introduction to atmospheric dispersion...

Introduction to atmospheric dispersion modelling

M.Sofiev

Content

• Main parts of the atmosphere

• Basic terms

atmospheric tracer

temporal and spatial scales

life time in the atmosphere

life cycle of atmospheric tracers

• Dispersion equation

• Langrangian and Eulerian dispersion models

• Parts of a dispersion model

• Model Quality Assurance

• Summary

Major layers of the atmosphere

• Troposphere (tropos=mixing): interaction with surface

• Stratosphere: ozone-uv heating

• Mesosphere: mixing again

• Thermosphere: O2, N2 – solar radiation heating

• Ionosphere: ions

• Exosphere: fast molecules escape to the open space

0

20

40

80

100

60

Altitude

, km

103

102

10

10-2

10-3

1

Pre

ssure

, hP

a

10-1

t r o p o p a u s e

s t r a t o p a u s e

m e s o p a u s e

troposphere

strato

sph

ere

mesosp

here

thermosphere

0 20 40-60 -40 -20-80

Temperature, C

Basic terms

• Atmospheric tracer: a compound, which is transported with atmospheric flows but does not affect them (sufficiently low concentration)

some impact exists always: feedback to meteorological processes

• Tracer lifetime: a time period needed for 2-fold (or e-fold) reduction of amount of the tracer in the atmosphere

condition- and process-dependent: e.g. lifetime with regard to advection

general meaning: lifetime is related to relaxation times for any process involving this tracer (not 1:1 though)

• Spatial and temporal scales

related to lifetime

temporal scales are translated into spatial ones via wind speed

processes and their imporatnce are related to scales 1sec

100sec

1hr

1yr

10yr

1 day

Tim

e s

cale

100km 1000km 10,000km100m 1km 10km1m

Spatial scale

10m

100yr CFCs

CH4

CH3Br

CH3CCl3

CO

Aerosols

SO2

Trop O3

NOx

DMS

C3H6

N2O

H2O2

C5H8

CH3O2

HO2

NO3

OH

Boundary layermixing time

Inter-hemispheric

mixing time

Hemispheric

mixing time

- - mesoscale

regional

scale

global

scale

micro

scale

local

scale

Pollution cycle in the troposphere

Chemical & physical reactionsTransportEmission

Diffusion in deep layersDiffusion in deep layers

LOADLOAD

Content


• Basic terms

atmospheric tracer








• Summary

Dispersion equation

• Mass conservation

transport

sources

sinks

• Scale separation

mean flow

turbulence

• Closure problem

K-theory turbulent diffusion coefficient

(x0,y0,z0)

x

z

y

u(x0-x/2) u(x0+x/2)

w(z0-z/2)

w(z0+z/2)

v(y0+y/2)

v(y0-y/2)

3

1

( )i

i i

cu c E R

t x

( / )( ) ( )

i ii

i i i

C CLC U C K R C E

t x x x

From concentration to load

p is a weight (sensitivity) function of the load:

– Population exposure

– Ecosystem damage

– Observational site

)(),( cityxxtxp ( , ) ( ) 1( )

ecosystemp x t p t A

0

( , ) load functional

T

M dt C p dx C p

( , ) ( ) 1( , , )site beg end

p x t t t t x x

Alternative way to get the load function

* * * *

* *

( , ) ( , ) ( , ) ( , )

adjoint dispersion equation

M C E C LC LC C p C

LC p

* ( ) ( )i ii

i i i

L U K R Ct x x x

, * 0

*( ) 0

x

C C

C t T

Consider some function C* satisfying

L* C* = p, where L* is adjoint to L.

Then

Dual features of dispersion problem

(1/ )( ) ; ; ( , )

i ii

i i i

L U K R LC E M p Ct x x x

* * * *( ) ; ; ( , )i ii

i i i

L U K R LC p M E Ct x x x

Forward problem:

Inverse (adjoint) problem

Content


• Basic terms

atmospheric tracer








• Summary

Classifications of models

Model principles

– Eulerian

– Lagrangian

– Gaussian

– statistical Monte-Carlo

Scales

– global

– continental

– regional

– local/urban

Classifications of models.2

Chemicals

– acid

– ozone

– greenhouse gas

– inert aerosol/dust

– radio-activity

– toxic

– persistent pollutants

Model media

– atmospheric

– multi-media

– integrated models

Classifications of models.3

Input data

– climatological

– real-time data

Time dimension: direction, horizon

– re-analysis

– now-casting

– forecasting

Problem to solve

– forward

– inverse

Content


• Basic terms

atmospheric tracer








• Summary

SILAM v.5: outlook

• Modules

8 chemical and physical transformation modules (6 open for operational use),

6 source terms (all open),

2 aerosol dynamics (one open)

3D- and 4D- Var

• Domains: from global to beta-meso scale (~1km resolution)

• Meteo input:

ECMWF

HIRLAM, AROME, HIRHAM, ECHAM, and any other who can write GRIB-1 or GRIB-2

WRF

SOx

Acid-basic

CB4

Pollen

General PM

Radioactive

Passive

self-decay Long-lived

multi-media

Transformations

Area

Point

Nuclear bomb

Source types

Map of

species masses

Em

iss

ion

Tra

nsfo

rma

tio

n

Dynamics

Advection

diffusion

Aerosol

dynamics

Bio-VOC

Pollen

Sea salt

Simple

Basic

Transformation

Dry

Wet

Deposition

Initialization,

3D-Var

Simulation control

forward adjoint 4D-Var

AROME

NWP model HIRLAM-MBE NWP model

Physiography,

forest mapping

Aerobiological

observations

FMI regional AQ assessment and forecasting

platform

Satellite

observations

Phenological

observations SILAM

CTM model

EVALUATION:

NRT model-measurement

comparison

Aerobiological

observations

Meteorological

data: ECMWF

Online AQ

monitoring

Phenological

models

Fire Assimilation

System

HIRLAM-RCR

NWP model

AQ

products

CLRTAP/EMEP

emission data

http://silam.fmi.fi

Satellite

observations

Global boundary cond.

+ own simulations

WRF

TVM

SILAM scales

• Global

• Continental

• Regional

• Meso-scale

SO2 3.5.2003

Content


• Basic terms

atmospheric tracer








• Summary

Model vs reality

I.Repin. Zaporozhje Cossacks are writing a letter to Turkish sultan

• Model is never a copy of reality

It represents only those features, which are deemed to be important for a

specific application

• The extent of their similarity is to be established in each specific case

W.Kandinski. Cossacks

Model Quality Assurance (QA)

• Tests of individual modules (development stage)

• Sensitivity runs

• Model-measurement comparison

• Model-model comparison

Model-measurement comparison

• The only connection between model and reality

• Data sets from different origin

point observations vs grid-mean model results

representativeness error

instrumental errors

• Observations are expensive => sparse

• Limited number of observed variables

• Specific statistical methods are required to obtain non-trivial conclusions

Model inter-comparison

• Useful if lacking measurements

• Similar features of data

• Large data sets => high accuracy

• Wide variety of analytical methods

• Ensemble model

• Possibly, no connection to reality

Summary

• Distribution of an atmospheric pollutant is described via dispersion equation, which is a representation of the mass conservation law

• Duality of dispersion problem allows for usage of forward and adjoint dispersion equations

identical final results – the load

choice depends on specific task

• Model quality assurance implies several actions and covers ALL stages of the model development

introduction to atmospheric dispersion...

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