Copyright University of Reading

QUANTIFYING THE UNCERTAINTY IN VOLCANIC ASH FORECASTS

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Department of Meteorology

Helen Dacre1, Natalie Harvey1, Kelsey Mulder1, Nathan Huntley2,

David Thomson3, Helen Webster3, Peter Webley4, Don Morton4

1. University of Reading, 2. University of Durham, 3. Met Office, 4. University

of Alaska, Fairbanks

USERS INFER UNCERTAINTY IN

VOLCANIC ASH FORECASTS

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Area of no-fly zone

Heat map showing

no-fly zones

Mulder et al. 2017 (WCAS)

Q. Given the forecast below, draw a no-fly zone

VOLCANIC ASH PREDICTION • Real time forecasts and future risk to infrastructure

• Airborne and surface concentrations

3 Meteorology Volcano characteristics Atmospheric physics

Dacre et al. 2013 (ACP)

QUANTIFYING UNCERTAINTY

Ensemble meteorology

Ensemble model

parameters

Ensemble dispersion

models

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1. ENSEMBLE METEOROLOGY

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METEOROLOGICAL UNCERTAINTY

6 Ash column load forecasts for two forecasts (top)

12 UTC on 7 May, (bottom) 00UTC on 8 May 2010

Ensemble member # 1 Ensemble member # 2

bifurcation

Satellite ash cloud

Missing

ash

TRAJECTORY SPREAD

• Particle trajectories rapidly diverge after the trajectories

encounter a bifurcation point 7

Flow separation along 20 particle trajectories released at 06UTC on 6 May

2010 transported using different 72 hour ensemble forecasts

FLOW SEPARATION

• In some meteorological situations the trajectories diverge

while in other situations they remain close together 8

Low wind-speed at

source

Evolution of ensemble spread for 18 simulations initialised between 15 April

and 7 May. Colours show along-trajectory accumulated flow separation

2. ENSEMBLE MODEL PARAMETERS

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EXPERT ELICITATION

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18 parameters

identified, 6

ESP’s & 12

internal

parameters

15 parameters

varied

independently

1700 parameter

sets created

Harvey et al. 2017 (NHESS)

EMULATION

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• An emulator is a simple approximation of the complicated

model that can be evaluated almost instantly

• Rather than approximating the entire output, concentrate on

average column integrated mass in given regions

• 3-4 regions / hour results in 75 emulators

Region 1

Region 2

Region 3

SEVIRI satellite retrieved ash column

loading 0 UTC 14 May 2010

EMULATION

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• Emulator estimates the expected value and the variance

for the summary f(x) should we run NAME at parameter

choice x

One dimensional example of an emulator. The points represent 6 NAME

simulations of ash column loading at parameter choices x. Emulator

prediction (black) ±3 standard deviations (red)

Average column

integrated ash

loading in region, f(x)

Parameter choice, x

IDENTIFY ACTIVE PARAMETERS

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• Emulators improved by focussing on important parameters

• Parameters removed in turn and R2 calculated, parameters

whose removal cause the smallest change were eliminated

• Only a small number of parameters contribute to the

uncertainty in each region

Parameter

Number of regions

where parameter is

active

Plume height 75

Mass eruption rate 75

Standard deviation of velocity for free tropospheric turbulence 61

Precipitation rate required for wet deposition 58

Particle size distribution scale parameter 18

Lagrangian timescale for free tropospheric turbulence 15

All other parameters 0-4

3. ENSEMBLE DISPERSION MODELS

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ENSEMBLE DISPERSION MODELS

15 Animation of volcanic ash simulations using 3 dispersion models. No ash in

any simulation (dots), ash in all simulations (hatching)

SUMMARY METEOROLOGICAL UNCERTAINTY

• Nearby ash particle trajectories can rapidly diverge leading poor

forecast accuracy for deterministic forecasts which do not represent

variability in wind fields at the synoptic-scale

INPUT AND INTERNAL PARAMETER UNCERTAINTY

• Statistical approximation (emulator) of NAME used to identified which

parameters contribute the most to prediction uncertainty allowing us to

focus areas for improved observations or model development

MULTI-MODEL UNCERTAINTY

• Using consistent meteorology and ESPs allows us to visualise multi-

model uncertainty and communicate forecast confidence where models

agree

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EXTRA SLIDES

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VOLCANIC ASH HAZARD • More than 80 volcanoes in Europe with over 1200 recorded eruptions

• Relatively small eruptions can cause major disruption

• Icelandic volcanoes erupt ~ every 5 years

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RISK APPETITE

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INPUT AND INTERNAL

PARAMETER UNCERTAINTY

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• Multi-parameter sensitivity analysis has 2 main advantages

• Amount of parameter space sampled is larger

• Includes interactions between parameters

• Disadvantage is that dispersion models run too slowly to

evaluate many (>3) parameter choices at the same time

2 parameters

= 102

3 parameters

= 103

1 parameter

= 101

4 parameters

= 104

VALIDATE EMULATORS

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• Able to perform an additional 1500 Fast NAME simulations

• Over the 75 regions, the proportion of successful

predictions from the validation ranged from 94.5% to 99%

Leave-one-out validation plot of emulator for 1st output (region 1).

Emulator expected value for parameter sets x_i (black) ± 3 standard

deviations (blue), NAME output at each parameter set (red).

Region 1