the physics background of the bde sc5 pilot cases
Post on 15-Apr-2017
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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.-16 www.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)
11-oct.-16 www.big-data-europe.eu
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”
11-oct.-16 www.big-data-europe.eu
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
11-oct.-16 www.big-data-europe.eu
Scales of atmospheric motions
11-oct.-16 www.big-data-europe.eu
• 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
11-oct.-16 www.big-data-europe.eu
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 11-oct.-16 www.big-data-europe.eu
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 11-oct.-16 www.big-data-europe.eu
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 11-oct.-16 www.big-data-europe.eu
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 11-oct.-16 www.big-data-europe.eu
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
11-oct.-16 www.big-data-europe.eu
Global-scale dispersion modelling
11-oct.-16 www.big-data-europe.eu
2 days 4 days 6 days
8 days 10 days 12 days
Regional scale dispersion modelling
11-oct.-16 www.big-data-europe.eu
Dispersion of ash from
the Eyjafjallajökull
volcano in Iceland
Meso-scale urban pollution
Ozone
concentrations
for different
emission
scenarios
11-oct.-16 www.big-data-europe.eu
Local scale dispersion modelling
11-oct.-16 www.big-data-europe.eu
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)
11-oct.-16 www.big-data-europe.eu
Inverse modelling example
Sensitivity of
ozone
concentration at
a specific site
and time on NO2
concentrations at
previous times
11-oct.-16 www.big-data-europe.eu
Inverse modelling example
Sensitivity of ozone
concentration at a
specific site and time
on NO2 emissions
accumulated until
that time
11-oct.-16 www.big-data-europe.eu
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 11-oct.-16 www.big-data-europe.eu
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.-16 www.big-data-europe.eu
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.-16 www.big-data-europe.eu
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