Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land...

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010

Image:

MODIS Land Group,

NASA GSFC

March 2000

A Cloud Object Based Volcanic Ash Detection Technique

A Cloud Object Based Volcanic Ash Detection Technique

Presented by

Michael PavolonisPresented by

Michael Pavolonis

2 Center for Satellite Applications and Research (STAR) Review

09 – 11 March 2010 Center for Satellite Applications and Research (STAR) Review

09 – 11 March 2010

Requirement, Science, and BenefitRequirement, Science, and Benefit

Requirement/Objective• Mission Goal: Commerce and Transportation

– Research Area: Provide accurate, timely, and integrated weather information to meet air and surface transportation needs.

Science • How can satellite data be used to quantitatively track dangerous

volcanic ash clouds? How can satellite data products be used to validate and improve forecasts of ash cloud dispersion?

Benefit• These products will allow forecasters to issue more timely and accurate ash cloud warnings and

forecasts to the aviation community, helping to reduce the risk of ash/aircraft encounters and limit the economic impact associated with rerouting aircraft around suspected ash clouds.

• The ash cloud property retrievals can be used to improve ash fall predictions. Ash fall poses a major hazard to life, property, and natural resources.



09 – 11 March 2010

Challenges and Path ForwardChallenges and Path Forward

• Science challenges– Product validation is difficult given the lack of in-situ observations of ash clouds.

• Next steps– Similar products are being developed for other sensors such as: GOES, MTSAT,

MODIS, SEVIRI, VIIRS, GOES-R, AIRS, IASI, and CrIS.– Our goal is an automated combined LEO/GEO global volcanic ash monitoring system

that will be a reliable tool for volcanic ash forecasters and modelers.

• Transition Path– The AVHRR component of this system is scheduled to be fully transitioned into

NESDIS operations by May/June 2010 (a PSDI funded effort).– We have developed the algorithm which will be used to generate the operational

GOES-R ash products.– Our goal is to transition the GOES products to NESDIS operations within the next

few years.– End users: Volcanic Ash Advisory Centers (VAACs), Air Force, NRL, Modeling

Community, Research Community

Center for Satellite Applications and Research (STAR) Review09 – 11 March 2010

Center for Satellite Applications and Research (STAR) Review09 – 11 March 2010 4

Ash Detection MethodAsh Detection Method

Volcanic Ash

Meteorological Clouds•In lieu of traditional brightness temperature differences, the ash detection algorithm utilizes effective absorption optical depth ratios (-ratios) (Pavolonis, 2010a and Pavolonis 2010b), which isolate the desired microphysical signatures.

•Spatially connected candidate volcanic ash pixels are grouped into cloud objects. Spectral and spatial object statistics are used to determine which objects are ash clouds.

Candidate ash objects

Algorithm Innovation #1: Spectral

Algorithm Innovation #2: Spatial



Ash Detection MethodAsh Detection Method

Volcanic Ash

Meteorological Clouds•In lieu of traditional brightness temperature differences, the ash detection algorithm utilizes effective absorption optical depth ratios (-ratios) (Pavolonis, 2010a and Pavolonis 2010b), which isolate the desired microphysical signatures.

•Spatially connected candidate volcanic ash pixels are grouped into cloud objects. Spectral and spatial object statistics are used to determine which objects are ash clouds.

Algorithm Innovation #1: Spectral

Algorithm Innovation #2: Spatial Filtered ash objects



Retrieval MethodRetrieval Method•An optimal estimation technique (Heidinger and Pavolonis, 2009) is applied to ash pixels to retrieve cloud temperature, emissivity, and a micro-physical parameter.

•The retrieved parameters are used to estimate cloud height, effective particle radius, and ash mass loading.

•An error estimate for each of the retrieved parameters is a by-product of the optimal estimation approach.

•These products can be used to improve ash dispersion and fallout forecasts.

Ash Loading

Ash Height Effective Radius

Quantitative Ash Products



E-mail Warning Product Quick-look

Automated Ash Warning SystemAutomated Ash Warning System

•The warning criteria is fully user configurable.

•In addition to the text message, an automatically generated, pre-analyzed false color image along with product images are supplied to the user.

Quantitative description of ash cloud needed to issue accurate advisory



False alarm

False alarmMarch 23 - 27

eruptions

April 1 and 4 eruptions

•During this 20 day period leading up to and including the 2009 eruptions of Redoubt, AK, only 2 false warnings occurred out of 474 full AVHRR scenes received directly at Gilmore Creek (GC), AK (0.5% of scenes received at GC).

•In other words, a forecaster can expect a false warning once every 7 to 10 days.

•Every eruptive event captured by the AVHRR was detected.

Automated Warning PerformanceAutomated Warning Performance



Unique Early Warning CapabilityUnique Early Warning Capability

•This is the first automated technique capable of identifying volcanic ash that is sequestered in ice, which is common in the early stages of the ash cloud lifecycle.

Early detection of new eruption (ash is largely sequestered in ice)

Remnant ash from previous eruption



09 – 11 March 2010

Challenges and Path ForwardChallenges and Path Forward

• Science challenges– Product validation is difficult given the lack of in-situ observations of ash clouds.

• Next steps– Similar products are being developed for other sensors such as: GOES, MTSAT,

MODIS, SEVIRI, VIIRS, GOES-R, AIRS, IASI, and CrIS.– Our goal is an automated combined LEO/GEO global volcanic ash monitoring system

that will be a reliable tool for volcanic ash forecasters and modelers.

• Transition Path– The AVHRR component of this system is scheduled to be fully transitioned into

NESDIS operations by May/June 2010 (a PSDI funded effort).– We have developed the algorithm which will be used to generate the operational

GOES-R ash products.– Our goal is to transition the GOES products to NESDIS operations within the next

few years.– End users: Volcanic Ash Advisory Centers (VAACs), Air Force, NRL, Modeling

Community, Research Community

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