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Data Assimilation Working Group
Dylan Jones (U. Toronto)Kevin Bowman (JPL)
Daven Henze (CU Boulder)
IGC74 May 2015
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Chemical Data Assimilation Methodology
• x is the model state (e.g., O3 distribution)
• xa is the a priori estimate of the state
• y is the observations
• H is the observation operator that maps the model state to the instrument space
• R is the observation error covariance matrix
• Bx is the a priori error covariance matrix
at time t+1, where p is the vector of sources (NOx and CO emissions) or sinks.
State optimization:
Source optimization:
Joint source/state optimization:
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Chemical Data Assimilation with GEOS-Chem
Kalman Filter• Full chemistry state estimation • CO2 state estimation
4-Dimensional Variation Data Assimilation (4D-Var)• Full chemistry state and source estimation• CO2, CH4, N2O source (flux) estimation
Ensemble Kalman Filter (EnKF)• CO2, CH4 source (flux) estimation
3D-Var• Full chemistry state estimation
Local Ensemble Transform Kalman Filter (LETKF)• CO2, CH4 source (flux) estimation
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3D-Var and 4D-var
• 4D-Var adjusts the initial state (initial conditions) to optimize the model trajectory to better match the observations distributed over the assimilation window
• 3D-Var does not account for the differences in the timing of the observations over the assimilation window
[ECMWF Lecture Notes, 2003]
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4D-var and the Kalman Filter
• Unlike 4D-Var, the Kalman filter used only the observations available at the specified timestep
[ECMWF Lecture Notes, 2003]
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Ensemble Kalman Filter (EnKF)
Determine model errors from the time evolution of an ensemble of initial model states
For an ensemble of initial states X, which is (n x Nens), the model error covariance Sm
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New Applications: Assimilation of AIRS-OMI Data
Kalman filter assimilation of AIRS-OMI O3 profiles for August 2006
[Thomas Walker, JPL]
[See poster A.22 by Thomas Walkerthis afternoon]
Absolute O3 Difference at 420 hPa
Absolute O3 Difference at 900 hPa
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New Applications: Weak Constraint 4D-Var
Estimated CO adjustments in the tropics to mitigate model bias during assimilation of MOPITT CO data for March 2006
[Keller et al., submitted, JGR]
[talk by Martin Keller on Tuesday afternoon]
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New Applications: 4D-Var Optimization of O3 Deposition
Mean a priori O3 bias (ppb)
Mean a posteriori O3 bias (ppb)
Ozone dry deposition velocity distribution
[Walker et al., submitted, JGR]
Inverse modeling of O3 deposition for August 2006 using AQS surface data
Background versus local anthropogenic contributions to Western US ozone pollution constrained by Aura TES and OMI observations
Investigation:Huang et al., JGR (in press) improved ozone source attribution by integrating Tropospheric Emission Spectrometer (TES) ozone and Ozone Monitoring Instrument (OMI) nitrogen dioxide into a state-of-the-art multi-scale assimilation system. Ozone attribution was estimated at surface monitoring sites when total ozone exceeded current and potential thresholds (Fig. c-d).
Key Findings:• Average background ozone was estimated at 48.3 ppbv or 76.7% of the total ozone in California-Nevada region in
summer 2008 (Fig. a-b) but was repartitioned between non-local pollution, which was enhanced by 3.3 ppbv from TES ozone assimilation, and local wildfires, which was reduced by 5.7 ppbv from OMI nitrogen dioxide assimilation.
• Background ozone varied spatially with higher values in many rural regions. Except Southern California, less than 10 ppbv of local anthropogenic ozone would be possible without violating a 60 ppbv threshold. Increases in non-local pollution and local wildfires will require additional reductions in local anthropogenic emissions to meet standards.
Science problem:Proposed reductions in EPA primary ozone standard increases the importance of accurate attribution of background (non-local and local natural) and local human ozone sources.
[Min Huang, JPL]
Comparison between 4D-Var and EnKF in CO2 flux estimation in TransCom regions with simulated GOSAT
Liu et al., 2015, in preparation
Black: truthBlue: prior fluxesRed: 4D-VarGreen: EnKF
EnKF
Monte Carlo method in 4D-Var
Monthly mean flux error reduction
Monthly mean flux error reduction
11 TransCom regions over land
• Comparable performance between EnKF and 4D-Var in CO2 flux estimation[Junjie Liu, JPL]
Now:• Current version matches one of
CT’s 16 flux scenario cases (MillerFossil,CASA-GFED2,TAKA-Interactive Atm fluxes)
Future work: • Residual modeling with EOFs
(MsTMIP uncertainty)• testing effect of filter window
length
Introducing GEOS-CarbonTracker (poster at GMAC 2015, Boulder)
[Andrew Schuh , CSU]
Based on EnKF approach
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Developments Since IGC6
• Increase in the suite of observation operators for assimilation of satellite data• Nested full chemistry 4D-Var• Joint source/state optimization• Multispecies 4D-Var• Weak constraint 4D-Var• Monte Carlo and hybrid 4D-Var approach for Hessian calculation• GEOS-CarbonTracker
Future Challenges
• Data assimilation with the massively parallel GEOS-Chem• Enhancing availability of the EnKF capability to the GEOS-Chem community
