weatherOz
Rodrigo Pires, Anna Hepworth, Rebecca O’Leary, Jonathan Carroll, Dean Marchiori, Paul Melloy, Mark Padgham, Hugh Parsonage, Keith Pembleton and Adam H. Sparks
Source:vignettes/weatherOz.Rmd
weatherOz.RmdIntroduction
{weatherOz} provides automated downloading, parsing, and formatting of weather data for Australia through API endpoints provided by the Department of Primary Industries and Regional Development (DPIRD) of Western Australia, and by the Science and Technology Division of the Queensland Government’s Department of Environment and Science (DES). As well as Australian Government Bureau of Meteorology (‘BOM’) précis and coastal forecasts, agriculture bulletin data, and downloading and importing radar and satellite imagery files.
DPIRD weather data are accessed through public APIs provided by DPIRD, https://www.agric.wa.gov.au/weather-api-20, providing access to weather station data from DPIRD’s own weather station network. Detailed information on using {weatherOz} with DPIRD data is available in the weatherOz for the DPIRD Weather 2.0 API vignette.
Australia-wide weather data are based on data from the Australian Bureau of Meteorology (BOM) data and accessed through SILO (Scientific Information for Land Owners) (Jeffery et al., 2001). More details on using {weatherOz} with SILO data are available in the weatherOz for the SILO API vignette.
The BOM also serves several types of data data as XML, JSON and SHTML
files. This package fetches these files, parses them and return a data
frame. Satellite and radar imagery files are also made available to the
public via anonymous FTP. {weatherOz} provides functionality to query,
fetch and create terra::SpatRaster() or {stars} objects of
GeoTIFF satellite imagery or a {magick} object of radar image.png files.
For detailed information on using {weatherOz} with BOM data, please see
the weatherOz for BOM Data
Resources vignette.
Following is a quick demonstration of some common tasks that you may wish to undertake while using {weatherOz}.
A Note on API Keys
The examples in this README assume that you have stored your API key in your .Renviron file. {weatherOz} will prompt you to set up your API keys automatically if you haven’t. For more information on the preferred method for setting up your API keys, see Chapter 8 in “What They Forgot to Teach You About R” by Bryan et al. for more on storing details in your .Renviron if you are unfamiliar.
To get a DPIRD API key, you can use get_key() and it
will direct you to the form to request a key and provides instructions
for using usethis::edit_r_environ() to add your key to your
.Renviron so that {weatherOz} will automatically find it. If you have
already set up an API key, this will return that value for you.
get_key(source = "DPIRD")You only need to provide an e-mail address for the SILO API. Using
get_key() will provide you with instructions on what format
to use in your .Renviron so that {weatherOz} will auto-recognise it and
if you have already set up an API key, this will return that value for
you.
get_key(source = "SILO")Note that you do not need to do this separately, any function requiring an API key will prompt you if you don’t have one set.
Using {weatherOz} to Fetch Daily Summary DPIRD Data
Daily summary weather data is frequently used. The following example will demonstrate how you can find and download weather station data for stations in or near Northam, WA for the year of 2022. This example assumes that you have stored your DPIRD API key in your .Renviron file.
library(weatherOz)
Northam <-
find_nearby_stations(
longitude = 116.6620,
latitude = -31.6540,
which_api = "dpird",
distance_km = 20
)
Northam
#> station_code station_name longitude latitude state elev_m
#> <fctr> <char> <num> <num> <char> <int>
#> 1: NO Northam 116.6942 -31.65161 WA 163
#> 2: MK Muresk 116.6913 -31.72772 WA 251
#> owner
#> <char>
#> 1: WA Department of Primary Industries and Regional Development (DPIRD)
#> 2: WA Department of Primary Industries and Regional Development (DPIRD)
#> distance_km
#> <num>
#> 1: 3.23
#> 2: 7.93There are two stations within 20km of the coordinates for Northam, WA that we provided. We’ll choose the closest, Northam, station_code NO to fetch the daily weather data for all air temperature, rainfall and relative humidity values.
dpird_daily <- get_dpird_summaries(
station_code = "NO",
start_date = "2022-01-01",
end_date = "2022-12-31",
values = c("airTemperature", "rainfall", "relativeHumidity")
)
dpird_daily
#> Key: <station_code>
#> station_code station_name longitude latitude year month day
#> <fctr> <char> <num> <num> <int> <int> <int>
#> 1: NO Northam 116.6942 -31.65161 2022 1 1
#> 2: NO Northam 116.6942 -31.65161 2022 1 2
#> 3: NO Northam 116.6942 -31.65161 2022 1 3
#> 4: NO Northam 116.6942 -31.65161 2022 1 4
#> 5: NO Northam 116.6942 -31.65161 2022 1 5
#> ---
#> 361: NO Northam 116.6942 -31.65161 2022 12 27
#> 362: NO Northam 116.6942 -31.65161 2022 12 28
#> 363: NO Northam 116.6942 -31.65161 2022 12 29
#> 364: NO Northam 116.6942 -31.65161 2022 12 30
#> 365: NO Northam 116.6942 -31.65161 2022 12 31
#> date air_tavg air_tmax air_tmax_time air_tmin
#> <Date> <num> <num> <POSc> <num>
#> 1: 2022-01-01 23.8 32.5 2022-01-01 17:02:00 16.7
#> 2: 2022-01-02 25.4 36.1 2022-01-02 16:58:00 14.4
#> 3: 2022-01-03 28.0 39.9 2022-01-03 14:42:00 16.6
#> 4: 2022-01-04 26.1 36.3 2022-01-04 15:15:00 17.3
#> 5: 2022-01-05 27.3 39.1 2022-01-05 16:19:00 16.1
#> ---
#> 361: 2022-12-27 23.0 40.2 2022-12-27 14:48:20 13.7
#> 362: 2022-12-28 22.2 40.5 2022-12-28 19:58:20 11.9
#> 363: 2022-12-29 26.0 40.2 2022-12-30 03:03:20 13.8
#> 364: 2022-12-30 26.6 39.2 2022-12-30 13:50:50 14.5
#> 365: 2022-12-31 22.7 31.1 2022-12-31 15:45:50 16.2
#> air_tmin_time rainfall rh_avg rh_tmax rh_tmax_time rh_tmin
#> <POSc> <num> <num> <num> <POSc> <num>
#> 1: 2022-01-01 05:17:00 0 39.9 71.5 2022-01-01 04:10:00 14.5
#> 2: 2022-01-02 05:19:00 0 35.2 63.8 2022-01-02 05:39:00 16.4
#> 3: 2022-01-03 05:18:00 0 38.0 68.5 2022-01-03 05:22:00 14.4
#> 4: 2022-01-04 04:22:00 0 45.9 74.2 2022-01-04 04:33:00 20.1
#> 5: 2022-01-05 05:14:00 0 42.1 77.2 2022-01-05 05:22:00 14.4
#> ---
#> 361: 2022-12-27 05:13:20 0 41.8 81.1 2022-12-26 16:03:20 14.6
#> 362: 2022-12-28 05:33:20 0 32.8 65.4 2022-12-28 05:35:00 9.2
#> 363: 2022-12-29 05:01:40 0 29.2 62.5 2022-12-29 05:08:20 9.5
#> 364: 2022-12-30 04:52:30 0 34.4 61.2 2022-12-30 05:06:40 7.9
#> 365: 2022-12-31 05:23:20 0 59.0 98.9 2022-12-31 05:30:00 24.7
#> rh_tmin_time
#> <POSc>
#> 1: 2022-01-01 18:03:00
#> 2: 2022-01-02 16:58:00
#> 3: 2022-01-03 16:15:00
#> 4: 2022-01-04 16:35:00
#> 5: 2022-01-05 17:21:00
#> ---
#> 361: 2022-12-27 16:47:30
#> 362: 2022-12-28 16:11:40
#> 363: 2022-12-29 17:31:40
#> 364: 2022-12-30 14:00:00
#> 365: 2022-12-31 16:01:40Using {weatherOz} to Fetch Patched Point SILO Data
Daily weather station observations with interpolated missing values are available from SILO. The following example will demonstrate how you can find and download weather station data for stations in or near Toowoomba, Qld for the year of 2022. This example assumes that you have stored your SILO API key (email address) in your .Renviron file.
library(weatherOz)
Tbar <-
find_nearby_stations(
longitude = 151.9507,
latitude = -27.5598,
which_api = "silo",
distance_km = 20
)
Tbar
#> station_code station_name longitude latitude state elev_m owner
#> <fctr> <char> <num> <num> <char> <num> <char>
#> 1: 041529 Toowoomba Airport 151.9134 -27.5425 QLD 640.9 BOM
#> 2: 041369 Moyola 151.8819 -27.5233 QLD 559.0 BOM
#> 3: 040096 Helidon Post Office 152.1246 -27.5504 QLD 155.0 BOM
#> 4: 041011 Cambooya Post Office 151.8650 -27.7072 QLD 476.0 BOM
#> 5: 041512 Cooby Creek Dam 151.9244 -27.3825 QLD 497.0 BOM
#> distance_km
#> <num>
#> 1: 4.149375
#> 2: 7.903524
#> 3: 17.172757
#> 4: 18.433372
#> 5: 19.881135There are 11 stations within 20km of the coordinates for Toowoomba, Qld that we provided in the SILO database. We’ll choose the closest, Toowoomba, station_code 041103 to fetch the daily weather data for all air temperature, rainfall and relative humidity values.
ppd <- get_patched_point(
station_code = "041103",
start_date = "2022-01-01",
end_date = "2022-12-31",
values = c("max_temp", "min_temp", "rain", "rh_tmax", "rh_tmin")
)
ppd
#> station_code station_name year month day date air_tmax
#> <fctr> <char> <num> <num> <int> <Date> <num>
#> 1: 041103 Toowoomba 2022 1 1 2022-01-01 22.8
#> 2: 041103 Toowoomba 2022 1 2 2022-01-02 27.7
#> 3: 041103 Toowoomba 2022 1 3 2022-01-03 29.4
#> 4: 041103 Toowoomba 2022 1 4 2022-01-04 30.0
#> 5: 041103 Toowoomba 2022 1 5 2022-01-05 29.5
#> ---
#> 361: 041103 Toowoomba 2022 12 27 2022-12-27 26.2
#> 362: 041103 Toowoomba 2022 12 28 2022-12-28 26.7
#> 363: 041103 Toowoomba 2022 12 29 2022-12-29 27.6
#> 364: 041103 Toowoomba 2022 12 30 2022-12-30 27.0
#> 365: 041103 Toowoomba 2022 12 31 2022-12-31 27.7
#> air_tmax_source air_tmin air_tmin_source elev_m extracted latitude
#> <int> <num> <int> <char> <Date> <num>
#> 1: 25 16.6 25 691.0 m 2024-11-17 -27.5836
#> 2: 25 14.2 25 691.0 m 2024-11-17 -27.5836
#> 3: 25 13.8 25 691.0 m 2024-11-17 -27.5836
#> 4: 25 16.8 25 691.0 m 2024-11-17 -27.5836
#> 5: 25 17.5 25 691.0 m 2024-11-17 -27.5836
#> ---
#> 361: 25 15.1 25 691.0 m 2024-11-17 -27.5836
#> 362: 25 13.0 25 691.0 m 2024-11-17 -27.5836
#> 363: 25 14.1 25 691.0 m 2024-11-17 -27.5836
#> 364: 25 17.0 25 691.0 m 2024-11-17 -27.5836
#> 365: 25 16.4 25 691.0 m 2024-11-17 -27.5836
#> longitude rainfall rainfall_source rh_tmax rh_tmax_source rh_tmin
#> <num> <num> <int> <num> <int> <num>
#> 1: 151.9317 7.6 25 72.1 26 100.0
#> 2: 151.9317 0.1 25 47.1 26 100.0
#> 3: 151.9317 0.0 25 38.8 26 100.0
#> 4: 151.9317 0.0 25 43.1 26 95.7
#> 5: 151.9317 0.0 25 49.2 26 100.0
#> ---
#> 361: 151.9317 0.0 25 47.9 26 95.0
#> 362: 151.9317 0.2 25 43.7 26 100.0
#> 363: 151.9317 0.0 25 44.1 26 100.0
#> 364: 151.9317 0.7 25 60.3 26 100.0
#> 365: 151.9317 0.4 25 50.9 26 100.0
#> rh_tmin_source
#> <int>
#> 1: 26
#> 2: 26
#> 3: 26
#> 4: 26
#> 5: 26
#> ---
#> 361: 26
#> 362: 26
#> 363: 26
#> 364: 26
#> 365: 26{weatherOz} offers much more functionality that is detailed in other vignettes that document how to use it to get station metadata for any station in the DPIRD or SILO databases, get extreme weather events for the DPIRD station network, get minute data for DPIRD stations, get APSIM formatted data from SILO, get ag bulletins, précis forecasts and various imagery files from BOM in the respective vignettes for DPIRD, SILO and BOM data available through {weatherOz}.
Appendix 1 - Map of DPIRD Station Locations
# this chunk assumes that you have your DPIRD API key in your .Renviron file
if (requireNamespace("ggplot2", quietly = TRUE) &&
requireNamespace("ggthemes", quietly = TRUE) &&
requireNamespace("gridExtra", quietly = TRUE) &&
requireNamespace("grid", quietly = TRUE) &&
requireNamespace("maps", quietly = TRUE)) {
library(ggplot2)
library(mapproj)
library(maps)
library(ggthemes)
library(grid)
library(gridExtra)
library(dplyr)
dpird_stations <-
get_stations_metadata(which_api = "DPIRD") |>
filter_at(vars(latitude, longitude),
all_vars(!is.na(.)))
Aust_map <- map_data("world", region = "Australia")
dpird_stations <-
ggplot(dpird_stations, aes(x = longitude, y = latitude)) +
geom_polygon(
data = Aust_map,
aes(x = long, y = lat, group = group),
color = grey(0.7),
fill = NA
) +
geom_point(color = grDevices::rgb(0.58, 0.20, 0.13),
size = 0.09) +
coord_map(ylim = c(-44, -10),
xlim = c(112, 154)) +
theme_map() +
labs(title = "DPIRD Station Locations Available in Weather 2.0 API",
caption = "Data: Western Australia Department of Primary Industries and Regional Development (DPIRD)")
# Using the gridExtra and grid packages add a neatline to the map
grid.arrange(dpird_stations, ncol = 1)
grid.rect(
width = 0.98,
height = 0.98,
gp = grid::gpar(
lwd = 0.25,
col = "black",
fill = NA
)
)
}
plot of chunk dpird-station-locations-map
Appendix 2 - Map of SILO Station Locations
if (requireNamespace("ggplot2", quietly = TRUE) &&
requireNamespace("dplyr", quietly = TRUE) &&
requireNamespace("ggthemes", quietly = TRUE) &&
requireNamespace("gridExtra", quietly = TRUE) &&
requireNamespace("grid", quietly = TRUE) &&
requireNamespace("maps", quietly = TRUE)) {
library(ggplot2)
library(mapproj)
library(maps)
library(ggthemes)
library(grid)
library(gridExtra)
silo_stations <- get_stations_metadata(which_api = "SILO") |>
filter_at(vars(latitude, longitude),
all_vars(!is.na(.)))
Aust_map <- map_data("world", region = "Australia")
SILO_stations <-
ggplot(silo_stations, aes(x = longitude, y = latitude)) +
geom_polygon(
data = Aust_map,
aes(x = long, y = lat, group = group),
color = grey(0.7),
fill = NA
) +
geom_point(color = grDevices::rgb(0.58, 0.20, 0.13),
size = 0.09) +
coord_map(ylim = c(-44, -10),
xlim = c(112, 154)) +
theme_map() +
labs(title = "BOM Station Locations Available in SILO Database",
caption = "Data: Australia Bureau of Meteorology (BOM)")
# Using the gridExtra and grid packages add a neatline to the map
grid.arrange(SILO_stations, ncol = 1)
grid.rect(
width = 0.98,
height = 0.98,
gp = grid::gpar(
lwd = 0.25,
col = "black",
fill = NA
)
)
}
plot of chunk silo-station-locations-map