interpretation of station data with an adjoint model maarten krol (imau) peter bergamaschi (ispra)...

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)