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Next-generation OMI SO 2 Retrieval Algorithm based on Principal Component Analysis

Next-generation OMI SO 2 Retrieval Algorithm based on Principal Component Analysis. Can Li 1,2 , Joanna Joiner 2 , Nick Krotkov 2 , P. K. Bhartia 2 1 ESSIC, University of Maryland College Park 2 NASA GSFC 18 th OMI Science Team Meeting KNMI, De Bilt , The Netherlands March 13, 2014.

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Next-generation OMI SO 2 Retrieval Algorithm based on Principal Component Analysis

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  1. Next-generation OMI SO2Retrieval Algorithm based on Principal Component Analysis Can Li1,2, Joanna Joiner2, Nick Krotkov2, P. K. Bhartia2 1ESSIC, University of Maryland College Park 2NASA GSFC 18th OMI Science Team Meeting KNMI, De Bilt, The Netherlands March 13, 2014 Email: Can.Li@nasa.gov

  2. Outline • Background and Motivation • Methodology (Framework) • Application to OMI • Results (Planetary Boundary Layer SO2) • Results (Volcanic SO2) • Data Continuity: Comparison of OMI and OMPS • Next Steps and Conclusions

  3. Background and Motivation Operational OMI SO2 (Sept. 2004 – 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 radiances Rayleigh and Mie scattering, surface reflectance etc. The Ring effect Various gas absorbers (O3, SO2 etc.) Plus additional measurement artifacts terms (e.g., wavelength shift, stray light, etc.) and/or radiance data correction schemes Utilization 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 fitting Measured N-value spectrum SO2 column amount PCs from SO2-free regions, (O3absorption, surface reflectance, RRS, measurement artifacts etc.) other than SO2 absorption Pre-calculated SO2Jacobians (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 OMI • Spectral window: 310.5-340 nm – avoid stray light at shorter wavelengths • Each row (scan position) processed individually – different characteristics between different rows of the 2-D CCD • Each 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 SO2Jacobians • 1st step: Simple Jacobians similar to those used in operational BRD algorithm for straight-forward comparison Step 1 Ps See Li et al., [GRL, 2013] for details

  7. Principle Components and Residuals Example PCs from entire row # 11, Orbit 10990 (Var.% 99.8492) PC #1: Mean spectrum (a-c) First few PCs Blue line: scaled reference Ring spectrum (Var.% 0.1264) PC #2: O3 absorption (Var.% 0.0217) PC #3: Surface reflectance (also Ring signature) PCs #4 and #5: likelymeasurement artifacts, noise (>99.99% variance explained) (Var.% 5.32E-5) (Var.% 4.79E-5) (d) Least squares fitting residuals for a pixel near Hawaii Smaller residuals with SO2Jacobiansfitted

  8. Results: noise and artifact reduction August, 2006 OMI operational BRD • PCA algorithm reduces retrieval noise by a factor of two as compared with the BRD algorithm • SO2Jacobians for PCA algorithm calculated with the same assumptions as in the BRD algorithm

  9. Results: Boundary layer pollution SO2 PCA Operational BRD eastern U.S., August 2006 PCA 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, 2006 BRD, 2006 only BRD, 2004-2012 Largely hidden by artifacts • One 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 withoutsignificantly 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 wavelengths • Iteration of SO2Jacobians (pre-calculated assuming loadingsof1- 500 DU) • Shift of spectral fitting window to longer wavelengths PCA closest to estimated released SO2 mass of ~2200 ktbased 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 OMPS • Our algorithm eliminates the need for explicit instrument-specific radiance correction schemes • Test on OMPS: minimal changes to algorithm • biggest isthe use of OMPS slit function for Jacobians • spectral window, etc.,same as in OMI • Reduces 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 sampling October, 2013

  15. OMI and OMPS comparison OMPS, Jan. 2013 OMI, Jan. 2013 Both 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 patternsandSO2signals over eastern India(several coal-fired power plants built in recent years) [Lu et al., 2013].

  17. Next Steps • Expanded table for SO2 Jacobians to more accurately account for measurement conditions (e.g.,O3amount, reflectivity, geometries) • Addition of scattering weight to output to allow convenient adjustment of SO2 column amount based on user-provided profile • Inclusion of error estimates • Operational implementation,public release • >1 year processed and currently under evaluation • Initial release for boundary layer pollution this year • ImprovedJacobians and volcanic data to follow soon • On 12 CPUs, 1-2 days to process a year of OMI data

  18. Conclusions • Significant improvements in retrieval quality –PCA algorithm uses full spectral content from OMI and similar instruments offering increased temporal resolution and source detection • Computation 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 schemes • Flexibility – fitting window can be easily adjusted to optimize sensitivity under different conditions

  19. Backups

  20. Results: Daily boundary layer SO2 August 14, 2006 August 13, 2006 August 15, 2006 August 16, 2006

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