Lecture 4 Tropospheric Chemistry Data Assimilationearth.esa.int/dragon/D5_L4_Elbern.pdfDay 5 Lecture...

Day 5 Lecture 4 Tropospheric Chemistry Data Assimilation Hendrik Elbern 1

DRAGON ADVANCED TRAINING COURSE IN ATMOSPHERE REMOTE SENSING

Lecture 4Tropospheric Chemistry

Data AssimilationH. Elbern

Rhenish Institute for Environmental Research at the University of Cologne

andVirt. Inst. for Inverse Modelling of Atmopheric Chemical

Composition



Objective of this lecture

Understand the• the bi- and multi-uncertainty aspect of

tropospheric chemistry data assimilation: the generalized inversion task

• consequences for system implementation

• Observe practical potential and limits of tropospheric chemistry data assimilation and air quality forecasts



Outline1. Objectives and special challenges of air quality

data assimilation

2. Implications for the methodology

3. Tropospheric chemistry 4Dvar examples

4. Summary



Motivation What is the public request?

As brief as possible:• to provide timely prediction of chemical weather

(summer: photo smog; winter: particulate matter)

• fine scale pollution monitoring for retrospective assessment of reduction measures and exposure times (most importantly particulate matter)

2 realisation attempts on a European level:EC 6FP GEMS, ESA GSE PROMOTE



Objectives of air quality data assimilation

• bring together air quality measurements and CTMs to provide optimal spatio-temporal reconstructions of air quality parameters,

• estimate variability, sources, sinks, and trends• provide better air quality predictions• reconstruct past changes,• act as a decision support system for protection

measures (which emissions are most critical?)



Which constituents/complexity really matters? • Human health:

– PM10, PM2.5, PM1 (= Particulate Matter )– POPs (Persistent Organic Pollutants):

• PAHs (Polycyclic Aromatic Hydrocarbons), • PCBs (PolyClorinated Biphenyls), • HCHs (HexaCloroHexanes), • benzene, • benzopyrene, ….

– Trace metals: Cd, Be, Co, Hg, Mo, Ni, Se, Sn, V, As, Cr, Cu, Mn, Zn, Pb

– Ozone, PAN, NO, NO2, SO2, CO– Pollen

• Crops:– Ozone

• Forests, lakes, other vulnerable ecosystems– “Acid rain”– ozone

∫x

drrm0

)(



Special challenges of Special challenges of tropospherictroposphericchemistry data assimilationchemistry data assimilation

The following problems prevail:1. strong influence of manifold processes including

emissions and deposition2. spatially highly variable “chemical regimes”3. chemical state observability (= “analyseability”)

hampered by manifold hydrocarbon species4. consistency with heterogeneous data sources:

satellite data and in situ observations

This invokes the application space-time data assimilation algorithms preserving the BLUEBLUE property (Best Linear Unbiased Estimator)



State of the Art ExamplesHow are today’s services designed?

1. solvers of a deterministic transport-diffusion-reaction equation, mostly operator-split-wise (except probably reaction-vert. diffusion)

2. continental, or regional nest (1 km < ∆x < 250 km horizontal resolution)

3. tropospheric gas phase chemistry, 40-60 species, < 200 reactions

4. basic aerosol treatment of at least NH3, HNO3, H2SO4



““UpgradeUpgradedata to data to informationinformation”

GISdata

Simulation of the“atmospheric processor”

healthinformation

legislationculturalheritage

farmingsupport

aviationcontrol

science

Meteodata

Emissioninventories

in situobservations

Satellite retrievals

Chemical Weather SystemChemical Weather System



Special challenge: which is the appropriate scale for air quality modelling?

• intercontinental / long range transport of pollutants

• many processes are local: point and line source emissions

• surface patterns/tesselation impacts skill

• boundary layer and convection simulation at least of like importance as in meteorology



Prognosis

www.eurad.uni-koeln.de

Europeds = 125 km

Central Europeds = 25 km

NRWds = 5 km

23 levels lowest level: ca. 40 mupper boundary ca. 15 km

Sequential nestingJülich example

ECHOds = 1 km

10 20 30 40 50 60 70

10

20

30

40

50

60

5.250 12.800 22.400 35.700 53.200 76.600 111.000 190.000 412.000

casename: grid: 79 x 69

KFZ kg/Tag echo

© b

y E

UR

AD

Wed

Nov

6 2

2:48

:26

2002



Hemispheric forecast PM10

Incentives•In readiness for tropospheric satellite data•boundary value provision for continental scale models for long range transported constituents

high resolution version150 x 150 gridDx = 125 km



Coarsest grid for intercontinental transportforecast 6.9.2005 today (∆x=250 km)

ozone and PM10



Daughter grids forecast 6.9.2005 todayEurope ∆x=125 km and central Europe ∆x=25 km



targeted regionsforecast 6.9.2005

5 km resolution



NOx1.5 x

1 x

0.6 x

VOCs

0.6 x 1 x 1.5 x

0.6 x 1 x 1.5 x

Note:notC-orthogonal,not max.sensitivitiesaligned

BERLIOZ NOx-VOC emissions variation

ensemble (20.7.1998,

15:00 UTC)



NOx1.5 x

1 x

0.6 x

VOCs

0.6 x 1 x 1.5 x

0.6 x 1 x 1.5 x

BERLIOZ NOx-VOC emissions variation

ensemble (21.7.1998,

15:00 UTC)

Note:notC-orthogonal,not max.sensitivitiesaligned



Empirical Kinetic Model Approach scheme

Isopleths of ozone production, due to NO2 and VOC

Nitrogene oxides and numerous hydrocarbons act highly nonlinearly as precursors of ozone. Chemical conditions are either controlled by NOx or VOC deficit, delineating the “chemical regime”.Both 4D-var and Kalman filter should start with the proper chemical regime.

EKMA diagram(Empirical Kinetic Model Approach) VOC sensitive

NOx sensitive

NOx

VOCs

approximate conditions 20.7. and 21.7.



Aerosol Chemistry in

MADEModal Aerosol Dynamics

for EURAD/Europe(Ackerman et al., 1998,

Schell 2000)

dMik/dt=nuki

k+coagiik+coagij

k

+condik+emiik

Mik:=kth Moment of ith Mode

Bridge from optical to chemicalpropertiesassimilation of aerosolBy sattelite retrievals: e.g.MERIS MODIS AATSR+SCIAMACHY,…



Example: chemical complexity:The EURAD Secondary ORGanic Aerosol

Model (SORGAM)



Advanced spatio-temporal data assimilation:4D -var and Kalman filtering

• Models do not passively accept external 3D analyses (probably react by engendering spurious relaxations)

• Rather contribute with its dynamics/ chemical kinetics as constraint to estimate the most probable state or parameter values

Best Linear Unbiased Estimate (BLUE)while using data and models consistentlyallows for Hypothesis testing



Advanced Advanced spatiospatio--temporal methods used in temporal methods used in tropospherictropospheric chemistry data assimilationchemistry data assimilation

Spacio-temporal BLUEs applied in troposphericchemistry data assimilation:

• 4D var: – with EURAD (Elbern and Schmidt, 1999, 2001), – with POLAIR (Issartel and Baverel, 2003)

• Kalman Filter – with LOTOS model (van Loon et al, 2000), (RRSQR)– with EUROS model (Hanea et al. 2004) (En+RRSQRKF)



Question: Which parameter to be optimized?Hypothesis: initial state and emission rates are least known



In the troposphere, for emission rates, the product(paucity of knowledge*importance) is high



Incremental Formulation

•Analysis State:

•New „State“ Variables:

•Cost Function:



Background Error Covariance Matrix B• must be provided as an operator (size is of order 1013)

• we would like to have an operator which caneasily be factorised by B=B1/2BT/2

• Weaver and Courtier (2001):–general diffusion equation serves for a valid operator

generating a positive definite covariance operator–diffusion equation is self adjoint–B1/2 and BT/2 by applying the diffusion operator half the

diffusion time



Background standard deviations Σ

• function of height and species:

• relative background errors:height level dependent

• absolute background errors:species dependent



4D-var configuration

MesoscaleMesoscale EURAD 4DEURAD 4D--var var datadata assimilationassimilation systemsystem

meteorologicaldriverMM5

meteorologicaldriverMM5

EURADemission model

EEMemission 1. guess

EURADemission model

EEMemission 1. guess

direct CTMdirect CTM

emission ratesemission rates

Initi

alva

lues

Initi

alva

lues

min

imis

atio

nm

inim

isat

ion

adjointCTM

adjointCTM

observationsobservationsanalysisanalysis

grad

ient

fore-cast



backward integration

1. step 2. step 3. step 4. step 5. step 6. step 7. step 8. step 9. step

2 level forward and backward integration schemetime direction

disk

forward integration

backward integration

intermediate storageLevel 1

store

recover

1. step 2. step 3. step 4. step 5. step 6. step 7. step 8. step 9. step



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storage sequence: level 2, operator split(each time step)


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vert. Diffusion

vert.. Advekt




Treatment of the inverse problem for emission rate inference



Normalised diurnal cycle of anthropogenic surface emissions f(t)emission(t)=f(t;location,species,day) * v(location,species)

day in {working day, Saturday, Sunday} v optimization parameter



Optimisation of emission ratesamplitude optimisation for each emittedspecies and grid cell

AS1

AS1 Fraglich, ob diese Folie gezeigt werden muss. Wenn Fragen kommen, dann zeigen.Achim Strunk, 22/04/2004



Background emission rate covariance matrix D



Reduction of the partial cost functionsrelative to observation type (coarse grid, 54 km)

GO ground in situSC satellite columns (NO2)SP satellite profiles (O3)TO total costsEM background emissionBG state background



Semi-rural measurement site Eggegebirge

assimilation interval forecast

7. August 8. August 1997

+ observationsno optimisation

initial value opt.

emis. rate opt.

joint emis + ini val opt.



Rhine-Main area box: (Frankfurt-Mainz)9.-10. August 1997

assimilation interval forecast+ observationsno optimisation

initial value opt.

emis. rate opt.




Error statisticsbias (top), root mean square (bottom)

assimilation window forecast

forecast

assimilation window

+ observationsno optimisation

initial value opt.

emis. rate opt.




∆x=54 km

WhichWhich isis thethe requestedrequested resolutionresolution??BERLIOZBERLIOZ grid designs and observational sites

(20. 21. 07.1998)

∆x=18 km∆x=6 km

∆x=2 km

Con

trol a

nd d

iagn

ostic

sC

ontro

l and

dia

gnos

tics



Some BERLIOZ examples of NOx assimilation (20. 21. 07.1998)

NO

NO2

Time series for selected NOx stations on nest 2. + observations, -- - no assimilation,-____ N1 assimilation (18 km), -____N2 assimilation.(6 km), -grey shading: assimilatedobservations, othersforecasted.



surf

ace

heig

ht la

yer ~

32-~

70m

NO2, (xylene (bottom), CO (top) SO2 .

Emission source estimates by inverse modellingOptimised emission factors for Nest 3



Nest 2: (surface ozone)(20. 21. 07.1998)

without assimilation with assimilation



CONTRACEConvective Transport of Trace Gases into

the upper Troposphere over

Europe: Budget and Impact of Chemistry

Coord.: H. Huntrieser, DLR

flight path Nov. 14, 2001



CONTRACE Nov. 14, 2001 north (= home) bound

O3

H2O2CO

NO

1. guess assimilation result observations flight height [km]



Assimilation of GOME NO2 tropospheric columns, 5.8.1997

GOME NO2 columns: Courtesy of A. Richter, IFE, U. Bremen

# m

olec

NO

2/cm

2

NOAA NOAA chch 3 (near infrared)3 (near infrared)

# m

olec

NO

2/cm

2#

mol

ecN

O2/c

m2

post assimilationforecaststarted:5.8.97 06:00 UTC

forecast without assimilation



NO2 tropospheric column assimilation IFE:Example: BERLIOZ episode 20.7.1998

GO

ME

retie

val

Cou

rtesy

: A. R

icht

er, I

FE B

rem

en

no a

ssim

ilatio

nw

ith a

ssim

ilatio

n

NOAA 1411:51 UTC



IUP Bremen GOME GOME forecast validation

ideal

no assimilation with assimilation

20.7.98

verificationforecast 21.7.98(not assimilated)

forecast forecast



GOME ozone profile assimilation BERLIOZ (20.7.1998)Data: Neuronal Network retrieval (Müller et al., 2003)

+ NNORSY ozone retrieval

first guess

after assimilation



Ozone observation increment 20.07.1998layer: 200-100 hParadius of influence: 200 km

height dependent influence radii



ComparisonSPURT flight campaign with NNORSY retrievalsflight Germany canary Islands 17. Jan. 2002

Time, location, and height of airplane-GOME match



Which revisit cycle is usefull for the middle and upper troposphere data assimilation?

average30°-40°N

average40°-50°N

average60°-70°Naverage

50°-60°N

NNORSY retrievalsnever DADA based forecastcurrent data assimi.



How long is satellite data assimilation sustained? (GOME retrievals)

no G

OM

E da

ta a

vaila

ble

ratio without any assimilation

normalized squared errors



MOPITT tropospheric CO assimilation

850 hPa, 1 July. 2003

first guess at 11:00 UTC;.

retrieved 850 hPaconcentrationsin ppbV.

4D-Var analysis for thesame time;

Note: Colour codes are not fully consistent!



Assimilation of Aerosol observations• In situ:

EEA Airbase: Database of groundstations of EU member countries & states:

• 450 stations for PM10 (2003)• No PM2.5. (4 stations in UK only)

• Satellite measurements:SYNAER (SYNergetic AErosol Retrieval, DLR-DFD, [Holzer-Popp, 2001])*

• combines GOME&ATSR-2, SCIAMACHY&AATSR measurementsaboard ERS-2/ENVISAT

• ATSR-2/AATSR:dark field detection, BLAOT (Boundary Layer Aerosol OpticalThickness) and albedo are calculated

• GOME/SCIAMACHY: Provides PM0.5, PM2.5 and PM10 columns and its composition (6 intrinsic species)



EEA PM10 stations

SYNAER PM10

retrievals

Aerosol observations (14.7.2003, ~10:00 UTC)



Background Error Covariance Matrix B (short design outline)

( )( )∑=

−−=K

nj

nji

niij xxxx

KB

1

1

K=# Ensembles; i,j neighboring cells

TL κ2=diffusion coefficients κ:

Correlation length L to neighboring gridcell:

, , ,

⇒

1. How to obtain the covariances?Ensemble/NMC approach:

2. How to process this information?Translate into Diffusion coefficients difusion paradigma



3Dvar aerosol assimilation (13.7.2003)

in s

ituon

lyin

situ

& SY

NAE

R

in s

itu&

SYN

AER

back

grou

nd



3Dvar aerosol assimilation (14.7.2003)biomass burning case in Spain

in s

ituon

ly

in s

itu&

SYN

AER

back

grou

nd

in s

itu&

SYN

AER



Do aerosol data assimilation effectsaccumulate? (14. July 2003)

assimilation on previous days 10 UTCAccumulation of retrieval information over 14 days

No previous assimilationonly 14. July 2003



Validation

wildfires5.3(28%)5.5 (24%)7.34914.07.2003none1.9 (70%)2.5 (61%)6.32013.07.2003none1.3 (32%)1.4 (30%)1.92912.07.2003

no sat data----11.07.2003wildfires6.9(26%)6.5 (30%)9.32710.07.2003

no sat data----09.07.2003wildfires6.7 ( -2%)7.1 ( -8%)6.61308.07.2003wildfires6.9 (28 %)6.8 (29 %)9.63207.07.2003

no sat data----06.07.2003In situ & satIn situ onlyno assim.

RemarksRMS [µg/m3] (improvement)# withheldstations

Date

Surface stations in grid cells with SYNAER retrievals withheld for validation



Summary

• chemical weather forecasts are a multiple scale problem

• chemical data assimilation rests on sparse and heterogeneous observations, with variable error characteristics (incl. error of representativity)

• initial value optimisation is insufficient, as at least emissions are less known and more important

• the ability for inversion is therefore required to optimize emission rates

• much is to be done for optimising multivariate covariance matrices

Lecture 4 Tropospheric Chemistry Data Assimilationearth.esa.int/dragon/D5_L4_Elbern.pdfDay 5 Lecture...

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