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Steven D. Miller Cooperative Institute for Research in the Atmosphere Colorado State University

Introducing DEBRA : A D ust E nhancement with B ackground R eduction A lgorithm Applicable to Next-Generation Optical Spectrum Imaging Radiometers. Steven D. Miller Cooperative Institute for Research in the Atmosphere Colorado State University 25 January 2012.

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Steven D. Miller Cooperative Institute for Research in the Atmosphere Colorado State University

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  1. Introducing DEBRA: A Dust Enhancement with Background Reduction Algorithm Applicable to Next-Generation Optical Spectrum Imaging Radiometers Steven D. Miller Cooperative Institute for Research in the Atmosphere Colorado State University 25 January 2012 92nd AMS Annual Meeting New Orleans 18 SATMET, Paper 8A.4

  2. Outline • On the relevance of mineral dust • Review of dust detection & retrieval methods • DEBRA algorithm description • Examples of capabilities and limitations • Preliminary comparisons against CALIPSO • Ongoing work Special thanks to Dr. Richard Bankert (NRL Monterey) for assistance in retrieving case studies in support of this research

  3. Why Should We Care About Dust? • Radiation Budget Dust on snow Phytoplankton Bloom • Hydrological Cycle • Ecosystem • Respiratory Health • Visibility Both direct and indirect affects, changing sources over time linked to climate change in a fully coupled system…

  4. A Spectrum of Dust Detection Methods • Ultraviolet • TOMS Aerosol Index • Blue Light • ‘NDDI’, ‘Deep Blue’ • Thermal IR • Split Window,Tri-Spectral • Active Systems • Lidar Cirrus Convection Dust • All techniques suffer from limitations in sensitivity, spatial resolution, temporal resolution, uncertainty in dust profile/properties. • DEBRA does not exploit new physics for detection, but applies, combines, and displays the results of detection tests in a new way.

  5. DEBRA Essential Elements • Weighted sum of scaled 10.8-8.7,12-10.8 µm BTDs & BT-contrast tests • Employs a simple front-end cloud mask • Includes post-mask dust restoral tests • Clear-sky background characterization (key element) • Cloud-cleared scenes (reference images, similar to Legrand et al. 2001) • Surface emissivity database (using Seemann et al., 2008) • Dynamic scaling indexed to background values • Reduces the false alarm field • Confidence factor [0,1] output • Allows for use as a quantitative mask • Results presented as enhanced imagery • Allows for use as a visual analysis aide • Retains native spatial resolution of sensor • Retains meteorological context of dust events • Day & night, land & water color consistency • Blended transition of day & night algorithms at the terminator • Simplifies user interpretation and training

  6. Daytime Enhancement  Objective is to isolate the dust signal and then enhance it in an obvious and consistent way, with minimal color distractions

  7. Nighttime Enhancement • Maintaining consistency across day/night improves the interpretation of 24-hr animations.

  8. Example Color Options • All hues in Red/Green/Blue color space are possible • Yellow-dust provides best contrast for most common forms of color blindness (important consideration for imagery products)

  9. Background Characterization Cloud-Cleared MSG-SEVIRI Data • 1. Cloud-Cleared BTD Backgrounds • Built from composites for each spectral band. Compute BTDs which then serve as the base value for scaling bounds. • Accounts for surface features that appear as false dust signals in conventional RGB algorithms used for dust enhancement. (3.9 m) (8.7 m) BTD 10.8-8.7 m BTD 12-10.8 m  “Bright” features in the background images are regions where potential for false alarms is high. -OR- • 2. Surface Emissivity Database • UW-BF surface emissivity database (global, monthly), interpolated to MSG/SEVIRI bands; (Seemann et al., JAM-C, 2008). • Estimate skin temperature from either satellite or NWP fields, compute BTs and BTDs. • Provides dynamic scaling with account for non-linear behavior of Planck Function. Global Land Surface Emissivity Data  Couple with land surface temperature (e.g., model analysis) to specify BTD dynamically.

  10. Dynamic Scaling Concept Lofted Dust Layer Top of Scaling Bounds (Shading represents the surface’s spectral similarity to the dust signal) Base of Scaling Bounds Oceans & Vegetation Barren Soils Desert Sand  Dynamic enhancement extracts more information and maintains feature continuity • DEBRA seeks to suppress false alarms caused by spectral complex surfaces • Enables a clearer depiction of significant dust extent with minimal loss of information

  11. Solar Zenith Angle  sun= 90 Blended Algorithm Day/Night Blending Concept  Smooth transition across algorithmic interfaces DEBRA (V4.3) VIS/IR (No Dust Enhancement) Daytime Algorithm Nighttime Algorithm  Some dawn terminator artifacts due to colder land surfaces.

  12. Simplified Feature Identification 10.8 µm Dust RGB (EUMETSAT) DEBRA Reduces visual distractions arising from clouds and from bright/ambiguous-colored clear sky regions. Quantitatively, DEBRA is simply a confidence factor [0-1] for dust detection, and can be used as a fuzzy-logic mask.

  13. Broken Cloud Conditions • Less overall information content, but refined to dust features • Tends to over-enhance dust over low-cloud situations (coupling strong BTD signals with relatively cooler TB (11µm) values) • Cannot detect dust under cloud layers

  14. Over-Water Performance • Marginal performance for low-level dust over-water (potential for daytime improvement when applied to sensors having a blue-light band—MODIS, VIIRS, future ABI and MTG) • Maintains consistent color scheme

  15. Preserving the Meteorological Context Libya Egypt Sudan Chad Thunderstorm Outflows In contrast to a simple mask, DEBRA retains meteorological context, helping to explain forcing & dynamic features.

  16. Comparisons with CALIPSO • Member of the NASA “A-Train” 1330 LTAN satellite constellation • 532 nm (primary) lidar, nadir pointing, non-scanning (X-Z plane “curtains”) • High sensitivity to aerosol and optically thin clouds • Vertically resolved information at ~30 m resolution below 8 km • Extract DEBRA confidence index [0,1] along the CALIPSO ground track CALIPSO

  17. 8 March 2007 1300 UTC (Daytime Pass) B A A B

  18. 9 March 2007 0100 UTC (Nighttime Pass) A B A B

  19. 9 March 2007 1400 UTC (Daytime Pass) B A A B

  20. 10 March 2007 0200 UTC (Nighttime Pass) A B A B

  21. DEBRA vs. CALIPSO AOD DEBRA is detection (mask) tool; serving as either visual aide or the first step in a physical retrieval of dust properties

  22. Ongoing Work / Plans • Improve cloud mask and column water vapor dependent scaling, optimize temporal stability and background information usage. • Additional validation, coupling of DEBRA dust mask to physical retrievals of aerosol optical thickness • Adapt to handle volcanic ash detection (include 7.3 µm SO2 band) • Apply to MODIS and VIIRS, augmenting algorithm in preparation for Advanced Baseline Imagers (ABI; on MTSAT and GOES-R) • Daytime blue-band (~480 nm) • 1.38 µm ‘cirrus’ band (filter) • Low light visible band on VIIRS (moonlight) • Test in other regions (China, Southwest U.S., Australia, S. America) • Coordinate with EUMETSAT & the WMO to demonstrate DEBRA as a complementary tool in the Sand and Dust Warning Advisory and Assessment System (SDS-WAS). Thanks! …Questions?

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