Modeling Volcanic Ash Transport and Dispersion: Expectations and Reality

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Modeling Volcanic Ash Transport and
DispersionExpectations and Reality
René Servranckx Peter Chen Montréal Volcanic
Ash Centre Canadian Meteorological Centre
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Presentation Topics Ash Transport Models

• Reality 20 years from now
• Expectations for Ash Transport Models (TM)
• Reality today / Limiting factors
• Areas for improvement

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June 22, 2024

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June 22, 2024
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June 22, 2004!

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• ACCURATE guidance on SPACE / TIME LOCATION / 3D
  STRUCTURE of airborne ash
• LITTLE (or no) UNCERTAINTY (ash / no ash)
• TIMELY delivery
• Implications for TM?

What area aviation users TM expectations?

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Components Ash Modeling Problem

• Accuracy and timeliness of TM guidance depends
  on
• Volcanic Ash Source (Source Term / eruption
  parameters)
• Meteorology
• Transport and Dispersion (TM)

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Despite uncertainties, TM

• Are of great value!
• Especially important for REAL-TIME, operational
  response
• Sometimes, the ONLY guidance available
• Must be used in conjunction with other tools
  (remote sensing, etc.)
• Can not be used blindly!

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Limiting Factors VOLCANIC ASH SOURCE

• Eruption parameters largely unknown / poorly
  quantified
• Detection of eruptions / airborne ash is
  problematic
• Poor quantitative estimates of atmospheric ash
  loading / only 2D
  ? 3D is needed for TM
• Threshold ash concentrations that pose threat to
  aviation (?) ? May be
  very small (NASA DC-8 Hekla incident)
• Visual Ash Cloud criterion on TM guidance
  is subjective
• DEFAULT SCENARIOS and LOW THRESHOLD values in TM
  guidance

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Limiting Factors METEOROLOGY

• HORIZONTAL and VERTICAL resolution of Numerical
  Weather Prediction (NWP) Models
• Vertical coordinates are not Flight Levels
  standard atmosphere
• Representation of earths surface (topography)
  in NWP models
• Mt Mckinley, AK 6194 m
  2640 m
• Incomplete knowledge of initial conditions of
  the atmosphere
• Predictability of atmosphere / Accuracy of NWP
  vary with flows / patterns

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Limiting Factors TRANSPORT / DISPERSION

• VOLCANIC ASH SOURCE component
• METEOROLOGY component
• Parameterization of dispersal, removal and
  deposition of ash
• Real time assimilation of airborne ash is not
  done
• Predictive ability varies with atmospheric
  conditions

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Areas for improvement
VOLCANIC ASH SOURCE COMPONENT

• 1998 and 2003 WMO / ICAO volcanic ash meetings
  Substantial improvements could be
  made in TM guidance if source term estimates were
  improved
• ICAO (IAVW Ops Group) to IAVCEI
  
  QUANTITATIVE estimates of eruption parameters for
  TM?
• NASA DC-8 encounter with Hekla diffuse plume
  damage from very small ash concentrations (?)

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Areas for improvement
VOLCANIC ASH SOURCE COMPONENT

• If unconditional ash-avoidance is the rule,
  small concentrations must be accurately predicted
  ? Good estimate of Source term is important!
• Remote sensing Any technological advancement
  that might improve quantitative estimates of the
  3D distribution of airborne ash
• Assimilation of volcanic ash data in TM
  Exploratory work has been done (Siebert et al.
  2002 NOAA Air Resources Laboratory)
• How much can we achieve? ? Highly dependent on
  remote sensing improvements (quantitative 3D
  distribution)

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Areas for improvement METEOROLOGY and DISPERSION
/ TRANSPORT Components

• Improvements to NWP Models are ongoing
• Improvements to TM also ongoing
• ENSEMBLE FORECASTING Already done for NWP
  Models applicable to TM
• Many runs (single or multiple models) using
  slightly different initial conditions
• BASIC IDEA AVERAGE of many runs BETTER than
  single run
• Spread among runs is gives an estimate of
  uncertainty

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Example Visual ash clouds from 4 TM runs
valid at same time

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Finagles Laws of Information

• The information you have is not what you want
• The information you want is not what you need
• The information you need is not what you can
  obtain
• The information you can obtain costs more that
  you want to pay

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Corollaries

• What you see / interpretation depend on
• Tools / Technology
• How information is presented
• How one looks at information
• What you see may not be what you get !

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Impact of changing visual ash cloud value

• ALL IMAGES TO FOLLOW ARE FROM SAME TRANSPORT
  MODEL RUN WITH SAME SOURCE TERM CONDITIONS
• 1 hour eruption of Cleveland starting 15 UTC 19
  Feb 2001
• Images valid 45 hours after start of eruption
• CANERM (TM) diagnostic average ash concentration
  in FL200 - FL350 (micrograms per cubic meter)
• ? perception of where ash is or is not present!

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100
80
200
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1
50
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Summary Transport Models

• Expectations are high
• Despite uncertainties, VALUABLE!
• Must be used with other sources of information
• Can not be used blindly /require careful
  interpretation / knowledge of uncertainties
• New ways of looking at information and estimating
  uncertainties (Ensemble forecasts)
• Accuracy can be increased by reducing
  uncertainties What can we do to bridge
  the gaps?