Satellite data assimilation for air quality forecast

1. Satellite data assimilation for air quality forecast Journées ARC – Grenoble - 18/10/2006

2. Objectives • Assimilation of satellite measurements of troposphere chemistry, in view of improving the quality of forecast. • Context: • ADOQA INRIA ARC • 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. 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. 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. 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. 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. Assimilation of simulated IASI data • Simulation 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. Examples of simulated IASI measurements Raw measurement: IR radiation spectrum Error on 0-6km O3 column : mean 27%, instead of expected 20%.

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. 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.