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Advanced Probabilistic Ash Detection Using IASI for Enhanced Risk Management

This paper presents a novel probabilistic detection method for volcanic ash using the Infrared Atmospheric Sounding Interferometer (IASI). By leveraging a wide array of channels and incorporating variable uncertainties, the approach offers several advantages, including computational efficiency and the ability to deal with varied atmospheric states (clear, cloudy, ashy) without pre-screening for clouds. The challenges encountered include the accurate representation of different ash clouds and their properties, making this method particularly valuable for risk managers in response to volcanic activities.

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Advanced Probabilistic Ash Detection Using IASI for Enhanced Risk Management

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  1. Probabilistic Ash Detection for IASI Shona Mackie, Matt Watson

  2. Outline • IASI • Probabilistic Detection Method • Advantages • How It works • Challenges

  3. IASI • Polar-Orbiting Platforms • 8461 Channels • Infra-Red

  4. Probabilistic Detection- Advantages • Allows for variable uncertainty • Useful for risk managers • Exploits scene-specific information • No pre-screening for cloud • Computationally efficient • Generic (in principle)

  5. How It Works • Possible atmospheric states: • CLEAR, CLOUDY, ASHY NWP DEM Emissivity Atlas Pixel-Specific PDF for each State convolve with uncertainties RTM

  6. How It Works • Possible atmospheric states: • CLEAR, CLOUDY, ASHY NWP DEM Emissivity Atlas Pixel-Specific PDF for each State convolve with uncertainties RTM Probability of observation, y Given prior info. x and state ci

  7. How It Works ci,j clear/cloudy/ashy y observation x prior information

  8. How It Works P(cash) set to 5% P(cclear) + P(ccloud) = 95% Season-, latitude- dependent P(ccloud) taken from ISCCP data

  9. Challenges ci,j clear/cloudy/ashy y observation x prior information

  10. Challenges • Clear Sky – run time, use current NWP • Cloudy – pre-calculate, using ECMWF profiles dataset: • Single-layer, single-phase approximations • Weight representations according to global cloud statistics

  11. Challenges • Ashy – pre-calculate using same dataset • RTM needs optical properties for ash

  12. Challenges • Ashy – pre-calculate using same dataset • Weight representation according to relative likelihood for: • Different altitudes • Different mass concentrations

  13. Challenges • Relative likelihood for different altitudes • Frequency of injection heights (historical eruption data) • Relative residence time • Function of tropopause height • Poorly constrained

  14. Challenges • Relative likelihood for different mass concentrations • Function of distance from source? • Shape of function? Unknown source?

  15. Challenges • Representation of different ash clouds needs to be weighted according to relative likelihood • Not enough data to define weights • Unrealistic PDF from model data

  16. Challenges • Use empirical PDF? • Paucity of observations • Biased towards a few eruptions • Detecting observations for inclusion in PDF – circular problem

  17. Ash Classed Pixels PLAY MOVIE Ash Probability PLAY MOVIE

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