1. Interfaces EO data with Atmospheric and Land Surface Model: Progress report Liang Feng, Paul Palmer

2.  Reference OSSE system  High-resolution model  CO:CO2 ratios Project Tasks

3. I. Progress overview Overall Aim: Developing a reference OSSE simulation for community researchers to evaluate impacts of space-borne atmospheric composition measurements on surface flux estimates. Outputs: A. OSSE framework with flexible modules and libraries. Status: Revised and ready for beta-test. B. One complete example OSSE system for 1) simulating OCO-like XCO2 observations; and 2) estimating regional CO2 fluxes by assimilating XCO2 observations using an Ensemble Kalman Filter (EnKF). Status: close to be finished. C. Detailed documents. Status: beta version due in Dec, 2012. D. Visualization Tools. Status: to be started. Observing System Simulation Experiment Tool

4. II. OSSE framework. III. Example OSSE system Today we will focus on:

5. II. OSSE framework  Data flow and directory structure  Main modules  Styles

6. Obs +random errors Flux Forecasts Obs operator Forecast ( 3-D concentrations ) CTM Prior + error Posteriori + error Observation Simulation EnKF assimilation ETKF Model Obs Ensemble Ensemble forecasts ( 3-D fields ) Surface flux Ensemble CTM Obs operator

7.  Model world: Object, process, measurements, and estimates. Measure Transport Top-down estimate Atmosphere Observation Surface flux Flux model inventory Met files Disk IO

8. observation ctm lib surface_flux atmosphere etkf instrument  We have hence constructed a directory structure for OSSE tools as:

9.  Main modules Module nameDescription etkf_m.pyCalculate a posteriori ; gain matrix (K); transform matrix (T), by using ETKF algorithm. state_vectort_c.pyClass for state vector and its projection to surface fluxes: a). construct, manage, and update state vectors and their perturbation ensembles. b). maintain a (virtual) projection to surface fluxes. construct_error_cor_m.pyConstruct (spatial) error correlations from regional flux perturbation (basis) functions contruct_state_vector_m.pyInterface for user to construct state vector from flux perturbation (basis) functions 1. etkf

10. Module nameDescription ctm_grid_c.pyClass for tempo-spatial grid for modelling atmospheric compositions. ctm_field_c.pyClass for gridded model data of single atmospheric tracer. It also contains horizontal and vertical interpolation functions etc. ctm_slice_c.pyClass for tracer profiles along sampling track etc. ctm_world_c.pyGeneral container for tracer data ( field or slice). It provides: a) member management (add, group, remove, update, search, retrieve etc) ; b) (virtual) disk IO. Io_file_c.pyClass for helping to access CTM outputs, it helps to : 1)group file names for parameters to be read. 2) finalize extensions for file names. ctm_file_read_m.pyInterface for reading data from CTM outputs ctm_file_write_m.pyInterface for writing data to be read by CTM. 2. atmosphere

11. Module nameDescription surface_grid_c.pyClass for tempo-spatial grid for surface fluxes. surface_flux_c.pyClass for gridded surface flux. It also contains functions for: a) disk IO; b) re-gridding. flux_pb_c.pyClass to contain perturbation (basis) flux maps. It has a virtual disk IO for flux files used to force CTM. region_map_c.pyClass for generating multi-layer region maps according to user’s choices. gen_region_pb_flux_m.pyconstruct class flux_pb_c from multi-layer regional maps and the map for flux errors over the world. 3. surface_flux

12. Module nameDescription observation_c.pyClass for observations. It also contains functions to a) read (write) observations from/to disk files; b) filter and weight observations for assimilation. ctm_observation_c.pyClass for sampling model outputs at observation time and locations. ens_observation_c.pyClass for sampling ensemble model outputs at observation time and locations. 4. observation Module nameDescription ctm_config_c.pyClass to generate configuration file to control CTM forecast simulations. ctm_restartfile_c.pyClass to (re-) construct CTM restart files. 5. ctm

13. Module nameDescription sat_orbit_c.pyClass for generating (reading ) satellite orbits. sat_avk_c.pyClass for generating satellite averaging kernels from pre- calculated tables. sat_oerr_c.pyClass for generating satellite observation errors from pre- calculated tables. check_cloud_m.pyFunctions for checking the probabilities of cloud contaminations along satellite observation tracks from given cloud cover (probability) map and the viewspot penalty function check_aerosol_m.pyFunction for calculating AOD along satellite tracks from given AOD probability maps. 6. instrument

14. Module nameDescription time_m.pyFunctions for time conversions. gp_axis_c.pyGeneral class for axis. It has functions for indexing and interpolating etc error_message_m.pyRecord and print error or warning message fort_lib.f90Functions for (faster and parallelized) array operations, and equation solving. 7. lib

15.  Styles 1. Simple, short and stable 2. Object - and task –orientation. Only use stable python features and well-established packages Example: ctm_field_c.py: class ctm_field: #members obj_id: : id of the class instance. name: : name of the tracer unit: : unit of the data grid: : grid. data: : gridded data id: : id of the tracer. category: : category of the tracer group: : tracer group time_tag: : time step for the data __attr_dict: : dictionary of the attribute # functions (12 in total) def get_profiles(self, lon_lst, lat_lst, time_lst=None):

16. For example: by writing less than 30 lines, we can use class region_map to generate a complicated map for estimating regional fluxes at different spatial resolutions. 3. Easy to use

17. 4. Easy to change/expand Example: In ctm_world_c.py, efforts have been made to minimize the efforts needed to communicate with user’s CTM via disk files. The user now can read their model input/outputs by overriding member function read_ctm_to_fields in class ctm_world, or by overriding the interface module ctm_file_read_m.py.

18. III. Example OSSE system Aim: use modules in OSSE framework to build a complete OSSE system for OCO-like measurements (Feng et al, 2009). The system uses GEOS-Chem (which is, however, not part of the delivered package) as the chemistry transport model.

19. Obs ETKF Flux Forecasts (climatology) Obs operator forecast GEOS-Chem Model Obs Ensemble Ensemble forecasts Surface CO2 Ensemble GEOS-Chem Obs operator Prior + error Posterior + error 1) Observation simulation 2) Data Assimilation (+Perturbations)

20.  Observation Simulation module A.Surface Fluxes  We use CO2 flux climatology as ‘the true fluxes’.  We provide interfaces to store them into binary files for GEOS-Chem CTM to read. B.Chemistry Transport Model  Functions in ctm_restart_c.py and ctm_config_c.py are changed (overridden) to generate input.geos and restart files for GEOS-Chem forward simulations.

21. C. Sampling We override IO interfaces so that we can use class ctm_world to read and sample 3D model fields along the satellite orbits. We used cloud and aerosol climatology files and the default algorithm as the inputs to screen cloud and aerosols. D. Vertical integration/conversion A standard module is used to convert model vertical profiles to satellite retrievals, by applying the averaged kernels read from pre-calculated tables for OCO-like instrument.

22. Example: Simulating OCO XCO2 1) Sampled along Aqua orbits GEOS-Chem transport model: Biosphere (CASA), Biomass (GFED), Fossil (+Bio) fuel (ODIAC), Ocean (Takahashi) 2) Scenes with cloud or AOD > 0.3 removed Jan 1-day 3) Averaging kernels applied Pressure [hPa] Glint mode Averaging kernels

23.  Top-down Optimal surface flux estimation x: regional surface fluxes y obs : measurements of atmospheric concentrations. H: Jacobian (CTM model). K=P f H T (HP f H T +R) -1 – Kalman gain matrix. P f : a priori uncertainty matrix R: observation error matrix Posteriori Priori gainobservation model  Data Assimilation Module (Ensemble Kalman Filter)

24.  Ensemble Approach

25. A. State vector and ensemble representation of its uncertainties

26. We use region_map_c.py to define 144 global regions. We use gen_region_pb_flux_m.py to generate BFs from biospheric GPP map and the multi-layer map for the 144 regions.

28. D. Inversion algorithm:Ensemble Transform Kalman Filter (ETKF)

29.  Algorithm validation: Comparison with the LSCE 4d-var system. In the comparison experiments, we have assimilated the same ACOS-GOSAT B210 XCO2 retrievals (real data) over 2009 and 2010, by using 2 different approaches: EnKF (UoE) and 4d-var (LSCE) (Feng et al, 2013).

31. VI. Summary OSSE system will be issued for beta test soon. Whole package, including detailed documents will be delivered in next march.

32. INTERNAL DEADLINES 1) Deliver revised code and documentation to Paul end of November 2012. This is before the Reading meeting. I will check the code and documentation. Return to LF a week later. 2) Circulate to Silvia, Annemarie, and Siegfried the week of the 10th December 2012. Start beta testing. 3) End of beta testing period at the end of January 2013. 4) Debug, edit, package, and produce online material by the week of the 21st February 2013. 5) Final edits and release the end of February. We are about two weeks behind the internal deadline, but months ahead the project schedule.