Uncertainty characterisation in atmospheric chemistry data assimilation and emission estimation

1Eskes, Leiden workshop, 10 Nov 2011

Uncertainty characterisation in atmospheric chemistry data assimilation and emission estimation

Henk EskesKNMI, the Netherlands

The Chemical Weather

Local, regional and global distributions of important trace gases and aerosols and their variabilities on time scales of minutes to hours to days, particularly in light of their various impacts, such as on human health, ecosystems, the meteorological weather and climate.

M. Lawrence et al, Environ. Chem., 2005

Monitoring and short-range forecastingof atmospheric composition

Towards an operational GMES service

Adrian SimmonsEuropean Centre for Medium-Range Weather Forecasts

GMES Atmosphere

Weather services

Atmosphericenvironmental services

Climate forcing by gases and aerosols

Long-range pollutant transport

European air quality

Dust outbreaks

Solar energy

UV radiation

• • • Environmental agencies

provide data & information on

Services related to the chemical and particulate content of the atmosphere

Project structure

MACC: 45 partners, plus third parties

MACC-II: 36 partners, plus third parties

Coordinated by the European Centre for Medium-Range Weather Forecasts

MACC Daily Service Provision

Air quality

Global Pollution

Aerosol UV index

http://www.gmes-atmosphere.eu/

MACC Reanalysis Service

Reanalysis

Ozone records

Flux Inversions

http://www.gmes-atmosphere.eu/

8Eskes, Leiden workshop, 10 Nov 2011

Satellite observations of air quality

Eskes, Leiden workshop, 10 Nov 2011

MACC: 30 yr ozone layer reanalysesla

titud

e

ozon

e la

yer

thic

knes

s (D

U)

- Use all available ozone column satellite data sets- Assimilate in a chemistry-transport model for ozone, based on a sub-optimal Kalman filter approach

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The October monthly mean over the Antarctic region.

sparse abundant

2009

2008

2007

5 October, 2006

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1987

1985

Ozo

ne

loss

(197

9 –

2009

)

1988

1986

1984

1983

MACC: 30 yr ozone hole reanalyses

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Sub-optimal Kalman filter approach:

Forecast covariance = time-dependent variance * fixed correlations

Correlation matrix: (static)function of the distance onlyfunctional form determined from OmF statistics

Variance: (time dependent)• Model error, growth of the forecast variance with time

consistent with OmF• Advection of the forecast variance (extra tracer)• Solve Kalman filter analysis equation for forecast variance

Ozone reanalyses: forecast error modelling

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Forecast covariance = time-dependent variance * fixed correlationsVariance:

• Model error, growth of the forecast variance with time• Advection of the forecast variance• Analysis equation for forecast variance


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Test: Compare OmF as modelled with OmF observedExtension of the famous chi-square test


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1% level

RMS of OmF (dotted) typically 2%Bias OmF (blue) and OmA (red) are less than 1%

-1% level

OmF and OmA: typical performance

Example for January 2008

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Example: Ozone retrieval bug

Validation of the

O3 column retrieval

for the

SCIAMACHY

satellite instrument

Plot OmF as a

function of

parameters relevant

for the retrieval

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MACC - Regional Air Quality

Regional air quality forecasts and reanalyses

Ensemble approach, based on the modelsEMEP, EURAD, CHIMERE, MATCH, SILAM, MOCAGE, LOTOS-EUROSData assimilation of surface and satellite data is developed for each of the models individuallySurface observations considered:• Ozone, NOx, PM10, PM2.5, SO2, ...Satelite data considered:• NO2 (OMI, SCIAMACHY, GOME-2)• Tropospheric Ozone (IASI)• AOD

Idea: ensemble spread represents uncertainty


MACC: Regional air quality forecasts

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Van Loon et al, Atm. Env. 41 (2007)

Ensemble forecasts: why ?

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Spread models represents uncertainty quite reasonablyBut why ?Vautard et al, GRL 2006

Ensemble forecasts: why ?


MACC: Assimilation techniques used

Large-scale problemmodel grid: longitude * latitude * altitude * component

order (100)^4 or 10^8 model state variables

Global reanalysis and daily analysesBased on ECMWF model

Atmospheric composition + meteorological observations4D-Var, 12h time window

Ozone 30-year reanalysis: sub-optimal Kalman filterRegional analysesSeveral techniques used:Statistical interpolation, 3D-Var, 4D-Var, Ensemble Kalman filterFlux inversionsInverse modelling techniques, 4D-Var, EnKF

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Khattatov, JGR 104, 18715 (1999)

Chemical system strongly coupled: Chemical covariance matrix (Kalman filter) becomes singular

Important to use advanced assimilationtechnique to exploit multivariate character:• 4D-Var• Ensemble Kalman Filter

Chemical data assimilation

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7. August 8. August 1997

+ observationsno optimisation

initial value opt.

emis. rate opt.

joint emis + ini val opt.

Source: Hendrik Elbern, Köln

Assimilation of state + emission sources

assimilation interval forecast

Information in species concentrations quickly gets lost. Emissions may store the information for longer periods.

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NO2 observations from OMI instrument

NO2 air pollution, observed by OMI, 2005-2007

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NO2 observations with satellites

Error analysis of NO2 retrievalBoersma et al, 2004

Error related to cloud fraction Error related to surface albedo

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Assimilation DOMINO v2 - 27 march 2007

Free model, Lotos-Euros

OMI NO2

Analysis (NOx emission adjustment)

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ACCENT+ AirQuality-Climate

OMI NO2 assimilation, 23 mar - 29 apr 2007

NOx emission adjustment factor

Emission scaling factor averaged over 5 week periodOnly significant > 2 σ points

OMI NO2 satellite data

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Conclusions

OmF and OmA•To specify spatial error correlations, time dependent error growth.•Extension of chi-square test: OmF observed vs. modelled•Powerful tool for satellite validation: OmF vs retrieval parameters Model ensemble air quality forecasts•Spread of models represents uncertainty. But why?

Chemical data assimilation•Chemical system stiff, strongly coupled•Near surface: little memory, information lost in hours to one day: focus on update of model parameters, such as emissions

Satellite observations of air pollution (example NO2)•Complicated retrieval errors: partly random, partly systematic•First applications to infer emissions (4D-Var, EnKF)

MACC project: http://www.gmes-atmosphere.eu/

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Thank you for your attention

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Analysis vs TOMS: 15 April 2001

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ACCENT+ AirQuality-Climate

Different OMI NO2 retrieval products

EOMINO (EMPA)

DOMINO v2

DOMINO v1

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Meteorology

Emissions

Land use

Boundary conditions

…

Input

Instantaneous

24HrData

Satellitedata

Observations

NO2

PM

O3

AOD

Emissons Chemistry

Aerosolphysics

Advection

WetDeposition

Verticalexchange

Dry Deposition

…

Chemistry transport model

EnKF filter

EnKF smoother

Data-assimilation

Lotos-Euros model

MACC Forecasts and reanalyses

EuropeanAir quality

Global Pollution

Aerosol

UV indexOzone records

Flux Inversions

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OMI NO2 versus AQ models

BOLCHEM CAC CAMx

CHIMERE EMEP EURAD

MATCH SILAM OMI

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Relaties weer, chemie, uitstoot en gezondheid

Radiation

Chemistry Meteorology

Pollutant concentrations

Emissions

Human health

Photolyse ratesTemperature

AerosolsActive radiative gases

Dispersion Chemical regimes…

Deposition Height of the boundary layerTransport…

ConvectionTransport

IntensityTemperature

Reduction of emissions

PoliciesClean technologies

Chemical regimesspeciation

Primary pollutants

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NO + NO2

Chemische reacties (oxidatie)

Primaire vervuiling

• Vluchtige organische verbindingen

• Stikstofoxiden (NOx)

• Deeltjes

• Zwaveldioxide (SO2)

• …

Koolwaterstoffen, aromaten, aldehydes, …• Ozon (OOzon (O3 3 ))

• Stikstofdioxide (NO2 )

• Salpeterzuur (HNO3 )

• Zwavelzuur (H2SO4 )

• PANs

• Aldehyden (HCHO, …)

Secundaire aerosolen …

Secundaire vervuiling

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Example: Ozone analyses

Example: Ozone hole simulations

Neumayer ozone sondesAssimilation: MLS, OMI, SBUV

Model without assimilation

August 2008 October 2008

Flemming et al, ACPD 10, 9173-9217, 2010

Documents

Uncertainty characterisation in atmospheric chemistry data assimilation and emission estimation