Data Assimilation Working Group

Dylan Jones (U. Toronto) Kevin Bowman (JPL)

Daven Henze (CU Boulder)

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IGC7 4 May 2015

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:

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Joint source/state optimization:

Chemical Data Assimilation with GEOS-Chem

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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

3D-Var and 4D-var

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• 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]

4D-var and the Kalman Filter

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• Unlike 4D-Var, the Kalman filter used only the observations available at the specified timestep

[ECMWF Lecture Notes, 2003]

Ensemble Kalman Filter (EnKF)

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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

New Applications: Assimilation of AIRS-OMI Data

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Kalman filter assimilation of AIRS-OMI O3 profiles for August 2006

[Thomas Walker, JPL]

[See poster A.22 by Thomas Walker this afternoon]

Absolute O3 Difference at 420 hPa

Absolute O3 Difference at 900 hPa

New Applications: Weak Constraint 4D-Var

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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]

New Applications: 4D-Var Optimization of O3 Deposition

Mean a priori O3 bias (ppb)

Mean a posteriori O3 bias (ppb)

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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: truth Blue: prior fluxes Red: 4D-Var Green: 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

Developments Since IGC6

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• 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