Assimilation of satellite tropospheric columns May 7, 2008 2 - - PowerPoint PPT Presentation

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Assimilation of satellite tropospheric columns

May 7, 2008

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Pollution by satellite

Pollution haze over China (Image from NASA) Striking visualizations of pollution (smog, plumes). Can this information be processed by air quality forecast systems?

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Objective

Aura satellite (NASA)

• Application: air quality forecast at European scale.
• Affected by high uncertainties (emissions, meteorological fields, chemical

scheme).

• Available data: air quality monitoring networks are irregularly scattered, of

variable accuracy.

• Availability of space-borne soundings of the troposphere: MOPITT, OMI

(NASA), SCIAMACHY, GOME2, IASI (ESA).

• Regular spatial sampling in clear sky conditions, daily revisit.
• Tropospheric columns of trace gases (O3, NO2, CH4…), e.g. IASI provides 0-

6km O3 column, OMI 0-10km NO2 columns.

• Aerosol optical properties.
• Objective: to assimilate satellite columns to improve air quality forecast at

ground level.

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Context

• ADOQA INRIA ARC (completed) on advanced data assimilation

methods.

• ESA/Eumetsat project on the feasibility of assimilating IASI

columns for regional air quality forecast.

• Participation to the TRAQ proposal for the future ESA tropospheric

mission (around 2012).

• Academic cooperation with IPSL/SA.

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Information content

• The tropospheric column is the total amount of a trace gas

contained in a vertical column over a given area, up to:

• Top of atmosphere: total columns, used for stratospheric studies,

greenhouse gases and ozone hole monitoring.

• Tropopause (12-16km): trans-continental transport.
• Lower troposphere (6-10km): adapted to regional air quality.
• Acquisition in the UV (GOME2, OMI) or IR (IASI) domains, then

inversion of a radiative transfer model. Limitations:

• Clear sky conditions (clouds may significantly lower the data availability).
• High uncertainties especially for lower troposphere column.
• Difficulty: the column provides a global information mixing all

altitudes, air quality forecast requires accurate information in the boundary layer (first few km).

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Simulated and measured NO2 column

• Polyphemus (left) and OMI (right) NO2 column (0-500mb) on 2005,

July 15th.

• Comparison shows a good agreement in Western Europe (Ruhr,

Benelux, UK), higher NO2 values measured by OMI on Eastern Europe

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Tropospheric column/ boundary layer

• Boundary layer ozone represents in average 14% of the 0-6km column.

Left: average boundary layer contribution to the ozone column in summer and afternoon conditions (July 2001, 15h).

• NO2 is much more concentrated at lowest levels. Right: typical NO2

profiles in non, moderately and highly polluted situations.

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Model sensitivity

• Numerical experiment with O3:
• Perturbation of a reference O3 at t=0 above 1500m.
• Comparison of Polyphemus simulation with/without perturbation.
• The maximal impact on boundary layer is observed 27hrs after the

perturbation, its amplitude is around one fourth of the initial perturbation.

• A better knowledge of upper troposphere ozone (through

assimilation of ozone columns) makes it possible to improve the boundary layer forecast.

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Assimilation of simulated IASI columns

• Twin experiment.

Reference Polyphemus Simulation Perturbated Polyphemus Simulation Analysis Simulated IASI column

RTM Instrument model Inversion code Perturbation of input data Assimilation

Optimal Interpolation

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Simulated IASI measurements

The simulated measure is inverted using SA-NN code, and compared to the reference column. The mean error is 27%, in agreement with IASI specifications (20-40%)

Outgoing IR radiation as measured by IASI (radiative

transfer using LBLRTM code, convolved with the instrument model).

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Assimilation of simulated IASI data

• Perturbated run:
• NO2 emission +30%
• O3 deposition -15%
• Boundary O3 +15%
• Comparison of O3 columns:

reference, perturbated, analysis

• A1, A2, A3: assimilation, the

analysis gets close to the reference.

• After A3, the analysis gets

back closer to the perturbated run

A1 A3 A2

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Assimilation of simulated IASI data

• Comparison of O3 at

ground level: reference, perturbated, analysis

• A1, A2, A3:

assimilation, the analysis gets close to the reference.

• After A3, the analysis

gets back closer to the perturbated run

A1 A3 A2

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Conclusions

• Feasibility study: IASI 0-6km O3 columns have the potential to

improve air quality forecast at ground level.

• Significant contribution of boundary layer O3 to the measure;
• Good sensitivity of boundary layer O3 to a knowledge of upper

troposphere ozone.

• Positive assimilation experiments, even with simulated data.
• Pending availability of IASI level 2 chemical data.
• Ongoing: assimilation of real OMI NO2 columns. Differences:
• Models are less good for NO2 than for O3 -> larger discrepancies

between model and satellite data.

• NO2 has shorter lifetime: can daily acquisition improve the forecast?