R/extract_patched_point.R
extract_patched_point.RdExtracts station-based weather data for the provided dates from the nearest
weather station to the provided longitude and latitude values in x.
Weather data for the single closest station(s) to the provided longitude
and latitude will always be returned.
extract_patched_point(
x,
start_date,
end_date,
n_stations = 1L,
values = "all",
api_key = NULL
)List. An object with names containing vector pairings of
longitude and latitude values expressed as decimal degree values in that
order, each individual vectors' items should be named "x" (longitude) and
"y" (latitude), respectively. The list item names should be descriptive
of the individual vectors and will be included in a "location" column of
the output.
A character string or Date object representing the
beginning of the range to query in the format “yyyy-mm-dd”
(ISO8601). Data returned is inclusive of this date.
A character string or Date object representing the end of
the range query in the format “yyyy-mm-dd” (ISO8601). Data
returned is inclusive of this date. Defaults to the current system date.
An integer value that indicates how many stations per
location should be returned. Defaults to one (1) station with only the
nearest station being returned. Values greater than 1 will result in the
next nearest stations being returned from 1 to n.
A character string with the type of weather data to
return. See Available Values for a full list of valid values.
Defaults to all with all available values being returned.
A character string specifying a valid email address to use
for the request. The query will return an error if a valid email address
is not provided.
a data.table::data.table with the weather data queried with the weather variables in alphabetical order. The first eight columns will always be:
station_code,
station_name,
longitude,
latitude,
elev_m (elevation in metres),
date (ISO8601 format, "YYYYMMDD"),
year,
month,
day,
extracted (the date on which the query was made)
Column names are converted from the default returns of the API to be
snake_case formatted and where appropriate, the names of the values that
are analogous between SILO and DPIRD data are named
using the same name for ease of interoperability, e.g., using
rbind() to create a data.table that contains data from both APIs.
The SILO documentation provides the following information for the PatchedPoint data.
These data are a continuous daily time series of data at either recording stations or grid points across Australia:
Data at station locations consists of observational records which have been supplemented by interpolated estimates when observed data are missing. Datasets are available at approximately 8,000 Bureau of Meteorology recording stations around Australia.
Data at grid points consists entirely of interpolated estimates. The data are taken from the SILO gridded datasets and are available at any pixel on a 0.05° × 0.05° grid over the land area of Australia (including some islands).
Which will return all of the following values
Rainfall
Maximum temperature
Minimum temperature
Vapour pressure
Vapour pressure deficit
Class A pan evaporation
Synthetic estimate1
Combination (synthetic estimate pre-1970, class A pan 1970 onwards)
Morton's shallow lake evaporation
Solar exposure, consisting of both direct and diffuse components
Relative humidity at the time of maximum temperature
Relative humidity at the time of minimum temperature
FAO564 short crop
ASCE5 tall crop6
Morton's areal actual evapotranspiration
Morton's point potential evapotranspiration
Morton's wet-environment areal potential evapotranspiration over land
Mean sea level pressure
Solar radiation: total incoming downward shortwave radiation on a horizontal surface, derived from estimates of cloud oktas and sunshine duration3.
Relative humidity: calculated using the vapour pressure measured at 9am, and the saturation vapour pressure computed using either the maximum or minimum temperature6.
Evaporation and evapotranspiration: an overview of the variables provided by SILO is available here, https://data.longpaddock.qld.gov.au/static/publications/Evapotranspiration_overview.pdf.
The data are supplied with codes indicating how each datum was obtained.
Official observation as supplied by the Bureau of Meteorology
Deaccumulated rainfall (original observation was recorded over a period exceeding the standard 24 hour observation period).
Interpolated from daily observations for that date.
Synthetic Class A pan evaporation, calculated from temperatures, radiation and vapour pressure.
Interpolated from daily observations using an anomaly interpolation method.
Interpolated from the long term averages of daily observations for that day of year.
Rayner, D. (2005). Australian synthetic daily Class A pan evaporation. Technical Report December 2005, Queensland Department of Natural Resources and Mines, Indooroopilly, Qld., Australia, 40 pp.
Morton, F. I. (1983). Operational estimates of areal evapotranspiration and their significance to the science and practice of hydrology, Journal of Hydrology, Volume 66, 1-76.
Zajaczkowski, J., Wong, K., & Carter, J. (2013). Improved historical solar radiation gridded data for Australia, Environmental Modelling & Software, Volume 49, 64–77. DOI: doi:10.1016/j.envsoft.2013.06.013 .
Food and Agriculture Organization of the United Nations, Irrigation and drainage paper 56: Crop evapotranspiration - Guidelines for computing crop water requirements, 1998.
ASCE’s Standardized Reference Evapotranspiration Equation, proceedings of the National Irrigation Symposium, Phoenix, Arizona, 2000.
For further details refer to Jeffrey, S.J., Carter, J.O., Moodie, K.B. and Beswick, A.R. (2001). Using spatial interpolation to construct a comprehensive archive of Australian climate data, Environmental Modelling and Software, Volume 16/4, 309-330. DOI: doi:10.1016/S1364-8152(01)00008-1 .
Other weather data:
extract_data_drill(),
extract_power()
Other SILO:
extract_data_drill()
if (FALSE) {
# Source data from a list of latitude and longitude coordinates in NSW & WA
locs <- list(
"Merredin" = c(x = 118.28, y = -31.48),
"Corrigin" = c(x = 117.87, y = -32.33),
"Tamworth" = c(x = 150.84, y = -31.07)
)
# Replace "your_api_key" with a valid email address.
w <- extract_patched_point(
x = locs,
start_date = "20210101",
end_date = "20210131",
api_key = "your_api_key"
)
}