The document outlines the methodology and goals of the BDE 2nd SC5 pilot project 'Demokritos', which focuses on computational modeling of atmospheric dispersion of hazardous pollutants. It discusses the integration of big data tools to efficiently estimate pollutant sources during emergencies, particularly in cases of nuclear accidents, by utilizing meteorological data and dispersion models. The project aims to create a framework that allows for pre-calculation and storage of various dispersion scenarios to rapidly identify emission sources when necessary.

1. THE RATIONALE AND
METHODOLOGY OF THE
BDE 2ND SC5 PILOT
NCSR “Demokritos”20-Dec.-16

2. Framework
¥ Computational modelling of atmospheric dispersion
of hazardous pollutants
¥ How can BigDataEurope Integrator tools contribute
to performing more efficiently computational tasks
related to atmospheric dispersion of hazardous
pollutants?
11-oct.-16www.big-data-europe.eu

3. Purposes and means
¥ Air pollution abatement / early warning / countermeasures
o Anthropogenic emissions: routine, accidental (nuclear, chemical),
malevolent (terrorist) – unannounced releases
o Natural emissions (e.g., volcanic eruptions)
¥ Measurements (from earth or space)
¥ Mathematical modelling
¥ Combination of the above → “forward” or “inverse” modelling
through “data assimilation”
11-oct.-16www.big-data-europe.eu

4. Input data for dispersion modelling
¥ Meteorology
¥ “Source term”: knowledge of the emitted pollutant(s)
source(s): Location, quantity and conditions of release,
timing
¥ Terrain characteristics, geometry of buildings etc.
¥ Depending on available input and measurement data:
“forward” or “inverse” modelling
11-oct.-16www.big-data-europe.eu

5. Cases of “inverse” computations
¥ 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
¥ Inverse “source-term” estimation techniques
11-oct.-16www.big-data-europe.eu

6. Inverse 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
o Special attention: multiple solutions
11-oct.-16www.big-data-europe.eu

7. Introducing the 2nd BDE SC5 Pilot
¥ The previously mentioned mathematical techniques require
large computing times
¥ Purpose: fast estimation of source location in emergencies
¥ Proposed solution: pre-calculate a large number of scenarios,
store them, and at the time of an emergency select the “most
appropriate”
¥ BDE will provide the tools to perform this functionality
efficiently
11-oct.-16www.big-data-europe.eu

8. Structure of the 2nd BDE SC5 Pilot
¥ Geographic area: Europe
¥ Cases of interest: accidents at Nuclear Power Plants
¥ Weather calculations:
o Re-analysis data for 20 years
o Clustering → “typical” weather circulation patterns
o Downscaling through WRF for the “typical” weather
circulation patterns
11-oct.-16www.big-data-europe.eu

9. Structure of the 2nd BDE SC5 Pilot
¥ Dispersion calculations:
o Calculation of dispersion patterns from NPPs for the
above downscaled typical weather circulation patterns
o Dispersion results: gridded and (optionally) at
monitoring stations
11-oct.-16www.big-data-europe.eu

10. Structure of the 2nd BDE SC5 Pilot
¥ In the event of radiation signals at some stations:
o Matching of current and recent weather to closest
typical circulation pattern
o From the stored dispersion results pertaining to the
matched weather circulation patterns select the one that
closest matches the monitoring data
o The matched dispersion pattern will reveal the most
probable emission source
11-oct.-16www.big-data-europe.eu

11. So far …
¥ Preliminary clustering studies on limited amount of
re-analysis data (while waiting for full download)
o On the basis of different variables on different
pressure levels
¥ Dispersion calculations for a selected NPP for the
revealed weather classes
11-oct.-16www.big-data-europe.eu

12. So far …
¥ Selected a random date, taken as “true” accident day
¥ Matching of the “true” day’s weather data with the closest
weather class from the clustering procedure
¥ Dispersion calculations with the weather data of the “true” day
¥ Comparison of dispersion results based on “true” and matched
weather data
11-oct.-16www.big-data-europe.eu

13. Workflow
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ECMWF
Weather
reanalysis data
(20+years)
WRF
Pre-processed
weather data
Clustering
Predominant
weather patterns
DIPCOT
Dispersions for
weather
patterns, for a
number of fixed
nuclear sites
Detector
Detection of
dangerous
release
Weather
service
Recent
weather (e.g.
3 days)
Batch processing
Interactive workflow Comparison
Candidate
release origins

14. Data
¥ ECMWF Reanalysis data
¥ NCAR-UCAR Archive
o Better compatibility with WPS/WRF
¥ 20-30 years
o Approx. 6 TB in total
¥ Grib2 format – again for better compatibility with WRF
o NetCDF via WPS
¥ Many variables at multiple geopotential heights
www.big-data-europe.eu

15. Architectural Overview
www.big-data-europe.eu
Possible additions as BDE pilot components:
(1) POSTGIS
(2) DIPCOT

16. Clustering
¥ Traditional methods
o Agglomerative hierarchical
o K-means
¥ Soon to implement
o NN-based feature extraction (e.g. autoencoders,
convolution nets)
o (Possibly) followed by k-means
www.big-data-europe.eu

17. Evaluation
¥ Incremental
o Clustering outcome
o Closeness of constituent weather within clusters / distance between
clusters
o Dispersion characteristics
o Different cluster descriptors for
v Creating cluster-based dispersions
v Matching “real data” to clusters
¥ Complete
o Compare cluster-based dispersion against
o “Real data” dispersion
v For a number of hypothetical scenarios
www.big-data-europe.eu

18. Preliminary results
¥ Clustering over 2-year period (1986, 1987)
o K=6 clusters
¥ Multiple geopotentials
¥ Other variables – notably wind speed – at
different heights
¥ “Visual comparison” against “real data” dispersions
¥ Incrementally combining more vars
www.big-data-europe.eu

19. Cluster quality / GHT 500hPa
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• 1986, 1987
• Resolution=
• Items (6-hr snapshots) =
• K-means, for K-6
• Geopotential height=500hPa
• Dispersions well differentiated for a
specific hypothetical origin
• Real data:

20. Different Clustering Algorithms
www.big-data-europe.eu

21. Immediate Future Work
¥ Feature extraction
o Taking into account multiple variables
o At more heights
¥ Automatic evaluation
o For a number of pre-selected scenarios
¥ Dockerisation and inclusion into the BDE architecture
www.big-data-europe.eu

22. 11-oct.-16www.big-data-europe.eu
Thank you for your attention!