get_data_drill {weatherOz}

Get DataDrill Weather Data From SILO

Description

Fetch nicely formatted weather data from the SILO API of spatially interpolated weather data (DataDrill). The daily climate surfaces have been derived either by splining or kriging the observational data. The returned values contain “source” columns, which denote how the observations were derived. The grid spans 112° to 154°, -10° to -44° with resolution 0.05° latitude by 0.05° longitude (approximately 5 km × 5 km).

Usage

get_data_drill(
  longitude,
  latitude,
  start_date,
  end_date = Sys.Date(),
  values = "all",
  api_key = get_key(service = "SILO")
)

Arguments

longitude

A single numeric value representing the longitude of the point-of-interest to the hundredths (e.g., 0.05) of a degree.

latitude

A single numeric value representing the latitude of the point-of-interest to the hundredths (e.g.., 0.05) of a degree.

start_date

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.

end_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.

values

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.

api_key

A character string containing your API key, an e-mail address, for the request. Defaults to automatically detecting your key from your local .Renviron, .Rprofile or similar. Alternatively, you may directly provide your key as a string here. If nothing is provided, you will be prompted on how to set up your R session so that it is auto-detected.

Value

a data.table::data.table() with the weather data queried with the weather variables in alphabetical order. The first eight columns will always be:

• longitude,

• latitude,

• elev_m (elevation in metres),

• date (ISO8601 format, YYYYMMDD),

• year,

• month,

• day,

• extracted (the date on which the query was made)

Column Name Details

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.

Available Values

all

Which will return all of the following values

rain (mm)

Rainfall

max_temp (degrees C)

Maximum temperature

min_temp (degrees C)

Minimum temperature

vp (hPa)

Vapour pressure

vp_deficit (hPa)

Vapour pressure deficit

evap_pan (mm)

Class A pan evaporation

evap_syn (mm)

Synthetic estimate¹

evap_comb (mm)

Combination (synthetic estimate pre-1970, class A pan 1970 onwards)

evap_morton_lake (mm)

Morton's shallow lake evaporation

radiation (Mj/m²)

Solar exposure, consisting of both direct and diffuse components

rh_tmax (%)

Relative humidity at the time of maximum temperature

rh_tmin (%)

Relative humidity at the time of minimum temperature

et_short_crop (mm)

FAO56⁴

short crop

et_tall_crop (mm)

ASCE⁵

tall crop⁶

et_morton_actual (mm)

Morton's areal actual evapotranspiration

et_morton_potential (mm)

Morton's point potential evapotranspiration

et_morton_wet (mm)

Morton's wet-environment areal potential evapotranspiration over land

mslp (hPa)

Mean sea level pressure

Value information

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.

Data codes

Data codes Where possible (depending on the file format), the data are supplied with codes indicating how each datum was obtained.

0

Official observation as supplied by the Bureau of Meteorology

15

Deaccumulated rainfall (original observation was recorded over a period exceeding the standard 24 hour observation period)

25

Interpolated from daily observations for that date

26

Synthetic Class A pan evaporation, calculated from temperatures, radiation and vapour pressure

35

Interpolated from daily observations using an anomaly interpolation method

75

Interpolated from the long term averages of daily observations for that day of year

Author(s)

Rodrigo Pires, [e-mail], and Adam H. Sparks, [e-mail]

References

1. 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.

2. 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.

3. 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.

4. Food and Agriculture Organization of the United Nations, Irrigation and drainage paper 56: Crop evapotranspiration - Guidelines for computing crop water requirements, 1998.

5. ASCE’s Standardized Reference Evapotranspiration Equation, proceedings of the National Irrigation Symposium, Phoenix, Arizona, 2000.

6. 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.

See Also

Other SILO: find_nearby_stations(), find_stations_in(), get_data_drill_apsim(), get_patched_point(), get_patched_point_apsim(), get_stations_metadata(), silo_daily_values

Other data fetching: get_ag_bulletin(), get_coastal_forecast(), get_data_drill_apsim(), get_dpird_apsim(), get_dpird_extremes(), get_dpird_minute(), get_dpird_summaries(), get_patched_point(), get_patched_point_apsim(), get_precis_forecast(), get_radar_imagery(), get_satellite_imagery()

Examples

## Not run: 
# requires an API key as your email address
# Source data from latitude and longitude coordinates (gridded data) for
# max and minimum temperature and rainfall for Southwood, QLD.
wd <- get_data_drill(
  latitude = -27.85,
  longitude = 150.05,
  start_date = "20221001",
  end_date = "20221201",
  values = c("max_temp", "min_temp", "rain"),
  api_key = "your_api_key"
)

## End(Not run)