In khufkens/phenor: An R phenology Modelling Framework

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(phenor)
library(ecmwfr)
options(keyring_backend="file")

# side load small ERA5 dataset (if feasible within CRAN footprint)

ERA5 data can be used to train, evaluate and forecast phenology models (using CMIP routines). The data used in the download sections is ERA5 land which is re-analysis data at a 10km resolution.

# ERA5 example
pr_dl_era5(
  path = "~/Desktop",
  user = "2088",
  product = "era5",
  extent = c(
    50.73149477111302,
    -7.08887567473501,
    40.365567456020266,
    12.748594284373073
    )
  )

# ERA5-land example
pr_dl_era5(
  path = "~/Desktop",
  user = "2088",
  product = "land",
  file = "era5-land.nc",
  extent = c(
    48.345183009475015,
    4.782986565238425,
    45.64153500799514,
    11.44515031394129
  )
)

# format ERA5 data for upscaling
data <- pr_fm_era5(
  path = "~/Desktop/",
  file = "era5-land.nc",
  year = 2019
)

# load the included data using
data("phenocam_DB")

# optimize model parameters
# using daymet data for training
set.seed(1234)
optim.par <- pr_fit(
  data = phenocam_DB,
  cost = rmse,
  model = "TT",
  method = "GenSA"
)

output <- pr_predict(
  optim.par$par,
  data = data,
  model = "TT"
  )

raster::plot(output)
maps::map("world", add = TRUE)