tno experience m. schaap, r. timmermans, h. denier van der gon, h. eskes, d. swart, p. builtjes on...
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![Page 1: TNO experience M. Schaap, R. Timmermans, H. Denier van der Gon, H. Eskes, D. Swart, P. Builtjes On the estimation of emissions from earth observation data](https://reader036.vdocuments.us/reader036/viewer/2022062407/56649d555503460f94a32bfc/html5/thumbnails/1.jpg)
TNO experience
M. Schaap, R. Timmermans, H. Denier van der Gon, H. Eskes, D. Swart, P. Builtjes
On the estimation of emissions from earth observation data
Experiences from TNO using the LOTOS-EUROS model
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26 Nov 2009TNO experience2
TNO has a large experience in emission inventories
• Emission inventories on global, European and national scale• Top-down and bottom-up• Much attention for spatial distribution• Delivered emissions to e.g. GEMS, MACC, EUCAARI,
MEGAPOLI
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26 Nov 2009TNO experience3
Emissions of Elemental Carbon in Europe
How can satellite data help to improve these maps?
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26 Nov 2009TNO experience4
Natural & Biogenic emissions – calculated online
Isoprene
Marine emissions
How can satellite data help to improve these algorithms?
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26 Nov 2009TNO experience5
Research aims at TNO i.r.t. Earth Observation
• To combine earth observation data and modelling to obtain an optimal assessment of the air quality over Europe.
• To quantify anthropogenic emission strengths by using EO data.
• Reanalysis as well as NRT
5 10 15 20 25 30 35 40 45 50 55
PM10 measurements (g/m3)
2 4 6 8 10 12 14 16 18 20 22 24 26
PM2.5 LOTOS-EUROS (g/m3)
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45
AOT (-)
Groundbased Model Satellite
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Meteorological forecastECMWF
EmissionsRegional / LocalPreprocessing
Transport Advection Turbulence
Deposition Wet and Dry
ChemistryGas phase
Aerosol
Gidded hourly simulated concentrations:
Gases O3 , NO2 , SO2 …
Aerosols Sulfate, Nitrate, sec. organic, primary…
Wet, dry deposition fluxes
Explicit CTM
Global chemical forcingclimatology / explicit model
Land use
Input data Numerical formulation
LOTOS-EUROS
• CTM directed at the lower troposphere (up to 5 Km)• Developed at TNO & RIVM• Used at KNMI, PBL, Univ. Berlin, Univ. Aveiro• Includes a data assimilation environment
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26 Nov 2009TNO experience7
Meteorology
Emissions
Land use
Boundary conditions
…
Input
Instantaneous
24HrData
Satellitedata
Observations
NO2
PM
O3
AOD
Schematic of LOTOS-EUROS modelling system:
Emissons Chemistry
Aerosolphysics
Advection
WetDeposition
Verticalexchange
Dry Deposition
…
Chemistry transport model
EnKF filter
EnKF smoother
Data-assimilation
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26 Nov 2009TNO experience8
Integration of earth observation data into models
A priory State
Assimilationprocedure
ObservationsAnalysedState (xa)
Modelintegration
AssimilationProcedure
EnKF
ObservationsSatellite dataIn-situ data
Analysed StateConcentrations
EmissionsOther parameters
Modelintegration
Model integration
Air Qualityforecast
Weatherforecast
Model input Meteorology
EmissionsNoise
OutputL4 data products
Emission estimates
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26 Nov 2009TNO experience9
Ozone measurements from the EMEP network assimilated
Single component assimilation – Ozone
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26 Nov 2009TNO experience10
0
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100 200 300 400 500 600
Measured Model Assimilation
Con
cen
tra
tion
Hour
Validation
Vredepeel
Assimilation station
Westmaas
Validation station
0
50
100
150
200
100 200 300 400 500 600
Measured Model Assimilation
Con
cen
tra
tion
Hour
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26 Nov 2009TNO experience11
Single component assimilation – PM10
Without assimilation With assimilation
From Denby et al. 2008, Atmospheric environment 42
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26 Nov 2009TNO experience12
The big challenges:
• To disentangle the uncertainty due to the emission input from other model uncertainties
• The assimilation “blames” all errors to a limited amount of parameters
• To keep the system realistic and balanced
• To combine different sources of data – multi component
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26 Nov 2009TNO experience13
Assimilation of SO2 and SO4 – a case study
SO2 SO4
Annual mean for 2003
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26 Nov 2009TNO experience14
Modelled annual mean concentrationsSO2 and SO4
SO2 SO4
OBS 1.6 2.5
MOD 2.3 1.8
OBS/MOD
1.4 0.7
RESID 1.5 1.4
RMSE 2.1 2.1
Cor 0.48 0.47
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26 Nov 2009TNO experience15
Results: SO2 annual cycle over all assimilation stations
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26 Nov 2009TNO experience16
Results: SO2 & SO4 annual cycle over all stations by including uncertain conversion rates
SO2 SO4
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26 Nov 2009TNO experience17
Annual mean estimated multiplication factors
Emissions Reaction rate
Also after acknowledging the shortcomings of the model it indicates that the shipping emissions and those in Poland may be too high
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26 Nov 2009TNO experience18
Assimilation OMI NO2 measurements with LOTOS-EUROS
Analysis NOx emissions / inventory (yellow=1)
Impact for ozone at the surface
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26 Nov 2009TNO experience19
AssimilationSurface ozone
Impact of assimilation on ozone peak value
No assimilation
AssimilationOMI NO2
NO2 bias in the model effects ozone negatively
Note, OMI NO2 may be ~25% high
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26 Nov 2009TNO experience20
Bias determination PM & AOD
AERONET AOD - model
Pro
babi
lity
AERONET AOD all data
Mod
eled
AO
D
Daily average AOD over all stations
AODaeronet = 1.6 * AODmodel
PM10 = 2 * PM10model
To use AOD for estimating PM concentrations and emissions
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26 Nov 2009TNO experience21
Assimilation strategy
• 2006• LOTOS-EUROS CTM
• Bias corr.: AOD = AOD * 1.6• Reduced domain
• MODIS data• Uncertainty: 0.05 + 0.15AOD• All pixels used
• EnKF Assimilation• Model uncertainty relative to anthropogenic emissions
• 30%, Daily, time correlation of three days• 12 ensemble members• Model simulation at overpass time stored during the day• Assimilation performed once a day at midnight• Localisation (ρ = 50 Km)
AERONET EMEP
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26 Nov 2009TNO experience22
Model to MODIS comparison
MODIS composite Modelled composite
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26 Nov 2009TNO experience23
Effect of Assimilation
After assimilationMODIS composite
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26 Nov 2009TNO experience24
Impact of assimilation on comparison with MODIS
RMSE
Cor
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26 Nov 2009TNO experience25
Verification with AERONETCorrelation
Model
Model
Ass
imila
tion
Ass
imila
tion
RMSE
1
10
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26 Nov 2009TNO experience26
PM10 measurementsCorrelation
RMSE
Ass
imila
tion
Ass
imila
tion
Model
Model
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26 Nov 2009TNO experience27
May 6th May 7th
Assimilation
Model
MODIS
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26 Nov 2009TNO experience28
Hamburg
1
2
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26 Nov 2009TNO experience29
Neuglobsow AOD
PM10
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26 Nov 2009TNO experience30
Exploring the impact of forest fire emissions (FMI) on calculated PM fields: May 6th, 2006
LOTOS-EUROS PM
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101
LE
Hamburg
ForestFires
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26 Nov 2009TNO experience31
Conclusions
• To quantify emissions from observations one needs a model• Data assimilation or inverse modelling of EO data is feasible
• We are able to provide level 4 products
• Data assimilation an objective framework
• To estimate emissions challenges are:• To disentangle the uncertainty due to the emission input from
other model uncertainties• To keep the system realistic and balanced• To combine different sources of data – multi component
• Hence, this is a long term scientific research line
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26 Nov 2009TNO experience32
Where can we use our present capabilities to provide information on emissions?
• Search for trends in the parameter estimates? • Does the EO data indicate that the emission trend is not as expected?• The system does the meteo correction, etc for you.
• To indentify locations of new and significant emission sources• The areas with consistently high model-measurement deviations
• To identify time profiles – needed: geostationary data
• Emission estimates• Only in hotspot locations, and/or with observations during the emission
itself.
• Direct variables such as land use, LAI, Fire Radiative Power, White cap, etc
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26 Nov 2009TNO experience33
Assimilation stations
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26 Nov 2009TNO experience34
Validation stations