institute of environmental physics and institute of remote sensing university of bremen
DESCRIPTION
Improving OMI NO 2 retrievals. TRYING TO IMPROVE. BETTER UNDERSTAND. OMI Science Team Meeting De Bilt , March 12, 2014. Andreas Richter , A. H ilboll, and J. P. Burrows. Institute of Environmental Physics and Institute of Remote Sensing University of Bremen. Introduction. - PowerPoint PPT PresentationTRANSCRIPT
Institute of Environmental Physics and Remote SensingIUP/IFE-UB Physics/Electrical Engineering
Department 1
Institute of Environmental Physics andInstitute of Remote Sensing
University of Bremen
Improving OMI NO2 retrievals
Andreas Richter, A. Hilboll, and J. P. Burrows
OMI Science Team MeetingDe Bilt, March 12, 2014
TRYING TO IMPROVE
BETTER UNDERSTAND
Introduction
• OMI data provides excellent coverage and unprecedented spatial detail• It will soon be followed by the TROPOMI / S5P instrument which has
many similarities but promises even better performanceÞ As preparation for S5P, IUP Bremen has started to look into OMI spectra
Choice of fitting window
• Starting point is transfer of GOME-2 settings to OMI data• First check: how consistent are results from different fitting windows?
– 425 – 497 nm vs 425 – 450 and 405 – 465 nm => very good• Second check: how consisitent are results with operational NASA product?
– Very good correlation with 405 – 465 nm, offset of 1.5x1015 molec cm-2
=> will use GOME-2 settings
Choice of fitting window
Why these specific settings?• Good results in GOME-2 data
– Little noise– Good sensitivity to tropospheric NO2
• The same window is used in our ground-based analysis
=> somewhat random choice
Spike removal
• In spite of the good signal to noise, OMI data in SAA region have large scatter
• Data can easily be flagged using RMS of fits, but can‘t we do better?
Spike removal
• Apply GOME-2 approach: – Run DOAS fit on original data– Identify “spikes“ using threshold of 10 x mean RMS – Re-run DOAS fit on reduced number of spectral points
Spike removal
• Application to data in SAA region strongly reduces the number of outliers compared to operational NASA product
• This approach ignores all flagging in lv1 data• Data elsewhere remains nearly unchanged• Is there an issue with potential for biases?
Spike removal
• The number of useful retrievals in the SAA region is strongly increased
Pacific background
• Although the NO2 product is rather robust when using the mean solar background, some “stripes” are apparent
• They can be removed using destriping• Use of earth-shine background leads to similar results => offset?
No destriping Destriping Pacific background
AMF dependence on NO2 column
• One of the main DOAS assumptions is, that the light path enhancement (AMF) for a trace gas is independent of its column amount
• For strong absorbers (O3, SO2, IR gases) this approximation is not good enough and the change of sensitivity with wavelength needs to be accounted in the fit ("modified“ DOAS)
• NO2 is generally considered to be a weak absorber, but is that still true for very polluted scenarios?
AMF dependence on NO2 column
• Up to a vertical column of about 1x1016 molec cm-2, small dependence of AMF on NO2 amount
• For larger columns, the AMF decreases and NO2 absorption structures appear
AMF dependence on NO2 column
• In relative units, the effect is – 5% for a column of 5x1016 molec cm-2
– 5 – 12% for 1x1017 molec cm-2
• The effect is larger at smaller wavelengths (larger absorption cross-section, more Rayleigh scattering)
AMF dependence on NO2 column
• Effect on DOAS fit on synthetic data is larger than on AMF alone as both smaller AMF and spectral structures reduce NO2 columns
• Relative effect increases with SZA• Effect is similar in typical fitting windows• Even at a column of 5x1016 molec cm2, the error is > 10%
Excursion: GOME-2 NO2 above China
• Monthly GOME-2 tropospheric NO2 data are missing most of the large values
• These were removed by cloud filtering as aerosol was so thick that data were classified as partially cloudy
No cloud screening
Is it only Aerosols?
• Even without cloud screening, there are data gaps over pollution hot spots on some days
• This is due to quality checking as these fits are poor
No Chisq. screening
Why are the fits poorer at strong pollution?
• There are large and clearly structured residuals in fits over pollution hot spots
• This is not random noise!
• Comparison to NO2 cross-sections shows that scaling of NO2 should change over fitting window
Wavelength dependence of Air Mass Factor
• For constant albedo, AMF of NO2 layer close to the surface increases with wavelength in a Rayleigh atmosphere
• For a surface layer, this can be a significant effect• With radiative transfer modelling and a formal inversion, this should
provide information on the altitude of the NO2
About +/- 20%
Empirical Approach for NO2(λ)
• Take standard NO2 x-section• Scale to increase amplitude with
wavelength• Orthogonalise to leave NO2
columns unchanged
When introduced in the fit, large residuals are fixed
Results Empirical Approach NO2(λ): GOME-2
• The empirical NO2 AMF proxy is found over the pollution hotspot in China
• It is not found at other locations where the NO2 slant column is large• There is some noise in the retrieval of the proxy
Results Empirical Approach NO2(λ): OMI
• As for GOME-2 data, the empirical NO2 AMF proxy is found over the pollution hotspot in China
• There is more noise than in GOME-2 data• Problems with row anomaly
Results Empirical Approach NO2(λ): OMI
• At very large NO2 columns, the wavelength dependency can clearly be found in both fitting windows
• The retrieval improves when including the empirical parameter• The fitting coefficient – relative to that of NO2 – should contain
information on the presence of BL NO2
Is there more than China? GOME-2
• Fit is improved by AMF proxy everywhere over pollution hotspots
Comparison to NO2 columns: GOME-2
• Overall pattern similar to NO2 map
• Differences in distributions of maxima
• Artefacts over water• noise
Impact of Clouds: GOME-2
• On many days in winter, very large NO2 slant columns are observed over Europe and the US
• The NO2 AMF proxy picks up only very few of these signals
• This is linked to the fact that most of the events are related to cloudy scenes or snow on the surface, resulting in small wavelength dependence
Sensitivity Study for NO2(λ) / NO2
• Ratio of AMF proxy and NO2 has strong dependence on NO2 layer height
• Dependence on albedo is small between 3% and 7%
Synthetic data:• Rayleigh atmosphere• Constant albedo• NO2 layer in different altitudes• DOAS fit on spectra• NO2 temperature dependence
corrected by using 2 NO2 x-sections• AMF proxy included
• Ratio of AMF proxy / NO2 to normalise signal
Sensitivity Study NO2(λ) / NO2: SZA
• Effect varies with SZA; larger effect at larger SZA• At large SZA, AMF proxy also found for high NO2
• Dependence on albedo is small between 3% and 7%
Sensitivity Study NO2(λ) / NO2: Bright Surfaces
Þ multiple scattering over bright surfaces is stronger at shorter wavelengths
Þ wavelength dependence of AMF is inverted
• Increasing albedo reduces effect as expected for reduced importance of Rayleigh scattering
• For large albedo (> 50%), negative fit factors are found for AMF proxy => wavelength dependence is inverted and only weakly dependent on altitude
NO2(λ) / NO2: Impact of Aerosols and Clouds
Shielding
• Similar effect for both, AMF proxy and NO2 => will cancel in ratio• Ratio will give layer height for cloud free part of pixel
Light path enhancement
• Light path enhancement in clouds / aerosols depends only weakly on wavelength
• Effect on NO2 but no effect on AMF proxy• Ratio will no longer be representative of NO2 layer height
NO2(λ) / NO2: Case Study Highveld: GOME-2
• NO2 plume from Highveld power plants can be tracked onto the ocean• NO2 SC values increase downwind of the source• AMF Proxy also has higher values within the plume, but
– Is more narrow– Has largest values at beginning of plume, not at the end of it
OMI NO2: The Indonesian Mystery
• Clear sea- land contrast• Strange shadows• Oszillation with lower
values in the North and higher values in the South
OMI NO2: The Indonesian Mystery
No cloud screening, simple stratospheric AMF
• Effect is also visible in pseudo-stratospheric columns (no surface a priori)
• IUP product seems less affected
OMI NO2: The Indonesian Mystery
IUP product has• Less “shadow“
structures• Less NO2 over the
islands (basically no NO2 over many of the smaller ones)
• Overall lower values
=> more detailed investigation needed!
NASA
IUP
No cloud screening, simple stratospheric AMF
Summary
• We have joined the OMI NO2 club• Application of our GOME-2 settings to OMI proved successful
– Good consistency between fitting windows but offset vs. Operational product
– Spike removal works very well• At large NO2 values (> 5 x 1016 molec cm-2), AMF becomes a clear
function of NO2 column which needs to be corrected (> 10% effect)• At large BL NO2 values, the wavelength dependence of the NO2 AMF
becomes relevant in the fit in particular in the GOME-2 fitting window and can be used to derive some information on NO2 layer height
• Over the Indonesian region, something strange is going on in the operational NO2 product
• We‘re looking forward to digging deeper into OMI (and TROPOMI) NO2 data! Funding by DLR Bonn
under Contract 50EE1247