Polynomial transformation and aggregation of climate data

The function staggregate_polynomial() aggregates climate data to the daily level, raises these daily values to the 1 through nth power, and aggregates the transformed values to the polygon level and desired temporal scale.

Usage

staggregate_polynomial(
  data,
  overlay_weights,
  daily_agg,
  time_agg = "month",
  start_date = NA,
  time_interval = "1 hour",
  degree
)

Arguments

data: The raster brick with the data to be transformed and aggregated
overlay_weights: A table of weights which can be generated using the function overlay_weights()
daily_agg: How to aggregate hourly values to daily values prior to transformation. Options are 'sum', 'average', or 'none' ('none' will transform values without first aggregating to the daily level)
time_agg: the temporal scale to aggregate data to. Options are 'hour', 'day', 'month', or 'year' ('hour' cannot be selected unless daily_agg = 'none')
start_date: the date (and time, if applicable) of the first layer in the raster. To be input in a format compatible with lubridate::as_datetime(), e.g. "1991-10-29" or "1991-10-29 00:00:00". The default is NA since the rasters usually already contain temporal information in the layer names and they do not need to be manually supplied.
time_interval: the time interval between layers in the raster to be aggregated. To be input in a format compatible with seq(), e.g. '1 day' or '3 months'. The default is '1 hour' and this argument is required if daily_agg is not 'none' or if the start_date argument is not NA.
degree: the highest exponent to raise the data to

Examples

polynomial_output <- staggregate_polynomial(
  data = temp_nj_jun_2024_era5 - 273.15, # Climate data to transform and
                                         # aggregate
  overlay_weights = overlay_weights_nj, # Output from overlay_weights()
  daily_agg = "average", # Average hourly values to produce daily values
                         # before transformation
  time_agg = "month", # Sum the transformed daily values across months
  start_date = "2024-06-01 00:00:00", # The start date of the supplied data,
                                      # only required if the layer name
                                      # format is not compatible with stagg
  time_interval = "1 hour", # The temporal interval of the supplied data,
                            # required if daily_agg is not "none" or if the
                            # start_date argument is not NA
  degree = 4 # Highest order
  )
#> Rewriting the data's temporal metadata (layer names) to reflect a dataset starting on the supplied start date and with a temporal interval of 1 hour
#> Averaging over 24 layers per day to get daily values
#> Executing polynomial transformation
#> Aggregating by polygon and month

head(polynomial_output)
#>     year month poly_id  order_1  order_2  order_3  order_4
#>    <num> <num>  <char>    <num>    <num>    <num>    <num>
#> 1:  2024     6     011 726.5687 17749.71 437408.2 10873488
#> 2:  2024     6     033 733.6797 18116.46 451620.0 11364291
#> 3:  2024     6     015 728.9664 17889.96 443345.9 11091933
#> 4:  2024     6     009 686.7632 15826.99 367227.1  8579171
#> 5:  2024     6     007 730.9276 17985.27 446880.3 11210400
#> 6:  2024     6     041 683.5539 15879.92 375830.3  9051704