1. 1 Satellite data assimilation for air quality forecast 10/10/2006

2. 2 Objectives Assimilation of satellite measurements of troposphere chemistry, in view of improving the quality of forecast. Context: Feasibility study concerning the future sensors GOME2 and IASI (to be launched in Oct 2006 on EPS/MetOp). PI of ESA/Eumetsat project. Future mission TRAQ (2012) for the evolution of air quality at regional (Europe) and global scales. Collaboration with IPSL/SA (C. Clerbaux) : participated to the design of the IASI sensor.

3. 3 Chemical measurements to be assimilated Ground-based monitoring network: Operated locally. Irregularly scattered throughout Europe, good temporal sampling. Non uniform quality. Nature: ground level concentrations, vertical profiles (LIDAR). Satellites : Continuous improvement of satellite measurements of troposphere chemistry: MOPITT, OMI (NASA), GOME, SCIAMACHY (ESA), futurs GOME2 and IASI (ESA). Regular spatial sampling (12 km), 1 acquisition/day, uniform quality. Nature: columns (O3, NOx, CH4, …), vertical profiles (O3, NOx), aerosols optical properties.

4. 4 Potential of IASI acquisitions Considered measure: 0-6km ozone column. What information it carries of the boundary layer? Contribution of boundary layer ozone to the column. Sensitivity (via model) of boundary layer O3 to modifications of upper troposphere O3. IASI assimilation experiments.

5. 5 Contribution of boundary layer ozone Computed from a reference atmosphere: Polyphemus analysis, July 2001. Mean contribution 14%, larger during day than during night. Irregularly scattered in space and time. Small but not negligible. 0h 15h

6. 6 Sensitivity to modification of upper troposphere ozone Experiment: Perturbation of reference: modification of O3 above 1500m. Perturbation of initial condition, or cyclic perturbation (simulating the assimilation of IASI data). Boundary layer O3 computed from Polyphemus and compared to the reference. Conclusions : Sensitivity app. 25%. Maximum impact on boundary layer observed 27h hours after perturbation. A better control of upper troposphere ozone (obtained by assimilating IASI data) makes it possible to improve the ozone forecast in the boundary layer.

7. 7 Assimilation of simulated IASI data S imulation of IASI data: – Atmosphere description (Polyphemus from 0 to 5km, standard atmosphere above). – Simulation of radiation: radiative transfer model LBLRTM (AER). – Simulation of raw measurements (radiances) : IASI instrument model, provided by IPSL/SA. – ESA operational inversion algorithm SA-NN, developed by IPSL/SA. Assimilation by Optimal Interpolation in a perturbated model:

8. 8 Examples of simulated IASI measurements Error on 0-6km O3 column : mean 27%, instead of expected 20%. Raw measurement: IR radiation spectrum

9. 9 Assimilation of simulated IASI data -red : reference. -black : perturbated model, mean error 13% : NO2 emissions +30% O3 deposition -15% O3 boundary conditions +15% -green : with assimilation, mean error 9%

10. 10 Conclusion -Yes, IASI can be used for improving air quality forecast: Small but significative contribution on boundary layer O3 to the measurement. Good sensitivity of boundary layer O3 to a control of upper troposphere O3. Encouraging assimilation experiments, despites the simulation of data and simple assimilation method. -Better results expected with NOx measurements: OMI, GOME2. -Hot topic in the community. -At Clime team: ESA projects (EPS MetOp, TRAQ proposal), collaboration with IPSL/SA.