1 / 17

Probabilistic Ash Detection for IASI

Probabilistic Ash Detection for IASI . Shona Mackie, Matt Watson. Outline. IASI Probabilistic Detection Method Advantages How It works Challenges. IASI. Polar- O rbiting Platforms 8461 Channels Infra-Red. Probabilistic Detection - Advantages. Allows for variable uncertainty

hayley
Download Presentation

Probabilistic Ash Detection for IASI

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related