tno experience m. schaap, r. timmermans, h. denier van der gon, h. eskes, d. swart, p. builtjes on...

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

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


Emissions of Elemental Carbon in Europe

How can satellite data help to improve these maps?


Natural & Biogenic emissions – calculated online

Isoprene

Marine emissions

How can satellite data help to improve these algorithms?


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

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


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


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


Ozone measurements from the EMEP network assimilated

Single component assimilation – Ozone


0

50

100

150

200

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


Single component assimilation – PM10

Without assimilation With assimilation

From Denby et al. 2008, Atmospheric environment 42


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


Assimilation of SO2 and SO4 – a case study

SO2 SO4

Annual mean for 2003


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


Results: SO2 annual cycle over all assimilation stations


Results: SO2 & SO4 annual cycle over all stations by including uncertain conversion rates

SO2 SO4


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


Assimilation OMI NO2 measurements with LOTOS-EUROS

Analysis NOx emissions / inventory (yellow=1)

Impact for ozone at the surface


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


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


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


Model to MODIS comparison

MODIS composite Modelled composite


Effect of Assimilation

After assimilationMODIS composite


Impact of assimilation on comparison with MODIS

RMSE

Cor


Verification with AERONETCorrelation

Model

Model

Ass

imila

tion

Ass

imila

tion

RMSE

1

10


PM10 measurementsCorrelation

RMSE

Ass

imila

tion

Ass

imila

tion

Model

Model


May 6th May 7th

Assimilation

Model

MODIS


Hamburg

1

2


Neuglobsow AOD

PM10


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


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


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


Assimilation stations


Validation stations

tno experience m. schaap, r. timmermans, h. denier van der gon, h. eskes, d. swart, p. builtjes on...

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