Tropospheric NO2 from space: retrieval issues and perspectives for the future

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Tropospheric NO2 from space retrieval issues and
perspectives for the future

• Michel Van Roozendael
• BIRA-IASB, Brussels, Belgium

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Overview

• Retrieval method (basics)
• Main issues regarding
• Spectral fitting
• Stratospheric correction
• Tropospheric AMFs
• Cloud correction
• How to assess the accuracy of our retrievals?
• Challenges for the future

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GOME tropospheric NO2 intercomparison

Why such differences?
Van Noije et al., ACP, 2006

Who is right?

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NO2 remote sensing using DOAS

• UV-Vis NO2 absorption is
• Structured
• Independent of pressure
• Weakly dependent on T
• Total atmospheric attenuation is small (ltlt 1)
• ? Atmospheric transmission follows Beer-Lambert
  law in a simple way

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The 3 steps to tropospheric NO2 VCDs

• STEP 1 DOAS ? NO2 SCD

Strat. NO2
NO2
Surface
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STEP 1 Spectral fitting issues

• Error on DOAS fit controlled by
• S/N ratio, limited by shot noise of detector
• Possible systematic bias due to
• Temperature dependence of NO2 cross-sections
• Interferences with unknown or badly known
  absorbers (e.g. absorption from water vapor
  and/or liquid water)
• Inaccurate correction for Raman scattering by air
  and/or water
• Instrumental artefacts. DOAS is insensitive to
  spectrally smooth radiometric errors, but very
  sensitive to offset type errors as well as to
  radiance errors displaying high frequency
  structures (e.g. polarisation, undersampling, )
• Choice of fitting interval ? trade-off between
  S/N and minimisation of bias effects. Differences
  in settings/correction schemes applied by
  different groups may result in significant SCD
  differences.

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Accuracy of measured radiances what does matter
for DOAS?

• S/N ratio ? the more photons the best (in
  practice trade-off between spatial/spectral
  resolution and S/N)
• Instrument/radiometric calibration issues
• Wavelength calibration
• Knowledge of instrumental slit function
• Dark-current correction
• Straylight correction
• Polarization correction
• Diffuser plate response

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Examples of known instrumental problems affecting
DOAS retrievals

• GOME diffusor plate spectral features interfering
  with NO2 absorption ? time-dependent bias,
  requiring special treatment

Richter Wagner, 2001
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STEP 2 Stratospheric correction

• Different methods can be used to extract the
  tropospheric signal from the total column seen
  from space (e.g. use cloud shielding effect,
  limb-nadir matching, wavelength dependence of
  AMFs, etc)
• By far, the most popular ones are
• The reference sector technique and its variants
  (e.g. harmonic analysis) ? use NO2 columns
  measured over unpolluted regions to infer the
  stratospheric part over source regions
• The model based technique ? use NO2 columns from
  3D-CTM constrained by observations over
  unpolluted regions
• The assimilation technique ? assimilate NO2 SCD
  in 3D-CTM (variant of model method)

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STEP 3 get VCDs using tropospheric AMFs

• Most complex and error prone part of the
  retrieval
• Tropospheric NO2 AMFs depend on

• Solar and viewing geometries
• Surface properties (albedo, ground elevation)
• Aerosols
• Cloud properties
• Shape of tropospheric NO2 profiles

Problem these properties are to a large extent
unknown, or there are known at inappropriate
resolution !
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Examples of solutions currently in use
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Cloud correction scheme

• Clouds shield surface NO2
• Clouds enhance sensitivity to NO2 located above
  or at cloud altitude

• Clouds generally treated as lambertian reflectors
  ? effective cloud fraction and scattering cloud
  top height

AMF (1-f).AMFclear f.AMFcloud AMFcloud
requires estimation of the NO2 column underneath
the cloud (ghost column) !
NO2 layer
Surface
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Impact of clouds on tropospheric AMFs
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Clouds as a mean to retrieve information on the
NO2 vertical distribution

• Idea because of their shielding effect and high
  albedo, clouds reduce the sensitivity to surface
  NO2 and increase the sensitivity to
  free-tropospheric NO2
• Possible applications
• Quantify NO2 produced by lightning (Boersma et
  al., 2005)
• Relate altitude of NO2 plumes to the location of
  sources (Beirle et al., 2007)
• Identify long-range transport events (TEMIS)

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How to assess the accuracy of our NO2 retrievals?

• Differences in retrieval strategies result in
  inconsistencies beteween NO2 products derived
  from different groups. Problem even larger when
  different instruments are analysed by different
  groups.

• Strategies to assess the accuracy of NO2
  retrievals
• Comprehensive error analysis (cf. Boersma et al.,
  2004)
• Intercomparison of satellite data sets (cf. van
  Noije et al., 2006)
• Validation using external correlative data sets

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Tropospheric NO2 validation a challenge

• Why is difficult to valide tropospheric NO2 from
  satellites?
• NO2 emissions are extremely variable in space in
  time ? the NO2 field as sampled by the satellite
  can hardly be matched by correlative
  measurements.
• Suitable validation data sets are currently
  limited
• In-situ surface measurements (difficult to
  compare with satellite columns)
• Remote-sensing network from NDACC (focus on
  stratospheric columns)
• In-situ aircraft (excellent but expensive -gt lack
  of statistics)
• MAXDOAS (promising technique under development
  need for network deployment)
• NO2 Lidar (interesting but expensive -gt lack of
  statistics)

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Status of tropospheric NO2 sounders
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Current statusGOME, SCIAMACHY, GOME-2 and OMI
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Requirements for future NO2 monitoring systems

• Driving requirements for air quality (Capacity
  study)
• Spatial resolution 5-20 km
• Revisit time 0.5 2h

• Can be met through
• Option 1 combination of (at least one)
  geostationary satellite and one sun-synchronous
  low earth orbit satellite (LEO)
• Option 2 constellation of several instruments in
  LEO a minimum of 3 instruments is needed to
  satisfy sampling requirements at mid-latitude

? Trade-off between Options 1 and 2 must be
evaluated (ongoing CAMELOT study)
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Challenges for the future (1)

• How to ensure the consistency of the global NO2
  observing system (GEOSS/GMES requirement) when
  the fleet of instruments expands more and more?
• Evolve towards common retrieval approaches?
• Rely on both operational (standardised) and
  scientific (state-of-art) retrieval approaches

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Challenges for the future (2)

• What to do to improve NO2 retrievals?
• A) Enhance sensitivity to detect lower levels of
  pollution
• Using better instruments ? improve S/N ratio
  through better photon collection efficiency
• Larger throughput (limited by weight and size!)
• Longer integration time (GEO)
• Multiply instruments
• Using improved algorithms
• Expand fitting range using direct-fitting ? puts
  high requirements on the quality of Level 1 data,
  and on data processing

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Challenges for the future (3)

• B) Improve treatment of radiative transport
• Use synergy with other (co-located) instruments
  to get better information on albedo, aerosols and
  clouds
• Use more advanced model data or higher resolution
• Improve cloud retrieval algorithms in synergy
  with those of NO2 (combined cloud-trace gas
  retrievals)
• C) Get more than the column (vertical profiling)
• Expand fitting range using direct-fitting and
  optimal estimation ? requirements on Level 1
  quality (cf. sensitivity)
• Further develop cloud slicing techniques
• Use dual/multiple view geometry?