Further developments and applications for the adjoint of cmaq
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Further Developments and Applications for the Adjoint of CMAQ . Amir Hakami, Kumaresh Singh, Adrian Sandu, John Seinfeld (Carleton, Caltech, Va Tech). 6 th Annual CMAS Conference Chapel Hill October 1, 2007. Overview. Brief introduction to adjoint sensitivity analysis

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Further developments and applications for the adjoint of cmaq l.jpg

Further Developments and Applications for the Adjoint of CMAQ

Amir Hakami, Kumaresh Singh, Adrian Sandu, John Seinfeld

(Carleton, Caltech, Va Tech)

6th Annual CMAS Conference

Chapel Hill

October 1, 2007


Overview l.jpg
Overview CMAQ

  • Brief introduction to adjoint sensitivity analysis

  • Implementation details

    • Current status

    • KPP integration

    • Forward (DDM/TLM) implementation

    • Process-by-process validation

    • Computational performance

  • Potential applications

  • Future developments

CMAS Conference Oct 1, 2007


Forward vs backward sensitivity analysis l.jpg
Forward CMAQ vs. BackwardSensitivity Analysis

Inputs/Sources

Outputs/Receptors

  • Adjoint analysis is efficient for calculating sensitivities of a small number of outputs with respect to a large number of inputs. Forward analysis is efficient for the opposite case.

  • Complementary methods (Source-based vs. Receptor-based), each suitable for specific types of problems.

CMAS Conference Oct 1, 2007


Ddm tlm and adjoint formulations l.jpg
DDM/TLM and adjoint formulations CMAQ

  • Forward model

  • Tangent linear model (TLM/DDM)

  • Adjoint model

CMAS Conference Oct 1, 2007


Current status of cmaq adj l.jpg
Current status of CMAQ-ADJ CMAQ

  • Developed in collaboration between Caltech and Va Tech

  • Developed for version 4.5

    • Pretty hard to keep up with CMAS releases!

  • Only gas-phase processes

  • Only uniform grid – no nesting

  • Only sequential simulation

  • Discrete adjoint with the exception of HADV

  • Availability:

    • Va Tech version:

      • http://www.cs.vt.edu/~asandu/Software/CMAQ_ADJ/CMAQ_ADJ.html

    • Caltech/Carleton: to be released soon

  • More details: Hakami et al. (2007), ES&T (in press)

CMAS Conference Oct 1, 2007


Kpp integration work precision diagram l.jpg
KPP integration: work-precision diagram CMAQ

  • Chemistry independent with 5 Rosenbrock and 4 Runge-Kutta solvers

CMAS Conference Oct 1, 2007


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DDM implementation CMAQ

  • More accurate than DDM-3D implementation

CMAS Conference Oct 1, 2007


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Backward simulation scheme CMAQ

CMAS Conference Oct 1, 2007


Chemistry l.jpg
Chemistry CMAQ

  • Chemistry-only simulations

    • Seminormalized sensitivity of ozone to initial NO

CMAS Conference Oct 1, 2007


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Vertical diffusion CMAQ

  • Chemistry + vertical diffusion

    • Seminormalized sensitivity of ozone to NO emissions

CMAS Conference Oct 1, 2007


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Horizontal advection CMAQ

BF (+100%)

BF (-10%)

Adjoint

DDM

  • Sensitivity of ozone in 20st column cross section to initial ozone in 20th column

    • Only HADV in x direction

  • Hence, continuous approach for HADV

    • Bott exhibits better behavior

CMAS Conference Oct 1, 2007


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A side note: what to validate? CMAQ

  • As developers, should we only validate our numerical routines for concentrations?

  • In light of increased attention paid to model sensitivities, it appears that validation efforts should include sensitivity information as well as concentrations

    • Even if not performing formal sensitivity analysis, we are routinely using (finite) differences.

  • It is imperative to make sure that our numerical routines do not produce response surfaces that are overly fractured/discontinuous.

CMAS Conference Oct 1, 2007


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HDIFF (top) and VADV CMAQ

CMAS Conference Oct 1, 2007


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Full model validation CMAQ

NO emissions

Initial ozone

CMAS Conference Oct 1, 2007


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Computational efficiency CMAQ

1- Values are normalized to forward simulation with EBI solver.

2- Values are normalized to the forward simulation with the same solver.

3- Values include the time required for concentration integrations.

CMAS Conference Oct 1, 2007


Potential applications environmental exposure l.jpg
Potential applications (environmental exposure) CMAQ

  • Different applications depending on the definition of the cost function.

  • As a receptor-based method, adjoint analysis is particularly powerful for policy applications

    • Nonattainment analysis (Hakami et al., 2006)

    • Most common uses in data assimilation and inverse modeling

  • Let’s look at few other examples

CMAS Conference Oct 1, 2007


Potential applications population exposure l.jpg
Potential applications – population exposure CMAQ

Population exposure metric:

Sensitivity to NOx emissions

Metric distribution

(Plots are normalized to the total metric)

CMAS Conference Oct 1, 2007


Potential applications vegetation stress l.jpg
Potential applications - vegetation Stress CMAQ

Vegetation damage (W126) metric:

Metric distribution

Sensitivity to NOx emissions

(Plots are normalized to the total metric)

CMAS Conference Oct 1, 2007


Potential applications temperature dependence l.jpg
Potential applications - temperature dependence CMAQ

Population exposure

Vegetation stress

NB: This only includes the effects through chemistry.

CMAS Conference Oct 1, 2007


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Future research plans CMAQ

  • Further development of the adjoint of CMAQ:

    • Clouds, aqueous, and aerosol processes.

      • Aerosol thermodynamics will be a significant challenge.

    • Parallelization.

    • Backward nesting.

    • Coupling with GEOS-Chem in backward mode.

      • That would give us a regional-to-global forward and backward sensitivity analysis platform

CMAS Conference Oct 1, 2007


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Summary and conclusions CMAQ

  • KPP integration with CMAQ provides users with good combination of accuracy and efficiency.

  • Both DDM and adjoint implementations show very good level of accuracy and computational efficiency.

  • Receptor-oriented nature of the adjoint method makes it ideal for policy applications and target-based analysis.

  • Problems with PPM advection adjoint indicates the need for the development community to validate sensitivities (differences) in addition to concentrations.

CMAS Conference Oct 1, 2007


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Acknowledgements CMAQ

  • Thanks to

    • Daewon Byun, Soontae Kim, and Qinbin Li

    • Funding Agencies: NSF and NASA

CMAS Conference Oct 1, 2007


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Questions? Comments? CMAQ

Thank you!!

CMAS Conference Oct 1, 2007


Adjoint analysis l.jpg
Adjoint analysis CMAQ

  • Target-based, receptor-oriented method: Depends on the definition of a cost function ( J ) for which sensitivity calculations are carried out.

  • Adjoint equations are integrated backward in time. At each location and time adjoint variables are gradients of the cost function with respect to state vector (concentrations).

CMAS Conference Oct 1, 2007


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