weatherOz for DPIRD

Working With DPIRD Data

Three functions are provided to streamline fetching data from the DPIRD Weather 2.0 API endpoints.

get_dpird_extremes(), which returns the recorded extreme values for the given station in the DPIRD weather station network.;
get_dpird_minute(), which returns weather data in minute increments for stations in the DPIRD weather station network with only the past two years being available; and
get_dpird_summaries(), which returns weather data in 15 and 30 minute, hourly, daily, monthly or yearly summary values for stations in the DPIRD weather station network.

A Note on API Keys

All examples in this vignette assume that you have stored your API key in your .Renviron file. 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.

Getting Extreme Weather Values

The get_dpird_extremes() function fetches and returns nicely formatted individual extreme weather summaries from the DPIRD Weather 2.0 API. You must provide a station_code and API_key, the other arguments, values and include_closed are optional.

Available Values for Extreme Weather

all (which will return all of the following values),
erosionCondition,
erosionConditionLast7Days,
erosionConditionLast7DaysDays,
erosionConditionLast7DaysMinutes,
erosionConditionLast14Days,
erosionConditionLast14DaysDays,
erosionConditionLast14DaysMinutes,
erosionConditionMonthToDate,
erosionConditionMonthToDateDays,
erosionConditionMonthToDateMinutes,
erosionConditionMonthToDateStartTime,
erosionConditionSince12AM,
erosionConditionSince12AMMinutes,
erosionConditionSince12AMStartTime,
erosionConditionYearToDate,
erosionConditionYearToDateDays,
erosionConditionYearToDateMinutes,
erosionConditionYearToDateStartTime,
frostCondition,
frostConditionLast7Days,
frostConditionLast7DaysDays,
frostConditionLast7DaysMinutes,
frostConditionLast14Days,
frostConditionLast14DaysDays,
frostConditionLast14DaysMinutes,
frostConditionMonthToDate,
frostConditionMonthToDateDays,
frostConditionMonthToDateMinutes,
frostConditionMonthToDateStartTime,
frostConditionSince9AM,
frostConditionSince9AMMinutes,
frostConditionSince9AMStartTime,
frostConditionTo9AM,
frostConditionTo9AMMinutes,
frostConditionTo9AMStartTime,
frostConditionYearToDate,
frostConditionYearToDate,
frostConditionYearToDateMinutes,
frostConditionYearToDateStartTime,
heatCondition,
heatConditionLast7Days,
heatConditionLast7DaysDays,
heatConditionLast7DaysMinutes,
heatConditionLast14Days,
heatConditionLast14DaysDays,
heatConditionLast14DaysMinutes,
heatConditionMonthToDate,
heatConditionMonthToDateDays,
heatConditionMonthToDateMinutes,
heatConditionMonthToDateStartTime,
heatConditionSince12AM,
heatConditionSince12AMMinutes,
heatConditionSince12AMStartTime,
heatConditionYearToDate,
heatConditionYearToDateDays,
heatConditionYearToDateMinutes, and
heatConditionYearToDateStartTime

Example 1: Get All Extremes for Northam, WA

In the first example, we illustrate how to fetch all extreme values available for Northam.

library(weatherOz)

(extremes <- get_dpird_extremes(
  station_code = "NO",
  api_key = Sys.getenv("DPIRD_API_KEY")
))

Example 2: Get Selected Extremes for Northam, WA

Fetch only soil erosion extreme conditions for Northam, WA. The documentation for get_dpird_extremes() contains a full listing of the values that are available to query from this API endpoint.

library(weatherOz)

(
  extremes <- get_dpird_extremes(
    station_code = "NO",
    values = "erosionCondition",
    api_key = Sys.getenv("DPIRD_API_KEY")
  )
)

Getting Minute Data

This function fetches nicely formatted minute weather station data from the DPIRD Weather 2.0 API for a maximum 24-hour period. You must provide a station_code and API_key, the other arguments, start_date_time, minutes and values are optional.

Available Values for Minute Data

all (which will return all of the following values),
airTemperature,
dateTime,
dewPoint,
rainfall,
relativeHumidity,
soilTemperature,
solarIrradiance,
wetBulb,
wind,
windAvgSpeed,
windMaxSpeed, and
windMinSpeed

Example 3: Get All Minute Data for the Past 24 Hours

library(weatherOz)

(
  min_dat <- get_dpird_minute(
    station_code = "NO",
    api_key = Sys.getenv("DPIRD_API_KEY")
  )
)
#> Key: <station_code>
#>       station_code           date_time air_temperature relative_humidity soil_temperature solar_irradiance rainfall
#>             <fctr>              <POSc>           <num>             <num>            <num>            <int>    <int>
#>    1:           NO 2024-04-18 13:05:00            30.7              23.5             35.1              761        0
#>    2:           NO 2024-04-18 13:06:00            31.0              23.1             35.1              763        0
#>    3:           NO 2024-04-18 13:07:00            30.9              23.1             35.2              761        0
#>    4:           NO 2024-04-18 13:08:00            30.9              23.2             35.2              761        0
#>    5:           NO 2024-04-18 13:09:00            31.1              22.9             35.2              761        0
#>   ---                                                                                                              
#> 1432:           NO 2024-04-19 12:56:00            28.7              22.9             33.3              776        0
#> 1433:           NO 2024-04-19 12:57:00            28.6              23.0             33.4              774        0
#> 1434:           NO 2024-04-19 12:58:00            28.5              23.0             33.4              773        0
#> 1435:           NO 2024-04-19 12:59:00            28.3              23.4             33.5              772        0
#> 1436:           NO 2024-04-19 13:00:00            28.2              23.3             33.5              773        0
#>       dew_point wet_bulb wind_height wind_avg_speed wind_avg_direction_compass_point wind_avg_direction_degrees
#>           <num>    <num>       <int>          <num>                           <char>                      <int>
#>    1:       7.5     17.5           3           4.18                               NE                         54
#>    2:       7.5     17.6           3           4.68                               NE                         34
#>    3:       7.4     17.5           3           3.17                              NNE                         13
#>    4:       7.5     17.5           3           5.69                              NNE                         26
#>    5:       7.5     17.6           3           6.19                                E                         79
#>   ---                                                                                                          
#> 1432:       5.5     16.0           3           3.17                              ENE                         77
#> 1433:       5.5     16.0           3           2.66                               NE                         52
#> 1434:       5.4     15.9           3           2.66                                E                         90
#> 1435:       5.4     15.9           3           3.17                              ENE                         73
#> 1436:       5.3     15.8           3           5.18                              ENE                         66
#>       wind_min_speed wind_max_speed
#>                <num>          <num>
#>    1:          1.656           6.19
#>    2:          0.648           9.22
#>    3:          0.648           7.20
#>    4:          2.160           8.21
#>    5:          3.672           8.71
#>   ---                              
#> 1432:          1.152           7.20
#> 1433:          1.152           5.69
#> 1434:          0.648           4.68
#> 1435:          1.152           5.69
#> 1436:          2.664           9.22

Example 4: Get Specific Time and Date Data for Specific Values

If you wish to supply a specific start date and time and values, you may do so as shown here.

library(weatherOz)

(
  min_dat_t_rad_wind <- get_dpird_minute(
    station_code = "NO",
    start_date_time = "2023-02-01 13:00:00",
    minutes = 1440,
    values = c("airTemperature",
               "solarIrradiance",
               "wind"),
    api_key = Sys.getenv("DPIRD_API_KEY")
  )
)
#> Error in switch(values, all = c(weatherOz::dpird_minute_values, "dateTime"), : EXPR must be a length 1 vector

Getting Summary Data

The function, get_dpird_summary(), fetches nicely formatted minute weather station data from the DPIRD Weather 2.0 API for a maximum 24-hour period. You must provide a station_code and API_key, the other arguments, start_date_time, minutes and values are optional.

Available Values for Summary Data

all (which will return all of the following values),
airTemperature,
airTemperatureAvg,
airTemperatureMax,
airTemperatureMaxTime,
airTemperatureMin,
airTemperatureMinTime,
apparentAirTemperature,
apparentAirTemperatureAvg,
apparentAirTemperatureMax,
apparentAirTemperatureMaxTime,
apparentAirTemperatureMin,
apparentAirTemperatureMinTime,
barometricPressure,
barometricPressureAvg,
barometricPressureMax,
barometricPressureMaxTime,
barometricPressureMin,
barometricPressureMinTime,
battery,
batteryMinVoltage,
batteryMinVoltageDateTime,
chillHours,
deltaT,
deltaTAvg,
deltaTMax,
deltaTMaxTime,
deltaTMin,
deltaTMinTime,
dewPoint,
dewPointAvg,
dewPointMax,
dewPointMaxTime,
dewPointMin,
dewPointMinTime,
erosionCondition,
erosionConditionMinutes,
erosionConditionStartTime,
errors,
etoShortCrop,
etoTallCrop,
evapotranspiration,
frostCondition,
frostConditionMinutes,
frostConditionStartTime,
heatCondition,
heatConditionMinutes,
heatConditionStartTime,
observations,
observationsCount,
observationsPercentage,
panEvaporation,
rainfall,
relativeHumidity,
relativeHumidityAvg,
relativeHumidityMax,
relativeHumidityMaxTime,
relativeHumidityMin,
relativeHumidityMinTime,
richardsonUnits,
soilTemperature,
soilTemperatureAvg,
soilTemperatureMax,
soilTemperatureMaxTime,
soilTemperatureMin,
soilTemperatureMinTime,
solarExposure,
wetBulb,
wetBulbAvg,
wetBulbMax,
wetBulbMaxTime,
wetBulbMin,
wetBulbMinTime,
wind,
windAvgSpeed, and
windMaxSpeed

What You Get Back

This function returns a data.table with station_code and the date interval queried together with the requested weather variables in alphabetical order. Please note this function converts date-time columns from Coordinated Universal Time ‘UTC’} to Australian Western Standard Time ‘AWST’. The first ten columns will always be:

station_code,
station_name,
longitude,
latitude,
year,
month,
day,
hour,
minute, and if month or finer is present,
date (a combination of year, month, day, hour, minute as appropriate)

Example 5: Get Annual Rainfall Since 2017

Use the default value for end date (current system date) to get annual rainfall since 2017 until current year for Capel.

library(weatherOz)

(
  annual_rain <- get_dpird_summaries(
    station_code = "CL001",
    start_date = "20170101",
    api_key = Sys.getenv("DPIRD_API_KEY"),
    interval = "yearly",
    values = "rainfall"
  )
)
#> Key: <station_code>
#>    station_code station_name longitude  latitude  year rainfall
#>          <fctr>       <char>     <num>     <num> <int>    <num>
#> 1:        CL001        Capel  115.6407 -33.60947  2017    711.4
#> 2:        CL001        Capel  115.6407 -33.60947  2018    822.0
#> 3:        CL001        Capel  115.6407 -33.60947  2019    660.6
#> 4:        CL001        Capel  115.6407 -33.60947  2020    862.4
#> 5:        CL001        Capel  115.6407 -33.60947  2021    928.0
#> 6:        CL001        Capel  115.6407 -33.60947  2022    670.4
#> 7:        CL001        Capel  115.6407 -33.60947  2023    570.0
#> 8:        CL001        Capel  115.6407 -33.60947  2024      2.2

Example 6: Get Monthly Rainfall Since 2017

Use the default value for end date (current system date) to get monthly rainfall since 2017 until current year for Capel.

library(weatherOz)

(
  monthly_rain <- get_dpird_summaries(
    station_code = "CL001",
    start_date = "20170101",
    api_key = Sys.getenv("DPIRD_API_KEY"),
    interval = "monthly",
    values = "rainfall"
  )
)
#> Key: <station_code>
#>     station_code station_name longitude  latitude  year month       date rainfall
#>           <fctr>       <char>     <num>     <num> <int> <int>     <Date>    <num>
#>  1:        CL001        Capel  115.6407 -33.60947  2017     1 2017-01-01      0.0
#>  2:        CL001        Capel  115.6407 -33.60947  2017     2 2017-02-01      0.0
#>  3:        CL001        Capel  115.6407 -33.60947  2017     3 2017-03-01     59.6
#>  4:        CL001        Capel  115.6407 -33.60947  2017     4 2017-04-01      0.0
#>  5:        CL001        Capel  115.6407 -33.60947  2017     5 2017-05-01     84.0
#>  6:        CL001        Capel  115.6407 -33.60947  2017     6 2017-06-01     57.8
#>  7:        CL001        Capel  115.6407 -33.60947  2017     7 2017-07-01    194.4
#>  8:        CL001        Capel  115.6407 -33.60947  2017     8 2017-08-01    175.8
#>  9:        CL001        Capel  115.6407 -33.60947  2017     9 2017-09-01     49.8
#> 10:        CL001        Capel  115.6407 -33.60947  2017    10 2017-10-01     34.0
#> 11:        CL001        Capel  115.6407 -33.60947  2017    11 2017-11-01     14.2
#> 12:        CL001        Capel  115.6407 -33.60947  2017    12 2017-12-01     41.8
#> 13:        CL001        Capel  115.6407 -33.60947  2018     1 2018-01-01     14.4
#> 14:        CL001        Capel  115.6407 -33.60947  2018     2 2018-02-01      0.2
#> 15:        CL001        Capel  115.6407 -33.60947  2018     3 2018-03-01     15.4
#> 16:        CL001        Capel  115.6407 -33.60947  2018     4 2018-04-01     42.8
#> 17:        CL001        Capel  115.6407 -33.60947  2018     5 2018-05-01    115.2
#> 18:        CL001        Capel  115.6407 -33.60947  2018     6 2018-06-01    168.8
#> 19:        CL001        Capel  115.6407 -33.60947  2018     7 2018-07-01    214.6
#> 20:        CL001        Capel  115.6407 -33.60947  2018     8 2018-08-01    156.8
#> 21:        CL001        Capel  115.6407 -33.60947  2018     9 2018-09-01     29.6
#> 22:        CL001        Capel  115.6407 -33.60947  2018    10 2018-10-01     45.8
#> 23:        CL001        Capel  115.6407 -33.60947  2018    11 2018-11-01     11.2
#> 24:        CL001        Capel  115.6407 -33.60947  2018    12 2018-12-01      7.2
#> 25:        CL001        Capel  115.6407 -33.60947  2019     1 2019-01-01      6.0
#> 26:        CL001        Capel  115.6407 -33.60947  2019     2 2019-02-01      0.0
#> 27:        CL001        Capel  115.6407 -33.60947  2019     3 2019-03-01     29.4
#> 28:        CL001        Capel  115.6407 -33.60947  2019     4 2019-04-01     33.0
#> 29:        CL001        Capel  115.6407 -33.60947  2019     5 2019-05-01     27.0
#> 30:        CL001        Capel  115.6407 -33.60947  2019     6 2019-06-01    239.6
#> 31:        CL001        Capel  115.6407 -33.60947  2019     7 2019-07-01     85.4
#> 32:        CL001        Capel  115.6407 -33.60947  2019     8 2019-08-01    117.8
#> 33:        CL001        Capel  115.6407 -33.60947  2019     9 2019-09-01     45.8
#> 34:        CL001        Capel  115.6407 -33.60947  2019    10 2019-10-01     67.0
#> 35:        CL001        Capel  115.6407 -33.60947  2019    11 2019-11-01      7.8
#> 36:        CL001        Capel  115.6407 -33.60947  2019    12 2019-12-01      1.8
#> 37:        CL001        Capel  115.6407 -33.60947  2020     1 2020-01-01      2.0
#> 38:        CL001        Capel  115.6407 -33.60947  2020     2 2020-02-01     31.8
#> 39:        CL001        Capel  115.6407 -33.60947  2020     3 2020-03-01     40.8
#> 40:        CL001        Capel  115.6407 -33.60947  2020     4 2020-04-01     41.6
#> 41:        CL001        Capel  115.6407 -33.60947  2020     5 2020-05-01    156.2
#> 42:        CL001        Capel  115.6407 -33.60947  2020     6 2020-06-01    132.4
#> 43:        CL001        Capel  115.6407 -33.60947  2020     7 2020-07-01    125.8
#> 44:        CL001        Capel  115.6407 -33.60947  2020     8 2020-08-01     96.0
#> 45:        CL001        Capel  115.6407 -33.60947  2020     9 2020-09-01    110.0
#> 46:        CL001        Capel  115.6407 -33.60947  2020    10 2020-10-01     32.6
#> 47:        CL001        Capel  115.6407 -33.60947  2020    11 2020-11-01     88.0
#> 48:        CL001        Capel  115.6407 -33.60947  2020    12 2020-12-01      5.2
#> 49:        CL001        Capel  115.6407 -33.60947  2021     1 2021-01-01      0.0
#> 50:        CL001        Capel  115.6407 -33.60947  2021     2 2021-02-01     76.2
#> 51:        CL001        Capel  115.6407 -33.60947  2021     3 2021-03-01     27.4
#> 52:        CL001        Capel  115.6407 -33.60947  2021     4 2021-04-01     92.0
#> 53:        CL001        Capel  115.6407 -33.60947  2021     5 2021-05-01    160.6
#> 54:        CL001        Capel  115.6407 -33.60947  2021     6 2021-06-01     81.2
#> 55:        CL001        Capel  115.6407 -33.60947  2021     7 2021-07-01    187.6
#> 56:        CL001        Capel  115.6407 -33.60947  2021     8 2021-08-01     90.4
#> 57:        CL001        Capel  115.6407 -33.60947  2021     9 2021-09-01     93.4
#> 58:        CL001        Capel  115.6407 -33.60947  2021    10 2021-10-01    109.4
#> 59:        CL001        Capel  115.6407 -33.60947  2021    11 2021-11-01      5.4
#> 60:        CL001        Capel  115.6407 -33.60947  2021    12 2021-12-01      4.4
#> 61:        CL001        Capel  115.6407 -33.60947  2022     1 2022-01-01      0.0
#> 62:        CL001        Capel  115.6407 -33.60947  2022     2 2022-02-01      4.4
#> 63:        CL001        Capel  115.6407 -33.60947  2022     3 2022-03-01      4.6
#> 64:        CL001        Capel  115.6407 -33.60947  2022     4 2022-04-01     44.0
#> 65:        CL001        Capel  115.6407 -33.60947  2022     5 2022-05-01    105.6
#> 66:        CL001        Capel  115.6407 -33.60947  2022     6 2022-06-01    103.8
#> 67:        CL001        Capel  115.6407 -33.60947  2022     7 2022-07-01    169.0
#> 68:        CL001        Capel  115.6407 -33.60947  2022     8 2022-08-01    121.4
#> 69:        CL001        Capel  115.6407 -33.60947  2022     9 2022-09-01     69.4
#> 70:        CL001        Capel  115.6407 -33.60947  2022    10 2022-10-01     29.0
#> 71:        CL001        Capel  115.6407 -33.60947  2022    11 2022-11-01     18.0
#> 72:        CL001        Capel  115.6407 -33.60947  2022    12 2022-12-01      1.2
#> 73:        CL001        Capel  115.6407 -33.60947  2023     1 2023-01-01      0.8
#> 74:        CL001        Capel  115.6407 -33.60947  2023     2 2023-02-01      0.0
#> 75:        CL001        Capel  115.6407 -33.60947  2023     3 2023-03-01     27.8
#> 76:        CL001        Capel  115.6407 -33.60947  2023     4 2023-04-01     81.2
#> 77:        CL001        Capel  115.6407 -33.60947  2023     5 2023-05-01     31.8
#> 78:        CL001        Capel  115.6407 -33.60947  2023     6 2023-06-01    130.0
#> 79:        CL001        Capel  115.6407 -33.60947  2023     7 2023-07-01    123.2
#> 80:        CL001        Capel  115.6407 -33.60947  2023     8 2023-08-01     74.8
#> 81:        CL001        Capel  115.6407 -33.60947  2023     9 2023-09-01     80.2
#> 82:        CL001        Capel  115.6407 -33.60947  2023    10 2023-10-01     17.2
#> 83:        CL001        Capel  115.6407 -33.60947  2023    11 2023-11-01      2.4
#> 84:        CL001        Capel  115.6407 -33.60947  2023    12 2023-12-01      0.6
#> 85:        CL001        Capel  115.6407 -33.60947  2024     1 2024-01-01      1.4
#> 86:        CL001        Capel  115.6407 -33.60947  2024     2 2024-02-01      0.6
#> 87:        CL001        Capel  115.6407 -33.60947  2024     3 2024-03-01      0.2
#> 88:        CL001        Capel  115.6407 -33.60947  2024     4 2024-04-01      0.0
#>     station_code station_name longitude  latitude  year month       date rainfall

Example 7: Get Daily Rainfall and Wind From Beginning of 2017 to End of 2018

Use the default value for end date (current system date) to get daily rainfall and wind records from 2017-01-01 to 2018-12-31 for Binnu. Note that the Binnu station has two wind heights, 3m and 10m.

library(weatherOz)

(
  daily_wind_rain <- get_dpird_summaries(
    station_code = "BI",
    start_date = "20170101",
    end_date = "2018-12-31",
    api_key = Sys.getenv("DPIRD_API_KEY"),
    interval = "daily",
    values = c("rainfall",
               "wind")
  )
)
#> Key: <station_code>
#>       station_code station_name longitude latitude  year month   day       date rainfall wind_avg_speed wind_height
#>             <fctr>       <char>     <num>    <num> <int> <int> <int>     <Date>    <num>          <num>       <int>
#>    1:           BI        Binnu  114.6958  -28.051  2017     1     1 2017-01-01        0           4.28           3
#>    2:           BI        Binnu  114.6958  -28.051  2017     1     1 2017-01-01        0           6.85          10
#>    3:           BI        Binnu  114.6958  -28.051  2017     1     2 2017-01-02        0           4.53           3
#>    4:           BI        Binnu  114.6958  -28.051  2017     1     2 2017-01-02        0           7.39          10
#>    5:           BI        Binnu  114.6958  -28.051  2017     1     3 2017-01-03        0           4.74           3
#>   ---                                                                                                              
#> 1456:           BI        Binnu  114.6958  -28.051  2018    12    29 2018-12-29        0          35.63          10
#> 1457:           BI        Binnu  114.6958  -28.051  2018    12    30 2018-12-30        0          29.19           3
#> 1458:           BI        Binnu  114.6958  -28.051  2018    12    30 2018-12-30        0          36.70          10
#> 1459:           BI        Binnu  114.6958  -28.051  2018    12    31 2018-12-31        0          32.52           3
#> 1460:           BI        Binnu  114.6958  -28.051  2018    12    31 2018-12-31        0          41.51          10
#>       wind_max_direction_compass_point wind_max_direction_degrees wind_max_speed       wind_max_time
#>                                 <char>                      <int>          <num>              <POSc>
#>    1:                              NNE                         31          17.57 2018-07-17 11:51:00
#>    2:                              ESE                        121          23.18 2017-07-30 11:38:00
#>    3:                               NE                         41          21.78 2017-08-10 13:29:00
#>    4:                                N                          1          19.55 2018-07-17 12:16:00
#>    5:                              WSW                        247          18.40 2017-06-14 09:05:00
#>   ---                                                                                               
#> 1456:                               SE                        134          60.91 2017-02-09 15:56:00
#> 1457:                              ESE                        105          62.03 2018-01-14 22:55:00
#> 1458:                              ESE                        113          69.77 2018-01-14 22:45:00
#> 1459:                              SSE                        163          58.64 2017-02-08 09:12:00
#> 1460:                              SSE                        167          64.40 2017-02-08 04:48:00

Example 8: Get Hourly Rainfall and Wind From Beginning of 2022 to Current

Use the default value for end date (current system date) to get hourly rainfall and wind records from 2022-01-01 to Current Date for Binnu. Note that the Binnu station has two wind heights, 3m and 10m.

library(weatherOz)

(
  hourly_wind_rain <- get_dpird_summaries(
    station_code = "BI",
    start_date = "20220101",
    api_key = Sys.getenv("DPIRD_API_KEY"),
    interval = "hourly",
    values = c("rainfall",
               "wind")
  )
)
#> Key: <station_code>
#>        station_code station_name longitude latitude  year month   day  hour                date rainfall
#>              <fctr>       <char>     <num>    <num> <int> <int> <int> <int>              <POSc>    <num>
#>     1:           BI        Binnu  114.6958  -28.051  2022     1     1     0 2022-01-01 00:00:00        0
#>     2:           BI        Binnu  114.6958  -28.051  2022     1     1     0 2022-01-01 00:00:00        0
#>     3:           BI        Binnu  114.6958  -28.051  2022     1     1     1 2022-01-01 01:00:00        0
#>     4:           BI        Binnu  114.6958  -28.051  2022     1     1     1 2022-01-01 01:00:00        0
#>     5:           BI        Binnu  114.6958  -28.051  2022     1     1     2 2022-01-01 02:00:00        0
#>    ---                                                                                                  
#> 40270:           BI        Binnu  114.6958  -28.051  2024     4    18    22 2024-04-18 22:00:00        0
#> 40271:           BI        Binnu  114.6958  -28.051  2024     4    18    23 2024-04-18 23:00:00        0
#> 40272:           BI        Binnu  114.6958  -28.051  2024     4    18    23 2024-04-18 23:00:00        0
#> 40273:           BI        Binnu  114.6958  -28.051  2024     4    19     0 2024-04-19 00:00:00        0
#> 40274:           BI        Binnu  114.6958  -28.051  2024     4    19     0 2024-04-19 00:00:00        0
#>        wind_avg_direction_compass_point wind_avg_direction_degrees wind_avg_speed wind_height
#>                                  <char>                      <int>          <num>       <int>
#>     1:                              NNE                         18           0.00           3
#>     2:                              SSW                        213           0.62          10
#>     3:                              NNE                         33           0.00           3
#>     4:                              SSW                        213           1.06          10
#>     5:                              ENE                         58           0.00           3
#>    ---                                                                                       
#> 40270:                              WNW                        286          55.23          10
#> 40271:                               NW                        313          43.20           3
#> 40272:                               NW                        312          57.27          10
#> 40273:                               NW                        317          44.32           3
#> 40274:                               NW                        315          58.52          10
#>        wind_max_direction_compass_point wind_max_direction_degrees wind_max_speed       wind_max_time
#>                                  <char>                      <int>          <num>              <POSc>
#>     1:                                N                          1           0.00 2023-01-20 05:01:00
#>     2:                               NW                        305           1.55 2023-04-08 00:58:00
#>     3:                              NNE                         22           0.00 2022-07-23 01:01:00
#>     4:                              WSW                        246           2.52 2023-04-08 02:00:00
#>     5:                                N                         11           0.00 2023-02-04 22:01:00
#>    ---                                                                                               
#> 40270:                              WNW                        282          78.37 2023-09-13 14:25:00
#> 40271:                               NW                        318          72.94 2023-09-13 12:39:00
#> 40272:                               NW                        314          90.58 2023-09-13 12:24:00
#> 40273:                               NW                        311          73.76 2023-09-13 11:40:00
#> 40274:                               NW                        311          91.19 2023-09-13 11:34:00

Getting APSIM Ready Data

For work with APSIM, you can use get_dpird_apsim() to get an object in your R session that’s ready for saving using [apsimx::write_apsim_met()] of DPIRD weather data. This function only needs the station_code, start_date, end_date and your api_key to return the necessary values.

What You Get Back

An object of {apsimx} ‘met’ class, compatible with a data.frame, that has daily data that include year, day, radiation, max temperature, min temperature, rainfall, relative humidity, evaporation and windspeed.

Example 9: Get APSIM Formatted Data for Binnu From 2022-04-01 to 2022-11-01

library(weatherOz)

(
  binnu <- get_dpird_apsim(
    station_code = "BI",
    start_date = "20220401",
    end_date = "20221101",
    api_key = Sys.getenv("DPIRD_API_KEY")
  )
)
#>  
#> Binnu.met 
#> latitude = -28.051000  (DECIMAL DEGREES) 
#> longitude = 114.695750  (DECIMAL DEGREES) 
#> tav = 16.78 (oC) ! Average ambient temperature. Based on 2022-04-01 to 2022-11-01. 
#> amp = 7.7 (oC) ! Amplitude in mean monthly temperature. Based on 2022-04-01 to 2022-11-01. 
#> year day radn maxt mint rain evap rh windspeed 
#> () () (MJ/m2/day) (oC) (oC) (mm) (%) (m/s) () 
#>   year day    radn maxt mint rain evap   rh windspeed
#> 1 2022  91  8176.3 26.9 20.4  2.1  1.2 84.4      3.66
#> 2 2022  92 13439.8 27.6 20.4  3.2  2.0 85.4      3.82
#> 3 2022  93 14764.3 28.7 20.7  3.4  5.8 88.9      4.28
#> 4 2022  94 16305.3 28.2 19.9  4.4  2.2 81.5      4.43
#> 5 2022  95 23568.2 28.7 16.5  6.4  0.2 70.9      4.53
#> 6 2022  96 22831.7 32.3 15.6  7.2  0.0 57.7      4.70

Working With DPIRD Metadata

Three functions are provided to assist in fetching metadata about the stations.

find_nearby_stations(), which returns a data.table with the nearest weather stations to a given geographic point or known station either in the DPIRD or BOM (from SILO) networks.
get_dpird_availability(), which returns a data.table with 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 a data.table with 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,
    api_key = Sys.getenv("DPIRD_API_KEY"),
    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  116.2559 -31.84210     WA    267
#>                                                                   owner distance_km
#>                                                                  <char>       <num>
#> 1: WA Department of Primary Industries and Regional Development (DPIRD)        0.00
#> 2: WA Department of Primary Industries and Regional Development (DPIRD)        7.62
#> 3: WA Department of Primary Industries and Regional Development (DPIRD)       29.23
#> 4:                  WA Department of Fire and Emergency Services (DFES)       30.90
#> 5:   WA Department of Biodiversity, Conservation and Attractions (DBCA)       37.83
#> 6:   WA Department of Biodiversity, Conservation and Attractions (DBCA)       47.79

Example 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,
    api_key = Sys.getenv("DPIRD_API_KEY"),
    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  116.2559 -31.84210     WA    267
#>                                                                   owner distance_km
#>                                                                  <char>       <num>
#> 1: WA Department of Primary Industries and Regional Development (DPIRD)        3.23
#> 2: WA Department of Primary Industries and Regional Development (DPIRD)        7.93
#> 3:                  WA Department of Fire and Emergency Services (DFES)       30.79
#> 4: WA Department of Primary Industries and Regional Development (DPIRD)       31.65
#> 5:   WA Department of Biodiversity, Conservation and Attractions (DBCA)       34.61
#> 6:   WA Department of Biodiversity, Conservation and Attractions (DBCA)       44.75

Example 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,
    api_key = Sys.getenv("DPIRD_API_KEY"),
    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  116.2559 -31.84210     WA    267
#> 25:       009066        Gidgegannup  116.1976 -31.79060     WA    290
#>     station_code       station_name longitude  latitude  state elev_m
#>                                                                    owner distance_km
#>                                                                   <char>       <num>
#>  1: WA Department of Primary Industries and Regional Development (DPIRD)    0.000000
#>  2:                                                                  BOM    3.369959
#>  3: WA Department of Primary Industries and Regional Development (DPIRD)    7.620000
#>  4:                                                                  BOM    9.878912
#>  5:                                                                  BOM   10.986728
#>  6:                                                                  BOM   20.914807
#>  7:                                                                  BOM   23.934879
#>  8:                                                                  BOM   24.889495
#>  9:                                                                  BOM   28.381350
#> 10:                                                                  BOM   28.808439
#> 11: WA Department of Primary Industries and Regional Development (DPIRD)   29.230000
#> 12:                                                                  BOM   29.790490
#> 13:                  WA Department of Fire and Emergency Services (DFES)   30.900000
#> 14:   WA Department of Biodiversity, Conservation and Attractions (DBCA)   37.830000
#> 15:                                                                  BOM   37.993263
#> 16:                                                                  BOM   41.128932
#> 17:                                                                  BOM   41.412805
#> 18:                                                                  BOM   42.226585
#> 19:                                                                  BOM   44.489821
#> 20:                                                                  BOM   45.457855
#> 21:                                                                  BOM   45.923973
#> 22:                                                                  BOM   46.778842
#> 23:                                                                  BOM   47.759247
#> 24:   WA Department of Biodiversity, Conservation and Attractions (DBCA)   47.790000
#> 25:                                                                  BOM   49.440793
#>                                                                    owner distance_km

Checking Station Uptime or Availability

Example 13: 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",
  api_key = Sys.getenv("DPIRD_API_KEY")
))
#> Key: <station_code>
#>    station_code station_name to9_am since9_am since12_am current_hour last24_hours last7_days_since9_am
#>          <fctr>       <char>  <int>     <int>      <int>        <int>        <int>                <int>
#> 1:        WS001     Westonia    100       100        100          100          100                  100
#>    last7_days_since12_am last14_days_since9_am last14_days_since12_am month_to_date_to9_am month_to_date_since12_am
#>                    <int>                 <int>                  <int>                <int>                    <int>
#> 1:                   100                   100                    100                  100                      100
#>    year_to_date_to9_am year_to_date_since12_am
#>                  <int>                   <int>
#> 1:                 100                     100

Example 14: 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",
    api_key = Sys.getenv("DPIRD_API_KEY")
  )
)
#> Key: <station_code>
#>    station_code station_name start_date   end_date availability_since_9_am availability_since_12_am
#>          <fctr>       <char>     <POSc>     <POSc>                   <int>                    <int>
#> 1:           BI        Binnu 2018-01-01 2018-01-31                     100                      100

Getting 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 15: 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",
                                  api_key = Sys.getenv("DPIRD_API_KEY")))
#>      station_code station_name      start        end  latitude longitude  state elev_m
#>            <char>       <char>     <Date>     <Date>     <num>     <num> <char>  <int>
#>   1:        AN001    Allanooka 2012-06-19 2024-04-19 -29.06361  114.9972     WA    131
#>   2:        AM001       Amelup 2019-10-09 2024-04-19 -34.27083  118.2685     WA    200
#>   3:        SH002      Babakin 2016-06-22 2024-04-19 -32.12548  118.0041     WA    313
#>   4:           BA  Badgingarra 2008-11-19 2024-04-19 -30.33805  115.5395     WA    284
#>   5:        BP001     Balingup 2014-10-24 2024-04-19 -33.79620  116.0640     WA    227
#>  ---                                                                                  
#> 226:           YS      Yilgarn 2008-11-01 2024-04-19 -31.91562  119.2561     WA    468
#> 227:        YE001    York East 2013-11-08 2024-04-19 -31.83588  116.9211     WA    229
#> 228:        YU001         Yuna 2012-06-21 2024-04-19 -28.33763  114.9898     WA    329
#> 229:        YU002      Yuna NE 2016-03-24 2024-04-19 -28.20032  115.2616     WA    267
#> 230:        YU003   Yuna North 2018-08-08 2024-04-19 -28.12086  114.9626     WA    264
#>                                                                    source status    wmo
#>                                                                    <char> <char> <lgcl>
#>   1: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA
#>   2: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA
#>   3: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA
#>   4: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA
#>   5: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA
#>  ---                                                                                   
#> 226: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA
#> 227: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA
#> 228: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA
#> 229: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA
#> 230: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA

Example 16: 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,
                                  api_key = Sys.getenv("DPIRD_API_KEY")))
#>      station_code station_name      start        end  latitude longitude  state elev_m
#>            <char>       <char>     <Date>     <Date>     <num>     <num> <char>  <int>
#>   1:        AN001    Allanooka 2012-06-19 2024-04-19 -29.06361  114.9972     WA    131
#>   2:        AM001       Amelup 2019-10-09 2024-04-19 -34.27083  118.2685     WA    200
#>   3:        SH002      Babakin 2016-06-22 2024-04-19 -32.12548  118.0041     WA    313
#>   4:           BA  Badgingarra 2008-11-19 2024-04-19 -30.33805  115.5395     WA    284
#>   5:        BP001     Balingup 2014-10-24 2024-04-19 -33.79620  116.0640     WA    227
#>  ---                                                                                  
#> 234:           YS      Yilgarn 2008-11-01 2024-04-19 -31.91562  119.2561     WA    468
#> 235:        YE001    York East 2013-11-08 2024-04-19 -31.83588  116.9211     WA    229
#> 236:        YU001         Yuna 2012-06-21 2024-04-19 -28.33763  114.9898     WA    329
#> 237:        YU002      Yuna NE 2016-03-24 2024-04-19 -28.20032  115.2616     WA    267
#> 238:        YU003   Yuna North 2018-08-08 2024-04-19 -28.12086  114.9626     WA    264
#>                                                                    source status    wmo probe_height
#>                                                                    <char> <char> <lgcl>        <num>
#>   1: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA         1.25
#>   2: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA         1.25
#>   3: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA         1.25
#>   4: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA         1.25
#>   5: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA         1.25
#>  ---                                                                                                
#> 234: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA         1.25
#> 235: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA         1.25
#> 236: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA         1.25
#> 237: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA         1.25
#> 238: WA Department of Primary Industries and Regional Development (DPIRD)   open     NA         1.25
#>      rain_gauge_height wind_probe_heights air_temperature battery_voltage delta_t dew_point pan_evaporation
#>                  <num>             <list>          <lgcl>          <lgcl>  <lgcl>    <lgcl>          <lgcl>
#>   1:               0.5                  3            TRUE            TRUE    TRUE      TRUE            TRUE
#>   2:               1.0                  3            TRUE            TRUE    TRUE      TRUE            TRUE
#>   3:               0.5                  3            TRUE            TRUE    TRUE      TRUE            TRUE
#>   4:               0.5                  3            TRUE            TRUE    TRUE      TRUE            TRUE
#>   5:               0.5                  3            TRUE            TRUE    TRUE      TRUE            TRUE
#>  ---                                                                                                       
#> 234:               0.5                  3            TRUE            TRUE    TRUE      TRUE            TRUE
#> 235:               0.5                  3            TRUE            TRUE    TRUE      TRUE            TRUE
#> 236:               0.5                  3            TRUE            TRUE    TRUE      TRUE            TRUE
#> 237:               0.5                  3            TRUE            TRUE    TRUE      TRUE            TRUE
#> 238:               0.5                  3            TRUE            TRUE    TRUE      TRUE            TRUE
#>      relative_humidity barometric_pressure rainfall soil_temperature solar_irradiance wet_bulb  wind1  wind2  wind3
#>                 <lgcl>              <lgcl>   <lgcl>           <lgcl>           <lgcl>   <lgcl> <lgcl> <lgcl> <lgcl>
#>   1:              TRUE               FALSE     TRUE            FALSE             TRUE     TRUE   TRUE  FALSE  FALSE
#>   2:              TRUE               FALSE     TRUE            FALSE             TRUE     TRUE   TRUE  FALSE  FALSE
#>   3:              TRUE               FALSE     TRUE             TRUE             TRUE     TRUE   TRUE  FALSE  FALSE
#>   4:              TRUE               FALSE     TRUE             TRUE             TRUE     TRUE   TRUE  FALSE  FALSE
#>   5:              TRUE               FALSE     TRUE             TRUE             TRUE     TRUE   TRUE  FALSE  FALSE
#>  ---                                                                                                               
#> 234:              TRUE               FALSE     TRUE             TRUE             TRUE     TRUE   TRUE  FALSE  FALSE
#> 235:              TRUE               FALSE     TRUE             TRUE             TRUE     TRUE   TRUE  FALSE  FALSE
#> 236:              TRUE               FALSE     TRUE            FALSE             TRUE     TRUE   TRUE  FALSE  FALSE
#> 237:              TRUE               FALSE     TRUE             TRUE             TRUE     TRUE   TRUE  FALSE  FALSE
#> 238:              TRUE               FALSE     TRUE             TRUE             TRUE     TRUE   TRUE  FALSE  FALSE
#>      apparent_temperature eto_short eto_tall frost_condition heat_condition wind_erosion_condition richardson_unit
#>                    <lgcl>    <lgcl>   <lgcl>          <lgcl>         <lgcl>                 <lgcl>          <lgcl>
#>   1:                 TRUE      TRUE     TRUE            TRUE           TRUE                   TRUE            TRUE
#>   2:                 TRUE      TRUE     TRUE            TRUE           TRUE                   TRUE            TRUE
#>   3:                 TRUE      TRUE     TRUE            TRUE           TRUE                   TRUE            TRUE
#>   4:                 TRUE      TRUE     TRUE            TRUE           TRUE                   TRUE            TRUE
#>   5:                 TRUE      TRUE     TRUE            TRUE           TRUE                   TRUE            TRUE
#>  ---                                                                                                              
#> 234:                 TRUE      TRUE     TRUE            TRUE           TRUE                   TRUE            TRUE
#> 235:                 TRUE      TRUE     TRUE            TRUE           TRUE                   TRUE            TRUE
#> 236:                 TRUE      TRUE     TRUE            TRUE           TRUE                   TRUE            TRUE
#> 237:                 TRUE      TRUE     TRUE            TRUE           TRUE                   TRUE            TRUE
#> 238:                 TRUE      TRUE     TRUE            TRUE           TRUE                   TRUE            TRUE
#>      chill_hour
#>          <lgcl>
#>   1:       TRUE
#>   2:       TRUE
#>   3:       TRUE
#>   4:       TRUE
#>   5:       TRUE
#>  ---           
#> 234:       TRUE
#> 235:       TRUE
#> 236:       TRUE
#> 237:       TRUE
#> 238:       TRUE

Rodrigo Pires, Anna Hepworth, Rebecca O’Leary and Adam H. Sparks

Working With DPIRD Data

A Note on API Keys

Getting Extreme Weather Values

Available Values for Extreme Weather

Example 1: Get All Extremes for Northam, WA

Example 2: Get Selected Extremes for Northam, WA

Getting Minute Data

Available Values for Minute Data

Example 3: Get All Minute Data for the Past 24 Hours

Example 4: Get Specific Time and Date Data for Specific Values

Getting Summary Data

Available Values for Summary Data

What You Get Back

Example 5: Get Annual Rainfall Since 2017

Example 6: Get Monthly Rainfall Since 2017

Example 7: Get Daily Rainfall and Wind From Beginning of 2017 to End of 2018

Example 8: Get Hourly Rainfall and Wind From Beginning of 2022 to Current

Getting APSIM Ready Data

What You Get Back

Example 9: Get APSIM Formatted Data for Binnu From 2022-04-01 to 2022-11-01

Working With DPIRD Metadata

Finding Nearby Stations

Example 10: Finding Stations Nearby a Known Station

Example 11: Finding Stations Nearby a Given Longitude and Latitude

Example 12: Finding Stations in Both the DPIRD and SILO Data Sets

Checking Station Uptime or Availability

Example 13: Checking Station Availability for Current Year

Example 14: Checking Station Availability for a Set Time Period

Getting Station Metadata for the DPIRD Network Stations

Example 15: Get DPIRD Station Metadata

Example 16: Get Rich DPIRD Station Metadata and Include Closed Stations

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