Uncertainty Quantification of Long-Range Atmospheric ... · Copyright © 2014 SCK•CEN Method for...

Copyright © 2014 SCK•CEN

Uncertainty Quantification of Long-Range Atmospheric Transport Models: Case Study

P. De Meutter1,2,3, J. Camps1, A. Delcloo2, B. Deconninck4, P.

Termonia2,3

1SCK•CEN, 2RMIB, 3UGent, 4IRE

[email protected]

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Overview

l Quantifying uncertainty by ensembles

l Results for radionuclide station RN33 l Summary and perspectives

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Overview

l Quantifying uncertainty by ensembles

l Results for radionuclide station RN33 l Summary and perspectives

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Introduction

Uncertainty quantification of atmospheric transport modelling is desired for decision support in the context of CTBT and emergency response in case of a nuclear accident. We aim to quantify dispersion forecast uncertainty arising from meteorological uncertainty by using ensembles

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(ECMWF)


Introduction

Uncertainty quantification of atmospheric transport modelling is desired for decision support in the context of CTBT and emergency response in case of a nuclear accident. We aim to quantify dispersion forecast uncertainty arising from meteorological uncertainty by using ensembles The main purpose of ensemble forecasts (in meteorology) are to: l  improve the forecasts l  provide an indication of reliability l  provide a basis for probabilistic forecasting l  forecast rare and extreme events

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Method for creating spread

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Meteorological data

Parameters

Dispersion model

Dispersion forecast


Method for creating spread

In EPS, perturbations are generated using: l Ensemble Data Assimilation (EDA) l Initial-time Singular Vectors l Stochastically Perturbed Parameterization

Tendency scheme (SPPT)

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Meteorological data

Parameters

Dispersion model

Dispersion forecast

10+1 members of the Ensemble Prediction System (EPS) of the European Centre for Medium-Range Weather Forecasts (ECMWF)


EPS: Ensemble data assimilation

3D Variational analysis: 4D Variational analysis: Unperturbed meteorological observations: control forecast (CF) Perturbed meteorological observations: members of the ensemble Perturbations are randomly drawn from a Gaussian distribution with zero mean and standard deviation equal to the observation error estimate used in 4D-Var The perturbations are symmetric

Buizza et al, 2008

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EPS: Initial-time singular vectors

To take into account weather forecast uncertainty, EPS uses initial-time singular vector perturbations Given the large computational cost of a weather forecast, how to select perturbations that grow?

Leutbecher and Palmer, 2008

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t0 tn


EPS: Initial-time singular vectors

To take into account weather forecast uncertainty, EPS uses initial-time singular vector perturbations Consider the following set of equations: We linearize these around a reference state: We define a solution operator M (the propagator): The right-handed singular vectors of M associated with the largest singular values span a subspace of fastest growing perturbations à draw perturbations from that subspace

Leutbecher and Palmer, 2008

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Overview

l Quantifying uncertainty by ensembles l Results for radionuclide station RN33 l Summary and perspectives

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Setup (1/2)

disperion model Flexpart radionuclide 133Xe receptor RN33 (Schauinsland) sources IRE 15’ emission data + constant emissions

from NPP in GE, FR, SW, BE, NL period Jan, Feb, Mar 2014 weather data ECMWF’s EPS: 10 members + CF

(data every 3 hours; 0 to 21h forecasts) horizontal grid spacings 0.5° domain roughly Europe

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Setup (2/2)

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RN33

IRE

RN33

IRE


Results: activity concentrations for RN33

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Results: activity concentrations for RN33 (IRE+NPPs)

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Results: statistical scores for RN33

CF Ensemble mean

CF (obs>1 mBq/m3)

Ensemble mean (obs > 1 mBq/m3)

ρ 0.66 0.63 0.54 0.50 FB 0.24 0.24 0.25 0.25 NMSE 4.37 4.48 1.94 1.99 WNNR1 2.39 2.57 1.08 1.17 NNR1 0.78 0.80 0.68 0.72 FMS2 0.47 0.50 0.5 0.54 GSS2 0.41 0.45 0.21 0.29

1Poli and Cirillo, 1993 2Threshold 2 mBq/m3

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Overview

l Quantifying uncertainty by ensembles l Results for radionuclide station RN33 l Summary and perspectives

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Summary

l Ensembles allow to quantify dispersion forecast uncertainty l The dispersion forecast spread is flow-dependent l As previous studies found, NPPs contribute mostly to the

measured activity at RN33 l However, the highest peaks at RN33 can be attributed to

IRE l Further work is necessary...

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Perspectives

l Should we include the 1200 UTC EPS run? l Can we use EDA directly instead of EPS? l Compare spread from EPS with spread using different

weather models (NCEP, GLAMEPS) l  Introduce dispersion model uncertainty l Systematic study of the role of NPPs on IMS stations l Assess activity concentrations around Australia (with

ANSTO data)

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Thank you for your attention

References Buizza, R., Leutbecher, M., & Isaksen, L. (2008). Potential use of an ensemble of analyses in the ECMWF Ensemble Prediction System. Quarterly Journal of the Royal Meteorological Society, 134(637), 2051-2066. Leutbecher, M., & Palmer, T. N. (2008). Ensemble forecasting. Journal of Computational Physics, 227(7), 3515-3539. Poli, A. A., & Cirillo, M. C. (1993). On the use of the normalized mean square error in evaluating dispersion model performance. Atmospheric Environment. Part A. General Topics, 27(15), 2427-2434.

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Copyright © 2014 - SCK�CEN

PLEASE NOTE! This presentation contains data, information and formats for dedicated use ONLY and may not be copied,

distributed or cited without the explicit permission of the SCK•CEN. If this has been obtained, please reference it as a “personal communication. By courtesy of SCK•CEN”.

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