How assumed composition affects the interpretation of satellite observations of volcanic ash

Shona Mackie*, Sarah Millington, I. M. Watson

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

18 Citations (Scopus)


The monitoring of volcanic ash in the atmosphere by satellite-borne instruments is highly important for generation of warnings of potential ash hazards to aviation, and to constraining model predictions of an ash cloud's anticipated evolution. The high economic cost of flight restrictions creates a demand for precise monitoring and forecasting; however, no scientific product can be considered precise unless presented with a robust estimate of its associated uncertainty. Data from infrared sensors are focused on, as these monitor the atmosphere both day and night. Most methods for the detection of ash, and the retrieval of its properties, rely on forward modelling to estimate the ash signal at the satellite. This requires assumptions to be made about the ash composition in order to constrain its optical properties as represented in a radiative transfer model. Ash composition may change through the course of an eruption, and is often unknown for new eruptions. Even in cases where the composition of the ash can be sampled, it is unlikely that it is homogeneous enough to match the composition of any of the available optical property datasets exactly (which properties are required for radiative transfer modelling). This often necessary assumption can affect the observed ash signal by an amount that varies with cloud altitude, thickness, and concentration from a few percent to 17.7% for the highest ash concentration examined in this study. This has implications for methods that rely on forward modelling of ash observations, and for the interpretation of real ash observations when ash composition is unknown.

Original languageEnglish
Pages (from-to)20-29
Number of pages10
JournalMeteorological Applications
Issue number1
Publication statusPublished - Jan 2014


  • remote sensing
  • hazards
  • modelling
  • DUST


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