Get weather data from SILO

Extract weather data for Australia from the SILO weather data resource for a set of environments with defined latitude and longitude coordinates.

Usage

get.SILO.weather(
  Envs,
  Lats,
  Lons,
  Years,
  ncores = NULL,
  verbose = TRUE,
  dlprompt = FALSE
)

Arguments

Envs: Vector of environment names character strings.
Lats: Vector of latitude numeric values for each environment in the same order as Envs.
Lons: Vector of longitude numeric values for each environment in the same order as Envs.
Years: Vector of year integer values for each environment in the same order as Envs.
ncores: Number (integer) of cores to use for parallel processing of gridded data up to 5 cores. Use 1 to run in series. The default (NULL) will use the maximum available cores up to 5. If running in parallel, an output log text file will be created in the working directory.
verbose: Logical. Should progress be printed? Default = TRUE.
dlprompt: Logical. Should the user be prompted approve the total download size? Default = FALSE

Value

A list of length 2:

$data is a list of matrices of weather data for each weather variable. Each data matrix has environment names as rows and days of the year as columns
$Env.info is a data frame of environment names and coordinate values for environments included in the data.

Details

Weather variables returned include:

daily_rain - Daily rainfall (mm)
max_temp - Maximum temperature (°C)
min_temp - Minimum temperature (°C)
vp_deficit - Vapour pressure deficit (hPa)
radiation - Solar exposure, consisting of both direct and diffuse components (MJ m^-2)
day_lengths - Time between sunrise and sunset (h) not taken from SILO

When there are only a few environments, point data will be sequentially downloaded from SILO. When there are many environments in each year, data will be downloaded and extracted from whole gridded data files more efficiently. Any locations outside of the Australian land area will return NA.

An internet connection with high download speed is suggested for downloading gridded data for many environments.

References

Jeffrey, S. J., Carter, J. O., Moodie, K. B., & Beswick, A. R. (2001). Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environmental Modelling & Software, 16(4), 309–330.

Author