1. 1 GOES-R AWG Aviation Team: SO 2 Detection Presented By: Mike Pavolonis NOAA/NESDIS/STAR

2. 2 Outline  Aviation Team  Executive Summary  Algorithm Description  ADEB and IV&V Response Summary  Requirements Specification Evolution  Validation Strategy  Validation Results  Summary

3. 33 Aviation Team  Co-Chairs: Ken Pryor and Wayne Feltz  Aviation Team »K. Bedka, J. Brunner »J. Mecikalski, M. Pavolonis, B. Pierce »W. Smith, Jr., A. Wimmers, J. Sieglaff  Others »Walter Wolf (AIT Lead) »Shanna Sampson (Algorithm Integration) »Zhaohui Zhang (Algorithm Integration)

4. 4 Executive Summary  The ABI SO 2 detection algorithm produces a yes/no SO 2 flag  Software Version 4 was delivered in January (2011). The CUTR was completed in January (2011). ATBD (100%) and test datasets are scheduled to be delivered in June (2011).  The SO 2 detection algorithm utilizes spectral and spatial information to identify SO 2 clouds. Only infrared measurements are used, so the algorithm methodology is day/night consistent.  Validation Datasets: Hyperspectral ultra-violet based retrievals of SO 2 total column loading from the Ozone Monitoring Instrument (OMI).  Validation studies indicate that the SO 2 loading algorithm is meeting the 100% accuracy specification.

5. 5 Algorithm Description

6. 6 Algorithm Summary  Input Datasets: 1). ABI channels 8, 10, 11, 14, and 15, 2). Satellite viewing angle, 3). Clear sky radiances and transmittances, 4). Cloud frequency of occurrence LUT  -ratios are calculated to help identify cloud microphysical “anomalies” possibly associated with SO 2 absorption.  Cloud objects are constructed from potential SO 2 pixels.  Spectral and spatial cloud object statistics are used to determine if all of the pixels that compose a cloud object contain SO 2 or do not contain SO 2.  A flag indicating the approximate SO 2 loading is also produced and stored in the quality flags.

7. 7 Algorithm Channel Selection ABI channel 11 coverage ABI channel 10 coverage The ABI algorithm utilizes SO 2 absorption lines located in the channel 10 (7.3  m) and channel 11 (8.5  m) bands in combination with the lack of SO 2 absorption in the 6.2, 11, and 12  m bands to detect SO 2 induced spectral anomalies.

8. 8 Spectral Information As in Pavolonis (2010), effective absorption optical depth ratios (  - ratios) are used to infer cloud microphysical information. Cloud microphysical anomalies (large deviations from theoretical values) are then used to identify potential SO 2 contaminated pixels. Spectral ratio of effective absorption optical depth Spectral ratio of scaled extinction coefficients This relationship provides a direct link to theoretical size distributions from the measurements

9. 9 Spectral Information Compare the spectral signature of an SO 2 cloud, an ice cloud,and a liquid water cloud. Water Cloud Ice Cloud SO 2 Cloud

10. 10 Spectral Information The SO 2 cloud is characterized by the simultaneous occurrence of large  (12, 11) and large  (8.5, 11). Ice clouds tend to have smaller  (8.5, 11) relative to SO 2 clouds. SO 2 clouds tend to have larger  (12, 11) relative to liquid water clouds when hydrometeors are co- located with the SO 2. SO 2 Liquid Water Ice  (8.5, 11),  (12, 11) SO 2 spectral signature

11. 11 Spectral Information The SO 2 cloud is characterized by the simultaneous occurrence of large  (7.3, 11) and large  (8.5, 11). Ice clouds tend to have smaller  (8.5, 11) and  (7.3, 11) relative to SO 2 clouds. Liquid water clouds are often present well below the peak of the 7.3  m weighting function, and hence does not noticeably impact the 7.3  m radiance. SO 2 Liquid Water Ice  (8.5, 11),  (7.3, 11) SO 2 spectral signature

12. 12 SO 2 Detection Processing Schematic Compute cloud emissivities and  - ratios INPUT: ABI and ancillary data Determine which pixels meet cloud object membership criteria Construct cloud objects and compute cloud object statistics Determine which cloud objects are SO 2 clouds Output SO 2 mask

13. 13 Example Output Cordon Caulle (Chile), June 06 2011 – 17:00

14. 14 Example Output Grimsvotn, May 22, 2010 - 13:00 UTC GOES-R SO 2 Product OMI SO 2 Product Iceland The GOES-R and OMI products are in good agreement (more on this later)

15. 15 Algorithm Changes from 80% to 100%  Improved SO 2 detection in the absence of a strong 7.3 μm signal (8.5 μm signal only).  Improved SO 2 detection when SO 2 overlaps or is mixed with thick ice cloud (more work is still needed though)  Metadata output added  Quality flags standardized

16. 16 ADEB and IV&V Response Summary  All ATBD errors and clarification requests have been addressed.  No substantive modifications to the approach were required.

17. 17 Requirements and Product Qualifiers SO 2 Detection NameUser &PriorityGeographicCoverage(G, H, C, M)VerticalResolutionHorizontalResolutionMappingAccuracyMeasurementRangeMeasurementAccuracyProduct RefreshRate/CoverageTime (Mode 3)Product RefreshRate/CoverageTime (Mode 4)Vendor AllocatedGround LatencyProductMeasurementPrecision SO 2 Detection GOES-RFull DiskTotal Column 2 km1 kmBinary yes/no detection from 10 to 700 Dobson Units (DU) 70% correct detection Full disk: 60 min Full disk: 5 min 806 secN/A NameUser &PriorityGeographicCoverage(G, H, C, M)TemporalCoverageQualifiersProductExtentQualifierCloud CoverConditionsQualifierProductStatisticsQualifier SO 2 DetectionGOES-RFull DiskDay and nightQuantitative out to at least 70 degrees LZA and qualitative at larger LZA Clear conditions down to feature of interest associated with threshold accuracy Over specified geographic area

18. 18 Validation Approach

19. 19 Validation Approach OMI is a Dutch-Finnish instrument on board the Aura satellite in NASA’s A-Train. OMI uses measurements of backscattered solar UV radiation to detect SO 2. OMI can detect SO 2 at levels less than 1 DU. Although OMI is very sensitive to SO 2, it only views a given area of earth once daily. The MODIS instrument on the Aqua satellite (also in the A-Train) observes the same area as OMI, which allows us to study any SO 2 clouds observed by OMI. The Ozone Monitoring Instrument (OMI) is used as “truth” The ABI SO 2 mask is validated as a function of OMI SO 2 loading

20. 20 Validation Approach  OMI SO 2 Quality Flag (QF) from the OMI data set is used to filter out poor quality SO 2 loading retrievals Before filteringAfter filtering Spurious data

21. Validation Approach 21 The OMI SO 2 loading product and the GOES-R proxy data (MODIS or SEVIRI) are matched up in time and re-mapped to the same grid to allow for quantitative comparisons.

22. 22 Validation Results

23. 23 SO 2 Validation The ABI SO 2 mask is validated as a function of OMI SO 2 loading. Only scenes that contained SO 2 clouds were used so this analysis reflects how the algorithm will perform in relevant situations The accuracy is 79% when the OMI SO 2 indicates 10 DU or more of SO 2. Accuracy Accuracy Requirement

24. 24 SO 2 Validation While not required the true skill score was also evaluated. The true skill score is 0.59 when the OMI SO 2 indicates 10 DU or more of SO 2. The true skill score is 0.70 when the OMI SO 2 indicates 14 DU or more of SO 2. True Skill Score

25. 25 F&PS requirements – SO 2 F&PS requirement: Product Measurement Range F&PS requirement: Product Measurement Accuracy Validation results Binary yes/no detection from 10 to 700 Dobson Units (DU) 70% correct detection 79% correct detection when loading is 10 DU or greater (0.70 true skill score when loading is 14 DU or greater) 25

26. 26 Summary  The ABI SO 2 Detection algorithm provides a new capability to objectively detect SO 2 clouds, which may be an aviation hazard and impact climate.  The GOES-R AWG SO 2 algorithm meets all performance and latency requirements.  The improved spatial and temporal resolution of the ABI, along with a 7.3 um band that is better suited for SO 2 detection, will likely lead to improved SO 2 detection capabilities (relative to SEVIRI and MODIS).  The 100% ATBD and code will be delivered to the AIT this summer.  Prospects for future improvement: 1). Express results as a probability, not a mask. 2). Correct for high level ice and ash clouds using bands not sensitive to SO 2 3). Improve quantitative estimates of SO 2 loading and possibly height