
How assumed composition affects the interpretation of satellite observations of volcanic ash
Author(s) -
Mackie Shona,
Millington Sarah,
Watson I. M.
Publication year - 2014
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.1445
Subject(s) - volcanic ash , environmental science , atmosphere (unit) , radiative transfer , satellite , composition (language) , volcano , meteorology , atmospheric sciences , remote sensing , geology , linguistics , philosophy , physics , quantum mechanics , aerospace engineering , seismology , engineering
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.