Presentation on theme: "18th OMI Science Team Meeting"— Presentation transcript:
1 18th OMI Science Team Meeting Next-generation OMI SO2 Retrieval Algorithm based on Principal Component AnalysisCan Li1,2, Joanna Joiner2, Nick Krotkov2, P. K. Bhartia21ESSIC, University of Maryland College Park2NASA GSFC18th OMI Science Team MeetingKNMI, De Bilt, The NetherlandsMarch 13, 2014
2 Outline Background and Motivation Methodology (Framework) Application to OMIResults (Planetary Boundary Layer SO2)Results (Volcanic SO2)Data Continuity: Comparison of OMI and OMPSNext Steps and Conclusions
3 Background and Motivation Operational OMI SO2 (Sept – Feb. 2008)Motivation: Band Residual Difference (BRD) algorithm fast and sensitive, but large noise and artifacts (only 3 pairs of wavelengths)Objective: develop an innovative approach to utilize the full spectral content from OMI while maintaining computational efficiency
4 Basis – Spectral fitting algorithms First look at the DOAS Equation:Measured sun-normalized radiancesRayleigh and Mie scattering, surface reflectance etc.Various gas absorbers (O3, SO2 etc.)The Ring effectPlus additional measurement artifacts terms (e.g., wavelength shift, stray light, etc.) and/or radiance data correction schemesUtilization of the full spectral content, but some terms are difficult to model (e.g., RRS)
5 Methodology (Framework): PCA Instead of explicit modeling of ozone, RRS, and other instrumental features, we use a data-driven approach based on principal component analysis (PCA) with spectral fittingMeasured N-value spectrumSO2 column amountPCs from SO2-free regions, (O3 absorption, surface reflectance, RRS, measurement artifacts etc.) other than SO2 absorptionPre-calculated SO2 Jacobians (assuming O3 profiles, albedo, etc.)Fitting of the right hand side to the spectrum on the left hand side -> SO2 column amount and coefficients of PCs(See Guanter et al., 2012; Joiner et al., 2013; Li et al., 2013)
6 Application to OMISpectral window: nm – avoid stray light at shorter wavelengthsEach row (scan position) processed individually – different characteristics between different rows of the 2-D CCDEach swath processed individually – account for orbit-varying dark current# of PCs determined dynamically – exclude SO2-related PCs and avoid overfitting by checking the correlation between PCs and SO2 Jacobians1st step: Simple Jacobians similar to those used in operational BRD algorithm for straight-forward comparisonStep 1 PsSee Li et al., [GRL, 2013] for details
7 Principle Components and Residuals Example PCs from entire row # 11, Orbit 10990(Var.% )PC #1: Mean spectrum(a-c) First few PCsBlue line: scaled reference Ring spectrum(Var.% )PC #2: O3 absorption(Var.% )PC #3: Surface reflectance (also Ring signature)(Var.% 5.32E-5)PCs #4 and #5: likely measurement artifacts, noise (>99.99% variance explained)(Var.% 4.79E-5)(d) Least squares fitting residuals for a pixel near HawaiiSmaller residuals with SO2 Jacobians fitted
8 Results: noise and artifact reduction August, 2006OMI operational BRDPCA algorithm reduces retrieval noise by a factor of two as compared with the BRD algorithmSO2 Jacobians for PCA algorithm calculated with the same assumptions as in the BRD algorithm
9 Results: Boundary layer pollution SO2 PCAOperational BRDeastern U.S., August 2006PCA algorithm reveals major SO2 point sources (circles), with much reduced noise and artifacts.
10 Ex. Sudbury, Canada (~220 kt in 2006) Ex. analyzed with pixel averaging (super sampling) reveals details of emission sources [e.g., Fioletov et al., 2011]PCA, 2006BRD, 2006 onlyBRD,Largely hidden by artifactsOne year’s worth of PCA retrievals yield results similar to that from 3-5 years worth of BRD data.Global survey shows that PCA SO2 removes most artifacts in BRD data without significantly altering signals from real sources (Fioletov and McLinden, personal communication)
11 Volcanic SO2: Kasatochi eruption August 7-8, 2008 For volcanic SO2, nonlinearity due to saturation at shorter wavelengthsIteration of SO2 Jacobians (pre-calculated assuming loadings of DU)Shift of spectral fitting window to longer wavelengthsPCA closest to estimated released SO2 mass of ~2200 kt based on observed decay of SO2 [Krotkov et al., 2010]
12 Transport of the plume August 10, 2008 August 11, 2008 August 12, 2008 Ln(SO2)
13 Comparison with OMPSOur algorithm eliminates the need for explicit instrument-specific radiance correction schemesTest on OMPS: minimal changes to algorithmbiggest is the use of OMPS slit function for Jacobiansspectral window, etc., same as in OMIReduces the chance of introducing artifacts/biases between different instruments
14 OMI and OMPS comparison OMPS and OMI PCA SO2 retrievals show good agreement despite somewhat different samplingOctober, 2013
15 OMI and OMPS comparison OMPS, Jan. 2013OMI, Jan. 2013Both OMI and OMPS PCA SO2 retrievals show enhanced SO2 loading over northern China in January 2013, when severe pollution attracted media and public attention.
16 OMI and OMPS comparison OMI and OMPS PCA SO2 data show similar seasonal patterns and SO2 signals over eastern India (several coal-fired power plants built in recent years) [Lu et al., 2013].
17 Next StepsExpanded table for SO2 Jacobians to more accurately account for measurement conditions (e.g., O3 amount, reflectivity, geometries)Addition of scattering weight to output to allow convenient adjustment of SO2 column amount based on user-provided profileInclusion of error estimatesOperational implementation, public release>1 year processed and currently under evaluationInitial release for boundary layer pollution this yearImproved Jacobians and volcanic data to follow soonOn 12 CPUs, 1-2 days to process a year of OMI data
18 ConclusionsSignificant improvements in retrieval quality – PCA algorithm uses full spectral content from OMI and similar instruments offering increased temporal resolution and source detectionComputation efficiency – over an order of magnitude faster than comparable spectral fitting algorithms; increasingly important given the greater data volumes expected from future missions (e.g., TROPOMI, TEMPO)Maximal data continuity between instruments – no need to develop instrument-specific radiance data correction schemesFlexibility – fitting window can be easily adjusted to optimize sensitivity under different conditions
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