atmospheric chemistry measurement and modeling capabilities are advancing on many fronts closer...
DESCRIPTION
Model vs. Observations Modeled O 3 vs. Measured O 3 Cost functional measures the model- observation gap. Goal: produce an optimal state of the atmosphere using: Model information consistent with physics/chemistry Measurement information consistent with reality All with errors +TRANSCRIPT
Atmospheric Chemistry Measurement and Modeling Capabilities are Advancing on Many
Fronts
Closer Integration is Needed
P-3B
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Tem
pera
ture
H2O
Win
d Sp
eed
O3
SO4
J[O
1D]
SO2
PAN
Ethe
ne
Prop
ane
CO
J[N
O2]
Etha
ne
Noy
Ethy
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RN
O3
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zene
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olue
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OH
AO
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HN
O3
NO
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NO
Cor
rela
tion
Coe
ffici
ent
R(<1KM)R(1-3KM)R(>3 KM)
Predictability – as Measured by Correlation Coefficient Met Parameters are Best
Performance decreases with altitude
< 1km
O3 predicted “better” than CO
Carmichael et al., JGR, 2003
Model vs. ObservationsModeled O3 vs. Measured O3
• Cost functional measures the model-observation gap.
• Goal: produce an optimal state of the atmosphere using:
Model information consistent with physics/chemistry
Measurement information consistent with reality
All with errors
+
Challenges in chemical data assimilation
• A large amount of variables (~100 concentrations of various species at each grid points)
– Memory shortage (check-pointing required)• Various chemical reactions (>200) coupled together
(lifetimes of different species vary from seconds to months) – Stiff differential equations
• Chemical observations are very limited, compared to meteorological data
– Information should be maximally used, with least approximation • Highly uncertain emission inventories
– Inventories often out-dated, and uncertainty not well-quantified
Data assimilation methods
• Simple data assimilation methods– Nudging– Optimal Interpolation (OI)– 3-Dimensional Variational data assimilation (3D-Var)– Ensembles
• Advanced data assimilation methods– 4-Dimensional Variational data assimilation (4D-Var)
• Fisher and Lary (1995), AutoChem model• CTMs with 4D-Var applications: STEM, EURAD, CHIMERE
– Kalman Filter (KF)• Many variations, e.g. Ensemble Kalman Filter (EnFK)• CTMs with KF applications: EUROS, LOTOS, MOZART,
EURAD
Extensive Real-Time Evaluation of Regional Forecasts – Stu McKeen
http://www.etl.noaa.gov/programs/2004/neaqs/verification/
Forecast Skill (One Model vs Ensemble) -- observation-based
bias corrections helpEnsemble (8 models)One CTM model
4D-Var data assimilation
(old forecast)
(new)
(initial condition for NWP)
x
4D-Var application with CTMs
Observations
Forward CTM model evolution
Backward adjoint model integration
Optimization
Cost function
Gradients
Update control variables
Checkpointing
Our Analysis Framework
MesoscaleMeteorological Model
(RAMS or MM5)MOZART Global Chemical
Transport Model
STEM Prediction Model with on-line
TUV & SCAPE
Anthropogenic & biomass burning Emissions
TOMS O3
Chemistry & TransportAnalysis
Meteorological Dependent Emissions
(biogenic, dust, sea salt)
STEM Tracer Model (classified tracers for
regional and emission types)
STEM Data-Assimilation
Model
Observations
Airmasses andtheir age & intensity
Analysis
Influence FunctionsEmission Biases/
Inversion
Assimilation of AIRNOW O3 surface observations for July 20, 2004Observations: circles, color coded by O3
mixing ratioSurface O3 (forecast)
Surface O3 (analysis)
Assimilation of elevated observations for July 20, 2004NOAA P3 flight observations Ozonesonde observations
(Rhode Island)
We are exploring these issues with a new NOAA GCP grant
Change of Initial O3 after Assimilation
• Date:
July 20, 2004
• Observations:
AirNow, P3-O3, Ozonesonde
• Isosurfaces of relative changes:
-20% (blue), +20% (yellow), +100% (red)
Effect of O3 Assimilation on Forecast
Courtesy John Reilly, MIT
Which species to assimilate?
A Key Issue Is Which Data To Assimilate -- Example Impact of
Assimilating NOy
Leads to improved prediction of NO, NO2, PAN, and HNO3
Modeling the Background Error Term
• AR Models• Improved 4D-Var Results
12 EDT July 20 (w/o (top) and w (bottom) assimilation)
4d-Var data assimilation results are visibly improved
when using the new AR background covariance
Observation error 8%; I.C. error 10ppbv; Initial ozone is control
Ensemble-based Chemical Data Assimilation
• Formulation and Challenges• Examples
Experimental setting of the ensemble-based data
assimilation system• 50 members, perturbed I.C., B.C., and emissions• 30% initial std, AR correlations + TESV perturbations• O3 and NO2 observations at 24 ground locations in 3 countries,
and in one vertical column. Perturbation 0.1% std, uncorrelated • Quality of analysis in a sub-domain including observation sites
Continued Improvement in the Forward Models are Needed: Effects of Physical
Removal Processes – which are significant sources of uncertainty
High Dry Dep Case Change in surface ozone (ppb)
With/W-o wet dep Change in column BC
Improving Emissions is a Top Priority: Models,
Emissions, and Observations are not
Perfect –Inverse Modeling
Where do we go from here?Example of Use of 3-D CFORS modeling system at TRACE-P Information Day in Hong Kong
Chemical Data AssimilationFeasible & necessary.Just the beginning—
more ??s than answers – we need test beds!
Important implications for measurement systems and models.
Need to grow the community.
PORT PHILLIP BAY
260 280 300 320 340 360EASTING (km)
DND
BRI
FTSPSY
PTC
MTC ALP
PTHGLS
GVD
PLP BXH
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HING(
km)
LIGHT
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HEAVY
AIR QUALITY FORECAST-MELBOURNE
AIR QUALITY FORECASTAIR QUALITY FORECAST--MELBOURNEMELBOURNE
NORTH EAST
HOUR
INDE
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NORTH EAST
HOUR
INDE
X
Tomorrow will be fine and sunnyTomorrow will be fine and sunny--with moderate to heavy air pollutionwith moderate to heavy air pollution
PORT PHILLIP BAY
260 280 300 320 340 360EASTING (km)
DND
BRI
FTSPSY
PTC
MTC ALP
PTHGLS
GVD
PLP BXH
5740
5760
5780
5800
5820
5840
NORT
HING(
km)
LIGHT
MODERATE
HEAVY
AIR QUALITY FORECAST-MELBOURNE
AIR QUALITY FORECASTAIR QUALITY FORECAST--MELBOURNEMELBOURNE
PORT PHILLIP BAY
260 280 300 320 340 360EASTING (km)
DND
BRI
FTSPSY
PORT PHILLIP BAY
260 280 300 320 340 360EASTING (km)
DND
BRI
FTSPSY
PTC
MTC ALP
PTHGLS
GVD
PLP BXH
5740
5760
5780
5800
5820
5840
NORT
HING(
km)
LIGHT
MODERATE
HEAVY
AIR QUALITY FORECAST-MELBOURNE
AIR QUALITY FORECASTAIR QUALITY FORECAST--MELBOURNEMELBOURNE
NORTH EAST
HOUR
INDE
X
NORTH EAST
HOUR
INDE
X NORTH EAST
HOUR
INDE
X
NORTH EAST
HOUR
INDE
X
Tomorrow will be fine and sunnyTomorrow will be fine and sunny--with moderate to heavy air pollutionwith moderate to heavy air pollution
Objectives:1. To ensure accurate, comprehensive global
observations of key atmospheric gases and aerosols;
2. To establish a system for integrating ground-based, in situ and satellite observations using atmospheric models;
3. To make the integrated observations accessible to users.
[email protected]@esa.int
An international process:
Panel of 19 experts from 12 countries and independent reviewers from 7 countries.
Integrated Global Atmospheric Chemistry Observation (IGACO) System
SatelliteObservations
Aircraft
Ground-based
IGACO System
Links to:
Space agencies, WCRP, GCOS, IGBP, IGOS themes
Implemented by WMOSee Overleaf
NO2
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