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Chemical Data Assimilation in Support of Chemical Weather
ForecastsGreg Carmichael, Adrian Sandu, Dacian Daescu, Tianfeng Chai,
John Seinfeld, Tad Anderson, Peter Hess, Dacian Daescu
Data Assimilation
Chemical Data Assimilation in Support of Chemical Weather
ForecastsOutline Motivation
Current State of Forward Models
Data Assimilation Framework (4d- Var) – Issues
Preliminary Results
Future Directions
Models are an Integral Part of Atmospheric Chemistry Studies
• Flight planning• Provide 4-Dimensional context of the
observations• Facilitate the integration of the different
measurement platforms • Evaluate processes (e.g., role of biomass
burning, heterogeneous chemistry….)• Evaluate emission estimates (bottom-up
as well as top-down)• Emission control strategies testing• Air quality forecasting
TRACE-P/Ace-Asia/ITCT-2K1 EXECUTION
Emissions-Fossil fuel-Biomass burning-Biosphere, dust
Long-range transport fromEurope, N. America, Africa
ASIA PACIFIC
Satellite datain near-real time:MOPITTTOMSSEAWIFSAVHRRLIS
3D chemical model forecasts: - x - GEOS-CHyEM - CFORS - z
FLIGHTPLANNING
Boundary layerchemical/aerosolprocessing
ASIANOUTFLOW
Stratosphericintrusions
PACIFIC
Forward Models Are becoming More Comprehensive
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
110 115 120 125 130 135 1400
1
2
3
4
5
6
7
CO Scale(ppbv)300+250 to 300200 to 250150 to 200100 to 15050 to 100
110 115 120 125 130 135 1400
1
2
3
4
5
6
7
K(ug/m3)1+0.8 to 10.6 to 0.80.4 to 0.60.2 to 0.40 to 0.2
Fight Planning: Frontal outflow of biomass burning plumes E of Hong Kong
Observed CO –Sacshe et al.
Observed aerosol potassium - Weber et al.
Biomass burning CO forecast
Longitude
100 ppb
P-3B
0.00
0.20
0.40
0.60
0.80
1.00
1.20
Te
mp
era
ture
H2
O
Win
d S
pe
ed
O3
SO
4
J[O
1D
]
SO
2
PA
N
Eth
en
e
Pro
pa
ne
CO
J[N
O2
]
Eth
an
e
No
y
Eth
yn
e
RN
O3
Be
nze
ne
+ T
olu
en
e
OH
AO
E
HN
O3
NO
2
NO
Co
rrela
tio
n C
oeff
icie
nt
R(<1KM)
R(1-3KM)
R(>3 KM)
Predictability – as Measured by Correlation Coefficients
Met Parameters are Best
Performance decreases with altitude
< 1km
Model vs. Observations
Modeled 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
+
Development of a General Computational Framework for the Optimal Integration
of Atmospheric Chemical Transport Models and Measurements Using Adjoints
(NSF ITR/AP&IM 0205198 – Started Fall 2002)
A collaboration between:
Greg Carmichael (Dept. of Chem. Eng., U. Iowa)Adrian Sandu (Dept. of Comp. Sci., Virginia Tech.)
John Seinfeld (Dept. Chem. Eng., Cal. Tech.)Tad Anderson (Dept. Atmos. Sci., U. Washington)
Peter Hess (Atmos. Chem., NCAR)Dacian Daescu (Dept. Math, Portland State)
http://atmos.cgrer.uiowa.edu/people/tchai/
Basic Idea of 4D-Var
0 0 b 1 0 b obs 1 obs
0
1 1( )
2 2
NT Tk k k kk
k
J c c c B c c c c R c c
•Define a cost functional
•Derive adjoint of tangent linear model
λ λ( λ ) ρ (ρ )λ φ
ρTi i
i iiu K F c
t
Where adjoint variables are the sensitivities of the cost functional with respect to state variables (concentrations), i.e.
ii c
J
•Update Initial conditions using the gradients
Useful by themselves !!
Assimilation ResultsAssimilate O3/NO2 with O3/NO2 observations in the window [0,6] GMT, March 01, 2001;Twin experiments framework;Full 3D simulation with SAPRC chemical mechanism.
O3
CO-assimilation
Observation Frequency vs Number of Species O
3
O3 - only
O3 & NO2
Recovery of O3 and NO2 is Different WHY?
NO2
O3
Most of the grid points values are recovered within in 1%; but some locations the error is > 20%.
1%
20%
Assimilation requires better algorithms (with known error behavior)
Additional details see Chai’s paper on Thursday
Overview of Research in Data Assimilation for Chemical Models. Solid lines represent current capabilities. Dotted lines represent new analysis capabilities that arise through the assimil. of chemical data.
Ensemble methods
Chemical Assimilation and
Big-Iron“BIGMAC”@VT
Ranked 3rd with measured performance = 10 Tflop/s.A Pentium class cluster with 16-24 processors has ~ 50 Gflop/sec.On such a cluster we run parallel STEM (TraceP): 1 hour simulation time / 5 minutes cpu timeOn the terrascale machine we can run in parallel an ensemble of 200 simulations for the same simulation / cpu time ratio.
Assimilation of Aerosol Dynamics
•Theoretical framework enables the solution of coupled coagulation and growth with minimal number of size bins;
•Piecewise polynomial discretizations;
•Adjoint/assimila-tion system built
Data FrequencyGradient Methods
Recovery of Initial Distribution
We plan to test some of these developments in an operational setting this summer as part of a large field experiment.
We are Developing General Software Tools to Facilitate the Close Integration of Measurements
and Models
The framework will provide tools for: 1) construction of the adjoint model; 2) handling large datasets; 3) checkpointing support; 4) optimization; 5) analysis
of results; 6) remote access to data and computational resources.
Adjoints being developed for MOZART, plans for WRF-Chem
http://atmos.cgrer.uiowa.edu/people/tchai/
Chemical Data Assimilation: The Future?
Feasible & necessary.Just the beginning—
more ??s than answers – but we have test beds!
Huge implications for measurement systems and models.
Need to grow the community.
PORT PHILLIP BAY
260 280 300 320 340 360
EASTING (km)
DND
BRI
FTSPSY
PTC
MTC ALP
PTHGLS
GVD
PLP BXH
5740
5760
5780
5800
5820
5840
NORTHIN
G(km)
LIGHT
MODERATE
HEAVY
AIR QUALITY FORECAST-MELBOURNE
AIR QUALITY FORECASTAIR QUALITY FORECAST--MELBOURNEMELBOURNE
NORTH EAST
HOUR
IND
EX
NORTH EAST
HOUR
IND
EX
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
260 280 300 320 340 360
EASTING (km)
DND
BRI
FTSPSY
PTC
MTC ALP
PTHGLS
GVD
PLP BXH
5740
5760
5780
5800
5820
5840
NORTHIN
G(km)
LIGHT
MODERATE
HEAVY
AIR QUALITY FORECAST-MELBOURNE
AIR QUALITY FORECASTAIR QUALITY FORECAST--MELBOURNEMELBOURNE
PORT PHILLIP BAY
260 280 300 320 340 360
EASTING (km)
DND
BRI
FTSPSY
PORT PHILLIP BAY
260 280 300 320 340 360
EASTING (km)
DND
BRI
FTSPSY
PTC
MTC ALP
PTHGLS
GVD
PLP BXH
5740
5760
5780
5800
5820
5840
NORTHIN
G(km)
LIGHT
MODERATE
HEAVY
AIR QUALITY FORECAST-MELBOURNE
AIR QUALITY FORECASTAIR QUALITY FORECAST--MELBOURNEMELBOURNE
NORTH EAST
HOUR
IND
EX
NORTH EAST
HOUR
IND
EX NORTH EAST
HOUR
IND
EX
NORTH EAST
HOUR
IND
EX
Tomorrow will be fine and sunnyTomorrow will be fine and sunny--with moderate to heavy air pollutionwith moderate to heavy air pollution
TWO-SCENARIO TWO-SCENARIO FORECASTFORECAST
http://www.wmo.ch/web/arep/gaw/urban.html
Air Quality Forecasting Research Elements
Summary of USWRP Air Quality Forecasting WorkshopApril 29 - May 1, 2003
Houston, TX
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