volcanic ash remote sensing at noaa/nesdis/star and cimss

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Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS Mike Pavolonis (NOAA/NESDIS/STAR) Justin Sieglaff and Wayne Feltz (CIMSS)

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Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS. Mike Pavolonis (NOAA/NESDIS/STAR) Justin Sieglaff and Wayne Feltz (CIMSS). Outline. Historical overview of volcanic ash support at CIMSS and NOAA/NESDIS/STAR - NASA ASAP’s role Recent NOAA/CIMSS research Future outlook. Outline. - PowerPoint PPT Presentation

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Page 1: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Volcanic Ash Remote Sensing at

NOAA/NESDIS/STAR and CIMSS

Mike Pavolonis (NOAA/NESDIS/STAR)

Justin Sieglaff and Wayne Feltz (CIMSS)

Page 2: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Outline

• Historical overview of volcanic ash support at CIMSS and NOAA/NESDIS/STAR - NASA ASAP’s role

• Recent NOAA/CIMSS research

• Future outlook

Page 3: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Outline

• Historical overview of volcanic ash support at CIMSS and NOAA/NESDIS/STAR - NASA ASAP’s role

• Recent NOAA/CIMSS research

• Future outlook

Page 4: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

NASA ASAP Supported Volcanic Ash Research at

CIMSS• The NASA ASAP provided the

resources needed for CIMSS researchers to apply their expertise in [meteorological] cloud remote sensing to volcanic clouds.

• Under NASA ASAP funding at CIMSS, the ash detection and height problems were studied (2003 - 2005).

Page 5: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Ash HeightsMike Richards’ M.S. research was focused on using MODIS to infer ash heights.

Volcanic clouds

Page 6: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Ash DetectionPavolonis et al. (2006) published an article on improvements in ash cloud detection. The goal was to develop an operational quality algorithm that can be used to automatically alert an analyst to the presence of a volcanic cloud.

Page 7: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

NASA ASAP Supported Volcanic Ash Research at

CIMSS• Research progress under the ASAP project

paved the way for new funding opportunities from NOAA/NESDIS to implement the algorithms on a variety of current and future operational sensors and transition them to operations.

• In 2005, M. Pavolonis began working for NOAA/NESDIS/STAR which helped to strengthen the relationship between research and NOAA/NESDIS operations.

Page 8: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Current NOAA Operational Volcanic Cloud Products

**OMI SO2 images are being produced on an experimental basis (soon to be operational).

Ellrod et al., 2004

Enhanced multi-spectral volcanic ash imagery

Page 9: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

NOAA/NESDIS/STAR Research to Operations Efforts

• Thanks to the efforts of Gary Ellrod, Don Hillger, and others at CIRA, special imagery for monitoring volcanic ash clouds is available in NOAA operations.

• While these products have proven to be very useful to volcanic ash forecasters, quantitative (e.g. ash probability, height, and mass loading) products are still lacking in operations.

• As such, NOAA has funded two additional projects aimed at improving operational products produced by current operational sensors.

• NOAA Product Systems Development and Implementation (PSDI) funding is supporting the implementation of an automated AVHRR based volcanic ash monitoring system.

• NOAA GOES Improved Measurements and Product Assurance Plan (GIMPAP) funds are being used to develop quantitative volcanic ash products for the current GOES Imager.

Page 10: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

NOAA’s Efforts for Future GOES

• In 2006, the GOES-R Algorithm Working Group (AWG) Aviation Application Team formed.– Ken Pryor (Co-chair, microburst potential)– Wayne Feltz (Co-chair, turbulence and overshooting tops)– Bill Smith Jr. (icing)– John Mecikalski (CI)– Mike Pavolonis (volcanic ash, SO2, and fog)

• The GOES-R requirements state that quantitative volcanic ash and SO2 products, in the form of ash height and mass loading and an SO2 mask, must be produced.

• NOAA has funded Pavolonis and CIMSS colleagues to do this work.

Page 11: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Outline

• Historical overview of volcanic ash support at CIMSS and NOAA/NESDIS/STAR - NASA ASAP’s role

• Recent NOAA/CIMSS research

• Future outlook

Page 12: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Remote Sensing Philosophy• Strive for globally and temporally consistent results (avoid regional

and seasonal tuning, if possible).– Accounting for the background conditions on a pixel-by-pixel basis

greatly improves the chance of producing globally consistent results.– The advent of more accurate fast RT models, higher quality NWP data,

surface emissivity databases, and faster computers allows us to calculate a reasonable estimate of the clear sky radiance for each pixel.

– We also seek IR-only approached when possible.

• Avoid approaches that require extensive tuning when applied to various sensors with similar spectral channels.– Since cloud transmittance varies gradually as a function of wavelength

compared to gasous transmittance, explicitly accounting for the spectral response function (assumed to be known) of a given band increases the chance of consistent results across a variety of sensors.

Page 13: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Pavolonis et al. (2006), JTECH

Traditional ash detection methods trigger numerous false alarms

How often does BTD(11 - 12 um) go negative?

Page 14: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Pavolonis et al. (2006), JTECH

Traditional ash detection methods trigger numerous false alarms

How often does BTD(11 - 12 um) go negative?

Algorithms should account for local background conditions

Page 15: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Physical Relationships

Rad()observed Rad()clear

[Rad()ac t()ac*B(,Teff )] Rad()clear

theoretical (1.0 1 g1)ext1

(1.0 2 g 2)ext 2

observed ln(1.0 1)

ln(1.0 2)

After Van de Hulst (1980) and Parol at al. (1991)…

theoretical observedEffective absorption ratios (similar to ratio of scaled absorption cloud optical depth)

Page 16: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Physical Relationships

Rad()observed Rad()clear

[Rad()ac t()ac*B(,Teff )] Rad()clear

theoretical (1.0 1 g1)ext1

(1.0 2 g 2)ext 2

observed ln(1.0 1)

ln(1.0 2)

After Van de Hulst (1980) and Parol at al. (1991)…

theoretical observedEffective absorption ratios (similar to ratio of scaled absorption cloud optical depth)

•The bottom line is that the cloud microphysical signal can be isolated from the surface and atmospheric contribution by converting the measured radiances to effective absorption optical depth and examining the spectral variation.

•This new data space allows us to largely avoid algorithm tuning and helps produce results that are much more spatially and temporally consistent.

Page 17: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

GOES-R AWG Efforts

• Project Goal: Develop, implement, validate, and document volcanic ash and SO2 algorithms for the GOES-R Advanced Baseline Imager.

• Products produced: ash probability, ash height, and mass loading

Page 18: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

June 23-26, 2008 <Project> Critical Design Review

Volcanic Ash Detection

Potential volcanic ash pixels are identified using a tri-spectral (8.5, 11, and 12 um) optical depth ratio approach.

Ash

Meteo Clouds

Using optical depth ratios results in a 10% increase in skill in correctly identifying volcanic ash clouds compared to BTD’s!

Max Skill:0.72

BTD’s

Max Skill:0.82

Beta

Page 19: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

June 23-26, 2008 <Project> Critical Design Review

GOES-R Examples

Karthala - 11/25/2005

Mount Etna - 11/24/2006

RGB Ash Detection

Ash Height Ash Loading

Page 20: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

June 23-26, 2008 <Project> Critical Design Review

Algorithm Development - Performance Estimates

•The ash detection algorithm is applied to every pixel in the full disk.

•The height and mass loading retrieval is only applied to pixels that potentially contain ash so as to prevent a non-volcanic ash pixel from being assigned a non-zero mass loading.

•Currently, the false alarm rate is on the order of 0.001%.

Karthala ash cloud

Page 21: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Difficult Scenes

Since volcanic ash monitoring is vital, an effort is made to account for various conditions, including ash overlapping a lower cloud layer.

Ash over low cloud

Page 22: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

GOES-R SO2 Detection

•SO2 clouds exhibit both an SO2 absorption and a small particle signature in the 7.3, 8.5, 11, and 12 m bands.

•Thus, SO2 clouds that contain ice/water can be inferred using the split-window, even though the split window lacks SO2 absorption.

Page 23: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Algorithm Development for the Current GOES Imager

• Project Goal: Apply modified versions of the GOES-R algorithms to the current series of GOES imagers. Once algorithms are deemed reliable, pursue a transition to operations.

• Products produced: ash probability, ash height, and mass loading

Page 24: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Example GOES-10 Products (May 6, 2008)

Mass Loading Ash Cloud Height

RGB (Chaiten eruption)Ash Detection Theory

Page 25: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Operational Implementation of AVHRR Volcanic Ash Products

• Project Goal: Implement volcanic ash algorithms in an operational AVHRR processing system (CLAVR-x), test in pre-operational mode (1/2009 - 1/2010), and begin operational processing (3/2010).

• Products produced: ash probability, ash height, and mass loading

Page 26: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Expected Outcomes•After each AVHRR data segment is processed, the output is checked for high probability ash. If present, an email alert will be sent to VAAC analysts.

•All products will also be viewable in McIDAS and AWIPS.

•We are working closely with the Washington, D.C. VAAC on this project. We also hope engage the Anchorage VAAC.

Page 27: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

•Hyperspectral measurements offer improved sensitivity to the presence of ash and its height.

•We are working towards routine hyperspectral processing (IASI and AIRS).

Hyperspectral Infrared

Page 28: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Outline

• Historical overview of volcanic ash support at CIMSS and NOAA/NESDIS/STAR - NASA ASAP’s role

• Recent NOAA/CIMSS research

• Future outlook

Page 29: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

Future Outlook• The ultimate goal is an automated multi-sensor global

monitoring system (GEO’s and LEO’s) to assist volcanic ash forecasters. We can process any GEO imager, AVHRR, and MODIS.

• Make better use of hyperspectral infrared measurements (e.g. combined imager/sounder retrievals for the best of both worlds).

• Can satellite-based retrievals be assimilated into dispersion models?

• Build collaborations (NRL, VAAC’s, NCAR, etc…)• Use current NASA ASAP volcanic ash funding to facilitate

collaboration with NRL.

Page 30: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

From: FAA Aviation Safety Journal Vol. 2 (3)

From David Innes

Page 31: Volcanic Ash Remote Sensing at NOAA/NESDIS/STAR and CIMSS

The Advanced Baseline Imager: ABI Current

Spectral Coverage16 bands 5 bands

Spatial resolution 0.64 m Visible 0.5 km Approx. 1 kmOther Visible/near-IR 1.0 km n/aBands (>2 m) 2 km Approx. 4 km

Spatial coverageFull disk 4 per hour Every 3 hoursCONUS 12 per hour ~4 per hourMesoscale Every 30 sec n/a

Visible (reflective bands) On-orbit calibration Yes No

Slide courtesy of Tim Schmit