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A Cloud Object Based Volcanic Ash Detection Technique Presented by Michael Pavolonis Center for A Cloud Object Based Volcanic Ash Detection Technique Presented by Michael Pavolonis Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000

Requirement, Science, and Benefit Requirement/Objective • Mission Goal: Commerce and Transportation – Research Area: Requirement, 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. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 2

Challenges and Path Forward • Science challenges – Product validation is difficult given the Challenges 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 Cr. IS. – 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) Review 09 – 11 March 2010 3

Ash Detection Method • In lieu of traditional brightnessvolcanic ash pixels are grouped into Ash Detection Method • In lieu of traditional brightnessvolcanic ash pixels are grouped into cloud Spatially connected candidate temperature differences, the ash detection Algorithm Innovation #1: Spectral objects. Spectral and spatial object statistics are used to ( -ratios) algorithm utilizes effective absorption optical depth ratiosdetermine which objects are 2010 a and Pavolonis 2010 b), which isolate the desired (Pavolonis, ash clouds. Meteorological Clouds microphysical signatures. Volcanic Ash Algorithm Innovation #2: Spatial Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Candidate ash objects 4

Ash Detection Method • In lieu of traditional brightnessvolcanic ash pixels are grouped into Ash Detection Method • In lieu of traditional brightnessvolcanic ash pixels are grouped into cloud Spatially connected candidate temperature differences, the ash detection Algorithm Innovation #1: Spectral objects. Spectral and spatial object statistics are used to ( -ratios) algorithm utilizes effective absorption optical depth ratiosdetermine which objects are 2010 a and Pavolonis 2010 b), which isolate the desired (Pavolonis, ash clouds. Meteorological Clouds microphysical signatures. Volcanic Ash Algorithm Innovation #2: Spatial Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Filtered ash objects 5

Retrieval Method • An optimal estimation technique (Heidinger and Pavolonis, 2009) is applied to Retrieval Method • An optimal estimation technique (Heidinger and Pavolonis, 2009) is applied to ash pixels to retrieve cloud temperature, emissivity, and a micro-physical parameter. Quantitative Ash Products Ash Loading • 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 byproduct of the optimal estimation approach. Ash Height Effective Radius • These products can be used to improve ash dispersion and fallout forecasts. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 6

Automated Ash Warning System • The warning criteria is fully user configurable. • In Automated 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. E-mail Warning Product Quick-look Quantitative description of ash cloud needed to issue accurate advisory Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 7

Automated Warning Performance False alarm March 23 - 27 eruptions False alarm April 1 Automated Warning Performance False alarm March 23 - 27 eruptions False alarm 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. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 8

Unique Early Warning Capability Remnant ash from previous eruption Early detection of new eruption Unique Early Warning Capability Remnant ash from previous eruption Early detection of new eruption (ash is largely sequestered in ice) • 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. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 9

Challenges and Path Forward • Science challenges – Product validation is difficult given the Challenges 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 Cr. IS. – 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) Review 09 – 11 March 2010 10