interpretation of station data with an adjoint model maarten krol (imau) peter bergamaschi (ispra)...
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Interpretation of station data with an adjoint Model
Maarten Krol (IMAU)
Peter Bergamaschi (ISPRA)
Jan Fokke Meierink, Henk Eskes (KNMI)
Sander Houweling (SRON/IMAU)
What is TM5?
• Global model with zoom option
• Two-way nesting
• Mass-conserving / Positive
• Atmospheric chemistry Applications
• Off-line ECMWF
• Flexible geometry
What is TM5?
6x4
3x2
1x1
Why an Adjoint TM5?
• Concentrations on a station depend on emissions
• Interesting quantity: dM(x,t)/dE(I,J,t’)– How does a ‘station’ concentration at t changes
as a function of emissions in gridbox (I,J) at time t’?
– Inverse problem: from measurements M (x,t)
--> E(I,J,t’)
Adjoint TM5
• dM(x, t)/dE(I,J) (constant emissions) can be calculated with the adjoint in one simulation
• M0(x, t) = f(E0(I,J))
• M(x, t) = M0+dM(t)/dE(I,J)*(E(I,J)-E0(I,J))
• Only if the system is linear!
Adjoint TM5 (4DVAR)
Finokalia MINOS 2001 measurements
Dirty
Clean
Finokalia
• Integrations from M(t) back to july, 15.
• Forcing at station rm(I,J,1) = rm(I,J,1) + f(t,t+dt) (during averaging period)
• Adjoint chemistry
• Adjoint emissions give analytically: dM(t)/dE(I,J)
Clean
Dirty
Clean
Dirty
Clean
Dirty
Clean
Dirty
Prior MCF emission distribution
Procedure
• Minimise
• With
Posterior MCF emissions:
Negatives
Emissions over sea
BETTER CONSTRAIN THE PROBLEM
Conclusions
• Emissions seem to come from regions around the black sea!
• Results sensitive to prior information• Not surprising: 8 observations <==> 1300
unknowns• Emissions required: 10-30 gG/year• How to avoid negatives?
Next Steps (to be done)
• Prior Information– non-negative– full covariance matrix
• Full 4Dvar, starting with obtained solution as starting guess emissions
• Influence station sampling, BL scheme, ….
• All observations separately (Movie)