH4) are the most important greenhouse gases (GHGs) whose atmosphericconcentrations continue to increase. The global trends and variability of these GHGs can be monitored bysatellite observations of the column-averaged mixing ratios of CO2 (XCO2) and CH4 (XCH4). The accuracy ofsatellite derived products is validated against reliable standards in some areas. But over oceans, limited
reference data hinders successful uncertainty quantification or bias correction efforts and precludes reliableconclusions about changes in the carbon cycle.
In the initial study, we obtained an “observation-based column-averaged CO2” (obs. XCO2) dataset byintegrating cargo-ship (Ship Of Opportunity, SOOP) and commercial aircraft (Comprehensive Observation
Network for Trace gases by Airliner, CONTRAIL) observations with the aid of state-of-the art atmosphericchemistry-transport model calculations. With the new dataset, we successfully identified and evaluatedimprovements in the GOSAT (Greenhouse gases observing satellite) and OCO-2 (Orbiting Carbon
Observatory) retrieval algorithms between 2014 and 2017 over the Western Pacific.
In the follow-up study, we made a temporal extension of the obs. XCO2 dataset using data of the Copernicus
Atmosphere Monitoring Service (CAMS) for the stratosphere this time. The same methodology was applied
to CH4 to obtain obs. XCH4. To cross-check the observation-based datasets, and as a complimentary approachto validate satellite and model derived data over oceans, a customized EM27/SUN Fourier Transform infrared
spectrometer was deployed on two research cruises in spring 2021 and winter 2021/2022. The semi-automatic
setup was developed by the Heidelberg University. With the aim of continuous ground-based measurements
of XCO2, XCH4, and XCO in coastal zones, this setup will be deployed on a cargo ship between Tokyo andFukuoka, Japan, in June 2022 for the first time. We will present first results and challenges, and give an outlook
on future applications.
Keywords: observation-based XCO2/XCH4, mobile EM27/SUN, validation

26

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)=;36(7$)138))27-2+)%796)1)28&-%7-(%627Monitoring facility-scale CO2 emission changes from space

Ray Nassar*1, Omid Moeini1, Jon-Paul Mastrogiacomo2, Christopher W. O’Dell3, Robert R.
Nelson4, Matthäus Kiel4, Greg Osterman4
1. Environment and Climate Change Canada, Canada
2. University of Toronto, Canada
3. Cooperative Institute for Research in the Atmosphere, Colorado State University, USA
4. Jet Propulsion Laboratory, California Institute of Technology, USA

The central objective of the United Nations Paris Agreement is for countries to reduce their greenhousegas emissions in order to limit future climate change. Transparent monitoring of CO2 emission reductions
is desirable at multiple spatial scales, including the scale of individual facilities such as fossil fuel burning
power plants. Here, we present a case study on monitoring CO2 emissions from Poland’s Bełchatów coalburning power plant (the highest CO2 emitting power plant in Europe), using space-based observations
from NASA’s Orbiting Carbon Observatory-2 and -3 (OCO-2 and OCO-3) missions, including multiple‘Snapshot Area Mapping’ (SAM) mode observations with OCO-3. The emission estimates are comparedwith reported power generation, demonstrating the ability to detect emission changes over time at thefacility scale. This research gives us a glimpse of the potential capabilities of future CO2 imaging missions
like the Copernicus Anthropogenic CO2 Monitoring Mission (CO2M) and reinforces the value of future
space-based CO2 data for verifying local-scale CO2 emission reductions with the potential to support thetransparency framework under the Paris Agreement.

Keywords: power plant, plume, OCO-2, OCO-3, transparency

28

Monitoring anthropogenic emissions from space: insights from
OCO-3’s Snapshot Area Mapping (SAM) mode
Abhishek Chatterjee*1, Robert Nelson1, Matthäus Kiel1, Sudhanshu Pandey1, Brendan Fisher1, Gary
Spiers1, Emily Bell2, Annmarie Eldering1, Thomas Kurosu1, John Lin3, Junjie Liu1, Chris O’Dell2,
Vivienne Payne1, Dustin Roten3, Tommy Taylor2, Paul Wennberg4, Dien Wu4, Cecilia Cheng1,
Ralph Basilio1
1. Jet Propulsion Laboratory, California Institute of Technology, USA
2. Cooperative Institute for Research in the Atmosphere, Colorado State University, USA
3. Department of Atmospheric Sciences, University of Utah, USA
4. Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena,
USA
As the world embarks on ambitious efforts to verify emission reduction goals, accurate andprecise monitoring of CO2 emissions from anthropogenic activities is essential. Over the last
decade, space-based retrievals of the dry-air mole fraction of column-mean carbon dioxide (XCO2)
from GOSAT and OCO-2 have provided significant new insights into our understanding of theanthropogenic and natural carbon cycles. However, neither of these missions were designed tomonitor emissions from anthropogenic “hot spots”, like large urban areas and megacities. GOSAT
and OCO-2 observations are often limited to a single overpass over a large urban area/megacity,
thus restricting their monitoring and detection capabilities to only a small portion of the domain.
The OCO-3 mission, on board the International Space Station, specifically includes anobservation strategy that is designed to monitor and quantify anthropogenic emissions. Thisviewing mode, called the Snapshot Area Mapping (SAM) mode, collects several adjacent swathsof XCO2 observations in a two-dimensional sweeping pattern over a spatial area of approximately
80 km by 80 km. This unique capability to map a given region of interest with the SAM mode
results in a rich dataset for urban- and local-scale carbon cycle studies. In this presentation, we
will go over this multi-swath measurement approach, technical challenges faced during the earlydays of operations, and the current data products available for scientific use. We will also highlightscientific findings from studies that are exploiting the information content in these dense, finescale maps. These studies are clearly demonstrating potential improvements in constraining urban
emissions by using the OCO-3 SAM mode observations. The presentation will conclude with a
discussion of proposed plans for terminating OCO-3 operations early next year, the resulting data
gap between OCO-3 and future wide-swath missions with similar global anthropogenic emission
monitoring capabilities (ESA CO2M), and implications for tracking city- and country-level
progress towards meeting proposed CO2 emission reduction goals.


29



Keywords: CO2 emissions, megacities, emission monitoring, NASA OCO-3, SAM mode30    
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31

Carbon Mapper: on-orbit performance predictions and airborne
prototyping
Riley Duren*1, 2,3, Daniel Cusworth1,2, Alana Ayasse1,2
1. CarbonMapper
2. University of Arizona
3. Jet Propulsion Laboratory
Efforts to mitigate methane (CH4) and carbon dioxide (CO2) emissions are complicated byinconsistencies between estimates derived from atmospheric measurements, greenhouse gas inventories, andself-reporting programs. Contributing to these discrepancies are a relatively small number of industrial
facilities that emit anomalously high amounts of greenhouse gases, often in an unpredictable and intermittent
fashion. Lack of transparency, spatio-temporal completeness, measurement sensitivity and costs are barriers
to diagnosing and mitigating point-source emissions at scale across geographically dispersed infrastructure.
We present the predicted on-orbit performance of the Carbon Mapper program, a constellation of small
satellites designed to track individual CH4 and CO2 point-source emitters in priority regions globally. Carbon
Mapper is implemented as a public-private partnership with an open data policy for CH4 and CO2. The firsttwo satellites are funded for launch in 2023 and will characterize the global distribution of point-sources inpriority regions at 30m spatial resolution with visible-infrared imaging spectrometers. The system is designed
for expansion to a larger satellite constellation in 2025 offering sub-daily to weekly sampling. We also present
findings from prototype airborne surveys of diverse regions and emissions sectors including lessons learned
from follow-up site-level measurements and mitigation by facility operators and regulators with implications
on the emerging global, multi-scale (tiered) observing system of systems.

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*(!#$% &# %#!"!33

Strong methane point sources contribute a disproportionate fraction of
total emissions across multiple basins in the U.S.
Daniel H. Cusworth1,2*, Andrew K. Thorpe3, Alana K. Ayasse1,2, David Stepp2, Joseph Heckler4,
Gregory P. Asner4,2, Charles E. Miller3, Vineet Yadav3, John W. Chapman3, Michael L. Eastwood3,
Robert O. Green3, Benjamin Hmiel5, David Lyon5, and Riley M. Duren1,2,3
1. Arizona Institutes for Resilience, University of Arizona, Tucson, AZ, USA
2. Carbon Mapper, Pasadena, CA, USA
3. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
4. Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ, USA
5. Environmental Defense Fund, Austin, TX, USA*Corresponding Author: Daniel Cusworth
Email: dcusworth@arizona.edu

Understanding, prioritizing, and mitigating methane (CH4) emissions requires quantifying methane budgets fromfacility scales to regional scales with the ability to differentiate between source sectors. We deployed a tiered
observing system for multiple basins in the United States (San Joaquin Valley, Uintah, Denver-Julesberg, Permian,
Marcellus). We quantify strong point source emissions (>10 kg CH4 h-1) using airborne high spatial resolution
imaging spectrometers, then attribute them to sectors, and assess their intermittency with multiple revisits. We
compare these point source emissions to total basin CH4 fluxes derived from inversion of Sentinel-5p satellite
observations. Across basins, point sources make up on average 40% of the regional flux. We sampled some basins
several times across multiple months and years and find a distinct bimodal structure to emission timescales: thetotal point source budget is split nearly in half by short-lasting and long-lasting emission events. With theincreasing airborne and satellite observing capabilities planned for the near future, tiered observing systems willmore fully quantify CH4 emissions from facility to regional scales, which is needed to effectively and efficiently
reduce methane emissions.

34

Summary statistics for each basin surveyed between 2019-2021 (Figure 1). Panel (a) shows a comparison between
aggregated point source emissions for each campaign with a top-down spatially/temporally synchronous
TROPOMI flux inversion and bottom-up emission from the 2012 EPA gridded inventory. Panel (b) shows the
cumulative distribution of airborne plume emissions quantified for each campaign. Panel (c) shows the relative
sector breakdown between airborne plume emissions and the bottom-up inventory for the following emission
sectors: oil & gas (dark/light red), waste management (dark/light yellow), manure management (dark/light green),
and coal (dark/light black).

35

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37

The impact of viewing geometry on the interpretation of highresolution measurements of CO2 plumes
Tufel Nicolas1*, Reum Friedemann1 and Marshall Julia1
1.Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institute for Atmospheric Physics,
Oberpfaffenhofen, Germany

The CO2Image mission being developed at DLR will be measuring column-integrated atmospheric carbon dioxideabundance at a spatial resolution of 50 m x 50 m. The aim of the mission is to quantify emissions from anthropogenic
point sources, providing information relevant for national emissions reporting under the Paris Agreement. By
targeting a high spatial resolution, smaller emission plumes can be detected, and sensitivity to point source emissions
as small as 1 MtCO 2/year is foreseen. The increasing resolution of the ground pixels also leads to some additional

complications in the interpretation of the measurements. This study focuses on the assumption of nadir viewingthat is often made when interpreting the retrieved column-integrated values. In reality, this column-integrated
value usually represents two slant columns, depending on the solar and viewer angles. This can result in aslight shift in the location of plume, and a broadening of the width of the plume. The problem is exacerbatedfor plumes aloft, such as those from high chimneys. If this effect is not considered, it can lead to errors in the
retrieved emission rates. By using both Gaussian plume and large eddy simulations, this study quantifies
differences in the retrieved plume as a function of solar and viewer angles, pixel size, and emission height.
Furthermore, we assess the difference in the resultant retrieved fluxes using common methods for emission
estimation, such as the integrated mass enhancement (IME) approach.

38

Urban XCO2 gradients from space: results from OCO-3 SAM’s and adense network of EM27/SUN over Mexico City
K. Che*1,2, T. Lauvaux2, N. Taquet3, Y. Xu4, W. Stremme3, M. Campos3,
Yi Liu1, Z. Cai1, M. Ramonet4 and M. Grutter3
1 Carbon Neutrality Research Center, Institute of Atmospheric Physics,
Chinese Academy of Science, Beijing, 100029, China
2 Groupe de Spectrométrie Moléculaire et Atmosphérique (GSMA), Université de ReimsChampagne Ardenne, UMR CNRS 7331, Moulin de la Housse,
BP 1039, 51687 CEDEX 2 Reims, France
3 Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México,
Mexico City, Mexico
4 Université Paris-Saclay, CEA, CNRS, UVSQ, Laboratoire des Sciences du Climat et del’Environnement (LSCE/IPSL), Gif-sur-Yvette, France
Large metropolitan regions across the world represent a significant fraction of fossil fuel CO2 emissions
at the global scale, but recent studies have shown that large uncertainties might affect their official SelfReported Inventories, most often leading to under-estimation of urban direct emissions. The metropolitan areaof Mexico City, a densely populated region of about 20 million inhabitants, has pledged for carbon neutrality
by 2050, an ambitious target that can be supported by atmospheric measurements. Mexico City has been
selected for frequent Snapshot Area Mode (SAM) by the NASA Orbiting Carbon Observatory-3 (OCO-3) . In
parallel, a French-Mexican research collaboration, called Mexico City’s Regional Carbon Impacts (MERCICO2), used a dense network of 7 ground-based column sensors located in the Mexico City metropolitan area
(MCMA), offering a unique opportunity to compare and evaluate the quantification of urban XCO2 gradients,
and possibly the monitoring of XCO2 emissions over the coming decades. The network included 1 permanenthigh resolution IFS 125 HR spectrometer at the high altitude Altzomoni observatory (4000 m a.s.l), andcontributing to the NDACC network since 2014, 2 permanent EM27-SUNs stations located in Mexico Cityand measuring since 2016 and 2019, and four additional EM27-SUNs instruments deployed across the urban
basin during a 7-month (October 2020 to May 2021) intensive field campaign.
During October 2020 to May 2021, an intensive 7-month field campaign included 7 solar-viewing
Fourier transform infrared (FTIR) spectrometers (6 portable Bruker EM27/SUN and 1 IFS 125 HR). Two
FTIRs were deployed in rural areas, in the basin and near mountain tops, while the other instruments werelocated across the urban basin of Mexico City. Thanks to NASA’s Orbiting Carbon Observatory-3 (OCO-3)
collecting urban-focused XCO2 measurements at high-resolution (SAM and targets), a total of 20 partial XCO2
maps are available over the same time period. After removing the background concentrations, we attempt toreconcile the observed intra-urban gradients and urban-rural gradients (ΔXCO2) from FTIRs and OCO-3. The
analysis is supported by column footprints, generated by X-Stochastic Time-Inverted Lagrangian Transport(X-STILT) driven by the Weather Research and Forecasting (WRF) model, to determine the observations lessinfluenced by urban emissions (optimal background). Urban gradients are of great importance in inverse
modeling; lower gradients would directly lead to lower estimation of the emissions over the city. This study
identifies potential differences in observed XCO2 gradients thanks to an unprecedented density of column
sensors over a single metropolitan area. We evaluate satellite urban gradients and quantify the potential toconstrain urban CO2 emissions from different observing strategies over Mexico City.

39

The Greenhouse gas Observations of Biospheric and Local Emissions
from the Upper sky (GOBLEU): New multi-species observations frompassenger aircrafts in support of monitoring of Japan’s anthropogenic
carbon emissions
Hiroshi Suto*1, Akihiko Kuze1, Tomohiro Oda2,3,4, Ayako Matsumoto5, Shigetaka Mori6, Yoichi
Nakamura7, Mayumi Shigetoh1, Chiharu Hoshino1, Kei Shiomi1, Yosuke Miyashita5, Yasuhiro
Tsubakihara5
1 Japan Aerospace Exploration Agency, Japan
2 Universities Space Research Association, Columbia, MD, USA
3 Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, US
4 Graduate School of Engineering, Osaka University, Suita, Osaka, Japan
5 ANA HOLDINGS INC., Japan
6 JASTECS, Japan
7 NEC, Japan
Japan Aerospace Exploration Agency (JAXA) and ANA HOLDINGS INC. (ANAHD) launched a newobservation program, the Greenhouse gas Observations of Biospheric and Local Emissions from the Upper
sky (GOBLEU). We introduced a “new passenger”, a carry-on luggage sized imaging spectrometer suites wenewly developed, to ANA’s domestic passenger flights, and have started collected high-resolution (~sub km
along track spatial resolution) spectra of nitrous dioxide (NO2), carbon dioxide (CO2) and solar induced
fluorescence (SIF), with moderated spectral resolution. Our “passenger” instrument can be mounted on apassenger seat without any modification to the aircraft, and collects data routinely and frequently as regularflights go. The aim of our program is to quantify Japan’s subnational carbon emissions, especially from cities
and individual sources with emission sectoral attribution in order to provide actionable information towards
Japan’s 46% 2030 greenhouse gas (GHG) reduction goal. Based on the same remote sensing technique as ones
used by GHG satellites, our program also prototypes the synergic use of CO2 and NO2 for enhancing theemission monitoring capability.
We will show data from early GOBLEU flights and present data analyses that examine the utility of the newdata we collect. We will also discuss the unique utility of our new aircraft observation and its potential
contribution to GHG emission monitoring and the upcoming Global Stocktakes (GST) with an expanded
observation coverage and frequency.

40

Monitoring instruments onboard Japanese passenger flights.

Keywords:
Global Stocktakes, Anthropogenic carbon emissions, passenger aircrafts

41

Using OCO-2 column CO2 retrievals to rapidly detect and estimate
extreme terrestrial biosphere carbon anomalies
Andrew F. Feldman*1,2, Zhen Zhang3, Yasuko Yoshida4, Abhishek Chatterjee5, Joanna Joiner6,
Benjamin Poulter1
1Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
2NASA Postdoctoral Program, NASA Goddard Space Flight Center, Greenbelt, MD, USA
3Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD,

USA
4Science Systems and Applications, Inc. (SSAI), Lanham, MD, USA
5Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
6Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center,Greenbelt, MD, USA
Abstract
The global carbon cycle is experiencing continued increases in atmospheric carbon concentrations that arelargely driving climatic change. Greenhouse gas satellites are designed to primarily observe seasonal tointerannual variations of these atmospheric carbon concentrations to assess their anthropogenic andnatural drivers. Recent work has shown that these satellites are further applicable to quantify surface
emissions at smaller spatiotemporal scales. For example, simple mass balance approaches have been used
to rapidly estimate surface methane fluxes from satellite atmospheric column methane measurements.
However, less attention has been placed on using these satellite observations and mass balance methods toevaluate surface CO2 fluxes from the terrestrial biosphere at shorter timescales. Such applications could
be useful to monitor, in real time, extreme biosphere carbon fluxes. Here, we assess the ability of Orbiting
Carbon Observatory-2 (OCO-2) column-averaged dry air CO2 (XCO2) retrievals to rapidly detect andestimate terrestrial biosphere CO2 emissions. Using CarbonTracker model reanalysis as a testbed, we first
demonstrate that a mass balance approach, previously used to estimate methane fluxes, can estimatemonthly surface CO2 flux anomalies in the Western United States. The method is optimal when thechosen target region is spatially extensive enough to account for atmospheric mixing and has consistentadvection conditions with contributions primarily from one background region. While errors in individual
soundings partially reduce the ability of OCO-2 XCO2 to estimate more frequent, weaker surface CO2
flux anomalies, we found that OCO-2 XCO2 can often detect and estimate larger surface flux anomalies.
Greenhouse gas satellites are therefore more useful for rapidly evaluating widespread terrestrial biospherecarbon anomalies due to extreme climatic events than currently appreciated. Any noise reduction inforthcoming greenhouse gas satellites and/or the existence of large surface carbon anomalies will increase
the ability to rapidly estimate surface fluxes at smaller spatiotemporal scales.

Key Words:
OCO-2, XCO2, biosphere, flux, satellite42

Is GOSAT XCO2 useful in the OCO era?
Christopher O’Dell*1, Tommy Taylor1, Sean Crowell2, and Frederic Chevallier3
1. Colorado State University, USA
2. University of Oklahoma, USA
3. Le Laboratoire des Sciences du Climat et de l'Environnement, France

GOSAT launched in 2009 as the first dedicated greenhouse gas monitoring instrument, followed by OCO-2 in2014 and OCO-3 in 2019. Among other quantities, these satellites observe the column-average dry air mole
fraction of CO2 (XCO2). They hold a significant advantage over the existing in-situ network of CO2 measurements
in terms of spatial coverage, sampling most of the global on timescales of 3-14 days. However, satellite-based CO2
flux products have been slow to be adopted by the larger science community, mainly because the requirements for
systematic errors in the retrievals have been exceptionally challenging to address. Recently though, after
incremental improvements to the ACOS retrieval algorithm, OCO-2 based flux products have gained more
acceptance, with mainstream publications on fluxes inferred from satellite products becoming more common.
However, GOSAT CO2 flux products have generally not received the same attention.
In this work, we attempt to show that the quality of GOSAT flux products can approach that using OCO-2 and is
not overly dominated by systematic errors in the retrievals. We first discuss the error statistics of several widely
available GOSAT XCO2 products. One of these, the ACOS version 9 GOSAT retrieval, was then assimilated into
two global inversion systems: a 4DVarbased system from the University of Oklahoma, and the CAMS variational
system from LSCE. We compare the results of these flux inversions with corresponding results from OCO-2 over
the overlap period 2015-2019 and show that they are generally comparable. This preliminary result implies thatGOSAT-based CO2 flux inversions may be used to complement OCO-2 and other more recent satellites both
spatially and temporally, providing potentially valuable information on terrestrial fluxes back to 2009.

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45

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47

Interannual variability of anthropogenic methane emission
estimated by an inverse model using GOSAT and surface
observations
Rajesh Janardanan1*, Shamil Maksyutov1, Fenjuan Wang1, Marielle Saunois2, Yukio
Yoshida1, Tsuneo Matsunaga1
1. National Institute for Environmental Studies, Japan
2. Laboratory of Climate and Environmental Sciences (LSCE), France
Abstract
We report the country level analysis of anthropogenic methane emissions estimated with a high-resolution
inverse model using observational data from global surface observation network and Greenhouse gases
Observing SATellite (GOSAT; NIES Level 2, v02.95) for the period 2009-2020. The high-resolution
inverse model is implemented by coupling a Lagrangian particle dispersion model FLEXPART to theglobal Eulerian transport model NIES-TM. The surface observational data include Obspack CH4 (v4.0) andobservations from ICOS network. For the anthropogenic prior emissions in the inversion, we used monthly
data from Emissions Database for Global Atmospheric Research (EDGAR v6.0). For emission frombiomass burning, Global Fire Emissions Database (GFED v4.1s) was used. Emissions from wetland andsoil sink were taken from Saunois et al (2020). Other flux categories include emission from termites,
geological and ocean sources, all fluxes provided at 0.1q spatial resolution. Flux corrections were estimatedfor six categories such as agriculture, biomass burning, waste, oil and gas sector, coal and wetlands on a biweekly time step. Variational optimization was applied to obtain flux corrections for the six categories asscaling factors of monthly varying prior uncertainty fields. For the GOSAT-based inversion, potential
biases were removed from satellite retrievals using a surface-data optimized forward simulation, on
monthly basis, for 5° latitude bands. The interannual variability and the trend of the estimated anthropogenic
emissions were analyzed for the major emitting countries. The trend of anthropogenic methane emissions
inferred from GOSAT observations for these countries were, China 0.38±0.17 Tg CH4 yr−2 (p=0.045), USA-0.23±0.05 (p=0.001), India 0.15±0.1 (p=0.18), Brazil 0.12±0.14 (p=0.39), Russia 0.11±0.03 (p=0.005),
Indonesia 0.35±0.05 (p<<0.0). These results are in conformity with recent country-specific studies onanthropogenic methane emission trends. Similar estimates were arrived by inversion using surface
observations, with China having 0.49±0.19, USA -0.13±0.1, India 0.03±0.18, Brazil 0.46±0.1, Russia
0.17±0.04 and Indonesia 0.38±0.04 Tg CH4 yr−2.
Keywords: Anthropogenic emissions, inverse modeling, GOSAT, methane trend48

Towards assimilation of GOSAT partial column retrievals in
CTE-CH4 atmospheric inverse model for estimation of global andregional CH4 budgets
Aki, Tsuruta*1, Akihiko, Kuze 2, Nobuhiro, Kikuchi2, Tuula, Aalto 1, Kei, Shiomi2, Fumie, Kataoka3,
and Hiroshi, Suto2
1. Finnish Meteorological Institute, Finland
2. Japan Aerospace Exploration Agency, Japan
3. Remote Sensing Technology Center of Japan, Japan
It has been shown that atmospheric inverse models benefit from satellite retrievals for constraining global
and regional greenhouse gas (GHG) budgets. Due to their spatial coverage, they have advantage over surface
in-situ observations for the estimation of GHG fluxes in regions with a limited in-situ observation network,
such as Tropics. However, assimilation of the satellite data has been challenging as both transport model andretrieval errors need to be taken into account. For methane (CH4), several studies showed large discrepancies
between modelled and retrieved values, even after the flux estimates are optimised based on surface
observations. Possible explanations lay in the upper atmosphere. Model may not be able to estimate
stratospheric profiles well due to e.g. limited number of model layers, incorrect distribution of thestratospheric sinks, and insufficient parameterization of troposphere-stratosphere exchange. For the satellitedata, it is challenging to accurately retrieve the shape of the prior profiles over the stratosphere, especially athigh latitudes.
In this study, we use the newly published JAXA/EORC’s GOSAT partial column retrievals of CH4 in
CTE-CH4 atmospheric inverse model for estimation of global and regional CH4 budgets. First comparison
with forward modelling showed a good agreement with the retrievals in the lower troposphere. Unlike thecomparison to total column values (XCH4), no strong systematic biases such as latitudinal dependencies were
found. This confirms that the discrepancies in XCH4 is mainly due to the upper atmosphere. However, theseasonal cycle of the partial column values did not agree entirely. At high northern latitudes, the seasonal
cycle amplitude was generally overestimated by the model in the lower troposphere, and underestimated in
the upper troposphere. The results show potential advantages in using the tropospheric partial column data inatmospheric inverse models, and increasing understanding of CH4 budgets.

Keywords: GOSAT, partial column retrievals, methane, atmospheric inversion

49Increasing Natural Gas Demand Threatens China’s Carbon Reduction
Fenjuan, Wang*1,2, Shamil, Maksyutov1, Rajesh, Janardanan1, Aki, Tsuruta3, Akihiko, Ito1, Isamu,
Morino1, Yukio, Yoshida1, Yasunori, Tohjima1, Johannes, W. Kaiser4, Xin, Lan5,6, Yong, Zhang7,
Miao, Liang7, Ivan, Mammarella8, Jost V., Lavric9 and Tsuneo, Matsunaga1
1. National Institute for Environmental Studies, Japan 2. National Climate Centre, China 3. Finnish
Meteorological Institute, Finland 4. Deutscher Wetterdienst, Germany 5. Cooperative Institute for
Research in Environmental Sciences, University of Colorado, USA 6. National Oceanic andAtmospheric Administration, USA 7 Meteorological Observation Center, China Meteorological
Administration, China 8 University of Helsinki, Finland 9 Max Planck Institute for
Biogeochemistry, Germany (now at Acoem Australasia, Melbourne, Australia)

Natural gas is a popular choice as an alternative to coal for most countries to reduce air pollution. China
promotes a coal-to-gas energy strategy since 2012 and achieves evident air pollution reduction. While methaneleakage to the atmosphere from dramatic increase of natural gas production and consumption to the atmospherecause serious climate warming issues. In this study, we analyze GOSAT satellite and surface-based methane
observations with a high-resolution inverse model, and provide evidence of emission source changes, with
implications for designing efforts for climate change mitigation at a regional level in China. we find that themethane emissions in China increase during 2010-2018 with the growth of natural gas use especially inNorthern China. Those findings highlight the side effect of natural gas use and serve to awake the attention
towards proper strategies facing the spread of natural gas use and pipeline expansion in China. Fugitive
emissions of methane from natural gas can be reduced to the economic and environmental benefit of by theincreasing natural gas use in China and call for methane emission reductions throughout the gas supply chain
and promotion of low emission end-use facilities.

Keywords: methane emission inversion, natural gas leakage, GOSAT

50

Evaluation of GOSAT-2 SIF with ground-based SIF in three common
vegetation types
Tomoki Morozumi*1, Hibiki M Noda1, Tomomichi Kato2, Hideki Kobayashi3, Yuma Sakai4,
Naohisa Nakashima5, Kanokrat Buareal2, Keisuke Ono6, Kenlo N Nasahara7, Tomoko K Akitsu8,
Shohei Murayama9, Hiroyuki Muraoka10
1. National Institute for Environmental Studies, Japan
2. Hokkaido University, Japan
3. Japan Agency for Marine-Earth Science and Technology, Japan
4. National Institute of Technology Tomakomai College, Japan
5. Obihiro Universityof Agriculture and Veterinary Medicine, Japan
6. National Agriculture and Food Research Organization, Japan
7. University of Tsukuba, Japan
8. Japan Aerospace Exploration Agency, Japan
9. National Institute of Advanced Industrial Science and Technology, Japan
10. Gifu University, Japan

The importance of terrestrial ecosystem monitoring is increasing as to be facing to impact of climatechange. Sun-induced chlorophyll fluorescence (SIF) is the radiation emitted from the photosynthetic process
in plants. Thus, remote sensing of SIF is used as an indicator to detect photosynthetic activity. SIF has beenmeasured with various platforms, including towers, UAVs, airborne, and satellites. However, these values are
not comparable. In general, the footprint of satellite SIF is much larger than those of ground-based SIF
capturing a single vegetation type. In addition, satellites can provide SIF data for overpassing time. As it islacking hourly variations within a day, the daily cumulative amount will be an approximate extrapolation.
Therefore, it is necessary to validate the satellite SIF with the ground-based SIF which is measured
continuously in common ecosystem types. The present study aimed to evaluate the GOSAT-2 satellite SIF(FTS-2 SWIR L2 Ver.01.07) with the ground-based SIF. We have installed fine resolution spectrometers at
three sites (deciduous broadleaved forest in Japan TKY: 36°N, 137°E, evergreen coniferous forest in Alaska
PFA: 65°N,147°W, and paddy field in Japan MSE: 36°N 140°E) and measured SIF continuously. Although theground-based and GOSAT-2 SIF were different from each other, those differences could be derived from thefootprint land cover and the target-to-viewer geometry such as field-of-view and view zenith. If carefully
considering those factors, the values were almost consistent with each other. We also demonstrate theuncertainty of each vegetation type.
Keywords: SIF, ground-based observation, vegetation types, forest, cropland

51

The US Plan for Measuring, Monitoring, Reporting, and Verifying(MMRV) GHG Emissions
Ken Jucks*1, Ariel Stein2, James Whetstone3
1. National Aeronautics and Space Administration (NASA), USA
2. National Oceanic and Atmospheric Administration (NOAA), USA
3. National Institute of Standards and Technology (NIST), USA

In January 2022, the current Government of the US announced a sweeping plan for reducing GHG emissions andmonitoring and reporting these reductions. Some of these new activities are currently funded through the recently
enacted Infrastructure law. In particular, the law includes funding for land reclamation for abandoned coal mines,
modernization of the natural gas pipeline system, identification of orphaned wells, and the launch of a methane
reduction infrastructure initiative. This announcement also called for the initiation of a Greenhouse Gas MMRVWorking Group ‘to help identify and deploy the best available tools and data systems to measure, monitor, report,
and verify (MMRV) carbon dioxide, methane, and other GHG emissions and removals”. This new working groupis split into two Technical Working Groups (TWG), one focusing on emissions from Agriculture and Forestry andone focusing on GHG measurements and reporting. This presentation will focus on the latter. The US agencies
that provide most of the observations of GHG observations are NASA (from space and aircraft remote sensing),
NOAA (in situ), and NIST (focused urban intensives). These are the agencies providing the leadership within the
TWG. The initial goals of this effort are to better coordinate the activities of these agencies and to focus initiallyon use cases where the existing observation programs can make substantial contribution. The intent is to startwith a domestic focus, and then see where it makes sense to advance the system more globally. To do that, thisa ct i v i t y w i l l at t em pt t o pr ov i d e c o nsi st en cy w i t h ot her i n t e r n at i o n al ef f or t s f o r MM R V .

52Mapping Emissions Over Offshore Oil and Gas platforms Using Sun
Glint with Carbon Mapper
Alana K Ayasse1,2, Riley Duren1,2,3, Andrew K Thorpe3, Daniel Cusworth1,2, Eric Kort4, Alan Gorchov
Negron4, Ravi teja Nallapu5, Bhavi Jagatia5, Philip Linden5, Joseph Heckler6, Gregory P Asner6
1

University of Arizona, 2CarbonMapper.org, 3Jet Propulsion Laboratory, California Institute ofTechnology, 4University of Michigan, 5Planet Labs PBC, 6Arizona State University

Offshore oil and natural gas platforms are responsible for about 30% of global oil and natural gas
production. Despite the large share of global production few studies have directly measured emissions
from these facilities. Offshore platforms are typically understudied due to their remote location. Satellites
offer the ability to regularly measure atmospheric methane and potentially carbon dioxide emitted fromthese facilities. However, passive satellites that rely on reflected radiance cannot always makemeasurements due to a lack of reflected radiance over water. To compensate satellite must rely on special
sun glint acquisitions to capture a target with enough illumination. Few current satellites have a specificglint mode and none with fine enough spatial resolution to map point source emissions.
The upcoming Carbon Mapper satellite system intends to have a glint mode to map point sources
emissions from offshore facilities. Carbon Mapper is scheduled to launch in 2023. In anticipation of theupcoming satellite mission, a pre-cursors airborne campaign was conducted in May and October of 2021
with the Global Airborne Observatory (GAO) platform, which is an aircraft equipped with a 432 band
Visible Short-Wave InfraRed (VSWIR) imaging spectrometer, similar to the future Carbon Mapper
instrument. In 2021, GAO used glint mode to survey over 150 offshore platforms and surrounding
infrastructure in US federal and state waters in the Gulf of Mexico. This represents ~ 8% of active shallow
water infrastructure in the US Gulf of Mexico and one of the largest surveys of offshore facilities to date.
Here we present the methods and results from the airborne imaging spectrometer campaign, new insights
learned about methane emissions from offshore oil and gas facilities, and plans for the Carbon Mapper
satellite glint mode.

Figure 1: Methane plume observed from GAO with an estimated emission rate of 2510.4 ± 374.5 kg hr1. The plume is overlaid on the GAO high resolution true color camera image.

53

Building a validation support service for CO2M and other GHG
satellite missions
Mahesh Kumar Sha*1, Nicolas Kumps1, Bavo Langerock1, Dietrich G. Feist2,3, Tijl Verhoelst1,
Martine De Mazière1, Jean-Christopher Lambert1, Max Reuter4, Michael Buchwitz4, Thomas
Kaminski5, Michael Vossbeck5, Ruediger Lang6
1. Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Belgium
2. Ludwig-Maximilians-Universität München, Germany
3. Deutsches Zentrum für Luft- und Raumfahrt, Germany
4. University of Bremen, Germany
5. The Inversion Lab Thomas Kaminski Consulting, Germany
6. EUMETSAT, Germany

The success of a satellite mission is critically dependent on its ability to reach the targeted accuracy andprecision requirements set for the mission. A well-developed calibration and validation concept for the mission
is key to ensure verification and traceability to in-situ reference measurements. In the framework of theCopernicus CO2M mission, we are developing a service to support the cal/val concept. As a first task, we aredoing an assessment of existing ground-based datasets relevant for continuous cal/val and monitoring of
CO2M products, and the availability of complementary parameters, which influence the target measurand. We
created a modular dynamic database describing the station characteristics, key instrument parameters, available
data products and their quality, and network characteristics. The database is stored as a NetCDF file and canbe accessed using an oPeNDAP interface with the possibility to request the parameters, filter and selectvariables via a web-based interface in json or ascii output. We created a web-based interface to project on a
global map, level-3 files (gridded level-2 files) of target gases from satellites (e.g., S5P CH4, S5P NO2, OCO2 CO2), emission inventory databases, make comparisons with each other, and over-plot the station list withparameters from the database. The service will facilitate the users to get an overview of the available
measurements with respect to emission hot-spots, suitability of adding additional measurements at certain
locations considering the criticality of emission, co-emitted species and pollution levels, and assist in
identifying locations to fill observational gaps in the currently existing measurements networks. In thispresentation, we will provide an overview of the service implemented on behalf of EUMETSAT for the CO2M
mission, which is also useful for other satellite missions and for the scientific community.
Keywords: Greenhouse gases, station database, satellite validation, emission inventories, CO2M

54

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;; ) 2"2#$ < = "> +   + ""  -4  "" "   ,?(@  >$  "> "$ "  ,?( >" <" @ "" $  + "@  A  ( "  @ +   +    >" 3  "  @$ >  $  > " + " ""@ +" @  " @ "   > "" < "  "   "  @" @"   > > " @     ""  $"  @ < @ >>"  " 2  " ">   >  ">   B"""  " 4  " $  $+   >" "  C"    $   > ""  " @  " $>   ">   "$ "     "  "   @     ""    @" "> $   "  +     $
 ""  $  " @  ">  "  "" "        <  "   ++   "  ,?( >" "   <   +  "   """ @   " "  ""     ""  $    > " @  ">  "   "" @   D@"E $" > >  "> > >

55

                              ! "  # $  %&' # ( )% & " +, &  - +  &./001    023 6 6   &.    7  /   80"   7/09:7/06/;76<7=
<" )  67  >>?(6  6!"   '.@ % .7..77 ( A3 6 6   / +       "    % ./    B%    A                 %   A      -    A     .
:       A           /A  //@&A -    A A      /@&   %   .   A       A -    %        "#  /@&    %  %        A A  

 %       A "    A     %  /@&   .

,         A       /@& ""  +A%  A /@&%    CD/@&EB A

  A      %   %    % . : 

% /@&A               + "    BAA  A   "    % /@& %  %  .,   A  /@&A   B " %   A   A B  

%  .;        D/@&    %   % A    %    A% +  :    A"    B   AA BA   .  A  A

   /@&A     % ., A  %  A       %    A "         A       B    "   . F  B A 
B     B   %    >GH.:    AB  +   A   %   B   A      %      A   % A
 .

56

I B  J/@&/" ;  ; B    

57

 

 !   

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

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 3@B=:/117  * =E;/<   /@@=::  

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/@B6&GAB3;"0A3@D/B7=<=A:/;=A!/B7=</:/0=@/B=@GB;=A>63@71&173<13A/<2:=0/:6/<537D7A7=<#/17471!=@B6E3AB!/B7=</:/0=@/B=@G%716:/<2*(&!":=0/: =<7B=@7<5/0=@/B=@G=C:23@"(&H/M/B;=A>63@71%3A3/@163<B3@%&B/B3 3B3=@=:=571/:53<1G=4&>/7< 3B'3<3@743
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
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60

Using Remote Sensing to Detect and Mitigate Methane Plumes in
California

Jorn Herner1*, Jason Schroeder1, Matthias Falk1, Elizabeth Scheehle1,
1. California Air Resources Board, Research Division, PO Box 2815, Sacramento, CA, 95812. USA

Satellite data has been used by the academic community to quantify the presence of greenhouse gases andother air pollutants over large areas. New satellites are starting to have sufficient spatial resolution to whereindividual emissions sources can be identified. This is less useful for overall stock take but can play a criticalrole in identifying and mitigating individual leaks. Specifically, these remote sensing technologies holdpromise to enable the detection of methane plumes at a frequency and scale not considered feasible withtraditional boots-on-the-ground methods, thus making these new satellites an attractive tool for regulatory
agencies. In California, pilot studies (airborne campaigns) with a hyperspectral imager, which can image andlocate individual plumes, have shown that these plumes exist in our infrastructure, and that often the emissions
are unintentional and can be easily fixed if operators are made aware of them. In California this concept has
been demonstrated with several airborne campaigns whereby California Air Resources Board staff worked
closely with partners in industry. In this presentation we will describe the specifics of the mitigation efforts,
show results from the airborne campaigns, and discuss the work being done to prepare for the launch of two
satellites that will deliver this type of data regularly.

Keywords: methane, plumes, mitigation

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63

A Role for Space-based CO2 and CH4 observations in Greenhouse Gas
Emissions Monitoring
David Crisp
Crisp Spectra LLC

Over the past decade, space-based remote sensing of CO2 and CH4 have come of age with Japan’s GOSAT and
GOSAT-2, NASA’s OCO-2 and OCO-3, China’s TanSat and Europe’s Sentinel 5 Precursor. Their data are now
yielding estimates of XCO2 with accuracies near 1 ppm and XCH4 with accuracies near 15 ppb on regional scales.
These data are not as accurate as those from surface and airborne sensors, but complement their measurementswith increased resolution and coverage. Top-down atmospheric models, including global and regional-scale
inverse models and discrete plume models have also advanced rapidly to better exploit the denser, but less accurate
space-based products.
These data and models are now providing useful tools for monitoring atmospheric CO2 and CH4 sources and sinks.
They are also attracting the attention of policy makers seeking ways to manage their greenhouse gas (GHG)
emissions to mitigate the impacts of climate change and meet their UNFCCC reporting requirements. Those
reports employ bottom-up inventories of emissions and removals GHGs from specific economic categories andsectors. These inventories provide accurate, actionable data in categories such as fossil fuel use for energy, where
activity data and emission factors are well understood, but are less reliable in the Agriculture, Forestry, and Other
Land Use (AFOLU) sector, where activity data and emission factors are more difficult to quantify. They also
neglect emissions changes on unmanaged (natural) land and oceans. For these reasons, policy makers and otherstakeholders and asking how atmospheric GHG data can be combined with bottom-up inventories to produce more
complete and accurate global stocktakes. As we started to address this question, we identified additional
capabilities needed by existing GHG missions and models. Some of these are addressed by future missions(GOSAT-GW, GeoCarb, CO2M), while others are not. This presentation will assess our progress and identify
gaps needing attention.

64

Advancing Arctic-Boreal carbon cycle science using OCO-2 retrievals
Abhishek Chatterjee*1, Nicole Jacobs2, Brendan Byrne1, Sourish Basu3,4, Hannakaisa Lindqvist5,
Alexey Lyapustin4, Nima Madani1, Joseph Mendonca6, Antti Mikonnen5, Charles E. Miller1, Ray
Nassar6, Nicholas Parazoo1, Debra Wunch7, Chris O’Dell2
1. Jet Propulsion Laboratory, California Institute of Technology, USA
2. Cooperative Institute for Research in the Atmosphere, Colorado State University, USA
3. Earth System Science Interdisciplinary Center, University of Maryland, USA
4. NASA Goddard Space Flight Center, USA
5. Finnish Meteorological Institute, Finland
6. Environment and Climate Change Canada, Canada
7. Department of Physical and Environmental Sciences, University of Toronto, Canada
One of the largest uncertainties in projected greenhouse gas concentrations and temperature trends is theimpact from terrestrial and marine carbon-climate feedbacks, especially in the northern high latitudes (i.e.,
circumpolar Arctic and Boreal region North of approximately 50° latitude). This lack of understanding ofArctic-Boreal carbon cycle dynamics can be directly attributed to the limited constraint provided byatmospheric CO2 measurements, both in terms of quantity (only a limited number of in situ observations
are available) and quality (large biases in retrievals from space-based sensors such as GOSAT and OCO2). In this presentation, we will discuss recent progress made in improving the quantity and the quality of
retrievals over the Arctic-Boreal region from NASA’s OCO-2 mission. These retrieval improvements relate
to both refinement of the filtering and bias correction approach, and modification to the core Atmospheric
CO2 Observations from Space (ACOS) retrieval algorithm by incorporating a new BRDF model that
captures snow surface reflection, polarization and spectral dependence at different wavelengths for snow
& ice-covered surfaces. Validation against measurements from high latitude TCCON sites and EM27/Sun
sites will be presented. In our talk, we will also highlight the potential value of these high-density satellite
retrievals for constraining the magnitude and spatiotemporal distribution of carbon flux patterns during theshoulder seasons (Mar – May, Sep – Nov). Our results clearly illustrate that space-based observations havethe potential to provide better insight into the most sensitive parts of the Arctic-Boreal carbon cycle system,
i.e., parts of the system in which carbon cycle responses will lead to substantial positive, or modificationsof negative feedbacks, to the climate system. We will finally conclude with how knowledge gained fromour retrieval developments will be valuable beyond a single mission (i.e., OCO-2) and aid retrievals fromfuture missions with similar aspirations (JAXA GOSAT-GW and ESA CO2M).

65

Keywords: NASA OCO-2, Arctic-Boreal carbon flux, snow/ice retrievals, snow BRDF

66

XCH4 retrieval from MethaneAIR using the CO2-Proxy Approach:First results over major US oil and gas basins
Christopher Chan Miller*1,2, Sebastian Roche1,2, Jonas Wilzewski1,2, Xiong Liu1, Amir Souri 1,
Eamon Conway1**, Jenna Samra1, Apisada Chulakadabba 2, Maryann Sargeant2, JonathanFranklin2, Josh Benmergui3, Kang Sun4, Ritesh Gautem5, Mark Omara5, Steven Wofsy 2
1. Harvard Smithsonian Center for Astrophysics, United States
2. Harvard University, United States
3. MethaneSAT LLC, United States
4. University of Buﬀalo, United States
5. Environmental Defense Fund, United States
MethaneAIR is the airborne precursor to MethaneSAT, an upcoming satellite mission commissioned toquantify methane emissions from the global oil and gas (O&G) sector. With near identical instrument
specifications to MethaneSAT, it has been designed as a testbed for the satellite’s data processing
algorithms prior to launch, which is expected to be mid next year. MethaneAIR also serves as a uniqueobserving mission in itself, capable of mapping accurate column averaged dry air methane mole fractions(XCH4) over a typical O&G basin at 5x25 m2 spatial resolution using just a few hours of flight time.
Here we present XCH4 retrieval results from the second campaign of MethaneAIR, conducted onboard
the NSF/NCAR HIAPER GV aircraft. The campaign concluded in August 2021, and surveyed the
Bakken, Uintah, and Permian O&G production regions. The retrieval is based on the CO2-proxyapproach, targeting the CO2 and CH4 absorption bands at 1.61 and 1.65 microns respectively, and will be
the primary approach used for the satellite mission. We show validation of the MethaneAIR XCH4
retrievals using comparisons of aircraft overpasses of ground based EM27 observations, and colocated
TROPOMI XCH4 data. We then estimate MethaneAIR’s point source detection limit based on its
observed precision and a set of WRF large-eddy simulations. Finally we show methane emission source
attribution and quantification highlights from the first basin-scale surveys, and discuss implications for theupcoming satellite mission.

67

An analysis of GOSAT-TIR observation sensitivity
to the near-surface CH4
Dmitry Belikov*1, Naoko Saitoh1 and Prabir K. Patra1,2
1

2

Center for Environmental Remote Sensing, Chiba University, Chiba, Japan;
Research Institute for Global Change (RIGC), Japan Agency for Marine-Earth Science andTechnology (JAMSTEC), Yokohama, JapanThe Thermal and Near Infrared Sensor for Carbon Observation Fourier-Transform Spectrometer (TANSOFTS) onboard the Greenhouse gases Observation SATellite (GOSAT) detects CH4 absorption spectra in thesolar short wave infrared (SWIR) and the thermal infrared (TIR) bands. The SWIR observations provide
information on column-averaged CH4 (XCH4) in cloud-free conditions during the daytime satellite overpass.
The TIR observations are also carried out at night and in cloudy conditions providing much greater
geographical and temporal coverage and resolving the vertical profiles of CH4 at 22 retrieval levels. The
sensitivity of TIR CH4 observations is stronger in the mid to upper troposphere and relatively low near thesurface because its spectral signatures depend on thermal contrast between the atmosphere and surface.
Nevertheless, the TIR instrument has sufficient sensitivity to provide critical information about the CH4
distribution at the top of the boundary layer.
In this work, we examined the TIR CH4 variability in comparison with ground-based observations from theWorld Data Centre for Greenhouse Gases (WDCGG), and simulations by MIROC4.0-based Atmospheric
Chemistry Tracer Model (MIROC4-ACTM) for the period 2009–2014. We studied the sensitivity of the TIR
retrievals at the near-surface layers (950-850 hPa) of the atmosphere on the basis of the prior assumptions.
Comparison with the monthly, daily, and hourly measurements showed that GOSAT TIR observations have asufficiently high density of observations to detect the variability of CH4 at different time scales. A good
improvement has been achieved with respect to the GOSAT prior CH4 for remote and coastal sites locatedmainly in East Asia, North America, and Europe. These achievements increase the reliability and confidenceof the global distribution of GOSAT TIR CH4 and the possibility of its application for the validation of threedimensional methane products.
Keywords: GOSAT, thermal infrared band, CH4, WDCGG, MIROC4-ACTM

68

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An overview of the new and improved OCO-3
ACOS v10 XCO2 data productM. Kiel*1, C. O’Dell2, B. Fisher1, R. Nelson1, T. Taylor2, T. Kurosu1, R. Rosenberg1, G. Keller1,
J. Laughner1, G. Osterman1, and A. Chatterjee1
1. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
2. Cooperative Institute for Research in the Atmosphere, Colorado State University,
Fort Collins, CO, USA
Since its launch on May 4, 2019, the OCO-3 instrument has collected millions of CO2 observations globally,
including dense, fine-scale XCO2 Snapshot Area Mapping mode (SAMs) observations of emission hotspots
like cities, power plants, and volcanoes. Evaluating these space-based measurements against independent data
sets provides information about the quality, potential biases, and errors in the OCO-3 data. Here, we present
comparisons of the new and improved OCO-3 v10.4 data version against groundbased measurements from the
Total Carbon Column Observing Network (TCCON). We focus on comparisons over land in nadir, target, andSAM mode and discuss sitedependent biases between OCO-3 and TCCON (GGG2020). The improved v10.4
data version reduces an XCO2 time-dependent bias that was present in the previous data version. Further, weevaluate differences between OCO-3 and TCCON related to different local overpass times which informs
about potential airmass and viewing geometry related biases. Finally, we analyze how well OCO-3 captures
the mean seasonal cycle amplitudes and growth rates over selected TCCON sites. The OCO-3 v10.4 data
product is a vast improvement over earlier OCO-3 data versions and is of comparable quality to the OCO-2
v10 data.
Copyright 2022 California Institute of Technology, U.S. Government sponsorship acknowledged.

72

Using the O2 column from the O2 singlet delta band to derive proxy
XCH4 from MethaneAIR observations
Sébastien Roche1,2, Christopher Chan Miller2, Amir Souri2, Jonas Wilzewski1,2, Eamon Conway2,*,
Jenna Samra3, Jonathan Franklin1, Kang Sun3, Kelly Chance2, Xiong Liu2, and Steven Wofsy1
1: Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge,
MA, USA
2: Center for Astrophysics | Harvard & Smithsonian, Cambridge, MA, USA
3: Research and Education in Energy, Environment and Water Institute, University at Buffalo, Buffalo,
NY, USA*Now at: Kostas Research Institute, Northeastern University, Burlington, MA, USA
The area-mapping MethaneSAT satellite will aim to estimate CH4 oil & gas emissions from over 80% ofemitters. It will use one spectrometer to retrieve CH4 from a window centered at 1.66 μm, and CO2 from awindow centered at 1.61 μm, and a second spectrometer to retrieve O2 and surface pressure in a window
centered at 1.27 μm. MethaneAIR is the airborne simulator for the MethaneSAT satellite, its observations
are used to test the retrieval algorithms that will be used to process MethaneSAT data. We will present
MethaneAIR Level 2 results (trace gas concentrations derived from observed spectra) obtained with the“proxy” method.
A “proxy” retrieval does not include the effect of aerosols on the light path in the forward model, it instead
uses the column of another gas retrieved from a band spectrally close to the target gas as proxy for theaerosol-induced light path changes, assuming that they are similar in the two neighboring spectral regions.
Typically, CO2 has been used as the proxy species for XCH4, but the variability in the modelled XCO2 canintroduce biases, especially over targets with sources of both CH4 and CO2. We investigate the use of theO2 column from the O2 singlet delta band to derive proxy XCH4. XO2 (~0.2095) is much less variable than
XCO2. However, the O2 window is more spectrally distant from the CH4 window than the CO2 window,
making the O2 proxy more sensitive to aerosols. Unlike MethaneAIR, MethaneSAT observations in the O2
window will be affected by airglow. We will investigate the effect of aerosols and airglow on the variabilityof the retrieved O2 column.

73

Validation of TROPOMI Methane in the GeoCarb Domain
Susan S. Kulawik1, Alba Lorente2, Sean Crowell3, Colm Sweeney4, Kathryn McKain4,5, Edward
J. Dlugokencky4, Sebastien C. Biraud6, John Miller4, Paul O. Wennberg7, Debra Wunch8, FrankHase9, Coleen M. Roehl7, Rigel Kivi10, Nicholas M. Deutscher11, Kei Shiomi12, Voltaire A.
Velazco11, Thorsten Warneke13, Kawakami Shuji14, Laura T. Iraci15, James R. Podolske15, Joshua
L. Laughner16
1. BAER Institute, 625 2nd Street, Suite 209, Petaluma, CA, USA
2. Earth science group, SRON Netherlands Institute for Space Research, Utrecht, the Netherlands
3. GeoCarb Mission, University of Oklahoma, Norman, OK, USA
4. National Oceanic and Atmospheric Administration, Earth System Research Global Monitoring
Laboratory, Boulder, CO, USA
5. University of Colorado, Cooperative Institute for Research in Environmental Sciences,
Boulder, CO, USA
6. Lawrence Berkeley National Laboratory, Earth Science Division, Berkeley, CA, USA
7. Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena,
California, USA
8. Department of Physics, University of Toronto, Toronto, Canada
9. Institute of Meteorology and Climate Research IMK-ASF., Karlsruhe Institute of Technology,
Karlsruhe, Germany
10. Gases and Satellite Methods group, Finnish Meteorological Institute, Sodankylä, Finland
11. Centre for Atmospheric Chemistry, School of Earth, Atmospheric and Life Sciences,
University of Wollongong, Wollongong, NSW 2522, Australia
12. Japan Aerospace Exploration Agency JAXA., Tsukuba, Japan
13. Institute of Environmental Physics, University of Bremen, Bremen, Germany
14. EORC Earth Observation Research Center, JAXA Japan Aerospace Exploration Agency
15. Atmospheric Science Branch, NASA Ames Research Center, Moffett Field, CA, USA
16. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
The GeoCarb mission will make geostationary observations of CO2, CH4, CO, and SIF over Western
Hemisphere land (50S-50N, 20W-125W), launching in 2024. In preparation for validation of GeoCarb, we
compare validation of TROPOMI CH4 within the GeoCarb domain to the global validation of TROPOMI.
We use the bias corrected v1 RemoTeC CH4 product downloaded from GES DISC, described in Lorente etal. (2021). To estimate systematic error, both correlated error and regional bias error are added inquadrature, and co-location error (model at satellite vs. model at validation) and validation error (systematic
error of validation estimates) are subtracted in quadrature. We compare validation versus GGG2020 global
TCCON (“the gold standard”), TCCON within the GeoCarb domain (4 sites), and NOAA Global
Monitoring Laboratory and DOE aircraft observations extended by the CAMS CH4 model in the GeoCarb
domain (8 sites). We find systematic error estimates versus global TCCON, North American TCCON, andaircraft give consistent results with error estimates differing by less than 2 ppb.

Keywords: TROPOMI, Methane, GeoCarb, TCCON, Aircraft74

Evaluation of XCO2 estimates from OCO-2 and OCO-3
observations using the NASA ACOS version 10 retrieval algorithm
Thomas E. Taylor1 , Christopher W. O’Dell1 , Brendan Fisher2 , Graziela R. Keller2 , Matthäus Kiel2 ,Thomas Kurosu2 , Joshua Laughner2 , Aronne Merrelli3 , Robert R. Nelson2 , Greg Osterman2 ,Robert Rosenberg2 , Gary Spiers2 , Abhishek Chatterjee2 , Vivienne Payne2 , and Peter Somkuti4
1

Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA.
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
3
Univ. Michigan, Ann Arbor, Michigan, USA.
4
Univ. Oklahoma, College of Atmospheric and Geographic Sciences, Oklahoma, USA.
2

Correspondence: Tommy Taylor (tommy.taylor@colostate.edu)
Keywords: ACOS, retrieval, XCO2 , OCO-2, OCO-3
The version 10 (v10) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm
has been applied to multi-year records of observations from NASA’s Orbiting Carbon Observatory -2 and -3 sensors (OCO-2
5

and OCO-3, respectively) to produce estimates of the column-averge dry-air mole fraction of carbon dioxide (XCO2 ). OCO-2
has been providing science measurements from a sun-synchronous polar orbit with an approximately 13:30 local overpass timesince September of 2014, while OCO-3 has been operating in a precessing orbit from the International Space Station (ISS)since August of 2019, providing varying time-of-day sampling.

10

Although there are signiﬁcant differences between OCO-2 and OCO-3 geolocation methodologies, and spectral and radiometriccalibration techniques, the XCO2 estimates are produced from Level 1b measurements using similar pre- and post-processing,i.e., cloud screening, quality ﬁltering (QF) and bias correction (BC), as well as the same version (v10) of the L2FP retrieval
algorithm. The shared heritage of the two sensors and the processing pipeline provides an ideal case for intercomparisons of
the XCO2 estimates.

15

Spatiotemporal analysis of the global XCO2 products for the overlapping time period highlights both similarities, a reﬂectionof shared heritage, and differences, driven mainly by differing orbit and sampling characteristics. A subset of several hundred
clusters of soundings, tightly collocated in time and space was used to assess agreement in the XCO2 estimates. Initial results
showed a time-dependent divergence, which was found to be highly correlated against OCO-3 zero-level-offset in the weak20

CO2 spectral band (First 4 panels of Figure 1). After application of a post-hoc correction to the OCO-3 v10 bias corrected
XCO2 (now L2Lite v10.4), the products show good agreement with no detectable time trend (Panel 5 of Figure 1). Comparisons of the satellite XCO2 estimates (v10 for OCO-2, and v10.4 for OCO-3) to those derived from atmospheric inversion

1

75

systems show qualitatively similar results (Figure 2).

Figure 1. Correction of OCO-3 v10 XCO2 estimates using the correlation between ΔXCO2 (OCO-3 minus OCO-2 for tightly collectedclusters of soundings) and the zero-level-offset in the weak CO2 spectral band. Results shown for footprint 4 (of 8) and Land soundings.

276Figure 2. Comparison of v10 XCO2 from OCO-2 (top) and OCO-3 (bottom) against model-derived XCO2 for Land (left) and Ocean (right).

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78

MethaneSAT: A new approach to quantifying area andpoint source methane emissions
Jonathan E. Franklin*1, Joshua Benmergui2, Kelly Chance3, Apisada Chulakadabba1, Bruce Daube1,
Ritesh Gautem4, Steven Hamburg2,4, Tom Ingersoll2, Yang Li5, Xiong Liu3, Bingkun Luo3, Daniel
McCleese6, Tom Melendez2, Sara Mikaloff-Fletcher7, Christopher Chan Miller1,3, Mark Omara4,
Sebastian Roche1,3, Jenna Samra3, Maryann Sargent1, Amir Souri3, Kang Sun8, Jonas Wilzewski1,3,
Steven Wofsy1
1. Harvard University, USA
2. MethaneSAT LLC, USA
3. Center for Astrophysics | Harvard Smithsonian, USA
4. Environmental Defense Fund, USA
5. Baylor University, USA
6. Caltech, USA
7. NIWA, NZ
8. University at Buffalo, USA
The MethaneSAT mission (launch ready in Q2 2023) intends to revolutionize the measurements and modelingof CH4 emissions across the globe. Commissioned by MethaneSAT LLC, a subsidiary of the Environmental
Defense Fund, MethaneSAT has been designed to systematically map and quantify CH4 emissions from regions
accounting for over 80% of global oil and gas production. The primary objective is motivating and enabling
rapid emission reductions. Secondary objectives include observations of agricultural and urban areas.
The mission aims to fill the critical data gap between global mapping satellites (low spatial resolution,
moderately high precision), and point-source missions (high spatial resolution, but small field of view and low
precision). MethaneSAT’s target observations will consist of a wide observing swath (~220 km @ nadir), ahigh spatial resolution (~100 m x 400 m), and a low detection threshold (~2 – 4 ppb precision @ 1.5 km2)
which will enable quantification of both concentrated as well as diffuse area-aggregate emissions associatedwith oil and gas production regions.
Over the past year substantial progress has been made in assembly, integration, and testing of MethaneSAT’s
two imaging spectrometers built by Ball Aerospace. Integration with the platform bus supplied by Blue Canyon
Technologies will commence in Q3 2022 followed by flight-system level characterization and calibration
activities. An airborne precursor instrument, MethaneAIR, was successfully deployed in July/August 2021
aboard the NSF/NCAR Gulfstream V and produced nearly 50 hours of atmospheric observations. These
measurements have been used to develop and challenge the trace gas retrievals and flux algorithms needed to79

complete the MethaneSAT mission. Future flights of MethaneAIR are planned to map additional US oil andgas production regions, measure emissions from a large selection of landfills, and, post-launch, participate inMethaneSAT validation activities.
Keywords: MethaneSAT, methane80

Operational product processing developments for the Copernicus
anthropogenic CO2 Monitoring (CO2M) mission
Ruediger Lang*1, Helmut Bauch1, Bojan Bojkov1, Leonid Butenko1, Hannah Clarke1, Paola
Colagrande1, Josef Gasteiger1, Bernd Husemann1, Timon Hummel1, Antoine Lacan1, Fabrizio Di
Loreto1, Cosimo Putignano1, Yu Ren1, Vincenzo Santacesaria1, Sruthy Sasi1, and Eduardo Valido
Cabrera1
1.

European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),
Darmstadt, Germany

As part of the Copernicus Programme of the European Commission, the European Space Agency (ESA) andthe European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) are expanding
the Copernicus Space Component to include measurements for anthropogenic CO2 emission monitoring. The
greatest contribution to the increase in atmospheric CO2 comes from emissions from the combustion of fossil
fuels and cement production. In support of well-informed policy decisions and for assessing the effectivenessof strategies for CO2 emission reduction, uncertainties associated with current anthropogenic emission
estimates at national and regional scales need to be improved. Satellite measurements of atmospheric CO2,
complemented by in-situ measurements and bottom-up inventories, will enable, by using advanced (inverse)modelling capabilities, the transparent and consistent quantitative assessment of CO2 emissions and theirtrends at the scale of megacities, countries, and at global scale.
This presentation provides an overview of the status of the CO2M operational mission data processing subsegment (MDPS) developments ongoing at EUMETSAT. We will describe the milestones for implementing
the full operational processing chain, which aims at maximising the available information content for theCO2, Methane and NO2 retrievals by combining the measurements from all three instruments on-board
CO2M (the CO2/NO2 push-broom grating spectrometer (CO2I/NO2I), the Multi Angle Polarimeter (MAP),
and the Cloud Imager (CLIM)). Key characteristics of top-of-atmosphere test data for all three instruments
will be using realistic orbit scenarios based on a common atmosphere and common surface modelling.
In addition, we present the latest status on the centralised processing approach of MAP, CLIM and auxiliary
data, co-located, co-registrated, and/or aggregated to the main CO2I/NO2I instrument footprint, and by thisfacilitating the creation of a three-sensor hyper-instrument for use in the downstream operational retrieval of
GHG and NO2 total column concentrations.
Keywords: CO2M, CO2, monitoring, products, processing

81Overview on the proposed Arctic Observing Mission (AOM)Ray Nassar*1, Chris Sioris1, Chris McLinden1, Joseph Mendonca1, Konstantin Baibakov2,
Shen-En Qian2, Isabelle Jean2, Fauve Strachan2, Helena van Mierlo2, Alec Casey1, Matt Arkett1,
Josep Aparicio1, Jinwoong Kim1,3, Mike Neish1, Saroja Polavarapu1, Feng Deng3, Dylan B.A.
Jones3, Anthony Girmenia4
1. Environment and Climate Change Canada (ECCC), Canada
2. Canadian Space Agency (CSA), Canada
3. University of Toronto, Canada
4. University of Waterloo, Canada

The Arctic Observing Mission (AOM) is a satellite mission concept that would use a highly elliptical orbit(HEO) to enable frequent observations of greenhouse gases (GHGs), air quality, meteorological variables
and space weather north of the usable viewing range of geostationary (GEO) satellites. AOM evolved fromthe Atmospheric Imaging Mission for Northern Regions (AIM-North) with and expansion of scope. AOM isenvisioned as a Canadian-led mission to be implemented with international partners. AOM is currently
undergoing a pre-formulation study to refine options for the mission architecture and advance other technicaland design aspects, investigate socio-economic benefits of the mission and better establish the roles andcontributions of partners. AOM would use an Imaging Fourier Transform Spectrometer (IFTS) with 4 nearinfrared/shortwave infrared (NIR/SWIR) bands to observe hourly CO2, CH4, CO and Solar Induced
Fluorescence spanning cloud-free land from ~40-80°N. The rapid revisit is only possible due to cloud
avoidance using ‘intelligent pointing’, which is facilitated by the availability of real-time cloud data from themeteorological imager and the IFTS scanning approach. Simulations suggest that these observations would
improve our ability to detect and monitor changes in the Arctic and boreal carbon cycle, including CO2 andCH4 emissions from permafrost thaw, or changes to northern vegetation carbon fluxes under a changing
climate. This presentation will give an overview of AOM with a focus on the GHG instrument, its expectedcapabilities and its potential for carbon cycle science and monitoring.

Keywords: Arctic, boreal, imaging Fourier transform spectrometer, permafrost

82

MethaneSAT OSSE Simulation to Assess Errors in XCH4 Derived
From the CO2 and O2 Proxy Methods
Amir H. Souri 1, Chris Chan Miller1,2,3, Xiong Liu1, Sebastien Roche1,2, Jonas Wilzewski1,2, Joshua
Benmergui4, Robert Spurr5, Kang Sun6,7, Jonathan E. Franklin2, Steven Wofsy2,8, Kelly Chance1
1

Center for Astrophysics | Harvard & Smithsonian, Cambridge, MA, USA

2

Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge,

MA, USA
3

Climate Change Research Center, University of New South Wales, Sydney, New South Wales, Australia

4

Environmental Defense Fund, Washington, DC, 20009, USA

5

RT Solutions, Inc., 9 Channing Street, Cambridge, MA 02138, USA

6

Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY, USA

7

Research and Education in Energy, Environment and Water Institute, University at Buffalo, Buffalo, NY,USA
8

Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA

Abstract. An Observing System Simulation Experiment (OSSE) has been designed to systematically analyze
and estimate the retrieval errors in XCH4 for the upcoming MethaneSAT mission – a new earth-observing
satellite under development by MethaneSAT, LLC, a subsidiary of the Environmental Defense Fund. Several
case studies are generated at both regional and global scales. To account for a wide range of atmosphericconditions and surface properties, we incorporate dynamical inputs into the Smithsonian PLanetary
ATmosphere interface to VLIDORT (SPLAT-VLIDORT) radiative transfer model to simulate radiances at
MethaneSAT O2 (1249–1305 nm) and CH4 (1598–1676 nm) channels. These inputs include particle size
distributions observed by AERONET, high-resolution anisotropic MODIS BRDFs, GEOS-FP reanalyses, andhigh-resolution CH4 fields derived from a Lagrangian inverse modeling. We retrieve the total concentrationsof CH4, O2, H2O, and CO2 without the consideration of multi-scattering (i.e., transmission only) from thesimulated radiance with expected SNR levels. We then leverage the retrieved CO2 and O2 as light-path proxies
to calculate XCH4 and compare it to the true state of XCH4 defined in the forward simulation. A primaryadvantage of the CO2 proxy is the close proximity of the spectral absorption bands of CH4 and CO2 permitting
non-scattering retrievals of XCH4 in the presence of aerosols. The robustness in the retrieval O2 proxy is
inhibited by the presence of dust particles. Nonetheless, the O2 proxy reveals a higher precision (18 ppbv at
100×400 m2) and a lower bias (<1%) under specific aerosol conditions relative to the CO2 proxy (26 ppbv at
100×400 m2, 3%). Results encourage a development of a combined product that should provide more highfidelity information than is possible when the proxy products are used individually.

83

Keywords: MethaneSAT, methane, OSSEs, errors

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Development of the MethaneSAT cloud and aerosol filter
Jonas Wilzewski*1,2, Sébastien Roche1,2, Chris Chan Miller2, Amir Souri2, Eamon Conway2,3,Jonathan Franklin1, Jenna Samra2, Jacob Hohl2, Kang Sun4, Xiong Liu2, Kelly Chance2, and Steven
Wofsy1
1. Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University,
Cambridge, MA, USA
2. Center for Astrophysics | Harvard & Smithsonian, Cambridge, MA, USA
3. Now at Kostas Research Institute, Northeastern University, Burlington, MA, USA
4. Research and Education in Energy, Environment and Water Institute, University at Buffalo,
Buffalo, NY, USA
The MethaneSAT satellite mission aims at quantifying anthropogenic methane emissions by measuringreflected solar radiation in two spectral windows in the short wave infrared range. In the 1,249 nm - 1,305 nm
spectral range the sensor will measure the oxygen (O2) singlet Delta band with a full width at half maximum(FWHM) of 0.17 nm. Additionally, the instrument will observe most of the 2v3 absorption band of methane(CH4) and a 3v1+v3 carbon dioxide (CO2) band in the spectral range 1,598 nm - 1,676 nm at a spectral
resolution of 0.23 nm at FWHM. Clouds and aerosols can introduce biases into the inversion of methanecolumn concentrations from solar backscatter measurements and we therefore develop retrieval processors tofilter out contaminated scenes.
One processor makes use of surface pressure retrievals to infer the presence of scattering particles in the fieldof view of the sensor. These retrievals specifically take into account O2 airglow emission (see Sun et al. 2018).
Surface pressure is retrieved by fitting spectra in the O2 band, assuming a non-scattering atmosphere. A
complementary algorithm takes advantage of differences in the atmospheric light path between the two spectral
windows of MethaneSAT in the presence of aerosols and clouds. Here, we retrieve water vapor (H2O) column
concentrations from the 1.3 μm and 1.6 μm windows under the assumption of the geometric lightpath. We
construct thresholds for both filters to identify aerosol and cloud contaminated scenes.
Both filtering approaches are applied to measurements of the MethaneAIR instrument (Staebell et al. 2021) todemonstrate their capacity in screening for clear scenes. In addition, we apply the cloud and aerosol processors
to an ensemble of synthetic MethaneSAT scenes at realistic noise levels to evaluate their performance. Finally,
we discuss our on-going efforts in developing a filter for observations affected by cloud shadows.
References
Sun, K., Gordon, I. E., Sioris, C. E., Liu, X., Chance, K., and Wofsy, S. C.: Reevaluating the use of O2 a1Δgband in spaceborne remote sensing of greenhouse gases, Geophysical Research Letters, 45, 5779–5787,
https://doi.org/10.1029/2018GL077823, 2018.
Staebell, C., Sun, K., Samra, J., Franklin, J., Chan Miller, C., Liu, X., Conway, E., Chance, K., Milligan, S.,
and Wofsy, S.: Spectral calibration of the MethaneAIR instrument, Atmospheric Measurement Techniques,14, 3737–3753, https://doi.org/10.5194/amt-14-3737-2021, 2021.

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88

Satellite (GOSAT-2 CAI-2) and surface (ARFINET) observations of
Aerosol Black Carbon over India: Regional and Global Inferences
Mukunda M. Gogoi1, S. Suresh Babu1, Ryoichi Imasu2, Makiko Hasimoto3
1. Space Physics Laboratory, Vikram Sarabhai Space Centre, ISRO, Thiruvananthapuram, India
2. Atmospheric and Ocean Research Institute, University of Tokyo, Japan
3. Space Technology Directorate I, Earth observation research center,
Japanese Aerospace Exploration Agency, JapanThe light-absorbing Black Carbon (BC) aerosols have very sensitive role in affecting the Earth’s radiationbudget and climate. In this study, we present the regional distribution of BC over India based on simultaneous
observations of aerosol and surface properties by Cloud and Aerosol-Imager-2 (CAI-2) on-board Greenhousegases Observing Satellite-2 (GOSAT-2). To evaluate and validate the spatio-temporal distribution of BC fromsatellite (CAI-2) retrieval, near surface BC mass concentrations measured across a network of aerosol
observatories (ARFINET) over India are used and the findings are extended to understand the global BC
features. Considering the limited spatial coverage of ground-based point-measurements as well as the biases inmodel simulated BC due to the inaccurate model inventories and meteorological input available for thesimulations, the satellite-based observations synchronizing with surface measurements is a novel idea to quantify
and classify the real BC environment across distinct geographic regions of the globe. The results indicate that,
despite the seasonal biases, the overall regional distribution of BC is very well represented by the satellite
retrieval, with close agreement with the ground-based measurements. The validation and closure studies between
the two data sets show RMSE < 2, with absolute difference < 2 μg m -3 during winter (R = 0.79) and premonsoon (R = 0.74). Over the hotspot regions of India, the satellite retrievals show soot volume fraction of ~5%, columnar single scattering albedo of ~ 0.8 and BC column optical depth of ~ 0.1. In terms of global spatiotemporal variability, higher BC tends to occur mostly in areas where biomass burning is intense. Overall, thisstudy highlights the effectiveness of first ever satellite retrieval of BC, which could be effectively used for the
regular monitoring of BC load arising out of vehicular/ industrial/ biomass burning activities across the globe.

89Figure: Regional distribution of monthly average BC from satellite (CAI2, 0.25 x 0.25 degree) and surface(climatological average) observations in the month of December (left panel). The surface BC is obtained frommulti-wavelength aethalometer (ATH) measurements in the ARFINET. The right panel shows the monthly
variation of regional average BC from satellite (CAI2) and surface (ATH) measurements (averaged over all thelocations considered for inter-comparison), along with the absolute difference between the two data sets.

90

Biases in XCH4 retrievals from the 2.3 ȝm spectral bandPeter Somkuti*1, Christopher O’Dell2, Greg McGarragh2, Sean Crowell1 and Lesley Ott3
1. University of Oklahoma, USA
2. Colorado State University, USA
3. NASA Goddard Space Flight Center – Global Modeling and Assimilation Office, USA

The TROPOMI instrument (currently in-orbit) as well as the upcoming GeoCarb mission are both utilizing
the methane absorption band at 2.3 ȝm. This band is convenient for greenhouse gas remote sensing, as it
contains absorption features of methane (CH4), carbon monoxide (CO) as well as water vapor (H2O)
allowing for the retrieval of those gas species.
As part of GeoCarb algorithm development activities, we have investigated the feasibility of single-band
CH4/CO/H2O retrievals. Using an established radiative transfer and instrument model simulator framework,
we conduct retrieval experiments with a focus on systematic errors and analyze the driving mechanisms thatcause these errors.
We find that weak aerosol scattering, with total column optical depths of less than 0.05, is sufficient to causea statistically significant surface albedo bias. This particular bias is qualitatively similar to one observed inreal TROPOMI XCH4 data and described in the work by Lorente et al. 2021. We further attempt an approachto prevent the bias from occurring by informing the retrieval algorithm with sufficient data regarding the true
aerosol scattering profiles.
Surface-related biases can be prohibitive in the use of space based XCH4 observations. This effect is amplified
in the vicinity of darker surfaces such as wetlands, which are of special interest to the methane science
community. Given the importance of space-based methane observations, we emphasize the need to better
understand the limits of stateRIWKHDUWUHWULHYDODOJRULWKPV

Keywords: XCH4, retrieval algorithm, systematic biases

91

Development of the MethaneSAT L0-1B processor
Bingkun Luo1*, Xiong Liu1, Jonathan Franklin2, Eamon Conway1,6, Kang Sun3, Amir Souri1,
Josh Benmergui4, Kelly Chance1, Steven Wofsy2,5
1. Center for Astrophysics | Harvard & Smithsonian, Cambridge, MA, USA
2. Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University,
Cambridge, MA, USA
3. Research and Education in Energy, Environment and Water Institute, University at Buffalo,
Buffalo, NY, USA
4. Environmental Defense Fund, Washington, DC, 20009, USA
5. Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA
6. Kostas Research Institute, Northeastern University, Burlington, MA, USA
MethaneSAT is an Environmental Defense Fund (EDF) satellite mission set to launch in mid-2023
to monitor methane emission from over 80% of the oil and gas industry and other sources withhigh precision and fine spatial resolution. The MethaneSAT instrument consists of two push-broom
imaging spectrometers: the CH4 spectrometer (1.598-1.676 ȝm) to detect CH4 and CO2
absorption near 1.65 and 1.61 ȝm, and the O2 spectrometer (1.249-1.305ȝm) to detect O2
absorption near 1.27 ȝm. It is currently being built by Ball Aerospace and under sensor-leveltesting. Integration with the spacecraft bus by Blue Canyon Technologies will occur in Q3 2022
followed by flight-system level testing. MethaneAIR is the airborne simulator for MethaneSAT. It
was successfully deployed in July/August 2021 aboard the NSF/NCAR Gulfstream V andproduced ~50 hours of observations. A L0-1b processor has been developed for MethaneAIR toconvert digital counts to radiometrically, spectrally, and geometrically calibrated L1b radiancespectra. The MethaneSAT L0-1b processor is being developed leveraging that the MethaneAIR
L0-1b processor and analysis of MethaneSAT instrument calibration test data.
We will present the details of the L0-1b processor to process nominal radiance measurements aswell as on-orbit dark, LED, lunar, and airglow measurements. First, image processing steps
including various corrections (e.g., gain, nonlinearity, residual image, Pixel Response NonUniform (PRNU), Bezel, dark current and straylight), radiometric calibration and noise calculation
will be performed to derive radiometrically calibrated data and measurement precision. Then, awavelength calibration will be conducted to correct wavelength shifts and monitor the changes inInstrument Spectral Response Functions (ISRFs). An orthorectification procedure will be used togeolocate the measurements and calculate viewing geometry with optional GEO-AKAZE to verify
geolocation accuracy again reference images and correct for potential errors. Finally, several
geolocation related information such as snow/ice and surface types, sun glint will be added. Pixel
flagging will be done at various steps. We will also present on-orbit utilities and methods tomonitor bad and RTS pixels from dark current measurements, measure detector gains andnonlinearity from LED measurements, trend radiometric calibration and degradation fromvicarious calibrations and lunar measurements.
Presentation Type: Poster
Session: 3 Retrieval algorithms and methods for inter-instrument and product Cal/Val

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94

A Neural Network Approach to Filtering OCO-2 Retrievals OverSnow
J. Mendonca*1, R. Nassar1, C. O’Dell2 and D. Wunch3
1. Environment and Climate Change Canada, Toronto, ON, Canada
2. Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort
Collins, CO, USA
3. Department of Physics, University of Toronto, Toronto, ON, Canada
Satellite retrievals of XCO2 at northern high latitudes currently have sparser coverage and lower data
quality than most other regions of the world. The OCO-2 B10 quality control filters out all retrievals
made over scenes with snow, despite some of these retrievals providing reasonably accurate results. In
order to recover some of these retrievals we have designed a new filtering scheme for the OCO-2 B10
bias-corrected XCO2 retrievals made over snow using a neural network (NN). We tested two different
truth proxies when training the NN: 1) the mean XCO2 from several models, and 2) the small area
approximation. To assess the performance of the NN filters we use Total Carbon Column Observing
Network (TCCON) data at selected northern high latitude sites as an independent data set for validation.
We found that the NN trained with the model mean data performed better compared to the NN trainedwith the small area data. In both cases we find that the throughput was much lower and the bias andprecision were slightly worse compared to non-snow retrievals.
Key words: OCO-2, snow, filtering, machine learning, neural network95

Recent status of GOSAT calibration and validation activities in 2022
Kei Shiomi*1, Fumie Kataoka2, Junko Kasuya1, Hiroshi Suto1, Nobuhiro Kikuchi1, Akihiko Kuze1
1. JAXA, Japan
2. RESTEC, Japan

JAXA’s GOSAT has been operating since 2009 and GOSAT-2 since 2018 to monitor the greenhouse gases(GHGs) carbon dioxide (CO2) and methane (CH4) by a Fourier Transform Spectrometer. The total columnaverage dry air mole fractions of CO2 (XCO2), and CH4 (XCH4) are measured from space using surfacereflected sunlight at near-IR wavelengths. The GOSAT-1/-2 and the other GHG missions, NASA OCO-2/-3
and ESA TROPOMI are collaborated in calibration and validation activities to maintain and improve GHG
observation accuracy.
JAXA has deployed the TCCON station at Saga University and started ground-based XCO2 and XCH4
observations since 2011. Long-term dataset of the Saga TCCON has been newly released in April 2022,
processed by the latest software GGG2020. GOSAT targets at Saga regularly every 3 days, GOSAT-2 in 2 daysof a 6-day revisit. OCO-2 and OCO-3 observe Saga after target selection process.
GOSAT team has conducted vicarious calibration campaign at the Railroad Valley every June - July since 2009
in collaboration with NASA OCO team to ensure the radiometric accuracy. We have also deployed a portable
EM27/SUN to measure XCO2 and XCH4 for validation since 2014. JAXA has released a website for theVicarious Calibration Portal for Space-borne GHGs Sensors to archive the vicarious calibration techniques anddatasets as part of the CEOS activity. GOSAT team will visit to join the Railroad Valley for the OCOTROPOMI-GOSAT joint calibration campaign in June 2022 after the last visit of 2019.
We will present the recent status of JAXA’s GOSAT calibration and validation activities of Saga TCCON andRailroad Valley campaign.

Keywords: GOSAT, OCO-2, TCCON, EM27

96

An overview of the tests and evaluations of the OCO-2 version 11
retrieval algorithm

Le Kuai*1, Vivienne Payne1, Fabiano Oyafuso1, Robert Nelson1, Brendan Fisher1, Chris O’dell2,
Joshua Laughner1, and the OCO-2 Algorithm Team
1. Jet Propulsion Laboratory, California Institute of Technology, USA
2. Cooperative Institute for Research in the Atmosphere, Colorado State University, USA

The OCO-2 Atmospheric Carbon Observations from Space (ACOS) column-averaged dry-air mole-fraction ofcarbon dioxide (XCO2) product has been widely used to improve our understanding of regional- and globalscale carbon fluxes. However, known outstanding issues remain with the existing v10 product. These include
spectral residuals above the instrument noise and regionally-dependent biases in retrieved surface pressure,which propagate into errors in the bias corrected XCO2.
Here, we show results of testing for the new version 11. The updates accepted for v11 include 1) updating thespectroscopy to ABSCO v5.2, 2) instrument line shape improvements, 3) South Atlantic Anomaly screening
modifications, 4) an updated CO2 prior and 5) new ABSCO scaling coefficients. For example, ABSCO v5.2
includes improvements to the water vapor absorption lines and an updated strong CO2 table and scale factor.
The initial v11 test retrievals are found to better estimate the H2O column and improve the reduced χ2, a metricof how well the residuals are being fit by the retrieval. New CO2 prior profiles and new CO2 ABSCO scale
factor resulted in a less biased XCO2 by reducing “co2_grad_del”, which is a metric that describes thedifference between the retrieved and prior CO2 profiles and is often highly correlated with XCO2 bias. We will
also show the impacts of other tests on the v11 products by comparing to v10.
Keywords: OCO-2, retrieval algorithm, version 1197    

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98Exploring the sensitivity of northern high latitude retrievalsfrom OCO-2 to the referenced digital elevation model
Nicole Jacobs*1, Christopher O'Dell*1
1. Cooperative Institute for Research in the Atmosphere,
Colorado State University, Fort Collins, CO, USA

Carbon exchange in the northern high latitude regions plays an important role in completing our understanding of
global carbon dynamics, and a paucity of atmospheric observations of CO2 in these regions hinders further
investigation. Polar orbiting satellites such as the NASA Orbiting Carbon Observatory 2 (OCO-2) have great
potential for providing an abundance of observations over northern high latitude regions if issues of retrieval
quality can be addressed. One reason satellite-based observations of high latitude regions are challenging is that
supporting information and observations necessary for trace gas retrievals are sparser, outdated, or of lower quality
in these regions than at mid-latitudes. Observations of atmospheric column average dry air mole fractions of CO2(XCO2) depend on accurate knowledge of the number density of dry air in the atmospheric column, which requires
accurate knowledge of surface pressure. For XCO2 retrievals from OCO-2, prior estimates of surface pressure areused that are adjusted for to the average elevation in the satellite footprint based on a Digital Elevation Model(DEM). A global empirical bias correction is applied to the retrieved XCO2, after the fact, that has the effect ofmoving the density of dry air in the column closer to that implied by the prior surface pressure and away from that
implied by the retrieved surface pressure. As a result, an accurate DEM is essential for defining an appropriate
bias correction for northern high latitude retrievals from OCO-2. Different DEMs for the northern high latitude
regions (north of ~50°N or ~60°N) can differ by as much as 50 m, implying differences in surface pressure of ~56 hPa, which can produce differences in retrieved, bias corrected XCO2 up to or more than 1 ppm. We will present
an exploration of the impact of changing the DEM on OCO-2 retrieved XCO2 in northern high latitude regions.

99

Carbon dioxide and methane measurements at Sodankylä, Finlandand comparisons with satellite borne observations
Rigel Kivi*1, Pauli Heikkinen1, Juha Hatakka1, Hannakaisa Lindqvist1, Johanna Tamminen1 andHuilin Chen2
1. Finnish Meteorological Institute, Finland
2. Groningen University, Netherlands

Ground based observations by Fourier Transform Spectrometers (FTS) are frequently used for comparisonswith satellite born sensors. At Sodankylä (67.4° N, 26.6° E) we installed an FTS instrument in early 2009. The
spectrometer is recording direct solar spectra in the near-infrared spectral region in the spectral range between
0.7 and 2,5 μm. From the spectra column-averaged abundance of CO2, CH4, N2O, HF, CO, H2O, and HDO are
retrieved. The FTS instrument (based on Bruker 125 HR) has been optimized for greenhouse gas measurements
and participates in the Total Carbon Column Observing Network (TCCON).
Here we present comparisons with the Greenhouse gases Observing SATellite (GOSAT) observations. We finda good agreement between GOSAT and ground-based observations. In case of CO2 the relative difference
between the two instruments has been -0.04 +/- 0.01 % and in case of CH4 the relative difference has been
calculated to be -0.04+/-0.02 %.
Secondly, we present results of AirCore profile measurements at Sodankylä. AirCore is a sampling system,
which is directly related to the World Meteorological Organization in situ trace gas measurement scales.
AirCore sampling is possible for both stratosphere and troposphere, which is a benefit, compared to the aircraftin situ measurements. Therefore, the AirCore sampling system can provide a ground-truth standard forcomparison with total column measurements from either ground-based FTS or satellites. We have used AirCore
profiles to compare with TCCON measurements. At Sodankylä we have made AirCore measurements during
all seasons, including springtime flights under polar stratospheric vortex conditions.  Keywords: TCCON, GOSAT, AirCore, carbon dioxide, methane100

Water and Temperature SVD Estimates to Improve OCO-2 XCO2
ErrorsRobert R. Nelson*1, Susan S. Kulawik2, Christopher W. O’Dell3
1. Jet Propulsion Laboratory, California Institute of Technology, USA
2. Bay Area Environmental Research Institute, USA
3. Cooperative Institute for Research in the Atmosphere, Colorado State University, USA

The NASA Orbiting Carbon Observatory-2 (OCO-2) was launched in 2014 with the goal of accurately andprecisely measuring column-mean carbon dioxide. In order to fit the measured near infrared radiances properly,
many physical parameters besides CO2 must be included in the optimal estimation state vector. Atmosphericwater vapor and temperature are two such parameters. Up until now, the NASA Atmospheric Carbon
Observations from Space (ACOS) retrieval simply solved for a multiplicative scaling factor on an a priori
water vapor profile and an additive offset on an a priori temperature profile. However, preliminary synthetic
studies have indicated that both water vapor and temperature have 2-3 degrees of freedom in the verticalatmospheric column and that solving for full profiles can lead to improved CO2 retrievals. In this work, we use
singular value decomposition (SVD) to determine the most common “patterns” of atmospheric water vapor
and temperature, then solve for scaling factors on these patterns. We assess retrieval errors statistics bycomparing against TCCON and other validation sources.

Keywords: OCO-2, XCO2, water vapor, temperature, singular value decomposition

101

Update on the retrieval algorithm for the NIES TANSO-FTS/GOSATSWIR L2 productYu Someya1*, Yukio Yoshida1, Hirofumi Ohyama1, Shohei Nomura1, Akihide Kamei1, Isamu
Morino1, Hitoshi Mukai1, Tsuneo Matsunaga1, and TCCON partners2
1. National Institute for Environmental Studies, Tsukuba, Japan
2. https://tccon-wiki.caltech.edu

National Institute for Environmental Studies (NIES) has provided the TANSO-FTS/GOSAT SWIR L2 productfor more than a decade. The latest versions of the L2 product are V02.90/91 and the related bias-corrected ones
based on the retrieval algorithm described in Yoshida et al. (2013). We recently updated the retrieval algorithm
by focusing on the increase of available scans and improvement of the spectral fitting accuracies to produce
the next version of the L2 product, V03.00. The main updates of the retrieval algorithm are:1.

estimate cirrus optical thickness and top pressure simultaneously with gases instead of the 2 Pm band
cloud screening2.

replace the TANSO-FTS degradation model with that of Someya and Yoshida (2020)3.

replace the solar irradiance spectra to that based on TSIS-HSRS

4.

update the gas absorption coefficient tables

The retrieval results show that the spectral residuals are decreased except for those in the strong CO2 sub-band
over ocean. Available scans are increased by 14% over land although that is decreased by 20% over ocean.
The data over ocean is decreased because of the residuals of the strong CO2 sub-band and the stricter thresholdsof the residuals in the post-screening. The validation result of XCO2 from V03.00 against TCCON with theversion of GGG2020 revealed almost the same quality as that from V02.90/91 over land and increased negative
bias over ocean. Therefore, bias correction is necessary for XCO2 over ocean. The XCH4 from V03.00 has anegative bias with several ppb opposite to the positive bias of that from V02.90/91. In addition, theinconsistency in the decadal CO2 growth rate with the field measurements over ocean which is recently foundedin V02.90/91 (NIES GOSAT project, 2021) is resolved in the V03.00 product.
Keywords: GOSAT, retrieval algorithm, Full Physics, CO2, CH4

102

Design of the retrieval algorithm and level 2 product for greenhousegases from GOSAT-GW
Yu Someya1*, Yukio Yoshida1, Hisashi Yashiro1, Hirofumi Ohyama1, Isamu Morino1, Tazu Saeki1,
and Tsuneo Matsunaga1
1. National Institute for Environmental Studies, Tsukuba, Japan

Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW) is planned to launch at theend of Japanese FY2023. The instrument for monitoring greenhouse gases (GHGs) onboard GOSAT-GW isnamed Total Anthropogenic and Natural emissions mapping SpectrOmeter-3 (TANSO-3). TANSO-3 has thethree spectral bands covering 420 – 490, 747 – 783, and 1590 – 1654 nm with the full-width half-maximumof 0.75, 0.075, and 0.3 nm respectively. The GHG retrieval algorithm is based on that for GOSAT-2 SWIR
level 2 (L2) using the 760 and 1600 nm bands. The full physics algorithm estimates column-averaged dry-air
mole fractions of CO2 and CH4 (XCO2 and XCH4) with the ancillary parameters such as water vapor, aerosol
effective height, total aerosol optical thickness, surface pressure, temperature shift, and surface albedo. A priori
values are taken from the calculations by Nonhydrostatic ICosahedral Atmospheric Model (NICAM) for
greenhouse gases and aerosols, and the Japanese reanalysis dataset, JRA-3Q for the other atmosphericconditions. In addition to the full physics retrieval, proxy and solar-induced fluorescence (SIF) retrievals areprocessed. Because no imaging sensors are onboard GOSAT-GW, cloud screening with two procedures isconducted by using the TANSO-3 spectra itself. One is the reflectance test which discriminates the brightsurfaces as clouds. The other one utilizes the estimated surface pressure from O2 A band same as the ACOS
retrieval algorithm. The GHG L2 product is planned to be provided as the combined one with the results of all
the above processing including main outputs, XCO2 and XCH4 from full physics retrieval, XCH4 from proxy
retrieval, and SIF.
Keywords: GOSAT-GW, retrieval algorithm, CO2, CH4103

GOSAT-2 TANSO-FTS-2 SWIR L2 Version 02 Products
Yukio Yoshida*1, Yu Someya1, Hirofumi Ohyama1, Hiroshi Suto2, Isamu Morino1, Tsuneo
Matsunaga1, and TCCON partners3
1. National Institute for Environmental Studies, Japan
2. Japan Aerospace Exploration Agency, Japan
3. https://tccon-wiki.caltech.edu/
The Greenhouse gases Observing SATellite-2 (GOSAT-2), the successor to the GOSAT (2009 - current), waslaunched on 29 October 2018, and its operational observation has started from February 2019.

The column-

averaged dry air mole fractions of carbon dioxide, methane, carbon monoxide, and water vapor (XCO2, XCH4,
XCO, and XH2O; hereinafter called Xgas), retrieved from Short-Wavelength InfraRed (SWIR) spectral data
(Bands 1, 2, and 3 at 0.75–0.77, 1.56–1.69, and 1.92–2.33 ȝm, respectively) using a so-called full-physics
retrieval method, are released as the "GOSAT-2 TANSO-FTS-2 SWIR L2 Column-averaged Dry-air Mole
Faction Product" (hereinafter called SWFP).

Retrieval uncertainties of Xgas are evaluated primarily bycomparing with the Total Carbon Column Observing Network (TCCON) data. As for the SWFP V01.07 landdata, the single-sounding precision values evaluated by TCCON GGG2014 data are 3.72 ppm, 16.3 ppb, 7.3
ppb, and 130.4 ppm for XCO2, XCH4, XCO, and XH2O, respectively.

Relatively lower precision of Xgas

seems to come from a small fluctuation in full-width at half maximum (FWHM) of the TANSO-FTS-2
instrumental line shape functions (ILSF), which depends on the on-orbit instrument temperature environment,
and a radiance offset, which depends on the incident signal level. To suppress the retrieval error due to thechange of the FWHM of ILSF and the radiance offset, several parameters will be added to the state vector in
the SWIR L2 V02 retrieval algorithm.

A tentative result shows the single-sounding precision of 2.01 ppm,

10.0 ppb, 7.1 ppb, and 124.2 ppm for XCO2, XCH4, XCO, and XH2O, respectively.

The error characteristics

and remaining issues of SWIR L2 V02 products will be shown in the session.

Keywords: GOSAT-2, retrieval algorithm, column-averaged dry air mole fraction of gases

104

Oxygen Airglow Studies in Support of the MethaneSAT Mission
Kang Sun*1,2, Mahdi Yousefi1, Christopher Chan Miller3,4,6, Kelly Chance3, Gonzalo Gonzalez
Abad3, Iouli E. Gordon3, Xiong Liu3, Ewan O'Sullivan3, Jenna Samra3, Christopher E. Sioris5,
Steven C. Wofsy6,7
1. Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo,
NY, USA
2. Research and Education in Energy, Environment and Water Institute, University at Buffalo,
Buffalo, NY, USA
3. Center for Astrophysics | Harvard & Smithsonian, Cambridge, MA, USA
4. Climate Change Research Center, University of New South Wales, Sydney, New South Wales,
Australia
5. Environment and Climate Change Canada, Toronto, Ontario, Canada
6. Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University,
Cambridge, MA, USA
7. Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA
The main topic of this work is an optimal estimation-based algorithm to retrieve number density of excitedoxygen (O2) molecules that generate airglow emissions near 0.76 μm (b1Σg+ or A band) and 1.27 μm (a1Δg or
1

Δ band) in the upper atmosphere. Both oxygen bands are important for the remote sensing of greenhousegases. The algorithm is applied to the limb spectra observed by the SCanning Imaging Absorption
spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument in both nominal (tangent heights
below ~90 km) and mesosphere-lower thermosphere (MLT) modes (tangent heights spanning 50-150 km). The
number densities of emitting O2 in the a1Δg band are retrieved in an altitude range of 25-100 km near daily in2010, providing a climatology of O2 a1Δg band airglow emission. This climatology will help disentangle
airglow from backscattered light in nadir remote sensing of the a1Δg band. The global monthly distributions of
the vertical column density of emitting O2 in a1Δg state show mainly latitudinal dependence without otherdiscernible geographical patterns. Temperature profiles are retrieved simultaneously from the spectral shapesof the a1Δg band airglow emission in the nominal limb mode (valid altitude range 40-100 km) and from both
a1Δg and b1Σg+ band airglow emissions in the MLT mode (valid range 60-105 km). The temperature retrievalsfrom both airglow bands are consistent internally and in agreement with independent observations from ACEFTS and MIPAS with absolute mean bias near or below 5 K and root mean squared error (RMSE) near or
below 10 K. Besides, the zenith airglow for the a1Δg band observed by the MethaneAIR instrument on boardof the NSF/NCAR Gulfstream V aircraft in August 2021 will be discussed.
Keywords: oxygen, airglow, SCIAMACHY, MethaneSAT, MethaneAIR105

XCO2 observation network for verification of Kanto intensive
observations and sun-glint observations by GOSAT and GOSAT-2

Ryoichi Imasu*1, Isamu Morino2, Hitoshi Mukai2, and Yutaka Matsumi3
1. Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba, Japan
2. National Institute for Environmental Studies, Ibaraki, Japan
3. Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan
Solar-tracking spectrometers developed by a group of the Nagoya University have been installed at four
observation sites in the Kanto Plain for validating columnar dry-air mixing ration of carbon dioxide(XCO2) observed by GOSAT and GOSAT-2 during the Kanto intensive observations. The system wasnamed " Compact Array Spectrometer Targeting Local Emissions of CO2 (CASTLE-CO2)". It consists of asun tracker and a grating spectrometer (Ocean Optics NIRQuest 512-1.7). One of CASTLE-CO2 wasplaced at a Total Carbon Column Observing Network (TCCON) site in Tsukuba city (Ibaraki) for its
calibration. On the other hand, The University of Tokyo (UTokyo) and the National Institute forEnvironmental Studies (NIES) have been carrying out simultaneous observations of GOSAT/ GOSAT-2
and onboard observations using a CASTLE-CO2 on a meteorological observation ship of the Japan
Meteorological Agency (JMA) as part of the joint observation of greenhouse gases based on theagreement between the JMA and the Ministry of the Environment (MOE). Within this framework, XCO2
obtained through the sun-glint observations by GOSAT and GOSAT-2 have been compared with thedata observed by a CASTLE-CO2. For the same purpose, CASTLE-CO2 was installed on a Pacificisland, Minamitorishima (24.28N, 153.98E) and XCO2 observation was started in 2019. Currently, weare preparing to install CASTLE-CO2 on another Pacific island, Amamioshima (28.32N, 129.37E) asa joint research with Atmosphere and Ocean Research Institute (AORI) and The institute of Medical Science(IMS) of UTokyo.
Keywords: ground-based remote sensing, XCO2, CASTLE-CO2, validation, remote island106                             !"" #$ %  !  

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 !( - !    ! ! Comparison of GOSAT-2 XCO2 and an independent inversion analysis
Takashi Maki *1, Takashi Nakamura2
1. Meteorological Research Institute, Japan
2. Japan Meteorological Agency, JapanGOSAT-2, designed to monitor the carbon budget, was launched in 2018 and has been
accumulating observation data continuously. Compared to the previous satellite GOSAT, this satellite
increases the number of observation data by expanding the observable area using intelligent pointing,etc. We compared the latest XCO2 version 0107 observed by GOSAT-2 with the JMA CO2 distribution,
which is an independent inversion analysis using only in-situ observations. The satellite observation
data and the JMA CO2 distribution were compared after taking monthly averages at 5-degree
horizontal grid points. The comparison period is 2019-2020. The GOSAT-2 observable area has
expanded compared to GOSAT, and the number of observation data per unit area has also increased.
On the other hand, the differences from the independent analysis results, which were small in GOSAT(NIES SWIRL2Ver. 2.9X), became larger overall, and in many areas, there was a tendency towardoverestimation compared to the analysis. More detailed comparison results will be reported at theworkshop.
Keywords: GOSAT-2, XCO2 Bias estimation, Inverse model108

Validation of recent results of the GOSAT-2 FTS-2 SWIR L2 products
Yukitomo Tsutsumi*1, Isamu Morino1, Osamu Uchino1, Atsushi Ugajin1, Tomoaki Tanabe1,
Matthias M. Frey1, Yukio Yoshida1, Tsuneo Matsunaga1, Akihide Kamei1, Makoto Saito1, Hibiki M.
Noda1, Tetsu Sakai2, Tomohiro Nagai2, Hiroshi Okumura3 and TCCON partners
1

National Institute for Environmental Studies (NIES), Tsukuba, Japan
2
Meteorological Research Institute (MRI), Tsukuba, Japan
3
Saga University, Saga JapanThe validation is made using the recent products of GOSAT-2 FTS-2 SWIR L2 Full Physics products (Ver.
01.04 & 01.07). The validation period of these products is March 1, 2019 ~ November 30, 2021 (notoverlapped). The area definitions are “Land” (Ӎ 10% of land fraction) and “Ocean” (< 10% of land fraction).
Only data with the quality flag “good” are used. No distinction is made by gain in these analyses.

Figure 1 Scatter Plot of GOSAT-2 SWIR L2 Full Physics product against TCCON
observations for XCO2, XCH4, XH2O, and XCO.

The results of validation on GOSAT-2 Full Physics product (Ver.01.04 & Ver.1.07) for XCO2, XCH4,
XH2O in the comparison area of ± 2º are as follows.
y The mean biases and their standard deviations of XCO2 over Land and Ocean are 1.93 ± 4.12 ppm(0.47 ± 1.00%) and -0.31 ± 5.60 ppm (-0.08 ± 1.36%), respectively.
y The mean biases and their standard deviations of XCH4 over Land and Ocean are -1.43 ± 19.76 ppb (0.07 ± 1.06%) and -10.28 ± 23.92 ppb (-0.56 ± 1.30%), respectively.
y The mean relative biases and their standard deviations of XH2O over Land and Ocean are 5.6 ± 23.2%
and 3.0 ± 19.9%, respectively. Their standard deviations are relatively large.
y The mean biases and their standard deviations of XCO over Land and Ocean are 21.75 ± 9.76 ppb(25.92 ± 11.69%) and 19.01 ± 7.63 ppb (23.42 ± 9.36%), respectively.
y The trends of bias for XCO2 and XCH4 show some variations during the observation term, the term is
not so long though.

109

The influences of aerosols and the validation results of GOSAT FTS SWIR L2 Ver 03.00 will bealso presented.
Keywords: GOSAT-2, Full Physics product (Ver. 01.04 & 01.07), GOSAT, validation, mean bias against
TCCON

110

The Information Content of Dense Carbon Dioxide Measurementsfrom Space: A Case Study with OCO-3
2

3,4,5

Dustin Roten*1, John C. Lin1, Lewis Kunik1, Derek Mallia1, Dien Wu , Tomohiro Oda
6Eric A. Kort

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Ǧ Ǧ    Ǧ͵
 ʹͲͳͻǡʹͲʹͲǡʹͲʹͳǤ  
    ͵ǤͲ   ȋʹͲͳͷȌǤ            ǡ 
  Ǥǯ    ǦǦʹ
 ǡǦǤǤKeywords: Urban carbon cycle, CO2 emissions, OCO-3, Bayesian inversion, emissions inventory111

Accurately Measuring Atmospheric CO2 Enhancements andQuantifying CO2 Emissions from Wildfires – an Airborne
Demonstration
Jianping Mao*1,2, James B. Abshire1,2, S. Randy Kawa2, Haris Riris2, Xiaoli Sun2,Paul T. Kolbeck1, Brad Weir2,3, and Niels Andela4
1. University of Maryland; 2. NASA Goddard Space Flight Center; 3. Universities Space
Research Association, USA;4. Cardiff University, UK
Vegetation fires are a major source of greenhouse gases. Recent wildfires in the Western United States have
had profound impacts on air quality and have emitted a record amount of CO2. The CO2 emissions fromfires estimated with fire emission models or databases have large discrepancies and uncertainties. Groundbased and airborne measurements of fire emissions are few and difficulty to obtain, and CO2 retrievals fromsatellite measurements using passive remote sensing are significantly degraded by scattering effects ofsmoke in the scene.
NASA Goddard Space Flight Center has developed the CO2 Sounder lidar, an integrated-path, differential
absorption lidar approach to measure global atmospheric column-averaged CO2 (XCO2) from space as a
candidate for NASA’s Planned ASCENDS mission. This pulsed laser approach measures CO2 absorption
at multiple wavelengths across a CO2 line centered at 1572.335 nm. Measurements of height-resolved laserbackscatter profiles allow this technique to accurately estimate XCO2 through fire smoke plumes.
We demonstrate this measurement capability over wildfires using lidar measurements from the summer
2017 ASCENDS/ABoVE airborne science campaign. During the August 8 flight over Vancouver Island,
there were dense smoke plumes from the record-breaking wildfires in the Canadian Rockies. The aircraft
overflow this region at a 9-km altitude, the lidar XCO2 retrievals showed an average enhancement of 4 ppm
from the wildfires. A spiral-down validation performed at Moses Lake airport, Washington, showed a biasof 0.1 ppm relative to in situ measurements and a standard deviation of 1 ppm in lidar XCO2 retrievals. We
compared these enhancements with those from the Goddard chemistry transport models using the Global
Fire Emission Database (GFED) and the Quick Fire Emissions Dataset (QFED). The comparison results
suggest that the CO2 emissions from GFED and QFED for these wildfires were underestimated by morethan a factor of 2. To our knowledge this is the first use of lidar to remotely sense CO2 enhancements fromlarge wildfires.
The results show that future airborne campaigns and spaceborne missions with this capability will
significantly reduce the uncertainty in emission modeling and improve estimates of the impact of wildfireson air quality and ecosystem. This capability will further benefit atmospheric transport process studies andestimates of carbon fluxes.
Keywords: CO2, wildfires, lidar, emissions, airborne

112(a) the cloud-free XCO2 retrievals from the CO2 Sounder lidar for the flight on August 8, 2017. Significant
XCO2 enhancements were seen in the flight segment cross Vancouver Island. The British Columbia
wildfires are marked in the north of these enhancements. (b) image of the smoke plumes from the wildfires
in Canadian Rockies as seen from DC-8 over Vancouver Island (Photo by Graham Allan). (c) true color
image from Aqua/MODIS showing the smoke and fires on the same day.
Mao, J., Abshire, J. B., Kawa, S. R., Riris, H., Sun, X., Andela, N., & Kolbeck, P. T. (2021). Measuring
atmospheric CO2 enhancements from the 2017 British Columbia wildfires using a lidar. GeophysicalResearch Letters, 48, e2021GL093805. https://doi.org/10.1029/2021GL093805

113

Urban Greenhouse Gas Emission Monitoring in Seoul and Tokyo
Matthias Max Frey*1, Astrid Mueller1, Hirofumi Ohyama1, Isamu Morino1, Hiroshi Tanimoto1,
Yugo Kanaya2, Koichi Tsukiyama3, Hayoung Park4, and Sujong Jeong4
1. National Institute for Environmental Studies, Japan
2. Japan Agency for Marine-Earth Science and Technology, Japan
3. Tokyo University of Science, Japan
4. Seoul National University, South Korea

Greenhouse gases (GHGs) are the main driver of anthropogenic excess radiative forcing and thereby largely
contribute to the current increase of global mean temperature. Out of the GHGs, carbon dioxide (CO2) andmethane (CH4) are the most important species with respect to radiative forcing. The importance of reducing
GHGs and achieve carbon neutrality has been acknowledged by international parties, leading to the Kyoto
protocol and subsequent Paris COP21 agreement. For the successful implementation of the mitigation goals
cities are of special importance, as over 50 % of the human population lives in urban areas and the population
growth is predicted to occur predominantly in urban centers. At present, cities are estimated to be responsiblefor 53 % - 87 % of fossil fuel CO2 emissions. While emissions of CO2 can be estimated rather precisely on thenational scale, higher uncertainties are reported for urban GHG emissions, especially CH4. The large
uncertainty of the global contribution of urban areas to GHG emissions is why a new generation of city-scaleobserving and modelling systems are needed. Here, mobile Fourier Transform spectrometers (EM27/SUN)
measuring column-averaged dry air mole fractions of trace gases (Xgas) are utilized together with additional
instrumentation to simultaneously measure city scale emissions of CO2 and CH4 in Seoul and Tokyo, two of
the most populous metropolises in the world. We present first results from observations carried out in February
2022 with a focus on the measurements in the Tokyo metropolitan area.

Keywords: Urban emission monitoring, GHG, EM27/SUN

114

Development of an Integrated Lightweight Multi-Rotor UAV Payload
for Atmospheric Carbon Dioxide Mole Fraction Measurements
Tonghui Zhao*1,2, Dongxu Yang1,2, and Yi Liu1,2
1. Carbon Neutrality Research Center (CNRC), Institute of Atmospheric Physics, CAS, China
2. Key Laboratory of Middle Atmospheric Physics and Global Environmental Observation(LAGEO), CAS, China

Records and projections of increasing global average temperature call for improvements of global stocktake
inputs, which are vital to achieving targets of intergovernmental agreements on climate change. Unmanned
Aerial Vehicle (UAV)-based atmospheric observation of greenhouse gas (GHG) concentrations is anupcoming addition to the top-down measurement methods due to its advantageous spatial-temporal
resolutions, greater coverage area and lower costs. Hence, we developed and tested a lightweight UAV
payload enclosure integrating a non-dispersive diffusion infrared (NDIR) spectrometer and twoelectrochemical sensors for measurements of carbon dioxide (CO2), carbon monoxide (CO) and nitrogendioxide (NO2). To achieve higher response times and maintain measurement qualities, we designed a custom
air inlet on the rotor-facing side of the enclosure to reduce measurement fluctuations caused by rotor
downwash airflow. To validate the payload design, we conducted a controlled test for comparing chamberedand chamber-less NDIR spectrometer measurements. From the test we observed a reduction of 0.48 hPa interms of standard deviation of pressure measurements and minimised downwash-flow-induced anomalous
biases (+0.49 ppm and +0.08 hpa for chambered compared to −1.33 ppm and −1.05 hpa for chamber-less asshown in the bottom left figure). We also conducted an outdoor in-situ measurement test with multiple
flights reaching 500 m above ground level (ABGL). The test yielded high resolution results representing
vertical distributions of mole fraction concentrations of three types of gases via two types of flight trajectory
planning methods (bottom right figure). Therefore, we provide an alternative UAV payload integration
method for NDIR spectrometer CO2 measurements that complement existing airborne GHG observation
methodologies. Additionally, we also introduced an aerodynamic approach in reducing measurement noises
and biases for a low response time sensor configuration.
Keywords: drone-based, NDIR, CO2, emission monitoring, lower atmosphere

P ublication: Zhao, T.; Yang, D.; Liu, Y.; Cai, Z.; Yao, L.; Che, K.; Ren, X.; Bi, Y.; Yi, Y.; Wang, J.; et al. Development of an Integrated Lightweight Multi-Rotor UAV Payload for
Atmospheric Carbon Dioxide Mole Fraction Measurements. Atmosphere 2022, 13, 855. https://doi.org/10.3390/atmos13060855

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116

Quantification of CO2 emissions over the Greater Toronto Area:
Comparison of model simulations with OCO and EM27/SUN
measurements
Omid Moeini*1, Ray Nassar1, Jinwoong Kim1,3, Michael Neish1, Felix Vogel1, Shuzhan Ren2, Nasrin
Mostafavi Pak1,3, Debra Wunch3
1) Environment and Climate Change Canada, Climate Research Division, Toronto, Canada,
2) Environment and Climate Change Canada, Air Quality Research Division, Toronto, Canada,
3) University of Toronto, Department of Physics, Toronto, Canada

Atmospheric carbon dioxide (CO2) is the most important greenhouse gas. Urban areas are estimated to be
responsible for more than 70% of global fossil fuel CO2 emissions. While many cities now have emission
reduction policies, the observation methods to verify their outcomes are limited. Satellite measurements and highresolution modelling are effective ways to monitor and quantify CO2 in the atmosphere. In this study, we present
and analyze the XCO2 (column averaged dry-air CO2 mole fraction) distributions over the Greater Toronto Area
(GTA) using the results of the GEM-MACH model (Global Environmental Multiscale model - Modelling Air
quality and Chemistry) with 2.5 km resolution for the period of April to August 2020. The simulated values arecompared with multiple OCO-3 SAM (Snapshot Area Map) and OCO-2 Target mode observations collected overthe GTA. The comparison between the XCO2 measurements by two EM27/SUN instruments located in the GTA
with the simulated values and the OCO observations are discussed as a step toward combining these datasets toimprove our understanding of GTA emissions.

Keywords: CO2, XCO2, GEM-MACH, OCO-3, GTA

117

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118

Detection of locally elevated methane concentrations by analyzing
Sentinel-5 Precursor satellite data
Steffen Vanselow*1, Oliver Schneising-Weigel1, Michael Buchwitz1, Heinrich Bovensmann1
and John P. Burrows1

1. Institute of Environmental Physics (IUP), University of Bremen FB1, Bremen, Germany

Methane CH4 is an important anthropogenic greenhouse gas and its rising concentration in theatmosphere contributes significantly to global warming. Satellite measurements of the column-averaged
dry-air mole fraction of atmospheric methane, denoted as XCH4, can be used to provide information onemission trends of emission sources, which can help to improve emission inventories and review policies
to mitigate climate change.
The Sentinel-5 Precursor (S5P) satellite with the TROPOspheric Monitoring Instrument (TROPOMI)
onboard was launched in October 2017 into a sun-synchronous orbit with an equator crossing time of
13:30. TROPOMI measures reflected solar radiation in different wavelength bands to generate various
data products and combines daily global coverage with high spatial resolution. TROPOMI's
observations in the shortwave infrared (SWIR) spectral range yield methane with a horizontal resolutionof typically ͹ ൈ ͹ km2.
We used a monthly XCH4 data set (2018-2020) generated with the WFM-DOAS retrieval algorithm,
developed at the University of Bremen, to detect temporally stable, locally enhanced methane
concentrations originating from potential emission sources. At first, we applied a spatial high-pass filter
to the XCH4 data set to filter out the large-scale methane fluctuations. The resulting anomaly ΔXCH4
maps show the difference of the local XCH4 values compared to its surroundings. We then analyzed themonthly anomaly maps to identify hot spot regions by utilizing different filter criteria, such as thenumber of months in which the local methane anomalies ΔXCH4 must exceed certain threshold values.
In the last step, we calculated some properties of the detected hot spots like the mean monthly methane
enhancement and attributed them to emission sources by comparing them with inventories ofanthropogenic methane emissions.
In this presentation, the algorithm and initial results concerning the detection of local methane
enhancements by spatially localized methane sources (e.g., wetlands, coal mining areas, oil and gas
fields) are presented.

Keywords: detection algorithm, hot spots, methane, Sentinel-5 Precursor

119NOx and CO inversions for the derivation of FFCO2 emissions in
EuropeA. Fortems-Cheiney*,1, G. Broquet1, I. Pison1, E. Potier1, A. Berchet1, R. Plauchu1, H. Denier
van der Gon2, S.N.C Dellaert2
1

Laboratoire des Sciences du Climat et de l’Environnement, LSCE-IPSL (CEA-CNRSUVSQ), Université Paris-Saclay, 91191 Gif-sur-Yvette, France.
2
Department of Climate, Air and Sustainability, TNO, P.O. Box 80015, 3508 TA Utrecht,
Netherlands.
We have developed an atmospheric inversion configuration to quantify monthly to annual budgets of
the national emissions of FFCO2 in Europe. These FFCO2 emission estimates are based on theassimilation of the long-term series of NO2 OMI and of CO MOPITT space-borne observations, with
the Community Inversion Framework (CIF) and the CHIMERE regional chemistry-transport model.
In a first step, variational regional inversions are performed to estimate monthly European NOx or CO
emissions at 0.5° horizontal resolution, using the TNO-GHGco-v3 inventory as a prior estimate ofthese emissions. In a second step, these maps are converted into estimates of the rescaling to be
applied to monthly budget of sectoral anthropogenic NOx, CO and then FFCO2 emissions per country.
The estimates of anthropogenic NOx emissions are increased by the inversion compared to the TNOGHGco-v3 inventory. As a natural consequence of the increase of the NOx emissions and of theindirect use of the emission ratios from the inventory, the inverted FFCO2 emissions from the NOx
inversions are larger than those from the TNO-GHGco-v3 inventory, and the differences between
these inverted emissions and the inventory are relatively low (below 5% for all annual budgets at
European scale). However, when the CO inversion is used to rescale jointly the monthly budgets ofsectoral CO and FFCO2 emissions from the inventory, it yields FFCO2 emissions that are smaller thanthose from the TNO-GHGco-v3 inventory. The emissions derived from the CO inversion are quiteconsistent with the inventory. However, their sign is opposed to those obtained when assimilating NO2
data. This demonstrates the need for a better understanding of the uncertainties, and in particular the
potential sources of biases in the datasets and in the two inversion processes before attempting at
synthesizing the CO and NO2-based estimates of the FFCO2 emissions.
Keywords: atmospheric inversion, NOx, CO, FFCO2

120

Spatial distribution and Seasonal cycle of northern high latitudes
CH4 fluxes inferred from an atmospheric inverse model using
TROPOMI XCH4
Aki, Tsuruta*1, Ella, Kivimäki1, Hannakaisa, Lindqvist1, Leif, Backman1, Janne, Hakkarainen1,
Tomi, Karppinen1, Oliver, Schneising2, Michael, Buchwitz2, Sander, Houweling3, Rigel, Kivi1,
Huilin, Chen4, Justus, Notholt2, Debra Wunch5, Johanna Tamminen1 and Tuula Aalto 1
1. Finnish Meteorological Institute, Finland
2. Institute of Environmental Physics (IUP), University of Bremen, Germany
3. Institute for Marine and Atmospheric Research Utrecht, Utrecht University, the Netherlands
4. Centre for Isotope Research, University of Groningen, the Netherlands
5. Department of Physics, University of Toronto, Canada
Methane (CH4) is an important anthropogenic greenhouse gas (GHG) with a global warming potential 28
times that of carbon dioxide on a 100-year time horizon. Recently, the number of atmosphericmeasurements from satellites is rapidly increasing and their retrieval quality is improving continuously.
As those measurements have much higher spatial coverage compared to the ground-based observations,the potential to better constrain GHG budgets is expected to be high. However, the availability of thesatellite data depends highly on the sunlight and clouds, and therefore, seasonality may not be as wellconstrained in regions such as northern high latitudes (NHL).
In this study, we examine the potential of the ESA Sentinel-5 Precursor TROPOMI XCH4 retrievals toconstrain CH4 budgets at northern high latitudes (NHL), using the CarbonTracker-Europe CH4
atmospheric inverse model. Fluxes are estimated by constraining the model using three sets ofobservations: 1) TROPOMI XCH4 retrievals of SRON operational data, 2) TROPOMI XCH4 retrieved
based on WFM-DOAS algorism, and 3) ground-based observations of surface CH4 from global andregional networks, e.g. ICOS and NOAA. The fluxes are estimated for 2018, and analysed by comparing
between the three setups, and to the ground-based non-assimilated data such as TCCON retrievals. The
estimated NHL wetland CH4 emissions are decreased from the prior when TROPOMI data areassimilated, especially in summer. The anthropogenic CH4 emissions also showed decrease from the priorin the TROPOMI inversions during spring and autumn. The evaluation against non-assimilated data
showed that the seasonality driven by the TROPOMI inversions may be associated with the seasonal bias
in the retrievals (Lindqvist et al., IWGGMS-18). For the spatial distributions, little differences were foundin the location of emission hot-spots. The results indicated both potential and complication of using the
TROPOMI data for estimation of NHL CH 4 budgets.
Keywords: TROPOMI, methane, atmospheric inversion, northern high latitudes

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122

Informing sector characteristics of urban CO2 emissions using coemitted trace gases
Dien Wu1,*, Junjie Liu2, Paul O. Wennberg1,3, Joshua L. Laughner2, Paul I. Palmer2,4, Robert R.
Nelson2, John C. Lin5, Eric A. Kort6, Annmarie Eldering2
1. Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena,
USA
2. Division of Engineering and Applied Science, California Institute of Technology, Pasadena, USA
3. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA
4. School of GeoSciences, University of Edinburgh, Edinburgh, UK
5. Department of Atmospheric Sciences, University of Utah, Salt Lake City, USA
6. Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor,
USA
Air pollutants (CO, NOx) are co-emitted with CO2 from many sources of combustion; their column abundances
can be retrieved from space-based sensors. While the joint use of satellite-based CO2 and co-emitted species
columns have been advocated to better understand anthropogenic CO2 emissions, few have demonstrated how
those measurements may facilitate the spatial allocation and sector attribution of CO2 emissions. Technicalchallenges including meteorological, chemical, biogenic, and retrieval influences, were less addressed whencombining multiple species/sensors.
Our goal is to inform sector characteristics of CO2 emissions by combining spatially-resolved column CO2
from OCO-3 with column CO and tropospheric NO2 (tNO2) data from TROPOMI. Instead of relying on CO2emission inventories, intra-city anthropogenic activities were informed from 1) a high-resolution urban climatezone dataset or 2) retrieved tNO2. To interpret tNO2 more effectively we modified a Lagrangian atmospherictransport model to incorporate a simplified representation of the nonlinear NOx chemistry and evaluated it overa few large US power plants with EPA-based emissions. The model identified a slight reduction in NOx
emission in 2020 compared to those in 2019 and 2021 over Phoenix, USA, possibly related to the COVID
lockdown.
We also present results that analyze multiple species. Combustion efficiencies associated with heavy industrial
activities in Shanghai are distinguishable from those in Los Angeles, as inferred from CO:CO2 enhancement
ratios. When the model is coupled with a chemical inversion system (LETKF), spatial distributions of urban
NOx emissions within Baotou, China and Phoenix were substantially improved. We plan to further constrain
and investigate spatially-resolved NOx:CO2 emission ratios at source locations, when observed XCO2 isingested into our framework. We argue that our analysis has the potential to provide independent information
about the spatial allocation and sector characteristics of urban CO2 emissions according to either enhancement
ratios or optimized emission ratios at emission sources.

123

Keywords: urban CO2 emission, sector characteristics, CO and NOx, inverse modeling

Figure. An example of the GFS-based modeled tNO2 plumes using X-STILT-NOx (upper row) vs. observed tNO2
plumes from TROPOMI and XCO2 enhancements from OCO-3 (lower row) over Baotou on Oct 3rd, 2020. The
modeled plumes are generated using annual mean ENOx from EDGAR with top emitters highlighted in light-grey
circles. Note that the observed XCO2 signals are scaled to fit in the color bar, meaning the maximum XCO2 signal
aggregated to the TROPOMI scale within the scene is about 2.66 ppm.

124

Seasonal and Interannual Variability of Australian Carbon Fluxes Seenby GOSAT
Eva-Marie Schömann*1, Sanam N. Vardag1,2, Sourish Basu3,4, Martin Jung5, Stephen Sitch6, André
Butz1,21
2Institute of Environmental Physics, Heidelberg University, Heidelberg, Germany

Heidelberg Center for the Environment (HCE), Heidelberg University, Heidelberg, Germany
3

4

Goddard Space Flight Center, NASA, Greenbelt, Maryland, USA

Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland,USA
5

Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena,
Germany
6

College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4RJ, UK

The semi-arid Australian continent significantly influences the interannual variability of the global terrestrial
carbon sink. Atmospheric inverse models and machine learning based extrapolations can be used to estimate landcarbon fluxes from CO2 measurements. However, the sparse spatial coverage of in-situ CO2 measurements overAustralia leads to large uncertainties in estimated carbon fluxes for the continent. Satellite measurements of CO 2offer an independent and spatially extensive source of information about the Australian carbon cycle. Combinedwith climatic variables and process-based ecosystem models, satellite data can reveal the underlying processes
leading to the Australian carbon flux variability.
Here, we examine the decadal data set (2009-2018) of atmospheric CO2 mole fractions delivered by theGreenhouse Gases Observing Satellite (GOSAT) above Australia. We estimate monthly satellite based land CO2
fluxes from those measurements via the TM5-4DVAR inverse model and discuss their seasonal and interannual
variability. We find that the seasonality of GOSAT atmospheric CO2 is different from that of in-situ based
atmospheric inverse models (CarbonTracker, TM5-4DVar, and CAMS). The largest difference occurs at the endof the dry season, when GOSAT shows around 1.5 ppm higher CO2 concentrations than the models. The CO2
concentration enhancements are reflected in strong CO2 pulses in the satellite based fluxes in this time. The CO2
pulses lead to a greater seasonal and inter-annual variability compared to fluxes of in-situ based atmospheric
inverse models, FLUXCOM and the ensemble mean of process-based dynamic global vegetation models from the
TRENDYv9 ensemble. We discuss implications and potential mechanisms for explaining the differences found.
Keywords: Semi-arid carbon cycle, Australia’s CO2 balance, satellite inversions, ecosystem modelling125

Estimation of GHG emission/absorption using
GOSAT satellite data in Mongolia
Masataka Watanabe*1, Akihiro Oba1, Zamba Batjargal2 and Gomboluudev Purevjav3
1Chuo University (Japan), 2Ministry of Environment and Tourism (Mongolia),
3Information and Research Institute for Meteorology, Hydrology,and Environment (Mongolia)
The Paris Agreement requires each country to report anthropogenic emissions of greenhouse gases (GHG).
However, there is a gap between actual GHG emission/sink and reported emission/sink due to the data qualityof national statistics and the limitation of the emission factor of the guideline. In addition, Biennial Update
Report (BUR) and Biennial Transparency Report (BTR) are rather difficult for many countries due to humanresource limitations and budget shortages. Therefore, to make this report more transparent but with limited
resources, vital needs exist to compare and verify their national GHG emission inventory data in BUR, BTR,
and the Global Stocktake using the satellite data observed by GOSAT and GOSAT-2. We jointly developed
an inverse modeling system that employs the inverse analysis based on the Green function using GOSATseries satellite observation and the WRF-Chem-based simulation results or WRF- Vegetation Photosynthesis
Respiration Model (VPRM) and estimated a posterior CO2 emission.
As a result of emissions, a posterior annual total CO2 emissions of the energy sector in 2018 was approximately
1 to 5 % higher than the Mongolian national GHG emissions inventory (pre-announcement).
We compared the amount of CO2 absorbed in forests and the model result in 2018 showed a relatively bigger
absorption than the Mongolian national GHG emission inventory 2018. Since there is no significant difference
between the flux observation result and the model result, it is in the process of further refinement.
The approach with this co-development is ideal for building BUR and BTR when Mongolia utilizes it in the
Quality Check (QC) or Verification part. Furthermore, by jointly preparing GHG emissions using satellite
observation with the above methodology for countries without sufficient inventory data, highly transparent
emission reports can be realized in the BUR and BTR. This research has been supported by the Ministry ofEnvironment, Japan.
Keywords: GOSAT, Inversion analysis, Greenhouse Gas (GHG) Emission/Absorption, WRF-Chem,
Biennial Update Report(BUR)/Biennial Transparency Report (BTR)
References/ Publications
Pillai, D., M. Buchwitz, C. Gerbig, T. Koch, M. Reuter, H. Bovensmann, J. Marshall and J.P. Burrows (2016):
Tracking city CO2 emissions from space using a high-resolution inverse modelling approach: a case study for
Berlin, Germany, Atmospheric Chemistry and Physics, Vol.16, pp.9591-9610.
Wang, J. S., S. R. Kawa, G. J. Collatz, M. Sasakawa, L. V. Gatti, T. Machida, Y. Liu, and M. E. Manyin㸦2018㸧:A global synthesis inversion analysis of recent variability in CO2 fluxes using GOSAT and in situ observations,
Atmospheric Chemistry and Physics, Vol.18, pp. 11097-11124.

126

Plume detection and characterization from XCO2 imagery:
Evaluation of Gaussian methods for quantifying
plant and city fluxes
Pascal Prunet1, Olivier Lezeaux1, Andrzej Klonecki1, Claude Camy-Peyret2,
François-Marie Bréon3, Grégoire Broquet3, and Diego Santaren3
1. SPASCIA, Toulouse, France
2. IPSL/UPMC-UVSQ, Paris, France
3. LSCE/IPSL, Gif-sur-Yvette, France

A comprehensive approach for the estimation of CO2 emissions of localized sources (power plants, cities)
from satellite imagery XCO2 has been implemented, based on an optimal estimation method (OEM)fitting the measured images with a Gaussian plume model dealing with multiple and/or extended sources.
It provides a full analysis of the retrieval uncertainty and its dependence to OEM model hypotheses,source characteristics, atmospheric conditions, and observation specifications (spatial resolution andspatial coverage).
In this paper, this approach is first used in an OSSE mode for assessing the potential of simulated satellite
measurements from MicroCarb city mode and GEOCarb concepts for quantifying emissions and for
estimating the performances of the flux retrieval method.
A comprehensive analysis of the expected precision on the source emissions over a representative set ofEuropean, US and South America target sites, considering the impact of the configuration mode and of
instrument/system defaults, of the complex behavior of the plume dynamics, and of realistic cloud
coverage, is provided.
A specific case study is performed by using Large Eddy Simulation (LES) model for the generation ofsynthetic observations, to evaluate the impact of atmospheric turbulence on the resulting XCO2 images
and on flux inversions uncertainty budget.
Then the performances and added-value of the gaussian-OEM method is evaluated in real conditions on
OCO-2 measurements.

127

Regional land carbon sink estimates with NIES inverse model using
ground-based, GOSAT and OCO-2 data.
Shamil Maksyutov*1, Rajesh Janardanan1, Tomohiro Oda2, Jiye Zeng1, Yukio Yoshida1, Tsuneo
Matsunaga1
1. NIES, Tsukuba, Japan
2. USRA, Columbia, MD, USA

Assimilating ground-based and satellite data from continental locations, especially in densely populated
regions, is obstructed by strong anthropogenic sources. High resolution transport modeling should help
reducing the source/sink signal “crosstalk” among various sources, such as anthropogenic/fossil, ecosystem
sink/respiration, and biomass burning. We estimated global CO2 fluxes with GOSAT and OCO-2 data using
our high resolution (0.1 degree) CO2 inverse modeling system, which includes several new developments, suchas (a) meteorology by ERA-5 reanalysis for improved interhemispheric transport rate and vertical mixings in
the troposphere, (b) observation-based upscaled fluxes by the terrestrial vegetation (Zeng et al 2020) andoceans (Landschutzer et al 2020), (c) the large transport matrixes stored on disk for running inversion of
GOSAT and OCO-2 satellite observations on general purpose computer systems. The land and ocean fluxes
were estimated from GOSAT data for 2009-2019 separately for four data cases: data from surface backgroundsites, surface background and continental data, and the two kinds of surface data with GOSAT. The estimatedtropical land sink appeared to be close to neutral, which agree with recent model estimates. The Boreal land
flux estimated at country scale showed a stronger uptake in Boreal Eurasia than in Boreal North America assimilarly seen in bottom-up estimates. Our inverse modeling setup with OCO-2 data followed the OCO-2 v10
MIP experiment protocol, while using a 1.3 sec satellite data averaging over land. The model-estimatedregional CO2 fluxes for 2015-2020 were within the range of the OCO-2 model ensemble and will be used in
the intercomparison and mean flux estimates.
Keywords: land carbon sink, inverse modelling, GOSAT, OCO-2

128

Global (2°×2.5°) and regional (0.5°×0.625°) daily surface CO2 flux
inferred from in-situ and OCO-2 B10 observations based on the
Carbon in Ocean-Land-Atmosphere (COLA) system
Zhiqiang Liu*1, Ning Zeng2
1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid
Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
2. Dept. of Atmospheric and Oceanic Science, University of Maryland College Park, Maryland,
USA

Atmospheric inversion of carbon dioxide (CO2) measurements to better understand carbon sources and sinks
has made great progress over the last two decades. However, most of the studies, including four-dimensionalvariational, ensemble Kalman filter, and Bayesian synthesis approaches, directly obtain only fluxes, while
CO2 concentration is derived with the forward model as part of a post-analysis. Kang et al. (2012) used the
local ensemble transform Kalman filter (LETKF), which updates the CO2, surface carbon flux (SCF), andmeteorology fields simultaneously. Following this track, a system with a short assimilation window and along observation window was developed (Liu et al., 2019). However, this data assimilation system faces thechallenge of maintaining carbon mass conservation. To overcome this shortcoming, here we apply aconstrained ensemble Kalman filter (CEnKF) approach to ensure the conservation of global CO2 mass. Aftera standard LETKF procedure, an additional assimilation is used to adjust CO2 at each model grid point andto ensure the consistency between the analysis and the first guess of the global CO2 mass. Compared to an
observing system simulation experiment without mass conservation, the CEnKF significantly reduces theannual global SCF bias from ~0.2 gigaton to less than 0.06 gigaton and slightly improves the seasonal andannual performance over tropical and southern extratropical regions. We show that this system can accurately
track the spatial distribution of annual mean SCF. And the system reduces the seasonal flux root-mean-squareerror from a priori to analysis by 48-90%, depending on the continental region. Moreover, the 2015-2016 El
Niño impact is well captured with anomalies mainly in the tropics.
Based on this system, Carbon in Ocean-Land-Atmosphere (COLA), we present a global (2°×2.5°) to North
America and Europe (0.5°×0.625°) daily surface CO2 flux inferred from in-situ and OCO-2 B10 observations.
COLA is one of the OCO2MIP-v10 model, and it is well consistent with the space-based and ground-based
estimations. COLA fully takes the advantages of EnKF that significantly reduced the computational costs
compared with traditional methods. For example, using 20 cores of CPU, the 5 years of 4°×5° global
inversion takes only 3 days and the 5 years of 0.5°×0.625° North America inversion takes ~30 days. We
believe that the native resolution global inversion could be possible on the HPC using the methods proposed
in COLA.

129

Global carbonyl sulfide budgets constrained by NOAA surface
network and MIPAS satellite
Jin Ma1, Maarten Krol1,2, Steve Montzka3, Norbert Glatthor4, Marc von Hobe5, Thomas Röckmann11Institute for Marine and Atmospheric Research Utrecht, Utrecht University, the Netherlands
Meteorology and Air Quality group, Wageningen University and Research, the Netherlands
3
Global Monitoring Laboratory, NOAA, USA
4
Karlsruher Institut für Technologie, Institut für Meteorologie und Klimaforschung, Karlsruhe,
Germany
5
Forschungszentrum Jülich GmbH, Germany
2

Carbonyl sulfide (COS) is a trace gas and becomes a diagnostic tool to better trace CO2 footprint and estimate
gross primary production (GPP). It is also important for its contribution to the stratosphere sulfur aerosols.
However, COS comes from various sources and absorbed by plants, soil, and oceans. Therefore, accuratequantification of COS global budgets is challenging. We implemented an inversion model within TM54DVAR to both assimilate NOAA surface observations and MIPAS satellite data from space, in order toimprove the inversion studies of COS. The two major precursors of COS, CS2 and DMS are also simulated in
the TM5 model. A bias correction scheme is developed to MIPAS satellite to handle the bias amongst different
observational data sources. A few inversion experiments are designed to evaluate the performance of inverse
system, and it proves that the bias correction helps fitting model to observations from both ground and space.
The inversions show that COS largest sink as biosphere gains reduction from a prior 1053 GgS/year in all
scenarios. Consequently, the other sources, e.g., COS ocean and CS2 ocean gains increased to balance thebudgets of COS. In general, the optimized sources and sinks of COS are well balanced to maintain COS in theatmosphere at relative equilibrium, as expected from long-term observations. Our study improves the datautilization of COS from ground and space, and provide new insights to COS global budget estimations.

130

Using a portable FTIR spectrometer for intercalibration of TCCON
stations: The COCCON travel standard
Benedikt, Herkommer*1, Frank Hase1, Jochen Groß1, Florian Dietrich2, Jia Chen2, Matthias Frey3,
Isamu Morino3, Angelika Dehn4 and Paolo Castracane4
1. Institute for Meteorology and Climate Research (IMK-ASF), Karlsruhe Institute ofTechnology (KIT), Karlsruhe, Germany
2. Environmental Sensing and Modeling, Technical University of Munich (TUM), Germany
3. National Institute of Environmental Studies (NIES), Japan
4. European Space Agency (ESA)
The precise knowledge of the global atmospheric concentrations of green-house gases (GHG) are crucial forunderstanding and monitoring climate change. Satellites for measuring GHGs are offering a global coverage,
however, they need precise ground-based reference data for validation. The Total Column Carbon Observing
Network (TCCON) measures column averages of GHG abundances at about 25 stations around the globe using
Fourier Transform Infrared (FTIR) spectrometer.
For achieving the high data quality needed for the validation of current and upcoming GHG measuring satellite
missions the control of site-by-site biases across the network is of utmost importance. So far, the verificationof the individual TCCON sites mainly depends on the collection of collocated in situ measurements of GHG
profiles, using airplane overflights or balloon air-core measurements, the use of calibrated HCl gas cells andon the evaluation of XLUFT.
In this work we present a new supplemental approach by using the portable EM27/SUN FTIR spectrometer,
which is the standard instrument of the Collaborative Carbon Column Observing Network (COCCON). This
instrument type has proven its high stability in various field campaigns and long-term studies. In the frameworkof the ESA project “Fiducial Reference Measurements for Green-House Gases II” we are exploiting thestaibility of then instrument to use it as a travel standard for TCCON. It is planned to visit sides in Japan,
Canada, Germany and Australia to perform side-by-side measurements with the TCCON spectrometer.
Between the visits, the stability of the travel standard will be monitored using the Karlsruhe TCCON site andthe COCCON reference spectrometer.
By the comparison of the different TCCON stations to a common reference we will be able to verify the levelof station-to-station consistency currently achieved by the TCCON and support further improvements. Here,
we present the first results of the travel standard visit at the TCCON site Tsukuba, Japan.
Keywords: Remote Sensing, FTIR, Satellite validation, TCCON, COCCON

131

The 2019 Methane Budget And Uncertainties At 1 Degree Resolution
And Each Country Through Bayesian Integration Of GOSAT Total
Column Methane Data And A Priori Inventory Estimates
John R. Worden*1, Daniel H. Cusworth1,4, Zhen Qu2, Yi Yin3, Yuzhong Zhang2,6, A. Anthony
Bloom1, Shuang Ma1, Brendan K. Byrne1, Tia Scarpelli2, Joannes D. Maasakkers5, David Crisp1,
Riley Duren4, and Daniel J. Jacob2
1. Jet Propulsion Laboratory / California Institute for Technology, USA
2. Harvard University, USA
3. California Institute for Technology, USA
4. University of Arizona, USA
5. SRON Netherlands Institute for Space Research, Netherlands
6. Westlake University, China
We use Optimal Estimation (OE) to quantify methane fluxes based on total column CH4 data from theGreenhouse Gases Observing Satellite (GOSAT) and the GEOS-Chem global chemistry transport model. We
then project these fluxes to emissions by sector at 1 degree resolution and then to each country using a new
Bayesian algorithm that accounts for prior and posterior uncertainties in the methane emissions. Theseestimates are intended as a pilot dataset for the Global Stock Take in support of the Paris Agreement. However,differences between the emissions reported here and widely-used bottom-up inventories should be used as astarting point for further research because of potential systematic errors of these satellite based emissions
estimates. We find that agricultural and waste emissions are ~263 +/- 24 Tg CH4/yr, anthropogenic fossil
emissions are 82 +/- 12 Tg CH4/yr, and natural wetland/aquatic emissions are 180 +/- 10 Tg CH4/yr. Theseestimates are consistent with previous inversions based on GOSAT data and the GEOS-Chem model. In
addition,anthropogenic fossil estimates are consistent with those reported to the United Nations Framework

Convention on Climate Change [80.4 Tg CH4/yr for 2019]. Alternative priors can be easily tested with our new
Bayesian approach (also known as prior swapping) to determine their impact on posterior emissions estimates.
We use this aproach by swapping to priors that include much larger aquatic emissions and fossil emissions(based on isotopic evidence) and find little impact on our posterior fluxes.

This indicates that these alternative

inventories are inconsistent with our remote-sensing estimates and also that the posteriors reported here are
due to the observing and flux inversion system and not uncertainties in the prior inventories. We find that total
emissions for approximately 57 countries can be resolved with this observing system based on the degrees-offreedom for signal metric (DOFS > 1.0) that can be calculated with our Bayesian flux estimation approach.
Below DOFS of 0.5, estimates for a countries total emissions are more weighted to our choice of priorinventories. The top five emitting countries (Brazil, China, India, Russia, USA) emit about half of the global132

anthropogenic budget, similar to our choice of prior emissions but with the posterior emissions shifted towards
the agricultural sector and less towards fossil emissions, consistent with our global posterior results. Our results
suggest remote sensing based estimates of methane emissions can be substantially different (although within
uncertainty) than bottom-up inventories, isotopic evidence, or estimates based on sparse in situ data, indicatinga need for further studies reconciling these different approaches for quantifying the methane budget.

Higherresolution fluxes calculated from upcoming satellite or aircraft data such as the Tropospheric Monitoring
Instrument (TROPOMI) and those in formulation such as the Copernicus CO2M, MethaneSat, or Carbon
Mapper can be incorporated in our Bayesian estimation framework for the purpose of reducing uncertainty
and improving the spatial resolution and sectoral attribution of subsequent methane emissions estimates.

Keywords: methane133

Observations of atmospheric CH4 enhancements due toanthropogenic emissions by the GOSAT-2 satellite.
Rajesh Janardanan1*, Shamil Maksyutov1, Yukio Yoshida1, Tsuneo Matsunaga1
1. National Institute for Environmental Studies, Japan
Abstract
We report the preliminary results of using observational data by the Greenhouse gases Observing
SATellite-2 (GOSAT-2) for estimating the signature of local anthropogenic methane emissions in
the column-averaged concentrations of atmospheric methane (XCH4) around the globe using highresolution transport modeling. We use the XCH4 observations data retrieved with NIES Level 2
algorithms v01.03 (proxy algorithm) and v01.04 (full physics algorithm). To estimate the observed
anomaly from XCH4 we use a high-resolution transport model FLEXPART which has spatial
resolution comparable to the size of the satellite observation footprint with an anthropogenic
methane emission inventory (Emission Database for Global Atmospheric Research, EDGAR v5)
to simulate the enhancements at all observation points. With a threshold simulated value wedelineate background observations and those with anthropogenic influence and estimate theanomalies from the mean background. To relate the observed anomalies to the inventory-based
estimates by the model in various regions, we do weighted linear regression with observed anomaly
as response variable. To reduce the impact of the observation error we aggregate the anomalies intomodel simulated enhancement bins over large region. We found that the local enhancements
observed by GOSAT-2 retrieved with both the algorithms scale linearly with inventory-based
simulations of XCH4 for the globe, confirming the potential for using GOSAT-2 observations forquantification of the methane emissions.

Keywords: anthropogenic methane emissions, GOSAT-2, emission inventory.

134