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Function to run random meteorological simulations on a deweather model

Source: R/simulate_dw_met.R
simulate_dw_met.Rd

This function performs random simulations to help isolate the effect of emissions changes from meteorological variability in air quality data. It works by repeatedly shuffling meteorological variables (like wind and air temperature) while keeping temporal patterns intact, then predicting pollutant concentrations using a trained deweather model.

Usage

simulate_dw_met(
  dw,
  newdata = deweather::get_dw_input_data(dw),
  vars = c("ws", "wd", "air_temp"),
  resampling = c("constrained", "all"),
  window_day = 30,
  window_hour = 2,
  n = 200,
  aggregate = TRUE,
  ...,
  .progress = rlang::is_interactive()
)

Arguments

dw

A deweather model created with build_dw_model().

newdata

Data set to which to apply the model. If missing the data used to build the model in the first place will be used.

vars

The variables that should be randomly varied. Note that these should typically be meteorological variables (e.g., "ws", "wd", "air_temp") and not temporal emission proxies (e.g., "hour", "weekday", "week").

resampling

The resampling strategy. One of:

  • "constrained" (default), meaning that only days of the year close to the target date are sampled. This option is used in conjunction with window_day and window_hour. For example, a window_day of 30 will sample +/-30 days of the date.

  • "all", meaning all dates are shuffled.

The argument for using constrained resampling is that it resamples conditions for a similar time of year and / or hour of the day to minimise the resampling of implausible conditions e.g. very warm temperatures during winter.

window_day, window_hour

The day of year (window_day) and hour of day (window_hour) windows to sample within when resampling = "constrained". For example, window_day = 30 samples within +/-30 days of any given date.

n

The number of simulations to use.

aggregate

By default, all of the simulations will be aggregated into a single time series. When aggregate = FALSE, all simulations will be returned in a single data frame with an .id column distinguishing between them.

...

Not currently used.

.progress

Show a progress bar? Defaults to TRUE in interactive sessions.

Value

a tibble

Parallel Processing

This function supports parallel processing using the {mirai} package. You will likely find that the performance of this function increases if "daemons" are set. The greatest benefits will be seen if you spawn as many daemons as you have cores on your machine, although one fewer than the available cores is often a good rule of thumb.

# set workers - once per session
mirai::daemons(4)

# run your function as normal
tune_dw_model(aqroadside, "no2", tree_depth = c(5, 10))

Author

David Carslaw

Jack Davison

Examples

if (FALSE) { # \dontrun{
dw <- build_dw_model(aqroadside, "no2")
simulate_dw_met(dw)
} # }