The physics background of the BDE SC5 pilot cases

THE PHYSICS
BACKGROUND OF THE BDE
SC5 PILOT CASES
NCSR “Demokritos”11-oct.-16

Common background
 The earth’s atmosphere is the common physical
background of the 2 SC5 BDE pilots
 BigDataEurope provides tools contributing to more
efficient management / processing of data related
to different aspects of studying the atmospheric
processes
11-oct.-16www.big-data-europe.eu

Why do we study the atmosphere?
 Weather prognosis
 Climate change prognosis
 Air pollution abatement / early warning /
countermeasures
o Anthropogenic emissions: routine, accidental (nuclear,
chemical), malevolent (terrorist) – unannounced releases
o Natural emissions (e.g., volcanic eruptions)

Methods and means
 How do we study the atmosphere?
o Measurements (from earth or space)
o Mathematical modelling
o Combination of the above → “forward” or “inverse”
modelling through “data assimilation”

Atmospheric motion
 Atmosphere is a fluid
o Energy supplier: the sun
o Energy and water exchanges with the soil and oceans
 Motions driven by “real” (pressure gradients, friction etc.) and
“apparent” forces (due to earth’s motion)
 Common characteristic of fluid flows: TURBULENCE
 Atmospheric turbulence consists of eddies with vast range of
size- and time-scales

Scales of atmospheric motions
• Motions are
connected
• Energy flows from
large to small scale
motions

Mathematical description
 Conservation equations for mass, momentum,
energy, humidity + equation of state
o Represent basic physical principles
 Partial differential equations
 NO analytical solution
 Numerical solution in computer codes - models

Numerical solution
 We split the “computational domain” to a “grid” of points or
volumes, “discretize” the equations
 For each variable: number of unknowns = number of grid
points
 How fine should this grid be (ideally)?
o Earth’s surface: 5.1 ×1014 m2
o Smallest eddies: 10-1 m
o Height: 1.2 ×104 m
o Time step: 1s
6.12 × 1020 grid cells
NOT POSSIBLE

Averaging / filtering
 We average – in space and time – the equations
o Sub-grid-scale motions are parameterized
 Split the earth’s surface in grids with steps of ¼ of
a degree and fewer vertical levels: 1.0 ×108 cells
 Big Data tools necessary here
 Possible, good enough for global weather
forecasting, not good enough for local scale motions

Downscaling / nesting
 Smaller computational domain(s) are defined over
area(s) of interest with finer resolution (~ 1km)
 Models simulate there in greater detail local weather
or climate change effects
 Smaller domains interact with larger ones and with
global data
 1st BDE SC5 Pilot contributes in the computational
simulation of this process

Example of nested domains

Towards the 2nd pilot case
 Atmospheric dispersion of pollutants
 Is totally driven by meteorology
 Different spatial scales involved: transport - diffusion
 Downscaled / nested meteorological data may be used
to “drive” the computational dispersion simulations
o Connection with 1st pilot case
 Crucial information: knowledge of the emitted pollutant(s)
source(s): where, when, how, how much and what

Examples of “forward” simulations
 A few examples of atmospheric dispersion
simulations will follow (performed by NCSRD),
involving (partially) known releases of substances
o We start from the pollutants release and move forward
in time as dispersion evolves

Global-scale dispersion modelling
2 days 4 days 6 days
8 days 10 days 12 days

Regional scale dispersion modelling
Dispersion of ash from
the Eyjafjallajökull
volcano in Iceland

Meso-scale urban pollution
 Ozone
concentrations
for different
emission
scenarios

Local scale dispersion modelling
Simulation of dispersion following
an explosion in a real city centre

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)

Inverse modelling example
 Sensitivity of
ozone
concentration at
a specific site
and time on NO2
concentrations at
previous times

Inverse modelling example
 Sensitivity of ozone
concentration at a
specific site and time
on NO2 emissions
accumulated until
that time

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
 “Source-term” estimation techniques

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

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, 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

Thank you for your attention!

The physics background of the BDE SC5 pilot cases

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The physics background of the BDE SC5 pilot cases