applications of inverse modeling for understanding of emissions and analysis of observations
DESCRIPTION
Applications of inverse modeling for understanding of emissions and analysis of observations. Rona Thompson , Andreas Stohl , Ignacio Pisso , Cathrine Lund Myhre, et al. Content of presentation. FLEXPART transport model - PowerPoint PPT PresentationTRANSCRIPT
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Applications of inverse modeling for understanding of emissions and analysis of observations
Rona Thompson, Andreas Stohl, Ignacio Pisso, Cathrine Lund Myhre, et al.
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Content of presentationFLEXPART transport model
Statistical analysis of observation data: Methane results for Zeppelin station
Inversion basics
Applications to halocarbon emissions
FLEXINVERT
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Lagrangian particle dispersion modelTurbulence and convection parameterizationsDry and wet depositionData input from ECMWF, GFS, MM5, WRF,…
Model descriptions in Atmospheric Environment,Boundary Layer Meteorology, Atmospheric Chemistry and Physics
Used at probably >100 institutes from several dozen countries
The FLEXPART model
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Can be run both forward (from sources) or backward (from measurement stations) in time, whatever is more efficient
Here: Backward in time for 20 days
Model output: 4-dimensional emission sensitivity field (3 space dimensions plus days backward in time)
Mixing ratio = emission sensitivity field x emission flux field
http://zardoz.nilu.no/~andreas/STATIONS/ZEPPELIN/Zeppelin_201001/ECMWF/polar_column_t/Zeppelin_201001.polar_column_t_1.html
Model set-up
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Footprint emission sensitivity maps averaged for the four seasons (upper panels) and normalized to annual mean
Transport climatology (2001-2012)
DJF MAM JJA SON
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Cluster analysisCluster analysis of trajectory output
(Dorling et al., 1992)
Cluster analysis can be used to stratify measurement data according to transport pathway
Disadvantage: no good control on the ”shape” of the clusters, no clear separation of sources, no quantitative information on emissions
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Cluster analysis (2001-2012)
Siberia and Central Asia = SCA, Western Arctic Ocean = WAO, Arctic Ocean = AO, Canada and Greenland = CGA, North Atlantic Ocean = NAO, East Asia and North Pacific = EA, Europe and North America = ENA, Siberia Northeast Asia = SNEA
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”Ashbaugh method”Ashbaugh, 1983; Ashbaugh
et al., 1985
Define a grid
Associate M measurements with trajectories and calculate total gridded residence time ST from individual gridded residence times
where i, j are grid indices. Then, select subset with L=M/10 highest 10% measured concentrations
To identify source/sink areas, calculate
If concentration not associated with transport: RP(i,j) = 0.1 everywhere
Where there is a source: RP(i,j) > 0.1
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”Ashbaugh method”Detrended and deseasonalized 2001-2012 CH4 data
Emission sensitivity
Sp
Emission sensitivity normalized by emission
sensitivity for all data
Rp
log(s m-3 kg-1)Highest 10% Lowest 10%
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”Ashbaugh method” – local scaleDetrended and deseasonalized 2001-2012 methane data
Emission sensitivity
Sp
Emission sensitivity normalized by emission
sensitivity for all data
Rp
log(s m-3 kg-1)Highest 10% Lowest 10%
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The inverse modeling problemNeeds a large set of atmospheric concentration measurements, ideally from many
stations and/or campaigns
Want to use these data to determine the emissions of the studied substance
Substance can be subject to removal processes (e.g., aerosols) or considered (almost) passive on short time-scales (e.g., CH4)
To use inverse modelling, the underlying atmospheric transport model must be able to account for these processes, i.e., it must be possible to establish quantitative source-receptor relationships
Systematic errors in the model would (likely) cause bias in retrieved emissions
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Aim: Determination of the emission sources from air concentration measurements
M ... M x N matrix of emission sensitivities from transport model calculations
… often called source-receptor relationshipx ... Emission vector (N emission values)y ... Observation vector (M observations)Difficulty: poorly constrained problem; large spurious emissions can easily result to satisfy even single measurement data points as there is no penalty to unrealistic emissions
Solution: Tikhonov regularization: ||x||2 is small
Bayesian inversion basics
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Slight reformulation if a priori information is available
yo ... Observation vector (M observations)xa ... A priori emission vector (N emission values)
Tikhonov regularization: ||x-xa||2 is small
We are seeking a solution that has both minimal deviation from the a priori, and also minimizes the model error (difference model minus observation)
Bayesian inversion basics
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1 2
Minimization of the cost function
1. Term: minimizes squared errors (model – observation)2. Term: Regularization term
x, o ... Uncertainties in the a priori emissions and the observationsdiag(a) … diagonal matrix with elements of a in the diagonal
The uncertainties of the emissions and of the „observations“ (actual mismatch between model and observations) give appropriate weights to the two terms
Bayesian inversion basics
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Halocarbon emissions in China
Example: HFC-23a by-product of HCFC-22 production
Black dots: 3 measurement stations
Top panel: emission distribution available a priori
Bottom panel: inversion result
Asterisks: known locations of HCFC-22 factories
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New development by Rona Thompson: FLEXINVERTDescription planned for Geosci. Mod. Dev.
• Planned as an open-source development
• Partly builds on Stohl et al. (2009) algorithm
• Algorithm specifically developed for long-lived greenhouse gases
• Allows coupling of 20-day FLEXPART backward runs with global model output
• Modular, so can be adjusted to different requirements (CH4, CO2, N2O, SF6, etc.)
• Allows flexible time resolution of the emissions (e.g., monthly)
• Facilitates error correlations of the prior emissions (spatially and temporally)
• Calculates posterior flux error covariances (i.e., errors in emissions)
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First application to East AsiaEmission sensitivity log(s m3 kg-1)
Variable grid resolution
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Application to East Asia (1)Atmospheric observations in nested domain
Institute Type No. sites
CAMS in-situ (CRDS) 4
NIES in-situ (GC-FID) 2
NOAA flask (GC-FID) 4
JMA in-situ (NDIR) 3
KMA in-situ (GC-FID) 1
NIER in-situ (GC-FID) 1
TOTAL 15
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Application to East Asia (2)
Source Dataset Total (TgCH4 y-1)
anthropogenic - rice cultivation - waste - fuels - animal agriculture
EDGAR-4.2 331
natural wetlands LPJ DGVM model 175
biomass burning GFED-3 13
geological based on Etiope et al. 2008 55
termites Sanderson et al. 1996 19
wild animals Olson et al. 1997 5
soils Ridgewell et al. 1999 -38
ocean Lambert and Schmidt, 1993 17
TOTAL 577
Prior emissions
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Results (1)
China a priori: 61.6 TgCH4/y
China a posteriori: 59.6 TgCH4/y
Annual mean fluxes for 2009
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Results (2)
OBSPRIORPOSTBKGND
0.270.53
0.450.57
0.330.57
0.370.49
0.380.50
0.120.26
0.400.71
0.640.79
0.520.72
0.410.69
0.290.35
0.270.71
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ConclusionsIn MOCA, we will use inverse modeling as a tool to analyze CH4 data
using station network (Zeppelin, Pallas, etc.)using campaign data
Algorithm (almost) ready but will need further development/testing
Will also utilize other means of analyzing data