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Modeling Volcanic Ash Transport and Dispersion: Expectations and Reality

Modeling Volcanic Ash Transport and Dispersion: Expectations and Reality. René Servranckx & Peter Chen Montréal Volcanic Ash Centre Canadian Meteorological Centre. Presentation Topics: Ash Transport Models. Reality 20 years from now Expectations for Ash Transport Models (TM)

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Modeling Volcanic Ash Transport and Dispersion: Expectations and Reality

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  1. Modeling Volcanic Ash Transport and Dispersion:Expectations and Reality René Servranckx & Peter Chen Montréal Volcanic Ash Centre Canadian Meteorological Centre

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

  3. June 22, 2024

  4. June 22, 2024

  5. June 22, 2004!

  6. 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?

  7. Components: Ash Modeling Problem • Accuracy and timeliness of TM guidance depends on: • Volcanic Ash Source (‘’Source Term’’ / eruption parameters) • Meteorology • Transport and Dispersion (TM)

  8. 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!

  9. 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

  10. Limiting Factors : METEOROLOGY • HORIZONTAL and VERTICAL resolution of Numerical Weather Prediction (NWP) Models • Vertical coordinates are not Flight Levels ‘’standard atmosphere’’ • Representation of earth’s 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

  11. 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

  12. 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 (?)

  13. 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)

  14. 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

  15. Example: ‘’Visual ash clouds’’ from 4 TM runs valid at same time

  16. Finagle’s 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

  17. 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 !

  18. 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!

  19. 100 80 200 10 1 50

  20. 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?

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