Abstract Collection

The 15th International Workshop on
Greenhouse Gas Measurements from Space(IWGGMS-15)

June 3rd – 5th, 2019
Hokkaido University, Sapporo, Japan

Japan Aerospace Exploration Agency (JAXA)
National Institute for Environmental Studies (NIES)
Ministry of the Environment (MOE)Contents
Session 1 “Ongoing and Near-term Satellite Missions and Calibration”
1.
2.

3.
4.
5.
6.
7.
8.

Sentinel-5 Precursor Mission: Status and Results about the Methane, Nitrogen Dioxide, Cloud& Aerosol Information Products (C. Zehner, ESA)TROPOMI Methane, Water Vapor Isotopologue and Carbon Monoxide Total Column
Measurements at Unprecedented Temporal and Spatial Resolution: Validation Results andApplications (J. Landgraf, SRON, Netherland)
Monitoring Global Carbon Dioxide from Space: the TanSat Mission and Carbon Flux
Investigation Study in China (Y. Liu, IAP, CAS, China)
In-Flight Performance of the TanSat Atmospheric Carbon Dioxide Grating Spectrometer (Z. D.
Yang, NSMC, CMA, China)
High-Resolution CH4 Observations with GHGSat: Plume Detections with GHGSat-D and
Next-Generation Satellite Characterization Results (D. Jervis, GHGSat, Canada)
Toward 20-year GHG Monitoring from Space by GOSAT: Operation, Calibration, Level 1
Dataset, Research Product, and Analytical Tools (A. Kuze, JAXA, Japan)
The Status and the Future Plan of GOSAT / GOSAT-2 Level 2 and 4 Products (T. Matsunaga,
NIES, Japan)
The OCO-3 Mission: Measuring Carbon Dioxide from the International Space Station Mission Goals and Instrument Status (A. Eldering, JPL, US)

Session 2 “Retrieval Algorithms and Uncertainty Quantification”
1. Accelerated MCMC for OCO-2’s CO2 retrieval (O. Lamminpää, FMI, Finland)
2. Recent Progress of GOSAT and GOSAT-2 SWIR L2 Products (Y. Yoshida, NIES, Japan)
3. PPDF-based Method to Account for Atmospheric Light Scattering in Spectroscopic
Observations of Greenhouse Gases from Space: Basic Principles, Validation, and Comparison
with Other Algorithms (S. Oshchepkov, INM, Belarus)
4. Errors in Retrieved Gases and Inferred Fluxes Arising from Non-uniform Scene Illumination:
A Case Study for the GeoCarb Mission (S. Crowell, Univ. Oklahoma, US, on behalf of J.
Nivitanont, Univ. Oklahoma, US)
5. Generation of Merged Level 2 and Level 3 XCO2 Data Products fromSCIAMACHY/ENVISAT, GOSAT and OCO-2 for the Copernicus Climate Change Service(M. Buchwitz, Univ. Bremen, Germany)
6. Retrieval of Aerosol Optical Properties Using GOSAT/TANSO-CAI and GOSAT2/TANSOCAI2 Measurements over the Ocean (C. Shi, JAXA, Japan)
7. Efficient Multiscale Gaussian Process Regression for Remote Sensing Data (J. Susiluoto, MIT,US)
8. Detecting Methane Point Sources from Space Using Hyperspectral Surface Imagers (D. H.
Cusworth, JPL, US)
- i -

9.

Information Content of Methane Flux Estimates from Joint AIRS and GOSAT LowerTroposphere Methane Retrievals (J. Worden, JPL, US)
10. Updates on AIRS CH4 and N2O Retrievals (J. Warner, Univ. Maryland, US)
11. Detection of XCO2 Anomaly Changes in Space and Time at a Global (S. Zhang, CAS, China)
12. A Spectral-sorting Approach for Constraining Coastal Aerosol Profile Using OCO-2 O2A
Measurements (D. Crisp, JPL, US, on behalf of Z. C. Zeng, Caltech, US)
Session 3 “Validation and Supporting Observations”
1.

First Validation Results of the Sentinel-5P Methane Using Global TCCON and NDACCIRWG Data (M. K. Sha, BIRA-IASB, Belgium)
2. Evaluation of Greenhouse Gas Satellite Observations at High Northern Latitudes (H. Lindqvist,
FMI, Finland)
3. GOSAT Observations of Tropospheric CO2 and CH4 Concentrations over Permafrost Regions
and Comparison with In situ Measurements of the ASCENDS/ABoVE 2017 Airborne Science
Campaign (N. Kikuchi, JAXA, Japan)
4. Real or Spurious? An Examination of the OCO-2 Version 9 XCO2 Data Set, and Curious
Features Therein (C. O'Dell, CSU, US)
5. A First Step Toward the Validation of the Merlin Satellite Mission: Magic Campaigns andSpatiotemporal Variability of Methane (C. Bès, CNES, France)
Session 4 “Flux Inversions from Space-based Greenhouse Gas Measurements”
1.
2.
3.
4.
5.
6.
7.
8.
9.

Ongoing Efforts to Develop Top-Down Atmospheric Flux Inventories for CO2 and CH4 (D.
Crisp, JPL, US)
CO2 Inverse Modeling with Satellite XCO2 Retrievals, Ground-based Observations and Highresolution Tracer Transport (S. Maksyutov, NIES, Japan)
Constructing a Carbon Flux Estimation System with Bias Corrected Satellite Data (T. Maki,
JMA/MRI, Japan)
Global and Regional Methane Budgets Derived from GOSAT Retrievals and Ground-based
Observations Using CTE-CH4 Atmospheric Inverse Model (A. Tsuruta, FMI, Finland)
Constraining Carbon Fluxes in Northern Regions by Combining Constraints from Multiple
Atmospheric CO2 Observing Systems (B. Byrne, JPL, US)
Resolving the Information in Large-scale Inversions: Application to CMS-Flux (K. Bowman,
JPL, US)
Exploring Constraints on a Wetland Methane Emission Ensemble with GOSAT (R. J. Parker,
Univ. Leicester, UK)
Difference of Detecting Anthropogenic CO2 Emission by GOSAT and OCO-2 Observations in
China (M. Sheng, CAS, China)
The Potential of the XCO2 High Resolution Imagery for the Monitoring of CO2 Emissions from
Large Cities and Industrial Plants: An Overview of the Studies at LSCE (G. Broquet, LSCE,
France)- ii -

10. Plume Detection and Characterization from XCO2 Imagery: Potential of Gaussian Methods forAnalyzing and Estimating Plant and City Fluxes in the Context of MicroCarb and GeoCarb (C.
Camy-Peyret, IPSL, France)
11. Quantifying Methane Emissions from Individual Coal Mine Vents with GHGSat-D Satellite
Observations (D. J. Varon, Harvard Univ., US)
12. Trends and Interannual Variation of African CH4 Fluxes Inferred from GOSAT XCH4
Retrievals (L. Feng, Univ. Edinburgh, UK)
Session 5 “Solar-induced Chlorophyll Fluorescence (SIF)” andSession 6 “Related Ground-based, Ship-borne, and Air-borne Measurements”
1.
2.
3.

4.
5.
6.

7.

8.

Solar-induced Chlorophyll Fluorescence from the Geostationary geoCARB Instrument (P.
Somkuti, CSU, US)
Seasonal Trends of GOSAT-SIF in Temperate Vegetations (H. M. Noda, NIES, Japan)
What Can We Learn About Effectiveness of Carbon Reduction Policies from Interannual
Variability of Carbon Emissions? Applying ODIAC Emission Estimates from East Asia of the2010s (L. D. Labzovskii, NIMS, Korea)
The COllaborative Carbon Column Observing Network (COCCON): Overview and Current
Status (M. Frey, KIT, Germany)
Airborne Demonstration of Atmospheric CO2 Concentration Measurements with a Pulsed
Multi-wavelength IPDA Lidar (J. Mao, Univ. Maryland, US)
Characterization of Aerosol Absorption over South Asia Based on Multi-platform
Measurements and CAI-2 Retrieval of AOD and Soot Volume Fraction (M. M. Gogoi, ISRO,
India)
Low-resolution FTIR Spectrometers Supplementing TCCON for the Validation of Space-borne
Observations of Greenhouse Gases (CO2, CH4) and CO (M. K. Sha, Royal Belgian Institute forSpace Aeronomy, Belgium)
Development of MRV System of Methane Emissions from Rice Paddies in the Mekong Delta(H. Arai, Univ. Tokyo, Japan)

Session 7 “Status of Future Satellite Missions about GHG Monitoring”
1.
2.
3.
4.

The Evolving Space-based Greenhouse Gas Measurement Fleet (D. Crisp, JPL, US)
NASA’s Plans for Greenhouse Gas Observations from Space (K. Jucks, NASA, US)
The GeoCarb Mission (B. Moore, Univ. Oklahoma, US)
The MicroCarb Project: Recent Achievements and Review of the Project Status (F. Buisson,
CNES, France)
5. The MicroCarb L1 & L2 Products (D. Jouglet, CNES, France)
6. Anthropogenic CO2 Monitoring with the European Candidate Copernicus Mission (T. Fehr,
ESA, on behalf of Y. Meijer, ESA)
7. EUMETSAT's Contribution to the CO2M Mission; Continuous Operations, Processing,Monitoring, and Cal/Val (R. Lang, EUMETSAT)- iii -

8.

AIM-North: The Atmospheric Imaging Mission for Northern Regions (J. Mendonca, ECCC,
Canada, on behalf of R. Nassar, ECCC, Canada)
9. Development and Field Validation of the PanFTS Instrument for Geostationary Measurementsof GHGs, Trace Gases and SIF (D. Crisp, JPL, US, on behalf of S. Sander, JPL, US)
10. Measuring Carbon Dioxide from Space Using Lidar: Status and Updates from the NASAASCENDS Development Activity (K. Jucks, NASA, US, on behalf of S. R. Kawa, NASA, US)- iv -

Poster Sessions
Topic 1. Ongoing and Near-term Satellite Missions and Calibration
1.
2.
3.
4.
5.
6.

Spectral-radiance Inter-comparsion between GOSAT/FTS, GOSAT-2/FTS-2, and OCO-2 (F.
Kataoka, RESTEC, Japan)
Atmospheric Variations in Column Integrated CO2 on Synoptic and Seasonal Time Scales over
the U.S. (Q. Wang, Univ. Oklahoma, US)
Sensitivity Evaluation of TANSO-FTS/GOSAT Using Principal Component Analysis (Y.
Someya, NIES, Japan)
GOSAT-2 Cal/Val Phase Operation Plan for Ensuring the Consistency with GOSAT (K.
Shiomi, JAXA, Japan)
The Ability of GeoCarb to Constrain the Interannual Variability of Carbon Gases over the
Amazon (B. Weir, USRA, US)
Progress in Atmospheric Carbon Monitoring Using NASA's Goddard Earth Observing System(GEOS) Model and Data from the OCO and GOSAT Missions (B. Weir, USRA, US)

7. The OCO-3 Mission: Global Observations of CO2 and Solar-Induced Fluorescence from theInternational Space Station - Snapshot Area Map and Target Mode Observations (T. Kurosu,
JPL, US)
Topic 2. Retrieval Algorithms and Uncertainty Quantification
8.

Exploring Improvements to the Aerosol Parameterization in the OCO-2 XCO2 Retrieval
Algorithm (R. R. Nelson, JPL, US)
9. Validation of Cloud Judgements in TANSO-FTS FOVs by Using Himawari-8/AHI Data (K.
Kitamura, Chiba Univ., Japan)
10. Carbon Dioxide Enhancement over Seoul from Space and Surface Measurements (C. Park,
Seoul National Univ., Korea)
11. Regeneration of CO2 Satellite Column Data Tailored to an Atmospheric Inversion Scheme (A.
Webb, Univ. Leicester, UK)
14. Characterization of OCO-2 and ACOS-GOSAT Biases and Errors for Flux Estimates (S.
Kulawik, BAER Inst., US)
15. Observing Patterns of Greenhouse Gases and Pollutants Across Cities Using Satellite Data (H.
Park, Seoul National Univ., Korea)
16. Preliminary Results from the ESA CH4TIR Project: Spectroscopy and Forward Model Error
Improvement for CH4 Retrieval in the TIR (C. E. Robert, IASB-BIRA, Belgium)
17. GOSAT-2/TANSO-CAI-2 and the Aerosol Product (M. Hashimoto, JAXA, Japan)
18. Assessing OCO-2 Northern High Latitude XCO2 Retrievals Over Snow (J. Mendonca, ECCC,
Canada)- v -19. A Journey of the OCO-2 XCO2 Data Set from Version 9 to Version 10: the ACOS Retrieval
Algorithm Validation (L. Kuai, JPL, US)
20. TanSat XCO2 Retrieval, Inter-comparison and Validation (D. Yang, Univ. Leicester, UK)
21. Observing Water Vapour in the Planetary Boundary Layer from the Short-Wave Infrared (T.
Trent, Univ. Leicester, UK)
22. Improvement and Application of PPDF-S Method for Retrieving XCO2 over Aerosol Dense
Areas (C. Iwasaki, Univ. Tokyo, Japan)
28. Variation of Carbon Dioxide at Upper Troposphere / Lower Stratosphere Derived from GOSATTANSO-FTS TIR (A. Honda, Kyushu Univ., Japan)
29. Validating Ratio Component XCH4/XCO2 of GOSAT Proxy Retrieval of Methane (H. Oshio,
NIES, Japan)
57. Simulation-retrieval Experiments over the Western Hemisphere with the GeoCarb Greenhouse
Gas Retrieval Algorithm (G. McGarragh, Colorado State Univ., US)
Topic 3. Validation and Supporting Observations
23. Variations in CO2 and CH4 in Upper Atmosphere: the Effects of Biomass Burning and Asian
Monsoon Transport Inferred from GOSAT/TANSO-FTS TIR Data (N. Saitoh, Chiba Univ.,
Japan)
24. Greenhouse Gas Measurements at the Sodankyla; TCCON Site and Comparisons with theSatellite Borne Observations (R. Kivi, FMI, Finland)
25. Profiles of Greenhouse Gases Measured in the 2018 STEAM Field Campaign (Z. Cai, CAS,
China)
26. Seasonal and Diurnal Opportunities for XCH4, XCO2, and XCO for the Amazonian Rainforest
Region Allowing Sampling and Validation (R. Chatfield, NASA, US)
27. Progress on Validation of the GOSAT and GOSAT-2 FTS SWIR L2 Products (I. Morino, NIES,
Japan)
Topic 4. Flux Inversions from Space-based Greenhouse Gas Measurements
30. Comparison between MOPITT and OCO-2 Flux Inversions: Analyze of CO-CO2 Correlation(H. Peiro, Univ. Oklahoma, US)
31. What Biogeochemical Processes Drive the Large Decrease of Atmospheric CO2 Growth Rate in
2017? (J. Liu, JPL, US)
32. Characteristics of Atmospheric Carbon Dioxide Concentrations Based on GOSAT and Its
Relations to Biomass Burning in China (Y. Shi, CAS, China)
33. Mesoscale Atmospheric Inversion of the CO2 Natural Fluxes in Amazonia Using GeoCarb andMicroCarb Data (S. K. Singh, LSCE, France)
34. Assessing Errors and Uncertainties in a Global, High-resolution, Fossil-fuel CO2 Emission
Dataset (T. Oda, NASA, US)- vi -

35. Solving Methane Fluxes at Northern Latitudes using Atmospheric and Soil Earth Observations
Data (H. Lindqvist, FMI, Finland)
36. Relationship between Methane Enhancements Observed by GOSAT and Country Scale
Anthropogenic Emissions in Asia (R. Janardanan, NIES, Japan)
37. Characterizing and Mitigating the Impact of Model Transport Errors on CO2 Flux Estimates in
the Assimilation of XCO2 Data from OCO-2 (D. Jones, Univ. Toronto, Canada)
38. The Seasonal and Inter-annual Variations of Regional CO2 and CH4 Fluxes Estimated fromGOSAT Data (H. Takagi, NIES, Japan)
39. Comparing National Methane Emissions Inventories with Estimates by the Global Highresolution Inverse Model (F. Wang, NIES, Japan)
40. lnverse Modeling of Anthropogenic Methane Emissions Based on Ground-based Monitoringand GOSAT Satellite Retrievals (A. Tsuruta, FMI, Finland)
Topic 5. Solar-Induced Chlorophyll Fluorescence (SIF)
41. Retrieval of Solar-induced Chlorophyll Fluorescence from TanSat Space Measurements (L.
Yao, CAS, China)
42. Long-term Evaluation of Zero-level Offset in GOSAT FTS O2 A-band and Consistency of the
Derived SIF with OCO-2 SIF (H. Oshio, NIES, Japan)
43. Assessing the Temporal Dynamics of Satellite-derived Photochemical Reflectance Index (PRI)and Solar-induced Fluorescence (SIF) in Climate-changing Mongolia (T. Kiyono, NIES, Japan)
44. Solar induced Fluorescence (SIF) Mapping from the Copernicus Anthropogenic CO2
Monitoring Mission (H. Boesch, Univ. Leicester, UK)
45. Implementing SIF Estimation Process to the Terrestrial Ecosystem Model VISIT and Applying
the Radiation Transfer Model (T. Miyauchi, NIES, Japan)
Topic 6. Related Ground-based, Ship-borne, and Air-borne Measurements
46. Methane Isotopologue Parameter Assessment of Multiple Spectral Databases Using TCCON (E.
Malina, ESA)
47. In situ Measurement of Vertical Distribution of CO2 and CH4 in the Troposphere by Aircraft
and Tethered Balloon (X. Sun, CAS, China)
48. Measuring In-situ CO2 Profile and Comparison with Satellites and Model (Y. Yi, CAS, China)
49. Provision of GOSAT Data from the WDCGG Website (A. Kinoshita, JMA, Japan)
50. Intercomparison of XCO2, XCH4, XCO Measurements Using EM27/SUN and IFS125HR in
Xianghe (K. Che, CAS, China)
51. Towards Tracking East Asian Transport of Pollution Using the Burgos TCCON Site and the
GOSAT Series Satellites (V. A. Velazco, Univ. Wollongong, Australia)
52. Intercomparison between TCCON XCO2 and XCH4 data in Japan and Philippines via a Portable
Fourier Transform Spectrometer (H. Ohyama, NIES, Japan)- vii -

53. Ground-based Measurement of Solar-induced Chlorophyll Fluorescence with High-resolution
Spectrum in Paddy Field Ecosystem, Japan (K. Buareal, Hokkaido Univ., Japan)
54. Quick Look Algorithm for GHG Source Detection by Using Airborne Imaging Spectrometer
Suite (T. Kawashima, RESTEC, Japan)
Topic 7. Status of Future Satellite Missions about GHG Monitoring
55. A Multi-wavelength Integrated Path Differential Absorption Lidar to Measure XCO2 fromSpace: Status (J. B. Abshire, NASA, US)
56. NASA's Carbon Cycle OSSE Initiative - Informing future space-based observing strategies
through advanced modeling and data assimilation (L. Ott, NASA, US)- viii -The Copernicus Sentinel-5 Precursor Mission: Status and Results
about products relevant for the future planned CO2 Sentinel Mission(Methane, Nitrogen Dioxide, Cloud & Aerosol Information)
Claus Zehner*1, Pepijn Veefkind2, Ilse Aben3, and Diego Loyola4
1. European Space Agency (ESA), Italy
2. Royal Netherlands Meteorological Institute (KNMI), The Netherlands
3. Netherlands Institute for Space Research (SRON), The Netherlands
4. German Aerospace Center (DLR), Germany
The Copernicus Sentinel-5 Precursor mission, launched on Oct. 13 2017, is the first atmospheric Sentinel and will support
Copernicus services in particular for atmospheric applications, including activities such as air quality, ozone and climate
monitoring. The instrument TROPOMI (Tropospheric Monitoring Instrument) is the single payload of the Sentinel-5
Precursor satellite and was co-funded by ESA and The Netherlands. Sentinel-5 Precursor ensures on the one handcontinuity of atmospheric satellite data provision from the ESA ERS (GOME), ENVISAT (SCIAMACHY), and the USA
EOS-AURA (OMI) missions in the various application and scientific domains and prepares on the other hand for the
future atmospheric Sentinel-4 and Sentinel-5 instruments hosted on EUMETSAT platforms. Key features of the
TROPOMI instrument are to have global coverage within one day and providing a spatial resolution of 7x3.5 km.
The Sentinel-5 Precursor mission has successfully finalised the Commissioning Phase on April 24 2018 and has been inroutine operations phase since March 05 2019. During Commissioning Phase only pre-operational sample data products
have been provided to selected Cal/Val experts. The staggered data release to the public has started during July 2018.
Currently Level 1B Radiance/Irradiance (Offline); Methane and trop. Ozone (Offline), Formaldehyde, Sulphur Dioxide,
Carbon Monoxide, Total Ozone, Nitrogen Dioxide, Aerosol Index and Cloud products (Offline and NRT) are available tothe public via the Copernicus Sentinel-5 Precursor Pre-Operations Data Hub–s5phub.copernicus.eu. This presentation
provides an overview about the Sentinel-5 Precursor Mission status and results about the Methane, Nitrogen, Cloud &Aerosol information products that are relevant for the future planned CO2 Sentinel mission.

Key words: Sentinel-5 Precursor, TROPOMI, Methane

1

TROPOMI methane, water vapor isotopologue and carbon monoxide
total column measurements at unprecedented temporal and spatial
resolution: Validation results and applications
Jochen Landgraf, Tobias Borsdorff, Alba Lorente1, Joost aan de Brugh, Andreas Schneider, Otto
Hasekamp
SRON Netherlands Institute for Space Research, Utrecht, Netherlands
The TROPOspheric Monitoring Instrument (TROPOMI) aboard of the Sentinel 5 Precursor (S5P) satellite is the first of
the ESA’s Sentinel missions to monitor air quality and climate change as part of the Copernicus programme. Since
November 2017, TROPOMI provides unique global measurements of atmospheric composition at an unprecedented
spatial resolution of 7x7 km2 with daily global coverage. Among the observed species is methane (CH4), the second mostimportant greenhouse gas, the water vapor isotopologues H216O and HD16O, and the air pollutant carbon monoxide (CO).
For the CH4 and CO products, SRON Netherlands Institute for Space Research developed the operational algorithms fordata processing, for the water vapor isotopologues a scientific data product will be released soon by SRON. In this work,
we present results on these product from the first year of TROPOMI measurements. The validation of the TROPOMI
CH4, CO and HD16O/H216O column observations with collocated ground-based measurements at different TCCON sites
shows a very good agreement. Because of the high signal-to-noise ratio of individual measurements, CO pollution hotspots can be detected from measurements of single orbit overpasses, which opens up new opportunities for emission
monitoring of pollution point sources on daily scales. For a set of cases, we demonstrate this application in more detail
discussing CO emission estimates from wild fires and industrial activities using TROPOMI observations. Moreover, forCH4 we show first case studies where we analyse the temporal and spatial variability of methane over several regions on
the US.

2

Monitoring Global Carbon Dioxide from space: the TanSat mission
and carbon flux investigation study in China
Yi Liu*,1, Zhaonan Cai1, Lu Yao1, Jing Wang1, Ke Che1, You Yi1, Hartmut Boesch2, Liang Feng3,
Palmer Paul3, Andre Butz4
1. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
2. University of Leicester, Leicester, UK
3. University of Edinburgh, Edinburgh, UK
4. University of Heidelberg, Heidelberg, German

The first scientific experimental CO2 satellite of China - Chinese carbon dioxide observation satellite (TanSat) was
launched in 22 Dec, 2016. After on-broad test and calibration, TanSat has been measuring the backscattered sunlight inscientific earth observation mode and produces XCO2 data for more than two years.
In this paper, we will introduce the recent improvement on TanSat retrieval and validation campaign in China. Instituteof Atmospheric Physics Carbon dioxide retrieval Algorithm for Satellite remote sensing (IAPCAS) is an optimal
estimation method (OEM) ‘full physics’ retrieval algorithm that developed for greenhouse gas satellite, especially for
TanSat measurement. IAPCAS-TanSat product has been retrieved and validated against TCCON measurements. In
additional, we compared the IAPCAS retrieval with other two well-known algorithms, the University of Leicester Full
Physical (UoL-FP) and the RemoTeC. The retrieval differences among algorithms have been investigated by verifying
them against TCCON measurement.
The solar induced chlorophyll fluorescence (SIF) can be approached from clear solar lines from TanSat O2A band
hyperspectral measurement. IAPCAS-TanSat-SIF product indicates the seasonal variations of vegetation growth. We will
introduce the results and learning from comparison on the regional distribution and seasonal variation of SIF productagainst OCO-2 measurements.
The validation campaign in Beijing and Inner Mongolia with multiple instruments coordinate measurement, incl.
EM27/SUN, AirCore show a preliminary result in Greenhouse satellite validations and will contribute more for TanSat
validation as well as new generation of greenhouse gas satellite, e.g. GOSAT-2, S5P and OCO-3.
The ecosystem carbon flux distribution and trend in China have been investigated using PYOSSE associated with in-situ
and satellite measurement. We found the changes on carbon flux in last 10 years and differences estimated result comparedwith and without satellite measurement involved.

Key words: TanSat, Retrieval, Validation, Carbon flux3

In-Flight Performance of the TanSat Atmospheric Carbon Dioxide
Grating Spectrometer
Zhong-Dong Yang, Yan-Meng Bi, Qian Wang, Cheng-Bao Liu, Song-Yan Gu,
Yu-Quan Zheng, Chao Lin, Zeng-Shan Yin, Long-Fei Tian
TanSat was successfully launched on 22 December 2016 and has been acquiring global measurements of CO2 and O2
spectral bands in reﬂected sunlight since early February 2017. The atmospheric carbon dioxide grating spectrometer(ACGS) is a spaceborne three-band grating hyperspectral spectrometer suite onboard TanSat. The ACGS is designed tomeasure high-spectral-resolution, co-boresighted spectra of reﬂected sunlight within the molecular oxygen (O2) A-band
range from 0.758 to 0.778 microns and the weak and strong absorption bands of carbon dioxide (WCO2 and SCO2)
ranging from 1.594 to 1.624 microns and from 2.042 to 2.082 microns, respectively; the spectral resolving power (λ/Δλ)of the ACGS is 19000, 12800 and 12250 in the O2 A-band, WCO2 band and SCO2 band, respectively. The in-ﬂight
radiometric calibration accuracy is better than 5%, which satisﬁes the required speciﬁcation. The spectral calibration
accuracy of the O2 A-band is 0.19 pm, that of the WCO2 band is 0.27 pm, and that of the SCO2 band is 4.75 pm, all ofwhich meet the 0.05 full width at half maximum (FWHM) requirement. The spectroscopic performance of the ACGS
surpasses mission requirements with a margin. The ACGS has noise levels that are comparable to or smaller than thoseobserved during prelaunch testing, and the noise has remained extremely stable in the three bands during on-orbit
operations. The SNR levels of the three bands meet the speciﬁed requirement. As expected, the ACGS radiometricperformance in the O2 A, WCO2 and SCO2 bands was fairly good during its ﬁrst sixteen months on orbit.

Index Terms
Spectrometer, Sounding, Atmosphere, Carbon Dioxide.

Zhongdong Yang is a Senior Scientist with the National Satellite Meteorological Centre. He is chief scientist of the ground segment project for the
FengYun-3 satellites and TanSat and takes a leading role in the data processing and algorithm development of products for the FengYun-3 meteorologicalsatellites and TanSat. (e-mail: zhongdong.yang@icloud.com and yangzd@cma.cn)
Yanmeng Bi, Qian Wang, Cheng-Bao Liu, Lei Yang, Song-Yan Gu are with the National Satellite Meteorological Centre (NSMC), CMA, China.
Yuquan Zhen, Chao Lin are with the Changchun Institute of Optics, Fine Mechanics and Physics (CIOFMP), CAS, China.
Zenshan Yin, Longfei Tian are with the Shanghai Engineering Centre for Microsatellites (SECM), CAS, China.

4

High-Resolution CH4 observations with GHGSat:
Plume Detections with GHGSat-D and Next-Generation Satellite
Characterization Results
Dylan Jervis*1, Jason McKeever1, David Gains1, Mathias Strupler1, Ewan Tarrant1, Daniel
Varon1,2, Stephane Germain1
1. GHGSat, Montréal, Canada
2. Harvard University, Cambridge, U.S.A.

The GHGSat demonstration satellite, GHGSat-D, is the first and only satellite on orbit designed for single-sitemeasurements of GHG emissions with high spatial resolution (< 50 m). We present previously unpublished singlepass measurements of point-source emission plumes from industrial sites in the San Juan and Permian basins, as well
as from other industrial sites around the world. Our high-resolution observations reveal the turbulent structure ofmethane plumes – a first from space – as well as allowing attribution of leaks to single pieces of equipment within
industrial sites whose infrastructure is often complex. Furthermore, we show that emission quantification estimatesderived from GHGSat-D satellite observation are corroborated by third-party estimates.
GHGSat will launch its next satellite, GHGSat-C1, in August 2019. We update on instrument construction, present
GHGSat-C1 characterization data that helps to illustrate our spectrometer concept, describe how our new retrievals
algorithm takes advantage of this characterization data, and provide an updated estimate on the performance limits of
GHGSat-C1. Finally, we show how the GHGSat-C1 characterization campaign informed a significant refinement inspectrometer design for our follow-on satellite, GHGSat-C2, scheduled for launch in Spring 2020.
Key Words: GHGSat, Methane, Plumes, High-resolution

5

Toward 20-year GHG monitoring from space by GOSAT: operation,
calibration, level 1 dataset, research product, and analytical tools
Akihiko Kuze*1, Shiomi Kei1, Nobuhiro Kikuchi1, Hiroshi Suto1, Makiko Hashimoto1, Fumie
Ktaoka2 and Takahiro Kawashima2
1. Japan Aerospace Exploration Agency
2. Remote Sensing Technology Center of Japan
Key words: GOSAT; calibration, GHG partial-column density of lower troposphere

Since 2009 Thermal And Near infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS)
onboard the Greenhouse gases Observing SATellite (GOSAT) TANSO-FTS onboard GOSAT has been monitoring arbon
dioxide (CO2) and methane (CH4) from space using solar-reflected light in shortwave infrared (SWIR) and thermal
infrared (TIR) light simultaneously with an agile pointing system.

Even though several anomalies had occurred during

the 10-year operation in orbit, TANSO-FTS has been providing uniform quality data by calibration and updating nonlinearity correction.

In June, we are planning to release Level 1B version 220 decade-long dataset.

The multiplex advantage of TANSO-FTS can measure both the SWIR light that passes from the top of the atmosphere to
the Earth’s surface providing column-averaged dry air mole fractions of CO2 and methane CH4 and TIR light of thethermal emission from the Earth’s atmosphere and surface.
thepartialcolumndensityoflower

On March 2018, we also released new research product of
and

upper

troposphereofmega-cityfromhttps://www.eorc.jaxa.jp/GOSAT/CO2_monitor/index_Ver.K.html.
Lastly, we have provided new analytical tools: a map of GHG emission source sectors for optimizing sampling pattern, a
decade-long radiance data of CEOS calibration sites, and GHG airplane observation campaign data over greater Nagoya
from https://www.eorc.jaxa.jp/GOSAT/index.html.

6

The Status and the Future Plan of GOSAT / GOSAT-2
Level 2 and 4 Products
Tsuneo Matsunaga*1, Isamu Morino1, Yukio Yoshida1, Makoto Saito1, Hibiki Noda1, Hirofumi
Ohyama1, Yosuke Niwa1, Akihide Kamei1, Fumie Kawazoe1, Tazu Saeki1, Ryoichi Imasu2,
Teruyuki Nakajima3, Takashi Nakajima4, Naoko Saitoh5, and Makiko Hashimoto3
1. National Institute for Environmental Studies (NIES), Japan
2. The University of Tokyo, Japan
3. Japan Aerospace Exploration Agency (JAXA), Japan
4. Tokai University, Japan
5. Chiba University, JapanGOSAT-2 (Greenhouse Gases Observing Satellite 2), launched in October 2018, is the second Japanese Earth observation
satellite for greenhouse gas observation from space. It is a joint mission promoted by Ministry of the Environment, JAXA
(Japan Aerospace Exploration Agency), and NIES (National Institute for Environmental Studies) as similar to the firstsatellite, GOSAT, launched in and operated since 2009. Both satellites are designed to measure atmosphericconcentrations of carbon dioxide (CO2) and methane (CH4) using Fourier transform spectrometers, FTS and FTS-2, witha help of moderate resolution imagers, CAI and CAI-2, for aerosol and cloud observation. The design lifetime of GOSAT2 is five years and the 15-year record of atmospheric CO2 and CH4 is now expected from these two satellites.
NIES is responsible for the generation of GOSAT-2's Level 2 and Level 4 standard products which are1) GOSAT-2 TANSO-CAI-2 L2 Cloud Discrimination Product2) GOSAT-2 TANSO-CAI-2 L2 Aerosol Property Product3) GOSAT-2 TANSO-FTS-2 SWIR L2 Chlorophyll Fluorescence and Proxy-method Product4) GOSAT-2 TANSO-FTS-2 SWIR L2 Column-averaged Dry-air Mole Fraction Product5) GOSAT-2 TANSO-FTS-2 TIR L2 Cloud and Aerosol Property Product6) GOSAT-2 TANSO-FTS-2 TIR L2 Temperature and Gas Profile Product7) GOSAT-2 L4A Global CO2 Flux Product8) GOSAT-2 L4A Global CH4 Flux Product9) GOSAT-2 L4B Global CO2 Distribution Product10) GOSAT-2 L4B Global CH4 Distribution Product
The public release of GOSAT-2 Level 2 and Level 4 products will start by the end of October 2019 and 2020, respectively.
In this presentation, the latest status and the future plan of GOSAT / GOSAT-2 Level 2 and Level 4 products will be
introduced.
Key words: GOSAT; GOSAT-2; Standard Product; Research Product7

The OCO-3 Mission: Measuring Carbon Dioxide from theInternational Space Station – Mission Goals and Instrument Status
Annmarie Eldering, Thomas P. Kurosu, Ryan P. Pavlick, Brendan. M. Fisher, and Gregory. B.
OstermanNASA Jet Propulsion Laboratory/California Institute of Technology
Pasadena, CA 91109 (USA)annmarie.eldering@jpl.nasa.gov
Phone: +1-818-219-9485

The Orbiting Carbon Observatory 3 (OCO-3) continues global CO2 and solar-induced chlorophyll
fluorescence (SIF) using the flight spare instrument from OCO-2. The instrument has been throughground testing and thermal vacuum, and has been shipped for installation on the International Space
Station (ISS), currently scheduled for launch on 25 April 2019. By early June, OCO-3 is expected to becompleting the decontamination cycle and First Light operations are imminent. This talk introduces the
OCO-3 mission and its science goals, presents the current instrument status, and provides updates on the
public release of L1b and L2 data products.
The low-inclination ISS orbit lets OCO-3 sample the tropics and sub-tropics across the full range ofdaylight hours with dense observations at northern and southern mid-latitudes (+/-52º). The combinationof these dense CO2 and SIF measurements provides continuity of data for global flux estimates as well
as a unique opportunity to address key deficiencies in our understanding of the global carbon cycle. The
instrument utilizes an agile, 2-axis pointing mechanism (PMA), providing the capability to look towards
the bright reflection from the ocean and validation targets. In addition to the nadir-, glint-, and targetmode geometries familiar from OCO-2, OCO-3 includes a new observation mode dedicated to mappingout larger spatial-scale emitters like cities. This Snapshot Area Map (SAM) mode will be used to map
areas of up to 80x80 km2 on the Earth surface with the standard OCO-3 ground footprints of 2x2 km2,
providing unprecedented high spatial resolution coverage of large-scale CO2 emitters worldwide.
Measurements over urban centers could aid in making estimates of fossil fuel CO2 emissions. Similarly,
the snapshot mapping mode can be used to sample regions of interest for the terrestrial carbon cycle.
This talk will focus on instrument status, mission design, science goals, and global operations, including
overviews of SAM and Target observation selection, sampling locations, frequency of revisit over theyear, hours of the day that are sampled, the strategy for making the data easily accessible to thecommunity, and updates on the public release schedule of L1b and L2 data products.

8

Accelerated MCMC for OCO-2’s CO2 retrieval
Otto Lamminpää*1, Jenny Brynjarsdottir2, Jonathan Hobbs3, Amy Braverman3,
Marko Laine1, Hannakaisa Lindqvist1, Johanna Tamminen1
1. Finnish Meteorological Institute, Finland
2. Case Western Reserve University, USA
3. Jet Propulsion Laboratory, USA
The Bayesian formalism and, in particular, Optimal Estimation (OE) has become the standard tool for computing theposterior mean as the answer to retrieval problems in atmospheric remote sensing. It is also the go-to option for retrievingcarbon dioxide columns in the Orbiting Carbon Observatory 2 (OCO-2) mission. While OE is a fast and simple-toimplement computational framework, it still only provides a simplification of the solution as a “most probable point” andcorresponding error estimates via linear Gaussian approximations, i.e., the solution is characterized with posteriormaximum and covariance matrix around it. However, the forward models in atmospheric retrievals are generally nonlinear and thus the resulting posterior distribution is not guaranteed to be Gaussian. This can lead to unpredictable errors
in the final solution introduced by wrong characterization of the uncertainties or, in the worst case, by converging to local
maxima.

Markov Chain Monte Carlo (MCMC) sampling methodology can be used as a remedy to this situation, providing a means
to calculate the actual posterior. To speed up the otherwise slow MCMC algorithm, we use the Likelihood Informed
Subspace (LIS) dimension reduction to reduce the size of the problem and apply this framework to the OCO-2 mission’s
CO2 retrieval. We utilize a simplified surrogate forward model and a set of measurements obtained from simulated
atmospheric states to compare OE and MCMC solutions.

We further identify the nonlinearities of the corresponding posterior distributions and characterize significant error
sources that are not seen by the OE method. Key findings of this work show strongly non-Gaussian behavior in the aerosolparameters of the OCO-2 state vector. This helps to explain the known irregularities in the retrieval caused by aerosol
misspecification, since the elements of the CO2 part of the state vector are strongly correlated with the aerosol part.

Keywords: OCO-2, MCMC, Dimension Reduction, Uncertainty Quantification, CO2

9

Recent progress of GOSAT and GOSAT-2 SWIR L2 products
Yukio Yoshida*1, Haruki Oshio1, Yu Someya1, Hirofumi Ohyama1, Akihide Kamei1, Isamu
Morino1, Osamu Uchino1, Tazu Saeki1, Yosuke Niwa1, Makoto Saito1, Hibiki Noda1, Tsuneo
Matsunaga1 and TCCON partner2
1. National Institute for Environmental Studies (NIES), Japan
2. https://tccon-wiki.caltech.edu
The Greenhouse gases Observing SATellite (GOSAT) was launched on January 23, 2009, and has been being operated
for more than ten years. GOSAT is equipped with two instruments: the Thermal And Near-infrared Sensor for carbon
Observation Fourier Transform Spectrometer (TANSO-FTS) and the Cloud and Aerosol Imager (TANSO-CAI). TANSOFTS has three bands in the short-wavelength infrared (SWIR) region (0.75-0.78, 1.56-1.72, and 1.92-2.08 µm) and athermal infrared (TIR) band (5.5-14.3 µm) at a spectral sampling interval of about 0.2 cm-1. TANSO-CAI can detect
optically thick clouds within the TANSO-FTS IFOV. The column-averaged dry air mole fractions of carbon dioxide,
methane, and water vapor (XCO2, XCH4, and XH2O; hereafter called Xgas) have been retrieved globally from cloud-free
SWIR spectral data of TANSO-FTS. Xgas are simultaneously retrieved using a so-called full-physics retrieval method,and its accuracy and precision are evaluated by comparing with the Total Carbon Column Observing Network (TCCON)
data. TCCON data is also utilized for an empirical bias correction of Xgas.
As a successor to the GOSAT mission, GOSAT-2 was launched on October 29, 2018, and its operational observation wasstarted from February 2019. GOSAT-2 is also equipped with two instruments: TANSO-FTS-2 and TANSO-CAI-2.
TANSO-FTS-2 has three SWIR bands (0.75-0.77, 1.56-1.69, and 1.92-2.33 µm) and two TIR bands (5.5-8.4 and 8.4-14.3µm) at a spectral sampling interval of about 0.2 cm-1. The SWIR L2 retrieval algorithm for GOSAT-2 is being developed
based on the latest retrieval algorithm for GOSAT. Since the spectral range of TANSO-FTS-2 is expanded to cover the
carbon monoxide (CO) absorption band at 2.3 µm, XCO is also retrieved simultaneously with the other gases in the fullphysics retrieval method. In addition to the full-physics-based XCO2, XCH4, XH2O, and XCO products, we are planning
to provide the proxy-based XCH4 product as well as solar induced chlorophyll fluorescence (SIF) product.

Key words: GOSAT; GOSAT-2; TCCON

10PPDF-based method to account for atmospheric light scattering inspectroscopic observations of green-house gases from space: basic
principles, validation, and comparison with other algorisms.
S. Oshchepkov 1, R. Imasu 2, C. Iwasaki 2 A. Bril 3, Y. Yoshida 4.
1
2

Centre for Climate Research, Institute for Nature Management, Minsk, Belarus

Atmosphere and Ocean Research Institute, the University of Tokyo, Kashiwa-shi, Chiba, Japan
3
4

Institute of Physics, Minsk, Belarus

National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan

We present a method to detect and simultaneously retrieve optical path modification due toatmospheric light scattering in space‐based greenhouse gas spectroscopic sounding. This method,which was applied to the analysis of radiance spectra measured by the Greenhouse Gases Observing
Satellite (GOSAT), is based on the consideration of the statistical properties of the Path length
Probability Density Function (PPDF) and on retrieval of PPDF parameters mostly from radiancespectra in the oxygen A‐band of absorption at 0.76 mm. We show that these parameters can beeffectively used to characterize the impact of atmospheric light scattering on carbon dioxide retrieval
in the atmospheric carbon dioxide (CO2) absorption bands at 1.6 mm and 2.0 mm. The threshold for
PPDF parameters when filtering aerosol contaminated atmospheric scenes is set so that the optical‐
path modification is negligible, and these settings are recommended as a basic guideline for selecting
the clearest atmospheric scenarios. We apply use the measurements from TCCON sites for validation
study and compare PPDF-based method with other algorithms.
Keywords: green-house gases, atmospheric light scattering, validation

11

Errors in Retrieved Gases and Inferred Fluxes Arising fromNon-uniform Scene Illumination: A Case Study for
the GeoCarb Mission
Jeff Nivitanont1, Sean Crowell*1, Chris O'Dell2, Greg McGarragh2, Eric Burgh3, Denis O'Brien4,
and Berrien Moore1
1. University of Oklahoma, 2. CIRA/Colorado State University, 3. Lockheed Martin Advanced
Technology Center, 4. University of Melbourne
Previous work (e.g. Hu et al (2016), Landgraf et al (2016) for the TROPOMI mission) has shownthat inhomogeneous illumination within the field of view (FOV) leads to retrieval bias due todistortions in the actual instrument spectral response function (ISRF) relative to the ISRF that ismeasured in pre-flight calibration and characterization. Laboratory studies have shown that this
distortion grows with wavelength. Various techniques have been employed to ameliorate theseissues, including continuously scanning across the slit and temporal oversampling, and more
recently hardware solutions have been developed, referred to as "slit homogenizers".
In this work, we demonstrate the impacts of the scene brightness inhomogeneity on retrievals of
XCO2, XCH4 and XCO using the case study of the GeoCarb instrument, which measures in the
0.76um, 1.6um, 2.05um, and 2.3um spectral bands. We find that the posterior scatter and bias growlinearly with the brightness coefficient of variation within a footprint, and that this effect is not
significantly ameliorated through the use of a continuous scan approach to operations.
The introduction of a slit homogenizer dramatically reduces, though doesn't eliminate, the worst ofthese effects. Quality filtering removes the worst offenders, though it also reduces coverage in areas
such as the Amazon.
In addition to retrieved gases, we examine the effect of the degraded coverage and scatter on
inferred flux estimates, and demonstrate that as long as errors are pseudo-random on 50+ km lengthscales (as was seen in the TROPOMI papers), regional flux constraints are maintained over the
GeoCarb field of regard due to the daily revisit time.
XCO2

XCO2
4.0
80

3.5
3.0

60

2.5
2.0

40

1.5
1.0

20

0.5
0.0

2.5

2

4

6

80

2.0
60
1.5
40
1.0
20

0.5
0.0

0
0

100

8 10 12 14 16 18 20

0

2

4

6

8 10 12 14 16 18 20

Fraction passed (%)

100
Non-uniformity scatter (ppm)

Non-uniformity scatter (ppm)

4.5

3.0

0

Coefficient of variation (%)

Coefficient of variation (%)

Figure 1 The results of the scene inhomogeneity retrieval study, where scene albedo coefficient of variation is plotted against theposterior error standard deviations both before (black line) and after (blue line) a quality filter is applied. The red line denotes
the percentage of soundings that pass the quality filter. The left figure shows the results when a slit homogenizer is not employed,
while the right figure shows the results when the slit homogenizer is included.

12

Generation of merged Level 2 and Level 3 XCO2 data products fromSCIAMACHY/ENVISAT, GOSAT and OCO-2 for the Copernicus
Climate Change Service
Michael Buchwitz*1, Maximilian Reuter1, Oliver Schneising1, Stefan Noël1,
Heinrich Bovensmann1 and John P. Burrows1
1. Institute of Environmental Physics (IUP), University of Bremen FB1, Otto Hahn Allee 1,28334 Bremen, Germany
The generation of long-term merged multi-sensor multi-algorithm satellite-derived Essential Climate Variable (ECV)Greenhouse Gas (GHG) data sets started several years ago within the framework of the GHG-CCI project of ESA’s
Climate Change Initiative (CCI, http://www.esa-ghg-cci.org/). This activity is now continued operationally in theframework of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/). The latest C3S GHG satellite
data set is available from the Copernicus Climate Data Store (CDS, https://cds.climate.copernicus.eu/) and covers thetime period 2003-2017. This data set contains merged Level 2 and Level 3 data products for XCO2 and XCH4 fromSCIAMACHY/ENVISAT and GOSAT. For the next release, which will cover the time period 2003-2018, it is planned toalso include OCO-2 XCO2. The current status of this activity will be presented including a short description of the latest
version of the Ensemble Median Algorithm (EMMA) used for merging the individual Level 2 data products and for
quality assessment of the individual sensor and the merged products.

Key words: SCIAMACHY; GOSAT; OCO-2; C3S; TCCON

Latest version of the C3S merged XCO2 data product (2003-2017) extended with preliminary GOSAT near-real-time
retrievals (2018) as generated at Univ. Bremen for CAMS (https://atmosphere.copernicus.eu/):

13

Retrieval of aerosol optical properties using GOSAT/TANSO-CAI and
GOSAT2/TANSO-CAI2 measurements over the ocean
Chong Shi*1, Teruyuki Nakajima1, Makiko Hashimoto1, Hideaki Takenaka1
1. Japan Aerospace Exploration Agency (JAXA), JapanA flexible inversion algorithm is proposed for the retrieval of aerosol optical properties over the ocean. To account for
the effect of the oceanic substances on the retrieval, the forward radiation calculation is performed by a coupledatmosphere-ocean radiative transfer model combined with a comprehensive bio-optical ocean module. A combined
multi-wavelength and multi-pixel constraint approach is used to estimate aerosol and oceanic substances simultaneously.
This sort of simultaneous inversion helps to correct the biases induced by the neglect of water-leaving radiance in the
traditional aerosol retrieval algorithms, particularly over the turbid waters; in addition, it can provide more aerosol
information than the standard ocean color approach. To investigate the availability of current scheme, we conduct the
retrieval using the synthetic and real measurements from the GOSAT/TANSO-CAI (hereafter, CAI). Inter-comparisons
between CAI-retrieved and counterpart satellite products, i.e., MODIS, as well as the ground-based observed aerosol
data, i.e., AERONET, are also performed. Generally, good agreement between CAI-AOT of fine and coarse aerosols
and those of MODIS/AERONET are identified. There are also promising retrieval over the sun glint and high turbid
water using current scheme. Finally, a preliminary result of the retrieved AOT based on the GOSAT2/TANSO-CAI2 isshown over the global ocean.

Key words: GOSAT; GOSAT-2; Aerosol; Remote sensing

14

Efficient Multiscale Gaussian Process Regression for
Remote Sensing Data
Jouni Susiluoto*1,2, Alessio Spantini1, Heikki Haario2, and Youssef Marzouk1
1. Massachusetts Institute of Technology, USA
2. Lappeenranta University of Technology, Finland

Observing greenhouse gas concentrations from space provides a global view to processes on
Earth that has unique benefits compared to measurements made on the ground. The global
coverage comes, however, with the price of spatial and temporal irregularities when the
retrievals are sometimes spoiled by external factors such as dust particles or prevailingweather conditions. In some applications, if the satellite does not make a measurement
exactly when and where information of the quantity of interest is needed, modeling to extract
the distributions of this quantity of interest can be performed.
We tackle these modeling and uncertainty quantification problems by designing andimplementing a computationally effective multi-scale Gaussian Process algorithm, which isable to compute expected values and uncertainties with arbitrary spatio-temporal resolution
and also draw samples from the random process, conditioning on even enormous amounts ofdata. We utilize multi-scale kernels, which are able to resolve the spatial heterogeneity
present in the data.
We learn the covariance kernel parameters with adaptive Markov Chain Monte Carlo andpresent a way of prescribing the mean function of the Gaussian Process by calculating
marginals of a Markov Random Field, also computing the associated uncertainties. The
validity of the multi-scale approach is presented and its limits discussed. The results arepresented in an OCO-2 v9 context.
Key words: Gaussian Process, OCO-2, Uncertainty Quantification, MCMC

15

Detecting methane point sources from space using hyperspectral
surface imagers
Daniel H. Cusworth*1,3, Daniel J. Jacob1, Daniel Varon1, Christopher Chan Miller2, Xiong Lu2,
Kelly Chance2, Andrew Thorpe3, Riley Duren3, Charles Miller3, and Christian Frankenberg3,4
1. School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
2. Atomic and Molecular Physics Division, Harvard Smithsonian Center for Astrophysics,
Cambridge, MA, USA
3. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
4. Division of Geology and Planetary Sciences, California Institute of Technology, Pasadena, CA,USA
Several new surface imaging satellites are set to be launched in the near future. These imagers observe radiancesin the shortwave infrared (SWIR), which is a spectral region with strong methane absorption. Methane observing satellites
typically have much finer spectral resolution than surface imagers. For example, the TROPOMI full-width at halfmaximum (FWHM) SWIR resolution is 0.25 nm whereas the EnMAP instrument’s FWHM is 10 nm. However, surface
imagers have the benefit of much finer spatial resolution than methane satellites, with pixels on the scale of 30 m compared
to several kilometers. We test if the coarser spectral resolution of the soon to be launched instruments can identify methane
absorption features in an atmospheric retrieval. We simulate EnMAP-like scenes using the EnMAP End-to-End
Simulation Tool (EeteS) with WRF-LES generated methane plumes. We find that EnMAP should be able to capture
methane enhancements within a methane plume with about 5.5% error, depending on the underlying surface. Previous
work found that an instrument with 5% error should be capable of constraining plumes of 170 kg h-1, which would accountfor a major fraction of point source emissions. Reducing the spectral resolution to 5 nm and 1 nm in simulated retrievals
further reduces the error to 3.6% and 2.3%, respectively. We spectrally and spatially downsample AVIRIS-NG images
taken during the California Baseline Methane Survey to match EnMAP instrument specification and perform retrievals
over methane emitting facilities. We find that these EnMAP-like retrievals still detect methane hotspots. The results fromthis study show that satellite imagers could play a transformative role in detecting and quantifying global methane point
source emitters.

Key words: Hyperspectral, EnMAP, AVIRIS-NG, WRF-LES

16

Information Content Of Methane Flux Estimates From Joint AIRS
and GOSAT Lower-Troposphere Methane RetrievalsJohn Worden, Yi Yin, and Anthony Bloom
Evaluating surface fluxes of CH4 using total column data requires models to accurately account for the transport andchemistry of methane in the free-troposphere and stratosphere, thus reducing sensitivity to the underlying fluxes. Here
we demonstrate the potential for estimating lower tropospheric CH4 concentrations through the combination of freetropospheric methane measurements from the Atmospheric Infrared Sounder (AIRS) and XCH4 (dry-mole air fraction of
methane) from the Greenhouse Gases Observing Satellite Thermal And Near Infrared for Carbon Observations (GOSATTANSO, herein GOSAT for brevity).

These retrievals, currently provided on a 5x4 (lon/lat) grid, have an uncertainty

that ranges from 15 to 35 ppb but with peak sensitivity at 908 hPa and little sensitivity to the upper troposphere andstratosphere.

These lower-tropospheric estimates can be compared to the surface network, and these comparisons show

a similar error structure as the calculated uncertainties.

The accuracy degrades with latitude such that we only use theinitial version of these data for evaluating tropical methane fluxes. Using an analytical (Bayesian-based) flux estimation
approach, We find these lower-tropospheric retrievals are almost directly sensitive to methane fluxes in S. America, Africa,and Asia, with degrees-of-freedom-for signal (DOFS) of these methane fluxes ranging from 0.7 to 0.9. Theoretical
reduction ranges from 30 to 70% for tropical wetland fluxes.

17

error

Updates on AIRS CH4 and N2O Retrievals
Juying Warner*1, Zigang Wei1, Xiaozhen Xiong2 and others to be added2
1. University of Maryland, Dept. of Atmospheric and Oceanic Science, Maryland, U.S.A.
2. NOAA/NESDIS/STAR, U.S.A.
The concentrations of methane (CH4) and nitrous oxide (N2O) in the atmosphere have been gradually increasing and may
continue, which plays a significant role in climate change. CH4 and N2O are two of major greenhouse gases besides
carbon dioxide (CO2). This study will ultimately make use of the high-quality global measurements of N2O and CH4 in
the thermal infrared domain of the TANSO-FTS-2 instrument to inter-calibrate with other similar hyperspectral spaceborne observations (e.g., GOSAT CH4, AIRS/CrIS CH4 and N2O, IASI N2O, and HIRAS CH4), to establish longterm
climate records. The concentrations derived from different space sensors may differ significantly due to sensor andalgorithm choices, and therefore, knowing these differences will improve our understanding of the variabilities in CH4
and N2O. We will present the updates of the retrievals and data distributions of CH4 and N2O from AIRS and CrIS sensors
at the IWGGMS-15. We will also discuss the short-term trends of CH4 and N2O starting in 2002 until present, as well ascomparisons with in situ ground and aircraft measurements.

Key words: AIRS; CH4; N2O

18

Detection of XCO2 anomaly changes in space and time at a global

Shaoqing Zhang12, Liping Lei*1, Zhonghua He12, Mengya Sheng*12, Shaoyuan Yang12, HuiZhong12, Zhaocheng Zeng3 and Bing Zhang12
1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, China
2. College of Resources and Environment, University of Chinese Academy of Sciences, China
3. Division of Geological and Planetary Sciences, California Institute of Technology, USA
It has been known that the anomaly change of atmospheric carbon dioxide (CO2) concentration is closely related
to the extreme climate. Detecting where and when the anomaly change of CO2 exactly occur can help us better tounderstand how the change of atmospheric CO2 concentration respond to the regional release and uptake of CO2
in the terrestrial biosphere which could be significantly impacted by the extreme climate. Zhonghua He et al.
propose a method for detecting the abnormal changes of atmospheric CO2 concentration by using mapping
GOSAT XCO2 data. This method makes use of the residual error value after fitting seasonal variation of yearly
detrended XCO2 for anomaly detection (hereafter referred to RE method). However this RE should be evaluatedas the fitting approach of seasonal variation is also applied in mapping XCO2 data. On the other hand, R. G.
Detmers et al. applied the monthly averaged XCO2 after detrended yearly (hereafter referred to as MA method) toanalyze the abnormal changes of atmospheric CO2 concentration.
We compare this two method using mapping GOSAT-XCO2 from Zhonghua He’s paper. As a result, we find thatthe anomaly change of XCO2 detected by two method generally show consistent anomaly period (see Fig.1)
where both of methods detected abnormal units that occurred in 2010 and 2015 when is same as the period of
ENSO event. MA method，however, did not detect the anomaly change in 2013 in North America (Num 9 in Fig.1(a)) which is detected by RE method. It is likely because the anomaly change in 2013 is a short-time while thedetecting time unit is month in MA method. The spatial distribution of detected anomaly change show somedifferences between two methods (see Fig.2). MA method detected the anomalies in North America (Num 9 inFig.2 (b)), Arabia (Num 7 in Fig.2 (b)) and India (Num 5 in Fig.2 (b)) around 2010 that RE method did not display
because these areas in RE method were not ranked in the top ten. Moreover, the spatial distribution range ofanomalies detected in the Eurasian continent (Num 1 in Fig.2) and North America (Num 6 in Fig.2) is smaller by
MA method than by RE method. In the next step, we will use auxiliary data for further comparative analysis andverification for the results of two methods.

Key words: XCO2, GOSAT, anomaly change, monthly mean of XCO2, fitting residuals for XCO2

19

（a）RE method
Figure.1（b）MA method

The time of anomaly changes of XCO2 detected by two methods using mapping GOSAT XCO2 data

respectively.

（a）RE method
Figure.2（b）MA method

The spatial distribution of anomaly changes of XCO2 detected by two methods using mapping GOSAT

XCO2 data respectively.

20

A spectral-sorting approach for constraining coastal aerosol profile
using OCO-2 O2A measurements
Zhao C. Zeng1, Vijay Natraj2, Feng Xu2, and Yuk Yung*1(Correspondences: zcz@gps.caltech.edu or yly@gps.caltech.edu)
1. California Institute of Technology, USA
2. Jet Propulsion Lab, California Institute of Technology, USA
Aerosols scattering effect is one of the largest sources of uncertainty in greenhouse gas retrievals from space by
influencing the path of atmospheric radiation. Especially, the vertical distribution of aerosols has a substantial impact on
the radiative transfer from aerosol scattering when trying to reduce the retrieval uncertainty. Satellite and ground-based
measurements have enabled accurate and continuous monitoring of total aerosol loading. However, these measurements
provide little or no information on the vertical distribution of aerosols. It has long been recognized that passive remotesensing using absorption spectroscopy of molecular oxygen has the potential for aerosol vertical profiling. Absorption in
the center of strong O2 lines is saturated, such that any radiance measured in these regions must originate from scatteringin the upper part of the atmosphere. In weak lines light can penetrate to lower atmospheric layers, allowing for thequantification of aerosols and other scatterers near the surface.
In this study, we applied a developed spectral-sorting algorithm (Zeng et al., 2018, GRL) to retrieve the verticalstructure of aerosols in the coastal regions using hyperspectral O2A measurements from OCO-2. The algorithm is applied
to infer the total aerosol optical depth (AOD) and the aerosol layer height (ALH) of the pollutant plumes over the coastal
regions of Sahara Desert and Northern China. The spectral sorting technique provides two advantages over a conventional
fitting scheme: (1) information related to aerosol loading and its vertical structure can be extracted in a straightforwardmanner from the observed radiance; (2) the spectral region(s) with the largest sensitivity to arbitrary geophysical retrieval
parameters (total AOD and ALH in this study) can be identified. The proposed retrieval algorithm to constrain aerosol
vertical distribution will potentially help quantify the aerosol direct radiative forcing and reduce bias in greenhouse gas
retrievals from space due to uncertainty caused by aerosol scattering.

Key words: OCO-2 ; O2A; Aerosols

21First validation results of the Sentinel-5P methane using global
TCCON and NDACC-IRWG data
Mahesh Kumar Sha*1, Bavo Langerock1, Martine De Mazière1, Dietrich G. Feist2, Ralf Sussmann3,Frank Hase4, Matthias Schneider4, Thomas Blumenstock4, Justus Notholt5, Thorsten Warneke5,
Rigel Kivi6, Yao Té7, Paul O. Wennberg8, Debra Wunch9, Laura Iraci10, Kimberly Strong9, David
W. T. Griffith11, Nicholas M. Deutscher11, Voltaire Velazco11, Isamu Morino12, Hirofumi Ohyama12,
Osamu Uchino12, Kei Shiomi13, Tae-Young Goo14, David F. Pollard15, Alba Lorente16, Haili Hu16,
Tobias Borsdorff16, Otto Hasekamp16, Jochen Landgraf16, Coleen Roehl8, Matthäus Kiel8, Geoffrey
Toon17 and TCCON and NDACC-IRWG team
1. Royal Belgian Institute for Space Aeronomy, Belgium
2. German Aerospace Center (DLR), Germany
3. Karlsruhe Institute of Technology, IMK-IFU, Germany
4. Karlsruhe Institute of Technology, IMK-ASF, Germany
5. University of Bremen, Germany
6. Finnish Meteorological Institute, Finland
7. LERMA-IPSL, Sorbonne Université, CNRS, PSL Research University, Paris
8. California Institute of Technology, USA
9. University of Toronto, Canada
10. NASA Ames Research Center, USA
11. University of Wollongong, Australia
12. National Institute for Environmental Studies, Japan
13. Japan Aerospace Exploration Agency, Japan
14. National Institute of Meteorological Research, Republic of Korea
15. National Institute of Water & Atmospheric Research Ltd, New Zealand
16. SRON Netherlands Institute for Space Research, Netherlands
17. NASA Jet Propulsion Laboratory, USA
The Sentinel-5 Precursor (S-5P) mission with the Tropospheric Monitoring Instrument (TROPOMI) onboard waslaunched on 13 October 2017. The S-5P provides daily global coverage of methane (CH4) with high spatial resolution of
7 x 7 km2. The S-5P CH4 standard and bias-corrected products are available over land surface. The ongoing
TCCON4S5P project led by the Royal Belgian Institute for Space Aeronomy (BIRA-IASB) focuses on the validation of
the S-5P CH4 and carbon monoxide (CO) products using the ground-based Total Carbon Column Observing Network(TCCON) data from the whole network, which includes currently about 26 globally distributed stations. The earlyvalidation of the S-5P CH4 data using the standard and rapid delivery TCCON and the Infrared Working Group of the
Network for the Detection of Atmospheric Composition Change (NDACC-IRWG) data was performed. The

22

NDACC-IRWG validation work is done as part of the S-5P mission performance centre (MPC). The validation results
contributed significantly to the quantification of the S-5P CH4 bias and precision (~0.3% bias and ~0.6% precision forthe bias corrected CH4 product), thereby leading to an official release of the S-5P CH4 data being recommended to theEuropean Space Agency (ESA).
The first validation results will be presented focusing on the identification and quantification of the bias and theprecision of S-5P CH4 products.

Keywords: methane-validation; S-5P; TROPOMI; TCCON; NDACC-IRWG

23

Evaluation of greenhouse gas satellite observations
at high Northern latitudes
Hannakaisa Lindqvist*1, Rigel Kivi2, Ella Kivimäki1, Tomi Karppinen2, Pauli Heikkinen2, Lena
Schreiner1, Janne Hakkarainen1, Iolanda Ialongo1, Otto Lamminpää1, Antti Mikkonen1, and JohannaTamminen1
1. Finnish Meteorological Institute, Helsinki, Finland
2. Finnish Meteorological Institute, Sodankylä, Finland

Satellite observations have tremendously increased the coverage of greenhouse gas measurements in the remote Arctic
and boreal regions. Monitoring these rapidly changing regions is vital for quantifying their contributions to the carbon
cycle. However, high Northern latitudes pose various challenges to the space-based retrievals of greenhouse gases,
including large solar zenith angles, frequent cloud coverage, and a seasonally snow-covered ground. These factors affectthe passive space-based observations and potentially the quality of the retrievals through biases in the retrieved products.
To help quantify these biases, we present results for high-latitude evaluation of XCO2 and XCH4 retrievals from theGreenhouse Gases Observing Satellite (GOSAT), the Orbiting Carbon Observatory -2 (OCO-2) and Sentinel 5 Precursor
TROPOMI (S5P-TROPOMI). We will primarily address comparisons against the high-resolution Fourier Transform
Spectrometer at the Sodankylä TCCON site operated by the Finnish Meteorological Institute (FMI). In addition to singlesounding differences, we evaluate also seasonally varying biases and quantify the seasonal cycle. The prior atmosphericprofiles used by the retrievals are evaluated against AirCore profile measurements. We will also introduce novel
measurements at Sodankylä that complement the existing measurements. In the coming years, FMI is investing a lot for
expanding the cryospheric, biospheric and atmospheric observations in Sodankylä to study the carbon and water cycles
at high Northern latitudes and thus provide valuable information also for GHG satellite validation activities. Finally, weplan to evaluate the satellite retrievals against model results in Northern Finland but also throughout high Northernlatitudes.

Keywords: CO2, CH4, Arctic, Boreal, GOSAT, OCO-2, TROPOMI, AirCore, TCCON, CTE-CH4, CAMS24

GOSAT observations of tropospheric CO2 and CH4 concentrations
over permafrost regions and comparison with in situ measurements of
the ASCENDS/ABoVE 2017 airborne science campaign
Nobuhiro Kikuchi*1, Akihiko Kuze1, Fumie Kataoka2, Kei Shiomi1, Makiko Hashimoto1, Hiroshi
Suto1, Joshua P. DiGangi3, Stephan R. Kawa4, and Jianping Mao4
1. Japan Aerospace Exploration Agency, Japan
2. Remote Sensing Technology Center of Japan, Japan
3. NASA Langley Research Center, USA
4. NASA Goddard Space Flight Center, USA
TANSO-FTS onboard GOSAT has spectral windows in both SWIR and TIR, which enable us to simultaneously observe
the Earth’s surface reflected sunlight and the thermal emission at the exact same footprint. SWIR spectra have informationon the total column amount of CO2 and CH4, while TIR spectra are sensitive to these gases mainly in the upper troposphere.
Combining these two windows, GOSAT is capable of retrieving concentrations of CO2 and CH4 in the lower troposphere,
which helps the surface flux estimate of these greenhouse gases.

It is widely accepted that a large amount of organic carbon is stored in permafrost, which can be released as a result ofwarming in high-latitude regions (Schuur et al., 2015, Nature). This additional emission of greenhouse gases may
accelerate global warming. Therefore, monitoring of the greenhouse gas emissions in permafrost regions is one of theimportant roles of satellite observations. In this study, we applied our JAXA/EORC experimental retrieval algorithm toderive CO2 and CH4 concentrations in the troposphere over the permafrost regions of North America, using both GOSATSWIR and TIR spectra. In our retrieval algorithm, the entire atmosphere is divided into 5 vertical layers, 2 of which areassigned to the lower and upper troposphere.

To validate our retrieval algorithm, we compared CO2 and CH4 concentrations in the troposphere to the in situ
measurements of the ASCENDS/ABoVE airborne science campaign conducted in the summer of 2017 in Alaska. There
are a total of 47 vertical profiles of CO2 and CH4 concentrations from the campaign, some of which can be directlycompared with co-located GOSAT observations. We found that the CO2 concentrations of the co-located GOSAT
observations were comparable to these in situ measurements. The co-located CH4 concentrations observed by GOSAT
seem to be a little underestimated, though monthly mean concentrations in North America is reasonable.

Key words: GOSAT; retrieval algorithm; validation, ASCENDS, ABoVE

25

Real or spurious? An examination of the OCO-2 version 9 XCO2
data set, and curious features therein
Christopher O'Dell 1, Annmarie Eldering 2, Aronne Merrelli 3, Emily Bell 1, David Crisp 2, Kenneth
Davis 4, Brendan Fisher 2, Matt Kiel 5, Thomas Lauvaux 6, Robert Nelson 2, Maximilian Reuter 7,
Paul Wennberg 5
1 CIRA, Colorado State University
2 NASA Jet Propulsion Laboratory
3 SSEC, University of Wisconsin-Madison
4 Penn State University
5 California Institute of Technology
6 Laboratoire des Sciences du Climat et l'Environnement
7 Institute for Environmental Physics, University of Bremen
It has long been recognized that space-based measurements of the column-mean dry air mole fraction of carbon dioxide(XCO2), if made with sufficient accuracy and precision, offer a potential goldmine of information on CO2 surface fluxes
at both small and large spatial scales.

However, possible biases in satellite XCO2 will create spurious fluxes and are alimiting factor in utilizing these data.

The current OCO-2 XCO2 data set, version 9, generally agrees well with truth

metrics, such as model data and collocated measurements from the TCCON network.

After post-retrieval filtering andbias correction, there are no obvious correlations of the XCO2 error versus any retrieved or auxiliary variable that is
globally consistent across either all land or all ocean measurements.

However, the bias correction is ad-hoc and largely

empirical, and is of the same order of magnitude as the change from the prior induced by the ACOS retrieval algorithm
itself.

This calls into question whether unexpected features in the data are real or spurious, in the absence of independentvalidation data.

In this presentation, we give an overview of the V9 XCO2 data set and its general features when compared against bothmodels and TCCON validation measurements.

We then explore some individual features found in the data at both small

and large scales, including an apparently false CO2 plume in the middle of the Arabian desert, apparent “XCO2 waves”
from an orbit track in northern Virginia, USA, and apparent large-scale biases with respect to models over the oceans.
These features are typically between 1 and several ppm. We will discuss how to create tests to determine if these features
are likely spurious or not, and show the results of these tests as applied to these particular features.

We will conclude

with a general discussion on how to use this knowledge to improve the ACOS algorithm in order to minimize spurious
data features at all scales.

26

A first step toward the validation of the Merlin satellite mission:
Magic campaigns and spatiotemporal variability of methane.
Caroline Bès1 Cyril Crevoisier2, François Danis2, Morgan Lopez3, Michel Ramonet3, Pascal
Jeseck4, Yao Té4, Lilian Joly5, Hervé Herbin6, Aurélien Bourdon7, Jean-Claude Rubio1, Jordi
Chinaud1, Denis Jouglet1, Véronique Tyrou1, Andreas Fix8, Bruno Millet1, Gerhard Ehret8, Philippe
Bousquet3
1. Centre National d’Etudes Spatiales, 31401 Toulouse, France
2. Laboratoire de Météorologie Dynamique, LMD/IPSL, CNRS, Ecole polytechnique, 91128 Palaiseau, France
3. Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université́ Paris
Saclay, 91191 Gif sur Yvette, France
4. LERMA-IPSL,Sorbonne Université, Observatoire de Paris, Université́ PSL, CNRS, 75005 Paris, France
5. Groupe de Spectrométrie Moléculaire et Atmosphérique
GSMA, CNRS, Université de Reims-Champagne Ardenne, 51687 Reims, France
6. Laboratoire d’Optique de Lille, LOA, Université de Lille, Villeneuve d’Ascq, France
7. Service des avions français instrumentés pour la recherche en environnement, SAFIRE, Météo-France/CNRS/CNES,
31270 Cugnaux, France
8. German Aerospace Center, Institute of Atmospheric Physics (DLR-IPA), Oberpfaffenhofen-Wessling

The MEthane Remote sensing Lidar missioN (MERLIN) will measure concentrations of atmospheric methane with ahigh accuracy, in order to quantify and locate emission sources and to improve surface CH4 flux estimates. MERLIN, set
up by CNES (French Space Agency) and DLR (German Space Agency) is scheduled to be launched in 2024.
MERLIN IPDA lidar measurements will enable to estimate the methane dry-air mixing ratio columns (XCH4). This mainlevel 2 product is expected to be provided to end users with a very low error: 1% relative random error and 0.2% relative
systematic error for a typical value of 1780 ppb. The Science Products Expertise (SPEX) entity is responsible for thecalibration, validation and quality control along the duration of the mission, in order to verify these binding requirements.
Long term and specific calibration and validation activities have to be carefully anticipated and planned to supply the
SPEX entity.
The first step of the validation activities is carried out through the MAGIC (Monitoring of Atmospheric composition andGreenhouse gases through multi-Instruments Campaign) campaigns. One of its goals is to evaluate the merits of several
instruments and their complementarity for comparison with Merlin XCH4 columns. MAGIC means will also be used forthe validation of XCO2 measured by the MicroCarb CNES mission.
The first issue of MAGIC was set up in January and May 2018. It involves several laboratories, several instruments which
perform surface, in-situ and remote sensing total column simultaneous measurements, hold by different platforms: aircraft,balloons, tower or ground.
An important issue for satellite mission validation is also the difficulty to perform simultaneous satellite and on-Earthmeasurements for comparison. CH4 spatiotemporal variability has been investigated through CH4 CAMS profiles data
analysis, in order to evaluate the impact of the imperfect colocalisations of measurements made by different instruments.

27

Ongoing Efforts to Develop Top-Down Atmospheric Flux
Inventories for CO2 and CH4David Crisp* (Jet Propulsion Laboratory, California Institute of Technology)
for the CEOS AC-VC Greenhouse Gas Team
Keywords: Remote Sensing, Greenhouse Gases, CO2, CH4, Fluxes
Advances in remote sensing instruments and analysis techniques are beginning to yield substantial
improvements in the precision, accuracy, resolution and coverage of space-based estimates ofatmospheric CO2 and CH4. Preliminary efforts are under way to integrate XCO2 and XCH4 estimatesfrom GOSAT and OCO-2 with measurements from ground-based and airborne instruments andassimilate these data into atmospheric inverse models to estimate CO2 and CH4 fluxes.
To coordinate ongoing efforts across the growing fleet of space-based greenhouse gas monitoringsatellites, the Committee on Earth Observation Satellites (CEOS) has endorsed an ambitious plan topromote the development of a prototype atmospheric flux product. The long-term objectives of thisproduct are to: (i) reduce uncertainty of national emission inventory reporting, (ii) identify additionalemission reduction opportunities and provide nations with timely and quantified guidance on progress
towards their emission reduction strategies, and (iii) track changes in the natural carbon cycle causedby human activities and climate change. CEOS has started to work with stakeholders in the policy and
GHG inventory community to refine these requirements. The initial effort will target the delivery of a
prototype global atmospheric CO2 and CH4 flux product by 2021, so that it is available in time to support
the 2023 global stocktake mandated by the Paris Agreement. This product may not meet the above
objectives in the developed world, but should provide useful insights into CO2 and CH4 emissions in
the rapidly developing world, where the bottom-up inventories are less mature. The lessons learned
from this prototype development effort are expected to play a key role in the plans for future, operational
constellations of space-based CO2 and CH4 sensors that will be deployed before the 2028 global
stocktake. This presentation summarizes these objectives and describes the rapidly evolving
implementation approach and near-term plans.

28

CO2 inverse modeling with satellite XCO2 retrievals, ground-based
observations and a high-resolution tracer transport.
Shamil Maksyutov*1, Rajesh Janardanan1, Tomohiro Oda2, Makoto Saito1, Yukio Yoshida1, Vinu
Valsala3, Johannes W. Kaiser4, Annmarie Eldering5, David Crisp5, Tsuneo Matsunaga1
1. National Institute for Environmental Studies, Japan,
2. USRA/NASA GSFC, USA
3. Indian Institute of Tropical Meteorology, India,
4. Deutscher Wetterdienst, Germany,
5. Jet Propulsion Laboratory, California Institute of Technology, USA
We apply a CO2 flux inversion system based on the high-resolution Lagrangian-Eulerian coupled tracer transport model
to estimate surface fluxes from atmospheric CO2 data observed at surface stations and XCO2 by NASA’s OCO-2 satellite
and GOSAT. We use the Lagrangian particle dispersion model (LPDM) FLEXPART to estimate surface flux footprints at
a 0.1-degree resolution for the OCO-2 observations (version V9r, over land) aggregated into 1 second averages and
GOSAT (v02.72) single shot data. The LPDM is coupled to a global atmospheric transport model (NIES-TM). The adjointof the coupled model is used in an iterative optimization procedure to derive surface flux corrections for the terrestrial
biosphere and oceans. High-resolution prior fluxes were prepared for anthropogenic emissions (ODIAC), biomassburning (GFAS), and the terrestrial biosphere (VISIT model). The prior flux uncertainty is based on monthly mean
MODIS GPP for land, and flux variability by the OTTM (Ocean Tracer Transport model) for ocean. The high-resolution
transport improves simulations of the anthropogenic plumes observed at continental continuous observation sites. Before
including satellite observations in the inversion, the monthly varying, latitude-dependent satellite data bias is estimatedby comparing satellite observations with the XCO2 with surface fluxes optimized using data from surface stations for theyear 2015. Monthly mean differences between the model and OCO-2 XCO2 estimates in 5-degree latitudinal bands are
below 1 ppm between 30° South and 50° North.

The bias correction of satellite data improves consistency between theflux estimates based on ground-based and satellite observations. Inverse modeling reduces the monthly mean mismatch
between OCO-2 and model to below 0.5 ppm for each latitudinal band, and to below 0.2 ppm in mid latitudes. Low biasof the model simulation made with optimized natural fluxes ensures it can be used as a reliable background for studying
the anthropogenic emissions with OCO-2 and GOSAT data.

Key words: OCO-2; GOSAT; inverse modeling; carbon cycle

29Constructing a carbon flux estimation system with bias correctedsatellite data
Takashi Maki*1, Tsuyoshi Thomas Sekiyama1, Keiihi Kondo1 and Takashi Nakamura2
1. Meteorological Researhc Institute (MRI), Japan
2. Japan Meteorological Agency (JMA), Japan
In recent years, many greenhouse gas observation satellites have been launched and operated (GOSAT, OCO2, TanSat, GOSAT-2, etc.). The satellite observation has advantages such as its wide observable area andspatial representation close to the model horizontal resolution. On the other hand, satellite observation has acritical issue of bias. This bias varies spatiotemporally. We need to properly evaluate and correct this bias incarbon cycle analysis. Many researchers have attempted to verify the bias of satellite observation data by direct
observations. However, ground observation sites are limited and it is insufficient to evaluate the bias of satellite
observation with a vast observable area in detail. We constructed a method to evaluate bias of satellite
observation data with independent inverse analysis data (JMA CO2 distributions) without using satellite
observation data. As we can obtain long-term observation data from 2009 to 2017 from GOSAT, we calculated
average bias data of satellite observation data by averaging differences from monthly satellite observation dataand independent analysis values in order to extract signal and remove noise. The global average bias of GOSAT
observation data (NIES SWIR L2 Ver. 2.8) throughout the whole period was -1.17 ppm (at 2.8 degrees grid)and -1.26ppm (at 5 degrees grid), almost consistent with verification results by ground observation. Looking
at the geographical distribution, the bias of the GOSAT observation data showed relatively large seasonal
fluctuation at the land area. By using this satellite observation data after this bias correction method for inverse
analysis, we can obtain CO2 flux analysis consistent with the existing inverse analysis. We have a plan to show
inverse model results using in-situ and bias corrected satellite data.
Keywords: GOSAT, Bias correction, Inverse modeling, carbon flux estimation

30

Global and regional methane budgets derived from GOSAT retrievals
and ground-based observations using CTE-CH4 atmospheric inversemodel
Aki Tsuruta*1, Janne Hakkarainen1, Leif Backman1, Yukio Yoshida2, Dietrich G.
Feist4,3, Toshinobu Machida2, Jost V. Lavric3, Ed Dlugokencky5, Juha Hatakka1,
Petri Keronen6, Elena Kozlova7, Paul Krummel8, Tuomas Laurila1, Zoe Loh8, Nikos
Mihalopoulos9, Simon O’Doherty9, Michel Ramonet11, Yasunori Tohjima3, Ray
Wang12, Doug Worthy13,and Tuula Aalto1
1. Finnish Meteorological Institute, Finland
2. National Institute for Environmental Studies (NIES), Japan
3. Max Planck Institute for Biogeochemistry, Department Biogeochemical Systems, Germany
4. Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre,
Germany
5. National Oceanic and Atmospheric Administration, Global Monitoring Division, USA
6. University of Helsinki, Institute for Atmospheric and Earth System Research / Physics, Finland
7. University of Exeter, College of Life and Environmental Sciences, UK
8. CSIRO Oceans and Atmosphere, Australia
9. Department of Chemistry, University of Crete, Greece
10. University of Bristol, School of Chemistry, UK
11. Laboratoire des Sciences du Climat et de l'Environnement, France
12. Georgia Institute of Technology, School of Earth and Atmospheric Sciences, Georgia
13. Environment and Climate Change Canada, Canada
Atmospheric methane (CH4) has in recent years increased as rapidly as at the end of the 20th century. The cause of therapid increase and its interannual variability are still under discussion due to lack of flux information and modellingcomplexity. Global CH4 budgets are fairly well understood, but the regional estimates still vary between models. In
order to obtain further understanding especially on the regional budgets, we present global and regional CH 4 emission
estimates from CarbonTracker Europe-CH4 (CTE-CH4) atmospheric inverse model that assimilates 1) the global
network of ground-based surface atmospheric CH 4 observations, and 2) column averaged dry-air mole fractions of CH 4
(XCH4) retrieved from GOSAT TANSO-FTS. Emissions from anthropogenic and natural (wetlands and other soils,
biofuel and biomass burning, termites, ocean and geological) sources are taken into account, and among those,31

emissions from anthropogenic and biospheric (wetlands and small sink to soils) sources are optimized simultaneously
based on the ensemble Kalman filter. In the GOSAT inversion, XCH 4 zonal mean differences at 5° latitudinal bands
between the GOSAT retrievals and 3D atmospheric CH 4 fields from an inversion assimilating surface observations were
removed from GOSAT XCH4 values before the inversion. The two inversions estimate similar global total CH 4
emissions for 2010-2017 (540-545 Tg CH 4 yr-1), with increasing trends in emissions during 2004-2008 and 2013-2016.
The seasonal cycle of the emission estimates were different in the two inversions in the Southern Hemisphere
extratropics, and the summer emissions in Northern Hemisphere temperate regions were greater in the GOSAT
inversion. The latitudinal gradient of the emissions were evaluated using non-assimilated observations from HIPPO
aircraft campaigns and TCCON, which showed a positive bias in the Northern Hemisphere. In addition, the seasonal
cycle and trend of the emissions, especially for the Tropics were evaluated using non-assimilated observations from
CONTRAIL and ATTO observatory.

Key words: GOSAT; CH4; atmospheric inversion

32

Constraining carbon fluxes in northern regions by combining
constraints from multiple atmospheric CO2 observing systems
Brendan Byrne*1, Junjie, Liu1, Meemong Lee1, Ian Baker2, Kevin W. Bowman1, John S. Kimball3,
Charles E. Miller1, Nicholas C. Parazoo1
1

Jet Propulsion Laboratory, California Institute of Technology, CA, USA
2Atmospheric Science Department, Colorado State University, Fort Collins, CO, USA
3
Numerical Terradynamic Simulation Group, W.A. Franke College of Forestry & Conservation,
The University of Montana, Missoula, MT 59812, USA
Northern latitudes (> 50°N) are experiencing the most rapid warming on Earth, resulting in major ecological change andpermafrost thaw. Yet many fundamental questions about the northern biosphere remain unanswered due to poor
observational coverage of these northern regions. To fill in observational gaps, we combine atmospheric CO2
measurements from the network of surface sites, GOSAT and TCCON in a single flux inversion analysis. We perform a
six-year flux inversion, producing data-constrained net ecosystem exchange of CO2 (NEE) in northern latitudes over
2010-2015. We further combine satellite observations of solar induced fluorescence (SIF) with NEE to disentangle fluxesof gross primary productivity (GPP) and ecosystem respiration (Re) for improved process attribution. These dataconstrained estimates of GPP and Re are then applied to evaluate terrestrial biosphere model estimates of the seasonal
cycle and interannual variability of NEE over northern ecosystems, giving insights into model deficiencies.

Key words: Flux inversion; GOSAT; TCCON; SIF

33

Resolving the information in large-scale inversions: application to
CMS-Flux
K. Bowman*1, N. Bousserez2, J. Liu1, M. Lee1, D. Henze3
1. Jet Propulsion Laboratory, California Institute of Technology, USA
2. European Centre for Medium Range Weather Forecasting, Reading, UK 3. Department of
Mechanical Engineering, University of Colorado, Boulder, USA
Large-scale 4D variational (4D-var) assimilation techniques, which can efficiently incorporate large datasets (e.g.
satellites), have migrated from numerical weather prediction to new areas in the Earth System including atmosphericchemistry and the carbon cycle. Of primary importance in these new applications is the inference of boundary conditions(e.g. fluxes), which need to be characterized in terms of uncertainty, resolution, and information content. However, these
metrics have been difficult to compute because they implicitly require large matrices. However, recent advances instochastic matrix decomposition and optimal dimension reduction techniques enable a new class of inversion methods
that can resolve independent spatio-temporal patterns of geophysical states. We show that these patterns are a function of
the observing system sampling and precision, the variability of the geophysical state itself, and the sensitivity of thosestates to observations. We then introduce the Fast Randomized Optimal approach for Diagnostic and Optimization(FRODO) algorithm that can practically compute these patterns along with critical information diagnostics, including the
averaging kernel and the degrees of freedom for signal. We integrate FRODO into the NASA Carbon Monitoring Flux
(CMS-Flux) carbon cycle data assimilation system to estimate optimal patterns of global CO2 fluxes and computeassociated diagnostics constrained by the NASA Orbital Carbon Observatory (OCO-2) sampling for the year 2015. Using
an Observing System Simulation Experiment (OSSE), we show how the spatial resolution of the flux estimate can vary
substantially even within a continent based upon the intersection of transport patterns and observations. The potential of
FRODO to quantify the impact of an emerging virtual constellation of carbon measurements to advance both policy andscientific objectives is explored.

34

Exploring Constraints on a Wetland Methane Emission Ensemble
with GOSAT
R. J. Parker*1,2,3, H. Boesch1,2,3, C. Wilson3,4, A. Bloom5, E. Comyn-Platt6, J. McNorton7,
M. Chipperfield3,41) Earth Observation Science, Department of Physics and Astronomy, University of Leicester,
Leicester, UK
2) Leicester Institute for Space and Earth Observation, University of Leicester, Leicester, UK
3) NERC National Centre for Earth Observation, UK
4) School of Earth and Environment, University of Leeds, UK5) Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
6) Centre for Ecology and Hydrology, Wallingford, UK
7) European Centre For Medium Range Weather Forecasts, Reading, UK
Methane (CH4) is an important greenhouse gas and contributes significantly to the increase in radiative forcing responsible
for the changing climate. Despite its importance in understanding the current and future Earth system, the past behaviourof atmospheric CH4 and crucially, its response in future climate scenarios is still poorly understood.

Natural wetlands are the largest contributors to global CH4 emissions but there remains a crucial lack of understandingon what drives the spatial and temporal variability of these emissions due to complex interactions between thehydrological and carbon cycles.

In this study we use an ensemble of CH4 emission data from the WetCHARTs dataset which derives emissions from a
combination of different wetland extent scenarios and temperature dependencies. This data is input into a global chemistrytransport model to produce total column amounts which can be directly compared to GOSAT CH4 observations.

Through comparison of these different ensemble members over different wetland regions, we are able to assess whichconstraints on wetland extent and temperature dependency provide the best agreement to observations and determine
which processes will be key in driving wetland CH4 emissions in the future climate.

35Difference of detecting anthropogenic CO2 emission by GOSAT and
OCO-2 observations in China
Mengya Sheng*12, Liping Lei*1, Shaoyuan Yang12, Zhonghua He12, Hui Zhong12, Shaoqing
Zhang12, Zhaocheng Zeng3 and Bing Zhang12
1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, China
2. College of Resources and Environment, University of Chinese Academy of Sciences, China
3. Division of Geological and Planetary Sciences, California Institute of Technology, USA
GOSAT takes measurements of reflected sunlight from Earth’s surface with a circular footprint of approximately
10.5km diameter while OCO-2 observes with a parallelogram footprint of 1.29km×2.25km. The column-averaged CO2
dry air mole fraction (XCO 2) retrieved from the two satellites observation could be different due to different sensors,viewing geometries, and repeat cycles for regional analysis of CO2. In this studying, we investigate their performancefor detecting anthropogenic emissions.
We collected GOSAT and OCO-2 XCO2 datasets (ACOS v7.3, OCO-2 v9) with consistent retrieval algorithms. To
evaluate the response differences between satellites to anthropogenic CO2 emissions, we select an emission region(Beijing-Tianjin-Hebei, high-density urban area) and a background region (western area of Inner Mongolia, sparsevegetation and low-population area) as contrasting area. The difference of XCO2 (∆XCO2) between emission area andbackground area are calculated as the contrast of emission to background during overlapping period of two satellites
observation, from September 2014 to May 2016.
The results show that the differences of monthly averaged XCO 2 between GOSAT and OCO-2 change from -1.62 ppm
to 1.20 ppm in the emission region, from -2.16 ppm to 1.30 ppm in the background region and the difference inemission region is lightly less than that in the background region. Both of maximum difference is in summer for
GOSAT and OCO-2.
When comparing monthly ∆XCO2 of GOSAT and OCO-2, the trends are approximately similar. Their differences are
generally -1ppm to 1ppm excluding 2.16 ppm in August 2015 and -1.28 ppm in December 2015, which are mainly
induced by the difference in the location where industrial heat sources and power plants are dense cluster and theamount of satellite observations, especially OCO-2 observations.
Our results indicate that regional CO2 anthropogenic emission detected by contrast between emission region andbackground region with OCO-2 and GOSAT should consider about the distribution of satellite observations.
Key words: GOSAT; OCO-2; XCO2; anthropogenic CO2 emission

36

(a)

(b)
Fig1. Timely variations of XCO2 footprints and Monthly averaged XCO2 from September 2014 to May 2016.
(a) Emission-region; (b) Background-region.

(a)

(b)

Fig2. CO2 observations of GOSAT and OCO-2 in August 2015 (a) and December 2015 (b). The dots with different
colors represent OCO-2 observations. The red dots represent GOSAT observations. The gray dots represent industrial
heat sources derived from VIIRS Nightfire product (Yongxue Liu et al.,2017). The black triangles represent power
plants from the database of Carbon Monitoring for Action (CARMA).

37

The potential of the XCO2 high resolution imagery for the monitoringof CO2 emissions from large cities and industrial plants: an overview
of the studies at LSCE
Grégoire Broquet*1, Diego Santaren1, Yilong Wang1, Anastasiia Prokaieva1, Franck Lespinas1,
Elise Potier1, François-Marie Bréon1, Frédéric Chevallier1, Philippe Ciais1, Michael Buchwitz2,
Maximilian Reuter2, Heinrich Bovensmann2, Cyril Crevoisier3, Vincent Cassé3, Greet JanssensMaenhout4, Tomohiro Oda5,6, Denis Simeoni7, Armin Löscher8 and Yasjka Meijer8
1. Laboratoire des sciences du climat et de l'environnement, CEA-CNRS-UVSQ, Université Paris
Saclay, Orme des Merisiers, 91191 Gif sur Yvette, France
2. Institute of Environmental Physics (IUP), University of Bremen FB1, Otto Hahn Allee 1, 28334
Bremen, Germany
3. Laboratoire de Météorologie Dynamique, Centre National de la Recherche Scientifique, Institut
Pierre-Simon Laplace, Palaiseau, France
4. Joint Research Center, Institute for Environment and Sustainability, Ispra (Va), Italy
5. Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD,
USA
6. Goddard Earth Sciences Technology and Research, Universities Space Research Association,
Columbia, Maryland, USA
7. Thales Alenia Space, Cannes, France
8. European Space Agency (ESA), ESTEC, Noordwijk, The Netherlands

The high-resolution spectro-imagery of vertically integrated CO2 concentrations (XCO2) from space may become acritical component of operational atmospheric inversion systems for the monitoring of CO2 anthropogenic emissions,allowing the detection of CO2 plumes downwind of large cities and industrial plants. Much remains to be understood anddeveloped to ensure that the constellation of the Copernicus Anthropogenic Carbon Dioxide Monitoring mission (CO2M,
to be deployed in 2025/2026), will feed a system quantifying the emissions with a coverage and accuracy that are relevant
for policy makers. LSCE has led or contributed to various projects (including ESA-LOGOFLUX, ESA-PMIF, H2020CHE, and TRACE in collaboration with Thales Alenia Space) for the preparation of XCO2 imaging missions such as
CarbonSat, CO2M, and the “city-mode” of MicroCarb. We present an overview of the high-resolution modeling systems
and of the Observing System Simulation Experiments (OSSEs) developed at LSCE to assess the potential of such
missions. These systems target all the large CO2 sources potentially seen from space across a country or the globe. They
are built on an automatic delimitation of the emission “clumps” corresponding to cities and large industrial plants inspatialized inventories. The ability to link information from successive images or from different cities has been assessed38

based on new analysis of the uncertainties in inventories. Results for cities and plants at the hourly to annual scale are
analyzed as a function of the emission rate, of observation parameters (precision, spatial resolution and swath) of typicalwind, cloud and sunlight conditions, and of the ability to disentangle neighbor diffuse and point sources and natural fluxes.
A 2x2km2 resolution and <1ppm precision imagery is required to achieve <10% uncertainty in estimates over few hours
for megacities emitting ~10MtCy-1. Results also show that cities and plants emitting <1MtCy-1, can hardly be monitored.
Key words: CO2 emissions; cities; XCO2 imagery; atmospheric inversion; CO2M

39

Plume detection and characterization from XCO2 imagery:
potential of Gaussian methods for analyzing and estimating
plant and city fluxes in the context of MicroCarb and GeoCarb
Claude Camy-Peyret

IPSL/SU-UVSQ, Paris, France
Pascal Prunet, Olivier Lezeaux

SPASCIA, Toulouse, France
François-Marie Bréon, Grégoire Broquet, Diego Santaren

LSCE/IPSL, Gif-sur-Yvette, France
We have used simulations over Western Europe of daily fields of column averaged dry air carbon
dioxide mixing ratio (XCO2) generated using kilometer scale emission inventories and an Euleriantransport model. Sixteen typical European sites covering power plants and cities in France,
Belgium, Germany, Great-Britain and the Netherlands have been selected as targets for the
inversion of emissions based on XCO2 images of the corresponding areas. These simulated images
have been used to assess the potential of satellite instruments able to acquire such images foremission estimates from LEO or GEO platforms. The simulated plumes have a realistic characterand have been fitted (using an optimal estimation method) with a Gaussian-based plume model
able to deal with multiple and/or extended sources. The number of retrieved parameters as well as
the dependence of their a posteriori uncertainties on the input XCO2 uncertainty (in ppmv) or on
the assumed spatial resolution (size of the pixels) and spatial coverage (area of the measurement
scene) are discussed. Systematic retrievals from MicroCarb and GeoCarb simulated images (withthe presently known characteristics of these satellite sounders) have been performed. These results
are used to identify suitable sites to be targeted by the MicroCarb “City Mode”. A quantification of
the expected precision on the derived source fluxes, considering the impact of the instrument
characteristics, of the complexity of the plumes, and of realistic assumptions on cloud coverage,
will be presented.

40

Quantifying Methane Emissions from Individual Coal Mine Vents
with GHGSat-D Satellite Observations
Daniel J. Varon*1,2, Daniel J. Jacob1, Dylan Jervis2, Jason McKeever2
1. Harvard University, 2. GHGSat, Inc.
GHGSat-D was launched in June 2016 as a demonstration instrument for the planned GHGSat satellite constellation,
which aims to quantify emissions from individual methane point sources using fine-resolution (< 50 m) observations ofmethane plumes. GHGSat-D has coarser precision (13%) than is planned for the constellation (1%-5%), but we show thatit can still detect plumes from individual coal mine vents after time averaging of wind-rotated observations on successive
overpasses. We present observations for the San Juan (USA), Appin (Australia), and Bulianta (China) coal mines anddeduce the corresponding emissions with error estimates using integrated methane enhancement (IME) and crosssectional flux methods.

41Trends and interannual variation of African CH4 fluxes inferred fromGOSAT XCH4 retrievals
Liang Feng1, Mark Lunt1, Paul Palmer1, Robert Parker2, Hartmut Boesch2
1. National Centre for Earth Observation (NCEO), University of Edinburgh, UK
2. National Centre for Earth Observation (NCEO), University of Leicester, UK
We infer global CH4 fluxes by using an Ensemble-Kalman filter (EnKF) to assimilate proxy GOSAT XCH4 retrievals for
8 years from 2019 to 2016, based on a global 3D chemistry transport model GEOS-Chem. We focus on the interannual
variation of the resulting fluxes and, find significant increases in CH4 emissions from the Eurasia temperate and from thetropical Africa regions between 2010 and 2016. Subsequently a more detailed investigation has been made over Tropical
Africa, which contains one of the largest river basins in the world and substantial wetlands which account for a significant
part of global methane emissions. We used a novel approach, based on the high-resolution nested regional version of the
GEOS-Chem model, to infer fluxes at much higher spatial and temporal resolution over Africa, which enables us to study
possible causes for annual and seasonal variations of the resulting fluxes, such as changes of temperature, precipitation
as well as the wetland area etc. We find that the robust positive trend in tropical Africa CH4 emissions is largely due toincreased emissions from the Sudd wetlands in South Sudan. Furthermore, we find a strong seasonality in emissions
across Northern Hemisphere Africa, with the timing of the seasonal emissions peak occurring in the latter part of thenorthern hemisphere wet season.

Keywords: CH4, GOSAT, Africa

42

Solar-induced chlorophyll fluorescence from the geostationary
geoCARB instrument
Peter Somkuti*1, Christopher O’Dell1, Greg McGarragh1
1. Colorado State University, USA
The geoCARB mission, to be launched in 2023, is aimed at retrieving atmospheric concentrations of carbon dioxide (CO2),methane (CH4) and carbon monoxide (CO) at a spatial resolution of 5.3x4.4 km2 in order to investigate science questions
related to the American carbon cycle as well as anthropogenic carbon emissions. Similar to other space-based missions
like GOSAT and OCO-2, the geoCARB instrument will feature bands in the shortwave infrared wavelength range,
including the O2 A-band at 0.76 µm. This band can be utilized to retrieve solar-induced chlorophyll fluorescence (SIF), a
byproduct of photosynthesis and a direct proxy for carbon uptake. Since GeoCARB will be placed in a geostationary orbit,
situated at a longitude between 65°W and 105°W, it will be the first time that space-based measurements of SIF can be
performed at a daily repeat cycle, covering most of the contiguous United States as well as the Amazon basin. Dependingon the chosen scanning strategy, even multiple measurements per day of a certain region are possible.
To assess the quality and quantity of SIF retrievals that can be expected from geoCARB, we perform (observing system)
simulation experiments based on realistic instrument specifications and observing geometries. Given the several millionsof measurements per day, we estimate the number of SIF retrievals that pass quality filtering and analyze the biases fortwo different retrieval algorithms. This provides the SIF community with a first indication of the amount and the qualityof SIF retrievals to expect from the geoCARB instrument.

Key words: Geocarb; SIF; Fluorescence43

Seasonal trends of GOSAT-SIF in temperate vegetations
Hibiki M. Noda*1, Haruki Oshio1, Yukio Yoshida1, Kouki Hikosaka2 and Tsuneo Matsunaga1
1. National Institute for Environmental Studies (NIES), Japan
2. Tohoku University, Japan
In recent decades, global warming has progressed owing to increase of CO2 emitted by human activities. Terrestrial
ecosystem absorbs about 30 % of anthropogenic CO2 through photosynthesis and the photosynthetic rate is quite sensitive
to meteorological conditions0. Thus, to deal effectively with global warming, it is necessary to monitor photophoretic
production with their underlying mechanisms including biogeochemical processes. Satellite remote sensing is a powerfultool to monitor spatial and temporal dynamics in the structure and function of terrestrial ecosystem those are responsible
for such photosynthetic CO2 absorption. Recently satellite-based SIF (solar-induced chlorophyll fluorescence) has beenutilized as a new technique to estimate GPP (gross primary production) of terrestrial ecosystems. Although typicalvegetation indices, e.g., NDVI and EVI are correlated to the amount of foliage, SIF is thought to be directly linked tophotosynthetic activities because the chlorophyll fluorescence is a radiation emitted from chlorophylls in thephotosynthetic process itself.
Since SIF is obtained by gas-measurement satellites, such as GOSAT, GOSAT-2, OCO-2 and GOME-2, special and/or
temporal resolutions of those are too rough to monitor vegetation condition. So, several studies have used average SIF oflarge area, such as 2 x 2o grid. However, because GOSAT measurements sparsely and spatial distribution of SIF data ishighly heterogeneous, this approach is not suitable for the monitoring in regional scale. In present study, to examine howmuch single-shot data of GOSAT-SIF reflects the vegetation activities, we selected five sites in Japan including deciduous
forest, evergreen forest, cropland and urban area and analyzed seasonal trend of SIF at those points observed by GOSAT
from 2009 – 2018. SIF at deciduous forest in cool-temperate region showed clear seasonal trend, similar to leaf phenology
of deciduous trees. In total, SIF of forest sites were two to four times higher than crop land.
Key words: GOSAT; GOSAT-2; SIF; phenology; vegetation monitoring

44

What can we learn about effectiveness of carbon reduction policies
from interannual variability of carbon emissions? Applying ODIAC
emission estimates from East Asia of the 2010s.
*Lev D. Labzovskii1 , Hugo Wai Leung Mak2 , Samuel Takele Kenea1 , Jae-Sang Rhee3, Azam Lashkari4, Shanlan
Li1, Tae-Young Goo1, Young-Suk Oh1, and Young-Hwa Byun1

1. Climate Research Division, National Institute of Meteorological Sciences (NIMS), Seogwipo,
Jeju-Do, 63568, Korea
2. Department of Mathematics, The Hong Kong University of Science and Technology, Clear
Water Bay, Kowloon, Hong Kong
3. Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Republic of
Korea

4. School of Environmental Science and Engineering, Southern University of Science and Technology,
Shenzhen, China
Most countries have submitted their Nationally Determined Contributions (NDC) but we still poorly understand what
policies are effective in terms of reduction of carbon abatement under the announced pledges. We estimate theimportance of national environmental policies of East Asia in terms of reduction in fossil fuel carbon emissions (FFCO2)
using ODIAC data. The national flagship policies in East Asia of the 2010s were generally beneficial in terms ofslowing down FFCO2 growth. When the polices were enacted, annual FFCO2 growth rate has either slowed down by
1% (South Korea), 5% (Mongolia), 8% (China) or even resulted in a decline (Japan) comparing to prior periods. We
find that the 12th Five-Year Plan (12th FYP) of China had the strongest footprint in FFCO2 and the recent slowest rate ofemission growth across East Asia (2011-2015) temporally corresponds to the 12th FYP. This rate is driven by
decrements in annual growth of FFCO2, coal use and cement production of China during the 12th FYP. We provide twobaseline projections of emission distribution in East Asia, by assuming that all policies are enacted (policy-on) or not(policy-off) in the future. The projections approve that policies were beneficial since policy-on scenario results in 24%,80%, 166% less FFCO2 emissions than policy-off scenario in East Asia by 2020, 2025 and 2030 respectively. This
progress is yet insufficient for reaching NDC goals by 2030. Even in policy-on scenario in 2030, East Asian countries
would either experience insufficient decline of FFCO2 like Japan (-13% of FFCO2 comparing to pledged -17%) or
increase of FFCO2 like South Korea (11%) and Mongolia (4%) comparing to 2010 level. For China, due to lack of
economy-independent goals, we were unable to assess NDC target compliance.
Key words: carbon emissions, carbon abatement, East Asia

45

The Collaborative Carbon Column Observing Network (COCCON):
overview and current status
Matthias Frey*1, Frank Hase1, Darko Dubravica1, Thomas Blumenstock1, Mahesh K. Sha2,
Matthäus Kiel3, Roland Harig4, Gregor Surawicz4, Nicholas M. Deutscher5, Kei Shiomi6, Jonathan
E. Franklin7, Hartmut Bösch8, Jia Chen9, Michel Grutter10, Hirofumi Ohyama11, Youwen Sun12,Zhensong Cao12, Andre Butz13, Gizaw Mengistu Tsidu14, Dragos Ene15, Debra Wunch16, Omaira
Garcia17, Michel Ramonet18, Felix Vogel19 and Johannes Orphal1
1. Karlsruhe Institute of Technology (KIT), IMK-ASF, Germany
2. Royal Belgian Institute for Space Aeronomy, Belgium
3. California Institute of Technology, USA
4. Bruker Optics GmbH, Germany
5. University of Wollongong, Australia
6. Japan Aerospace Exploration Agency, Japan
7. Harvard University, USA
8. University of Leicester, UK
9. Technische Universität München, Germany
10. Universidad National Autonoma de Mexico, Mexico
11. National Institute for Environmental Studies, Japan
12. Anhui Institute of Optics and Fine Mechanics, China
13. Universität Heidelberg, Germany
14. Botswana International University of Science and Technology, Botswana
15. National Institute for Research and Development in Optoelectronics, Romania
16. University of Toronto, Canada
17. Izana Atmospheric Research Centre, Spain
18. Laboratoire des sciences du climat et de l’environment, France
19. Environment and Climate Change Canada, Canada
Precise measurements of atmospheric abundances of greenhouse gases (GHGs), especially carbon dioxide (CO2) andmethane (CH4), are of utmost importance for the estimation of emission strengths and flux changes. Furthermore, thesemeasurements offer the prospect of being usable for the evaluation of emission reductions as specified by international
treaties, e.g. the Paris COP21 agreement. The existing Total Carbon Column Observing Network (TCCON) measurescolumn-averaged dry air mole fractions of CO2 and CH4 with reference quality. However, the instruments used by thisnetwork need large infrastructure to be set up as well as expert maintenance. Therefore TCCON stations have sparse
global coverage. Current satellites like the Orbiting Carbon Observatory-2 and the Greenhouse Gases Observing Satellite
offer global coverage. Nonetheless, they suffer from coarse temporal resolution. The Collaborative Carbon Column

46

Observing Network (COCCON) is intended to be a lasting framework for creating and maintaining a GHG observing
network based on common instrumental standards and data analysis procedures. Currently, 20 working groups operating
EM27/SUN Fourier Transform Infrared spectrometers are contributing. We expect that COCCON will increase the global
density of column-averaged greenhouse gas observations and will especially contribute to the quantification of local
sources. Therefore, the COCCON results can be used for the validation of satellite data. For achieving an optimal network
performance, common standards for instrumental calibration, quality checks for new spectrometers before deployment,
and finally a centralized processing and data storage facility is desirable. This framework will be provided by COCCON.
Within the ongoing ESA supported project COCCON-PROCEEDS, a common preprocessing tool and a centralized data
handling facility (CPDHF) demonstrator is under construction. We will present the already operational components of
COCCON (quality checks of new spectrometers, instrumental calibration of all devices) and the current status of
COCCON-PROCEEDS.

Key words: COCCON; TCCON; GHG

47

Airborne Demonstration of Atmospheric CO2 Concentration
Measurements with a Pulsed Multi-wavelength IPDA Lidar
Jianping Mao*1, James B. Abshire2, S. Randy Kawa2, Haris Riris2, Graham R. Allan3,
William E. Hasselbrack3, Kenji Numata2, Jeff Chen2, Xiaoli Sun2, Julie M. Nicely1,
Joshua P DiGangi4 and Yonghoon Choi4
1. UMD; 2. NASA Goddard Space Flight Center; 3. Sigma Space Inc., Greenbelt, MD 20771;
4. NASA Langley Research Center, Hampton, VA 23681, USA
Globally distributed atmospheric CO2 concentration measurements with high precision, low bias and full seasonal
sampling are crucial to advance carbon cycle sciences. The passive remote sensing of column-averaged atmospheric CO2
mixing ratio (XCO2) from space using Earth’s surface-reflected sunlight present significant local biases, which are likely
related to atmospheric scattering effects and to the low measurement sensitivity due to insufficient sensor spectral
resolution. NASA Goddard Space Flight Center has developed an integrated-path, differential absorption (IPDA) lidar
approach to measure global XCO2 from space as a candidate for NASA’s Active Sensing of CO2 Emissions over Nights,
Days, and Seasons (ASCENDS) mission. This pulsed laser approach uses a step-locked laser diode source and a highefficiency detector to measure atmospheric column CO2 absorption at multiple wavelengths across a CO2 line centered at
1572.335 nm at very high spectral resolution and high sensitivity to atmospheric CO2 variation.
Measurements of time-resolved laser backscatter profiles from the atmosphere allow this technique to estimate XCO2 andrange to cloud tops in addition to those to the ground with precise knowledge of the photon path-length regardless of
atmospheric scattering.

We demonstrate this measurement capability using airborne lidar measurements from summer 2017 ASCENDS airborne
science campaign in Alaska. It is the first time our airborne lidar measurement was extended to the Arctic region where
atmosphere is more dynamic and measurement conditions are more challenging. We have developed an “uninformative”
retrieval algorithm that does not depend on a prior information and minimizes bias by taking advantage of the highly
sensitive lidar measurements at multiple wavelengths. We show retrievals of XCO2 to a variety of cloud tops in addition
to the ground. We will also demonstrate how the partial column XCO2 to cloud tops and cloud slicing approach help
resolving vertical and horizontal gradient of CO2 in cloudy conditions. The XCO2 retrievals from the lidar are validated
against on-board in situ measurements of CO2 vertical profiles during flight spiral down maneuvers.

Adding this measurement capability to the future lidar mission for XCO2 will provide full global and seasonal data
coverage and some information about vertical structure of CO2. This unique facility is expected to benefit atmospherictransport process studies, carbon data assimilation in models, and global and regional carbon flux estimation.

48

Characterization of aerosol absorption over south Asia based on
multi-platform measurements and CAI-2 retrieval of AOD and sootvolume fraction
Mukunda M Gogoi*1, S Suresh Babu1 and Ryoichi Imasu2

1. Space Physics Laboratory, Vikram Sarabhai Space Centre, ISRO, India
2. Atmospheric and Ocean Research Institute, University of Tokyo, Japan

Improved understanding of the large heterogeneity in the nature and sources of aerosols and their climate impact is ofparamount importance over the south Asian region, because of the rapid changes in anthropogenic emissions associatedwith rapid urbanization and industrialization coupled with distinct topography, meteorology and land-use of the region. In
this context, assessment and application of aerosol retrievals by cloud-aerosol imager-2 (CAI-2) onboard GOSAT-2
satellite, in conjunction with multi-platform measurements of aerosols over South-Asia is very important. Based on
ground based estimates of aerosol physical and optical properties across a dense network of observatories (ARFINET)
over India, it is revealed that the seasonal variation of near surface aerosol concentration is different from the columnar
aerosol loading over most of the Indian regions. The near surface Black Carbon (BC) mass concentrations show highvalues during winter and post-monsoon; while columnar Aerosol Optical Depth (AOD) peaks during pre-monsoon. Thispattern changes at high altitudes where pre-monsoon enhancement is seen in both AOD and BC. Despite the seasonal andregional distinctiveness, the regions with high AOD coincide with the regions having high BC, occurring mainly over the
Indo-Gangetic Plains (IGP), followed by northeast India and western India; while peninsular region and southern India
show relatively lower values. Extensive aircraft measurements of the vertical distribution of aerosols revealed a definite
enhancement in aerosol absorption in the lower free troposphere over the IGP during spring, along with a reduction near
the surface. On the other hand, aerosol absorption depicted region specific absorption characteristics just prior to the onsetof the Indian summer monsoon, leading to a west–east gradient in the vertical structure of single scattering albedo (SSA).
Hence a synergistic approach of combining both ground based and space borne observations is necessary for the accuratecharacterizations of aerosols over south Asia. It is proposed to combine the CAI-2 retrievals of AOD and soot volume
fraction with the ground based ARFINET data to retrieve a more accurate regional picture of aerosol absorption over thesouth Asian region.

Research Field: Data application/ related ground-based, ship-borne, and air-borne measurements;49

Low-resolution FTIR spectrometers supplementing TCCON for thevalidation of space-borne observations of greenhouse gases (CO2,
CH4) and CO
Mahesh Kumar Sha*1, Bavo Langerock1, Martine De Mazière1, Christian Hermans1, Justus Notholt2,
Christof Petri2, Huilin Chen3, David Griffith4, Nicholas Jones4, Frank Hase5, Thomas Blumenstock5,
Qiansi Tu5, Rigel Kivi6, Pauli Heikkinen6, Damien Weidmann7, Neil Macleod7, Angelika Dehn8
1. Royal Belgian Institute for Space Aeronomy, Belgium
2. University of Bremen, Germany
3. University of Groningen, Netherlands
4. University of Wollongong, Australia
5. Karlsruhe Institute of Technology, IMK-ASF, Germany
6. Finnish Meteorological Institute, Finland
7. Rutherford Appleton Laboratory, United Kingdom
8. European Space Agency (ESA)/ESRIN
Ground-based FTIR passive remote sensing networks like the TCCON and NDACC-IRWG have been the key facilities
for the validation of satellites measuring greenhouse (CO2, CH4) and pollution (CO) gases. Even though TCCON is thebaseline network for high-precision and accurate total column measurements of these gases, the number of stations(currently about 26) available worldwide is limited. The 20 NDACC-IRWG stations, add to the list of stations used forsatellite validation. Even though, some of these stations are based on the same instruments as TCCON. Nadir viewing
satellite instruments measure gas concentration globally. To improve on the satellite measurement validation a denser
distribution of zenith-looking ground-based measurements covering different surface conditions (albedo), latitudinaland seasonal distribution, as well as stations in the southern hemisphere are needed. For this reason, several groups areinvestigating alternative portable low-cost instruments, which can complement the existing networks and enhance thevalidation of satellite measurements.
The ongoing campaign “Fiducial Reference Measurements for Ground-Based Infrared Greenhouse Gas Observations(FRM4GHG)” at the Sodankylä (Finland) TCCON site, funded by the European Space Agency (ESA), aims at
characterizing the assessment of several low-cost portable spectrometers for precise solar absorption measurements of
CO2, CH4 and CO. These measurements are performed simultaneously next to the TCCON instrument since 2017. In
addition, regular AirCore launches are performed from the site to provide in-situ reference profiles of these gases; this isuseful for the verification of the instrument calibration. The intercomparison results show that the low-resolution
instruments tested provide high quality data comparable to that of TCCON. The data collected during 2 years were used
for the validation of CO2 measured by OCO-2 and CH4, CO measured by Sentinel-5P satellite missions.
The results of the validation exercise will be presented and the added value of the portable FTIR remote-sensing

50

instruments will be discussed.

Keywords: S-5P; OCO-2; TCCON; FRM4GHG; validation

51

Development of MRV system of Methane emissions from rice paddies
in the Mekong delta
Hironori Arai* (the University of Tokyo, RIKEN Center for Computational Science, Japan)
Wataru Takeuchi (Institute of Industrial Science, the University of Tokyo, Japan)
Kei Oyoshi (Earth Observation Research Center, Japan Aerospace Exploration Agency, Japan)
Lam Dao Nguyen (Ho Chi Minh City Space Technology Application Center, Vietnam)
Towa Tachibana (Faculty of Law, Politics and Economics, Chiba University, Japan)
Ryuta Uozumi (Uozumi sustainability research institute, Japan)
Koji Terasaki (RIKEN Center for Computational Science, Japan)
Takemasa Miyoshi (RIKEN Center for Computational Science, Japan)
Hisashi Yashiro (RIKEN Center for Computational Science, Japan)
Kazuyuki Inubushi (Graduate School of Horticulture, Chiba University, Japan)

Monitoring sustainability and implementation status of a water-saving irrigation in tropical rice paddies
Greenhouse gas (GHG) emission observation/reduction technologies are attracting greater deal of attention from policy
makers to achieve Sustainable Development Goals. In terms of GHG accounting, Monitoring, Reporting and Verification(MRV) systems have become significantly important for the countries which ratified Paris Agreement by promising
Intended Nationally Determined Contributions (INDC). Not only evaluation of the amount of GHG emitted from thecountries, but also the mitigation’s effect and its dissemination status need to be monitored by the policy makers. In thisregard, the societies require the MRV systems with transparency and high cost-performance. To address such concern,
the authors are building an efficient/transparent MRV system in a tropical rice cropping system based on satellite remotesensing data. We are developing a long-term consistent bottom-up approaching method with high spatio-temporal
resolution, based on the Japanese earth observation technology (e.g., ALOS-2, AMSR-E/2, GCOM-C). In order to validate
the outputs from the bottom-up approaching method, now we are also challenging to build an independent top-down
approaching method based on the other satellites data (GOSAT, SCIAMACHY) using NICAM-LETKF with 1waymultivariate variable localization, which can estimate the surface fluxes without requiring any direct observation or apriori information of the fluxes with K-computer.

Kewords: Methane, Synthetic Aperture Radar, GOSAT, data-assimilation,

52

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NASA’s plans for Greenhouse Gas Observations from space.
Ken Jucks
NASA Headquarters
NASA has a robust set of observations for Greenhouse gas observations currently in orbit and in the plans for the near
future.

OCO-2 has been in operation for 5 years now and still producing high quality spectra that are used for retrievalsof CO2.

As long as OCO-2 is still producing high quality data, NASA intends to maintain continues operation of thesatellite.

OCO-3 recently launched and was deployed to the International Space Station.

The instrument is still in on

orbit checkout, though the expectation will be data that are of similar quality to OCO-2.

In the future, the GeoCARB

mission will launch to Geostationary orbit and observe the Americas completely (south of ~50 degrees latitude) on a daily
basis or multiple times of day.

The current expected launch date is 2023.

The mission was selected under the Earth

Venture Mission solicitation, which is part of the regularly solicitated calls for new space assets for NASA and are highly
cost and schedule constrained.

It is expected that the Earth Venture series of solicitations will continue in the future.

The recently released Decadal Survey called for prioritized science observations for NASA to pursue over the next decadeor more.

There were five observables that came out as the Highest priority for NASA to attempt to develop missions

within an appropriate cost framework.

Also called out for were six areas of high science priority observations for NASAto consider and make available for competed science missions, with the expectation that NASA would be able to afford
three of them.

Greenhouse gas observations fell within that category.

I will discuss that the implications are forgreenhouse gas observations now that NASA has the Decadal Survey guidance to consider.

54

The GeoCarb Mission
Berrien Moore III (University of Oklahoma) and Sean Crowell (University of Oklahoma)This paper presents an update on the NASA Earth Venture-2 Mission, Geostationary Carbon Cycle Observatory(GeoCarb), which would provide measurements of atmospheric carbon dioxide (CO2), methane (CH4), and carbon
monoxide (CO) from geostationary orbit. The GeoCarb mission would deliver daily maps of column integrated mixing
ratios of CO2, CH4, and CO over the observed landmasses at a spatial resolution of roughly 5 x 10 km, which will establish
the scientific basis for CO2 and CH4 flux determination at ecosystem/weather relevant time and space scale.

The instrument would exploit the four spectral regions: The Oxygen A-band for pressure and aerosols, the weak andstrong bands of CO2 near 1.61 and 2.06 microns, and a region near 2.32 microns for CO and CH4. The O2 and CO2 bands
are very similar to the instruments aboard OCO-2, and so we envision OCO-2 in geostationary orbit with the addition ofa fourth channel to measure CO and CH4, but without an oceanic capability. The O2 A-band also provides for retrieval of
Solar Induced Fluorescence (SIF).

The GeoCarb Mission persistent fine-scale daily mapping measurements, under changing conditions also enablesignificant advances on an important range of CO2 biotic issues, including: CO2 fertilization, change in primary
production because of nitrogen deposition, and the influence of broad climatic patterns on terrestrial sources and sinks.
This probes the mechanisms of the observed inter-annual variability in the atmospheric concentration of CO2. In sum,
GeoCarb attacks the primary question of the nature of the net terrestrial sink of CO2.

Wetland ecosystems, rice paddies and livestock are major, and highly uncertain, sources of CH4. Several approaches havebeen used to scale up from measurements at individual plots to estimations of CH4 emissions at the landscape scale.
However, there has been little large-scale top-down validation. Industrial sources are also poorly quantified. The GeoCarb
Mission’s high space- and time-measurements of CH4 enable important analyses of human impacts via agriculture andindustry vs. natural phenomena on methane sources.

55

The MicroCarb project: recent achievements and review of the
project status.
Francois Buisson (*)(1), Didier Pradines(1), Pascal Prieur(1), Véronique Pascal(1), Denis Jouglet(1),Arnaud Varinois(1), Emilie Limasset(1), Carole Deniel(1), Francois Marie Breon(2)(1)
Centre National d’Etudes Spatiales – Toulouse - France(2) Laboratoire des Sciences du Climat et de l’Environnement – Gif sur Yvette - France
The MicroCarb satellite is fully dedicated to the monitoring of the atmospheric CO2 fluxes, thanks to a passive grating
spectrometer with high spectral resolution in 4 narrow bands (0.76µ and 1.27 µ for O2, 1.6µ and 2.06 µ for CO2)
The MicroCarb project development is conducted by the CNES in partnership with several European agencies andorganisms and in close cooperation with national research laboratories.
A major milestone has been recently passed with the successful completion of the detailed design phase resulting into an
authorization to proceed with the realization phase heading for a launch in 2021.
Besides, the definition of the algorithms for processing the data and the different methods for calibrating the instrument
on ground and in orbit and also for validating the data have significantly progressed.
The presentation will give an overview of the progress and the organization of the project.
It will highlight the original features of the design and the way they will be validated.
It will conclude with the provisions taken for the next phases of the project.

Keywords: greenhouse gas, satellite, CO2.

56

The MicroCarb L1 & L2 products
Denis Jouglet*1, Pierre Lafrique1, Christelle Pittet1, Charlotte Revel1, Bruno Vidal1, Leslie David2,
François-Marie Bréon2, Simon Prady3, François Buisson1, Didier Pradines1
1. Centre National d’Etudes Spatiales – Toulouse - France
2. Laboratoire des Sciences du Climat et de l’Environnement – Gif sur Yvette - France
3. Magellium – Toulouse – France
MicroCarb is a European initiative for the monitoring of global CO2 fluxes and a better understanding of the mechanismsthat control these fluxes. It will provide atmospheric CO2 column integrated concentrations data to the scientificcommunity, in the continuation or in parallel to the on-going operational programs. 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. The aimed random error precision is <1ppm for a regional bias <0.2ppm. After a first talk (F. Buisson) dedicated toa status of the project, we here focus on the L1 and L2 products.

We present an overview of the MicroCarb L1 and L2 scientific algorithms. The ATBDs are defined and are prototyped orclose to be. L1 is decomposed in L1A (calibrated for radiometry and spectrometry at pixel detector level), L1B (calibrated
for radiometry, spectrometry and geometry at footprint level) and L1C (corrected calibrated spectra using geophysicaldata). L1C includes the cloud detection, based on intra-FOV information resulting from the temporal intermediate
readings of the detector. L2 is based on the 4ARTIC retrieval tool, based on optimal estimation. The prior knowledge will
be obtained from ECMWF, CAMS for CO2 and aerosols, Sentinel 2 for surface albedo and SRTM for the Digital
Elevation Model. The 4ARTIC performances are under estimation using the OCO-2 data and TCCON.

A quick overview of the cal/val methodology will also be presented.

Key words: MicroCarb, algorithms, performances, CO2

57

Anthropogenic CO2 monitoring with the European candidate
Copernicus mission
Yasjka Meijer*1, Jean-Loup Bezy1, Mark Drinkwater1, Thorsten Fehr1, Valerie Fernandez1,
Armin Loescher1, Herbert Nett1, Bernd Sierk1, Bernard Pinty2, Hugo Zunker2, Hartmut Boesch3,
Michael Buchwitz3, Philippe Ciais3, David Crisp3, Oleg Dubovik3, Richard Engelen3,
Sander Houweling3, Greet Janssens-Maenhout3, Jochen Landgraf3, Ruediger Lang3,
Hannakaisa Lindqvist3, Masakatsu Nakajima3, Pepijn Veefkind3
1. European Space Agency (ESA), The Netherlands
2. European Commission, DG-GROW, Belgium
3. Copernicus CO2 Monitoring Mission Advisory Group, ESA
As part of the European Copernicus Programme, the European Commission and the European Space Agency (ESA)
together with the support of EUMETSAT and ECMWF are considering the further development of the first generation
Copernicus Space Component to include measurements for fossil 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 effectiveness of CO2 emission strategies, uncertainties
associated with current anthropogenic emission estimates at national and regional scales need to be improved.

Satellite atmospheric measurements, in addition to in-situ measurements and bottom-up inventories, would enable thetransparent and consistent quantitative assessment of CO2 emissions and their trends at the scale of megacities, regions,countries, and the globe as well. Such a capacity would provide the European Union with a unique and independent sourceof information, which can be used to assess the effectiveness of policy measures, and to track their impact towards
decarbonizing Europe and meeting national emission reduction targets.

This presentation will provide an overview of the candidate Copernicus CO2 monitoring mission objectives, theconsolidated observational requirements on CO2 and auxiliary measurement capabilities. Operational monitoring ofanthropogenic emissions requires high precision CO2 observations (0.5–0.7 ppm) with 2–3 days geometrical revisit timeat mid latitudes. These observations will be obtained from NIR and SWIR spectra, and will be supported by (1) multiangle polarimeter observations, to minimize biases due to incorrect light path corrections, and (2) NO2 observations as
tracer for high temperature combustion. Retrieval of CO2 is further facilitated with a cloud imager to identify
measurements contaminated by low clouds and high altitude cirrus. In addition, an update will be presented of activities
and studies currently undertaken to prepare for the implementation of the space component.

Keywords: CO2, CH4, NO2, aerosol, Copernicus

58

EUMETSAT’s contribution to the CO2M mission – continuous
operations, processing, monitoring, and Cal/Val
Ruediger Lang*1, Vincenzo Santacesaria1, and Bojan Bojkov1
1. EUMETSAT , Germany

The European Commission (EC) has identified a set of new missions, including one dedicated to CO2 Monitoring (CO2M),
as part of the possible evolution of the Copernicus program. The CO2M mission will be the space component of aEuropean integrated anthropogenic CO2 emission monitoring system, which will also include in-situ observations andinversion modelling. The European partners involved in this project (EC, ECMWF, ESA, EUMETSAT and ICOS) haveagreed on a CO2 monitoring roadmap, with the objective to have this system in place to support the 2nd stock-taking in
2028 as planned by the Paris Agreement.

In a cooperation’s agreement between EUMETSAT, the EC and ESA, EUMETSAT will take on the responsibility fordeveloping the Payload Data Ground Segment (PDGS) which facilitates the continuous processing, monitoring, validation
and, where needed, vicarious calibration of the payload data-products and their operational dissemination to users.
EUMETSAT will also be responsible for the routine operations of the CO2M platforms, while ESA will be responsiblefor developing the space-component and its instrument payload, together with an end-to-end testing and in-orbit
verification system activities and the operations of the platforms before starting the routine activities.

For CO2M the availability of robust and well maintained ground based and in-situ networks, complemented by relevant
and ideally operational satellite data from other missions, as well as the usage of model data for continuous verification
and Cal/Val will be of vital importance for monitoring and maintaining the stringent long-term performance requirementsof the mission. We will outline how such a monitoring, verification and validation system, relying on robust data-streams
from many different sources, might be established in close collaboration with our European partner agencies, the EC andICOS, and what its key-elements will be in the context of an operational Cal/Val and monitoring environment to beestablished as part of a future PDGS at EUMETSAT. We will also address some key-challenges for such a continuous
monitoring and Cal/Val environment for a future operational European CO2M mission.

This paper provides an overview of the key elements of the CO2M operational processing system for starting the CO2MPDGS Phase A activities at EUMETSAT. In particular we provide an overview of the key elements for a future continuous
monitoring, verification, validation, and calibration system, which will be established using and/or expanding on existing
multi-mission monitoring and Cal/Val elements at EUMETSAT.

Key words: CO2M; OPERATIONS; CAL/VAL

59

AIM-North:
The Atmospheric Imaging Mission for Northern Regions
Ray Nassar1, Chris McLinden1, Chris Sioris1, Joseph Mendonca*1, Ryan Cooney2, Helena van
Mierlo2, Natasha Jackson2, Louis M. Moreau3, Sebastien Roche4, Debra Wunch4, Kim Strong4,
Dylan B.A. Jones4, Feng Deng4, Saroja Polavarapu1, Michael Neish1, Cameron MacDonald5, Bruce
Kuwahara5, Charles E. Miller6, Stanley J. Sander6, Jean-Francois Blavier6
and the rest of the AIM-North Team
1. Environment and Climate Change Canada
2. Canadian Space Agency
3. ABB
4. University of Toronto
5. University of Waterloo
6. NASA Jet Propulsion Laboratory, California Institute of Technology

The Atmospheric Imaging Mission for Northern Regions (AIM-North, www.aim-north.ca) is satellite mission conceptthat began Phase 0 study for the Canadian Space Agency (CSA) in 2019. AIM-North would observe greenhouse gases(GHGs), air quality (AQ) species and solar induced fluorescence (SIF) using a pair of satellites in a highly elliptical orbit(HEO) formation to give quasi-geostationary coverage over northern land (~40-80°N) multiple times per day. This
presentation gives an update on mission status and describes new instrument and science studies. In Phase 0, both an
Imaging Fourier Transform Spectrometer (IFTS) and a dispersive spectrometer have been considered as options to record
spectra of reflected near infrared (NIR) and shortwave infrared (SWIR) solar radiation to image XCO2, XCH4, XCO and
SIF, while a dispersive ultraviolet-visible spectrometer (UVS) would image NO2, O3, aerosols and other species. Recent
retrieval studies have determined signal-to-noise ratio (SNR) and other instrument requirements to meet the Level 2
precision and accuracy targets for different instrument designs. Simulations on the use of a cloud imager to locate clearsky areas to facilitate an intelligent pointing approach have shown significant improvements to the yield of cloud-freeobservations relative to standard pointing. Simulated AIM-North XCO2 observations are being used in Observing System
Simulation Experiments (OSSEs) to assess their ability to quantify northern CO2 fluxes from regions of permafrost, boreal
forests and other northern vegetation.

60

Development and Field Validation of the PanFTS Instrument for
Geostationary Measurements of GHGs, Trace Gases and SIF
Stanley Sander*1, Jean-Francois Blavier1, Yen-Hung Wu1, Maxwell Bryk1, Thomas Pongetti1,
Zhao-Cheng Zeng2, Liyin He2 and Yuk L. Yung2
1. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California USA
2. Geological and Planetary Sciences, California Institute of Technology, Pasadena,
California USA
The most recent Earth Science Decadal Survey from the U.S. National Research Council identified several key science
questions and observables related to atmospheric composition. These include questions related to vertical profiling ofaerosols; emission, transformation and long-range transport of tropospheric pollutants and greenhouse gases that drive
radiative forcing. The next generation of instruments and platforms must address the specific requirements that flowfrom the measurement requirements articulated in the Decadal Survey.
The Panchromatic Fourier Transform Spectrometer (PanFTS) is a NASA Earth Science Technology Program-funded
development to demonstrate a geostationary instrument capable of meeting or exceeding measurement requirements
posed by the Decadal Survey. The PanFTS flight instrument design combines measurement capabilities for the 0.2615 micron spectral range in a single package. The wide spectral coverage is important for retrieving the entire suite oftarget molecules, including several in widely different wavebands. Measurement of the same species in different
spectral regions significantly enhances the information content of the vertical profile retrievals.
PanFTS combines an imaging Michelson interferometer design with several unique features. The PanFTS instrument
is a hybrid based on spectrometers like TES (Tropospheric Emission Spectrometer) that measures thermal emission,and those like OCO (Orbiting Carbon Observatory) and OMI (Ozone Monitoring Instrument) that measure scatteredsolar radiation. As such the PanFTS design has two parallel optical trains, one for infrared wavelengths and one for
UV-Vis. These channels incorporate imaging focal planes to meet requirements for high spatial resolution (few km)
and large instantaneous field of regard from geostationary orbit.
This presentation will discuss the development status and future evolution of the PanFTS instrument. We will present
results obtained from versions of the PanFTS instrument located at JPL’s California Laboratory for Atmospheric
Remote Sensing (CLARS) on Mt. Wilson, California, overlooking the Los Angeles Basin. These will include longterm measurements of greenhouse gas emissions and solar-induced fluorescence (SIF) from vegetation.
Copyright 2019, California Institute of Technology

Keywords: PanFTS, geostationary, greenhouse gases, SIF, air quality, CLARS

61

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62

Spectral-radiance inter-comparsion between GOSAT/FTS,GOSAT-2/FTS-2, and OCO-2
Fumie Kataoka*1, Akihiko Kuze2, Kei Shiomi2, and Hiroshi Suto2
1. Remote Sensing Technology Center of Japan, 2-1-1 Sengen, Tsukuba-city, Ibaraki, JAPAN2. Japan Aerospace Exploration Agency, 2-1-1 Sengen, Tsukuba-city, Ibaraki, JAPANThe Greenhouse gases Observing SATellite-2 (GOSAT-2) was launched on 29 October 2018. GOSAT-2 is equipped with
the Thermal and Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer-2 (TANSO-FTS-2). FTS2 observes sunlight reflected light and thermal infrared radiation from the Earth’ surface and atmosphere with five spectral
bands.

FTS-2, GOSAT/TANSO-FTS, and Orbiting Carbon Observatory 2 (OCO-2) have the common spectral bands at 0.76, 1.6
and 2.0 µm. The spectral radiances of these three spectrometers were compared at temporally coincident and spatiallycollocated points. GOSAT and GOSAT-2 have 2-axis agile pointing system. We planned to target the same observation
points by uploading pointing angles in the GOSAT-2 initial calibration phase. The surface reflectance differences caused
by observation geometries have been reduced by correcting with Suomi-NPP VIIRS surface bidirectional reflectance
distribution function (BRDF) product.

The thermal infrared spectra of FTS and FTS-2 were also compared with Aqua/AIRS and Suomi-NPP/CrI with simulated
spectra using a radiative transfer model.

63

Atmospheric Variations in Column Integrated CO2 On Synoptic andSeasonal Time Scale Over the U.S.
Qingyu Wang, Sean Crowell**

School of Meteorology, University of Oklahoma, Norman, OK 73072

Past studies have demonstrated that synoptic events play an important role in the spatial and temporal variations of carbon
dioxide (CO2). In this study, in order to investigate whether cold fronts have impact on synoptic CO2 concentrations, wecollect 83 cold frontal cases over United Sates, east Pacific Ocean and west Atlantic Ocean from 2015 to 2017 with datafrom Orbiting Carbon Observatory-2 (OCO-2), Version 8 (V8) and Modern-Era Retrospective analysis for Research andApplications, Version 2 (MERRA-2), calculate the Column-averaged carbon dioxide dry air mole fraction (XCO2)
difference anomalies across the fronts, and apply significance test to decide whether cold fronts relate to CO2 changes.
The results show cold fronts in the summer in these three years do relate to synoptic CO2 changes, spring may also, butother seasons are not apparently related to CO2 changes though in our study. Frontal gradients from OCO-2 Version 9(V9) data show the similar pattern, however spring cases are less significant than those with V8, which indicates frontal
gradients in V9 data are only significant in summer.

64

Sensitivity evaluation of TANSO-FTS/GOSAT using principal
component analysis
Yu Someya*1 and Yukio Yoshida1
1. National Institute for Environmental Studies (NIES), Japan
Reportedly, the sensitivities of the short-wavelength infrared (SWIR) band of Thermal And Near-infrared Sensor for
carbon Observation - Fourier Transform Spectrometer (TANSO-FTS) have been degraded (Kuze et al., 2012) withwavenumber dependencies (Yoshida et al., 2012). In the current retrieval system for the SWIR level 2 product provided
by National Institute for Environmental Studies (NIES), the degradations are evaluated according to Yoshida et al. (2012).
Although this paper assumes that wavenumber dependency is slightly changed with time, we found that there are timeindependent variations. In this work, we developed a new algorithm to evaluate these degradations from on-orbit solar
calibration spectra using principal component analysis (PCA). The effectiveness of this algorithm is to be able todistinguish the time-dependent components of the spectral variability from the independent ones. This possibly enables
us to evaluate the temporal change of sensitivity more precisely. The degradation models were constructed with thefollowing sequence.
1.

Normalizing observed spectra with calculated spectra2.

Decomposing the dataset defined as a set of the differences of the normalized spectra from the referenced spectrum
into eigenvectors and principal component (PC) scores by singular vector decomposition for each band

3.

Correcting the incident angle and outgoing angle dependencies of PC scores

4.

Fitting the temporal variations of the corrected PC scores

5.

Reconstructing the temporal variations of the normalized spectra from the eigenvectors and the fitting functions of
PC scores accounting for exceeding 0.95 of cumulative proportions

This PCA-based degradation model is planned to be applied to the next version of the SWIR L2 retrieval system.

Key words: GOSAT; on-orbit calibration; PCA; retrieval algorithm

65

GOSAT-2 cal/val phase operation plan for ensuring the consistency
with GOSATKei Shiomi*1, Hiroshi Suto1, Makiko Hashimoto1, Shin Ishida1, Hiroko Imai1, Fumie Kataoka2,
Akihiko Kuze1 and Masakatsu Nakajima1
1. Japan Aerospace Exploration Agency (JAXA), Japan
2. Remote Sensing Technology Center of Japan (RESTEC), JapanGOSAT has been operating since 2009 to monitor greenhouse gases carbon dioxide (CO2) andmethane (CH4) by a Fourier Transform Spectrometer (FTS) and cloud and aerosol information by afine-resolved imager (CAI) in 3-day revisit. The column-average dry air mole fractions of CO2(XCO2), and CH4 (XCH4) are measured from space using surface-reflected sunlight at near-IR
wavelengths and the profiled densities of CO2 and CH4 using surface and atmospheric radiation with
ten-kilometer resolution by pointing system of wide swath. GOSAT-2 has been operating since
October 2018 with upgraded FTS-2 and CAI-2 in 6-day revisit. The FTS-2 observes XCO2, XCH4
and an additional XCO by an upgraded detector to enhance the combustion sources discrimination
than the FTS. The CAI-2 has two slant views of forward and backward directions with UV bands to
avoid overlapping sunglint area for aerosol application by the FTS-2.
GOSAT-2 initial commissioning phase has finished by the end of January 2019 and the following
calibration and validation phase has started since February. The FTS-2 observation was optimized toregular 5-grid observation points in swath, target observations at over 50 large cities, CEOS
calibration sites over desert, and ground-based TCCON, CONTRAIL / NOAA aircraft validation sites.
All the observation points of the FTS-2 can be selected within the rotating angle range of along-track(AT) and cross-track directions by the upgraded pointing system. The FTS-2 AT pointing expands +/40 degrees twice the AT range of the FTS. Hence, the FTS-2 can be allocated about 32 observation
points for one target location. GOSAT and GOSAT-2 observations almost synchronize every 6 days.
The FTS-2 is targeted the same locations as the FTS in 2 days out of 6 days for calibration andvalidation of inter-comparison opportunities. The GOSAT-2 observation will be further optimized byusers’ feedback to improve the observation plan for better measurements.
Key words: GOSAT; GOSAT-2; operation

66

The ability of GeoCarb to constrain the interannual variability ofcarbon gases over the Amazon
Brad Weir*1,2, Sean Crowell3, S. Randolph Kawa2, Lesley Ott2, Abshishek Chatterjee1,2, Steven
Pawson2 and Berrien Moore3
1. Universities Space Research Association (USRA), Maryland, USA
2. NASA Goddard Space Flight Center, Maryland, USA
3. University of Oklahoma, Oklahoma, USA
We perform a number of idealized assimilation experiments with the GEOS constituent data assimilation system to test
the ability of GeoCarb retrievals of CO, CO2, and CH4 to constrain the interannual variability of these gases over the
Amazon. Retrievals for instruments on other satellites which observe in similar channels (e.g. MOPITT, GOSAT, andOCO-2) are limited due to persistent cloud coverage. Given its ability to sample the same location multiple times in one
day, the expectation is that GeoCarb retrievals will return more soundings than those from previous missions. The goalof the assimilation experiments is to understand which scanning strategies lead to the best sounding densities and thus
have the best chance of constraining interannual variability in the carbon species. The experiments each begin by picking
a given year at random from a nature run (i.e., a model simulation meant to represent the truth). The model fields are
sampled according to a given strategy and then screened to account for cloud coverage. Next, we pick another year at
random and assimilate the synthetic GeoCarb samples into the GEOS model for that year. The output of the assimilation,
6-hourly, 3D fields of each constituent, is then directly comparable to the nature run. This comparison allows us toevaluate the ability of GeoCarb measurements to constrain the interannual variability of each gas.
Key words: GeoCarb; GEOS; Amazon

67

Progress in atmospheric carbon monitoring using NASA's Goddard
Earth Observing System (GEOS) model and data from the OCO andGOSAT missions
Brad Weir*1,2, Lesley Ott2, Abshishek Chatterjee1,2, Tomohiro Oda1,2 and Steven Pawson2
1. Universities Space Research Association (USRA), Maryland, USA
2. NASA Goddard Space Flight Center, Maryland, USA
NASA's Global Modeling and Assimilation Office (GMAO) produces a variety of carbon products based the synthesis ofsatellite remote sensing data and outputs of the Goddard Earth Observing System (GEOS). This includes bottom-up
surface fluxes due to fossil fuel emissions, biomass burning, terrestrial biospheric exchange, and ocean exchange
constrained by measurements of nighttime lights, fire radiative power, normalized difference vegetation index, and ocean
color. These fluxes are the basis of top-down estimates of carbon concentrations and fluxes. In particular, the GMAO
system processes retrievals of column carbon dioxide (XCO2) from GOSAT and OCO-2 to produce a high-resolution,
long-term global analysis of CO2 in three dimensions every 6 hours. Here, we discuss the potential applications of suchproducts for satellite intercomparison and evaluation against independent, non-coincident data. We also highlight theability to provide monthly global atmospheric growth rates inferred from the assimilated CO2 concentration product.
Finally, we discuss the challenges facing such products including bias correction and the estimation and analysis of modeltransport errors.
Key words: GOSAT; OCO; GEOS

Snapshot of the CO2 field produced by the GEOS analysis of OCO-2 retrievals.

68

The OCO-3 Mission: Global Observations of CO2 and Solar-Induced
Fluorescence from the International Space Station – Snapshot Area Map
and Target Mode Observations
thomas p. kurosu, annmarie eldering, ryan p. pavlick, brendan. m. fisher, gregory. b. osterman,
and The OCO Teams
NASA Jet Propulsion Laboratory/California Institute of Technology
Pasadena, CA 91109 (USA)thomas.kurosu@jpl.nasa.gov
phone: +1-818-354-2432
The Orbiting Carbon Observatory 3 (OCO-3) continues and extends global CO2 and solar-induced
chlorophyll fluorescence (SIF) using the flight spare instrument from OCO-2. The instrument has been
through ground testing and thermal vacuum, and has been shipped for installation on the International
Space Station, currently scheduled for launch on 25 April 2019. By early June, OCO-3 is expected tobecompleting the decontamination cycle and First Light operations are imminent. This talk focuses on the
new Snapshot Area Mode (SAM) as well as Target Mode observations, including observation selection,
sampling locations, spatial coverage, frequency of revisit over the year, and hours of the day that aresampled.
The low-inclination ISS orbit lets OCO-3 sample the tropics and sub-tropics across the full range ofdaylight hours with dense observations at northern and southern mid-latitudes (+/- 52º). The combinationof these dense CO2 and SIF measurements provides continuity of data for global flux estimates as well
as a unique opportunity to address key deficiencies in our understanding of the global carbon cycle. The
instrument utilizes an agile, 2-axis pointing mechanism, providing the capability to look towards thebright reflection from the ocean and validation targets. In addition to the nadir-, glint-, and target-mode
geometries familiar from OCO-2, OCO-3 includes a new observation mode dedicated to mapping out
larger spatial-scale emitters like cities. This Snapshot Area Map (SAM) mode will be used to map areasof up to 80x80 km2 on the Earth surface with the standard OCO-3 ground footprints of 3.5 km2, providing
unprecedented high spatial resolution coverage of large-scale CO2 emitters worldwide. Measurements
over urban centers could aid in making estimates of fossil fuel CO2 emissions. Similarly, the snapshot
mapping mode can be used to sample regions of interest for the terrestrial carbon cycle. In addition, there
is potential to utilize data from the currently operating ISS instruments ECOSTRESS (ECOsystem
Spaceborne Thermal Radiometer Experiment on Space Station) and GEDI (Global Ecosystem Dynamics
Investigation), which measure other key variables of the control of carbon uptake by plants, tocomplement OCO-3 data in science analysis.
In early June 2019, OCO-3 will be completing the decontamination phase and First Light operationsare
imminent. This talk focuses on the planned observation strategies for SAM and Target measurement
modes, including statistics of spatial coverage, sampling locations, sampling times of year and hours ofthe day, and the expected performance of the SAM and Target selection and coverage algorithm. We alsopresent updates on the public data release schedule for L1b and L2 data products.

69

Exploring Improvements to the Aerosol Parameterization in the
OCO-2 XCO2 Retrieval Algorithm
Robert R. Nelson*1, Christopher W. O’Dell2, Annmarie Eldering1,

1. Jet Propulsion Laboratory, California Institute of Technology, USA
2. Cooperative Institute for Research in the Atmosphere, USA
The scattering effects of clouds and aerosols are one of the primary sources of error when making space-based
measurements of carbon dioxide. This work describes multiple investigations into optimizing how aerosols areparameterized in retrievals of the column-averaged dry-air mole fraction of carbon dioxide (XCO2) performed onnear-infrared measurements of reflected sunlight from the Orbiting Carbon Observatory-2 (OCO-2). The primary goal
is to enhance both the precision and accuracy of the XCO2 measurements by improving the way aerosols are handled
in the NASA Atmospheric CO2 Observations from Space (ACOS) retrieval algorithm. Two studies were performed:
one on ingesting more intelligent aerosol priors into the retrieval and another on reducing the complexity of the aerosolparameterization. It was found that using co-located, instantaneous aerosol information from the Goddard Earth
Observing System Model, Version 5 (GEOS-5) resulted in a small improvement against multiple validation sources
but that the improvements were restricted by the accuracy and limitations of the model. Implementing simplified
aerosol parameterizations that solved for fewer parameters sometimes resulted in small improvements in the retrieved
XCO2, but further work is needed to determine the optimal way to handle the scattering effects of clouds and aerosols
in near-infrared measurements of XCO2. Finally, we present work on investigating the characteristics and physicalcauses of multiple sources of bias within the XCO2 retrieval algorithm. With several multi-million dollar space-based
greenhouse gas measurement missions scheduled and in development, the massive amount of measurements will be
an incredible boon to the global scientific community, but only if the precision and accuracy of the data are sufficient.

Key words: OCO-2; retrieval; aerosol

70Validation of cloud judgements in TANSO-FTS FOVs by using
Himawari-8/AHI data
Katsuki Kitamura1 and Naoko Saitoh*1
1. Center for Environmental Remote Sensing (CEReS), Chiba University, Japan

In the retrieval processing of greenhouse gases from the Thermal and Near Infrared Sensor for Carbon Observation(TANSO)-Fourier Transform Spectrometer (FTS) on board Greenhouse Gases Observing Satellite (GOSAT), cloudcontamination in the field of views (FOVs) of TANSO-FTS has been judged by TANSO-Cloud and Aerosol Imager (CAI)
in the daytime and by the thermal infrared (TIR) band of TANSO-FTS in the nighttime. We compared the cloud
judgements in the TANSO-FTS FOVs by TANSO-CAI or TANSO-FTS TIR band with cloud judgements by the Advanced
Himawari Imager (AHI) on board a Japanese geostationary satellite, Himawari-8. The AHI on board Himawari-8 has 16
bands from visible through thermal infrared regions, which is of great advantage to detect clouds in the FOVs of TANSOFTS.
About 90% of cloud judgements by TANSO-CAI and Himawari-8 agreed with each other over the ocean. TANSO-CAI
used only reflectance data in its visible bands for the cloud detections, whereas Himawari-8 used both reflectance andbrightness temperature data in its visible and infrared bands, respectively, which may cause their ~10% disagreements. In
contrast, agreements in cloud judgements between TANSO-FTS TIR band and Himawari-8 became worse by more than
10% compared to the TANSO-CAI and Himawari-8 cloud judgements. This is because that the FOVs of TANSO-FTS(10.5 km) is larger than those of TANSO-CAI and Himawari-8 (0.5-2 km), and TANSO-FTS TIR band uses only
brightness temperature values for the cloud detections. In the case of TANSO-FTS FOVs judged as “clear” by TANSOFTS TIR band and “cloud” by Himawari-8, small-scale clouds partly existed there judging from relatively low brightness
temperature values of the coincident Himawari-8 data. This suggests the possibility that TANSO-FTS TIR band cannot
detect such small-scale clouds, which may degrade the accuracy of CO2 and CH4 retrieval from the TANSO-FTS TIR
band.

Key words: GOSAT/TANSO-FTS, Himawar-8/AHI, cloud detection

71Carbon dioxide enhancement over Seoul from space and surface
measurements
Chaerin Park*, Su-Jong Jeong
Department of Environmental Planning, Graduate School of Environmental Studies, Seoul
National University, Seoul, Korea
Assessment of urban carbon emission is a critical issue to understand the global carbon cycle. In this research, weevaluate the spatial and temporal variations in atmospheric carbon dioxide concentrations over the Seoul Capital Area,
where is one of apparent Megacities across the world. This study analyzed urban CO2 variations using column integrated
XCO2 from Orbiting Carbon Observatory-2 from 2014 to 2018 and ground-based measurements over Seoul capital area.
Results show that Seoul has higher values of carbon dioxide concentration than its neighbor background value. From
XCO2, Seoul’s carbon dioxide concentration is increasing from 398.622ppm to 409.233ppm during the research period.
XCO2 concentration of Seoul is 1.387 ppm to 2.851 ppm higher compared to the nearby background area of Mt. Jiri. A
comparison of ground-based CO2 concentrations between Seoul and Anmyeond-do (GAW site) shows that concentrationsin Seoul is on average 30.3 ppm higher than that of Anmyeond-do. More details in our study will be presented in theworkshop. Our Carbon dioxide anomaly statistics and distribution of Seoul can provide valuable insight forcomprehending carbon dioxide emissions over the megacity across the world.

Key words: OCO- comparison of2; GOSAT; CT; Surfaced based measurements; Seoul* This work was funded by the Korea Meteorological Administration Research and Development Program under Grant
KMI (2018-03711).

72Regeneration of CO2 Satellite Column Data tailored to an
Atmospheric inversion Scheme
A.Webb. H. Boesch, F. Chevallier, C. ODell
Current atmospheric inversion schemes assume rather realistic prior information (to the first order)when projected in concentration space while satellite retrieval schemes try to maximize themeasurement contribution in the retrievals by giving a very weak weight to prior information. Suchinconsistent statistical hypotheses between XCO2 retrieval and atmospheric inversion schemes may be
a significant cause of error in atmospheric inversions assimilating satellite data.
By using sounding-specific covariances from model covariances representing the prior flux
uncertainties of the CAMS model projected in space and time, we can derive satellite CO2 columns
tailored towards and consistent with the specific assumptions of the CAMS atmospheric inversion
scheme. Using global level-2 CO2 column data from the NASA OCO-2 v9, we reconstruct CO2columns with prior information from the CAMS model using linear approximations thus avoiding theneed for time-consuming re-retrieval of the satellite observations.
In this presentation, we will describe the approach that is used for re-constructing the satellite CO2columns and we will evaluate the re-constructed CO2 columns from OCO-2 against the CAMS modeland ground-based TCCON data before using them for a formal atmospheric inversion with the CAMS
model.

73

Characterization of OCO-2 and ACOS-GOSAT biases and errors forflux estimates*Susan S. Kulawik1, Sean Crowell2, David Baker3, Junjie Liu4, Kathryn McKain5, Colm
Sweeney5,6, Sebastien C. Biraud7, Steve Wofsy8, Christopher W. O'Dell3, Gregory B. Osterman4,
Paul O. Wennberg9, Debra Wunch10, Coleen M. Roehl9, Nicholas M. Deutscher11,12, Matthäus
Kiel9, David W.T. Griffith13, Voltaire A. Velazco11, Justus Notholt12, Thorsten Warneke12, Christof
Petri12, Martine De Maziere14, Mahesh K. Sha14, Ralf Sussmann15, Markus Rettinger15, Dave
Pollard16, Isamu Morino17, Osamu Uchino17, Frank Hase18, Dietrich G. Feist, Kimberly Strong19,
Rigel Kivi20, Laura Iraci21, Kawakami Shuji22, Manvendra K. Dubey23, Eliezer Sepulveda24, Omaira
Elena Garcia Rodriguez24, Yao Te25, Pascal Jeseck25, Pauli Heikkinen20, Edward J. Dlugokencky5,
Michael R. Gunson4, Annmarie Eldering4, David Crisp4, Brendan Fisher4
1 BAER Institute: 625 2nd St, Ste 209, Petaluma, CA, USA
2 College of Atmospheric and Geographic Sciences, University of Oklahoma, Norman, OK, USA
3 Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins,
CO, USA
4 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
5 National Oceanic and Atmospheric Administration, Earth System Research Laboratory, Boulder,
CO, USA
6 University of Colorado, Cooperative Institute for Research in Environmental Sciences, Boulder,
CO, USA
7 Lawrence Berkeley National Laboratory, Earth Science Division, Berkeley, CA, USA
8 Harvard University, Cambridge, MA, USA
9 Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA,USA
10 Department of Physics, University of Toronto, Toronto, Canada
11 University of Wollongong, Wollongong, Australia
12 University of Bremen, Bremen, Germany
13 Centre for Atmospheric Chemistry, University of Wollongong, Wollongong, Australia
14 Royal Belgian Institute for Space Aeronomy, Brussels, Belgium
15 Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMKIFU), Garmisch-Partenkirchen, Germany
16 National Institute of Water and Atmospheric Research, Lauder, New Zealand
17 National Institute for Environmental Studies (NIES), Tsukuba, Japan
18 Karlsruhe Institute of Technology, IMK-ASF, Karlsruhe, Germany
19 University of Toronto, Toronto, Canada

74

20 Finnish Meteorological Institute, Sodankylä, Finland
21 NASA Ames Research Center, Moffett Field, CA, USA
22 Japan Aerospace Exploration Agency (JAXA), Tsukuba, Japan
23 Los Alamos National Laboratory, Los Alamos, NM, USA
24 Izaña Atmospheric Research Center, Meteorological State Agency of Spain (AEMet), Tenerife,
Spain
25 ERMA-IPSL, Sorbonne Universités, UPMC Univ Paris 06, CNRS, Observatoire de Paris,
PSL Research University, 75005 Paris, France
We characterize the systematic errors for OCO-2 v8 and ACOS-GOSAT v7.3 by comparisons to TCCON, ATom, ESRL
aircraft, and surface observations.

Although ACOS-GOSAT has larger random errors than OCO-2 and land observations

have larger random errors than ocean observations, the systematic errors are very similar across both satellites and surface
types, 0.6 +- 0.1 ppm.

We find consistent estimates of systematic error whether dynamic versus geometric coincidences

or ESRL aircraft (land) versus TCCON are used for validation, once validation and co-location errors are accounted for.
We also find areas with sparse throughput (due to quality flags and preprocessor selection) over land have ~double theerror of high-throughput regions.

We characterize both raw and bias-corrected results, finding that bias correction

improves systematic errors by a factor of 2 for land observations and improves errors by ~0.2 ppm for ocean.
Additionally, we validate the lowermost tropospheric (LMT) product for OCO-2 and ACOS-GOSAT by comparison toaircraft and surface sites, finding systematic errors of ~1.0 ppm, which is good considering LMT has 2-3 times thevariability of XCO2.

OCO-2 v9 has better throughput (up to 5x better in some areas) than v8 with similar systematic

error; v9 restricted to v8 quality results in a 0.1 improvement in the v9 land systematic error over v8.

We characterize

the time and distance scales of correlations for XCO2 errors, and find that the scales are similar to the scale length of thebias correction term.

Assimilation of the OCO-2v7 bias correction term is used to estimate flux errors resulting from

OCO-2 seasonal biases, finding annual flux errors on the order of 0.3 PgC/yr for Transcom-3 regions.
Key words: GOSAT; OCO-2; TCCON; error estimates

75

Observing patterns of greenhouse gases and pollutants across cities
using satellite data
Hayoung Park*, Su-Jong Jeong
Graduate School of Environmental Studies, Seoul National University, Seoul, Korea

Cities are home to half of the global population and, as a result, cities and urban areas are a major source of anthropogenic
emissions of CO2 as well as other pollutants and greenhouse gases which have a great impact on the environment, air
quality, and climate change. Therefore, reducing emissions is one of the biggest concerns of major metropolitan cities.
Studies have shown that reducing greenhouse gas emissions often reduce co-emitted air pollutants, bringing co-benefits
for air quality and slowing climate change, yet there is still a need for more research to be done on the relationships
between CO2 and other air pollutants. In this study, we observe the concentrations of CO2, CO, and aerosol optical depth(AOD) using satellite data from OCO-2, TROPOMI, and MODIS/Aqua across more than 30 different cities around theworld. This study aims to see the relationships between different anthropogenic emissions and pollutants. We also observe
the difference of emission characteristics across regions as well as the relations in chemistry of the pollutants.

Key words: Cities, OCO-2, TROPOMI, MODIS AOD, Greenhous Gases* This work was funded by the Korea Meteorological Administration Research and Development Program under Grant
KMI (2018-03711).

76

Preliminary results from the ESA CH4TIR project: Spectroscopy andforward model error improvement for CH4 retrieval in the TIR.
Charles E. Robert1, Ann-Carine Vandaele1, Justin Erwin1, Nicolas Kumps1, Séverine Robert1,
Martine de Mazière1 and Claude Camy-Peyret2
1. Institute for Space Aeronomy (IASB-BIRA), Brussels, Belgium
2. Institut Pierre Simon Laplace (IPSL), Paris, France.
Atmospheric methane is measured continuously from space, providing valuable information at global/regional scales foratmospheric monitoring as well as for surface flux estimates. Instruments in the SWIR provide a total atmospheric column
with rather uniform sensitivity up to the tropopause, while nadir instruments in the TIR provide profile information, withtypically a few pieces of information in the middle to upper troposphere. The synergistic exploitation of these two typesof measurements has a great potential for capturing CH4 information in the lowest part of the atmosphere, for better
understanding, quantifying and monitoring surface processes as well as sources and sinks of this target gas.

However, as shown by several recent studies and publications [e.g. Liuzzi, 2016; Siddans, 2017; De Wachter, 2017; deLange, 2018], CH4 atmospheric concentration retrievals from TIR nadir sensors exhibits significant biases compared toindependent observations, or when intercompared between different TIR and SWIR/TIR sensors products. It is necessary
to analyze the possible causes of biases and to investigate potential processing improvements and/or bias-correction forproper and consistent CH4 measurements in the TIR. Results from [Alvarado, 2015] and [De Wachter, 2017] highlighted
that incomplete spectroscopic knowledge could be a major source for these biases. However, this should be consolidated
and other sources of error, such as systematic forward modelling error or instrument biases, could also contribute.

The aim of the ESA CH4TIR project is to explore and consolidate the possible sources of CH4 biases in retrievals from
TIR sensors, and to establish and perform necessary actions and work for their mitigation. This shall be done by first
performing an exhaustive analysis of possible error sources: spectroscopic knowledge and input data of radiative transfer
models (RTM), forward model errors, and instrument biases. Then, a sensitivity analysis investigating the impact of thesethree types of errors on the final CH4 systematic error, using a state-of-the-art forward/inverse model and retrieval scheme
applied to IASI/GOSAT observations. Identifying the main bias contributors and provide specifications in terms ofaccuracy requirements on these three potential contributors in order to mitigate the biases in CH4 TIR retrievals.

In this work, we will present preliminary results of error and sensitivity analysis for the CH4 retrievals performed withTANSO-FTS and IASI [De Wachter, 2017] data in the TIR region using the ASIMUT-ALVL software [Vandaele, 2006].
We will assess the impact of the extension of the retrieval spectral range, the use of different spectroscopic databases andthe inclusion of different species, notably CF4 and HNO3.

77

REFERENCES[de Lange, 2018] de Lange, A and Landgraf, J: Methane profiles from GOSAT thermal infrared spectra, Atmos Meas Tech,
2018.
[De Wachter, 2017] De Wachter E, Kumps N, Vandaele AC, Langerock B, De Mazière M, Retrieval and validation of
MetOp/IASI methane. Atmos Meas Tech 2017.
[Liuzzi, 2016] Liuzzi G, Masiello G, Serio C, Venafra S, Camy-Peyret C, Physical inversion of the full IASI spectra:
Assessment of atmospheric parameters retrievals, consistency of spectroscopy and forward modelling. J Quant Spectrosc
Radiat Transf 2016.
[Siddans, 2017] Siddans R, Knappett D, Kerridge B, Waterfall A, Hurley J, Latter B, Boesch H, Parker R, Global heightresolved methane retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) on MetOp.
[Vandaele, 2006] Vandaele A, Kruglanski M, De Mazière M, Modelling and retrieval of atmospheric spectra using
ASIMUT. Proc of the first atmospheric science conference, ESA ESRIN, Frascati, Italy, 8-12 May 2006.

Key words: CH4; TIR; IASI; GOSAT; ASIMUT

78

GOSAT-2/TANSO-CAI-2 and the aerosol product
Makiko Hashimoto*1, Hideaki Takenaka1, Akiko Higurashi2 and Teruyuki Nakajima1
1. Japan Aerospace Exploration Agency (JAXA), Japan
2. National Institute for Environmental Studies (NIES), JapanGOSAT-2 (Greenhouse Gases Observing Satellite 2) was launched on October 29th, 2018. GOSAT-2 has two sensors,
Fourier Transform Spectrometer 2 (FTS-2) and Cloud and Aerosol Imager 2 (CAI-2). CAI-2 observes near UV to nearinfrared regions with ten bands and seven wavelengths, such as 340, 380, 443, 550, 674, 870 and 1630nm, and performing
two-directional observation in forward and backward directions. The spatial resolution (IFOV) is 920 m at a wavelengthof 1630 nm and 460 m at the other wavelengths. CAI-2 is characterized by a wavelength in ultraviolet (UV) region of
340 nm that is sensitive to aerosol light absorption, and it is expected to increase the retrieval accuracy of light absorbing
aerosol particles, such as soot and dust. The goals for CAI-2 are monitoring PM2.5 and soot volume fraction (sf) fromspace, improving accuracy of CO2 from FTS-2 by providing AOT at a wavelength of 1600nm (AOT1600). CAI-2 also
provides aerosol optical thickness at a wavelength of 550nm (AOT550) as amount of the atmospheric aerosols and aerosolÅngstrom exponent (AE) as particle size information. Accurate retrieval of fine mode AOT is necessary to derive PM2.5
in urban region from space. Therefore, an aerosol retrieval algorithm called MWPM (Multiple wavelengths and pixels
method) (Hashimoto and Nakajima, 2017) was developed. The method simultaneously retrieves fine and coarse mode
AOT and single scattering albedo by using several wavelengths and spatial difference of surface reflectance. The method
is useful for aerosol retrieval over spatially inhomogeneous surface like an urban area. We will show the results of aerosol
properties from CAI-2 by the algorithm, MWPM.
Key words: GOSAT-2; CAI-2; Aerosol

79Assessing OCO-2 Northern High Latitude XCO2 Retrievals OverSnow
Joseph Mendonca*1, Ray Nassar1, Debra Wunch2
1. Environment and Climate Change Canada, Toronto, Canada
2. University of Toronto, Department of Physics, Toronto, Canada
Measurements of CO2 in the Arctic and Boreal regions can help us understand how climate change has impacted thecarbon cycle in these regions. Satellites measurements of reflected sunlight (as made by OCO-2) can be used to retrieve
column averaged dry air mole fractions of CO2 (XCO2) to provide insight into the carbon cycle in regions where groundbased atmospheric measurements of CO2 are sparse (such as the Arctic and Boreal forest). However, retrievals of CO2
from spectra that are acquired over snow-covered surfaces are challenging. The albedo of snow in the CO2 bands (usedby OCO-2) varies with snow grain size, thus varying the signal-to-noise ratio (SNR), which impacts the bias and precisionof the retrieved XCO2. In this study, we will examine the bias and precision of XCO2 retrievals made over snow-covered
surfaces by comparing OCO-2 XCO2 to up-looking measurements from the Total Carbon Column Observing Network(TCCON). Then, we will investigate the OCO-2 filters in order to more optimally remove bad XCO2 retrievals over snowcovered surfaces. Finally, we will modify the OCO-2 retrieval algorithm to attempt to improve the retrievals for selectedcases of soundings over snow near high latitude TCCON sites.

Key words: OCO-2; TCCON; Retrievals

80A journey of the OCO-2 XCO2 data set from version 9 to version 10:
the ACOS retrieval algorithm validation
Le Kuai1, Brendan Fisher1, Vivienne Payne1, David Crisp1, Chris O’Dell2, Annmarie Eldering1
1. Jet Propulsion Laboratory, California Institute of Technology
2. Colorado State University
The OCO-2 version 9 column-mean carbon dioxide (CO2) dry air mole fraction (XCO2) product derived using the
Atmospheric CO2 Observations from Space (ACOS) retrieval algorithm has shown reduced uncertainties in regional andglobal carbon fluxes (Liu, 2017). The OCO-2 algorithm team continues to improve the ACOS algorithm to reduce data
scatter and bias, and understand the land-ocean contrast. This comprehensive, “full physics” retrieval algorithm couples
multiple physical processes, such as CO2 and O2 absorption, aerosol scattering, and surface reflectance, together withmodeled meteorological a priori values. Because of this, changes in any one part of the algorithm can introduce
unanticipated changes to the outputs. In this study, we review the tests of retrieval algorithm as one parameter is updated
in each test. For example, we describe the impacts of changes in 1) the O2 gas absorption cross section (ABSCO) tables
specifying the O2 A-band line and collision induced absorption, 2) scaling of the O2 A-band ABSCO table to minimize
bias, 3) relaxing the surface pressure constraint in the prior, and 4) the top-of-atmosphere solar flux for better agreementwith recent measurements. The retrieval results for each test are compared to the baseline retrieval results in the earlier
product (version 8). For each case, we try to understand the reason of the changes, and determine if the update helps toimprove the XCO2 output.

81

TanSat XCO2 retrieval, Inter-comparison and validation
Dongxu Yang1,2,*, Peter Somkuti1,3,4, Boesch Hartmut1,3, Yi Liu2, A. Webb1,3, Zhaonan Cai2, R.
Parker1,3, Lu Yao2, J. Anand1,3, Xi Chen2, A. Di Noia1,3
1. Earth Observation Science, Department of Physics and Astronomy, University of Leicester, UK
2. Institute of Atmospheric Physics, Chinese Academy of Sciences, China
3. National Centre for Earth Observation, University of Leicester, UK
4. Now at Colorado State University, Fort Collins, CO, USA
Atmospheric Carbon Dioxide (CO2), which is a main anthropogenic greenhouse gas whose concentration increase leads
to heating of the troposphere and subsequently to global warming. We need to characterize the distribution and trend
globally and investigate human emission contributions to the atmospheric CO2 growth. Following the ESASCIAMACHY/ENVISAT, which is the first space-based instrument to provide SWIR CO2 band hyperspectral detection,
the next generation satellites GOSAT and OCO-2 have been launched in 2009 and 2014, respectively. On 22 Dec 2016,
China launched a carbon dioxide observation satellite, TanSat, which is the first Chinese greenhouse gas monitoringsatellite that has been supported by the Ministry of Science and Technology of China, the Chinese Academy of Sciences,and the China Meteorological Administration, and recently joined the ESA 3rd party mission program. In-orbit calibration
tests were completed in the summer of 2017, and the performance of the instrument has since been evaluated in test
sessions. The TanSat XCO2 retrieval algorithm is the Institute of Atmospheric Physics Carbon dioxide retrieval Algorithm
for Satellite remote sensing (IAPCAS), which has also been used for GOSAT (ATANGO) and OCO-2 retrieval studies.
The University of Leicester (UoL) algorithm has been used for GOSAT retrieval and provide XCO2 product to the ESA
Climate Change Initiative and the Copernicus Climate Change Service. The retrieval accuracy and precision of both, the
IAPCAS and UoL algorithm, has been well investigated by verifying them against TCCON measurement. We will
introduce the application of the IAPCAS and UoL algorithms to TanSat XCO2 retrievals. The fitting residual has been
analyzed, and PCA based analysis method show an improvement on residual. Validation against TCCON measurementshow a good agreement on both algorithms. The method used in this study and results can help to improve the XCO2
retrieval from TanSat and subsequently the L2 products.

Key words: CO2, TanSat, retrieval algorithm, validation82

Observing Water Vapour in the Planetary Boundary Layer from the
Short-Wave Infrared
Tim Trent1,2, Hartmut Boesch*1,2, Peter Somkuti3 and Noëlle A. Scott4
1. Earth Observation Science, Department of Physics & Astronomy, University of Leicester,
University Road, Leicester LE1 7RH,
2. National Centre for Earth Observation, Department of Physics & Astronomy, University ofLeicester, University Road, Leicester LE1 7RH, UK
3. Department of Atmospheric Science, Colorado State University, CO, USA
4. Laboratoire de Météorologie Dynamique, Ecole Polytechnique–CNRS, 91128 Palaiseau, France;

Water vapour is a key greenhouse gas in the Earth climate system. In this golden age of satellite remote sensing, global
observations of water vapour fields are measured from numerous instruments operating between the ultraviolet/visible,
through the infrared bands, and into to the microwave regions of the electromagnetic spectrum. While these observations
provide a wealth of information on columnar, free-tropospheric and upper troposphere/lower stratosphere water vapour
amounts, there is still an observational gap regarding resolved bulk planetary boundary layer (PBL) concentrations. In
this study, we demonstrate the ability of the Greenhouse Gases Observing SATellite (GOSAT), to bridge this gap fromhighly resolved measurements in the shortwave infrared (SWIR). These new measurements of near-surface columnar
water vapour are free of topographic artefacts and cab be interpreted as a proxy for bulk PBL water vapour. Validation(over land surfaces only) of this new data set against global radiosondes show low biases that vary seasonally between 2% to 5%. Analysis of broad latitudinal bands show biases between -3% and 2% moving from high latitudes to theequatorial regions. Finally, with the extension of the GOSAT program out to at least 2027, we discuss the potential for a
new GOSAT PBL water vapour Climate Data Record (CDR).

Keywords: GOSAT; boundary layer; water vapour; validation; radiosonde83Improvement and application of PPDF-S method for retrieving XCO2
over aerosol dense areas
Chisa Iwasaki*1, Ryoichi Imasu1, Bril Andrey2, Sergey Oshchepkov2, Yukio Yoshida3, Tatsuya
Yokota3, 4, Vyacheslav Zakharov5,6, Konstantin Gribanov5, Nikita Rokotyan5
1. Atmosphere and Ocean Research Institute (AORI), The University of Tokyo, Japan
2. Center for Optical Remote Sensing, Institute of Physics of National Academy of
Sciences of Belarus (IPNASB), Belarus
3. National Institute for Environmental Studies (NIES), Japan
4. Department of Information Networking for Innovation and Design, Faculty ofInformation Networking for Innovation and Design, Toyo University, Japan
5. Laboratory of Climate and Environmental Physics, Ural Federal University, Russia
6. Institute of Mathematics and Mechanics, Russia
The photon path length probability density function-simultaneous (PPDF-S) algorithm is effective for retrieving columnaveraged concentrations of carbon dioxide (XCO2) and methane (XCH4) from Greenhouse gases Observing Satellite(GOSAT) spectra in ShortWavelength InfraRed (SWIR). Using this method, light-path modification attributable to lightreflection/scattering by atmospheric clouds/aerosols is represented by the modification of atmospheric transmittanceaccording to PPDF parameters. We optimized PPDF parameters for a more accurate XCO2 retrieval under aerosol dense
conditions based on simulation studies for various aerosol types and surface albedos. The optimization was applied toretrieval analysis of the GOSAT data that was measured when biomass burning was active in Western Siberia. The results
demonstrated that optimization enabled retrieval, even under smoky conditions, and that the total number of retrieved
data increased by about 70%. Furthermore, the results showed that atmospheric aerosol types that affected CO2 analysis

were identifiable by the PPDF parameter value. We will compare the GOSAT data analysis results with aerosol
characteristics detected by satellite images such as MODerate resolution Imaging Spectroradiometer (MODIS) over
the target area, and will discuss the usage of the PPDF parameters for the identification.
Key words: GOSAT; retrieval; XCO2; photon path length probability density function (PPDF), aerosol dense conditions

84

Variation of carbon dioxide at upper troposphere/lower stratospherederived from GOSAT TANSO-FTS TIR
Akihiro Honda1, Nawo Eguchi*2, Naoko Saito3 and Yousuke Niwa4
1. Interdisciplinary Graduate School of Engineering and Sciences, Kyushu University, Japan
2. Research Institute for Applied Mechanics, Kyushu University, Japan
3. Center for Environmental Remote Sensing, Chiba University, Japan
4. Center for Global Environmental Research, National Institute for Environmental Studies, Japan
Stratospheric cooling was reported, it could be caused by increasing the carbon dioxide (CO2), which is a major
greenhouse gas. While, the concentration of CO2 in the stratosphere is not well understood, nor are the exchange processes
between the upper troposphere and lower stratosphere (UT/LS ; 250-100hPa). The present study investigated the intraseasonal, seasonal and inter-annual variations of CO2 to understand the CO2 concentration at UT/LS and the Stratosphereand Troposphere exchange process.
We used the vertical profile data (Level 2) of CO2 derived from thermal infrared (TIR) region (Band 4: 5.5 - 14.3 μm) of
the Thermal And Near-infrared Sensor for carbon Observation (TANSO) - Fourier Transform spectrometer (FTS) on
board Greenhouse gas Observing SATellite (GOSAT). The analysis period is four years from January 2010 to December
2013. We adapted the bias correction values derived from Saitoh et al. [AMT, 2016] which validated the TIR CO2 profiles
at UT/LS region with the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL). For reference,
the atmospheric transport model, NIES-TM (ver.5) and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM)–
based Transport Model (TM) were used [Niwa et al., 2011; 2017].
The growth rate of CO2 concentration at UT/LS were approximately 2 ppmv/year, which one at southern hemisphere wererelatively larger than one at northern hemisphere. The seasonal variation of CO2 concentration, showed that the maximum
peak existed after a few month with respect to the peak at the middle or lower troposphere. The inter-annual variations
were affected by the ENSO cycle; the higher (lower) CO2 concentration at UT were seen during La Nina (Normal / ElNino) period. Finally, the intra-seasonal variation CO2 concentration at UT/LS were associated with both the vertical andhorizontal transportation due to the deep convection and the Asian monsoon anticyclonic circulation, respectively.

Key words: GOSAT; carbon dioxide; Stratosphere-Troposphere Exchange

85

Validating ratio component XCH4/XCO2 of GOSAT proxy retrieval ofmethane
Haruki Oshio*1, Yukio Yoshida1, Tsuneo Matsunaga1 and TCCON partners
1. National Institute for Environmental Studies (NIES), Japan
Monitoring methane concentration in the atmosphere from space is important to understand and predict the global carbon
cycle and the climate change. Proxy method and physics-based method have been used to retrieve the column-averaged
dry air mole fraction of methane (XCH4) from satellite measurements while accounting for the light pass modification byatmospheric scattering. We have been providing the physics-based XCH4 data as the Greenhouse gases Observing
SATellite (GOSAT) product. In addition to that, for GOSAT-2, we are planning to provide the proxy XCH4 product. A few
studies validated the ratio component XCH4/XCO2 of the proxy retrieval using the Total Carbon Column Observing
Network (TCCON) data; however, it has not fully been investigated that to what extent of cloud and aerosol load thescattering related errors is canceled in the ratio component. Here we compared the ratio component between GOSAT and
TCCON while considering the cloud fraction, the existence of cirrus, and other associated factors. Cloud and cirrus
screening was conducted by investigating the variation of difference between GOSAT and TCCON according to the cloud
fraction and the normalized radiance with its noise level at the strong water vapor absorptive channels in the 2 μm band.
Large bias and precision error were found for data whose footprint was partly covered by snow and ice. To exclude suchdata, it was required to adjust the threshold of blended albedo that has been used to screening the snow and ice cover or
to use a snow index calculated by GOSAT O2 A-band and weak CO2 band similar to the Normalized Difference Snow
Index (NDSI). Subsequently, negative correlation of the bias in the ratio component with the airmass and the difference
in retrieved albedo between the CO2 band and the CH4 band was corrected.
Key words: GOSAT; GOSAT-2; TCCON; methane; proxy method86

Simulation-retrieval experiments over the Western Hemisphere with
the GeoCarb greenhouse gas retrieval algorithm
Gregory McGarragh, Christopher O’Dell, Denis O’Brien, Thomas Taylor, Eric Burgh, and Sean
Crowell

The Geostationary Carbon Cycle Observatory (GeoCarb) mission aims to measure the spatial and temporal patterns ofgreenhouse gases with the ultimate goal of addressing fundamental questions in carbon cycle science, such as why andhow the global carbon cycle is changing.

GeoCARB will retrieve column integrated CO2, CH4, and CO concentrations

over North and South America from shortwave-infrared measurements in four hyperspectral bands made from ageostationary satellite platform.

The techniques used come from heritage algorithms developed for the polar-orbiting

Orbital Carbon Observatories (OCO) 2 and 3 missions, but will include an additional band for CH4 and CO sensitivity.
Other differences include spatial and temporal scan configurations.

In our presentation we will discuss the modifications

made to the OCO-2/3 heritage retrieval algorithm for application to GeoCARB, additional challenges that must be
addressed, and the sensitivity of the retrievals to various sources of uncertainty.

We will present results from syntheticscans over the Western Hemisphere that will shed some light on retrieved gas concentration errors expected by GeoCarb
in both in space and in time.

87

Variations in CO2 and CH4 in upper atmosphere: the effects of biomassburning and Asian monsoon transport inferred from GOSAT/TANSOFTS TIR data
Naoko Saitoh*1, Shingo Kosaka1, and Hinaoko Kobayashi1
1. Center for Environmental Remote Sensing (CEReS), Chiba University, Japan

Greenhouse Gases Observing Satellite (GOSAT) has continued its observations of greenhouse gases for more than 10
years since its launch on 23 January 2009. The thermal infrared (TIR) band of the Thermal and Near Infrared Sensor for
Carbon Observation (TANSO)-Fourier Transform Spectrometer (FTS) on board the GOSAT has observed concentrationsof CO2, CH4 and other greenhouse gases in several vertical layers. We have evaluated the data quality of the TANSO-FTS
TIR Level 2 Version 1 (V1) CO2 and CH4 products (Saitoh et al., 2016, 2017; Holl et al., 2016; Zou et al., 2016; Olsen etal., 2017).
We applied the evaluated bias-correction values to the TANSO-FTS TIR V1 CO2 data and then compared variations inconcentrations of the TIR CO2 data with those of Measurement Of Pollution In The Troposphere (MOPITT) CO data
(Deeter et al., 2014) in upper troposphere over the northern Africa. The comparison result showed that both TANSO-FTS
TIR CO2 and MOPITT CO concentrations became larger there almost at the same time in March through May, whichsuggests air mass with high CO and CO2 emitted due to biomass burning was transported to upper atmosphere.
As TANSO-FTS TIR V1 CH4 profiles generally agreed with aircraft CH4 profiles to within 10-15 ppb in the tropospherein low and middle latitudes, we analyzed seasonal variations in CH4 concentrations in each of the five atmospheric layers
over India by using the TIR V1 CH4 data. Our results were similar to the results reported in Chandra et al. (2017), butshowed larger impact of CH4 variations in lower troposphere on seasonal variations in XCH4 concentrations over India.
Key words: GOSAT/TANSO-FTS TIR band, CO2, biomass burning, CH4, Asian monsoon

88Greenhouse gas measurements at the Sodankylä TCCON site andcomparisons with the satellite borne observations

Rigel Kivi*1, Pauli Heikkinen1, Tuomas Laurila1, Huilin Chen2, Hannakaisa Lindqvist1 and JohannaTamminen1
1. Finnish Meteorological Institute, Finland
2. University of Groningen, the Netherlands

High-resolution FTS measurement program was established at Sodankylä in early 2009. Since then regular ground-based
measurements of CO2, CH4 and other gases have been performed at the site. Sodankylä participates in the Total Carbon
Column Observing Network (TCCON). The ground based measurements at the TCCON sites have been commonly used
to validate satellite based retrievals. The relevant satellite missions include the Greenhouse Gases Observing SATellite(GOSAT) and the Greenhouse gases Observing SATellite-2 (GOSAT-2). GOSAT-2 was launched recently, on October 29,
2018, while GOSAT became operational in early 2009. Here we have used FTS SWIR level 2 CO2/CH4 column amountproducts provided by NIES (National Institute for Environmental Studies). We found good agreement between the
GOSAT and collocated ground-based measurements at the Sodankylä TCCON site, based on ten years of measurements.
The relative difference calculated as (satellite-ground based FTS)/ (ground based FTS) for XCO2 was 0.09 +/- 0.02 percent
and for XCH4 the relative difference over the ten year period was 0.06 +/- 0.02 percent. In addition we also performed
AirCore in situ observations using a balloon based platform during all seasons. The AirCore profiles were obtained by acavity ring-down spectrometer, the mole fractions for the reference and fill gas were calibrated to World MeteorologicalOrganization in situ trace gas measurement scales. The in situ AirCore measurements were used to confirm the accuracyof total column retrievals of the ground based FTS.

Key words: TCCON; GOSAT; GOSAT-2

89

Profiles of greenhouse gases measured in the 2018 STEAM field
campaign
Zhaonan Cai, Yi Liu, Dongxu Yang, You Yi, Zhixuan Bai, KeChe, Sihong Zhu, Xiaoyu Ren,
Jianchun Bian
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
The stratosphere and troposphere exchange experiment over Asian summer monsoon (STEAM) is a five-year field
campaign project sponsored by Chinese Academy of Science (CAS) to improve the understanding of the chemical anddynamical processes in the upper troposphere and lower stratosphere (UTLS). During the field campaigns in June andNovember 2018, we launched four research soundings and AirCore, to measure the vertical distribution of aerosol, ozone,
water vapor, CH4, CO, CO2 in Inner Mongolia, with coincident column measurements using EM27. For the aircore system,
we use the zero pressure balloon as platform. When the ascent stops, and the balloon will hover at a constant altitude. The
sounding will hover for tens of minutes in order to stabilize, then cutting information will be send to the cutter, the payload
separates from the balloon and a suitable parachute opened to control the descent speed enough slow that makes thepressure inside and outside of tube equivalent. In order to maximize resolution, we constructed an entire mobile analyzing
system to shorten the time cost before recovery. For June campaign, we released Aircore in Xilin Hot on 13 June and 14
June and retrieved the vertical distribution of CO and CO2 from ground to ~25 km. Profiles of the two days shows severechanges in vertical gradient because of long-range airmass transport, the comparison with satellites and model indicatethat the column-averaged mixing ratio of CO and CO2 are identical but specific profile are different when a cold vortex
passes Xilin Hot. The aircore system was further improved in the November campaign, we released Aircore in Urad
Middle Banner on 12 November and 13 November, and derived profiles of CO2, CO, CH4 and N2O.

90

Seasonal and Diurnal Opportunities for XCH4, XCO2, and XCO for the
Amazonian Rainforest Region Allowing Sampling and Validation
Robert Chatfield*1, Robert Esswein1.2, Manvendra Dubey3, Sajian Heerah3
1. NASA Ames Research Center, Moffett Field, California, USA
2. Bay Area Environmental Research Institute, Moffett Field, California, USA
3. Los Alamos National Laboratories, Los Alamos, New Mexico, USA
There is considerable interest in the aggregate methane emissions from the Amazon and similar moist tropical regions,and XCH4 measurements are well suited to constrain sources to the global atmosphere. Similarly, XCO2 measurements
constrain CO2 in the region. XCO helps to partition CO2 patterns among burning and respiration processes. GeoCarb may
allow these column measurements over the Western Hemisphere, but satellite retrieval require exacting calibration andvalidation by sun-focused Fourier transform spectrometers (FTS). The rarity of sufficiently large gaps in the cloud cover
over the Amazon and similar rainforests restricts the validation opportunities for useful FTS observations and even more
the opportunities for accurate retrievals. TropOMI observational statistics are extremely poor for the region. We have used
two data sources to evaluate FTS opportunities at Manaus, Brazil, an FTS operated for 8 months near Manaus by
Mavendra Dubai, and also sun-photometer measurements at several stations. We report initial results on five questions:(1) how frequent are observing opportunities of FTS?, (2) What evidence is there that gaps in clouds are wide enough forsatellite retrievals at an appropriate accuracy?

(3) What is the diurnal and seasonal variability of cloud gaps? and(4)

What limitations are currently suggested for unbiased FTS measurement of XCH4 due to diurnal effect? And (5) Whatevidence is there for incidence of problematic high aerosol extinction at higher layers of the troposphere (800 hPa to 120hPa) which alter the XCH4 light-paths?
Key words: GeoCarb, TropOMI; TCCON, AERONET, Validation, Observation Opportunity

91

Progress on validation of the GOSAT and GOSAT-2 FTS SWIR L2
products
Isamu Morino*1, Osamu Uchino1, Hirofumi Ohyama1, Tran Thi Ngoc Trieu1, Yukio Yoshida1,
Tsuneo Matsunaga1, Akihide Kamei1, Makoto Saito1, Hibiki Noda1 and TCCON partners2
1. National Institute for Environmental Studies (NIES), Japan
2. https://tccon-wiki.caltech.edu

The Greenhouse gases Observing SATellite (GOSAT), launched on 23 Jan. 2009, is the world's first satellite dedicated tomeasuring the concentrations of the two major anthropogenic greenhouse gases, carbon dioxide (CO2) and methane (CH4),
from space. Column-averaged dry air mole fractions of CO2 and CH4, and water vapor (H2O) (denoted as XCO2, XCH4
and XH2O) are retrieved from the Short-Wavelength InfraRed (SWIR) spectral data observed with the Thermal And Nearinfrared Sensor for carbon Observation - Fourier Transform Spectrometer (TANSO-FTS) onboard GOSAT. The present
NIES full physics SWIR retrieval algorithm (Ver. 02.80) for land high-gain soundings showed small biases and standard
deviations: −0.33 ppm and 2.17 ppm for XCO2, −1.9 ppb and 13.4 ppb for XCH4, and −0.78% and 33.4% for XH2O,
respectively. (The mean value of the Total Carbon Column Observing Network (TCCON) data measured at each site
within ±30 min. of the corresponding GOSAT overpass time at around 1:00 pm local time in most of the sites is comparedwith GOSAT data measured within ±2-degree longitude/ latitude box centered on the TCCON site. The observation periodof the GOSAT data used for comparison is April 23, 2009 through June 30, 2018. Their comparisons are not considering
the difference in column averaging kernels) Retrievals from the GOSAT TANSO-FTS SWIR spectra for more than nine
years are already being used for scientific research applications.
A successor of GOSAT, GOSAT-2 was launched on 29 Oct. 2018, and is now in operational. In addition to XCO2, XCH4,
and XH2O, XCO (column-averaged dry air mole fraction of carbon monoxide) is retrieved from GOSAT-2 TANSO-FTS2 SWIR spectra. The main approach of acquiring validation data for GOSAT-2 is the same as those for GOSAT. Groundbased FTSs data from TCCON and NDACC IRWG (Network for the Detection of Atmospheric Composition ChangeInfraRed Working Group) sites will be used, and some sites will be co-located with lidar and sky-radiometer systems forextensive validation data acquisition. In-situ measurements and/or sampling of CO2, CH4, and CO onboard commercial
and charter aircraft by CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner) and NOAA(National Oceanic and Atmospheric Administration) will also be employed.
In this presentation, we focus on the current status of the GOSAT product validation and an overview of the GOSAT-2
validation plan, as well as preliminary validation result of the GOSAT-2 products.
Key words: GOSAT; GOSAT-2; FTS-SWIR; Validation; TCCON

92

Comparison between MOPITT and OCO-2 flux inversions :analyze of CO-CO2 correlation
Helene Peiro 1 and Sean Crowell 1
1

University of Oklahoma, Norman, United States

Carbon monoxide (CO) and carbon dioxide (CO2) are both emitted from combustion. CO is a product of inefficient
combustion often used as a tracer of CO2 for combustion. Observed CO-CO2 correlation slopes thus provide useful
constraints to characterize emissions sources. According to the concentrations of CO and CO2, we can determine the typesof sources. Most of the previous studies used CO measurements to constrain and improve CO2 flux inversions. But thecorrelation between CO and CO2 can also be used to improve and analyze the variability of both CO and CO2 flux
inversions. Satellites measuring CO and CO2 by Infrared remote sensing allow to improve our ability to map CO and CO2
and to understand their variability.

The goal of this study is to observe the correlation between two independent inversions of CO and CO2 and to analyze
CO and CO2 variability with these two flux inversions over the tropics for 2015.
Two independent inversions are compared : a CO2 inversion with the OCO-2 (Orbiting Carbon Observatory-2)
measurements and a CO inversion with the MOPITT (Measurements Of Pollution in The Troposphere) data. We apply afour-dimensional variational (4D-VAR) data assimilation system to optimize CO and CO2 emissions in the two separate
inversions using the chemistry transport model TM5. The 6°x4° global version of TM5 with 25 vertical sigma hybrid
levels is used.

We will observe the positive and strong correlation between CO and CO2 with these two independent inversions using
MOPITT and OCO-2 data for the Southern America, Indonesia and Africa. CO-CO2 correlation slopes will be study toobserve seasonal correlation. A stronger correlation will be particularly observe during the boreal and austral winter thanduring the growing season. A deeper analyze will be able to give information on the flux sources (determination of thetype of sources between power plants, biomass burning or car traffic).

Keywords : inversions, CO, CO2 , MOPITT, OCO-2

93

What biogeochemical processes drive the large decrease of
atmospheric CO2 growth rate in 2017?
Junjie Liu*1,2, Kevin Bowman1, David Schimel1, Nick Parazoo1, Anthony Bloom1, Meemong Lee1,Tom Oda3, Paul Wennberg2, Annmarie Eldering1
1. Jet propulsion Lab, Caltech
2. California Institute of Technology
3. Goddard Space Flight Center
Atmospheric CO2 growth rate has large interannual variability that is closely related to El Nino Southern Oscillation cycle(ENSO). In general, it is anomaly high during El Nino years when tropical temperature is high, and anomalously low
during La Nina years when tropical temperature is low. Atmospheric CO2 growth rate during 2015-2016 El Nino was ~3ppm, the highest since the record started in 1958. Correspondingly, the land surface temperature was also the highest. In
2017, the atmospheric CO2 growth rate quickly dropped to 2.2 ppm in 2017, ~ 0.2 ppm lower than the mean value between
2010 and 2017. However, the global land surface temperature was the third highest since 1880, only lower than 2015 and2016. So what caused the low atmospheric CO2 growth rate in spite of high land surface temperature in 2017?

To address this question, we will quantify air-sea net carbon fluxes, net biosphere production (NBP), and its component
fluxes including biomass burning and gross primary production (GPP) for the period of 2010 to 2017 by assimilatingcolumn CO2 observations from GOSAT and OCO-2, CO observations from MOPITT, and Solar Induced chlorophyll
Fluorescence (SIF) observations from GOSAT into NASA Carbon Monitoring System (CMS) – Flux inversion andoptimization framework. In this talk, we will present the dominant regions and biogeochemical processes that havecontributed to the low atmospheric CO2 growth rate in 2017, and further discuss the relationship between carbon cycle
responses and climate driver anomalies to reconcile between the low atmospheric CO2 growth rate and high land surface
temperature in 2017. In the end, we will discuss implications for future carbon-climate projections.

94

Characteristics of Atmospheric Carbon Dioxide Concentrations Basedon GOSAT and Its Relations to Biomass Burning in China
Yusheng Shi*1, Zhenghan Lv2
1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, China
2. Harbin Normal University, China
Carbon dioxide (CO2), the main greenhouse gas, has been increasing over the past few decades at a rate of ~2 ppm/aworldwide. China, the largest emitter of the CO2 emission, poses an important impact on the increase in global
atmospheric CO2 concentrations. In order to explain the spatial and temporal variation of atmospheric XCO2 concentrationand its influencing factors in China. We selected the satellite-retrieved atmospheric carbon dioxide column average dry
air mixing ratio (XCO2) dataset from greenhouse gas observation satellite (GOSAT) and three types of ancillary data
(NDVI, ODIAC and GFED). We emphasized the effects of biomass burning emissions on the change of XCO2 in different
terrestrial ecosystem. Based on these long-term series of satellite-retrieved datasets, we analyzed the spatial and temporalcharacteristics of XCO2 in China during 2009-2016 growing season by using standard deviation ellipse and trend analyses.
The results showed that the concentration of XCO2 in China increased at an average rate of 2.28 ppm/a with significant
seasonal variation of 6.78 ppm annually. South China presented higher incremental rate of XCO2 than other regions across
China with obvious differences in seasonality. Pixel-based analysis indicates a significant difference between eastern andwestern China: high XCO2 values were generally observed in the Southeast China, while low XCO2 concentration were
found in high latitude area in China with the annual median center of XCO2 concentration shifting to the northwest during
2009-2016. Then, we quantified the relative contribution of biomass burning to the change of atmospheric XCO2
concentration. The biomass burning emission had a great contribution and influence on the atmospheric CO 2 change inthe low latitudes of South China in autumn and the Yunnan Province in spring, respectively.

Key words: Atmospheric Carbon Dioxide Concentration; Spatial and Temporal Variation; Biomass Burning Emissions

95

Mesoscale atmospheric inversion of the CO2 natural fluxes inAmazonia using GeoCarb and MicroCarb data
Sarvesh Kumar Singh1*, Grégoire Broquet1, Luis Molina1,2, Yilong Wang1, Francois-Marie Bréon1,
Philippe Ciais1, Diego Santaren1, Carsten Standfuss3, Pascal Prunet3, Claude Camy-Peyret4,
Sean Crowell5, Berrien Moore5
1: Laboratoire des sciences du climat et de l'environnement, CEA-CNRS-UVSQ,Université Paris Saclay, Orme des Merisier, 91191 Gif Sur Yvette, France
2: International Center for Tropical Agriculture - Regional Office Asia, Hanoï, Vietnam
3: SPASCIA, 10 Avenue de l’Europe, 31520 Ramonville Saint Agne, France
4: IPSL, Sorbonne université 4 place Jussieu, Paris, France
5: GeoCarb, University of Oklahoma, 301, David L. Boren Blvd., 4 Partners Place,
Suite 1120, Norman, OK 73072 USA
Amazon forests contain nearly half of the tropical forest carbon stocks. and play a major but uncertain role in the global
carbon budget. Flux estimates based on atmospheric inversions are hampered by the lack of surface and satellite
observations of atmospheric CO2 in this region, and can hardly resolve uncertainties in the seasonal and inter-annual
variations of the Amazon carbon cycle. The second NASA Earth Venture Mission, Geostationary Carbon Cycle
Observatory (GeoCarb) is expected to provide daily images of the column concentrations of atmospheric CO 2 (XCO2)
from Geostationary orbit over tropical and mid-latitude America. In particular, it should provide basin wide images overAmazonia at a spatial resolution of typically 20-30 km 2 and once to several times during days with relatively low cloud
cover. Here, we investigate the potential of assimilating such images in a regional inverse modelling system to estimate
the Net Ecosystem Exchange (NEE) of CO2 in Amazonia based on Observing System Simulation Experiments(OSSEs). We use the CHIMERE transport model and the variational inversion system PYVAR-CHIMERE to conduct
inversions at 35 km and 6-hour resolution. A particular attention is paid to the consistency between the statisticalcharacterization of the uncertainties in the prior NEE estimate and in the transport model, and the actual errors imposedin the frame of the OSSEs. The use of different meteorological forcings or estimates of the NEE to define the truetransport and NEE as an alternative to random perturbations that are perfectly consistent with the assumptions made by
the inversion challenges this consistency and the traditional assumptions underlying the theoretical framework of theinversion. OSSEs with the assimilation of XCO 2 data from the CNES MicroCarb mission are also conducted to further
investigate the requirement and potential in the next decade for spaceborne monitoring of the NEE in the region.
Key words: carbon dioxide; GeoCarb; MicroCarb, transport-model errors; variational inversion.

96

Assessing errors and uncertainties in a global, high-resolution, fossilfuel CO2 emission dataset
Tomohiro Oda*1,2, Rostyslav Bun3,4, Vitaliy Kinakh3, Sha Feng5, Thomas Lauvaux6, JamesWang2,7, Randy Kawa7, Shamil Maksyutov8, Miguel Roman9, Zhousen Wang10,11, Gregg
Marland12, Lesley Ott1 and Steven Pawson1
1. Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, USA
2. Goddard Earth Sciences Technology and Research, Universities Space Research Association,
USA
3. Lviv Polytechnic National University, Ukraine
4. WSB University, Poland
5. Department of Meteorology and Atmospheric Science, Penn State, USA
6. Laboratoire des Sciences du Climat et de l'Environnement, France
7. Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, USA
8. Center for Global Environmental Research, National Institute for Environmental Studies(NIES), Japan
9. Earth from Space Institute, Universities Space Research Association, USA
10. Terrestrial Information Systems Laboratory, NASA Goddard Space Flight Center
11. Earth System Science Interdisciplinary Center, University of Maryland, USA
12. Appalachian State University, USA
The Open-source Data Inventory for Anthropogenic CO2 (ODIAC) is a global, high-resolution, gridded emission productthat provides CO2 emission estimates from fossil fuel combustion, cement production and gas flaring. ODIAC is basedon spatial disaggregation of country-level emission estimates made by the Carbon Dioxide Information Analysis Center(CDIAC) and provides a complete high-resolution picture of the global fossil-fuel CO2 emission based on a multiplicity
of geospatial information. While ODIAC was originally designed for atmospheric tracer transport simulations and inverse
flux calculations using data collected from carbon observing satellites, such as the Japanese Greenhouse gas Observing
SATellite (GOSAT) and NASA’s Orbiting Carbon Observatory (OCO), ODIAC has played a key role in many carbon
budget analyses across different spatial domains, from global, through regional, to city scales, using data collected fromdifferent observation platforms. It has also been used to guide observing system design studies for future carbon missions.
Thus, the errors and uncertainties in ODIAC are now of great interest to the wide variety of the data users. This study
attempts to assess errors and uncertainties in ODIAC and their impacts in studies associated with the use of ODIAC. The
evaluation of the gridded emission data products like ODIAC is fundamentally challenging because of the lack of physicalmeasurements. We started with comparing ODIAC to GESAPU, a multi-resolution emission data product for Poland, inorder to characterize errors and biases in the ODIAC emission disaggregation. We also employed NASA’s PCTM and
Penn State’s WRF to evaluate ODIAC at global and urban scales, in relation to other emission datasets, such as CDIAC’s

97

global gridded data and the Hestia urban emission product. Our analyses show a promise of the continued, significant
utility of the updated ODIAC emissions for ongoing/future missions, such as GOSAT-2, OCO-3 and beyond.

Key words: ODIAC, fossil fuel CO2 emission, emission inventory, GOSAT, OCO

98Solving Methane Fluxes at Northern Latitudes using Atmospheric andSoil Earth Observations Data

Hannakaisa Lindqvist*1, Aki Tsuruta1, Ella Kivimäki1, Tuula Aalto1, Vilma Kangasaho1, Tomi
Karppinen2, Tuomo Smolander1, Maria Tenkanen1 and Kimmo Rautiainen1
1. Finnish Meteorological Institute, Helsinki, Finland
2. Finnish Meteorological Institute, Sodankylä, Finland

Combined Earth Observations of atmospheric greenhouse gases and the cryosphere have the potential to fundamentally
increase our understanding of the carbon cycle specifically at high Northern latitudes. In this work, we investigate theconnection of the fluxes to the soil freezing and thawing at boreal and Arctic latitudes. We combine methods for thequantification of CH4 emissions by applying data from several Earth Observing (EO) satellites, ground-based retrievals,
in-situ measurements, and global atmospheric methane inversion model estimates. The EO data consist of a global soil
freeze/thaw estimate obtained from the ESA Soil Moisture and Ocean Salinity (SMOS) mission as well as retrievals of
atmospheric column-averaged methane (XCH4) obtained from the Greenhouse Gases Observing Satellite (GOSAT) andSentinel 5 Precursor TROPOMI (S5P-TROPOMI) observations. These EO data will be used in global atmosphericmethane inversion model, CarbonTracker Europe – CH4, simulations, focusing on the identification and quantificationof CH4 sources in the Northern Hemisphere and a trend analysis on the total methane emissions. EO data will be used toassess the spatial variability of the emissions and to better quantify the contributions from regions dominated byanthropogenic emissions and by natural emissions, especially those from wetlands. Further, EO data will be used to create
proxies of the seasonality of the natural methane emissions, focusing on the timing and length of the autumn freezing
period and springtime melting period. The combined use of several Earth Observation datasets together with inverse
modeling will increase the accuracy of the methane flux estimates at the remote Arctic and boreal regions where in-situ
measurements are still rare, and also lead to a better understanding of the longer-term link between the changes in the soil
conditions and the resulting methane fluxes.
Keywords: CH4, Arctic, Boreal, cryosphere, GOSAT, SMOS, TROPOMI, CTE-CH4

99

Relationship between methane enhancements observed by GOSAT
and country scale anthropogenic emissions in Asia
Rajesh Janardanan1*, Shamil Maksyutov1, Akihiko Ito1, Yukio Yoshida1, Tsuneo Matsunaga1
1 National Institute for Environmental Studies, Japan
Anthropogenic greenhouse gas emissions need to be better quantified to strengthen the efforts for climate change
mitigation plans. Here we report the update result of our attempt to estimate anthropogenic signal from GOSAT
XCH4 observations for a period from 2009 to 2016 (NIES L2 v02.72) and to use their relation to the underlying
emission on spatial scale of large countries over Asia for an independent verification purpose. We employ a global
Eulerian transport model (NIES-TM) coupled to a Lagrangian particle dispersion model (FLEXPART) with highresolution methane emission inventories to simulate GOSAT XCH4 observations. The anthropogenic emissions weretaken from EDGAR (v4.3.2), wetland emissions were calculated using VISIT model and biomass burning emissions
taken from GFAS, all at 0.1° resolution and meteorological fields were from JCDAS at 1.25°. We used the
Lagrangian simulations using anthropogenic emission alone at GOSAT observation locations to classify theobservations into polluted or background scans using XCH4 threshold. We then use observations and the coupledmodel simulations to calculate monthly regional background values and the XCH4 anomalies as deviations fromtheir respective backgrounds of model simulations and observations. The bin-averaged pair of anomalies were used
for a weighted linear regression accounting both errors in simulated and observation bins, with simulated against
GOSAT observed over various regions over Asia. The regression slope is a measure of how the observed anomalies
relate to the model simulation based on inventory over the region and can be used to correct inventory towards
fitting to GOSAT observed enhancements. Our results suggest that the anthropogenic methane emission in India andMiddle East is possibly lower than EDGAR inventory, while in China it is close to inventory. Over Japan, GOSAT
observed anomalies suggest slightly higher emissions than inventory though the uncertainty in this estimate is larger
owing to the limited number of observations.
Key words: GOSAT; anthropogenic methane emissions; emission verification

100Characterizing and mitigating the impact of model transport errors
on CO2 flux estimates in the assimilation of XCO2 data from OCO-2
Dylan Jones *1, Feng Deng1, Tailong He1, Ilya Stanevich1, and Xuesong Zhang1
1. Department of Physics, University of Toronto, Toronto, Ontario, Canada
Inverse modeling of atmospheric CO2 holds the promise of providing better estimates of regional sources and sinks of
CO2. However, discrepancies in the atmospheric transport models employed in these analyses have posed a challenge toobtaining robust estimates of the sources and sinks. Here we use a weak constraint four-dimensional variational (4D-Var)data assimilation scheme to assimilate atmospheric CO2 data from the surface observing network and from the Orbiting
Carbon Observatory (OCO-2) to optimize the distribution of atmospheric CO2 in the GEOS-Chem chemical transportmodel. We investigate the adjustments to the CO2 distribution produced by the weak constraint 4D-Var scheme to quantify
transport errors in GEOS-Chem. In particular, we examine the inferred spatial and temporal variability in the optimized
CO2 distribution and compare the information provided by the surface and OCO-2 data on atmospheric CO2 tocharacterize the model errors. We also quantify the impact of these errors on regional flux estimates of CO2.
Key words: OCO-2, Flux Inversion

101

The seasonal and inter-annual variations of regional CO2 and CH4 fluxes
estimated from GOSAT data
Hiroshi Takagi1*, Heon-Sook Kim2, Shamil Maksyutov1, Makoto Saito1, and Tsuneo Matsunaga1
1National Institute for Environmental Studies (NIES), Japan
2 Pusan National University, Busan, Republic of Korea

Determining in detail how the Earth’s carbon cycle is being modified by human activities and changing climate is anurgent societal need, and this called for establishing a coordinated system of space-based GHG monitoring platforms that
can augment the existing networks of ground-based GHG observation in spatial and temporal coverage. The Greenhousegases Observing SATellite (GOSAT), launched in early 2009 from Japan, is one of the pioneering spacecrafts dedicatedtoward that purpose, and has been making global measurement of reflected light spectra from which column-averaged
concentrations of CO2 and CH4 are retrieved. At NIES, these retrieved concentrations are combined with ground-based
observations to estimate monthly CO2 and CH4 surface fluxes on a sub-continental scale. The results of the flux estimation
are made public as GOSAT Level 4 data products.
We herein present the latest version of the Level 4 CH4 product (coverage: Jun. 2009 – Sep. 2015) and a candidate for the
next release of the Level 4 CO2 product (Jun. 2009 – Oct. 2017). Notable findings in the CH4 flux estimates include alarge year-to-year increase in CH4 global total flux found in 2013-2014, which is in line with a sharp rise in annual CH4
growth trends monitored by NOAA base-line stations. The largest contribution to this global emission rise was found tocome from middle South America, where anomalously high precipitations were observed in 2011 and 2014; the regional
CH4 flux was found to be highly correlated with the region’s water balance indicators. The 101-month-long Level 4 CO2
product candidate was produced using a bias-corrected version of GOSAT Level 2 column-averaged CO2 distribution(v02.75) and climatological VISIT biospheric flux data as an a priori information, both of which are new to the Level 4
CO2 data processing. The result of evaluation is presented.

102

Comparing national methane emissions inventories withestimates by the global high-resolution inverse model
Fenjuan Wang1,2, Aki Tsuruta1,3, Shamil Maksyutov1, Rajesh Janardanan1, Akihiko Ito1, Motoki
Sasakawa1, Toshinobu Machida1, Isamu Morino1, Yukio Yoshida1, Johannes W. Kaiser4, Greet
Janssens-Maenhout5, Ed Dlugokencky6, Tsuneo Matsunaga1
1. National Institute for Environmental Studies (NIES), Japan
2. National Climate Center, China
3. Finnish Meteorological Institute, Finland
4. Deutscher Wetterdienst, Germany
5. European Commission, Joint Research Centre (JRC), Italy
6. National Oceanic and Atmospheric Administration, USA
The global and regional methane emissions were evaluated using the newly developed global 0.1° × 0.1° high spatial
resolution inverse model NIES-TM-FLEXPART-VAR (NTFVAR) and atmospheric methane observation data at
global in-situ network archived at WDCGG and NIES. Prior fluxes at 0.1° resolution were prepared with two
variants of anthropogenic emissions, one is EDGAR v4.3.2 and the other is EDGAR gridded emissions scaled tomatch country totals by national reports to UNFCCC for 15 top emitting countries, augmented by biomass burning(GFASv1.2) and wetlands (VISIT). We compared the national reports of anthropogenic methane emission to
UNFCCC and EDGAR4.3.2. The ratio of global anthropogenic methane emission scaled to UNFCCC to that by
EDGAR is 97.5%, but the ratio varies by region, from nearly 200% in Russia to 84% in China and 62% in India.
We selected the year 2012 to compare the globally and regionally optimized methane emissions to both priors. Our
inverse model yields a net methane source of 528 Tg in the year 2012 with pronounced regional corrections to thepriors. With changing prior from EDGAR country totals to UNFCCCC totals emissions in India is reduced, butinversion corrects emissions upward. For Russia, increasing prior to match national report leads to lowering theemissions after correction by inversion. Estimated anthropogenic emissions over large regions (US, China, India
and Russia) are comparable to GCP-CH4 top-down estimates and inverse model estimates based on GOSAT data.
As there are differences between country scale inventories and model estimates, biases of the inversion need to beunderstood and reduced, and further effort is needed to achieve robust separation of the natural (wetlands) andanthropogenic emissions on a global scale, in the tropical band and in the extra-tropics.
Key words: methane emissions; inverse model; GOSAT

103

IWGGMS15, 3-5 June 2019, Sapporo, Japanlnverse modeling of anthropogenic methane emissions based on
ground-based monitoring and GOSAT satellite retrievals
Aki Tsuruta*1, Shamil Maksyutov2, Rajesh Janardanan2, Akihiko Ito2, Johannes
Kaiser3, Greet Janssens-Maenhout4, Ed Dlugokencky5, Motoki Sasakawa2,
Toshinobu Machida2, Yukio Yoshida2, Tsuneo Matsunaga2
1. Finnish Meteorological Institute, Finland
2. National Institute for Environmental Studies, Japan
3. Deutscher Wetterdienst, Germany
4. European Commission, Joint Research Centre (JRC), Italy
5. National Oceanic and Atmospheric Administration, Global Monitoring Division, USA
We perform a global high-resolution methane flux inversion to estimate methane emissions using atmospheric methane
data available from the WDCGG archive, ICOS and JR-STATION networks, and GOSAT satellite retrievals. For betteraccounting of anthropogenic emissions localized around large cities, we use the Lagrangian particle dispersion model
FLEXPART to model local tracer transport at 0.1° spatial resolution. FLEXPART is coupled to a global atmospheric
tracer transport model (NIES-TM). Prior fluxes at 0.1° resolution were prepared for anthropogenic emissions (EDGAR
v4.3.2), biomass burning (GFAS), and wetlands (VISIT). The inverse model NIES-TM-FLEXPART-VAR (NTFVAR)
applies variational optimization to two categories of fluxes: anthropogenic and natural (wetlands). Bi-weekly emissions
are estimated for 2009 to 2017. The coupled transport model manages to better reproduce the ground-based continuous
observations than using only NIES-TM, because it can resolve anthropogenic emission plumes. Monthly meandifferences between the GOSAT data and the inversion-optimized forward simulation are estimated for each 5° latitude
band and then subtracted from GOSAT retrievals before including them in the inversion. The bias correction is designed
to remove large scale biases in GOSAT retrievals, while retaining local scale variability that contains most of theinformation on anthropogenic emissions. Estimated anthropogenic emission trends showed a slight increase in the US
and India, and a significant increase in China during 2010-2017. The estimated emission trends and seasonal cycles arefurther evaluated by comparing with other available estimates.

Key words: GOSAT; CH4; atmospheric inversion

104Retrieval of solar-induced chlorophyll fluorescence from
TanSat space measurements
Lu Yao*1,2, Yi Liu1, Dongxu Yang1
1. Institute of Atmospheric Physics, Chinese Academy of Sciences
2. University of Chinese Academy of Sciences, China

Solar-induced chlorophyll fluorescence (SIF), re-emitted during the conversion of sunlight to chemical energy in plantleaves, constitutes a very small additional offset to the reflected radiance, which can be observed by sensitive remotesensing detectors. The Chinese global carbon dioxide monitoring satellite (TanSat) is the third satellite aiming at acquiring
greenhouse gas column density. The advanced technical characters of the hyper-spectrum grating spectrometer (ACGS)
onboard TanSat enable the retrieval of SIF from the space observation with just the spectrum in near-infrared band. We
retrieved one year of global SIF data from OCO-2 and TanSat measurements with a simple and robust method to makean inter-comparison of the two instruments performance. The global seasonal distribution shows that the SIF retrieved
with the KI Fraunhofer lines around 771nm from two satellite missions share the same spatial pattern in all seasons, which
agrees with the OCO-2 L2 SIF official product. With one year of data, the regional temporal variation of SIF varies fromdifferent kinds of vegetation types, as well as the surrounding climate conditions mainly dominated by the geographic
location. The consistency of temporal variation between TanSat and OCO-2 makes the comprehensive usage of these twokinds of products possible. Overall, the preliminary SIF result from TanSat observation could help to make a morevaluable global SIF map and that will promote the research on global vegetation photosynthesis from space measurement.

Key words: TanSat; SIF; Retrieval

105Long-term evaluation of zero-level offset in GOSAT FTS O2 A-band
and consistency of the derived SIF with OCO-2 SIF
Haruki Oshio*1, Yukio Yoshida1 and Tsuneo Matsunaga1
1. National Institute for Environmental Studies (NIES), Japan
Satellite remote sensing of solar induced chlorophyll fluorescence (SIF) has attracted attention as a method for improving
the estimation accuracy of photosynthesis activity of terrestrial vegetation in recent years. Evaluating the artifact signal(e.g., zero-level offset caused by non-linearity in the analogue circuit in case of Greenhouse gases Observing SATellite(GOSAT)) is effective to infer the instrument status and important to retrieve SIF from satellite measurements. Although
the zero-level offset of GOSAT has been evaluated, the detailed spatiotemporal variation has not been investigated.
Recently, SIF data have been provided by several satellites, and we are planning to provide SIF data as the GOSAT-2
product; therefore, the consistency among satellite SIFs is important to utilize those data. Agreement among SIF data
derived from satellite sensors has partly been reported, but difficulty due to difference in the observation pattern remains.
Here we investigate the criteria for identifying vegetation-free areas to evaluate the GOSAT zero-level offset and theoffset correction method, while comparing the derived SIF with Orbiting Carbon Observatory-2 (OCO-2) SIF at multiple
spatial scale (footprint to global). Criteria were determined as small variation in radiance within the GOSAT instantaneous
field of view for cloudy ocean scenes and higher albedo in 2.0 μm band than in 1.6 μm band for bare soil scenes, whichwere slightly different from the previously used criteria. GOSAT SIF that was most consistent with OCO-2 SIF was
obtained when the zero-level offset was evaluated from bare soil over the globe, with bias of about 0.1 mW m-2 nm-1 sr-1.
Our results support the consistency among the present satellite SIF data. Temporal variation of the zero-level offset for 9
years suggests that the radiometric sensitivity of the GOSAT spectrometer changed after switching the optics path selector
in January 2015.
Key words: solar induced chlorophyll fluorescence (SIF); GOSAT; GOSAT-2; OCO-2

106

Assessing the temporal dynamics of satellite-derived photochemicalreflectance index (PRI) and solar-induced fluorescence (SIF) in climatechanging Mongolia
Tomoki Kiyono*1, Hibiki Noda1, Kouki Hikosaka2, Tomo'omi Kumagai3, Yukio Yoshida1, andHaruki Oshio1
1. National Institute for Environmental Studies (NIES), Japan
2. Tohoku University, Japan
3. The University of Tokyo, Japan
Photosynthetic production is a fundamental process of ecosystems and it is sensitive to hydrometeorologicalconditions. In several studies, the vegetation indices which correlate with foliage amount, e.g., NDVI have been used tomonitor temporal changes in gross primary production (GPP) [e.g., Muraoka et al. 2012]. However, using only such
indices has difficulties in arid region [Turner et al. 2005] and evergreen forest, where light use efficiency (LUE) becomemuch important for GPP. The recent advent of satellite remote sensing enables us to obtain two optical indices, which are
directly linked to LUE; photochemical reflectance index (PRI) and solar-induced fluorescence (SIF). In a photosynthetic
process, a part of absorbed light energy is consumed as heat dissipation and some other part is emitted as fluorescence.
PRI correlates with the state of the xanthophyll cycle which is related to heat dissipation [Gamon et al. 1992] and SIF is
the fluorescence observed under solar-radiation. Thus, PRI and SIF may give us complementary information. However,previous studies have simply discussed the seasonality and/or trends in the correlations between satellite-PRI/SIF and
GPP. To use PRI and SIF, we have to consider physiological processes more deeply.
In the present study, we examined temporal changes in satellite-PRI and SIF in and around Mongolia (100°E–120°E
and 40°N–50°N), includes desert, grassland, and boreal forest. This region is under rapid warming and experienced
historic drought events in the late 1990s and 2000s [Hessl et al. 2018]. To reveal the temporal changes in PRI and SIF
under the drought event, we analyzed satellite data, metrological data, and ground-measured CO2 flux data. PRI was
obtained from new MODIS surface product, version 6 [Vermote and Wolfe 2015] for both morning (Terra-MODIS) andafternoon (Aqua). SIF was retrieved from GOSAT, according to Frankenberg et al. (2011). In this presentation, we will
discuss the drought effect on photosynthetic activity in grassland and boreal forest in this region.

Key words: PRI; SIF; Drought Response; MODIS; GOSAT

107

Solar induced Fluorescence (SIF) Mapping from the Copernicus
Anthropogenic CO2 Monitoring Mission
H. Boesch*,1,2, A. Webb1,2, P. Somkuti1,5, D. Yang1, A. Di Noia1, R. Parker1,2, M. Buchwitz3,
Y. Meijer4
1

2

Earth Observation Science Group, University of Leicester, Leicester, UK
National Centre for Earth Observation NCEO, University of Leicester, Leicester, UK
3
IUP, University of Bremen, Bremen, Germany
4
ESA, Noordwijk, The Netherlands
5
Now at Colorado State University, Fort Collins, USA

Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. Unused
absorbed photosynthetically-active radiation is released by plants primarily as heat with a small fraction being re-emitted
as fluorescence in the shortwave-infrared range. Thus, the fluorescence signal is related to the photosynthetic process andoffers thereby a potential to constrain the gross primary production (GPP) and to gain a more mechanistic understandingof the ecosystem carbon exchange. Although this fluorescence signal is small (about 1% of the reflected sunlight), it canbe well measured from space and global fluorescence dataset are now available from sevel satellite missions.

The Copernicus Anthropogenic CO2 Monitoring Mission is the space segment of a proposed operational capacity tomonitor fossil fuel CO2 emissions by the European Commission (EC) and the European Space Agency (ESA) in the
support of the Paris Agreement. The space system will measure images of total column CO2 from a multiple (low Earthorbiting) satellite constellation each with an across-track swath width of ~200 km providing global coverage every 3-dayswith a ground pixel size of 4 km2. The satellite will also measure the vegetation fluorescence signal which is needed as acorrection for the CO2 retrieval procedure but which will also provide valuable carbon cycle information that can be usedin synergy with the CO2 observations.

In this this presentation, we will describe the expected performance of the fluorescence retrieval obtained from theCopernicus Anthropogenic CO2 Monitoring Mission. We will discuss the implication of the improved coverage andresolution obtained from this mission compared to existing missions. We will also discuss different sources of potential
biases in the fluorescence retrieval, in particular from instrument calibration uncertainties and we will investigate how
well these biases can be corrected using soundings over non-vegetated areas.

108Implementing SIF estimation process to the terrestrial ecosystemmodel VISIT and applying the radiation transfer model
Tatsuya Miyauchi*1, Makoto Saito1, Hibiki M. Noda1, Yukio Yoshida1, Haruki Oshio1 and Akihiko
Ito1
1. National Institute for Environmental Studies (NIES), Japan
Since the solar-induced chlorophyll fluorescence (SIF) was reported that it could be retrieved from spectrum data
observed by Greenhouse Gases Observing Satellite (GOSAT), many studies using SIF for GPP estimation and model
improvement are actively conducted. The estimation of CO2 absorption and emission in the terrestrial vegetation isimportant to estimate global CO2 fluxes. The Vegetation Integrative Simulator for Trace graces (VISIT) is used to computethe prior fluxes in terrestrial ecosystem to produce GOSAT L4 products. To improve VISIT using SIF can contribute to
upgrade L4 products. To adjust the model and its parameters using GOSAT SIF, the model is required to compute the SIF
as prior value. We implemented the SIF estimation based on the biochemical process about the energy balance between
photochemistry, thermal deactivation, fluorescence and non-photochemical quenching (NPQ) to VISIT. To compare
computed SIF to GOSAT SIF, SIF calculated by VISIT must be converted SIF of satellite-observed direction. Moreover,
SIF emitted sunshade leaf is too complex to calculate by high temporal and spatial resolution. Therefore, we used lookup-table (LUT) created by pre-computation using the radiation transfer model. We used SCOPE model, which is used inmany previous studies for radiation transfer calculation in canopy. By using LUT for two ratios (1: shade leaf SIF to sunleaf SIF, 2: SIF of observed direction to SIF of sun zenith direction), we converted SIF calculated by VISIT to satelliteobserved SIF using the LUT and compared with GOSAT SIF. In our presentation, we will introduce the description of
ongoing developing VISIT-SIF model and our future plan applicating satellite-observed SIF to the model.

Key words: SIF; VISIT; SCOPE; GOSAT; GOSAT-2

109

Methane isotopologue parameter assessment of multiple spectral
databases using TCCON
Edward Malina*1, Ben Veihelmann1, Dietrich G. Feist2,3 and Isamu Morino4
1
2

Earth and Mission Science Division, ESA/ESTEC, the Netherlands

German Aerospace Center (DLR), Institute of Atmospheric Physics, Germany
3

Max Planck Institute for Biogeochemistry, Jena, Germany
4

National Institute for Environmental Studies, Japan

Copernicus Sentinel-5P with TROPOMI, and the future Sentinel 5 with the UVNS (Ultra-violet, Visible, Near
InfraRed, Shortwave Infrared) instrument will enable daily global measurements of methane (and other trace gases).
Both instruments, as well as the recently launched GOSAT-2/TANSO-FTS-2 exploit the spectral range 2305-2385
nm in the Short Wave InfraRed (SWIR) to retrieve methane total column information.
In this study, we assess TCCON retrievals of the two main methane isotopologues 12CH4 and 13CH4 in the
TROPOMI SWIR band, and the 1630-1700 nm spectral region using the TCCON GGG retrieval environment. Four
separate spectroscopy databases are used for this study: the TCCON spectroscopy database, the HITRAN2016
database, the GEISA2015 database, and the ESA SEOM-IAS database. The latter supports advanced treatment ofline shapes by including non-Voigt line shape parameters, which is not included in the other databases assessed here.
In this study, we analyse high spectral resolution irradiance spectra measured in solar occultation from two TCCON
sites, Ascension Island (for background variations) and Tsukuba, Japan (for variable seasons).
Retrieval experiments have been conducted for a variety of cases with different spectroscopic databases and spectral
fit windows. We find notable differences in the retrieved volume mixing ratios of methane isotopologues for both
TCCON sites. Fitted mixing ratios vary depending on the spectral band under investigation (in some cases up to 5%
for 12CH4). Differences are persistent throughout the day, and across different sites. In most cases, however, there ismore bias between the spectral bands, rather than between the spectroscopy databases. The SEOM-IAS databasetypically shows the best-fit metrics, i.e. lowest fit residuals and lowest posteriori retrieval errors. 13CH4 isparticularly challenging, with large variations in fit results and retrieval quality both across the spectral bands anddatabases (up to 20% retrieval volume mixing ratio bias), and non-uniformity between the 2300 nm and the 1650 nm
spectral ranges.
Keywords: Copernicus; TCCON; methane; spectroscopy; non-Voigt.

110

In situ measurement of vertical distribution of CO 2 and CH4 in the
troposphere by aircraft and tethered balloon

Xiaoyu Sun*1, Minzheng Duana,1, Xiangao Xia1, Disong Fu1 and Zhongdong Yang2
1. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing
2. National Satellite Meteorological Center, CMA, Beijing

Several Satellites have been launched into space to monitoring the greenhouse gases concentration, by observing theback-scattered hyper-spectral radiance in the SWIR, in the atmosphere therefore the vertical profile of greenhouse gases,
especially carbon dioxide, and aerosol could greatly modulate the retrievals. To investigate how the interplay process of
the CO2 and aerosol scattering in the atmosphere, which is blamed for the uncertainty of the retrieval results of satellite
measurements. Therefore, knowledge of CO2 vertical distribution is crucial for the development of satellite-borne retrieval
methods and algorithm. Aircraft in situ measurements of carbon dioxide mixing ratio and methane over Jiansanjiang(46.77 ゜ N, 131.99 ゜ E) (August 2018) were conducted, and compared to the retrieval results of Orbiting Carbon
Observatory 2 (OCO-2) and Chinese Carbon Dioxide Observation Satellite (TanSat). The aircraft measurements werecarried out between altitudes of 0.6 to 7 km, and obtained vertical profile of CO2 and CH4 by an Ultra-Portable Greenhouse
Gas Analyzer (Los Gatos Research, LGR). A constant increase of an averaged 15.26 ppm in CO2 mixing ratio wereobserved between altitude 2 to 7 km during the flight period. The methane measurements show an averaged 0.5 ppm in
CH4 mixing ratio increased below 2.0 to 0.6 km caused by the large emission from wide range paddy field below, and themixing ratio above 2 km varies between 1.951 to 1.976 ppm without large mutation.
Another vertical profile measurements of CO2 and CH4 on tether-balloon platform was conducted on Changshou(107.00° E, 29.84° N) (January 2019). In this study, vertical profiles of CO2 mixing ratio measurements was made at low
troposphere within altitude 0 to 1000 km. A strong vertical mixture of CO2 was observed between 0 to 700 km, and mostprofiles presents declining trends of CO2 and CH4 mixing ratio with the increase of altitude, since the experiment site
located in an industrial park with a large greenhouse gases emission source. The results would enhance the understandingof the spatial variation of CO2 and CH4.
Key words: Aircraft; tethered balloon; TanSat; OCO-2

111

Measuring in-situ CO2 profile and comparison with satellites
and model
You Yia, b, Yi Liua, b, Zhaonan Caia, Shuangxi Fangc, Dongxu Yanga, Yong Wanga, Miao Liangc,
Bo Yaoc, Qianli Mada

Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, Chinab
University of Chinese Academy of Sciences, Beijing 100049, Chinac
Meteorological Observation Centre(MOC), China Meteorological Administration (CMA),
Beijing 100081, ChinadZhejiang Lin’an Regional Atmosphere Watch Station, Zhejiang 311307, China
corresponding to caizhaonan@mail.iap.ac.cn

The vertical distribution of carbon dioxide in the atmosphere is critical to validate the vertical columns measured bysatellite and ground-based spectrometers and estimate the sinks and sources of carbon dioxide. This work describes thein-situ air-sampler system (Aircore). Two profiles of CO2 and CO were retrieved from two soundings of this system in
Xilin Hot on 13 and 14 June, 2018 and are compared with Orbiting Carbon Observatory-2 (OCO-2), Tropospheric
Monitoring Instrument (TROPOMI) and Copernicus Atmosphere Monitoring Service model (CAMS) respectively.
Results show that the column-averaged mixing ratio of CO2 (XCO2) of OCO-2 agrees well with XCO2 calculated fromthe in-situ CO2 profile, with a small mean differences of 5±1.89 ppb, TRPOMI XCO agrees well with XCO calculated
from in-situ CO profile with a slight bias of 0.4±0.6 ppm. OCO-2 a posterior profile has an obvious bias against in-situ
CO2 profile on 13 June with an underestimation between 600 hPa and 250 hPa and an overestimation below 600 hPa.
CAMS also has the same bias against in-situ CO profile, however, CAMS agrees well with in-situ CO profile on 14 Junein the troposphere because a weak cold vortex passed Xilin Hot on 13 June and disappeared the other day. In thestratosphere, there are significant bias in the vertical degradation. The striking agreement of the column-averaged mixing
ratio and the difference of vertical distribution indicate new exciting applications for the profile product.

112

Provision of GOSAT data from the WDCGG website
Atsuya Kinoshita*1, Saki Ohkubo1 and Seiji Miyauchi1
1. Japan Meteorological Agency (JMA), Japan
The World Data Centre for Greenhouse Gases (WDCGG; a World Data Centre (WDC) of the World MeteorologicalOrganization (WMO)) has been operated since 1990 by the Japan Meteorological Agency (JMA).

As the only WDC

specializing greenhouse gases, it serves to collect, archive and distribute data on such gases (e.g., CO2, CH4, CFCs andN2O) and other related gases (such as CO) in the atmosphere.

On 19 March 2019, WDCGG began online provision of CO2 observation data* from Japan’s Ibuki Greenhouse gases
Observing SATellite (GOSAT) for the period from April 2009 (https://gaw.kishou.go.jp/satellite) in addition to existing
surface-based data.

Integration of remote sensing satellite data and existing surface-based in situ data is expected topromote the wider use of this information and facilitate long-term monitoring of global distribution and sub-continental
CO2 emission/absorption estimates.

WDCGG plans to continue improving its services for the collection, archiving and distribution of satellite data worldwide,
including for GOSAT-2 (the successor to GOSAT), to support the monitoring of climate change and assist policy making,thereby helping to reduce environmental risks to society.

Overview and objective of the website are presented anddiscussed.

*The original GOSAT data products are distributed by the National Institute for Environmental Studies, Japan (NIES).
GOSAT Data Archive Service (GDAS, https://data2.gosat.nies.go.jp/index_en.html)

Key words: GOSAT; GOSAT-2; WDCGG

113

Intercomparison of XCO2、XCH4、XCO measurements using
EM27/SUN and IFS125HR in Xianghe
Ke Che*1, Yi Liu1, Dongxu Yang1, Zhaonan Cai1, Pucai Wang2, Yang Yang2, You Yi2, Sihong
Zhu2
Key Laboratory for Middle Atmosphere and Global Environment Observation
Institute of Atmospheric Physics, Chinese Academy of Scinces
We present work on use a compact solar-tracking Fourier Transform Spectrometer (Bruker EM27/SUN) to precisely
derive total column averaged amount of Greenhouse gases.
In order to ensure the high quality of the retrieved data, the total column averaged XCO2 measured from Bruker
EM27/SUN are compared to well calibrated IFS125HR in Xianghe (39.798˚N, 116.958˚E, 50m a.s.l.) as reference. We
also processed the EM27/SUN data using PROFFAST and GGG2014 for further investigation the performance of the
EM27/SUN. The comparison between EM27/SUN and IFS125HR shows a 0.24% bia with GGG2014+EGI, while a biasof 0.53% approached by PROFFAST. To further characteristics the differences between the two algorithms, Xgas has
been measured by EM27/SUN in Beijing (IAPCAS) with coordinate weather record by WS500 weather station. The
GGG2014 and PROFAST are involved in data processing, but found a bias of 0.20%, 1.23%, -1.0% for XCO2, XCH4
and XCO respectively. Xair calculated by the above two algorithms are approximately 1.0012、0.9831. The correlation
coefficient is 0.9979 for daily median XCO2 between the result of these two retrieval algorithms. Reasons for thesedifferences could be attributed to the difference in pre-processing method, instrument lines shape model, gas spectroscopy.
Furthermore, field campaign collaborating EM27/SUN and aircore soundings will contribute to the greenhouse gases
validation for TanSat, Sentinel-5P as well as other greenhouse gas satellites, GOSAT and OCO-2.

Key words: EM27/SUN; intercomparison; validation

114

Towards Tracking East Asian Transport of Pollution using the Burgos
TCCON site and the GOSAT Series Satellites
Voltaire A. Velazco*1,2, Isamu Morino3, Osamu Uchino3, Nicholas M. Deutscher1 and David W. T.
Griffith1
1. University of Wollongong, Australia
2. OML Center for Climate Change and Disaster Risk Management Foundation, Philippines
3. National Institute for Environmental Studies (NIES), JapanThe Burgos TCCON station, which started operations in March 2017, has been validated using aircraft profiles within
the EMeRGe mission, providing the last step towards a “full TCCON” site status. In this new project, we plan to
combine existing resources at the Burgos TCCON site in the Philippines and nearby sondes from Laoag airport withGOSAT and GOSAT-2 data to measure, monitor and understand the formation and transport of pollutants fromSoutheast Asia. As a first step, we aim to analyze measurements taken at the Burgos TCCON site and investigate thecorrelation of aerosols properties, water vapor and thin clouds on the column of trace gases measured from TCCON. We
will combine this analysis with data from an aircraft campaign (NASA-CAMP2Ex). As a second step, we plan tocompare GOSAT-2 retrievals over Ocean of XH2O, XCO, XCO2, and XCH4 with the ground based measurements. In
the third step, we will utilize a model to analyze the transport and address the question of how anthropogenic emissions
from East Asian countries are formed and transported to the tropical western Pacific.

Key words: GOSAT; GOSAT-2; Pollution, Validation; TCCON

115

Intercomparison between TCCON XCO2 and XCH4 data in Japanand Philippines via a portable Fourier transform spectrometer
Hirofumi Ohyama*1, Isamu Morino1, Akihiro Hori1, Kei Shiomi2, Nobuhiro Kikuchi2, Voltaire A.
Velazco3 and David W. T. Griffth3
1. National Institute for Environmental Studies (NIES), Japan
2. Japan Aerospace Exploration Agency (JAXA), Japan
3. University of Wollongong, Australia
Accuracies and precisions of column-averaged dry-air mole fractions of CO2 and CH4 (XCO2 and XCH4) obtained fromspace-borne measurements become higher due mainly to an improvement of retrieval algorithm, and therefore
requirements for the consistency between data from the Total Carbon Observing Network (TCCON), which are widelyused for validating the space-borne XCO2 and XCH4 data, is growing. In the present study, site-by-site biases across thefour TCCON sites in Japan and Philippines (Rikubetsu, Tsukuba, and Saga in Japan and Burgos in the Philippines) were
evaluated by using a portable and robust Fourier transform spectrometer (EM27/SUN) as a reference. Both the TCCON
and EM27/SUN data were analyzed with the GGG2014 software. The obtained XCO2 and XCH4 data for each instrument
were averaged every 10 minutes and were directly compared (i.e., not considering the difference in averaging kernels).
The mean differences in TCCON XCH4 with respect to EM27/SUN XCH4 were consistent across the four sites withnegative biases of 10.0–14.2 ppb. To probe the causes of the negative biases, the effects of the difference in spectral
resolution on the XCH4 data were examined. For this purpose, the raw TCCON interferograms were truncated to the
maximum optical path difference of the EM27/SUN and were analyzed in the same procedure as the original TCCON
spectra. We found that the XCH4 biases were reduced to a few ppb and most of the biases are attributed to the differencein the spectral resolution. As for XCO2, the mean differences were 1.57, 0.03, 0.75, and 0.60 ppm for Rikubetsu, Tsukuba,
Saga, and Burgos, respectively. Although the causes of the site-by-site XCO2 biases are under discussion, the candidate
may be a parameter related to a phase correction of spectra because a change in the parameter resulted in decreases in thesite-by-site XCO2 biases.
Key words: TCCON; EM27/SUN; CO2; CH4116

Ground-based measurement of solar-induced chlorophyll fluorescencewith high-resolution spectrum in paddy field ecosystem, Japan
Kanokrat Buareal*1, Tomomichi Kato2 and Keisuke Ono3
1. Graduate School of Global Food Resources, Hokkaido University, Japan
2. Research Faculty of Agriculture, Hokkaido University, Japan
3. National Institute for Agro-Environmental Sciences, JapanThe terrestrial ecosystem photosynthesis plays a crucial role in the global carbon cycling estimation. Accurate trackingof terrestrial gross primary productivity (GPP) can provide more important information about the CO 2 dynamics between
land and atmosphere as ecosystem level. Recently, tracking changes in photosynthesis at a global scale could be
investigated through Solar-Induced Chlorophyll fluorescence (SIF) by satellite. Although satellite-derived SIF showed ahigh correlation with GPP, the coarser spatial-temporal scale by satellite needs to be validated by performing a groundbased SIF assessment to better understand the dynamics of CO2 uptake by plants. SIF was measured by the high-resolution
spectrum at the Mase eddy flux site (36.05°N, 140.03°E), a rice paddy field (Oryza sativa L.; cultivar Koshihikari),
Tsukuba, Japan. The measurement was conducted from April to December using five spectroradiometers; FLAME,
HR4000 x2, QE Pro x2 (Ocean Optics, Dunedin, FL, USA), with 0.11-1.08 nm of the full-width at half maximum(FWHM). The spectroradiometers were connected via fiber switches (FSM1x8, Piezosystem Jena GmbH, Jena, Germany,and MOL-1x8-600-H, LEONI Fiber Optics GmbH, Föritztal, Germany) with to three optical fiber cables: the first one
was set looking downward at a viewing zenith angle of 45° as bare fiber (FOV of 25°), the second one was vertically set
looking downward (FOV of 180°), and the third one was vertically set looking upward to the sky (FOV of 180°). The
multispectral methods derived from the Fraunhofer Line Depth (FLD) principle, 3FLD, iFLD and Spectral Fitting Method(SFM) were used to estimate SIF and then compare with other conventional vegetation indices such as the normalized
difference vegetation index (NDVI) and Enhanced Vegetation Index (EVI). This study illustrated the preliminary resultof the ground-based measurement of SIF to verify that the SIF has the potential to be a promising proxy of photosynthesis
tracking in the paddy field ecosystem.

Key words: Fraunhofer Line Depth; Gross primary productivity; Photosynthesis; Spectroradiometers

117Quick look algorithm for GHG plume detection by using airborne
imaging spectrometer suite
Takahiro Kawashima1, Fumie Kataoka1 , Akihiko Kuze2 , Hiroshi Suto2, Kei Shiomi2 , Nobuhiro
Kikuchi2, Makiko Hashimoto2 and Shigetaka Mori3
1. Remote Sensing Technology Center of Japan,
2. Japan Aerospace Exploration Agency
3. JASTECS Corporation
Key words: Airborne, imaging spectrometer, GHG, methane, plume

In almost Japanese megacities, various CO2 and CH4 emission source like industrial activity (power plant, landfills,
gas factory, water processing plants), and agricultural activity (rice cultivation, pig farm) are concentrated within a few
tens kilometers region. An air borne imaging-spectrometer suite oriented for such CO2 and CH4 emission source
observation was newly developed by JAXA. The instrument consists of NIR spectrometer for O2-A band measurement
and SWIR spectrometer for CO2/CH4 measurement. NIR and SWIR spectrometers have wavelength coverage of 747–
783 nm with 0.09nm resolution and, 1560-1670nm with 0.17nm resolution respectively. Ground sampling distance isabout 50m with sampling time interval of 0.5 s.
We found enhancement of CO2 column density looking like ‘plume’ above Hekinan coal power plant located in Aichi
prefecture during a flight on February 16 2018. As a methodology for detection of the phenomenon, very simple band
ratio approach was enough because its location was predictable because of its large emission rate and already found bysatellite observation ( e.g. GOSAT, OCO-2 ) in the past.
On the other hand, detection of methane source was difficult, because of its relatively small variation of column
density to background (< 10%) as well as its un-known location with limited spatial extent (~ 100m). One ofremarkable features of the imaging spectrometer suite is its high spectral resolution which can improve an error in
determination of absorption depth caused by overlapping among lines as well as by spectral un-flatness of backgroundradiance. We discuss various methodologies like Continuum Interpolated Band Ratio (CIBR), reference spectral fitting
and matched filter for methane detection. We also discuss instrumental characterization and its correction like thermalvariation, optical aberration and frequency response of analogue circuit chain.

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119

NASA's Carbon Cycle OSSE Initiative - Informing future space-based
observing strategies through advanced modeling and data assimilation
Lesley Ott* , Sean Crowell , Dave Schimel , Chris O’Dell , David Baker , Andrew Schuh , Stephan
R. Kawa , Abhishek Chatterjee , Kevin Bowman , Berrien Moore III , and Piers Sellers
1

2

3

1

4

1,5

3

4

4

2

1

1. NASA Goddard Space Flight Center, Greenbelt, Maryland, U.S.A.
2. University of Oklahoma, Norman, Oklahoma, U.S.A.
3. Jet Propulsion Laboratory, Pasadena, California, U.S.A.
4. Colorado State University, Fort Collins, Colorado, U.S.A.
5. Universities Space Research Association, Columbia, Maryland, U.S.A.
Satellite observations of CO and CH are a critical constraint on the contemporary carbon budget, providing insights into
2

4

carbon-climate coupling, and demonstrating the ability to observe human emissions under certain conditions. Despite
these advances, substantial gaps in coverage exist that limit our ability to estimate fluxes at regional and global scales.
Next generation satellites will improve coverage, either through active remote sensing, a geostationary vantage point, ora combination of increased swath width and revisit frequency. Observing system simulation experiments (OSSEs) canquantify the relative strengths and weaknesses of the current observing system, identify key gaps, and evaluate the impactthat future satellites may have. As such, OSSEs are essential tools to guide implementation of future carbon-observing
missions.

To address these needs, a significant subset of the US carbon modeling community has come together with support fromNASA to conduct a series of OSSEs, with close collaboration in framing experiments and analyzing results. This effort
has produced realistic, model-based synthetic CO and CH datasets for use in inversion and signal detection experiments
2

4by the international community. NASA’s Goddard Earth Observing System Model (GEOS) has been used to create alibrary of simulations representing the current state of atmospheric carbon and dozens of expected flux change scenarios.
Last year, we presented an introduction to this effort including the OSSE framework and synthetic datasets. Here, we
present an overview of lessons learned. This will include preliminary results of coordinated inversion experiments thatassess the ability of different systems to observe subtle changes in the strength of the land carbon sink at continental
scales. We also discuss the need for new metrics to assess performance that are model independent, presenting suggestions
that could be incorporated in future mission planning. Finally, we summarize the challenges facing current OSSEs andhighlight advances that are needed.

Key words: OSSE, flux inversion, OCO-2, GeoCarb, ASCENDS, future missions

120