TROPOMI NO2
Prototype Algorithm
Folkert Boersma, Jos van Geffen, Bram Maasakkers,
Maarten Sneep, and Pepijn Veefkind
1st S5P-TROPOMI Verification Workshop, DLR, De Bilt, 15-16 April 2013s
Algorithm overview
3-step approach
(1) DOAS spectral fitting to obtain NO2 slant
column densities (SCDs)
(2) Separation of stratospheric contribution to
the SCD by data assimilation in TM5
(3) Converting the tropospheric and
stratospheric SCDs into vertical columns
through air mass factors (AMFs)
(1) Spectral fitting – DOAS
Non-linear least-squares fitting of modeled
spectrum to observed reflectance spectrum
Wavelength calibration of lv1 radiance spectra
prior to and separate of spectral fit
Baseline retrieval settings:
• 415 - 465 nm window
• NO2, O3, H2O, & Ring reference spectra fitted
• 5th order polynomial for broadband effects
(1) Spectroscopic reference data
NO2: Vandaele et al. [1998]
O3: Brion et al. [1998] or Serdyuchenko et al. [2012]
H2O: from HITRAN
Ring: TBD (for OMI synthetic from De Haan)
Hi-res spectra convolved with TROPOMI slit
function input to spectral fit
Temperature dependency NO2 cross section to be
corrected for in later steps (need T, NO2 profile)
(1) Spectral fitting
Find Ysim(λ) that minimizes chi-squared merit:
Probable fitting function:
(2) Stratospheric contribution
Assimilation of TROPOMI slant columns in the
3-D TM5 chemistry transport model (1° × 1°)
Purpose is to update model state to be in close
agreement with TROPOMI measurements
Kalman Filter approach
Adjustment of
O-F
model state
Realistic model (P) & observation error estimates (R) required
Strat. SCD
(2) Stratospheric contribution
Rii = σo2 with
TM5 troposheric
slant column
TM5 stratospheric
slant column
A >> B reflecting larger uncertainty in tropospheric
contribution to the error
Assimilation updates: NO, NO2, NO3, N2O5, HNO4
Lifetime NOy: ~weeks
info from TROPOMI stored over long periods
(3) Air mass factor approach
Decouple total AMF in altitude-dependent air
mass factors and vertical NO2 profile
Independent pixel approximation to account
for (partially) cloudy scenes
ml (¶N s ¶xa,l) calculated with DAK v3.2 and
stored in LUT
(3) Air mass factor approach
ml calculated as a function of forward model
parameters (LUT):
• Solar zenith angle
• Viewing zenith angle
• Relative azimuth angle
• Lambertian surface albedo
• Surface pressure
(3) A priori data to be used
•
•
•
•
Solar zenith angle:
TROPOMI (observed)
Viewing zenith angle:
TROPOMI
Relative azimuth angle: TROPOMI
Lambertian albedo:
Monthly climatology
MERIS (442 nm 0.25°x0.25°)
• Surface pressure:
Daily TM5 (1°×1°)
& DEM_3KM
• A priori NO2 profile:
Daily TM5 (1°×1°)
Fig B1, OMI stratospheric
VCDs compared to GOME2 and SCIAMACHY values
over the Pacific
stratospheric NO2 columns measured by different instruments
MI retrieves higher values than other satellite instruments (Fig B1)
sed instruments. This difference in NO2 values is markedly higher
pected due to the difference in measurement time.
have now coupled our new version 3 retrieval to the TM5 global 3D CTM, which
provides a priori profiles with a 1°×1° resolution. As suggested previously by
Boersma et al. (2007) and demonstrated by Heckel et al. (2011), the better resolved
a priori profile shapes lead to a better understanding of pollution gradients observed
from space. Figure C1 shows the difference between modeled tropospheric NO2
columns of TM5 at 3°×2° (left) and 1°×1° (right). Figure C2 shows the difference
between OMI tropospheric NO2 columns retrieved with TM5 at 3°×2° and 1°×1°.
c
E
wavelength calibration of the OMI lv1b data reduces the retrieved
mn density (SCD) by about 13% and the root-mean-square (RMS) of
about 20%. The RMS can be further reduced by changing the OMI
from the current 405-465 nm to 415-465 nm (Fig B2), which also
D. Combining the two changes reduces the SCD over the Pacific
23% and the RMS by about 25%.
A priori NO2↑ ► Lower tropospheric AMF↓ ► Retrieved NO2 ↑
Fig B2, OMI SCD and
accompanying RMS using the
improved calibration, as a
function of the wavelength
window used for the NO2 fit
Fig C2, Tropospheric NO2 OMI columns retrieved with TM5 at 1°×1° - retrieved with TM5 at 3°×2°
over Europe for 3-8 October 2004
TM5 cy3 with full chemistry
1° x 1° on one PC with 16 GB work
memory
1 day takes ±75 minutes on 2 processors
[B
v
t
[H
lo
[B
Q
N
(3) A priori data to be used
• Snow/ice cover:
ECMWF, NISE, …
• Cloud radiance fraction w TROPOMI FRESCO v6
(observed)
Consistent approach &
a priori information with
TROPOMI NO2:
• Lambertian surface
• Independent pixel appr.
• ECMWF, NISE snow/ice
• DEM_3KM terrain
height
Challenges (1)
Spectral fitting:
• Different fit windows, functions, and ref. spectra
• O2-O2
• T-correction on slant columns or through AMF?
• Raman scattering in water
• Liquid water absorption
• Sand signature?
Challenges (2)
Stratospheric correction:
• Difficult to evaluate without real data
• Strongly associated with accuracy spectral fitting
and assumptions therein
• Relies on (model) assumptions of tropospheric
NO2 over reference/unpolluted region(s)
Challenges (3)
Air mass factor:
•Cloud correction (IPA) or just cloud filtering?
•Lambertian reflector model for terrain & clouds
•Choice of RTM – polarization, sphericity
•Inclusion of background aerosols vs. effective
scattering …
•Risk that AMF verification comes down to
comparing a priori input data sets (albedo, terrain
height, a priori NO2, …)
Verification needs
Distinguish avoidable from unavoidable differences
Avoidable:
• Spectral fitting (synthetic spectra)
• Altitude-dependent AMFs (synthetic spectra)
• Sensitivity (AMF) to a priori data
Verification needs
Distinguish avoidable from unavoidable differences
Unavoidable (related to retrieval philosophy)
• Choice for stratospheric separation
• Choice for albedo dataset, terrain height, BRDF
• Correcting vs. filtering for residual clouds
• Choice for including background aerosol
correction
• Choice for source of NO2 profiles
Verification needs
Success criteria hard to define for NO2 final products
Proposal for separate criteria:
(1) Accuracy and precision of spectral fit
(2) Minimum number of negative tropospheric slant
columns
(3) Quantitative agreement on avoidable differences
(altitude-dependent AMFs, sensitivity to a priori
data)