sda_bias_correction — sda_bias_correction • PEcAnAssimSequential

This function helps to correct the forecasts' biases based on ML (random forest) training on the previous time point.

Usage

sda_bias_correction(
  site.locs,
  t,
  all.X,
  obs.mean,
  state.interval,
  cov.dir,
  pre.states,
  py.init = NULL
)

site.locs: data.frame: data.frame that contains longitude and latitude in its first and second column.
t: numeric: the current number of time points (e.g., t=1 for the beginning time point).
all.X: list: lists of data frame of model forecast from the beginning to the current time points that has n (ensemble size) rows and n.var (number of variables) times n.site (number of locations) columns. (e.g., 100 ensembles, 4 variables, and 8,000 locations will end up with data.frame of 100 rows and 32,000 columns)
obs.mean: List: 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.
state.interval: matrix: containing the upper and lower boundaries for each state variable.
cov.dir: character: physical path to the directory contains the time series covariate maps.
pre.states: list: containing previous covariates for each location.
py.init: R function: R function to initialize the python functions. Default is NULL. the default random forest will be used if `py.init` is NULL.

list: the current X after the bias-correction; the ML model for each variable; predicted residuals.

Dongchen Zhang