5 September 2022, IPCC TFI Expert Meeting on Use of Atmospheric Observation Data in Emission Inventories

Pilot national-scale estimates of CO2 and CH4 emissions andremovals from space-based measurements

Masataka Watanabe
Chuo University (Japan)

Focus Points on Needs for Satellite Monitoring
1. Missing-source and missing-sink
There is a difference in emission/sink between actual
measured GHG and reported in UNFCCC.
2. Difficulties with biennial reports of the GHG
inventory
A biennial update report (BUR) is hard for many
countries, non-Annex I party in particular(74 countries among 154 non-Annex I countries
did not submit BUR).
Human resource limitations, internally long process forcompleting reports, and budget, are significant reasons.

3. Transparency
All Parties to the agreement must submit a GHGI as apart of the Biennial Transparency Report (BTR) under
the Enhanced Transparency Framework (ETF).

Sink

SourceActual GHG sinkReported GHG
sink

Missing-sink of
GHG inventory

Actual GHG emission
Reported GHGemission

Missing-source of
GHG inventory

Supporting accurate
GHG inventory©JAXA

Satellite based estimation of
GHG emission/absorptionUlaanbaatar’s air pollution is the worst in the world
Road emissions
9％
Coal Stovesin ger area
29%

Copyright 2011-2018 AvaxNews
http://avax.news/fact/A_Look_at_Life_in_Ulaanbaatar.htm
lSummer in UB

Winter in UB

Coal Power
Plants60％
Copyright 2011-2018 AvaxNews
http://avax.news/fact/A_Look_at_Life_in_Ulaanbaatar.html

CO2 emission inventory
in Ulaanbaatar (2015）

Raw coalMeteorological characteristics of Ulaanbaatar
The atmospheric transport model simulated the two main features associated with micro circulation around Ulaanbaatar
during winter time.
• In winter during early morning, a local dominant wind near ground surface blows from north east to south west along Tuulriver basin. It is characterized by topography and dynamic factors. According to observation, easterly wind are 70-80%
dominant in the total frequency of wind during winter.
• Model well simulates inversion layer within 700-800 hPa levels as well as opposite direction between upper low levelandNorthnear surface wind (Figure 1). In winter, GHG emission comes from the ground which was under the inversion layerand inversion layer is most important factor for air pollution.
b)west-easta) south west-north east

Ulaanbaatar

Boundary layer

SouthFigure 1. Wind field results at 07:00am

Figure 2. Vertical cross section (wind direction and temperature) at 07:00am

Challenge 1) GOSAT Partial-column retrieval (Kuze et al., 2022)
A retrieval algorithm for a partial collumn density of two tropospheric and three stratosphere by combined use of theSWIR and TIR bands by the maximum a posteriori solution method was developed by JAXA/EORC .
The Product allows for removals of aerosol and thin cloud contamination (Kuze et al., 2022).
Therefore GOSAT-EORC-Daily-Partial-Column-GHGs products increase the GOSAT data availability even in areas withheavy air pollution, such as Ulaanbaatar.
In Ulaanbaatar, available data in 2018 was 2.2 times larger than that of the XCO2 NIES product, NIES V02.95-02.97.
This product is suitable for emission estimates in urban areas.

(Kuze et al.,2022)
JAXA/EORC GOSAT-EORC-Daily-Partial-Column-GHGs products

Challenge 2) Chimneys in coal power plants and industries
The height of the chimney, the initial temperature of emitted air, and ambient air temperature determine the initial
mixing and distribution of CO2. We should simulate plumes of significant CO2 emitted from a chimney in coal power
plants in Ulaanbaatar.

Distribution of CO2 Concentrationby using the Atmospheric Transport Model in the Whole ofMongolia and Surrounding Ulaanbaatar（Sep, 2017）

Source：Chuo University. Information And Research Institute Of Meteorology, Hydrology And Environment, Mongolia. Ministry of Environment, Japan.

Unit: ppm(Absolute)Inversion Analysis to Estimate CO2 Emission in Ulaanbaatar
Approach:Resolution：
10.5kmUpper Troposphere (UT)Global circulated CO2

We developed the model toestimate posterior emissions
volumes of a GHG emissions
inventory by inverse analysis
based on the Green Function
using GOSAT-series satellite
observation results

Lower Troposphere (LT)c(column- averaged concentrationof CO2 (XCO2 (LT)) )Minimizing thedifference withmodeled XCO2(LT)XCO2 (LT), provided by“GOSAT/GOSAT-2 EORC Daily
Partial Column GHGs
(JAXA/EORC)”, was used for ourinversion analysis••••

GOSAT: A satellite thatobserves column
averaged GHG
concentration(Resolution: 10.5km)©JAXA• Power plants• HOBsBoundary layer

Inversion Analysis

Φ（GHG flux）

Comparison / Validation

http://www.meted.ucar.edu/EUMETSAT/atmos_comp/media/graphics/crisp_whatisXCO2.jpg• Gers• Vehicles

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Targeted Observation of XCO2 in Ulaanbaatar city
by GOSAT（19 Sep. 2018）and control volume(blue line) and grid points on the WRF-Chem model

National GHG Inventory

When the a priori error of observation in XCO2 (Column CO2) concentration by GOSAT and the CO2 emission is two ppmv and 20%,respectively, the estimated a posteriori CO2 emission from Ulaanbaatar in 2018 is estimated to be almost the same as the inventory
from the EDGAR v6.0.
Comparing the energy sectoral CO2 emissions estimated using the top-down with BUR2 and the EDGAR inventory in 2018 showsa difference of a few % higher than BUR2 and several % smaller than the EDGAR, respectively.

“2018 Winter meansfrom October 2018 to March 2019”

Banned consumption ofraw coal in Ulaanbaatar
on May 2019～2018 WinterRaw Coal Use
1.2 million tons a year

Coal briquette conversion policy
and the reduction of CO2 emissions

2019 Winter2020 Winter2021 WinterSince the calorific value of the improved briquettes is twice
as high as raw coal, household coal consumption in
Ulaanbaatar’s ger area is expected to decrease up to 50%.
Refined coal briquette Production
0.3 million tons a year
New plant opened

Total coal usage would be down.
The reduction of CO2 emissions is unknown because it varies
depending on the combustion efficiency of the stove used.

Refined coal briquette Production
0.6 million tons a year

The atmospheric transport model outputs confirmed that due to the development of strong inversion layer inUlaanbaatar, velocity under the inversion layer is very small and emissions flux from the ger area remain within LT. This
indicated that XCO2LT enhancement by Kuze et al. (2022) is mainly due to surface CO2 emission in Ulaanbaatar. The
winter XCO2LT enhancement is significantly lower in 2019 Winter compared to 2018 Winter, suggesting that the briquetteconversion policy has reduced CO2 emissions. Inversion analysis is ongoing for estimating the CO2 emission during
the Coal briquette conversion policy.

NEE (g/m2) simulation results using WRF-Chem 4.0 (with VPRM) (2018 accumulation)

The model was calibrated with CO2 Fluxes observed at four stations (in the steppe at Nalaikh, Ovorzaisan, and Hustai)and (in the forest at Mongonmorit).
The difference between the observed and our model result was 0.09 μmol/m2/s on average, in good agreement.

Comparison of NEE (g/m2) between simulation results (accumulated in 2018) using WRFChem 4.0 (using VPRM) and GHG emission inventory (LULUFC, CO2 absorption by ecosystems)of Mongolia (2014) and Kazakhstan (2013)
Table. CO2 emission (LULUCF) of national GHG Inventory in Mongolia and Kazakhstan
CO2 emission (Gg, minus means absorption)
Year LULUCF in Mongolia
Total NEE in Mongolia by WRF
LULUCF in Kazakhstan(forest)(forest)2013-10,886,600
2014-30,7022018-30,215-177,975
Source:Kazakhstan<https://unfccc.int/files/national_reports/biennial_reports_and_iar/submitted_biennial_reports/application/pdf/brtext_eng_kz.pdf>
Mongolia <https://unfccc.int/files/national_reports/nonannex_i_parties/ica/technical_analysis_of_burs/application/pdf/mongolia-bur1-1-nir.pdf>

Table. Area of land use related to sinks in Mongolia and Kazakhstan
Steppe including
Area (km2)
Forest (km2)
agriculture area (km2)
Mongolia
1,553,560
1,129,035
125,528
Kazakhstan
2,724,902
2,169,920
33,090Source:Kazakhstan <https://tradingeconomics.com/kazakhstan/forest-area-percent-of-land-area-wb-data.html>
Mongolia <https://tradingeconomics.com/mongolia/forest-area-percent-of-land-area-wb-data.html>

Overview of the GOSAT-GW (TANSO-3)
GHG sensor

TANSO-FTS

TANSO-FTS-2

TANSO-3

satelliteGOSAT

GOSAT-2

GOSAT-GW

methodFTSFTSgrating

Observation gas

CO2, CH4

CO2, CH4, CO

CO2, CH4, NO2

Function to automatically avoid
clouds and observe

Wide mode, Focus mode*1

Others*1: The observation plan is under consideration.

GOSAT
10km
（TANSO）©JAXA

GOSAT-2 10km
（TANSO-2）

GOSAT-GW（TANSO-3） 10km×92、3km×30

©JAXA

©JAXA

Observing one element with a
FOV Φ10km intervals gridwidth 160 km. If there areclouds in the FOV ,the GHG
concentrations cannot becalculated.

It is possible to observe thespecified point with oneelement with FOV Φ10km.Thesensor can detect clouds byitself and automatically avoidthem.

It is possible to observe theentire globe with a spatial
resolution of 10 km in thewide mode, or the specified
range (90 km width) with aspatial resolution of 3 km in
the focus mode.

Summary The estimated a posteriori CO2 emission from Ulaanbaatar in 2018 is estimated to be almost the same as theenergy sector inventory from the EDGAR v6.0.
 Comparing the energy sectoral CO2 emissions estimated using the top-down with BUR2 and the EDGAR
inventory in 2018 in Mongolia shows a difference of a few percentages higher than BUR2 and several
percentages smaller than the EDGAR, respectively.
 Total NEE by WRF-VPRM in forest in 2018 were almost 6 times larger than CO2 absorption by the forest in
the Mongolian National inventory. Continuous research is needed.
 Mongolia's BUR would utilize and include these developed emission results in the chapter 3.1 Inventoryoverview; Additional Information/best practice.

Acknowledgement
The Ministry of Environment, Japan has supported this research, whose projects named "Project on Development ofInnovative Green Technology and MRV Method for JCM in Mongolia," "Project on Assessment of the accuracy of GHG
emission estimates using GOSAT-series satellite data in Mongolia," and "Project on Upgrading Techniques for Estimating
Greenhouse Gas Emissions using Satellite Observation data in Mongolia."