assimilation of satellite data for the environment · summary of introduction {use of satellite...
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ECMWF seminar ‘07 F. Chevallier
Assimilation of satellite data for the environment
Frédéric Chevallier, Peter RaynerLaboratoire des Sciences du Climat et de l’EnvironnementCEA/CNRS/UVSQ, IPSL, France
Richard EngelenECMWF, Reading
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ECMWF seminar ‘07 F. Chevallier slide 2
Thanks
Audrey Fortems, Philippe Peylin and Sophie Szopa
LSCE
The members of the FP6 GEMS consortiumTony HollingsworthAntje Dethof and Angela Benedetti
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ECMWF seminar ‘07 F. Chevallier slide 3
Outline
Introduction: CO2 from spaceOptimization of atmospheric concentrationsOptimization of surface fluxesOptimization of surface model parametersConclusion
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ECMWF seminar ‘07 F. Chevallier slide 4
The atmosphere from space
IASI Level 1C Spectra 29/11/2006, 13:42:11 UTCSource CNES-CNRS ETHER
aerosols aerosolsaerosols
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ECMWF seminar ‘07 F. Chevallier slide 5
CO2 as noise
HIRS 15-micron channels
“The brightness temperature differences can be as large as 1 K for a 30-ppmv CO2 increase and a seasonal variation of a few tenths of a Kelvin may exist” (Turner, 1993).Impact on temperature retrievals
Turner, 1993
Tb(CO2 variable) –Tb(CO2@330ppm) [K]
CO2 [ppm]
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ECMWF seminar ‘07 F. Chevallier slide 6
CO2 as signal
First retrievals of CO2 concentrations (Chédin et al., 2003).Upper troposphere in tropical latitudesHIRS+MSU1987-1991Neural network
Model peak to peaktwice as less
Chédin et al., 2003
344 ppm 368344 ppm 368
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ECMWF seminar ‘07 F. Chevallier slide 7
CO2 as signal (cont’ed)
Extension to high-spectral resolution measurementsAIRSUpper troposphere in tropical latitudes
Engelen et al., 20041D-Var
370 ppm 384372 ppm 381
Crevoisier et al., 2004Neural networkAugust 2003
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ECMWF seminar ‘07 F. Chevallier slide 8
CO2 as signal (cont’ed)
Extension to high-spectral resolution measurementsSCIAMACHYTotal column over lands
Buchwitz et al., 2005DOAS
Model simulations of CO2column [ppm]Olsen and Randerson, 2004
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ECMWF seminar ‘07 F. Chevallier slide 9
The same planet?
Total column vs. upper tropospheric columnRetrieved vs. measured
Barkley et al., 2006SCIAMACHY (red) andAIRS (blue) over North America
In situ measurements of CO2mixing ratios in the free troposphere (FT) and in the continental boundary layer (CBL), and of NEE (Hurwitz et al. 2004).
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ECMWF seminar ‘07 F. Chevallier slide 10
CO2 as primary target
CO2 concentrations higher than at any time within the last 650,000 years
OCO (NASA)Launch Dec’ 2008
GOSAT (JAXA, NIES, MoE)Launch Dec’ 2008
More projectsA-SCOPE, ACCLAIM,
CARBOSAT, …
IPCC (2007)
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ECMWF seminar ‘07 F. Chevallier slide 11
Beyond NWP within NWP systemsErrors on atmospheric concentrations of gases and aerosols affect NWP systems
NWP systems flexible and powerful enough to tackle environmental issues
Expertise in data mergingExpertise in satellite observationsExpertise in atmospheric modelling
Surface and soil properties
NOAA-10 channel 10Calculated minus obsPierangelo et al., 2004
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ECMWF seminar ‘07 F. Chevallier slide 12
FP6 GEMS project
Part of the Global Monitoring for Environment and Security (GMES, funded by EC & ESA) Atmosphere theme
31 consortium members, 4 years (started in March 2005)
Coordinated by ECMWF
Creation of a pre-operational global monitoring system for greenhouse gases, reactive gases, and aerosols in the troposphere and in the stratosphere
Near-real-time and retrospective global analyses for monitoring atmospheric composition, and short-range forecasts to drive regional air-quality models.
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ECMWF seminar ‘07 F. Chevallier slide 13
Summary of introduction
Use of satellite data for the environment = emerging topicIncreasing interest from the NWP communitySignal-to-noise may be challenging for some species
MODIS/Terra, August 2003, daytimeAerosol optical depth @0.55 micron
MISR/Terra, August 2003Aerosol optical depth @0.55 micron
A. Benedetti
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ECMWF seminar ‘07 F. Chevallier slide 14
Outline
IntroductionOptimization of atmospheric concentrationsOptimization of surface fluxesOptimization of surface model parametersConclusion
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ECMWF seminar ‘07 F. Chevallier slide 15
Mathematical framework
Bayes’ theorem
The optimal solution minimizes the following cost function
x: state vector (atmospheric concentrations)y = Hx+ε : observation (atmospheric concentrations)H: linear observation operator
(short-range chemical transport + interpolation)B: background error covariance matrixR: observation error covariance matrix
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ECMWF seminar ‘07 F. Chevallier slide 16
CO2 analysis
Analysed CO2
Analysis minus free runAugust 2003
CO2 4D-Var analysis using AIRSAugust 2003Started in January 2003
R. Engelen
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ECMWF seminar ‘07 F. Chevallier slide 17
Individual Profiles
10 ppmv
11 May 2003
Comparisons with flight data over Hawaii (courtesy of Pieter Tans, NOAA/ESRL) shows a clear improvement of the analysis over the free-running model.But, background error is important.
25 September 2003R. Engelen
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ECMWF seminar ‘07 F. Chevallier slide 18
Individual Profiles
10 ppmv
11 May 2003
Comparisons with flight data over Hawaii (courtesy of Pieter Tans, NOAA/ESRL) shows a clear improvement of the analysis over the free-running model.But, background error is important.
25 September 2003R. Engelen
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ECMWF seminar ‘07 F. Chevallier slide 19
Individual Profiles
10 ppmv
11 May 2003
Comparisons with flight data over Hawaii (courtesy of Pieter Tans, NOAA/ESRL) shows a clear improvement of the analysis over the free-running model.But, background error is important.
25 September 2003
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ECMWF seminar ‘07 F. Chevallier slide 20
Molokai, Island, Hawaii
Blue: free-running model
Red: reanalysis
Black: observationsR. Engelen
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ECMWF seminar ‘07 F. Chevallier slide 21
Analysis of CO
CO2 lifetime ~ 100 yearsCO lifetime ~ 2 monthsCO interacts with OH
Surface sources (combustion)Chemical production in the atmosphereChemical loss in the atmosphere
Observed by MOPITT satellite since 2000
GEMS analysis system : 2-way coupling between IFS and a chemistry-transport model
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ECMWF seminar ‘07 F. Chevallier slide 22
Analysis of COAssimilation of MOPITT dataFree running
15-30 July 20031018 modelcules/cm2
Assimilation minus free run
A. Dethof
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ECMWF seminar ‘07 F. Chevallier slide 23
Analysis of CO (cont’ed)
Assimilation of MOPITT CO columns leads to improved fit to profile observations from MOZAIC flights
15-30 July 2003MOSAIC data over Osaka, JPN
MOZAIC flight
Free-running CO model
Assimilation of MOPITT data
A. Dethof
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ECMWF seminar ‘07 F. Chevallier slide 24
Assimilation of POLDER data within the LMDZ-INCA model
Aerosol optical thickness
AN-FG AN-FG POLDER
Generoso et al. (2007)
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ECMWF seminar ‘07 F. Chevallier slide 25
Assimilation of POLDER data within the LMDZ-INCA model
AN-FG AN-FG POLDER
Generoso et al. (2007)
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ECMWF seminar ‘07 F. Chevallier slide 26
Assimilation of POLDER data within the LMDZ-INCA model
Generoso et al. (2007)
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ECMWF seminar ‘07 F. Chevallier slide 27
Outline
IntroductionOptimization of atmospheric concentrationsOptimization of surface fluxesOptimization of surface model parametersConclusion
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ECMWF seminar ‘07 F. Chevallier slide 28
Mathematical framework
Bayes’ theorem
The optimal solution minimizes the following cost function
x: state vector (surface fluxes)y = Hx+ε : observation (atmospheric concentrations)H: linear observation operator
(long-range chemical transport + interpolation)B: background error covariance matrixR: observation error covariance matrix
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ECMWF seminar ‘07 F. Chevallier slide 29
CO emissions from MOPITT
Comparison of a priori (grey symbols) and a posteriori (black symbols) monthly biomass burning sources in Africa with van der Werf et al. (2004) inventory (white symbols)
Petron et al. (2004)
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ECMWF seminar ‘07 F. Chevallier slide 30
CO2 fluxes from AIRS
11-month inversionMarch 2003, GEMS test re-analysisAN-FG, gC/m2/month
Inversion from satellite data
Inversion from in situ data(P. Peylin, pers. comm.)
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ECMWF seminar ‘07 F. Chevallier slide 31
CO2 fluxes from AIRS
11-month inversionMay 2003 , GEMS test re-analysisAN-FG, gC/m2/month
Inversion from satellite data
Inversion from in situ data(P. Peylin, pers. comm.)
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ECMWF seminar ‘07 F. Chevallier slide 32
Multi-tracer inversions
Simplified atmospheric chemistryComputing timeLimited observation information content
Hydrocarbon oxidation chain
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ECMWF seminar ‘07 F. Chevallier slide 33
Multi-tracer inversion from MOPITT+ surface MCF
Mean January 2007MOPITT L2V5.9.4700 hPa (ppb)31,400 retrievals
First-Guess equivalent Analysis equivalent
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ECMWF seminar ‘07 F. Chevallier slide 34
Multi-tracer inversion from MOPITT+ surface MCF
CO flux increments(g/m2 for 1 month)
HCHO-productionscaling factor (0-1)
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ECMWF seminar ‘07 F. Chevallier slide 35
Independent HCHO observation
Too much HCHO in the free model
F. Wittrock, 2006Bremen University
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ECMWF seminar ‘07 F. Chevallier slide 36
Outline
IntroductionOptimization of atmospheric concentrationsOptimization of surface fluxesOptimization of surface model parametersConclusion
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ECMWF seminar ‘07 F. Chevallier slide 37
Mathematical framework
Bayes’ theorem
The optimal solution minimizes the following cost function
x: state vector (model parameters)y = Hx+ε : observation (atmospheric concentrations + …)H: linear observation operator
(surface model +long-range chemical transport + interpolation)B: background error covariance matrixR: observation error covariance matrix
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ECMWF seminar ‘07 F. Chevallier slide 38
Con: involving development
More complex observation operator
LMDZT transport model includes ~ a few thousands lines of codeORCHIDEE model of the terrestrial vegetation includes ~ 40,000 lines of code
1 1( ) 2 ( ) 2 ( [ ])TJ H− −∇ = +bx B x - x H R y - x
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ECMWF seminar ‘07 F. Chevallier slide 39
Con: model as hard constraint
Changes the observation errors as seen by the inversion system
[Observation error] = [Measurement error] + [representativeness error] + [Model error]
Biases / Variances / Correlations
We may not have enough information from the observations to introduce a weak constraint formulation
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ECMWF seminar ‘07 F. Chevallier slide 40
Impact of observations error correlations
Surface fluxes from OCOHypothetical O.5 along-track correlation Correlations ignored in the inversionUncertainty reduction 1-sig(post)/sig(prior)
Increment in error reductionReference error reduction
Chevallier (2007)
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ECMWF seminar ‘07 F. Chevallier slide 41
Pro: assimilate more than atmospheric concentrations
020406080
100120140160
Free Run MODIS-Assimilated Run
evergreen needle-leafforestevergreen broad-leaved forestdecidious broad-levedforestmixed forest
natural C3
agricultural C3Demarty et al. (2007)
Assimilation of MODIS LAI within the ORCHIDEE vegetation modelRMS difference between simulated gross primary production and independent FLUXNET data (40 sites)
gC/m2/month
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ECMWF seminar ‘07 F. Chevallier slide 42
Pro: spread increments
Background error correlations
CO2 fluxes:Prior error spatialcorrelationsat a series of sitesChevallier et al. (2006)
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ECMWF seminar ‘07 F. Chevallier slide 43
Pro: spread increments (cont’ed)
Optimizing generic parameters may be more efficient than prior errors in spreading the observation information in space and time
Error reductionfor the inversion of CO2surface fluxes from CO2concentrations at two sites. 4-day period.
No prior error spatial correlations.
Transport from MesoNHat 8-km resolution.
Lauvaux et al. (2007)0.1 0.9
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ECMWF seminar ‘07 F. Chevallier slide 44
Pro: predictive capability
Rayner et al. (2005, 2007)
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ECMWF seminar ‘07 F. Chevallier slide 45
Summary
From the assimilation of satellite data to the inversion of parametersComprehensive approachIncreased sophisticationLarge networks of expertise required
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ECMWF seminar ‘07 F. Chevallier slide 46
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
NASAEurNOAAJAXA
IASI
DOAS Sounders
GREENHOUSE Gases : Main Satellite Provision 2003-2019
Advanced Sounders
AIRS
ENVISAT (SCIAMACHY) Uncertainty
GOSAT
NPP/ CrIS
NPOESS / CrIS
OCO
Greenhouse gas provision
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ECMWF seminar ‘07 F. Chevallier slide 47
Reactive gas provision
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
NASAEurNOAAJAXA
AURA (TES, OMI)
AURA (TES, OMI)
ENVISAT (MIPAS, SCIAMACHY, GOMOS)
Lower Troposphere
ENVISAT (SCIAMACHY)
NPP/ OMPS (~sbuv+toms)
OMPS-Nadir (Npoess)
GOME (Metop)
REACTIVE Gases (O3, N2O, SO2, CH2O) : Main Satellite Provision 2003-2019
Upr. Trop. -Lower StratUncertainty
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ECMWF seminar ‘07 F. Chevallier slide 48
Aerosol provision
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
NASAEurNOAAJAXA
V-IR Imager
V-IR Imager
Uncertainty
NPOESS/ VIIRS
MERIS
AURA (TES, OMI)
APS (Glory)
ParasolPolarimeter
AEROSOLS, Albedo, Ocean Colour, Vegetation: Main Satellite Provision 2003-2019
NPP/ VIIRS
MODIS (Aqua)
MODIS (Terra)