Folkert Boersma

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Folkert Boersma
Reducing errors in using tropospheric NO2 columns
observed from space
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EMEP
Main use of satellite observations estimating
emissions of NOx

• What is so uncertain about emissions?
• quantities
• locations
• times
• trends

But we can see the NOx sources from space
?chem 4-24 hrs
Emissions
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Satellite observations

• Pros
• sensitivity to lower troposphere
• improving horizontal resolution
• global coverage

• Cons
• daytime only
• column only
• clouds
• sensitivity to forward model parameters
  assumptions

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Retrieval method
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Retrieval method

• 3-step procedure
• obtain slant column along average light path
• separate stratospheric and tropospheric
  contributions
• convert tropospheric slant column in vertical
  column

In equation
Ns, Ns,st, Mtr are all error sources
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Retrieval method
aerosols
surface pressure
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State-of-science
van Noije et al., ACP, 6, 2943-2979, 2006
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Systematic differences
van Noije et al., ACP, 6, 2943-2979, 2006
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Stratospheric column
Accounting for zonal variability or not?
41.5N
E. J. Bucsela NASA GSFC
Model information Reference Sector
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Stratospheric column
Without correction errors up to 1?1015 molec.cm-2
March 1997
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Stratospheric column

• Alternative limb-nadir matching
• Limb observes zonal variability
• Stratospheric column estimate may introduce
  offsets from limb-technique

Courtesy of E. J. Bucsela NASA GSFC
A. Richter et al. IUP Bremen
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Stratospheric column

• In summary
• Reference sector method questionable
• Assimilation nadir-limb correct known
  systematic errors
• Assimilation self-consistent uncertainty
  0.21015
• Validation needed
• - SAOZ network (sunrise, sunset)
• Brewer direct sun (Cede et al.) in unpolluted
  areas

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Air mass factor
Retrieval method
Tropospheric air mass factor Mtr - Computed with
radiative transfer model and stored in tables Mtr
f(xa,b) xa a priori tropospheric NO2 prf b
forward model parameters - cloud fraction -
cloud pressure - surface albedo - aerosols ( -
viewing geometry)
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Air mass factor errors
A priori profile

• Large range in sensitivities between 200 1000
  hPa, especially in the BL
• Low sensitivity in lower troposphere for dark
  surfaces

• Clear pixel, albedo 0.02
• Clear pixel, albedo 0.15
• Cloudy pixel with fcl 1.0, pcl 800 hPa

Eskes and Boersma, ACP, 3, 1285-1291, 2003
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Air mass factor errors
A priori profile from CTMs

• Shapes reasonably captured by CTMs
• Effect of model assumptions on BL mixing lead to
  errors lt10-15
• Models are coarse relative to latest retrievals

Martin et al., JGR, 109, D24307, 2004
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Air mass factor errors
Effect of choice of CTM on retrieval
MOZART-2 (2?2) vs. WRF-CHEM (0.2?0.2)
A. Heckel et al. (IUP Bremen)
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Air mass factor errors
Effect of choice of CTM on retrieval
Effect 10
A. Heckel et al. (IUP Bremen)
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Air mass factor sensitivities
M w?Mcl (1-w)?Mcr
Cloud fraction
Boersma et al., JGR, 109, D04311, 2004
Cloud pressure
Albedo
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AMF errors surface albedo
?M ?M/?asf ?asf ?asf ?0.02 (GOME-TOMS)
()
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AMF errors cloud fraction
?M ?M/?fcl ?fcl ?fcl ?0.05 (FRESCO)
()
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AMF errors cloud pressure
?M ?M/?pcl ?pcl ?pcl ?50.0 (FRESCO)
()
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Air mass factor errors - aerosols

• If NO2 present, then also aerosol
• Aerosols affect radiative transfer dep. on
  particle type

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Air mass factor errors - aerosols

• Aerosols affect radiative transfer
• Cloud fraction sensitive to aerosols (?? 1.0
  ? ?fcl 0.01)

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Air mass factor errors surface pressure

• Surface pressure from CTMs (2 3)
• Strong differences with hi-res surface pressures

GOME
SCIAMACHY
Schaub et al., ACPD, 2007
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Error top-10

• Cloud fraction errors 30
• Surface albedo 15 resolution effect?
• Vertical profile 10 resolution effect?
• Aerosols 10? More research needed
• Cloud pressure 5
• Surface pressure depends on orography

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Is there a recipe for reducing all these errors?
1. Better estimates of forward model parameters A
good example surface pressures (Schaub et
al.) What should be done - a validation/improvem
ent of surface albedo databases - a
validation/improvement of cloud retrievals -
investigate effects aerosols on (cloud)
retrievals - validation vertical profiles -
higher spatial resolution (sfc. albedo, pressure,
profile)
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Is there a recipe for reducing all these errors?
2. How do we know if better forward model
parameters improve retrievals? We need an
extensive, unambiguous and well-accessible
validation database Testbed for retrieval
improvements - in situ aircraft NO2 (Heland,
ICARTT, INTEX) - surface columns (SAOZ, Brewer,
(MAX)DOAS) - in situ profiles (Schaub/Brunner) -
surface NO2 (regionally)
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Is there a recipe for reducing all these errors?

• 3. Towards a common algorithm/reduced errors?
• Difficult!
• Without testbed, verification of improvements is
  hard
• Improvements for one algorithm may deteriorate
  other algorithms, depending on retrieval
  assumptions
• Improved model parameters may work for some
  regions and some seasons, but not for others

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Is there a recipe for reducing all these errors?

• 3. Towards a common algorithm/reduced errors?
• Worth the try!
• Systematic differences can be reduced (emission
  estimates)
• Requires scientific will enormous task
• Collection of validation set
• Flexible algorithms digesting various model
  parameters
• Intercomparison leading to recommendations
• Fits purpose ACCENT/TROPOSAT