Comparison of adjoint and analytical approaches for solving atmospheric chemistry inverse problems. Monika Kopacz 1 , Daniel J. Jacob 1 , Daven Henze 2 , Colette Heald 3 , David G. Streets 4 , Qiang Zhang 5 October 11, 2006. - PowerPoint PPT Presentation
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Comparison of adjoint and analytical approaches for solving atmospheric chemistry
inverse problems
Monika Kopacz1, Daniel J. Jacob1, Daven Henze2, Colette Heald3, David G. Streets4, Qiang Zhang5
Number of constraints (x) limited by the inverse methodology
Advantages of satellite instruments (recent tropospheric measurements):
• offer dense, daily global coverage (from 1 to 16 days orbit repeat)
• observations over/close to the sources not background like surface station in remote atmosphere and oceans
• can constrain more emissions regions???
Measurement of Pollution In the Troposphere (MOPITT)
ˆ ) T -1 -1 -1 T -1a x x a x ax = x + ( F S F + S ) F S (F( x y)
Solving an inverse problem
1 1( ) ( ( ) ) ( ( ) ) ( ) ( )T Ta a aJ
x F x y S F x y x x S x xminimize
T 1 1x( ) = 2( 2 ( )aJ
x ax F(x)) S (F(x) y) + S x xcompute
Objective:
Analytical method Adjoint method
( ) xJ xcompute
use optimization algorithm to iteratively find
ˆ xSolve explicitly for
ˆ x
explicitly compute Jacobian matrix:
F( ) = x x K
( ) = 0xJ xset
MAP solution
T 1 1 1 T 1a aˆ + ( + ) ( )a
x x K S K S K S y Kx
using adjoint model
Analytic vs. adjoint solution
T 1 1 1 T 1a aˆ + ( + ) ( )a
x x K S K S K S y Kx
How the analytical approach becomes infeasible…
Not computing Jacobian matrix explicitly fortran code used to represent itUsing reverse mode efficient
Increasing the size of the optimized vector O(10) O(10^5)Constructing full Jacobian matrix KInverting large matrices
How an adjoint addresses problems of the analytical approach…
Assumptions we can’t avoid…Gaussian errors Linearization of nonlinear processes using gradient descent
( )
T 1 1x( ) = ( ( )x aJ
ax F(x)) S (F(x) y) + S x x( )
Method comparison project
Comparison objective: Perform a (adjoint) inversion similar to a previous (analytical) inversion using the same observations, emissions inventory, time frame, error characterization and forward model (but not resolution!)
Inversion objective: Constrain Asian CO emissions during the Spring 2001
≠
average model CO concentration average satellite (MOPITT) concentration
Heald et al, 2004: Comparative inverse analysis of satellite (MOPITT) and aircraft (TRACEP) observations to estimate Asian sources of carbon monoxide, J. of Geophys. Res.
Constructing error covariance
%
Observational errorObs. error variance: Relative Residual Error (RRE) method, ie. Computing deviation from an ensemble mean error (model bias due to error in sources)
All error covariance: set to zero
aS
S
A priori source error variance: from emissions inventory for each country and source type
S Includes model error, representation error and instrument error
CO constraints using adjoint inversion
Red: a priori underestimate
China, Northern India
Blue: a priori overestimate
East India, Southeast Asia, Philippines
A posteriori emissions scaling factors
Comparison: coarse (adjoint) vs. averaged detail (adjoint) source estimates
analytical adjoint1. C. China (ChCE) 1.83 1.342. SE Asia (SEAs) 0.63 0.673. Philippines (Ph) 0.89 0.734. Indonesia (Id) 0.96 0.905. India (In) 0.50 0.686. rest of world (RoW) 1.16 0.91 oxidation source 1.11
analytical adjoint1. W. China (ChW) 2.38 1.162. S. China (ChSE) 0.31 1.18 3. N. China (ChNE) 0.76 1.024. Japan (Jp) 1.88 0.99 Korea (Ko) 1.025. Europe (EU) 0.75 1.00
11 regions (state vector elements) from Heald et al, 2004
Potentially affected by aggregation error
Analytical bias Adjoint bias
a priori
a posteriori
Limitations and challenges of using the adjoint for inverse modeling
Do we have enough observations???
How computationally efficient is it?
If we use adjoint approach for inversion, what is the best optimization algorithm, considering the requirements of: - quick convergence - accuracy - non-negative solution, ie. will not yield negative emissions! - no bias?
fwd + adj run for simple CO chemistry (69 days): 4h
L-BFGS (Liu and Nocedal, 1989)
END
Adjoint model development
Based on GEOS-Chem forward model (GEOS-3, v6-05-07)
• advection
• deep convection
• turbulent mixing
• CO chemistry
• CO sources
• integration with MOPITT observations
Daven Henze at Caltech
Harvard
Note: The adjoint model also contains aerosol thermodynamics (full chemistry adjoint), wet and dry deposition and aerosol emissions components all developed by Daven Henze