1. Data Assimilation Working Group Dylan Jones (U. Toronto) Kevin Bowman (JPL) Daven Henze (CU Boulder) 1 IGC7 4 May 2015

2. Chemical Data Assimilation Methodology x is the model state (e.g., O 3 distribution) x a 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 B x 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: 2 Joint source/state optimization:

3. Chemical Data Assimilation with GEOS-Chem 3 Kalman Filter Full chemistry state estimation CO 2 state estimation 4-Dimensional Variation Data Assimilation (4D-Var) Full chemistry state and source estimation CO 2, CH 4, N 2 O source (flux) estimation Ensemble Kalman Filter (EnKF) CO 2, CH 4 source (flux) estimation 3D-Var Full chemistry state estimation Local Ensemble Transform Kalman Filter (LETKF) CO 2, CH 4 source (flux) estimation

4. 3D-Var and 4D-var 4 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]

5. 4D-var and the Kalman Filter 5 Unlike 4D-Var, the Kalman filter used only the observations available at the specified timestep [ECMWF Lecture Notes, 2003]

6. Ensemble Kalman Filter (EnKF) 6 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 N ens ), the model error covariance S m

7. New Applications: Assimilation of AIRS-OMI Data 7 Kalman filter assimilation of AIRS-OMI O 3 profiles for August 2006 [Thomas Walker, JPL] [See poster A.22 by Thomas Walker this afternoon] Absolute O 3 Difference at 420 hPa Absolute O 3 Difference at 900 hPa

8. New Applications: Weak Constraint 4D-Var 8 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]

9. New Applications: 4D-Var Optimization of O 3 Deposition Mean a priori O 3 bias (ppb) Mean a posteriori O 3 bias (ppb) 9 Ozone dry deposition velocity distribution [Walker et al., submitted, JGR] Inverse modeling of O 3 deposition for August 2006 using AQS surface data

10. 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]

11. Comparison between 4D-Var and EnKF in CO 2 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 11 TransCom regions over land Comparable performance between EnKF and 4D-Var in CO 2 flux estimation [Junjie Liu, JPL]

12. 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

13. Developments Since IGC6 13 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