The rationale behind the development of the 2nd BDE SC5 pilot

1. THE 2ND SC5 PILOT:
BACKGROUND AND
RATIONALE
NCSR “Demokritos”
6 November
2017

2. Background of SC5 pilot use
cases
 The pilot use cases for SC5 concern
applications related to the earth’s atmosphere
 Demonstrate how tools provided by BDE can
contribute to more efficient management /
processing / use of data related to different
aspects of atmosphere-related applications
11-oct.-16www.big-data-europe.eu

3. SC5 pilot use cases
 1st pilot use case concerns:
o Weather prognosis
o Climate change prognosis
 2nd and 3rd pilot use cases concern
o Atmospheric dispersion of hazardous pollutants
o Identification of unknown sources
11-oct.-16www.big-data-europe.eu

4. Dispersion of radioactive
species
11-oct.-16www.big-data-europe.eu

5. Generic flow diagram of dispersion
modelling procedure
11-oct.-16www.big-data-europe.eu
Dispersion model
Source term
Meteorological
data
Topography,
land cover
Concentration of
pollutants
Doses

6. SC5 2nd pilot case
 Atmospheric dispersion of pollutants
 Driven by meteorology
o Downscaled / nested meteorological data may be used
to “drive” the computational dispersion simulations
 Different spatial scales involved: transport - diffusion
 Crucial information: knowledge of the emitted
pollutant(s) source(s): where, when, how, how
much and what
11-oct.-16www.big-data-europe.eu

7. “Forward” dispersion
simulations
 When the releases of substances are (at least
partially) known
o We start from the time of pollutants release and
move forward in time as dispersion evolves
o We solve transport equation(s) for the emitted
substances
o Using prognostic weather data
11-oct.-16www.big-data-europe.eu

8. Forward dispersion modelling
11-oct.-16www.big-data-europe.eu
 Local scale dispersion:
o Simulation of dispersion following
an explosion in a real city centre
Urban Dispersion INternational Evaluation Exercise
(UDINEE), coordinated by JRC Ispra, plots by
ENSEMBLE system
ADREA-
HF,
NCSRD

9. Forward dispersion modelling
 ECURIE exercises, nuclear power plant hypothetical
accidents
o DIPCOT model, NCSRD, prognostic weather data by HNMS
11-oct.-16www.big-data-europe.eu

10. Cases of “inverse”
computations (1)
 The pollutant emission sources are known
(location and strength) and we want to assess:
o The sensitivity of pollutant concentrations at
specific locations to different emission sources
o The sensitivity of pollutant concentrations at
specific locations to concentrations of other
pollutants (photochemistry)
11-oct.-16www.big-data-europe.eu

11. Inverse modelling example
 Sensitivity of
ozone
concentration
at a specific
site and time
on NO2
concentrations
at previous
times
11-oct.-16www.big-data-europe.eu
Adjoint CMAQ, run by
NCSRD

12. Inverse modelling example
 Sensitivity of
ozone
concentration at a
specific site and
time on NO2
emissions
accumulated until
that time
11-oct.-16www.big-data-europe.eu
Adjoint CMAQ, run by
NCSRD

13. Cases of “inverse”
computations (2)
 The pollutant emission sources are NOT
known: location and / or quantity of emitted
substances
o Technological accidents (e.g., chemical, nuclear),
natural disasters (e.g., volcanos): known location,
unknown emission
o Un-announced technological accidents (e.g.
Chernobyl), malevolent intentional releases
(terrorism), nuclear tests 11-oct.-16www.big-data-europe.eu

14. Source-term estimation
 Available information:
o Measurements indicating the presence of air
pollutant
o Meteorological data for now and recent past
 Mathematical techniques blending the above
with results of dispersion models to infer
position and strength of emitting source
11-oct.-16www.big-data-europe.eu

15. Methods for source term
estimation
 1st method: forward in time modelling
o Multiple dispersion runs from potential sources,
adjustment of sources to achieve best agreement
between computations and observations
 Bayesian updating/inference methods, using
stochastic Monte Carlo (MC)
Markov Chain Monte Carlo (MCMC) sampling
11-oct.-16www.big-data-europe.eu

16. Methods for source term
estimation
 2nd method: backward-in-time modelling from
receptors to sources
o High degree of uncertainties
o Additional information / constraints to achieve
solution
 Adjoint and tangent linear models
Kalman filters
 Variational data assimilation
11-oct.-16www.big-data-europe.eu

17. Introducing the 2nd BDE SC5
Pilot
 The previously mentioned mathematical
techniques require large computing times: not
suitable to run in emergency response
 Way out: pre-calculate a large number of
scenarios, store them, “train” the system and
at the time of an emergency select the “most
appropriate”
 BDE will provide the tools to perform this11-oct.-16www.big-data-europe.eu

18. Questions?
29/11/2017www.big-data-europe.eu
 BigDataEurope Web site:
https://www.big-data-europe.eu
 Big Data Integrator:
https://github.com/big-data-europe
 Thank you for your attention!