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GOES-R AWG Aviation Team:

SO2 Detection

Presented By: Mike Pavolonis NOAA/NESDIS/STAR

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Outline

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

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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)

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Executive Summary The ABI SO2 detection algorithm produces a yes/no SO2 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 SO2 detection algorithm utilizes spectral and spatial information to identify SO2 clouds. Only infrared measurements are used, so the algorithm methodology is day/night consistent.

Validation Datasets: Hyperspectral ultra-violet based retrievals of SO2 total column loading from the Ozone Monitoring Instrument (OMI).

Validation studies indicate that the SO2 loading algorithm is meeting the 100% accuracy specification.

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Algorithm Description

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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 SO2 absorption.

Cloud objects are constructed from potential SO2 pixels.

Spectral and spatial cloud object statistics are used to determine if all of the pixels that compose a cloud object contain SO2 or do not contain SO2.

A flag indicating the approximate SO2 loading is also produced and stored in the quality flags.

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Algorithm Channel Selection

ABI channel 11 coverage

ABI channel 10 coverage

The ABI algorithm utilizes SO2 absorption lines located in the channel 10 (7.3 m) and channel 11 (8.5 m) bands in combination with the lack of SO2 absorption in the 6.2, 11, and 12 m bands to detect SO2 induced spectral anomalies.

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Spectral InformationSpectral 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 SO2 contaminated pixels.

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observed = ln(1.0 −ελ1)ln(1.0 −ελ 2)

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theoretical = (1.0 −ωλ1gλ1)σ extλ 1

(1.0 −ωλ 2gλ 2)σ extλ 2

Spectral ratio of effective absorption optical depth

Spectral ratio of scaled extinction coefficients

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theoretical ≈ βobservedThis relationship provides a direct link to theoretical size distributions from the measurements

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Spectral InformationSpectral Information

Compare the spectral signature of an SO2 cloud, an ice cloud ,and a liquid water cloud.

Water Cloud

Ice Cloud

SO2 Cloud

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Spectral InformationSpectral Information

•The SO2 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 SO2 clouds.

•SO2 clouds tend to have larger (12, 11) relative to liquid water clouds when hydrometeors are co-located with the SO2.

SO2

Liquid Water

Ice

(8.5, 11), (12, 11) SO2 spectral signature

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Spectral InformationSpectral Information•The SO2 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 SO2 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.

SO2

Liquid Water

Ice

(8.5, 11), (7.3, 11) SO2 spectral signature

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SO2 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 SO2 clouds

Output SO2 mask

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Example OutputCordon Caulle (Chile), June 06 2011 – 17:00

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Example OutputGrimsvotn, May 22, 2010 - 13:00 UTC

GOES-R SO2 ProductOMI SO2 Product

Iceland

IcelandThe GOES-R and OMI products are in good agreement (more on this later)

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Algorithm Changes from 80% to 100%

Improved SO2 detection in the absence of a strong 7.3 μm signal (8.5 μm signal only).

Improved SO2 detection when SO2 overlaps or is mixed with thick ice cloud (more work is still needed though)

Metadata output added

Quality flags standardized

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ADEB and IV&V Response Summary

All ATBD errors and clarification requests have been addressed.

No substantive modifications to the approach were required.

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Requirements and Product Qualifiers

SO2 Detection

Nam

e

User &

Priority

Geographic

Coverage

(G, H

, C, M

)

Vertical

Resolution

Horizontal

Resolution

Mapping

Accuracy

Measurem

entR

ange

Measurem

entA

ccuracy

Product Refresh

Rate/C

overage Tim

e (Mode 3)

Product Refresh

Rate/C

overage Tim

e (Mode 4)

Vendor A

llocated Ground

Latency

Product Measurem

ent Precision

SO2 Detection

GOES-R Full Disk Total Column

2 km 1 km Binary yes/no detection from 10 to 700 Dobson Units (DU)

70% correct detection

Full disk: 60 min

Full disk: 5 min

806 sec N/A

Nam

e

User &

Priority

Geographic

Coverage

(G, H

, C, M

)

Tem

poral Coverage Q

ualifiers

Product Extent Q

ualifier

Cloud C

over Conditions

Qualifier

Product Statistics Qualifier

SO2 Detection GOES-R Full Disk Day and night Quantitative 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

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Validation Approach

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Validation ApproachValidation 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 SO2. OMI can detect SO2 at levels less than 1 DU.

•Although OMI is very sensitive to SO2, 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 SO2

clouds observed by OMI.

The Ozone Monitoring Instrument (OMI) is used as “truth”

The ABI SO2 mask is validated as a function of OMI SO2 loading

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Validation Approach Validation Approach OMI SOOMI SO22 Quality Flag Quality Flag

(QF) from the OMI data (QF) from the OMI data set is used to filter out set is used to filter out poor quality SOpoor quality SO22 loading loading retrievalsretrievals

Before filtering After filtering

Spurious data

Validation ApproachValidation Approach

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•The OMI SO2 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.

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Validation Results

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SO2 Validation•The ABI SO2 mask is validated as a function of OMI SO2 loading.

•Only scenes that contained SO2 clouds were used so this analysis reflects how the algorithm will perform in relevant situations

•The accuracy is 79% when the OMI SO2 indicates 10 DU or more of SO2.

Accuracy

Accuracy Requirement

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SO2 Validation•While not required the true skill score was also evaluated.

•The true skill score is 0.59 when the OMI SO2 indicates 10 DU or more of SO2.

•The true skill score is 0.70 when the OMI SO2 indicates 14 DU or more of SO2.

True Skill Score

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F&PS requirements – SO2

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)

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Summary The ABI SO2 Detection algorithm provides a new capability to objectively

detect SO2 clouds, which may be an aviation hazard and impact climate.

The GOES-R AWG SO2 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 SO2 detection, will likely lead to improved SO2 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 SO2 3). Improve quantitative estimates of SO2 loading and possibly height