folkert boersma
DESCRIPTION
Folkert Boersma. Reducing errors in using tropospheric NO 2 columns observed from space. Blond et al. (2007). SCIAMACHY. EMEP. Main use of satellite observations: estimating emissions of NO x. What is so uncertain about emissions? quantities locations times trends. - PowerPoint PPT PresentationTRANSCRIPT
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Folkert Boersma
Reducing errors in using tropospheric Reducing errors in using tropospheric NONO22 columns observed from space columns observed from space
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What is so uncertain about emissions?• quantities• locations• times• trends
Main use of satellite observations: estimating emissions of NOx
But we can see the NOx sources from space
Emissions
EMEP
SCIAMACHY
Blond et al. (2007)
chem= 4-24 hrs
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Pros• sensitivity to lower troposphere• improving horizontal resolution• global coverage
Satellite observations
Cons• daytime only• column only• clouds• sensitivity to forward model parameters assumptions
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Retrieval method
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3-step procedure• obtain slant column along average light path• separate stratospheric and tropospheric contributions • convert tropospheric slant column in vertical column
Retrieval method
In equation:
Ns, Ns,st, Mtr are all error sources
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Retrieval method
aerosols
surface pressure
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IUP Bremen Dalhousie KNMI/BIRA
Ns,st Ref. sector scaled to SLIMCAT strat.
Ref. Sector Data-assimilation in TM4
Cloud fraction FRESCO <0.2 cloud fraction; only cloud selection, no further correction
GOMECAT FRESCO
Cloud pressure Not used GOMECAT FRESCO
Albedo GOME GOME TOMS/GOME
Profile shape MOZART-2 run for 1997, monthly averages on 2.8 x 2.8 °
GEOS-Chem (2x2.5)
TM4 (3x2)
Temperature correction
No Based on U.S. std. atmosphere
Based on ECMWF T-profiles
Aerosols Based on LOWTRAN
Based on GEOS-Chem
No
‘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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Accounting for zonal variability or not?
E. J. Bucsela – NASA GSFC
41.5°N
Stratospheric column
Model information
Reference Sector
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Without correction errors up to 11015 molec.cm-2
Stratospheric column
March 1997
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Alternative: limb-nadir matching
• Limb observes zonal variability
• Stratospheric column estimate may introduce offsets from limb-technique
Courtesy of E. J. Bucsela – NASA GSFC
Stratospheric column
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 ~0.2×1015
• Validation needed
- SAOZ network (sunrise, sunset)
- Brewer direct sun (Cede et al.) in unpolluted areas
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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)
Air mass factor
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A priori profile
(a) Clear pixel, albedo = 0.02
(b) Clear pixel, albedo = 0.15
(c) Cloudy pixel with fcl = 1.0, pcl = 800 hPa
Air mass factor errors
• Large range in sensitivities between 200 & 1000 hPa, especially in the BL
• Low sensitivity in lower troposphere for dark surfaces
Eskes and Boersma, ACP, 3, 1285-1291, 2003
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A priori profile from CTMs
Air mass factor errors
• Shapes reasonably captured by CTMs
• Effect of model assumptions on BL mixing lead to errors <10-15%
• Models are coarse relative to latest retrievals
Martin et al., JGR, 109, D24307, 2004
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Effect of choice of CTM on retrieval
Air mass factor errors
MOZART-2 (2°2°)
vs.
WRF-CHEM (0.2°0.2°)
Jun-Aug 2004 SCIAMACHY NO2
MOZART-2 AMF
A. Heckel et al. (IUP Bremen)
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Effect of choice of CTM on retrieval
Air mass factor errors
Effect ~10%Jun-Aug 2004 SCIAMACHY NO2
WRF-Chem AMF
A. Heckel et al. (IUP Bremen)
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Cloud fraction
Albedo
Cloud pressure
Air mass factor sensitivities
M = wMcl + (1-w)Mcr
Boersma et al., JGR, 109, D04311, 2004
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M = M/asf asf
asf = 0.02 (GOME-TOMS)
AMF errors – surface albedo
(%)
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M = M/fcl fcl
fcl = 0.05 (FRESCO)
AMF errors – cloud fraction
(%)
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M = M/pcl pcl
pcl = 50.0 (FRESCO)
AMF errors – cloud pressure
(%)
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• If NO2 present, then also aerosol• Aerosols affect radiative transfer dep. on particle type
Air mass factor errors - aerosols
Martin et al., JGR, 108, 4537, 2003
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• Aerosols affect radiative transfer• Cloud fraction sensitive to aerosols ( = +1.0 fcl +0.01)
Air mass factor errors - aerosols
Direct correction
Indirect correction through M=wMcl+(1-w)Mcr
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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
1. Cloud fraction errors ~30%
2. Surface albedo ~15% + resolution effect?
3. Vertical profile ~10% + resolution effect?
4. Aerosols ~10%? More research needed
5. Cloud pressure ~5%
6. 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/improvement 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