overview of volcanic ash remote sensing at the...
TRANSCRIPT
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August 6, 2015
Overview of volcanic ash remote sensing at the University of Bristol
Ma# Watson, Shona Mackie, Luke Western, Kate Wilkins
[email protected] School of Earth Sciences, University of Bristol, UK
Research at University of Bristol:
• Volcanology and hazards
• Probabilis=c risk assessment
• Integra=on of remote sensing
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Outline:
• Research at Bristol • Probabilis=c detec=on of ash • IASI retrieval • Comparison
WMO SCOPE-Nowcasting activity 2015 3
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Volcanic ash research: • Ash detec=on and retrieval algorithms
• Understanding sensi=vity and uncertainty
• Data inser=on • Probabilis=c ash forecas=ng
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• Ash detec=on: • Probabilis=c detec=on (Mackie & Watson 2014) • Adapted SDI (Taylor et al., in prep)
• Ash retrieval: • Op=mal es=ma=on using IASI (Western et al., in prep)
• ‘1D-‐Var’ retrieval (Met Office, Francis et al 2012) • Very fast look-‐up table retrievals
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• Ash detec=on: • Probabilis=c detec=on (Mackie & Watson 2014) • Adapted SDI (Taylor et al., in prep)
• Ash retrieval: • Op=mal es=ma=on using IASI (Western et al., in prep)
• ‘1D-‐Var’ retrieval • Very fast look-‐up table retrievals
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Probabilistic ash detection: NWP-Dependent Approach
P ci y, x( ) =P ci( )P y x,ci( )P cj( )P y x,cj( )
j∑
ci,j clear/cloud/ash y observation x prior information
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How it actually works
P yspec x,ci( ) =exp −1
2yobs − ymod( )T HTBH + R( )
−1yobs − ymod( )
"
#$%
&'
2π( )n2 HTBH + R
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H - Forward Modeling sensitivity to NWP fields B - Error Covariance Matrix for NWP fields R - Sensor Noise, and Forward Modeling Error yobs - Observed BTs ymod - Forward-Modeled BTs (from NWP) n - No. channels used
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PDFs
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Classification
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Advantages and disadvantages
ü Probabilis=c detec=on
ü Removes user thresholds
ü Accounts for atmospheric variability
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✗ Classified states are mutually exclusive
✗ Some false posi=ves from desert dust
✗ PDFs currently not fully representa=ve
Optimal estimation using IASI
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−2 lnP(x | y) = y−F(x)( )T Sε-1 y−F(x)( )+ x− xa( )T Sa-1 x− xa( )
xa =
500 hPa5gm-2
3µm
2.0, 0.15
!
"
######
$
%
&&&&&&
x =
pLre
σ g, b
!
"
#####
$
%
&&&&&
Assumptions made: • Refractive index (Eyjafjallajökull – Dan Peters) • Density (2738 kg m-3, Bonadonna et al 2011) • ECMWF ERA-Interim NWP profiles
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Clerbaux et al 2009, Atmos. Chem. Phys.
re= 3µm σg = 2.0 b = 0.15
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Log-‐normal:
Gamma:
Bayesian detection scheme (Mackie & Watson 2014)
Eyjafjallajökull eruption, 6th May 2010 20:15 UTC
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Coincident airborne lidar from FAAM Bae-146 and IASI overpass on 17th May 2010
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Advantages and disadvantages
ü Considers spread as state vector
ü Returns uncertain=es
ü Reliable in many volcanic scenarios
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✗ Covariance matrix from ash-‐free scenes
✗ Does not allow mixed species (SO2/water)
✗ Currently only for IASI
WMO SCOPE-Nowcasting activity 2015 24
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WMO SCOPE-Nowcasting activity 2015 25
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Summary
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• Movement towards remote sensing as a part of an integrated approach to risk assessment
• Necessitates (if we are thinking about risk) a move from determinis=c to probabilis=c frameworks
• Be#er integra=on of modelling and satellite observa=ons key to further advances
