daymetr (version 1.7.1)

download_daymet: Function to download single location 'Daymet' data

Description

Function to download single location 'Daymet' data

Usage

download_daymet(
  site = "Daymet",
  lat = 36.0133,
  lon = -84.2625,
  start = 2000,
  end = as.numeric(format(Sys.time(), "%Y")) - 2,
  path = tempdir(),
  internal = TRUE,
  silent = FALSE,
  force = FALSE,
  simplify = FALSE
)

Value

Daymet data for a point location, returned to the R workspace or written to disk as a csv file.

Arguments

site

the site name.

lat

latitude (decimal degrees)

lon

longitude (decimal degrees)

start

start of the range of years over which to download data

end

end of the range of years over which to download data

path

set path where to save the data if internal = FALSE (default = NULL)

internal

TRUE or FALSE, if TRUE returns a list to the R workspace if FALSE puts the downloaded data into the current working directory (default = FALSE)

silent

TRUE or FALSE (default), to provide verbose output

force

TRUE or FALSE (default), override the conservative end year setting

simplify

output data as a tibble, logical FALSE or TRUE (default = TRUE)

Examples

if (FALSE) {
# The following commands download and process Daymet data
# for 10 years of the >30 year of data available since 1980.
daymet_data <- download_daymet(
"testsite_name",
 lat = 36.0133,
 lon = -84.2625,
 start = 2000,
 end = 2010,
 internal = TRUE
 )

# We can now quickly calculate and plot
# daily mean temperature. Also, take note of
# the weird format of the header. This format 
# is not altered as to keep compatibility
# with other ways of acquiring Daymet data
# through the ORNL DAAC website.

# The below command lists headers of 
# the downloaded nested list.
# This data includes information on the site
# location etc. The true climate data is stored
# in the "data" part of the nested list.
# In this case it can be accessed through
# daymet_data$data. Other attributes include
# for example the tile location (daymet_data$tile),
# the altitude (daymet_data$altitude), etc.
str(daymet_data)

# load the tidyverse (install if necessary)
if(!require(tidyverse)){install.package(tidyverse)}
library(tidyverse)

# Calculate the mean temperature from min
# max temperatures and convert the year and doy
# to a proper date format.
daymet_data$data <- daymet_data$data |>
 mutate(
 tmean = (tmax..deg.c. + tmin..deg.c.)/2,
 date = as.Date(paste(year, yday, sep = "-"), "%Y-%j")
 )

# show a simple graph of the mean temperature
plot(daymet_data$data$date,
     daymet_data$data$tmean,
     xlab = "Date",
     ylab = "mean temperature")
 
# For other practical examples consult the included
# vignette. 
}