Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 1 Lecture 3 Tropospheric Chemistry Data Assimilation H. Elbern Rhenish Institute for Environmental Research at the University of Cologne and Institute for Chemistry and dynamics of the Geosphere-2: Troposphere (ICG-2), Research Centre Jülich, Germany
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Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 1
Lecture 3Tropospheric Chemistry
Data AssimilationH. Elbern
Rhenish Institute for Environmental Research at the University of Cologne
andInstitute for Chemistry and dynamics of the Geosphere-2: Troposphere (ICG-2), Research Centre Jülich, Germany
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 2
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
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 3
Outline1. Objectives and special challenges of air quality
data assimilation
2. Implications for the methodology
3. Tropospheric chemistry 4Dvar examples
4. Summary
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 4
Motivation What is the public request?
As brief as possible:• to provide timely prediction of chemical
• 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
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 5
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?)
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 6
Which constituents/complexity really matters? • Human health:
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 17
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.
A prototypelon-linearityexample:
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 18
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,…
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 19
Example: chemical complexity:The EURAD Secondary ORGanic Aerosol
Model (SORGAM)
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 20
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
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 21
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)
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 22
Question: Which parameter to be optimized?Hypothesis: initial state and emission rates are least known
emission biased model state
only emission rate opt.
only initial value opt.true state
observations
time
conc
entra
tion
joint opt.
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 23
In the troposphere, for emission rates, theproduct (paucity of knowledge*importance)
is high
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 24
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
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 25
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
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 26
4D-var configuration
MesoscaleMesoscale EURAD EURAD 4D4D--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
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 27
Comparison of NO2 tropospheric columns in molecules/cm2 for July 6th, 2006, 09-12 UTC.
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 47
Data assimilation result in terms of troposphericcolumns for July 6th, 2006. NO2 model columns based on OMI and SCIAMACHY assimilation within interval,
09-12 UTC.
Difference field giving implied changes for tropospheric columns by assimilation (middle), and induced surface concentration changes by NO2 ppb (right)
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 48
Data assimilation result in terms of tropospheric columns for July 7th, 2006. NO2 model columns based on OMI and SCIAMACHY assimilation
within the assimilation interval, 09-12 UTC.
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 49
Qualitative assessment of emission correction factors for July 7th, 2006
Emission inventoryvalues
to be increased
to be reduced
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 50
Control run (OmC) (no data assimilation at all,) black bold line, forecasted values (OmF) green bold line, analyses (OmA) blue bold line. For comparison: Gaussian fit to OmF pdf by mean and standard deviation given by broken purple line.
SCIAMACHYOSCIAmXOMIT probability density functions
for July 6th (left), and July 8th, (right).
Day 5 Lecture 3 Module name - Tropospheric Chemistry Data Assimilation 51