ICOS Science Conference, Session 1

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Plume detection and characterization from XCO2 imagery: Evaluation of
Gaussian methods for quantifying plant and city fluxes
Pascal Prunet1, Olivier Lezeaux1, Andrzej Klonecki1, Claude Camy-Peyret2,1,
François-Marie Bréon3, Grégoire Broquet3, Diego Santaren3
1: SPASCIA
2: IPSL
3: LSCE
Work supported by CNES in the frame of MicroCarb and GeoCARB-Fr scientific supports
ICOS Science Conference
15 September 2020
Abstract number 28
session 1, Part 1- Theme 2: Urban observations and detection of human emission
Introduction

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Project objectivesOUTLINE
 A full processing approach of atmospheric XCO2 imagery is presented for point source plume
characterization and emission inversion :
 Based on Gaussian modelling of the plume, adapted for single/multiple point sources and for extended
sources
 Optimal estimation method (OEM) inversion scheme fitting all Gaussian parameters in the state vector
consistently with well characterized a priori values and uncertainties
 An effective wind speed derived from ancillary data is used for the emission estimate
 A comprehensive uncertainty budget is provided together with the estimated flux
 Data filtering based on objective criteria allows the identification of favorable observation cases for the
quantification of the emissions
 Validation and performance analysis in an OSSE like mode, exploiting realistic synthetic XCO2
images of plumes generated by CHIMERE simulations (LSCE) for a representative set of city/power
plant sites :
 Demonstrate the ability of a Gaussian model to deal with realistic plumes and space-based image
characteristics
 Estimate the performances and provide a realistic uncertainty budget on the quantification of CO2 emissions
from various types of localized sources
 Prototype a processing chain for exploiting real XCO2 imagery measurements
 Apply to the analysis of two planed missions : MicroCarb (in its specific City Mode) and GeoCARB
(GEOCARB-like in this preliminary analysis)
 Demonstrated as an effective processing tool of XCO2 imagery, Providing good compromises
between accuracy, simplicity, flexibility

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Methodology : Direct Gaussian plume modelling
 Gaussian Plume formulation (punctual source)
𝑥
𝑦 =
𝑐𝑜𝑠  𝑈 𝑠𝑖𝑛  𝑈
−𝑠𝑖𝑛  𝑈 𝑐𝑜𝑠  𝑈
𝑋 − 𝑋𝑆
𝑌 − 𝑌 𝑆
 𝑈 direction of the wind vector in XOY (positive in the trigonometric direction)
 Note the distinction between the coordinates XOY (origin at bottom left corner of the image) and the
local reference system xSy of the Gaussian plume from the source S (Sx longitudinal axis of the plume,
Sy transversal axis perpendicular to Sx)
 Capacity to simulate multiple and extended sources
 Extended source : considered as a disc centered in (XS,YS) with radius 𝑟𝑆. An elementary
plume is computed from each point grid within the disc, then integrated for computing the
whole plume.
 Gaussian model observation operator within an OEM framework
9 parameters
Gaussian plume +
background
Extended
source
Multiple
sources
𝑋 𝐶𝑂2 𝑥, 𝑦 = 𝑋 𝐵𝐾𝐺 𝑥, 𝑦 + 𝑋 𝐺𝐴𝑈𝑆𝑆 𝑥, 𝑦
𝑋 𝐵𝐾𝐺 𝑥, 𝑦 = 𝑉𝑀𝑅0 + 𝑃𝑥 𝑥 + 𝑃𝑦 𝑦
𝑋 𝐺𝐴𝑈𝑆𝑆 𝑥, 𝑦 =
𝐹
𝑈
𝑒
−
1
2
𝑦
𝜎 𝑦 𝑥
2
2𝜋𝜎 𝑦 𝑥
with 𝜎 𝑦 𝑥 = 𝜎0
𝑥
𝑥0
𝑏
transverse spread
𝑋 𝐶𝑂2 𝑥, 𝑦 = 𝑓(𝑉𝑀𝑅0, 𝑃𝑥, 𝑃𝑦, 𝐹,  𝑈, 𝑏, 𝑈, 𝜎0, 𝑥0)
Forward model linearisation : 𝒚 = 𝒇(𝒙, 𝒃) + 𝜺 𝒚 = 𝒇 𝒙 𝒂, 𝒃 𝒂 + 𝑲 𝒙 𝒕 − 𝒙 𝒂 + 𝑲 𝒃 𝒃 𝒕 − 𝒃 𝒂 + 𝜺 𝒚
Cost function minimisation : 𝝌 𝟐 = )𝒚 − 𝒇(𝒙, 𝒃 𝑻 𝑺 𝒚
−𝟏
)𝒚 − 𝒇(𝒙, 𝒃 + 𝒙 − 𝒙 𝒂
𝑻 𝑺 𝒙
−𝟏
𝒙 − 𝒙 𝒂
A posteriori error budget : 𝜺 𝒙 = 𝑰 − 𝑨 𝜺 𝒙 + 𝑮𝑲 𝒃 𝜺 𝒃 + 𝑮𝜺 𝒚
Retrieved state vector 𝒙
Model parameters 𝒃
(site-specific characterisation)

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Project objectivesMethodology and study approach
 Methodology for XCO2 image processing over
power plants and city sites:
1. Configuration of the site (number and
position of point sources and extended
sources, caracterisation of x0 and 0)
2. A site-specific parameterisation of the
effective wind U derived from ECMWF
wind fields (surface wind and boundary
layer averaged wind) is done
3. The OEM retrieval of Gaussian plume
parameters and source(s) emissions,
together with their uncertainty, is
performed from XCO2 synthetic images
 For different instrument configurations (MicroCarb
City Mode, GeoCarb-like, testing several noise
levels)
 For a set of 17 sites (7 power plants, 10 cities) with
two months of 30-day times series of daily images
 For 3 inversion scenarios (without observation
noise and wind uncertainty, with wind
uncertainties, with different levels of observation
noise)
GEO-like mode :
6 km x 6 km resolution
Variable extension
1 ppm noise
MicroCarb mode:
2 km x 2 km resolution
40 km x 40 km extension
1 ppm noise
𝑈𝑒𝑓𝑓 = 𝐴1 𝑈𝑠𝑢𝑟𝑓 + 𝐴2 𝑈 𝐵𝐿
Effective wind computed by site-specific linear
combination of ECMWF surface and BL winds

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GEO and IntroductionREALISTIC DATASETS FOR MICROCARB AND GEOCARB-LIKE
“True” realistic observations are built from CHIMERE simulation by extracting “imagettes” from the
whole domain, then degrading resolution (for GEOCARB-like) and adding noise (for MicroCarb and
GEOCARB-like)
30 “imagettes” are generated for each site and each instrument configuration
XCO2 map and Sites illustrations in the CHIMERE domain
(XCO2 from IER emission only are shown, for the 4th of July 2016 at 12:00)
Plants Emission
[MtC/yr]
𝑿 𝑪𝑶𝟐 𝒎𝒂𝒙
[ppmv]
Dunkerque 1.8 1.0
Weisweiler 6.9 5.1
Niederaussem 10.5 4.5
Duisburg 6.4 4.5
Völklingen 1.0 1.5
Karlsruhe 1.8 1.9
Amer 1.6 1.8
Cities Emission
[MtC/yr]
[𝒌𝒕𝑪/𝒚𝒓
/𝒌𝒎𝟐]𝑬𝒎𝒊𝒔𝒔𝒊𝒐𝒏 𝒅𝒆𝒏𝒔𝒊𝒕𝒚
Courtrai 2.1 5.3
Gand 1.8 4.7
Liège 2.2 3.6
Rotterdam-west 1.7 8.2
Rotterdam-east 6.4 11.1
Nimègue 1.7 4.4
Paris 9.1 7.8
London 8.4 4.2
Luton 1.0 5.6
Frankfort 3.3 2.4
Stuttgart 2.4 2.1

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Scenario 1 : No noise : perfomance of the Gaussian model
Scenario 2 : Impact of effective wind uncertainty
Scenarios 3,4,5 : Impact of observation noises (0.2, 0.5, 1.0 ppm)
 Instantaneous uncertainty estimates
 Fraction of “valid” successful retrieval (Gaussian model
reproduce the realistic plume
 Test_khi2 : mean of quadratic (obs-calc) deviations
normalised by the obs noise < 1.15
 Test_DOFS : DOFS on emission flux > 0,6
Emission inversion over power plants
CO2 flux retrieval, compared to reference flux (from emission
inventory used by CHIMERE simulations)
TRUE OBS RETRIEVAL
04/07/16
07/07/16
𝛿𝐹 𝐹 = MEAN,STD of estimated relative uncertainties
∆𝐹 𝐹𝑟𝑒𝑓 = MEAN, STD of obtained relative errors
Uncertainty estimate, statistical error computation, quality flags
Illustration for Weisweiler retrieval results over 30 days (July 2016), MicroCarb

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Scenario 1 : No noise
Valid = 77%
DOFS = 0.99
𝛿𝐹/𝐹 = 10 ± 1 %
∆𝐹/𝐹𝑟𝑒𝑓 = 3 %
Scenario 2 : wind uncertainty
Valid = 70%
DOFS = 0.99
𝛿𝐹/𝐹 = 11 ± 2 %
∆𝐹/𝐹𝑟𝑒𝑓 = 8 %
Scenario 3 : noise 0.2 ppm
Valid = 70%
DOFS = 0.98
𝛿𝐹/𝐹 = 11 ± 3 %
∆𝐹/𝐹𝑟𝑒𝑓 = 10 %
Scenario 4 : noise 0.5 ppm
Valid = 77%
DOFS = 0.96
𝛿𝐹/𝐹 = 16 ± 4 %
∆𝐹/𝐹𝑟𝑒𝑓 = 17 %
Scenario 5 : noise 1 ppm
Valid = 83%
DOFS = 0.93
𝛿𝐹/𝐹 = 26 ± 9 %
∆𝐹/𝐹𝑟𝑒𝑓 = 20 %
Emission inversion over power plants
Weisweiler results over 30 days (July 2016), MicroCarb City mode configuration

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Scenario 3 : 0.2 ppm noise
Valid = 85%
DOFS = 0.97
𝛿𝐹/𝐹 = 16 ± 4 %
∆𝐹/𝐹𝑟𝑒𝑓 = 16 %
Scenario 4 : 0.5 ppm noise
Valid = 89%
DOFS = 0.92
𝛿𝐹/𝐹 = 23 ± 6 %
∆𝐹/𝐹𝑟𝑒𝑓 = 21 %
Scenario 5 : 1.0 ppm noise
Valid = 89%
DOFS = 0.83
𝛿𝐹/𝐹 = 37 ± 12 %
∆𝐹/𝐹𝑟𝑒𝑓 = 25 %
Paris results over 30 days (March 2016)
MicroCarb City mode configuration
19/03/16
Emission inversion over cities
24/03/16

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Project objecti
MICROCARB CITY MODE (MCM) FOR TESTING POWER PLANT AND CITY
CO2 EMISSIONS MEASUREMENTS FROM SPACE
 A dedicated, agile processing is developed for exploiting MCM XCO2 imagery, based on a customized gaussian plume model.
 Development and valuation with datasets of realistic MCM “imagettes” derived from CHIMERE simulations (provided by LSCE)
 Performance analysis over a representative set of target sites and meteorological situations : power plants and cities over France and Europe
Isolated
source
Extended
source
Non
Gaussian
plume
Complex
background
88 % successful processing of
realistic cases for retrieving
XCO2 emissions
 Flux retrieval
 Uncertainty estimate
 Quality flags
Multiples
sources
Too much
complex
background
Site type
Reference
flux [Tc/h]
Retrieved
flux [Tc/h]
Successful
processing
%
Instantaneous
uncertainty %
20-measure
averaged
Uncertainty %
Dunkerque plant 152 170 100 61 +/-35 15
Weisweiler plant 700 656 80 22 +/-7 5
Duisburg plant 552 515 76 25 +/-7 6
Niederaussem plant 575 545 83 33 +/-25 7
Karlsruhe plant 92 73 93 72 +/-31 26
Amer plant 195 196 83 66 +/-43 10
Paris city 1713 1709 90 37 +/-12 10
Anvers city 420 289 100 100 +/-0 26
Mulhouse city 71 82 90 67 +/-26 16
Lille city 111 108 93 88 +/-43 22
Le Mans city 42 54 87 85 +/-41 18

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Emission inversion over cities: GEOCARB-like vs MicroCarb
Site Paris : 19/03/2016, Scenario 1, MC/GC Site Paris : 24/03/2016, Scenario 1, MC/GC
Scenario 5 : noise 1 ppm
Valid = 70%
DOFS = 0.83
𝛿𝐹/𝐹 = 36 ± 16 %
∆𝐹/𝐹𝑟𝑒𝑓 = 35 %
MicroCarb GEOCARB-like
Scenario 5 : 1.0 ppm noise
Valid = 89%
DOFS = 0.83
𝛿𝐹/𝐹 = 37 ± 12 %
∆𝐹/𝐹𝑟𝑒𝑓 = 25 %

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Project objectives
 Key questions and challenges:
 How to deal with simplified dynamic of the gaussian plume ? definition and robust estimation of the
“effective wind” driving the gaussian plume
 Toward a comprehensive analysis of error budget associated with the emission estimate : Careful
evaluation of model error

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Project objectivesEFFECTIVE WIND : EVALUATION FROM REAL DATA FOR CITIES
 Plume direction : Evaluation of the ECMWF effective wind first guess, based on
real data (S5P)
 First evaluation of the ECMWF effective wind direction quality at plume scales
Methodology :
• Select plumes from measured S5P images
• Estimate the direction of the plume
(fitting with a simple Gaussian model of
the plume)
• Select ECMWF wind field coregistered
with plume source (spatio-temporal
interpolation)
• Estimate the ECMWF effective wind
direction (using ad hoc formulations of
the effective wind)
• Compare the wind direction from data
and from ECMWF
Data :
• 30 days of clear-sky S5P NO2 products on
the « CHIMERE » domain (Summer 2018)
• Analysis of 6 sites over the CHIMERE
domain (Paris, London, Cologne,
Hamburg, Gent, Amsterdam)ESA Copernicus Sentinel 5 Precursor NO2 product
(05-05-2018, T1238)

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Project objectives
 A proxy of the effective wind driving the plumes is derived from ECMWF surface and boundary layer winds
 Effective wind direction computed from ECMWF provides a reliable first guess for flux retrieval: (statistics over about
20 days and 6 city plumes (Paris, Cologne, Amsterdam, Cologne, London, Hamburg, Gand)
Comparison of plume direction (solid line) and ECMWF
effective wind (arrow) for two illustrative cities and days
Good consistency between « S5P-
observed » and « ECMWF-
derived » wind directions :
Stdv : [7°-11°]
Bias < 3°
Statistics over ~ 20 days and 6 cities
EFFECTIVE WIND : ESTIMATION FROM ECMWF ANALYSES,
VALIDATION FROM MEASURED S5P PLUME IMAGERY
Paris
London
Histogram of the differences between « S5P-
observed » and « ECMWF-derived » winds

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Project objectives
NCAR EULAG LES model (Wyszogrodzki et al., 2012) is used for the simulation of a specific study case (CoMet campaign
over Bełchatów, Poland, 2018-06-07). COSMO simulation (provided by EMPA) is used to initialize and force EULAG.
CoMet measurements from in situ aircraft (DLR) and MAMAP (Univ Bremem) are used for simulation evaluation.
Statistic at different time and space scales are explored and compared with other plum modelling approaches
(mesoscale models, Gaussian models)
PLUME MODELLING ERROR : A TENTATIVE ANALYSIS AND ESTIMATE
FROM LARGE EDDY SIMULATIONS (LES)
Scatter plot of the pixel XCO2 spatial
variability as a function of the pixel XCO2
temporal mean, for different pixel sizes
XCO 2 original
resolution
(200 m, 2 mn,
enhancement only)
XCO2 spatio-temporal
averaging (2200 m, 1 hour)
Spatial averaging (from 200 to 2200 m)
Temporalaveraging(from2mnto1hour)
On going work from CHE project, funded by the European Union’s Horizon 2020 program under grant agreement No 776186

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 Gaussian_OEM offers an efficient method for retrieving localised source emission from XCO2
imagery:
 Gaussian plume model able to properly deal with realistic plumes in ~ 80-90% of the tested cases (OSSE)
 preliminary estimation of the model error impact on emission uncertainty < 10%.
 Flexible and robust method : use ECMWF information to derive wind direction and module, and provide a
comprehensive error budget : main contributors are the measurement noise and the effective wind
uncertainty (module and direction).
 Performance analysis :
 For supporting the instrument/system design of the MicroCarb city mode. Recommandation for selecting
targets of interest for demonstrating MicroCab City mode: large and medium power plants (Weisweiler,
Dunkerque), and large and smaller cities (Paris, Gand)
 for evaluating the potential and preparing the exploitation of GeoCARB imagery for City/Plant
 A prototype processing chain, including preprocessing (“effective wind” estimation from ECMWF)
postprocessing (comprehensive uncertainty analysis, filtering of bad cases) has been designed.
 On going work : Validation with real data (OCO-2, OCO-3, S5P)
Synthesis