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Probabilistic detection of volcanic ash using a Bayesian approach.

Mackie S, Watson M - J Geophys Res Atmos (2014)

Bottom Line: Such techniques are generally specific to data from particular sensors, and most approaches result in a binary classification of pixels into "ash" and "ash free" classes with no indication of the classification certainty for individual pixels.The technique has already been successfully applied to other detection problems in remote sensing, and this work shows that it will be a useful and effective tool for ash detection.Presentation of a probabilistic volcanic ash detection schemeMethod for calculation of probability density function for ash observationsDemonstration of a remote sensing technique for monitoring volcanic ash hazards.

View Article: PubMed Central - PubMed

Affiliation: School of Earth Sciences, University of Bristol Bristol, UK.

ABSTRACT

: Airborne volcanic ash can pose a hazard to aviation, agriculture, and both human and animal health. It is therefore important that ash clouds are monitored both day and night, even when they travel far from their source. Infrared satellite data provide perhaps the only means of doing this, and since the hugely expensive ash crisis that followed the 2010 Eyjafjalljökull eruption, much research has been carried out into techniques for discriminating ash in such data and for deriving key properties. Such techniques are generally specific to data from particular sensors, and most approaches result in a binary classification of pixels into "ash" and "ash free" classes with no indication of the classification certainty for individual pixels. Furthermore, almost all operational methods rely on expert-set thresholds to determine what constitutes "ash" and can therefore be criticized for being subjective and dependent on expertise that may not remain with an institution. Very few existing methods exploit available contemporaneous atmospheric data to inform the detection, despite the sensitivity of most techniques to atmospheric parameters. The Bayesian method proposed here does exploit such data and gives a probabilistic, physically based classification. We provide an example of the method's implementation for a scene containing both land and sea observations, and a large area of desert dust (often misidentified as ash by other methods). The technique has already been successfully applied to other detection problems in remote sensing, and this work shows that it will be a useful and effective tool for ash detection.

Key points: Presentation of a probabilistic volcanic ash detection schemeMethod for calculation of probability density function for ash observationsDemonstration of a remote sensing technique for monitoring volcanic ash hazards.

No MeSH data available.


Related in: MedlinePlus

Restrictions and weights imposed on the representation of clouds within the PDF: (a, b) Temperature-dependent weighting applied to the representation of water clouds relative to ice clouds (the axes are scaled differently in Figures 5a and 5b to show the weighting functions applied in different temperature regimes), (c) the maximum cloud water path represented in the PDF as a function of cloud altitude for water clouds, (d) same as Figure 5c but for ice clouds, and (e) the ratio of cloud-filled to cloud-edge pixels in the ECMWF data set, used to weight the representation of clouds with 100% pixel coverage more heavily than those with fractional coverage.
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fig05: Restrictions and weights imposed on the representation of clouds within the PDF: (a, b) Temperature-dependent weighting applied to the representation of water clouds relative to ice clouds (the axes are scaled differently in Figures 5a and 5b to show the weighting functions applied in different temperature regimes), (c) the maximum cloud water path represented in the PDF as a function of cloud altitude for water clouds, (d) same as Figure 5c but for ice clouds, and (e) the ratio of cloud-filled to cloud-edge pixels in the ECMWF data set, used to weight the representation of clouds with 100% pixel coverage more heavily than those with fractional coverage.

Mentions: Observations corresponding to ice phase clouds were removed from the distribution where the ambient temperature, T, exceeds 273.15 K and water phase clouds are removed where T is less than 233.15 K (at which temperature homogeneous freezing of water drops occurs [Rogers and Yau, 1989]). The likelihood of ice clouds forming where T > 233.15 K depends on the abundance of freezing nuclei, which are generally active when T < ~ 264.15 K, and deposition nuclei, which are generally become active around T ~ 253.15 K [Rogers and Yau, 1989; Moran and Morgan, 1997]. Ice clouds may therefore be present around T ~ 264 K but become more likely at T ~ 253 K, when both freezing and deposition nuclei are available. A relationship between the likely presence of ice nuclei and T is used to weight the representation of ice clouds, relative to that of water clouds, at each cloud altitude (i.e., for each value of T). A linear weighting was assigned to ice clouds where 273.15 K > T > 253.15 K and an exponential weighting where T < 253.15 K, reaching a value of 1 at T = 233.15 K. This follows the relationship between T and ice nuclei abundance presented by Fletcher [1962]. Supercooled water clouds can exist anywhere where T > 233.15 K, depending on the number of ice nuclei present [Rogers and Yau, 1989]. Therefore, water clouds are assigned a weight of one minus the Temperature-dependent ice cloud weighting where 273.15 K > T > 233.15 K. These weightings are summarized in Figure 5.


Probabilistic detection of volcanic ash using a Bayesian approach.

Mackie S, Watson M - J Geophys Res Atmos (2014)

Restrictions and weights imposed on the representation of clouds within the PDF: (a, b) Temperature-dependent weighting applied to the representation of water clouds relative to ice clouds (the axes are scaled differently in Figures 5a and 5b to show the weighting functions applied in different temperature regimes), (c) the maximum cloud water path represented in the PDF as a function of cloud altitude for water clouds, (d) same as Figure 5c but for ice clouds, and (e) the ratio of cloud-filled to cloud-edge pixels in the ECMWF data set, used to weight the representation of clouds with 100% pixel coverage more heavily than those with fractional coverage.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4379904&req=5

fig05: Restrictions and weights imposed on the representation of clouds within the PDF: (a, b) Temperature-dependent weighting applied to the representation of water clouds relative to ice clouds (the axes are scaled differently in Figures 5a and 5b to show the weighting functions applied in different temperature regimes), (c) the maximum cloud water path represented in the PDF as a function of cloud altitude for water clouds, (d) same as Figure 5c but for ice clouds, and (e) the ratio of cloud-filled to cloud-edge pixels in the ECMWF data set, used to weight the representation of clouds with 100% pixel coverage more heavily than those with fractional coverage.
Mentions: Observations corresponding to ice phase clouds were removed from the distribution where the ambient temperature, T, exceeds 273.15 K and water phase clouds are removed where T is less than 233.15 K (at which temperature homogeneous freezing of water drops occurs [Rogers and Yau, 1989]). The likelihood of ice clouds forming where T > 233.15 K depends on the abundance of freezing nuclei, which are generally active when T < ~ 264.15 K, and deposition nuclei, which are generally become active around T ~ 253.15 K [Rogers and Yau, 1989; Moran and Morgan, 1997]. Ice clouds may therefore be present around T ~ 264 K but become more likely at T ~ 253 K, when both freezing and deposition nuclei are available. A relationship between the likely presence of ice nuclei and T is used to weight the representation of ice clouds, relative to that of water clouds, at each cloud altitude (i.e., for each value of T). A linear weighting was assigned to ice clouds where 273.15 K > T > 253.15 K and an exponential weighting where T < 253.15 K, reaching a value of 1 at T = 233.15 K. This follows the relationship between T and ice nuclei abundance presented by Fletcher [1962]. Supercooled water clouds can exist anywhere where T > 233.15 K, depending on the number of ice nuclei present [Rogers and Yau, 1989]. Therefore, water clouds are assigned a weight of one minus the Temperature-dependent ice cloud weighting where 273.15 K > T > 233.15 K. These weightings are summarized in Figure 5.

Bottom Line: Such techniques are generally specific to data from particular sensors, and most approaches result in a binary classification of pixels into "ash" and "ash free" classes with no indication of the classification certainty for individual pixels.The technique has already been successfully applied to other detection problems in remote sensing, and this work shows that it will be a useful and effective tool for ash detection.Presentation of a probabilistic volcanic ash detection schemeMethod for calculation of probability density function for ash observationsDemonstration of a remote sensing technique for monitoring volcanic ash hazards.

View Article: PubMed Central - PubMed

Affiliation: School of Earth Sciences, University of Bristol Bristol, UK.

ABSTRACT

: Airborne volcanic ash can pose a hazard to aviation, agriculture, and both human and animal health. It is therefore important that ash clouds are monitored both day and night, even when they travel far from their source. Infrared satellite data provide perhaps the only means of doing this, and since the hugely expensive ash crisis that followed the 2010 Eyjafjalljökull eruption, much research has been carried out into techniques for discriminating ash in such data and for deriving key properties. Such techniques are generally specific to data from particular sensors, and most approaches result in a binary classification of pixels into "ash" and "ash free" classes with no indication of the classification certainty for individual pixels. Furthermore, almost all operational methods rely on expert-set thresholds to determine what constitutes "ash" and can therefore be criticized for being subjective and dependent on expertise that may not remain with an institution. Very few existing methods exploit available contemporaneous atmospheric data to inform the detection, despite the sensitivity of most techniques to atmospheric parameters. The Bayesian method proposed here does exploit such data and gives a probabilistic, physically based classification. We provide an example of the method's implementation for a scene containing both land and sea observations, and a large area of desert dust (often misidentified as ash by other methods). The technique has already been successfully applied to other detection problems in remote sensing, and this work shows that it will be a useful and effective tool for ash detection.

Key points: Presentation of a probabilistic volcanic ash detection schemeMethod for calculation of probability density function for ash observationsDemonstration of a remote sensing technique for monitoring volcanic ash hazards.

No MeSH data available.


Related in: MedlinePlus