Working With DPIRD Metadata
Three functions are provided to assist in fetching metadata about the stations.
find_nearby_stations(), which returns adata.tablewith the nearest weather stations to a given geographic point or known station in either the DPIRD or BOM (from SILO) networks.find_stations_in(), which returns adata.tablewith the weather stations falling within a given geographic area in either the DPIRD or BOM (from SILO) networks.get_dpird_availability(), which returns adata.tablewith the availability for weather stations in the DPIRD network providing the up time and data availability for a given period of time.get_stations_metadata(), which returns adata.tablewith the latest and most up-to-date information available from the Weather 2.0 API on the stations’ geographic locations, hardware details, e.g., wind mast height, and recording capabilities.
Finding Nearby Stations
Example 10: Finding Stations Nearby a Known Station
Query WA only stations and return DPIRD’s stations nearest to the Northam, WA station, “NO”, returning stations with 50 km of this station.
library(weatherOz)
(
wa_stn <- find_nearby_stations(
station_code = "NO",
distance_km = 50,
which_api = "dpird"
)
)
#> 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
#> 3: YE001 York East 116.9211 -31.83588 WA 229
#> 4: BTSB DFES-B Talbot West 116.6898 -31.96060 WA 357
#> 5: ROGR Rolling Green 116.3184 -31.69527 WA 315
#> 6: BCAA DBCA-A Portable 116.2560 -31.84210 WA 268
#> owner
#> <char>
#> 1: WA Department of Primary Industries and Regional Development (DPIRD)
#> 2: WA Department of Primary Industries and Regional Development (DPIRD)
#> 3: WA Department of Primary Industries and Regional Development (DPIRD)
#> 4: WA Department of Fire and Emergency Services (DFES)
#> 5: WA Department of Biodiversity, Conservation and Attractions (DBCA)
#> 6: WA Department of Biodiversity, Conservation and Attractions (DBCA)
#> distance_km
#> <num>
#> 1: 0.00
#> 2: 7.62
#> 3: 29.23
#> 4: 30.90
#> 5: 37.83
#> 6: 47.78Example 11: Finding Stations Nearby a Given Longitude and Latitude
Using the longitude and latitude for Northam, WA, find all DPIRD stations within a 50km radius of this geographic point.
library(weatherOz)
(
wa_stn_lonlat <- find_nearby_stations(
longitude = 116.6620,
latitude = -31.6540,
distance_km = 50,
which_api = "dpird"
)
)
#> 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
#> 3: BTSB DFES-B Talbot West 116.6898 -31.96060 WA 357
#> 4: YE001 York East 116.9211 -31.83588 WA 229
#> 5: ROGR Rolling Green 116.3184 -31.69527 WA 315
#> 6: BCAA DBCA-A Portable 116.2560 -31.84210 WA 268
#> owner
#> <char>
#> 1: WA Department of Primary Industries and Regional Development (DPIRD)
#> 2: WA Department of Primary Industries and Regional Development (DPIRD)
#> 3: WA Department of Fire and Emergency Services (DFES)
#> 4: WA Department of Primary Industries and Regional Development (DPIRD)
#> 5: WA Department of Biodiversity, Conservation and Attractions (DBCA)
#> 6: WA Department of Biodiversity, Conservation and Attractions (DBCA)
#> distance_km
#> <num>
#> 1: 3.23
#> 2: 7.93
#> 3: 30.79
#> 4: 31.65
#> 5: 34.61
#> 6: 44.75Example 12: Finding Stations in Both the DPIRD and SILO Data Sets
Query stations nearest DPIRD’s Northam, WA station, “NO” and return both DPIRD and SILO/BOM stations within 50 km of this station.
library(weatherOz)
(
wa_stn_all <- find_nearby_stations(
station_code = "NO",
distance_km = 50,
which_api = "all"
)
)
#> station_code station_name longitude latitude state elev_m
#> <fctr> <char> <num> <num> <char> <num>
#> 1: NO Northam 116.6942 -31.65161 WA 163
#> 2: 010111 Northam 116.6586 -31.65080 WA 170
#> 3: MK Muresk 116.6913 -31.72772 WA 251
#> 4: 010150 Grass Valley 116.7969 -31.63580 WA 200
#> 5: 010152 Muresk Institute 116.6833 -31.75000 WA 166
#> 6: 010115 Quellington 116.8647 -31.77140 WA 220
#> 7: 010125 Toodyay 116.4703 -31.55170 WA 140
#> 8: 010244 Bakers Hill 116.4561 -31.74690 WA 330
#> 9: 010311 York 116.7650 -31.89970 WA 179
#> 10: 010023 Warradong Farm 116.9411 -31.50030 WA 240
#> 11: YE001 York East 116.9211 -31.83588 WA 229
#> 12: 010091 Meckering 117.0081 -31.63220 WA 195
#> 13: BTSB DFES-B Talbot West 116.6898 -31.96060 WA 357
#> 14: ROGR Rolling Green 116.3184 -31.69527 WA 315
#> 15: 010138 Wooroloo 116.3413 -31.81500 WA 277
#> 16: 010058 Goomalling 116.8269 -31.29940 WA 239
#> 17: 010134 Wattening 116.5150 -31.31190 WA 240
#> 18: 010165 Green Hills 116.9839 -31.94080 WA 244
#> 19: 010163 Jaroma 117.1433 -31.77060 WA 265
#> 20: 010009 Bolgart 116.5092 -31.27440 WA 240
#> 21: 010160 Quella Park 117.1194 -31.45330 WA 265
#> 22: 009007 Chidlow 116.2658 -31.86220 WA 300
#> 23: 010120 Doodenanning 117.0986 -31.90920 WA 290
#> 24: BCAA DBCA-A Portable 116.2560 -31.84210 WA 268
#> 25: 009066 Gidgegannup 116.1976 -31.79060 WA 290
#> station_code station_name longitude latitude state elev_m
#> owner
#> <char>
#> 1: WA Department of Primary Industries and Regional Development (DPIRD)
#> 2: BOM
#> 3: WA Department of Primary Industries and Regional Development (DPIRD)
#> 4: BOM
#> 5: BOM
#> 6: BOM
#> 7: BOM
#> 8: BOM
#> 9: BOM
#> 10: BOM
#> 11: WA Department of Primary Industries and Regional Development (DPIRD)
#> 12: BOM
#> 13: WA Department of Fire and Emergency Services (DFES)
#> 14: WA Department of Biodiversity, Conservation and Attractions (DBCA)
#> 15: BOM
#> 16: BOM
#> 17: BOM
#> 18: BOM
#> 19: BOM
#> 20: BOM
#> 21: BOM
#> 22: BOM
#> 23: BOM
#> 24: WA Department of Biodiversity, Conservation and Attractions (DBCA)
#> 25: BOM
#> owner
#> distance_km
#> <num>
#> 1: 0.000000
#> 2: 3.369959
#> 3: 7.620000
#> 4: 9.878912
#> 5: 10.986728
#> 6: 20.914807
#> 7: 23.934879
#> 8: 24.889495
#> 9: 28.381350
#> 10: 28.808439
#> 11: 29.230000
#> 12: 29.790490
#> 13: 30.900000
#> 14: 37.830000
#> 15: 37.993263
#> 16: 41.128932
#> 17: 41.412805
#> 18: 42.226585
#> 19: 44.489821
#> 20: 45.457855
#> 21: 45.923973
#> 22: 46.778842
#> 23: 47.759247
#> 24: 47.780000
#> 25: 49.440793
#> distance_kmExample 13: Finding Stations in the Southwest Agriculture Region of Western Australia
Using find_stations_in() is different than
find_nearby_stations() as it finds any stations that fall
within a boundary that you provide rather than using a single point to
search from. For detailed examples using named places or bounding boxes,
see the “weatherOz for SILO” vignette.
The {sf} object, south_west_agricultural_region, is
provided with {weatherOz} under the CC BY 4.0
Licence from the Department of Primary Industries and Regional
Development (DPIRD), so we can extract stations within this area of
Western Australia.
First, we can plot the south_west_agricultural_region to
see what it looks like.
library(weatherOz)
library(ggplot2)
library(ggthemes)
library(sf)
ggplot(south_west_agricultural_region) +
geom_sf() +
theme_map()
plot of chunk view_sw_ag_region
Now we can use that to find stations that fall only within that part of Western Australia. We’ll use the coordinate reference system (CRS) provided by this {sf} object and find all stations, including those that have closed.
sw_wa <- find_stations_in(
x = south_west_agricultural_region,
include_closed = TRUE,
crs = sf::st_crs(south_west_agricultural_region)
)
sw_wa
#> station_code station_name start end latitude
#> <fctr> <char> <Date> <Date> <num>
#> 1: 009804 Adina 1969-01-01 2024-07-07 -33.88110
#> 2: 008000 Ajana 1917-01-01 2024-07-07 -27.96070
#> 3: 009500 Albany 1877-01-01 2024-07-07 -35.02890
#> 4: 009741 Albany Airport Comparison 1942-01-01 2014-01-01 -34.94140
#> 5: 010501 Aldersyde Post Office 1909-01-01 1976-01-01 -32.36670
#> ---
#> 1034: 008146 Ytiniche 1913-01-01 2024-07-07 -30.07060
#> 1035: 008147 Yuna 1909-01-01 2024-07-07 -28.32500
#> 1036: YU001 Yuna 2012-06-21 2024-07-07 -28.33763
#> 1037: YU002 Yuna NE 2016-03-24 2024-07-07 -28.20032
#> 1038: YU003 Yuna North 2018-08-08 2024-07-07 -28.12086
#> longitude state elev_m
#> <num> <char> <num>
#> 1: 122.2167 WA 60
#> 2: 114.6336 WA 210
#> 3: 117.8808 WA 3
#> 4: 117.8022 WA 68
#> 5: 117.2833 WA NA
#> ---
#> 1034: 116.2092 WA 300
#> 1035: 114.9589 WA 270
#> 1036: 114.9898 WA 329
#> 1037: 115.2616 WA 267
#> 1038: 114.9626 WA 264
#> source
#> <char>
#> 1: Bureau of Meteorology (BOM)
#> 2: Bureau of Meteorology (BOM)
#> 3: Bureau of Meteorology (BOM)
#> 4: Bureau of Meteorology (BOM)
#> 5: Bureau of Meteorology (BOM)
#> ---
#> 1034: Bureau of Meteorology (BOM)
#> 1035: Bureau of Meteorology (BOM)
#> 1036: WA Department of Primary Industries and Regional Development (DPIRD)
#> 1037: WA Department of Primary Industries and Regional Development (DPIRD)
#> 1038: WA Department of Primary Industries and Regional Development (DPIRD)
#> status wmo
#> <char> <num>
#> 1: open NA
#> 2: open NA
#> 3: open 94801
#> 4: closed 95802
#> 5: closed NA
#> ---
#> 1034: open NA
#> 1035: open NA
#> 1036: open NA
#> 1037: open NA
#> 1038: open NAWe need to convert the sw_wa object from a
data.table to an sf object and transform it to
use the same CRS as the south_west_agricultural_region
object to map the results.
sw_wa <- st_as_sf(
x = sw_wa,
coords = c("longitude", "latitude"),
crs = "EPSG:4326"
)
sw_wa <- st_transform(x = sw_wa, crs = st_crs(south_west_agricultural_region))Now we can use {ggplot2} to plot the stations indicating whether they are still open or they are closed.
ggplot(south_west_agricultural_region) +
geom_sf(fill = "white") +
geom_sf(data = sw_wa,
alpha = 0.65,
size = 2,
aes(colour = status)) +
theme_map()
plot of chunk plot_sw_land_div_map
Checking Station Uptime or Availability
Example 14: Checking Station Availability for Current Year
Check the availability of the Westonia station since the start of the
current year using the default functionality with no
start_date or end_date.
library(weatherOz)
(WS001 <- get_dpird_availability(
station_code = "WS001"
))
#> Key: <station_code>
#> station_code station_name to9_am since9_am since12_am current_hour
#> <fctr> <char> <int> <num> <num> <int>
#> 1: WS001 Westonia 100 96.6 99.3 80
#> last24_hours last7_days_since9_am last7_days_since12_am
#> <num> <num> <num>
#> 1: 99.6 99.9 99.9
#> last14_days_since9_am last14_days_since12_am month_to_date_to9_am
#> <int> <int> <num>
#> 1: 100 100 99.9
#> month_to_date_since12_am year_to_date_to9_am year_to_date_since12_am
#> <num> <int> <int>
#> 1: 99.9 100 100Example 15: Checking Station Availability for a Set Time Period
Check the availability of the Binnu station for January of 2018. When
a custom start_date is provided an end_date
must also be provided.
library(weatherOz)
(
BI_201801 <- get_dpird_availability(
station_code = "BI",
start_date = "2018-01-01",
end_date = "2018-01-31"
)
)
#> Key: <station_code>
#> station_code station_name start_date end_date availability_since_9_am
#> <fctr> <char> <POSc> <POSc> <int>
#> 1: BI Binnu 2018-01-01 2018-01-31 100
#> availability_since_12_am
#> <int>
#> 1: 100Getting Station Metadata for the DPIRD Network Stations
The get_stations_metadata() function is shared with the
SILO functions as well, so this function will retrieve data from both
weather APIs. Shown here is how to use it for DPIRD data only and with
an example of DPIRD specific information, namely including closed
stations and rich metadata.
Example 16: Get DPIRD Station Metadata
The get_stations_metadata() function allows you to get
details about the stations themselves for stations in the DPIRD and SILO
(BOM) networks in one function. Here we demonstrate how to get the
metadata for the DPIRD stations only.
library(weatherOz)
(metadata <- get_stations_metadata(which_api = "dpird"))
#> station_code station_name start end latitude longitude state
#> <char> <char> <Date> <Date> <num> <num> <char>
#> 1: AN001 Allanooka 2012-06-19 2024-07-07 -29.06361 114.9972 WA
#> 2: AM001 Amelup 2019-10-09 2024-07-07 -34.27083 118.2685 WA
#> 3: SH002 Babakin 2016-06-22 2024-07-07 -32.12548 118.0041 WA
#> 4: BA Badgingarra 2008-11-19 2024-07-07 -30.33805 115.5395 WA
#> 5: BP001 Balingup 2014-10-24 2024-07-07 -33.79620 116.0640 WA
#> ---
#> 227: YS Yilgarn 2008-11-01 2024-07-07 -31.91562 119.2561 WA
#> 228: YE001 York East 2013-11-08 2024-07-07 -31.83588 116.9211 WA
#> 229: YU001 Yuna 2012-06-21 2024-07-07 -28.33763 114.9898 WA
#> 230: YU002 Yuna NE 2016-03-24 2024-07-07 -28.20032 115.2616 WA
#> 231: YU003 Yuna North 2018-08-08 2024-07-07 -28.12086 114.9626 WA
#> elev_m
#> <int>
#> 1: 131
#> 2: 200
#> 3: 313
#> 4: 284
#> 5: 227
#> ---
#> 227: 468
#> 228: 229
#> 229: 329
#> 230: 267
#> 231: 264
#> source
#> <char>
#> 1: WA Department of Primary Industries and Regional Development (DPIRD)
#> 2: WA Department of Primary Industries and Regional Development (DPIRD)
#> 3: WA Department of Primary Industries and Regional Development (DPIRD)
#> 4: WA Department of Primary Industries and Regional Development (DPIRD)
#> 5: WA Department of Primary Industries and Regional Development (DPIRD)
#> ---
#> 227: WA Department of Primary Industries and Regional Development (DPIRD)
#> 228: WA Department of Primary Industries and Regional Development (DPIRD)
#> 229: WA Department of Primary Industries and Regional Development (DPIRD)
#> 230: WA Department of Primary Industries and Regional Development (DPIRD)
#> 231: WA Department of Primary Industries and Regional Development (DPIRD)
#> status wmo
#> <char> <lgcl>
#> 1: open NA
#> 2: open NA
#> 3: open NA
#> 4: open NA
#> 5: open NA
#> ---
#> 227: open NA
#> 228: open NA
#> 229: open NA
#> 230: open NA
#> 231: open NAExample 17: Get Rich DPIRD Station Metadata and Include Closed Stations
You can fetch additional information about the DPIRD stations as well
as getting data for stations that are no longer open like so with the
rich and include_closed arguments set to
TRUE.
library(weatherOz)
(metadata <- get_stations_metadata(which_api = "dpird",
include_closed = TRUE,
rich = TRUE))
#> station_code station_name start end latitude longitude state
#> <char> <char> <Date> <Date> <num> <num> <char>
#> 1: AN001 Allanooka 2012-06-19 2024-07-07 -29.06361 114.9972 WA
#> 2: AM001 Amelup 2019-10-09 2024-07-07 -34.27083 118.2685 WA
#> 3: SH002 Babakin 2016-06-22 2024-07-07 -32.12548 118.0041 WA
#> 4: BA Badgingarra 2008-11-19 2024-07-07 -30.33805 115.5395 WA
#> 5: BP001 Balingup 2014-10-24 2024-07-07 -33.79620 116.0640 WA
#> ---
#> 234: YS Yilgarn 2008-11-01 2024-07-07 -31.91562 119.2561 WA
#> 235: YE001 York East 2013-11-08 2024-07-07 -31.83588 116.9211 WA
#> 236: YU001 Yuna 2012-06-21 2024-07-07 -28.33763 114.9898 WA
#> 237: YU002 Yuna NE 2016-03-24 2024-07-07 -28.20032 115.2616 WA
#> 238: YU003 Yuna North 2018-08-08 2024-07-07 -28.12086 114.9626 WA
#> elev_m
#> <int>
#> 1: 131
#> 2: 200
#> 3: 313
#> 4: 284
#> 5: 227
#> ---
#> 234: 468
#> 235: 229
#> 236: 329
#> 237: 267
#> 238: 264
#> source
#> <char>
#> 1: WA Department of Primary Industries and Regional Development (DPIRD)
#> 2: WA Department of Primary Industries and Regional Development (DPIRD)
#> 3: WA Department of Primary Industries and Regional Development (DPIRD)
#> 4: WA Department of Primary Industries and Regional Development (DPIRD)
#> 5: WA Department of Primary Industries and Regional Development (DPIRD)
#> ---
#> 234: WA Department of Primary Industries and Regional Development (DPIRD)
#> 235: WA Department of Primary Industries and Regional Development (DPIRD)
#> 236: WA Department of Primary Industries and Regional Development (DPIRD)
#> 237: WA Department of Primary Industries and Regional Development (DPIRD)
#> 238: WA Department of Primary Industries and Regional Development (DPIRD)
#> status wmo probe_height rain_gauge_height wind_probe_heights
#> <char> <lgcl> <num> <num> <list>
#> 1: open NA 1.25 0.5 3
#> 2: open NA 1.25 1.0 3
#> 3: open NA 1.25 0.5 3
#> 4: open NA 1.25 0.5 3
#> 5: open NA 1.25 0.5 3
#> ---
#> 234: open NA 1.25 0.5 3
#> 235: open NA 1.25 0.5 3
#> 236: open NA 1.25 0.5 3
#> 237: open NA 1.25 0.5 3
#> 238: open NA 1.25 0.5 3
#> air_temperature battery_voltage delta_t dew_point pan_evaporation
#> <lgcl> <lgcl> <lgcl> <lgcl> <lgcl>
#> 1: TRUE TRUE TRUE TRUE TRUE
#> 2: TRUE TRUE TRUE TRUE TRUE
#> 3: TRUE TRUE TRUE TRUE TRUE
#> 4: TRUE TRUE TRUE TRUE TRUE
#> 5: TRUE TRUE TRUE TRUE TRUE
#> ---
#> 234: TRUE TRUE TRUE TRUE TRUE
#> 235: TRUE TRUE TRUE TRUE TRUE
#> 236: TRUE TRUE TRUE TRUE TRUE
#> 237: TRUE TRUE TRUE TRUE TRUE
#> 238: TRUE TRUE TRUE TRUE TRUE
#> relative_humidity barometric_pressure rainfall soil_temperature
#> <lgcl> <lgcl> <lgcl> <lgcl>
#> 1: TRUE FALSE TRUE FALSE
#> 2: TRUE FALSE TRUE FALSE
#> 3: TRUE FALSE TRUE TRUE
#> 4: TRUE FALSE TRUE TRUE
#> 5: TRUE FALSE TRUE TRUE
#> ---
#> 234: TRUE FALSE TRUE TRUE
#> 235: TRUE FALSE TRUE TRUE
#> 236: TRUE FALSE TRUE FALSE
#> 237: TRUE FALSE TRUE TRUE
#> 238: TRUE FALSE TRUE TRUE
#> solar_irradiance wet_bulb wind1 wind2 wind3 apparent_temperature
#> <lgcl> <lgcl> <lgcl> <lgcl> <lgcl> <lgcl>
#> 1: TRUE TRUE TRUE FALSE FALSE TRUE
#> 2: TRUE TRUE TRUE FALSE FALSE TRUE
#> 3: TRUE TRUE TRUE FALSE FALSE TRUE
#> 4: TRUE TRUE TRUE FALSE FALSE TRUE
#> 5: TRUE TRUE TRUE FALSE FALSE TRUE
#> ---
#> 234: TRUE TRUE TRUE FALSE FALSE TRUE
#> 235: TRUE TRUE TRUE FALSE FALSE TRUE
#> 236: TRUE TRUE TRUE FALSE FALSE TRUE
#> 237: TRUE TRUE TRUE FALSE FALSE TRUE
#> 238: TRUE TRUE TRUE FALSE FALSE TRUE
#> eto_short eto_tall frost_condition heat_condition wind_erosion_condition
#> <lgcl> <lgcl> <lgcl> <lgcl> <lgcl>
#> 1: TRUE TRUE TRUE TRUE TRUE
#> 2: TRUE TRUE TRUE TRUE TRUE
#> 3: TRUE TRUE TRUE TRUE TRUE
#> 4: TRUE TRUE TRUE TRUE TRUE
#> 5: TRUE TRUE TRUE TRUE TRUE
#> ---
#> 234: TRUE TRUE TRUE TRUE TRUE
#> 235: TRUE TRUE TRUE TRUE TRUE
#> 236: TRUE TRUE TRUE TRUE TRUE
#> 237: TRUE TRUE TRUE TRUE TRUE
#> 238: TRUE TRUE TRUE TRUE TRUE
#> richardson_unit chill_hour
#> <lgcl> <lgcl>
#> 1: TRUE TRUE
#> 2: TRUE TRUE
#> 3: TRUE TRUE
#> 4: TRUE TRUE
#> 5: TRUE TRUE
#> ---
#> 234: TRUE TRUE
#> 235: TRUE TRUE
#> 236: TRUE TRUE
#> 237: TRUE TRUE
#> 238: TRUE TRUE