modelling the local dispersion and deposition of...

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Modelling the Local Dispersion and Deposition of Radionuclides from FukushimaSusan Leadbetter, Andrew Jones, Matthew Hort

Acknowledgments: Helen Webster, Met Office, Roland Draxler, NOAA, WMO Task Team

Talk Outline

• Met Office radiological responsibilities

• Fukushima Dai-ichi incident

• UNSCEAR - WMO Task Team

• NAME modelling

• Wet deposition

• Comparison to observation


Radiological: Responsibilities

• PACRAM• UK, short range (~1-10km) impact

• DECC-MOD Accidents• Impact on UK interests anywhere in world• 5 day forecast within 1 hour• Source identification

• RSMC – IAEA+WMO• Joint (UK + France) lead for Europe

& Africa• Support for entire world

• CTBTO• Global source identification



Fukushima Dai-ichi


Met Office Response to Fukushima

• IAEA

• CTBTO

• Daily NAME model runs for releases of 5 radionuclides with colleagues from

• Health Protection Agency (HPA)

• Food Standards Agency (FSA)

• Office for Nuclear Regulation (ONR)

• Long range forecasts

NAME simulation: Integrated air concentration up to 1200 UTC 21/03/11


UNSCEAR

• Report on Fukushima for 60th session of committee in 2013

• Assessment of radiation risks of Fukushima Dai-ichinuclear power plant accident

• Determine the source

• Look at the atmospheric and oceanic dispersion

• Look at public exposure

• World Meteorological Office task team to look at impacts of meteorology on dispersion of radionuclides


WMO Task Team

• USA, UK, Canada, Japan, Austria

• Ensemble to study impacts of meteorology on dispersion:

• 5 dispersion models

• 5 meteorological models

• Radar rainfall data

• Report available on WMO website: • http://www.wmo.int/pages/prog/www/CBS-

Reports/documents/WMO_fnpp_final_AnnexIII_24Jan.pdf

ATDM-Meteorology

CMC

NOAA

ECMWF

UKMET

JMA

CMC-MLDP0 C C

JMA-RATM C,R

NOAA-HYSPLIT C,R C,R C,R

UKMET-NAME C C C,R

ZAMG-FLEXPART C,R C,R


Dispersion Modelling


Region of Study


Dispersion Modelling I

• Modelled as series of independent 3-hour sources each followed for 72 hours

• Each source has 1Bq/hr release rate

Draxler et al. (2011)

+ + + …


Dispersion Modelling I

• Modelled as series of independent 3-hour sources each followed for 72 hours

• Transfer coefficient matrix. Number of releases (i), number of sampling periods (j), number of grid points (k)

• Source term applied offline

ijkTCM

ijki TCMDQCijk

Release rate

Decay rate

Draxler et al. (2011)


Dispersion Modelling II

• Caesium-137

• Non-sedimenting

• Wet and dry depositing

• Released at Fukushima Dai-ichi at 100m

• Meteorology from Global UM, ECMWF, JMA and radar rainfall

• Output 0-100m air concentration and total deposition at a resolution of 0.05° by 0.05°


Caesium-137 Source Term


Caesium-137 - Modelled

• Deposition high on 15 March

• Combination of wind direction and precipitation

Air Concentration (Bq/m3) Total Deposition (Bq/m2)


Caesium-137 Modelled Deposits: 11-31 March


Caesium-137 Observed Deposits

• MEXT soil sampling (MEXT, 2011)

• US DoE aerial measurements (DoE, 2011)

Cs137 Deposits - Modelled

k Bq/m2


Deposits: Model – Obs.

k Bq/m2


Deposition


Wet Deposition

• Removal of material based on depletion equation:

• C is concentration, Λ is the scavenging coefficient, given by:

• r is rainfall rate in mm/h and A and B are coefficients

CdtdC

BAr


Types of Wet Deposition in NAME

In-cloud(Rainout)

Below-cloud(Washout)

In-cloud(Rainout)

Below-cloud(Washout)

Rain

Below-cloud

(washout)

A = 8.4 x 10-5

B = 0.79

In-cloud (rainout)

A = 3.36 x 10-4

B = 0.79

BAr


Impact of Varying Scavenging Coefficients


Deposits: Model - Obs.

k Bq/m2


Deposits: Model – Obs.(Reference model)

k Bq/m2


Statistical Comparison

Correlation

00.10.20.30.40.50.60.70.80.9

1

V5a V5b V5c V5d V6a V6b V6c V6d

Fractional Bias

-0.4-0.3-0.2-0.1

00.10.20.30.40.5


Percent Within a Factor of 2

0

10

20

30

40

50

60


Kolmogorv-Smirnov Parameter

0

5

10

15

20

25

30

35


No single “best model”


Air Concentration – Tokai

• Peaks well captured by all models

• Measured air concentration remains high between peaks


Air Concentration - Model versus Observations

• Models show a negative bias


Statistical ComparisonFractional Bias

-1.6-1.4-1.2

-1-0.8-0.6-0.4-0.2

0V5a V5b V5c V5d V6a V6b V6c V6d

Percentage Within factor 2

0

5

10

15

20

25


• Statistics less robust due to only 41 measurements

• All models have negative bias

• V5a – decreased in-cloud scavenging - performs best in contrast to deposition results


Summary

• Modelled region of deposits compares well to observed deposits

• Predictions very sensitive to specific events/periods

• Current (standard) scavenging coefficients result in positive bias in deposits

• No single “best set” of scavenging coefficients

• Deposition/scavenging data very scarce


Any Questions?

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