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.-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)


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 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

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 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


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 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


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


Thank you for your attention!

The physics background of the BDE SC5 pilot cases

Technology

Transcript of The physics background of the BDE SC5 pilot cases