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ARTICLE IN PRESS
Atmospheric Environment 38 (2004) 4607–4617
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doi:10.1016/j.at
Ensemble dispersion forecasting—Part I: concept,approach and indicators
S. Galmarinia,*, R. Bianconib, W. Klugc, T. Mikkelsend, R. Addise,S. Andronopoulosf, P. Astrupd, A. Baklanovg, J. Bartnikif, J.C. Bartzish,R. Bellasiob, F. Bompayi, R. Buckleye, M. Bouzomi, H. Championm,
R. D’Amoursn, E. Davakisf, H. Eleveldj, G.T. Geertsemak, H. Glaabl, M. Kollaxo,M. Ilvonenu, A. Manningm, U. Pechingerp, C. Perssono, E. Polreichp, S. Potemskiq,
M. Prodanovar, J. Saltbonesh, H. Slaperj, M.A. Sofievt, D. Syrakovr,J.H. Sørenseng, L. Van der Auweras, I. Valkamat, R. Zelaznyq
a IES/REM, European Commission, Joint Research Center, TP 441, 21020 Ispra, ItalybEnviroware srl, C.Dir. Colleoni, Pzo Andromeda 1 20041 Agrate Brianza, Italy
cMittermayer weg 49, Darmstadt, GermanydRISØ National Laboratory, Wind Energy Department, P.O.Box 49, DK-4000, Roskilde, Denmark
eSavannah River Technology Center, Savannah River Site, Aiken, SC 29808, USAfEnvironmental Research Laboratory, NCSR Demokritos, 15310 Aghia Paraskevi Attikis, Greece
gDanish Meteorological Institute, Lyngbyvej 100, DK-2100, Copenhagen, DenmarkhNorwegian Meteorological Institute, P.O. Box 43, Blindern, N-0313 Oslo, NorwayiMETEOFRANCE, Dir.Prod., Serv./Environ., 42 av. Coriolis, 31057 Toulouse, France
jRIVM, Laboratory of Radiation Research, P.O. Box 1, Bilthoven, NetherlandskKNMI, P.O. Box 201, 3730 AE De Bilt, Netherlands
lGerman Weather Service (DWD), P.O. Box 10 04 65, 63004 Offenbach a.M., GermanymMeteorological Office, FitzRoy Road, Exeter EX1 3PB, UK
nCanadian Meteorological Centre, 2121 Voie de Service Nord, Rte Transcan., Dorval, QC, Canada H9P 1J3oSMHI, SE-601 76 NORRKOPING, Sweden
pCentral Institute for Meteorology and Geodynamics, Hohe Warte 38, A-1191 Vienna, Austriaq Institute of Atomic Energy, Research Group B1P, 05-400 Otwock-Swierk, Poland
rNMHI, 66 Tzarigradsko shaussee, Sofia 1784, BulgariasKMI, Ringlaan 3, 1180 Brussels, Belgium
tFMI, Air Quality Reas., Sahaajankatu 20 E, FIN-00810 Helsinki, FinlanduVTT-Processes, P.O. Box 1604, FIN-02044 VTT, Espoo, Finland
Received 16 January 2004; received in revised form 16 May 2004; accepted 26 May 2004
Abstract
The paper presents an approach to the treatment and analysis of long-range transport and dispersion model
forecasts. Long-range is intended here as the space scale of the order of few thousands of kilometers known also as
continental scale. The method is called multi-model ensemble dispersion and is based on the simultaneous analysis of
several model simulations by means of ad-hoc statistical treatments and parameters. The models considered in this
study are operational long-range transport and dispersion models used to support decision making in various countries
ing author.
ess: stefano.galmarini@jrc.it (S. Galmarini)
e front matter r 2004 Elsevier Ltd. All rights reserved.
mosenv.2004.05.030
ARTICLE IN PRESSS. Galmarini et al. / Atmospheric Environment 38 (2004) 4607–46174608
in case of accidental releases of harmful volatile substances, in particular radionuclides to the atmosphere. The
ensemble dispersion approach and indicators provide a way to reduce several model results to few concise
representations that include an estimate of the models’ agreement in predicting a specific scenario. The parameters
proposed are particularly suited for long-range transport and dispersion models although they can also be applied to
short-range dispersion and weather fields.
r 2004 Elsevier Ltd. All rights reserved.
Keywords: Long-range transport and dispersion; Multi-model ensemble dispersion forecast and analysis
1. Introduction
Ensemble modeling, originally developed for weather
prediction, is lately being extended to atmospheric
dispersion applications (Krishnamurti et al., 1999;
Dabberdt and Miller, 2000; Straume, 2001; Galmarini
et al., 2001; Draxler, 2002; Warner, 2002; Delle
Monache and Stull, 2003). Ensemble Dispersion Model-
ing (EDM) can be defined as the statistical analysis and
treatment of several dispersion simulations of a common
case study that aims at estimating the dependency of the
dispersion patterns on:
* The atmospheric flow fields used to calculate the
dispersion;* The approach to dispersion modeling;1
* The source characteristics;* The level of agreement among different model
approaches (flow and dispersion).
The estimate of the dependency on these factors aims at
determining the most probable dispersion patterns and
at the reduction of model and/or case-study uncertainty.
In general, in EDM, the atmospheric dispersion
model simulations constituting the ensemble (ensemble
members) can be obtained by means of a variety of
techniques, (the approaches presented here are depicted
in the sketch of Fig. 1) namely:
1. Single circulation model and single dispersion model
with perturbation of initial conditions for circulation: For
a given case study, a number of atmospheric flow
simulations are performed by means of a single model
that uses different initial conditions. The flow fields can
in turn be the result of ensemble weather forecasting as
shown by Straume et al. (2001). The dispersion case is
simulated by means of a single dispersion model that
uses the various flow fields. As it was demonstrated, the
ensemble dispersion produced with this technique
accounts for the dependency of the case study on the
atmospheric circulation variability.
1 In this context dispersion modeling is intended as dispersion
from a single point-source. There is no specification on the scale
to which the circulation or the dispersion models relate. The
categorization presented applies to dispersion at any scale from
short range to global.
2. Single circulation model and single dispersion model
with ensemble perturbation of initial conditions for
dispersion: Given a single flow field obtained from a
circulation model, a single dispersion model is used to
simulate several dispersion scenarios. The latter are
obtained by varying the dispersion case configuration
(e.g. emission height, vertical distribution of mass
released). The dispersion cases can be obtained from
ad-hoc definitions or by means of Monte Carlo
techniques (e.g. Burmaster and Anderson, 2003), which
produce a number of cases randomly selected from a
predefined distribution of the dispersion initial para-
meters. The resulting ensemble takes into account the
dependency of the dispersion simulation on the disper-
sion initial conditions. Warner et al. (2002) presented
such an analysis for an application of dispersion models
at the mesoscale in the Arabic peninsula.
3. Multi circulation models with single dispersion
models: A suite of models generates circulation fields
of the same case study. The models can be different from
one another or be the same model used with different
parameterization schemes for physical or sub-grid
processes (Warner et al., 2002). By means of these fields,
a single dispersion model produces an ensemble of
dispersion fields. This approach is somewhat similar to
1, although the ensemble includes also the impact of
the circulation modeling approach on the dispersion
simulation.
4. Single circulation model and multi dispersion models:
A suite of dispersion models uses the results of a single
circulation model simulation. The ensemble obtained
accounts for the impact of dispersion modeling on the
case study. Examples of this type of application are
ATMES (Klug et al., 1992) and ATMES-II (Mosca
et al., 1998) in which however such an approach was
used for dispersion model validation studies rather than
for ensemble treatment.
5. Multi-circulation models and multi-dispersion mod-
els: Different circulation models calculate the circulation
fields. Dispersion models, which are conceptually
different from one another, use atmospheric circulation
fields from different models to produce dispersion
simulations on the same dispersion case study. The
dispersion fields resulting from each of the models
constitute the members of an ensemble. This is quite a
ARTICLE IN PRESS
Fig. 1. Schematic representation of 5 methods to obtain dispersion ensemble members.
S. Galmarini et al. / Atmospheric Environment 38 (2004) 4607–4617 4609
general case with respect to the others in the sense that
the variety of the cases produced is strictly dependent on
the approach to both atmospheric circulation and
dispersion modeling (Galmarini et al., 2001). The use
of dispersion fields produced by different models based
on different meteorological fields is particularly suitable
when the latter are forecasted fields for which no
measurements are available for model validation and
tuning. The multi-circulation, multi-dispersion model
ensemble allows the analysis of a wide spectrum of
scenarios resulting from different NWP simulations and
approaches to dispersion modeling. Examples on the use
of this approach applied to climate predictions and air-
quality analysis are given by Krishnamurti et al. (1999)
and Delle Monache and Stull (2003).
Once an ensemble of dispersion simulations is
obtained by means of one of the techniques listed
above, a series of methods can be applied for the
ensemble analysis. Ensemble analysis turns out to be an
efficient method to obtain probabilistic results, which
are useful for estimating the sensitivity of the simulation
to initial conditions, physics described by the models,
algorithm implementation, numerics, representation of
surface properties, boundary conditions, source term
description, source representation. In this respect, the
ensemble results contain more information than single-
model results because they are not the simple conse-
quence of a particular choice of model and case study
configuration. This aspect becomes more evident when
the dispersion simulations are forecasts for which no
monitoring data can be used for the evaluation and
whenever the model results are used for support to
decision making or regulatory purposes.
This paper presents the results of a study that aims at
identifying statistical treatments and indicators for
EDM analysis, focusing on approach 5. The models
used to produce the ensemble are operational numerical
weather prediction (NWPs) models, as far as the
atmospheric circulation is concerned, and operational
long-range transport and dispersion models used by
several national meteorological services and environ-
ment protection agencies to forecast trans-boundary
dispersion of harmful materials dispersed in the atmo-
sphere. The main difference among the model results
constituting the ensemble members resides in the
approach to long-range transport modeling, and in
the meteorological forecasts that are used to simulate
transport and dispersion. While this paper will focus on
the ensemble treatment and analysis of dispersion model
simulations and the presentation of indicators for the
ARTICLE IN PRESSS. Galmarini et al. / Atmospheric Environment 38 (2004) 4607–46174610
multi-model ensemble analysis, Part II will show a
specific application of the technique and a quantitative
evaluation of the ensemble approach.
2. Framework: the ENSEMBLE project
The ENSEMBLE project2 was set up with the intent
of developing a system for the rapid exchange of long-
range transport and dispersion forecasts produced by
meteorological offices and radiation-protection agencies
in Europe and around the world. The dispersion models
are used in case of accidental releases of radioactive
material in the atmosphere. ENSEMBLE not only is
intended as a platform where several forecasts are
exchanged, but also as a tool for the simultaneous
analysis of the results of several models and for the
ensemble analysis. Briefly, ENSEMBLE is an Internet-
based server-side system (http://ensemble.ei.jrc.it) that
collects in real-time the long-range dispersion forecast
produced by 15 European, one US, and one Canadian
institute (Table 1). The forecasts are produced by
operational long-range transport and dispersion models
based on different concepts that use meteorological
fields produced by different NWP models (namely
ECMWF, various versions of HIRLAM, ARPEGE,
ALADIN, RAMS, GME, UM and LM). Model results
are produced over a domain bounded at (15E, 60W) and
(30N, 75N) with 0.5� resolution N–S and E–W and at 3-
hourly intervals for 60 h in the future from the notified
release time. More details on the ENSEMBLE system
and its technical specifications are described elsewhere
(Bianconi et al., 2004) and go beyond the scope of this
paper.
The ENSEMBLE system has been used during
exercises consisting of the notification to all the
participating institutes on the occurrence of a fictitious
release with defined characteristics (location of the
source, radio-nuclide released, release time, release
duration, release rate, release height). Upon notification
each institute produces a forecast of: air concentration
of a radio nuclide at 0, 200, 500, 1300, 3000m a.g.l.; time
integrated concentration at 0m a.g.l.; cumulated dry
deposition; cumulated wet deposition; Precipitation as
obtained from NWP. In total, 11 exercises were
performed as from Table 2. For each exercise, each
institute is requested to provide several dispersion
forecasts depending on the meteorological forecast
available during the evolution of the exercise. Therefore,
all dispersion forecasts submitted are initially based on a
completely forecasted meteorology, then on partly
forecasted and partly analyzed meteorology and finally
on completely analyzed meteorology. We will refer to
2Sponsored by the European Union DG-Research Technical
Development.
the age of the meteorology to distinguish among the
various meteorological sets used in the course of an
exercise. The sketch of Fig. 2 exemplifies the case in
which updated meteorological forecasts are available
every 12 h and therefore after 60 h, 7 dispersion fields are
produced.
3. Indicators for multi-model ensemble-dispersion
forecast
Within concept of multi-model ensemble dispersion
forecast (Galmarini et al., 2001) and in order to perform
the ensemble dispersion analysis, a series of ad-hoc
indicators have been developed that aim at determining
the level of agreement between the model results and the
uncertainty associated to the dispersion forecast. The
indicators refer to three possible ways of analyzing the
results:
* Space: All model results for the variables requested
can be consulted with respect to space: i.e. over the
whole simulation domain at a fixed time. In the case
of air concentration for example, this can be
performed for any of the 5 altitudes at which the
results are produced over the whole horizontal
domain or as vertical profiles at a fixed location.* Time: All model results are analyzed at a single point
in space as a function of time.* Global: All model results are analyzed regardless of
their space and time location.
For the sake of synthesis, most of the examples provided
here will relate to the Space analysis but the concept and
indicators can be extended to the other two types.
3.1. Ensemble members
The first step in the ensemble treatment of the model
results is the selection of the members that will constitute
the ensemble. Model results can be grouped and
compared in terms of single model vs. group of models
or group vs. group. The criteria followed to create an
ensemble of model results can be:
* Dispersion model formulation and characteristics:
e.g. Eulerian models, Lagrangian models, models
sharing common parameterization of specific physi-
cal processes;* Atmospheric circulation fields used to calculate the
dispersion: e.g. all dispersion models using ECMWF
or HIRLAM meteorological input;* Age of the meteorological fields used to run the
dispersion: as mentioned earlier, the dispersion model
results are based on meteorological fields with
different ages and with a variable fraction of
meteorological analysis.
ARTIC
LEIN
PRES
S
Table 1
Institutes and models participating to ENSEMBLE
Institute Dispersion model (DM) DM type NWP References on DM models
Canadian Meteorological Centre CANERM E GEM Global D’Amours (1998)
Danish Meteorological Institute DERMA L-P DMI-HIRLAM-E Sørensen (1998), Baklanov and
Sørensen (2001), Sass et al. (2002)
DERMA L-P DMI-HIRLAM-G
DERMA L-P ECMWF
Deutscher Wetterdienst GME-LPDM L DWD-GME Glaab et al. (1998)
LM-LPDM L DWD-LM
Finnish Meteorological Institute SILAM L FMI-HIRLAM
Institut Royal Meteorologique de Belgique BPaM4D L ECMWF
KNMI (NL) NPK-PUFF L ECMWF and HIRLAM
Meteo-France MEDIA E ARPEGE Amodei et al. (2001); Michou et al.
(2001)
MEDIA-nested E ARPEGE-ALADIN
Met Office (UK) NAME L UM Malcolm et al. (1999), Ryall and
Maryon (1998)
National Institute of Public Health and
Environment (NL)
NPK-PUFF L HIRLAM and ECMWF
National Centre for Scientific Research DIPCOT L ECMWF
‘‘Demokritos’’ (G) DIPCOT P ECMWF
National Institute of Atomic Energy Agency
(PO)
RODOS-MATCH L-E DMI-HIRLAM
National Institute of Meteorology and
Hydrology (BG)
EMAP E DWD-GME Syrakov (1995), Syrakov and
Prodanova (1998)
Norwegian Meteorological Institute SNAP L HIRLAM Saltbones et al. (1995)
Risø National Laboratory (DK) RODOS-LSMC P DMI-HIRLAM Mikkelsen et al. (1997),
RODOS-MATCH L-E DMI-HIRLAM Astrup et al. (2001)
Savannah River Technology Center (US) LPDM L RAMS3a
Swedish Meteorological and Hydrological
Institute
MATCH L-E HIRLAM Robertson et. al. (1999)
Zentralanstalt fuer Meteorologie und TAMOS L ECMWF T319L50 Pechinger et al. (2001)
Geodynamik (A) TAMOS L ECMWF T319L50
L=Lagrangian, E=Eulerian, P=Puff.
S.
Ga
lma
rini
eta
l./
Atm
osp
heric
En
viron
men
t3
8(
20
04
)4
60
7–
46
17
4611
ARTICLE IN PRESSS. Galmarini et al. / Atmospheric Environment 38 (2004) 4607–46174612
As from Table 1, the variety of models available
allows the construction of ensembles according to the
above-mentioned criteria that can contain several
members. Within the ENSEMBLE system a remote
user can perform the model grouping and analysis
in real time and on-the-fly as described in Bianconi
et al. (2004).
Table 2
List of locations and release characteristics of the 11
ENSEMBLE exercises
Ex.no. Release location Nuclide Release height (m)
a.g.l., duration (h)
01, Lerwick (UK) 137Cs 0, 6
02, Carcassonne (F) 137Cs 300, 3
03, London (UK) 137Cs 0, 6
04, Nantes (F) 137Cs Through A.B.L., 6
05, Stockholm (S) 137Cs 2, 10
06, Dublin (IRL) 137Cs 0, 15
07, Glasgow 137Cs 0–500m, 4
08, Mochovce (Slovak
Rep.)
137Cs 25, 12
09, Bratislava (Slovak
Rep.)
137Cs 25, 24
10, London (UK) 137Cs 350, 0.25
11, London (UK) 241Pu 350, 0.25
F T0+72 h A
F T0+ 60 hA T1-12 h
F T0+48 hA T2-24 h
F T0A T
T0
F Tr+60h
1st Ensemble
DF Tr+60 h
2nd Ensemble
DF Tr+60 h
3rd Esemble
DF T
4th E
WeatherWeather
Dispersion
time
Only forecasted Partly forecasted
Release time Tr
T1 T2
Fig. 2. Time evolution of the dispersion forecast update as new weat
forecast and analysis, respectively.
3.2. Ensemble treatment and indicators
3.2.1. Ensemble treatments
Once the model grouping has been made two optional
treatments are available:
1. Comparison of model results within one ensemble.
2. Comparison of ensembles. In this case two treat-
ments are possible to compare the behavior of two
groups:
2.1. Median: The ‘‘median treatment’’ in space,
refers to the determination of the median of
the values produced by the groups of models at
every point in space for a fixed time interval.
The median was selected since the model results
distribution is unknown a-priori.
2.2. Maximum: The ‘‘maximum’’ treatment, for
example in space, refers to the calculation of
the maximum value of the variable produced by
the ensemble at every point of the domain for a
fixed time interval.
Although these treatments are very simple, they allow
condensing in a single representation the results of
several models and the results of the two groups can be
compared, to obtain a more conservative analysis of all
model simulations and to outline the worst scenario
predicted.
+36 h 3-36 h
F T0+24 hA T4-48 h
F T0+12 hA T5-60 h
r+60 h
nsemble
DF Tr+60 h
5th Ensemble
DF Tr+60 h
6th Ensemble
DF Time horizon
60 h
DF Tr+60 h
7th Ensemble
F A T6-72 h
partly analysed Only analysed
T3 T4 T5 T6
her forecasts is available. In the figure, ‘‘F’’ and ‘‘A’’ stand for
ARTICLE IN PRESS
Fig. 4. Space overlap. Application to exercise 03. Contour level
0.01Bqm�3 of 137Cs 24 h after release. (a) Six model results
obtained using HIRLAM weather forecasts; (b) Four model
results obtained using distinct weather forecasts; (c) Orange
surface: maximum concentration distribution produced by the
four models of figure (a), Blue surface: maximum concentration
distribution produced by the six models of figure (b), Red
surface: overlap of orange and blue area.
S. Galmarini et al. / Atmospheric Environment 38 (2004) 4607–4617 4613
3.2.2. Space overlap
An example of treatments 1 and 2 is presented in
Figs. 3 and 4 (a, b and c).
Fig. 3. shows an example of analysis as a function of
the ‘‘age of the meteorology’’. The 137Cs contours at
0.2 Bqm�3 produced by one dispersion model during
exercise 06 using meteorological fields of different age
are overlapped and compared. The four meteorological
fields relate to 12 h before and 12, 36 and 60 h after the
release date and time. The first dispersion results is
therefore based on totally forecasted meteorology, the
two middle ones are based on partly analyzed and partly
forecasted meteorological fields, whereas the last one is
obtained only with analyzed meteorology. This type of
analyses allows one to verify the impact of meteorolo-
gical forecast variability on the dispersion pattern.
Fig. 4a and b exemplifies the result of a model
grouping or comparison of ensembles. Both plots refer
to exercises 03, and they show the 24-h forecasts of the
0.01Bqm�3 contour of ground concentration of 137Cs
produced by 10 models. Fig. 4a shows the results of six
dispersion models that use the meteorology produced by
the HIRLAM NWP model versions whereas Fig. 4b
shows the results of four models that used circulation
fields from four distinct NWP models. In spite of the
differences in the characteristics of dispersion models
(see Table 1) the effect of the circulation fields on model
results can be seen clearly. In Fig. 4a the contour levels
show a blobby structure whereas in Fig. 4b the contour
lines are elongated in the W–E direction. By determining
the maximum concentration produced by the model
groups at all grid nodes, it is possible to compare the
relative behavior of the two groups as shown in Fig. 4c.
Fig. 3. Space overlap of the 0.2Bqm�3 contour of 137Cs
calculated by one model with meteorological fields with
different ages. 12 h before the release (blue), 12 h after the
release (orange), 36 h after the release (yellow), 60 h after the
release (red).
The 10 model results are presented in the figure as two
clouds (orange and blue), the red part shows the region
of overlap. This treatment summarizes the results of 10
models, which have been grouped according to a well-
defined criterion, and allows one to produce a quanti-
tative estimate of the prediction agreement as well as an
estimate of the impact of meteorological fields on the
various simulations. Being the dispersion fields fore-
casted, this type of treatment turns out to be quite
revealing since it allows a quantitative analysis of several
model results and at the same time an estimate of their
level of agreement.
3.2.3. Agreement in threshold level
The agreement in threshold level (ATL) is an
indicator that allows one to summarize in a single plot
the results of several models with an estimate of the
geographical distribution of the models’ agreement. The
ATL is defined as the surface given by the normalized
number of models (k=1,2,y,M) that, at a given time,
predict values above a given threshold (CT, for example
ARTICLE IN PRESS
Fig. 5. (a) Agreement in threshold level for exercise 03 and 14
dispersion models. Time-integrated surface concentration of137Cs, 57 h after the release. Threshold value: 5 Bqhm�3. (b)
Same as (a) with the overlap of a single model result (hatched
surface). (c) Same as (a) without the contribution of outlier.
S. Galmarini et al. / Atmospheric Environment 38 (2004) 4607–46174614
an intervention level) namely:
ATLðx; y; tÞ ¼100
M
XM
k¼1
dk;
wheredk ¼ 1 if ckðx; y; tÞXcT;
dk ¼ 0 otherwise:
�ð1Þ
The ATL is based on the assumption of equal
importance of predictions, but it could be weighted
and biased towards some specific model or using model-
performance criteria. Figs. 5a, b and c give an example
of application of ATL referring to the Carcassonne-
release (exercise 06). Given a threshold level of
5Bqhm�3 for time-integrated concentration of 137Cs
and 14 model results, the plot shows the spatial
distribution (57 h after the release) of the models’
agreement in predicting that the variable’s values will
be equal to or greater than the threshold. In the absence
of experimental evidence the ATL indicator gives a clear
estimate of the portion of the domain that is interested
by the dispersion process and of the number of models
that agree in predicting the plume passage in the region.
Although the agreement of several models in determin-
ing the area covered by the plume does guarantee on the
actual occurrence of the event, the fact that the models
are conceptually different from one another and that
they use independent weather fields provides a quanti-
tative estimate of the likelihood of occurrence of the
event. Fig. 5a shows the presence of an extended area in
which a low percentage of models (o10%) forecast athreshold exceedence. In order to analyze this result,
within this type of representation it is possible to overlay
the behavior of a single model or an ensemble of models
constructed according to criteria 2.1 or 2.2 given in
Section 3.2.1. For the case of Fig. 5a, a single model is
responsible for the low agreement region as shown in
Fig. 5b. On the ground of this result, a series of
considerations can be made that may allow one to label
qualitatively the outlier and eventually disregard it. Such
considerations might be based, for example, on the fact
that the outlier is the only one with results substantially
different from other models that use the same meteor-
ological fields or the fact that the outlier results are
based on meteorological data produced at an earlier
time with respect to the others and that is therefore less
reliable. For example in the case of Fig. 5b, the outlier
uses the same meteorology used by 8 other models, but
its plume travels faster than the ones simulated by the
others. Since the 8 dispersion models are different from
one another, the results of the outliers can be given a
lower importance. It should be clear that this result
should not be intended as an evaluation of a specific
model but as a method to make a better use of the
information provided by all models for a specific
application case. The ATL without the contribution of
the outlier identified in Fig. 5b is shown in Fig. 5c. As
one can see, the area interested by the threshold
exceedence is now more confined in space and char-
acterized by a high level of agreement. Given appro-
priate justification to disregard the results of the outlier,
a decision maker presented with the results of Fig. 5c
can definitely better base his judgment and decisions.
ARTICLE IN PRESS
Fig. 6. Agreement in percentile level for time-integrated
concentration of 137Cs. Application to release 03, dispersion
field 57 h after release as produced by: (a) 50th percentile of 11
model results (Bq hm�3); (b) 90th percentile of 11 model results
(Bq hm�3).
S. Galmarini et al. / Atmospheric Environment 38 (2004) 4607–4617 4615
3.2.4. Agreement in percentile level (APL)
The agreement in percentile threshold (APL) provides
a representation of a specific variable once a model
agreement or confidence level has been specified. Let
D(x,y,t) be the set of the predictions made by an
ensemble of M models for the location (x, y) at time t,
and sorted in ascending order. The pth percentile of this
distribution of discrete values is obtained as
Dpðx; y; tÞ ¼Diðx; y; tÞ þ ð0:01pM � iÞ�ðDiþ1ðx; y; tÞ
� Diðx; y; tÞÞ; ð2Þ
where Di and Di+1 are two consecutive values of the
sorted series of predictions. The index i satisfies the
following condition:
ip0:01ppi þ 1: ð3Þ
The APL is defined as the two-dimensional surface
obtained for all (x, y) locations of the domain at time t
for which:
APLðx; y; tÞ ¼ Dpðx; y; tÞ for evey ðx; yÞ: ð4Þ
Figs. 6a and b give an example of APT for time-
integrated concentration 57 h after a release from
London (exercise 03) predicted by 11 models. The
Figs. 6a and b show the space distribution of the
variable predicted by the 50th and the 90th percentile of
the models’ distribution, respectively. The results of the
two figures can be regarded as the results of two
additional models: the Median Model and the 90th
percentile Model constructed from the distribution of
the 11 model results at each grid node of the domain.
ATL allows not only to verify actual values of the
selected variable space distribution at a specific time
interval, but also to know the percentage of models that
produce the estimates. With respect to the ATL
indicator presented in the previous section, the APL
condenses the results of several models in a single
representation in which the actual variables’ values are
presented and they are selected upon the definition of
confidence level. Through the APL one can identify for
example the presence of regions with high concentra-
tions and also have a quantitative estimate of the relative
confidence of the result obtained.
4. Relevance of ensemble dispersion forecasting to
decision-making
The EDM technique presented is now discussed,
placed in the right context and analyzed critically. The
aspect to be discussed is why we should approach long-
range atmospheric dispersion modeling by means of
ensemble techniques. Does EDM provide any improve-
ment from the application viewpoint? Over the past 20
years atmospheric dispersion modeling and its applica-
tion to the continental scale have experienced a vast
development in application. Several institutions world-
wide use state-of-the-art long-range atmospheric disper-
sion models operationally to provide support to decision
making in case of accidental releases of harmful
pollutants (e.g. radioactive gases from nuclear power
plants, toxic pollutants from chemical plants) to the
atmosphere. As demonstrated in several occasions, the
state of maturity of the atmospheric dispersion discipline
is yet to be such that the various modeling systems used
provide converging results (e.g. Klug et al., 1992;
Graziani et al., 1998a,b). Moreover, no systematically
successful model could be identified as the modeling tool
to be used as support to decision making. On the other
hand, the use of model forecast is presently vital for the
organization of countermeasures and the assessment of
ARTICLE IN PRESSS. Galmarini et al. / Atmospheric Environment 38 (2004) 4607–46174616
an accident’s consequences in the early phase. In this
context EDM proves to be a methodology that may
allow one to reduce the consequences of relying on a
single model. The methodology proposed allows to
consider all available model results to answer the
questions: Where is the cloud going? When shall we
expect it to reach a given location? How serious will the
expected concentration levels and consequences be?
Decisions based only on the forecast of the outlier of
Fig. 5b could lead to severe consequences in terms of
countermeasure adoption and civil protection in the
countries affected by the plume passage. On the
contrary, the simultaneous analysis of several model
results provided by EDM leaves at least room for doubt
on the correctness of the results of the outlier. One
should not forget that under operational activities and
emergency conditions a mistake could always be made
that could jeopardize the final result. Without a term of
comparison, the mistake cannot be identified. The use of
EDM however allows one to outline immediately an
anomaly that can be verified. In a way, EDM provides a
way to cope with the present single model deficiencies.
Within the type of EDM considered in this study (5. cf.
Section 1), the intrinsic diversity of modeling tools and
model results is considered an important asset. This
aspect should obviously take into consideration the fact
that the modeling systems producing the forecast are not
totally independent and that a certain bias towards a
particular situation may be present regardless of the
model differences. However the technique presented
points in the direction of a wide coverage of cases and
scenarios. It could be argued that the analysis of a large
number of forecasts produced by different models can
include an aspect of complementarity, thus providing a
more complete view on the evolution of the process and
the possibility to base decisions on a more educated
basis. It goes without saying that the absence of
experimental evidence to support the model results, the
results of EDM does not provide any guarantee that the
ensemble would be more reliable (paradoxically, all
models may agree in giving the wrong prediction).
However, the analysis of a large number of model
simulations and the estimate of forecast variability in
predicting the dispersion reduces such a risk and
enriches the decision-making process.
5. Conclusions
An approach to ensemble dispersion modeling (EDM)
has been presented, for the forecast of long-range
transport and dispersion of volatile radioactive material.
The concept and methodology consists of the analysis of
the forecasts produced by several models used oper-
ationally by national meteorological services and en-
vironmental protection agencies worldwide. The models
are state-of-the-art tools developed to forecast the
consequences of accidental releases of harmful materials
to the atmosphere. EDM takes into account the intrinsic
diversities among the various models and the fact that
they use meteorological forecasts produced by different
numerical weather prediction systems. EDM has been
presented together with some indicators for the model
treatment and analysis. The indicators were devised with
the scope of combining the results of the various models
in order to analyze and compare the results of coherent
groups and to estimate the level of agreement of the
various results. The final scope of EDM is to reduce the
uncertainty connected to the single deterministic fore-
cast and to provide a comprehensive analysis for
support to decision-making.
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