Towards the Subnational CO2 Emission Monitoring
Using Airborne and Space Sensors- JAXA’s Greenhouse Gases Monitoring Activities in Support ofCarbon Cycle Science and Climate Monitoring Hiroshi Suto
Japan Aerospace Exploration Agency (JAXA)
E-mail: suto.hiroshi@jaxa.jp
IPCC TFI Expert Meeting on Use of Atmospheric Observation Data in Emission Inventories 5-7 September 2022
1
WMO HQ, Geneva- Switzerland

Contributing to the GHG observation history from space
420
NOAA Global monthly mean CO2
JAXA/GOSAT GHG Global monthly mean CO2_LT
JAXA/GOSAT GHG Global monthly mean CO2_UP
JAXA/GOSAT GHG Global monthly mean CO2_TOT

410

CO2 concentration [ppm]

400

390

380

Ground-basedobservation station

370

360

Satellite-based observation
350
1990

1995

2000

2005
Year

2010

2015

2020

GOSAT satellite data presents 12 years of global CO2 concentration and its global
2changes from 2009 to 2021.

How to estimate CO2 emission from observation ?
Motivation:- Provide subnational emission estimates for potential QA/QC and verification of reported
national emission inventories (NEIs).
Key role for observation:- Collect spatially dense GHG data in a timely manner for detecting emissions hots spots andquantify the emissions and their changes.
Challenges for airborne and spaceborne observation (especially for CO2):- Quantity CO2 concentration enhancements due to particular surface sources (e.g. power plant,cities, industrial areas, etc).

3

JAXA’ GHG observation missions
Missions

GOSAT

GOSAT-2

GOBLEU

Platform

Satellite

Satellite

Passenger aircrafts

Launch2009/1/23

2018/10/29

2020

Local
observation
time

13:00

13:00

On-demand

Revisit time

3 days6 days

-xxx

Image

Observation
targetCO2, CH4,SIF(Solar-induced chlorophyllfluorescence)

Grid

CO2, CH4, CO, N2O
SIF(Solar-induced chlorophyllfluorescence)

CO2, NO2
SIF(Solar-induced chlorophyllfluorescence)

TargetObservation
image
4

JAXA’s approach
JAXA’s concept for estimating CO2 emission from remote sensing data:- Retrieving upper and lower CO2 concentration data from GOSAT satellite observations.
- Collecting NO2 observation data as proxy for fossil fuel combustion.
JAXA’s approach for estimating both CO2 enhancement and background concentration

JAXA’s Missions

Background concentration

CO2 enhancement

GOSAT(Spaceborne sensor)

Upper troposphere(4 km to 12 km altitude)

Lower troposphere(ground to 4 km altitude)

GOSAT-2(Spaceborne sensor)

Upper troposphere(4 km to 12 km altitude)

Lower troposphere(ground to 4 km altitude)

GOBLEU(Airborne sensor)

Small footprint withsimultaneous NO2 observation
as CO2 emission marker

Small footprint withsimultaneous NO2 observation
as CO2 emission marker

5

JAXA patrial column GHG product- JAXA developed a new retrieval algorithm to derive the partial column.
- GOSAT observes both solar reflected light and thermal emission.
- Products are free available (https://www.eorc.jaxa.jp/GOSAT/Global_GHGs_Map/index.html).
Upper troposphere:
Serves as a new reference (background) CO2
concertation for local analysis.
Lower troposphere:
Better reflects CO2 changes due to local emissions.
Conventional
Method

JAXA/EORC
new Method

Use only solarreflected light

Use both solarreflected light&thermal©MOE/JAXA/NIES

Solar reflected

0.2Psur=~12km
0.6*Psurf=~4km

CO2 emission and enhanced densityof the lower troposphereThermalemission

6Latitudinal gradient of JAXA CO2 products
430

420

N70-N50
N50-N30
N30-N10
N10-S10
S10-S30
S30-S50

CO2 concentration low atmosphere [ppm]

CO2 total cloumn concentration [ppm]

430

410

400

390

380

370

420

N70-N50
N50-N30
N30-N10
N10-S10
S10-S30
S30-S50

410

400

390

380

370
2010/01/01

2012/01/01

2014/01/01

2016/01/01
Year

2018/01/01

Total column concentration

2020/01/01

2010/01/01

2012/01/01

2014/01/01

2016/01/01
Time

2018/01/01

2020/01/01

Lower tropospheric concentration- Seasonal amplitude of lower tropospheric CO2 concentration is larger than that of total
column concentration.
- Latitudinal gradient of lower tropospheric concentration is more clear.

7

Comparison of XCO2 between OCO-2 and GOSAT
Matchup conditions
Coincident time:OCO2 observation time =< GOSAT+/- 1hour (time period: Sep. 2014 toOct. 2021.)
Data handling:
Average OCO-2 (more than 5 data)
data within GOSAT observation circle

Difference between OCO2 v10r and JAXA/GOSAT v02

JAXA/GOSAT XCO2 and OCO-2 products are in good agreement.
NO temporal and NO geolocational biases are observed.
8Comparison to the AirCore data
- The vertical CO2 gradient of the partial CO2 products was evaluated using the AirCore data.
- Vertical concentration of CO2, CH4, water vapor are in good agreement at Lamont, OK.

Karion, A., Sweeney, C., Tans, P., andNewberger, T., (2010) AirCore: An
Innovative Atmospheric Sampling
System, Journal of Atmospheric andOceanic Technology, Nov. 2010, doi:
10.1175/2010JTECHA1448.1.9

Model-based evaluation (ongoing)
The version of theMIP: OCO-2 MIP (v9)Figure 3.10. Latitudinal distributions of zonal mean of XCO(LT) and upper troposphere (UT) as observed with GOSAT
OCO-2 MIP IS over land and ocean in 2016 NH summer. Th
differences of XCO2 between the LT and UT. The shadings
2 MIP. Results with the OCO-2 MIP LNLG experiment are2016 NH summer
Daviset al.
working progress
Figure 3.10. Latitudinal distributions of zonal mean ofZhang,
XCO within
the lower
troposphere
Figure 3.6. Global maps of XCO2 within the lower troposphere(LT)anduppertroposphere
2MIPISexperiment.
NotethatGOSAT
retrievals over Gree(LT) and upper troposphere (UT) as observed with GOSAT and aircraft and simulated with- asGOSAT
XCO2
in the lower
troposphere
overallhassimilar
spatialpatternthat in OCO-2 MIP LNLG
(UT)
observedwith GOSAT
and simulatedwith(LT)
theOCO-2
OCO-2
MIP
LNLG
experimentin theas
MIP IS
overlandand ocean
in 2016 NH summer.
The rightmost panels show theexcluded
here.
Figure3.10. Latitudinal
distributions
ofzonalof XCO
withintheinandlowertroposphe
differencesof XCO
betweenthe LT andmean
UT. Theshadings
denotemodel spreads
OCO-tropics).
2 theensembleHemisphereat a global
scaleSummer.
(lowerinThenorthernhighandmid-latitudes
andhigher
inlow-altitudes
2016 Northern(NH)rightmostpanelsshow
thedifferences
2 MIP. Results with the OCO-2 MIP LNLG experiment are very similar to that with the OCOThe
latitudinal
distributions
ofthezonal
meanofISGOSAT
LT
data
inregridded
theNH
agreedwell
withand wi(LT)anduppertroposphere(UT)
as Noteobserved
withGOSAT
andaircraft
simulated
MIP
experiment.
thatretrievals
overGreenland
and arctic
landsACT,
areandNOAA
between GOSAT and OCO-2 MIP. Satellite retrievals 2andmodelresults
areGOSAT
allinto
werefoundat
40-50S
andaroundtheequator.
In
contrast,
Th
10
excluded here.
AToM
airborneobservations.
OCO-2 MIP IS over land and ocean in 2016 NH summer. The rightmost panels show the2.5º×2.5º.
22A ner
TS or TA SO- TS-2 are re uired to obtainfrom the partial-column density ased on what we obtained from we
Estimating
megacity
emissions
fromthepartial
columndistribution estimated from the O data will also provide secsuggest future applications for estimating emissions from megacities
formationfor individual sources The classi cation of emission
Averaging with more sampling points per day and more fre uent obproductthe source sectors is re uired to monitor human activity ocati
servations per month can reduce random errors in the estimation ore
T25HPRWH 6HQVLQJ RI (QYLURQPHQW   

New Delhi

Beijing

fre uent observations will also provide various wind speeds that reduce
estimate uncertainty nstead of assuming a spatially uniform emissionma or point sources such as power plants and steel factories arenown but their emission amounts and spatial distributions at ve

Comparison between our estimates and the ODIAC inventory estimates.
Kuze et al., RSE, 2022

NYC

RiyadhShanghaiTokyo

stributions ofTo yo.

circles obtained from target observations inarch 2for aei ing, bewelhi, cew or City, diyadh, eSpatial distribution of XCO2_LT ig. . CO emission estimates of a city area from GOSAT against the city-wide O AC inventory estimates Error bars show uncertainties in the emission estim
results
was encouraging
towards
obtained from target observation inan individual city The
by a leasts uare regression
The calculated correlation
coefmegacity
cient p was emission estimates using the partial
11
column product.
March 2019
direction varies much more in the summer than in theespecially in iyadh. All three megacities show lower. Advanced analysis methods that
determination
orological information and biological activities are
mission estimates for seasons other than winter.
tive value of the averaged CO2 emissions
2 i a e from. ecity i can be calculated from the coef cient α i expressed in E .
2used the partial air mass factor A p i for the entire city area. n the caseof ei ing, the coef cient in E .
is 2 . ppm m s in winter months
2and 2 2 , and the estimated emissions from the city area asde ned in ig. 2are .
tC month city.
To demonstrate the effectiveness of this analysis, we plotted theObservation by research aircraft
Demonstration of simultaneous CO2 and NO2 observation over point source
Challenging for coverage and observation frequency
With 1.6μm, 0.76μm NO2Imaging spectrometer2018/02/16 Over Nagoya area
Flight altitude: ~3km
~40km

~10km

Research aircraft

Power plant12

Suto et al., in prep.

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GOBLEU:

Greenhouse gas Observations of Biospheric and Local Emissions from the Upper sky- Cities are responsible for more than 70 % of theglobal total GHG emissions.
- 30 % of the Japan’s total CO2 emissions are emitted
between Tokyo and Fukuoka area (shaded in red).
- To achieve the net zero goal, the sectoral emissions
and their relative magnitude are expected to change
drastically over the next decade.

EDGAR CO2

Our objectives:- Monitoring Japan’s subnational ~ local climate
mitigation progress (e.g. emission reduction andsink enlargement) using high-resolution GHG andAQ measurements.
- Providing an objective evaluation for reported
inventory emission estimates.

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GHG remote sensing from a passenger aircraft
Our concepts:- NO hardware modification to aircraft*- Compact instruments on cabin seats- Observing through cabin window- Small power consumption with mobile
battery operation- 3 modules: 450nm, 740nm and 1.6um
bands for NO2, SIF and CO2 with fibercoupling.
Commercial airliners can make repeatable andfrequent observations over mega-cites withlower cost than research flights!.

Suto et al., in prep.
*Limitation of size and wight, the capacity of15battery, electronical magnetic conduction frominstruments have to be passed the certifications.

The first high resolution NO2 observations from GOBLEU-High NO2 were observed over emission hot spots (cities, point sources, and traffic)
In megacity Nagoya, spatial pattern of NO2 is different from GOBLEU(GB) and emission inventory.
Suto et al., in prep.

16Observing NO2 as a CO2 emission marker
35.0

35.0
TROP 12:30 LT35.0

GB 10:45 LT35.0

GB LT10:45

EDGAR v50 2018 CO2 Inventory

34.8

34.8

34.6

34.6

34.6

34.6

34.4

34.4

34.2

34.2
0510

34.4

1020304050 x 1015

135.2

135.4

135.6

Lon. [deg]

135.0
135.2
Lon [deg]

TROPOMI: NO2 observation
0.05°x 0.05°grid
Local time 12:30 (UT+9)

GB: NO2 observation
0.005°x 0.005°grid
Local time 10:45 (UT+9)-134.8135.4

468 10 x 1015

0NO2 [mol/cm^2]134.6

134.6

2134.8

135.0

135.2

135.4

135.6

135.6

234 5 x 10-6

34.0
134.6

134.8

135.0

135.2

135.4

135.6

Lon [deg]

Lon. [deg]GB: NO2 observation
0.05°x 0.05°grid
Local time 10:45 (UT+9)(TROP. grid)

GB provides fine spatial structures of NO2 concentration.
GB clearly indicate the emission from industry while it was notclear in satellite observation (due to time and spatial resolution).

1CO2 emissions [tons/yr]

34.0

NO2 [mol/cm^2]

34.0
135.0

34.2
00

NO2 [mol/cm^2]

134.834.4

34.2

15 20 x 1015

34.0134.6

Lat [deg]Lat [deg]

Lat. [deg]34.8

Lat. [deg]34.8

EDGAR: CO2 inventory
0.1°x 0.1°grid

Note: observation time of TROPOMI and GB aredifferent.

Suto et al., in prep.

17

NO2 Spatial correlation with ground-based observation

200
0.2

100

0.00-10111213
GBobs_Osaka Local time (JST)1415500

GB_Nagoya
TROP_Nagoya
NOx Inventory_Nagoya

400

TROPobs

0.6

300
0.4
200
0.2NOx emission [kg/hour]

0.4NOx emission [kg/hour]

300

90.8400TROPobs0.6

Spatial correlation

500

GB_Osaka
TROP_Osaka
NOx Inventory_Osaka

Spatial correlation

0.8

1000.00910111213
GBobs_Nagoya Local time (JST)

14NO2 spatial correlation between GB and ground-based observation are in good agreement.
Especially in Nagoya, TROP show less agreement with ground-based NO2 observation.
The result highlight the significance of the co-located CO2 and NO2.

15

Suto et al., in prep.

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Outlook- JAXA partial column concentration has vertical information for GHG concentration and will
support to estimate local CO2 emission.
- Regular GOBLEU flight (1 or 2 flights/month) started in this summer.
-Cities CO2 emission estimate is ongoing with observed NO2 as CO2 emission marker.

- JAXA continuously observe the global and local GHG concentration by satellite and passenger
aircrafts.

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