State Variable Data Assimilation: Ensemble Kalman Filter and Generalized ensemble filter

Check out SDA_control function for more details on the control arguments.

Usage

sda.enkf.multisite(
  settings,
  obs.mean,
  obs.cov,
  Q = NULL,
  restart = NULL,
  pre_enkf_params = NULL,
  ensemble.samples = NULL,
  control = list(TimeseriesPlot = FALSE, OutlierDetection = FALSE, send_email = NULL,
    keepNC = TRUE, forceRun = TRUE, run_parallel = TRUE, MCMC.args = NULL, merge_nc =
    TRUE, execution = "local"),
  debias = list(cov.dir = NULL, start.year = NULL),
  ...
)

Arguments

settings

PEcAn settings object

obs.mean

Lists of date times named by time points, which contains lists of sites named by site ids, which contains observation means for each state variables of each site for each time point.

obs.cov

Lists of date times named by time points, which contains lists of sites named by site ids, which contains observation covariances for all state variables of each site for each time point.

Q

Process covariance matrix given if there is no data to estimate it.

restart

Used for iterative updating previous forecasts. Default NULL. List object includes file path to previous runs and start date for SDA.

pre_enkf_params

Used for passing pre-existing time-series of process error into the current SDA runs to ignore the impact by the differences between process errors.

ensemble.samples

list of ensemble parameters across PFTs. Default is NULL.

control

List of flags controlling the behavior of the SDA. `TimeseriesPlot` for post analysis examination; `OutlierDetection` decide if we want to execute the outlier detection each time after the model forecasting; `send_email` contains lists for sending email to report the SDA progress; `keepNC` decide if we want to keep the NetCDF files inside the out directory; `forceRun` decide if we want to proceed the Bayesian MCMC sampling without observations; `run_parallel` decide if we want to run the SDA under parallel mode for the `future_map` function; `MCMC.args` include lists for controling the MCMC sampling process (iteration, nchains, burnin, and nthin.). `merge_nc` determine if we want to merge all netCDF files across sites and ensembles. If it's set as `TRUE`, we will then combine all netCDF files into the `merged_nc` folder within the `outdir`. `execution` decide the way we want to execute model including `local` ,where we execute the model locally; `qsub`, where we use the traditional `start_model_runs` function for submission; `qsub_parallel`, where we first combine jobs and submit them into the SCC.

debias

List: R list containing the covariance directory and the start year. covariance directory should include GeoTIFF files named by year. start year is numeric input which decide when to start the debiasing feature.

...

Additional arguments, currently ignored

Value

NONE

Details

Restart mode: Basic idea is that during a restart (primary case envisioned as an iterative forecast), a new workflow folder is created and the previous forecast for the start_time is copied over. During restart the initial run before the loop is skipped, with the info being populated from the previous run. The function then dives right into the first Analysis, then continues on like normal.

Author

Michael Dietze, Ann Raiho and Alexis Helgeson dietze@bu.edu