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
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
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
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
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
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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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
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
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)
First application to East AsiaEmission sensitivity log(s m3 kg-1)
Variable grid resolution
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
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
Results (1)
China a priori: 61.6 TgCH4/y
China a posteriori: 59.6 TgCH4/y
Annual mean fluxes for 2009
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
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