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Iterative Retrieval of Large SO 2 from Volcanic Eruptions

Iterative Retrieval of Large SO 2 from Volcanic Eruptions. Kai Yang 1,3 , Nikolay Krotkov 1,3 , Arlin Krueger 2 , Simon Carn 2 Pawn K. Bhartia 3. 1 GEST, UMBC, Baltimore, MD, USA 2 JCET, UMBC, Baltimore, MD, USA 3 GSFC, NASA, Greenbelt, MD, USA. Detect and Quantify SO 2.

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Iterative Retrieval of Large SO 2 from Volcanic Eruptions

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  1. Iterative Retrieval of Large SO2from Volcanic Eruptions Kai Yang1,3, Nikolay Krotkov1,3, Arlin Krueger2, Simon Carn2 Pawn K. Bhartia3 1GEST, UMBC, Baltimore, MD, USA 2JCET, UMBC, Baltimore, MD, USA 3GSFC, NASA, Greenbelt, MD, USA

  2. Detect and Quantify SO2

  3. SO2 Algorithms Retrieval Challenge: Wide range of loadings and vertical distributions Existing Algorithms: • Band Residual Difference (BRD) method • Linear Fit (LF) method Both methods are used in making OMI NRT and operational OMSO2 products. Improvements in addressing these two questions: • How small can OMI see? • How large can OMI measure?

  4. Combo O3 and SO2 Retrievals • Profile shapes: O3 (Ω) uses V8 climatology, SO2 (Ξ) uses Gaussian specified by a central height and a width. • Initial guess: latitude based O3 guess or an adjacent pixel O3 value, no SO2, derive MLER cloud fraction (Fc) at λ=333 nm, using OMCLDRR cloud pressure. • Iterative Spectral Fitting (ISF) Method:

  5. Sample Results:Eruption of Sierra Negra 10/23/2005 10/22/2005

  6. Sierra Negra Eruption 10/23/2005OMI Retrievals: ISF vs. LF ISF SO2 Mass LF SO2 Mass 1740 kilotons 950 kilotons (-45%)

  7. Largest SO2 Pixel Value Orange: lava flows Aqua MODIS Bands 7, 2, 1 Composite

  8. Ozone Consistency:Inside and Outside of the Plume Along track plots of O3 and SO2 (Along track)

  9. Ozone Consistency:Inside and Outside of the Plume Maps of O3: ISF vs. LF

  10. How Good are ISF Retrievals? High SO2 Fitting Residuals 1 N-value = -2.3% Radiance Window = 317.85–333.00 nm Error = 0.58 %

  11. High Precision Fitting Medium SO2 Low SO2 Fitting Error = 0.25% Fitting Error = 0.15% Background SO2 Fitting Error = 0.14%

  12. SO2 Vertical Distribution Simulated Retrievals Assumed profile too low Imeas<Iassum at short λ Assumed profile too high Imeas>Iassum at short λ

  13. Improving Retrieval Accuracy:Adjusting SO2 Central Height

  14. Improving Retrieval Accuracy:Adjusting SO2 Central Height

  15. Comparison with AIRS SO2 SO2 central height

  16. Anatahan Eruption: 04/06/05 Iterative Spectral Fit Linear Fit: OMSO2 TRM Derived SO2 central height ≈ 10.5 KM Assumed Central height = 7. 5 KM

  17. Anatahan Eruption: 04/06/05OMI – AIRS Comparison Iterative Spectral Fit AIRS ΔBT

  18. Anatahan Eruption: 04/06/05Aerosol Index Iterative Spectral Fit OMTO3 Linear Fit, OMSO2 Terra MODIS

  19. Nyamuragira Eruption:11/28/06SO2 & Aerosol Index ISF SO2 ISF AI OMTO3 AI OMSO2 AI

  20. Height & Distance Sierra Negra, Galápagos Islands Oct 23 – Nov 01, 2005 SO2 Plume height ~ 3 KM Nyamuragira, DR Congo Nov 28 – Dec 04, 2006 SO2 Plume height ~ 7 KM

  21. Height & Distance Soufriere Hills, Montserrat, West Indies May 20 – June 11, 2006 SO2 Plume height ~ 13 KM on May 20 Distance tracked ~ 20000 KM

  22. Summary • Operational OMSO2 products tend to underestimate SO2 column amounts, more so when they are large ( > 50 DU). • New algorithm provides more consistent ozone values inside and outside of volcanic plumes, and more precise SO2 columns. Record SO2 column retrieved. • OMI UV2 measurements contain SO2 vertical distribution info. Derived central plume heights are consistent with near simultaneous AIRS observations. • The slope of theFc-λ curve yielded from the fitting algorithm may be used as a better aerosol index.

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