1 Assimilation of EPS tropospheric ozone for air quality forecast B. Sportisse, M. Bocquet, V....

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1 Assimilation of EPS tropospheric ozone for air quality forecast B. Sportisse, M. Bocquet, V. Mallet, I. Herlin, JP. Berroir, H. Boisgontier ESA EUMETSAT EPS/MetOp RAO Workshop, May 2006

Transcript of 1 Assimilation of EPS tropospheric ozone for air quality forecast B. Sportisse, M. Bocquet, V....

Page 1: 1 Assimilation of EPS tropospheric ozone for air quality forecast B. Sportisse, M. Bocquet, V. Mallet, I. Herlin, JP. Berroir, H. Boisgontier ESA EUMETSAT.

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Assimilation of EPS tropospheric ozone for air quality forecastB. Sportisse, M. Bocquet, V. Mallet, I. Herlin, JP. Berroir, H. Boisgontier

ESA EUMETSAT EPS/MetOp RAO Workshop, May 2006

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Project objectives

•Applicative context: air quality forecast at European Scale. Air quality -> boundary layer concentrations

•Objective: • to assess the feasibility of assimilating O3 columns and profiles, to be

provided by MetOp (IASI, GOME): numerical experiments to be performed before the launch of MetOp

• to perform experiments with real O3 data when available

• validation and quantification by comparison with forecast obtained without assimilation, or with assimilation of ground stations measurements

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Outline

•The POLYPHEMUS air quality modelling system

•Preliminary studies:• Relative weight of boundary layer ozone in the IASI 0-6km O3 column

• Sensitivity of boundary layer ozone to IASI O3 column

•Data assimilation feasibility• Based on twin numerical experiments

• Based on simulated IASI columns

•Perspectives: assimilation of real data

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The Polyphemus air quality modelling system

•Developed by CEREA (joint ENPC-EdF laboratory, Clime team shared between ENPC and INRIA)

•Modular architecture:• Access to raw input data• Input data processing: physical parameterizations, …• Numerical heart: CTM, its linear tangent and adjoint models• High level applications:

– Direct forecast– Sensitivity studies– Impact studies– Ensemble forecast– Data assimilation– …

• Visualisation

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POLYPHEMUS architecture

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POLYPHEMUS applications: ozone forecast

European scale run

ECMWF met. Fields

EMEP emissions

Extensively validated

http://www.enpc.fr/cerea/polyphemus

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POLYPHEMUS applications: ensemble forecast

0 5 10 15 20Hours

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110 O3 vs time for different physical parameterization

Need of better constraining models

Data assimilation

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Preliminary studies

Is assimilation of EPS O3 information within regional CTMs feasible?

Problem:• EPS O3 mainly representative of upper troposphere

• Air quality application interested in boundary layer ozone

Feasible if:• Boundary layer has a significant weight in the 0-6km column

• Boundary layer ozone, as forecast by Polyphemus, is sensitive to changes in upper tropospheric ozone

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Relative weight of boundary layer O3

•Computed from a reference situation (validated Polyphemus analysis – July 2001)

•Average contribution of boundary layer 03 to 0-6km column: 14% (more during day because boundary layer is higher)

•Irregularly scattered in space and time

Mean at 0h

Mean at 15h

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Sensitivity of boundary layer O3

•Experiment: • Consider a reference run

• Apply perturbation to upper tropospheric ozone: at initial date, every 24h

• Run forecast with perturbated O3

• Compare forecast with reference

•Conclusion, from initial perturbation.• Max. sensitivity: 50%

• Max difference with reference observed 27h after simulation

•Conclusion, from cyclic pertubation• Perturbated runs quicly diverges from reference

•Overall conclusion: Yes, upper tropospheric ozone can be used to constrain boundary layer O3

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Sensitivity of boundary layer ozone

-50% perturbation applied on UT O3

Graph: mean relative difference at ground level between perturbation and reference

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Sensitivity of boundary layer O3

Same experiment

Relative difference 27h after perturbation

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Sensitivity of boundary layer O3

Perturbation applied every 24h

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Feasibility of data assimilation

•Numerical experiments:

•Columns generated from reference situation, 5% perturbation

•Assimilation within perturbated runs:• Method: Optimal Interpolation

• Different choices of model/observation covariance matrices

• Assimilation every 24h

• 2 types of perturbated runs:

– Perturbation of initial condition: assimilation should speed up the convergence to reference

– Pertubation of physical parameterization (Kz): perturbation always different from reference, assimilated run should lie in between.

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Feasibility of assimilation: perturbation of initial condition

0 20 40 60 80 100 120 140 160 1800

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Relative difference with reference

Blue: without DA

Others: DA with different values of obs. error.

Quicker convergence to reference

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Feasibility of assimilation: simulated IASI column

•Context: cooperation with SA (C. Clerbaux)

•Simulation of IASI radiances from:• Polyphemus reference (0-5km)

• UGAMP climatologies (6-60km)

• Interpolation (5-6km)

• LBLRTM code + IASI instrument model

•Inversion of IASI radiances:• Atmosphit model (for evaluation, 2 dates)

• SA NN operational code

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Simulation of IASI measurements

Example of profiles/inversion error (Atmosphit)

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Simulation of IASI measurements: issues

•Preparing the assimilation of real data: • Realistic noise (instrument, inversion procedure)

• Set up of observation operator

• Set up of observation covariance matrices

•Influence of a priori information:• 6-60km (O3, T, p)

• LST

•Ongoing, awaiting results

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Conclusions

•Twin numerical experiments tend to show that assimilation of 0-6km is feasible and can improve the analysis of boundary layer O3, in the case of high quality measurements

•Realistic accounting for instrument and inversion noise: experiment with simulated IASI columns going on

•Waiting for real data…