Nothing
weatherOz for BOM
In weatherOz: An API Client for Australian Weather and Climate Data Resources
title: "weatherOz for BOM"
author: "Jonathan Carroll, Dean Marchiori, Paul Melloy, Mark Padgham, Hugh Parsonage, Keith Pembleton and Adam H. Sparks"
output:
rmarkdown::html_vignette:
toc: true
vignette: >
%\VignetteIndexEntry{weatherOz for BOM}
%\VignetteEngine{knitr::rmarkdown_notangle}
%\VignetteEncoding{UTF-8}
Using {weatherOz} With BOM Data
Several functions are provided by {weatherOz} to retrieve Australian Bureau of Meteorology (BOM) data.
A family of functions retrieve data files from BOM or parse local files from BOM and return data frames;
get_precis_forecast() and parse_precis_forecast(), which return the précis (short) forecast;get_ag_bulletin() and parse_ag_bulletin(), which return the agriculture bulletin (defunct as of version v2.0.0);get_coastal_forecast() and parse_coastal_forecast(), which return coastal waters forecasts for each state.
Using get_precis_forecast()
This function only takes one argument, state. The state parameter allows the user to select the forecast for just one state or a national forecast.
States or territories are specified using the official postal codes or full name with fuzzy matching performed via agrep().
-
ACT - Australian Capital Territory
-
NSW - New South Wales
-
NT - Northern Territory
-
QLD - Queensland
-
SA - South Australia
-
TAS - Tasmania
-
VIC - Victoria
-
WA - Western Australia
-
AUS - Australia, returns national forecast including all states, NT and ACT.
Results
The function, get_precis_forecast(), will return a data frame of the weather forecast for the daily forecast for selected towns.
See Appendix 1 for a full description of the fields and values.
Example 1: Getting a Forecast
Following is an example fetching the forecast for Queensland.
library(weatherOz)
(QLD_forecast <- get_precis_forecast(state = "QLD"))
#> ----------- Australian Bureau of Meteorology (BOM) Precis Forecast. -
#> The HTML version of Short Form (Precis) Forecast for
#> QLD can be found at:
#> <http://www.bom.gov.au/qld/forecasts/state.shtml>
#> Please note information at the page
#> <http://www.bom.gov.au/catalogue/data-feeds.shtml#precis>.
#> --------------------------------------------------------------------------------
#> index product_id state town aac lat lon elev start_time_local end_time_local utc_offset
#> <fctr> <char> <char> <char> <char> <num> <num> <num> <POSc> <POSc> <fctr>
#> 1: 0 IDQ11295 QLD Brisbane QLD_PT001 -27.48080 153.0389 8.1 2025-02-25 09:01:53 2025-02-26 10:00
#> 2: 1 IDQ11295 QLD Brisbane QLD_PT001 -27.48080 153.0389 8.1 2025-02-26 00:00:00 2025-02-27 10:00
#> 3: 2 IDQ11295 QLD Brisbane QLD_PT001 -27.48080 153.0389 8.1 2025-02-27 00:00:00 2025-02-28 10:00
#> 4: 3 IDQ11295 QLD Brisbane QLD_PT001 -27.48080 153.0389 8.1 2025-02-28 00:00:00 2025-03-01 10:00
#> 5: 4 IDQ11295 QLD Brisbane QLD_PT001 -27.48080 153.0389 8.1 2025-03-01 00:00:00 2025-03-02 10:00
#> ---
#> 787: 2 IDQ11295 QLD Port Douglas QLD_PT254 -16.48681 145.4635 70.4 2025-02-27 00:00:00 2025-02-28 10:00
#> 788: 3 IDQ11295 QLD Port Douglas QLD_PT254 -16.48681 145.4635 70.4 2025-02-28 00:00:00 2025-03-01 10:00
#> 789: 4 IDQ11295 QLD Port Douglas QLD_PT254 -16.48681 145.4635 70.4 2025-03-01 00:00:00 2025-03-02 10:00
#> 790: 5 IDQ11295 QLD Port Douglas QLD_PT254 -16.48681 145.4635 70.4 2025-03-02 00:00:00 2025-03-03 10:00
#> 791: 6 IDQ11295 QLD Port Douglas QLD_PT254 -16.48681 145.4635 70.4 2025-03-03 00:00:00 2025-03-04 10:00
#> start_time_utc end_time_utc minimum_temperature maximum_temperature lower_precipitation_limit
#> <POSc> <POSc> <num> <num> <num>
#> 1: 2025-02-24 23:01:53 2025-02-25 14:00:00 NA 31 NA
#> 2: 2025-02-25 14:00:00 2025-02-26 14:00:00 22 30 0
#> 3: 2025-02-26 14:00:00 2025-02-27 14:00:00 21 30 0
#> 4: 2025-02-27 14:00:00 2025-02-28 14:00:00 21 31 NA
#> 5: 2025-02-28 14:00:00 2025-03-01 14:00:00 21 30 0
#> ---
#> 787: 2025-02-26 14:00:00 2025-02-27 14:00:00 23 32 0
#> 788: 2025-02-27 14:00:00 2025-02-28 14:00:00 23 32 0
#> 789: 2025-02-28 14:00:00 2025-03-01 14:00:00 22 32 NA
#> 790: 2025-03-01 14:00:00 2025-03-02 14:00:00 22 34 0
#> 791: 2025-03-02 14:00:00 2025-03-03 14:00:00 23 34 0
#> upper_precipitation_limit precis probability_of_precipitation
#> <num> <char> <num>
#> 1: NA Partly cloudy. 30
#> 2: 1 Partly cloudy. 30
#> 3: 1 Partly cloudy. 30
#> 4: NA Mostly sunny. 20
#> 5: 1 Partly cloudy. 30
#> ---
#> 787: 1 Partly cloudy. 30
#> 788: 1 Partly cloudy. 30
#> 789: NA Sunny. 20
#> 790: 1 Mostly sunny. 30
#> 791: 1 Possible shower. 40
Using get_coastal_forecast()
This function only takes one argument, state.
The state parameter allows the user to select the forecast for just one state or a national forecast.
States or territories are specified using the official postal codes or full name with fuzzy matching performed via agrep()
-
ACT - Australian Capital Territory
-
NSW - New South Wales
-
NT - Northern Territory
-
QLD - Queensland
-
SA - South Australia
-
TAS - Tasmania
-
VIC - Victoria
-
WA - Western Australia
-
AUS - Australia, returns national forecast including all states, NT and ACT.
Results
The function, get_coastal_forecast(), will return a data frame of the coastal waters forecast for marine zones in each state.
See Appendix 6 for a full description of the fields and values.
Example 2: Getting a Coastal Forecast
Following is an example fetching the forecast for Queensland.
library(weatherOz)
(QLD_coastal_forecast <- get_coastal_forecast(state = "QLD"))
#> ------- Australian Bureau of Meteorology (BOM) Coastal Waters Forecast. -------
#> Please note information at the foot of
#> <http://www.bom.gov.au/cgi-bin/wrap_fwo.pl?IDQ11290.html>,
#> the HTML version of Coastal Waters Forecast for
#> QLD.
#> Also see
#> <http://www.bom.gov.au/catalogue/observations/about-coastal-observations.shtml>.
#> --------------------------------------------------------------------------------
#> index product_id type state_code dist_name pt_1_name pt_2_name
#> <fctr> <char> <char> <char> <char> <char> <char>
#> 1: <NA> IDQ11290 <NA> QLD Queensland <NA> <NA>
#> 2: <NA> IDQ11290 <NA> QLD Queensland Gulf of Carpentaria <NA> <NA>
#> 3: <NA> IDQ11290 <NA> QLD North Queensland <NA> <NA>
#> 4: <NA> IDQ11290 <NA> QLD South Queensland <NA> <NA>
#> 5: 0 IDQ11290 <NA> QLD South East Gulf of Carpentaria: QLD-NT Border to Cape Keerweer <NA> <NA>
#> ---
#> 45: 1 IDQ11290 <NA> QLD Gold Coast Waters: Cape Moreton to Point Danger <NA> <NA>
#> 46: 2 IDQ11290 <NA> QLD Gold Coast Waters: Cape Moreton to Point Danger <NA> <NA>
#> 47: 0 IDQ11290 <NA> QLD Great Barrier Reef Offshore Waters <NA> <NA>
#> 48: 1 IDQ11290 <NA> QLD Great Barrier Reef Offshore Waters <NA> <NA>
#> 49: 2 IDQ11290 <NA> QLD Great Barrier Reef Offshore Waters <NA> <NA>
#> aac start_time_local end_time_local utc_offset start_time_utc end_time_utc
#> <char> <POSc> <POSc> <fctr> <POSc> <POSc>
#> 1: QLD_FA001 2025-02-25 10:00:22 2025-02-25 10:00:22 10:00 2025-02-25 10:00:22 2025-02-25 10:00:22
#> 2: QLD_FA002 2025-02-25 05:00:00 2025-02-25 05:00:00 10:00 2025-02-25 05:00:00 2025-02-25 05:00:00
#> 3: QLD_FA003 2025-02-25 05:00:00 2025-02-25 05:00:00 10:00 2025-02-25 05:00:00 2025-02-25 05:00:00
#> 4: QLD_FA004 2025-02-25 05:00:00 2025-02-25 05:00:00 10:00 2025-02-25 05:00:00 2025-02-25 05:00:00
#> 5: QLD_MW001 2025-02-25 10:00:00 2025-02-25 10:00:00 10:00 2025-02-25 10:00:00 2025-02-25 10:00:00
#> ---
#> 45: QLD_MW014 2025-02-26 00:00:00 2025-02-26 00:00:00 10:00 2025-02-26 00:00:00 2025-02-26 00:00:00
#> 46: QLD_MW014 2025-02-27 00:00:00 2025-02-27 00:00:00 10:00 2025-02-27 00:00:00 2025-02-27 00:00:00
#> 47: QLD_MW015 2025-02-25 10:00:00 2025-02-25 10:00:00 10:00 2025-02-25 10:00:00 2025-02-25 10:00:00
#> 48: QLD_MW015 2025-02-26 00:00:00 2025-02-26 00:00:00 10:00 2025-02-26 00:00:00 2025-02-26 00:00:00
#> 49: QLD_MW015 2025-02-27 00:00:00 2025-02-27 00:00:00 10:00 2025-02-27 00:00:00 2025-02-27 00:00:00
#> forecast_seas forecast_weather
#> <char> <char>
#> 1: <NA> <NA>
#> 2: <NA> <NA>
#> 3: <NA> <NA>
#> 4: <NA> <NA>
#> 5: Below 1 metre. Mostly sunny. The chance of a thunderstorm.
#> ---
#> 45: 1 to 1.5 metres. Partly cloudy.
#> 46: Around 1 metre, increasing to 1 to 1.5 metres offshore. Partly cloudy.
#> 47: 1.5 to 2.5 metres. Partly cloudy. 50% chance of showers.
#> 48: 2 to 3 metres. Partly cloudy. 70% chance of showers.
#> 49: 2 to 3 metres. Partly cloudy. 60% chance of showers.
#> forecast_winds
#> <char>
#> 1: <NA>
#> 2: <NA>
#> 3: <NA>
#> 4: <NA>
#> 5: East to southeasterly 10 to 15 knots becoming variable about 10 knots in the middle of the day.
#> ---
#> 45: Southeasterly 15 to 20 knots.
#> 46: Southeasterly 15 to 20 knots.
#> 47: Southeasterly 20 to 25 knots increasing to 25 to 30 knots in the early afternoon.
#> 48: Southeasterly 25 to 30 knots.
#> 49: Southeasterly 25 to 30 knots.
#> forecast_swell1 forecast_swell2
#> <char> <char>
#> 1: <NA> <NA>
#> 2: <NA> <NA>
#> 3: <NA> <NA>
#> 4: <NA> <NA>
#> 5: Below 0.5 metres. <NA>
#> ---
#> 45: Easterly 1.5 metres. <NA>
#> 46: Easterly 1.5 to 2.5 metres. <NA>
#> 47: East to southeasterly 1.5 to 2.5 metres. <NA>
#> 48: East to southeasterly 1 to 2 metres, increasing to 2 to 3 metres through the Capricorn Channel. <NA>
#> 49: East to southeasterly 1.5 to 2.5 metres, increasing to 2.5 to 4 metres through the Capricorn Channel. <NA>
#> forecast_caution marine_forecast tropical_system_location forecast_waves
#> <char> <char> <lgcl> <lgcl>
#> 1: <NA> <NA> NA NA
#> 2: <NA> <NA> NA NA
#> 3: <NA> <NA> NA NA
#> 4: <NA> <NA> NA NA
#> 5: <NA> <NA> NA NA
#> ---
#> 45: <NA> <NA> NA NA
#> 46: <NA> <NA> NA NA
#> 47: <NA> <NA> NA NA
#> 48: <NA> <NA> NA NA
#> 49: <NA> <NA> NA NA
Working with BOM Image Files
A second family of functions retrieve information pertaining to satellite and radar imagery.
get_available_imagery(), which returns available satellite imagery;get_satelllite_imagery() which returns a [terra::SpatRaster] or [stars] object as requested;get_available_radar(), which returns available radar images; andget_radar_imagery() which returns radar images as a [magick] object.
Using {weatherOz} to retrieve BOM satellite imagery
{weatherOz} provides functionality to retrieve high-definition GeoTIFF satellite imagery provided by BOM through public FTP with the following types of imagery being available: i.) Infrared images, ii.)
Visible images and iii.) Clouds/surface composite.
Working with Satellite Imagery
Valid BOM satellite Product IDs for GeoTIFF files include:
Product ID Description Type Delete time IDE00420 AHI cloud cover only 2km FD GEOS Satellite 24 IDE00421 AHI IR (Ch13) greyscale 2km FD GEOS Satellite 24 IDE00422 AHI VIS (Ch3) greyscale 2km FD GEOS Satellite 24 IDE00423 AHI IR (Ch13) Zehr 2km FD GEOS Satellite 24 IDE00425 AHI VIS (true colour) / IR (Ch13 greyscale) composite 1km FD GEOS Satellite 24 IDE00426 AHI VIS (true colour) / IR (Ch13 greyscale) composite 2km FD GEOS Satellite 24 IDE00427 AHI WV (Ch8) 2km FD GEOS Satellite 24 IDE00430 AHI cloud cover only 2km AUS equirect. Satellite 24 IDE00431 AHI IR (Ch13) greyscale 2km AUS equirect. Satellite 24 IDE00432 AHI VIS (Ch3) greyscale 2km AUS equirect. Satellite 24 IDE00433 AHI IR (Ch13) Zehr 2km AUS equirect. Satellite 24 IDE00435 AHI VIS (true colour) / IR (Ch13 greyscale) composite 1km AUS equirect. Satellite 24 IDE00436 AHI VIS (true colour) / IR (Ch13 greyscale) composite 2km AUS equirect. Satellite 24 IDE00437 AHI WV (Ch8) 2km AUS equirect. Satellite 24 IDE00439 AHI VIS (Ch3) greyscale 0.5km AUS equirect. Satellite 24 Information gathered from Australian Bureau of Meteorology (BOM)
Using get_available_imagery()
get_available_imagery() only takes one argument, product_id, a BOM identifier for the imagery that you wish to check for available imagery.
Using this function will fetch a listing of BOM GeoTIFF satellite imagery from ftp://ftp.bom.gov.au/anon/gen/gms/ to display which files are currently available for download.
These files are available at ten minute update frequency with a 24 hour delete time.
This function can be used see the most recent files available and then specify in the _imagery() function.
If no valid Product ID is supplied, defaults to all GeoTIFF images currently available.
Example 3: Checking Available Imagery
library(weatherOz)
(avail <- get_available_imagery(product_id = "IDE00425"))
#> [1] "IDE00425.202502240410.tif" "IDE00425.202502240420.tif" "IDE00425.202502240430.tif" "IDE00425.202502240440.tif"
#> [5] "IDE00425.202502240450.tif" "IDE00425.202502240500.tif" "IDE00425.202502240510.tif" "IDE00425.202502240520.tif"
#> [9] "IDE00425.202502240530.tif" "IDE00425.202502240540.tif" "IDE00425.202502240550.tif" "IDE00425.202502240600.tif"
#> [13] "IDE00425.202502240610.tif" "IDE00425.202502240620.tif" "IDE00425.202502240630.tif" "IDE00425.202502240640.tif"
#> [17] "IDE00425.202502240720.tif" "IDE00425.202502240730.tif" "IDE00425.202502240740.tif" "IDE00425.202502240750.tif"
#> [21] "IDE00425.202502240800.tif" "IDE00425.202502240810.tif" "IDE00425.202502240830.tif" "IDE00425.202502240840.tif"
#> [25] "IDE00425.202502240850.tif" "IDE00425.202502240900.tif" "IDE00425.202502240910.tif" "IDE00425.202502240920.tif"
#> [29] "IDE00425.202502240930.tif" "IDE00425.202502240940.tif" "IDE00425.202502240950.tif" "IDE00425.202502241000.tif"
#> [33] "IDE00425.202502241010.tif" "IDE00425.202502241020.tif" "IDE00425.202502241030.tif" "IDE00425.202502241040.tif"
#> [37] "IDE00425.202502241050.tif" "IDE00425.202502241100.tif" "IDE00425.202502241110.tif" "IDE00425.202502241120.tif"
#> [41] "IDE00425.202502241130.tif" "IDE00425.202502241140.tif" "IDE00425.202502241150.tif" "IDE00425.202502241200.tif"
#> [45] "IDE00425.202502241210.tif" "IDE00425.202502241220.tif" "IDE00425.202502241230.tif" "IDE00425.202502241240.tif"
#> [49] "IDE00425.202502241250.tif" "IDE00425.202502241300.tif" "IDE00425.202502241310.tif" "IDE00425.202502241320.tif"
#> [53] "IDE00425.202502241330.tif" "IDE00425.202502241340.tif" "IDE00425.202502241350.tif" "IDE00425.202502241400.tif"
#> [57] "IDE00425.202502241410.tif" "IDE00425.202502241420.tif" "IDE00425.202502241430.tif" "IDE00425.202502241450.tif"
#> [61] "IDE00425.202502241500.tif" "IDE00425.202502241510.tif" "IDE00425.202502241520.tif" "IDE00425.202502241530.tif"
#> [65] "IDE00425.202502241540.tif" "IDE00425.202502241550.tif" "IDE00425.202502241600.tif" "IDE00425.202502241610.tif"
#> [69] "IDE00425.202502241620.tif" "IDE00425.202502241630.tif" "IDE00425.202502241640.tif" "IDE00425.202502241650.tif"
#> [73] "IDE00425.202502241700.tif" "IDE00425.202502241710.tif" "IDE00425.202502241720.tif" "IDE00425.202502241730.tif"
#> [77] "IDE00425.202502241740.tif" "IDE00425.202502241750.tif" "IDE00425.202502241800.tif" "IDE00425.202502241810.tif"
#> [81] "IDE00425.202502241830.tif" "IDE00425.202502241840.tif" "IDE00425.202502241850.tif" "IDE00425.202502241900.tif"
#> [85] "IDE00425.202502241910.tif" "IDE00425.202502241920.tif" "IDE00425.202502241930.tif" "IDE00425.202502241940.tif"
#> [89] "IDE00425.202502241950.tif" "IDE00425.202502242000.tif" "IDE00425.202502242010.tif" "IDE00425.202502242020.tif"
#> [93] "IDE00425.202502242030.tif" "IDE00425.202502242040.tif" "IDE00425.202502242050.tif" "IDE00425.202502242100.tif"
#> [97] "IDE00425.202502242110.tif" "IDE00425.202502242120.tif" "IDE00425.202502242130.tif" "IDE00425.202502242140.tif"
#> [101] "IDE00425.202502242150.tif" "IDE00425.202502242200.tif" "IDE00425.202502242210.tif" "IDE00425.202502242220.tif"
#> [105] "IDE00425.202502242240.tif" "IDE00425.202502242250.tif" "IDE00425.202502242300.tif" "IDE00425.202502242310.tif"
#> [109] "IDE00425.202502242320.tif" "IDE00425.202502242330.tif" "IDE00425.202502242340.tif" "IDE00425.202502242350.tif"
#> [113] "IDE00425.202502250000.tif" "IDE00425.202502250040.tif" "IDE00425.202502250050.tif" "IDE00425.202502250100.tif"
#> [117] "IDE00425.202502250110.tif" "IDE00425.202502250130.tif" "IDE00425.202502250140.tif" "IDE00425.202502250150.tif"
#> [121] "IDE00425.202502250200.tif" "IDE00425.202502250210.tif" "IDE00425.202502250220.tif" "IDE00425.202502250230.tif"
#> [125] "IDE00425.202502250250.tif" "IDE00425.202502250300.tif" "IDE00425.202502250310.tif" "IDE00425.202502250320.tif"
#> [129] "IDE00425.202502250330.tif" "IDE00425.202502250340.tif" "IDE00425.202502250350.tif" "IDE00425.202502250400.tif"
#> [133] "IDE00425.202502250410.tif" "IDE00425.202502250420.tif" "IDE00425.202502250430.tif" "IDE00425.202502250440.tif"
#> [137] "IDE00425.202502250450.tif" "IDE00425.202502250500.tif"
Using get_satellite_imagery()
get_satellite_imagery() fetches BOM satellite GeoTIFF imagery, returning a SpatRaster object and takes two arguments.
Files are available at ten minute update frequency with a 24 hour delete time.
It is suggested to check file availability first by using get_available_imagery().
The arguments are:
-
product_id, a character value of the BOM product ID to download.
Alternatively, a vector of values from get_available_imagery() may be used here.
This argument is mandatory.
-
scans a numeric value for the number of scans to download, starting with the most recent and progressing backwards, e.g., 1 - the most recent single scan available , 6 - the most recent hour available, 12 - the most recent 2 hours available, etc.
Negating will return the oldest files first.
Defaults to 1.
This argument is optional.
Example 6: Fetching Satellite Imagery and Viewing It
library(weatherOz)
# Specify product ID and scans
i <- get_satellite_imagery(product_id = "IDE00425", scans = 1)
terra::plot() has been re-exported to simplify visualising these files while using {weatherOz}.
plot(i)
Using {weatherOz} With BOM Radar Imagery
{weatherOz} provides functionality to retrieve the latest radar imagery provided by BOM through a public FTP server.
These are the latest snapshots for each radar locations at various radar ranges e.g., 512km, 256km, 128km and 64km for some stations.
Using get_available_radar()
get_available_radar() fetches the available radar imagery from the BOM FTP and returns a data frame for reference.
This data frame contains the product_id, which is required when using the get_radar_imagery() function.
The files available are the latest .png files of BOM radar imagery which are typically updated each 6-10 minutes. Only the most recent image is retrieved for each radar location.
There are usually several radar ranges available for each radar location, such as 512km, 256km, 128km and possibly 64km.
The arguments are:
radar_id which is the BOM radar ID number; this defaults to 'all' which will return a data frame of all radar IDs in Australia.
Example 7: Getting Available Radar Imagery
library(weatherOz)
x <- get_available_radar()
head(x)
#> product_id LocationID range Name Longitude Latitude Radar_id Full_Name IDRnn0name IDRnn1name State
#> <char> <char> <char> <fctr> <num> <num> <int> <fctr> <fctr> <fctr> <fctr>
#> 1: IDR641 64 512km Adelaide 138.4689 -34.6169 64 Adelaide (Buckland Park) BuckPk BucklandPk SA
#> 2: IDR642 64 256km Adelaide 138.4689 -34.6169 64 Adelaide (Buckland Park) BuckPk BucklandPk SA
#> 3: IDR643 64 128km Adelaide 138.4689 -34.6169 64 Adelaide (Buckland Park) BuckPk BucklandPk SA
#> 4: IDR644 64 64km Adelaide 138.4689 -34.6169 64 Adelaide (Buckland Park) BuckPk BucklandPk SA
#> 5: IDR311 31 512km Albany 117.8163 -34.9418 31 Albany Albany Albany WA
#> 6: IDR312 31 256km Albany 117.8163 -34.9418 31 Albany Albany Albany WA
#> Type Group_ Status Archive as.is
#> <fctr> <fctr> <fctr> <fctr> <lgcl>
#> 1: Doppler Yes Public BuckPk TRUE
#> 2: Doppler Yes Public BuckPk TRUE
#> 3: Doppler Yes Public BuckPk TRUE
#> 4: Doppler Yes Public BuckPk TRUE
#> 5: Doppler Yes Public Albany TRUE
#> 6: Doppler Yes Public Albany TRUE
Using get_radar_imagery()
get_radar_imagery() fetches the latest BOM radar imagery for a given product ID.
The files available are the latest .png files of BOM radar imagery, which are typically updated each 6-10 minutes.
Only the most recent image is retrieved for each radar location.
There are usually several radar ranges available for each radar location, such as 512km, 256km, 128km and possibly 64km.
The only argument is:
product_id the BOM product_id associated with each radar imagery file.
These can be obtained from the get_available_radar() function.
This value must be specified and the function will accept only one at a time.
Example 8: Fetching Radar Imagery
library(weatherOz)
y <- get_radar_imagery(product_id = "IDR032")
plot(y)
References
Australian Bureau of Meteorology (BOM) Weather Data Services
Australian Bureau of Meteorology (BOM) FTP Public Products
Australian Bureau of Meteorology (BOM) Weather Data Services Observation of Rainfall
Australian Bureau of Meteorology (BOM) High-definition satellite images
Appendix 1 - Output From get_precis_forecast()
The functions, get_precis_forecast() or `parse_precis_forecast(), will return a data frame of the 7 day short forecast with the following fields:
- index
- Forecast index number, 0 = current day ... 7 day
- product_id
- BOM Product ID from which the data are derived
- state
- State name (postal code abbreviation)
- town
- Town name for forecast location
- aac
- AMOC Area Code, _e.g._, WA_MW008, a unique identifier for each location
- lat
- Latitude of named location (decimal degrees)
- lon
- Longitude of named location (decimal degrees)
- elev
- Elevation of named location (metres)
- start_time_local
- Start of forecast date and time in local TZ
- end_time_local
- End of forecast date and time in local TZ
- UTC_offset
- Hours offset from difference in hours and minutes from Coordinated Universal Time (UTC) for `start_time_local` and `end_time_local`
- start_time_utc
- Start of forecast date and time in UTC
- end_time_utc
- End of forecast date and time in UTC
- maximum_temperature
- Maximum forecast temperature (degrees Celsius)
- minimum_temperature
- Minimum forecast temperature (degrees Celsius)
- lower_precipitation_limit
- Lower forecast precipitation limit (millimetres)
- upper_precipitation_limit
- Upper forecast precipitation limit (millimetres)
- precis
- Précis forecast (a short summary, less than 30 characters)
- probability_of_precipitation
- Probability of precipitation (percent)
Appendix 2 - Output From get_coastal_forecast()
The output of get_coastal_forecast() or parse_coastal_forecast() will return a data frame with coastal waters forecast values of each area within the given state with the following fields:
- index
- Forecast index number. 0 = current day
- product_id
- BOM Product ID from which the data are derived
- type
- Forecast Region type e.g. Coastal
- state_code
- State name (postal code abbreviation)
- dist_name
- Name of forecast district
- pt_1_name
- Start of forecast district
- pt_2_name
- End of forecast district
- aac
- AMOC Area Code, _e.g._, WA_MW008, a unique identifier for each location
- start_time_local
- Start of forecast date and time in local TZ
- end_time_local
- End of forecast date and time in local TZ
- UTC_offset
- Hours offset from difference in hours and minutes from Coordinated Universal Time (UTC) for `start_time_local` and `end_time_local`
- start_time_utc
- Start of forecast date and time in UTC
- end_time_utc
- End of forecast date and time in UTC
- forecast_seas
- Forecast sea conditions
- forecast_weather
- Forecast weather summary
- forecast_winds
- Forecast winds summary
- forecast_swell1
- Forecast primary swell summary
- forecast_swell2
- Forecast seondary swell summary (not always provided)
- forecast_caution
- Forecast caution issued (not always provided)
- marine_forecast
- Additional marine forecast warning information (not always provided)
Try the weatherOz package in your browser
Any scripts or data that you put into this service are public.
weatherOz documentation built on April 16, 2025, 9:07 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
title: "weatherOz for BOM" author: "Jonathan Carroll, Dean Marchiori, Paul Melloy, Mark Padgham, Hugh Parsonage, Keith Pembleton and Adam H. Sparks" output: rmarkdown::html_vignette: toc: true vignette: > %\VignetteIndexEntry{weatherOz for BOM} %\VignetteEngine{knitr::rmarkdown_notangle} %\VignetteEncoding{UTF-8}
Using {weatherOz} With BOM Data
Several functions are provided by {weatherOz} to retrieve Australian Bureau of Meteorology (BOM) data. A family of functions retrieve data files from BOM or parse local files from BOM and return data frames;
get_precis_forecast()andparse_precis_forecast(), which return the précis (short) forecast;get_ag_bulletin()andparse_ag_bulletin(), which return the agriculture bulletin (defunct as of version v2.0.0);get_coastal_forecast()andparse_coastal_forecast(), which return coastal waters forecasts for each state.
Using get_precis_forecast()
This function only takes one argument, state. The state parameter allows the user to select the forecast for just one state or a national forecast.
States or territories are specified using the official postal codes or full name with fuzzy matching performed via agrep().
-
ACT - Australian Capital Territory
-
NSW - New South Wales
-
NT - Northern Territory
-
QLD - Queensland
-
SA - South Australia
-
TAS - Tasmania
-
VIC - Victoria
-
WA - Western Australia
-
AUS - Australia, returns national forecast including all states, NT and ACT.
Results
The function, get_precis_forecast(), will return a data frame of the weather forecast for the daily forecast for selected towns.
See Appendix 1 for a full description of the fields and values.
Example 1: Getting a Forecast
Following is an example fetching the forecast for Queensland.
library(weatherOz) (QLD_forecast <- get_precis_forecast(state = "QLD")) #> ----------- Australian Bureau of Meteorology (BOM) Precis Forecast. - #> The HTML version of Short Form (Precis) Forecast for #> QLD can be found at: #> <http://www.bom.gov.au/qld/forecasts/state.shtml> #> Please note information at the page #> <http://www.bom.gov.au/catalogue/data-feeds.shtml#precis>. #> -------------------------------------------------------------------------------- #> index product_id state town aac lat lon elev start_time_local end_time_local utc_offset #> <fctr> <char> <char> <char> <char> <num> <num> <num> <POSc> <POSc> <fctr> #> 1: 0 IDQ11295 QLD Brisbane QLD_PT001 -27.48080 153.0389 8.1 2025-02-25 09:01:53 2025-02-26 10:00 #> 2: 1 IDQ11295 QLD Brisbane QLD_PT001 -27.48080 153.0389 8.1 2025-02-26 00:00:00 2025-02-27 10:00 #> 3: 2 IDQ11295 QLD Brisbane QLD_PT001 -27.48080 153.0389 8.1 2025-02-27 00:00:00 2025-02-28 10:00 #> 4: 3 IDQ11295 QLD Brisbane QLD_PT001 -27.48080 153.0389 8.1 2025-02-28 00:00:00 2025-03-01 10:00 #> 5: 4 IDQ11295 QLD Brisbane QLD_PT001 -27.48080 153.0389 8.1 2025-03-01 00:00:00 2025-03-02 10:00 #> --- #> 787: 2 IDQ11295 QLD Port Douglas QLD_PT254 -16.48681 145.4635 70.4 2025-02-27 00:00:00 2025-02-28 10:00 #> 788: 3 IDQ11295 QLD Port Douglas QLD_PT254 -16.48681 145.4635 70.4 2025-02-28 00:00:00 2025-03-01 10:00 #> 789: 4 IDQ11295 QLD Port Douglas QLD_PT254 -16.48681 145.4635 70.4 2025-03-01 00:00:00 2025-03-02 10:00 #> 790: 5 IDQ11295 QLD Port Douglas QLD_PT254 -16.48681 145.4635 70.4 2025-03-02 00:00:00 2025-03-03 10:00 #> 791: 6 IDQ11295 QLD Port Douglas QLD_PT254 -16.48681 145.4635 70.4 2025-03-03 00:00:00 2025-03-04 10:00 #> start_time_utc end_time_utc minimum_temperature maximum_temperature lower_precipitation_limit #> <POSc> <POSc> <num> <num> <num> #> 1: 2025-02-24 23:01:53 2025-02-25 14:00:00 NA 31 NA #> 2: 2025-02-25 14:00:00 2025-02-26 14:00:00 22 30 0 #> 3: 2025-02-26 14:00:00 2025-02-27 14:00:00 21 30 0 #> 4: 2025-02-27 14:00:00 2025-02-28 14:00:00 21 31 NA #> 5: 2025-02-28 14:00:00 2025-03-01 14:00:00 21 30 0 #> --- #> 787: 2025-02-26 14:00:00 2025-02-27 14:00:00 23 32 0 #> 788: 2025-02-27 14:00:00 2025-02-28 14:00:00 23 32 0 #> 789: 2025-02-28 14:00:00 2025-03-01 14:00:00 22 32 NA #> 790: 2025-03-01 14:00:00 2025-03-02 14:00:00 22 34 0 #> 791: 2025-03-02 14:00:00 2025-03-03 14:00:00 23 34 0 #> upper_precipitation_limit precis probability_of_precipitation #> <num> <char> <num> #> 1: NA Partly cloudy. 30 #> 2: 1 Partly cloudy. 30 #> 3: 1 Partly cloudy. 30 #> 4: NA Mostly sunny. 20 #> 5: 1 Partly cloudy. 30 #> --- #> 787: 1 Partly cloudy. 30 #> 788: 1 Partly cloudy. 30 #> 789: NA Sunny. 20 #> 790: 1 Mostly sunny. 30 #> 791: 1 Possible shower. 40
Using get_coastal_forecast()
This function only takes one argument, state.
The state parameter allows the user to select the forecast for just one state or a national forecast.
States or territories are specified using the official postal codes or full name with fuzzy matching performed via agrep()
-
ACT - Australian Capital Territory
-
NSW - New South Wales
-
NT - Northern Territory
-
QLD - Queensland
-
SA - South Australia
-
TAS - Tasmania
-
VIC - Victoria
-
WA - Western Australia
-
AUS - Australia, returns national forecast including all states, NT and ACT.
Results
The function, get_coastal_forecast(), will return a data frame of the coastal waters forecast for marine zones in each state.
See Appendix 6 for a full description of the fields and values.
Example 2: Getting a Coastal Forecast
Following is an example fetching the forecast for Queensland.
library(weatherOz) (QLD_coastal_forecast <- get_coastal_forecast(state = "QLD")) #> ------- Australian Bureau of Meteorology (BOM) Coastal Waters Forecast. ------- #> Please note information at the foot of #> <http://www.bom.gov.au/cgi-bin/wrap_fwo.pl?IDQ11290.html>, #> the HTML version of Coastal Waters Forecast for #> QLD. #> Also see #> <http://www.bom.gov.au/catalogue/observations/about-coastal-observations.shtml>. #> -------------------------------------------------------------------------------- #> index product_id type state_code dist_name pt_1_name pt_2_name #> <fctr> <char> <char> <char> <char> <char> <char> #> 1: <NA> IDQ11290 <NA> QLD Queensland <NA> <NA> #> 2: <NA> IDQ11290 <NA> QLD Queensland Gulf of Carpentaria <NA> <NA> #> 3: <NA> IDQ11290 <NA> QLD North Queensland <NA> <NA> #> 4: <NA> IDQ11290 <NA> QLD South Queensland <NA> <NA> #> 5: 0 IDQ11290 <NA> QLD South East Gulf of Carpentaria: QLD-NT Border to Cape Keerweer <NA> <NA> #> --- #> 45: 1 IDQ11290 <NA> QLD Gold Coast Waters: Cape Moreton to Point Danger <NA> <NA> #> 46: 2 IDQ11290 <NA> QLD Gold Coast Waters: Cape Moreton to Point Danger <NA> <NA> #> 47: 0 IDQ11290 <NA> QLD Great Barrier Reef Offshore Waters <NA> <NA> #> 48: 1 IDQ11290 <NA> QLD Great Barrier Reef Offshore Waters <NA> <NA> #> 49: 2 IDQ11290 <NA> QLD Great Barrier Reef Offshore Waters <NA> <NA> #> aac start_time_local end_time_local utc_offset start_time_utc end_time_utc #> <char> <POSc> <POSc> <fctr> <POSc> <POSc> #> 1: QLD_FA001 2025-02-25 10:00:22 2025-02-25 10:00:22 10:00 2025-02-25 10:00:22 2025-02-25 10:00:22 #> 2: QLD_FA002 2025-02-25 05:00:00 2025-02-25 05:00:00 10:00 2025-02-25 05:00:00 2025-02-25 05:00:00 #> 3: QLD_FA003 2025-02-25 05:00:00 2025-02-25 05:00:00 10:00 2025-02-25 05:00:00 2025-02-25 05:00:00 #> 4: QLD_FA004 2025-02-25 05:00:00 2025-02-25 05:00:00 10:00 2025-02-25 05:00:00 2025-02-25 05:00:00 #> 5: QLD_MW001 2025-02-25 10:00:00 2025-02-25 10:00:00 10:00 2025-02-25 10:00:00 2025-02-25 10:00:00 #> --- #> 45: QLD_MW014 2025-02-26 00:00:00 2025-02-26 00:00:00 10:00 2025-02-26 00:00:00 2025-02-26 00:00:00 #> 46: QLD_MW014 2025-02-27 00:00:00 2025-02-27 00:00:00 10:00 2025-02-27 00:00:00 2025-02-27 00:00:00 #> 47: QLD_MW015 2025-02-25 10:00:00 2025-02-25 10:00:00 10:00 2025-02-25 10:00:00 2025-02-25 10:00:00 #> 48: QLD_MW015 2025-02-26 00:00:00 2025-02-26 00:00:00 10:00 2025-02-26 00:00:00 2025-02-26 00:00:00 #> 49: QLD_MW015 2025-02-27 00:00:00 2025-02-27 00:00:00 10:00 2025-02-27 00:00:00 2025-02-27 00:00:00 #> forecast_seas forecast_weather #> <char> <char> #> 1: <NA> <NA> #> 2: <NA> <NA> #> 3: <NA> <NA> #> 4: <NA> <NA> #> 5: Below 1 metre. Mostly sunny. The chance of a thunderstorm. #> --- #> 45: 1 to 1.5 metres. Partly cloudy. #> 46: Around 1 metre, increasing to 1 to 1.5 metres offshore. Partly cloudy. #> 47: 1.5 to 2.5 metres. Partly cloudy. 50% chance of showers. #> 48: 2 to 3 metres. Partly cloudy. 70% chance of showers. #> 49: 2 to 3 metres. Partly cloudy. 60% chance of showers. #> forecast_winds #> <char> #> 1: <NA> #> 2: <NA> #> 3: <NA> #> 4: <NA> #> 5: East to southeasterly 10 to 15 knots becoming variable about 10 knots in the middle of the day. #> --- #> 45: Southeasterly 15 to 20 knots. #> 46: Southeasterly 15 to 20 knots. #> 47: Southeasterly 20 to 25 knots increasing to 25 to 30 knots in the early afternoon. #> 48: Southeasterly 25 to 30 knots. #> 49: Southeasterly 25 to 30 knots. #> forecast_swell1 forecast_swell2 #> <char> <char> #> 1: <NA> <NA> #> 2: <NA> <NA> #> 3: <NA> <NA> #> 4: <NA> <NA> #> 5: Below 0.5 metres. <NA> #> --- #> 45: Easterly 1.5 metres. <NA> #> 46: Easterly 1.5 to 2.5 metres. <NA> #> 47: East to southeasterly 1.5 to 2.5 metres. <NA> #> 48: East to southeasterly 1 to 2 metres, increasing to 2 to 3 metres through the Capricorn Channel. <NA> #> 49: East to southeasterly 1.5 to 2.5 metres, increasing to 2.5 to 4 metres through the Capricorn Channel. <NA> #> forecast_caution marine_forecast tropical_system_location forecast_waves #> <char> <char> <lgcl> <lgcl> #> 1: <NA> <NA> NA NA #> 2: <NA> <NA> NA NA #> 3: <NA> <NA> NA NA #> 4: <NA> <NA> NA NA #> 5: <NA> <NA> NA NA #> --- #> 45: <NA> <NA> NA NA #> 46: <NA> <NA> NA NA #> 47: <NA> <NA> NA NA #> 48: <NA> <NA> NA NA #> 49: <NA> <NA> NA NA
Working with BOM Image Files
A second family of functions retrieve information pertaining to satellite and radar imagery.
get_available_imagery(), which returns available satellite imagery;get_satelllite_imagery()which returns a [terra::SpatRaster] or [stars] object as requested;get_available_radar(), which returns available radar images; andget_radar_imagery()which returns radar images as a [magick] object.
Using {weatherOz} to retrieve BOM satellite imagery
{weatherOz} provides functionality to retrieve high-definition GeoTIFF satellite imagery provided by BOM through public FTP with the following types of imagery being available: i.) Infrared images, ii.) Visible images and iii.) Clouds/surface composite.
Working with Satellite Imagery
Valid BOM satellite Product IDs for GeoTIFF files include:
| Product ID | Description | Type | Delete time |
|---|---|---|---|
| IDE00420 | AHI cloud cover only 2km FD GEOS | Satellite | 24 |
| IDE00421 | AHI IR (Ch13) greyscale 2km FD GEOS | Satellite | 24 |
| IDE00422 | AHI VIS (Ch3) greyscale 2km FD GEOS | Satellite | 24 |
| IDE00423 | AHI IR (Ch13) Zehr 2km FD GEOS | Satellite | 24 |
| IDE00425 | AHI VIS (true colour) / IR (Ch13 greyscale) composite 1km FD GEOS | Satellite | 24 |
| IDE00426 | AHI VIS (true colour) / IR (Ch13 greyscale) composite 2km FD GEOS | Satellite | 24 |
| IDE00427 | AHI WV (Ch8) 2km FD GEOS | Satellite | 24 |
| IDE00430 | AHI cloud cover only 2km AUS equirect. | Satellite | 24 |
| IDE00431 | AHI IR (Ch13) greyscale 2km AUS equirect. | Satellite | 24 |
| IDE00432 | AHI VIS (Ch3) greyscale 2km AUS equirect. | Satellite | 24 |
| IDE00433 | AHI IR (Ch13) Zehr 2km AUS equirect. | Satellite | 24 |
| IDE00435 | AHI VIS (true colour) / IR (Ch13 greyscale) composite 1km AUS equirect. | Satellite | 24 |
| IDE00436 | AHI VIS (true colour) / IR (Ch13 greyscale) composite 2km AUS equirect. | Satellite | 24 |
| IDE00437 | AHI WV (Ch8) 2km AUS equirect. | Satellite | 24 |
| IDE00439 | AHI VIS (Ch3) greyscale 0.5km AUS equirect. | Satellite | 24 |
| Information gathered from Australian Bureau of Meteorology (BOM) | |||
Using get_available_imagery()
get_available_imagery() only takes one argument, product_id, a BOM identifier for the imagery that you wish to check for available imagery.
Using this function will fetch a listing of BOM GeoTIFF satellite imagery from ftp://ftp.bom.gov.au/anon/gen/gms/ to display which files are currently available for download.
These files are available at ten minute update frequency with a 24 hour delete time.
This function can be used see the most recent files available and then specify in the _imagery() function.
If no valid Product ID is supplied, defaults to all GeoTIFF images currently available.
Example 3: Checking Available Imagery
library(weatherOz) (avail <- get_available_imagery(product_id = "IDE00425")) #> [1] "IDE00425.202502240410.tif" "IDE00425.202502240420.tif" "IDE00425.202502240430.tif" "IDE00425.202502240440.tif" #> [5] "IDE00425.202502240450.tif" "IDE00425.202502240500.tif" "IDE00425.202502240510.tif" "IDE00425.202502240520.tif" #> [9] "IDE00425.202502240530.tif" "IDE00425.202502240540.tif" "IDE00425.202502240550.tif" "IDE00425.202502240600.tif" #> [13] "IDE00425.202502240610.tif" "IDE00425.202502240620.tif" "IDE00425.202502240630.tif" "IDE00425.202502240640.tif" #> [17] "IDE00425.202502240720.tif" "IDE00425.202502240730.tif" "IDE00425.202502240740.tif" "IDE00425.202502240750.tif" #> [21] "IDE00425.202502240800.tif" "IDE00425.202502240810.tif" "IDE00425.202502240830.tif" "IDE00425.202502240840.tif" #> [25] "IDE00425.202502240850.tif" "IDE00425.202502240900.tif" "IDE00425.202502240910.tif" "IDE00425.202502240920.tif" #> [29] "IDE00425.202502240930.tif" "IDE00425.202502240940.tif" "IDE00425.202502240950.tif" "IDE00425.202502241000.tif" #> [33] "IDE00425.202502241010.tif" "IDE00425.202502241020.tif" "IDE00425.202502241030.tif" "IDE00425.202502241040.tif" #> [37] "IDE00425.202502241050.tif" "IDE00425.202502241100.tif" "IDE00425.202502241110.tif" "IDE00425.202502241120.tif" #> [41] "IDE00425.202502241130.tif" "IDE00425.202502241140.tif" "IDE00425.202502241150.tif" "IDE00425.202502241200.tif" #> [45] "IDE00425.202502241210.tif" "IDE00425.202502241220.tif" "IDE00425.202502241230.tif" "IDE00425.202502241240.tif" #> [49] "IDE00425.202502241250.tif" "IDE00425.202502241300.tif" "IDE00425.202502241310.tif" "IDE00425.202502241320.tif" #> [53] "IDE00425.202502241330.tif" "IDE00425.202502241340.tif" "IDE00425.202502241350.tif" "IDE00425.202502241400.tif" #> [57] "IDE00425.202502241410.tif" "IDE00425.202502241420.tif" "IDE00425.202502241430.tif" "IDE00425.202502241450.tif" #> [61] "IDE00425.202502241500.tif" "IDE00425.202502241510.tif" "IDE00425.202502241520.tif" "IDE00425.202502241530.tif" #> [65] "IDE00425.202502241540.tif" "IDE00425.202502241550.tif" "IDE00425.202502241600.tif" "IDE00425.202502241610.tif" #> [69] "IDE00425.202502241620.tif" "IDE00425.202502241630.tif" "IDE00425.202502241640.tif" "IDE00425.202502241650.tif" #> [73] "IDE00425.202502241700.tif" "IDE00425.202502241710.tif" "IDE00425.202502241720.tif" "IDE00425.202502241730.tif" #> [77] "IDE00425.202502241740.tif" "IDE00425.202502241750.tif" "IDE00425.202502241800.tif" "IDE00425.202502241810.tif" #> [81] "IDE00425.202502241830.tif" "IDE00425.202502241840.tif" "IDE00425.202502241850.tif" "IDE00425.202502241900.tif" #> [85] "IDE00425.202502241910.tif" "IDE00425.202502241920.tif" "IDE00425.202502241930.tif" "IDE00425.202502241940.tif" #> [89] "IDE00425.202502241950.tif" "IDE00425.202502242000.tif" "IDE00425.202502242010.tif" "IDE00425.202502242020.tif" #> [93] "IDE00425.202502242030.tif" "IDE00425.202502242040.tif" "IDE00425.202502242050.tif" "IDE00425.202502242100.tif" #> [97] "IDE00425.202502242110.tif" "IDE00425.202502242120.tif" "IDE00425.202502242130.tif" "IDE00425.202502242140.tif" #> [101] "IDE00425.202502242150.tif" "IDE00425.202502242200.tif" "IDE00425.202502242210.tif" "IDE00425.202502242220.tif" #> [105] "IDE00425.202502242240.tif" "IDE00425.202502242250.tif" "IDE00425.202502242300.tif" "IDE00425.202502242310.tif" #> [109] "IDE00425.202502242320.tif" "IDE00425.202502242330.tif" "IDE00425.202502242340.tif" "IDE00425.202502242350.tif" #> [113] "IDE00425.202502250000.tif" "IDE00425.202502250040.tif" "IDE00425.202502250050.tif" "IDE00425.202502250100.tif" #> [117] "IDE00425.202502250110.tif" "IDE00425.202502250130.tif" "IDE00425.202502250140.tif" "IDE00425.202502250150.tif" #> [121] "IDE00425.202502250200.tif" "IDE00425.202502250210.tif" "IDE00425.202502250220.tif" "IDE00425.202502250230.tif" #> [125] "IDE00425.202502250250.tif" "IDE00425.202502250300.tif" "IDE00425.202502250310.tif" "IDE00425.202502250320.tif" #> [129] "IDE00425.202502250330.tif" "IDE00425.202502250340.tif" "IDE00425.202502250350.tif" "IDE00425.202502250400.tif" #> [133] "IDE00425.202502250410.tif" "IDE00425.202502250420.tif" "IDE00425.202502250430.tif" "IDE00425.202502250440.tif" #> [137] "IDE00425.202502250450.tif" "IDE00425.202502250500.tif"
Using get_satellite_imagery()
get_satellite_imagery() fetches BOM satellite GeoTIFF imagery, returning a SpatRaster object and takes two arguments.
Files are available at ten minute update frequency with a 24 hour delete time.
It is suggested to check file availability first by using get_available_imagery().
The arguments are:
-
product_id, a character value of the BOM product ID to download. Alternatively, a vector of values fromget_available_imagery()may be used here. This argument is mandatory. -
scansa numeric value for the number of scans to download, starting with the most recent and progressing backwards, e.g.,1- the most recent single scan available ,6- the most recent hour available,12- the most recent 2 hours available, etc. Negating will return the oldest files first. Defaults to 1. This argument is optional.
Example 6: Fetching Satellite Imagery and Viewing It
library(weatherOz) # Specify product ID and scans i <- get_satellite_imagery(product_id = "IDE00425", scans = 1)
terra::plot() has been re-exported to simplify visualising these files while using {weatherOz}.
plot(i)
Using {weatherOz} With BOM Radar Imagery
{weatherOz} provides functionality to retrieve the latest radar imagery provided by BOM through a public FTP server. These are the latest snapshots for each radar locations at various radar ranges e.g., 512km, 256km, 128km and 64km for some stations.
Using get_available_radar()
get_available_radar() fetches the available radar imagery from the BOM FTP and returns a data frame for reference.
This data frame contains the product_id, which is required when using the get_radar_imagery() function.
The files available are the latest .png files of BOM radar imagery which are typically updated each 6-10 minutes. Only the most recent image is retrieved for each radar location.
There are usually several radar ranges available for each radar location, such as 512km, 256km, 128km and possibly 64km.
The arguments are:
radar_idwhich is the BOM radar ID number; this defaults to 'all' which will return a data frame of all radar IDs in Australia.
Example 7: Getting Available Radar Imagery
library(weatherOz) x <- get_available_radar() head(x) #> product_id LocationID range Name Longitude Latitude Radar_id Full_Name IDRnn0name IDRnn1name State #> <char> <char> <char> <fctr> <num> <num> <int> <fctr> <fctr> <fctr> <fctr> #> 1: IDR641 64 512km Adelaide 138.4689 -34.6169 64 Adelaide (Buckland Park) BuckPk BucklandPk SA #> 2: IDR642 64 256km Adelaide 138.4689 -34.6169 64 Adelaide (Buckland Park) BuckPk BucklandPk SA #> 3: IDR643 64 128km Adelaide 138.4689 -34.6169 64 Adelaide (Buckland Park) BuckPk BucklandPk SA #> 4: IDR644 64 64km Adelaide 138.4689 -34.6169 64 Adelaide (Buckland Park) BuckPk BucklandPk SA #> 5: IDR311 31 512km Albany 117.8163 -34.9418 31 Albany Albany Albany WA #> 6: IDR312 31 256km Albany 117.8163 -34.9418 31 Albany Albany Albany WA #> Type Group_ Status Archive as.is #> <fctr> <fctr> <fctr> <fctr> <lgcl> #> 1: Doppler Yes Public BuckPk TRUE #> 2: Doppler Yes Public BuckPk TRUE #> 3: Doppler Yes Public BuckPk TRUE #> 4: Doppler Yes Public BuckPk TRUE #> 5: Doppler Yes Public Albany TRUE #> 6: Doppler Yes Public Albany TRUE
Using get_radar_imagery()
get_radar_imagery() fetches the latest BOM radar imagery for a given product ID.
The files available are the latest .png files of BOM radar imagery, which are typically updated each 6-10 minutes.
Only the most recent image is retrieved for each radar location.
There are usually several radar ranges available for each radar location, such as 512km, 256km, 128km and possibly 64km.
The only argument is:
product_idthe BOM product_id associated with each radar imagery file. These can be obtained from theget_available_radar()function. This value must be specified and the function will accept only one at a time.
Example 8: Fetching Radar Imagery
library(weatherOz) y <- get_radar_imagery(product_id = "IDR032") plot(y)
References
Australian Bureau of Meteorology (BOM) Weather Data Services
Australian Bureau of Meteorology (BOM) FTP Public Products
Australian Bureau of Meteorology (BOM) Weather Data Services Observation of Rainfall
Australian Bureau of Meteorology (BOM) High-definition satellite images
Appendix 1 - Output From get_precis_forecast()
The functions, get_precis_forecast() or `parse_precis_forecast(), will return a data frame of the 7 day short forecast with the following fields:
- index
- Forecast index number, 0 = current day ... 7 day
- product_id
- BOM Product ID from which the data are derived
- state
- State name (postal code abbreviation)
- town
- Town name for forecast location
- aac
- AMOC Area Code, _e.g._, WA_MW008, a unique identifier for each location
- lat
- Latitude of named location (decimal degrees)
- lon
- Longitude of named location (decimal degrees)
- elev
- Elevation of named location (metres)
- start_time_local
- Start of forecast date and time in local TZ
- end_time_local
- End of forecast date and time in local TZ
- UTC_offset
- Hours offset from difference in hours and minutes from Coordinated Universal Time (UTC) for `start_time_local` and `end_time_local`
- start_time_utc
- Start of forecast date and time in UTC
- end_time_utc
- End of forecast date and time in UTC
- maximum_temperature
- Maximum forecast temperature (degrees Celsius)
- minimum_temperature
- Minimum forecast temperature (degrees Celsius)
- lower_precipitation_limit
- Lower forecast precipitation limit (millimetres)
- upper_precipitation_limit
- Upper forecast precipitation limit (millimetres)
- precis
- Précis forecast (a short summary, less than 30 characters)
- probability_of_precipitation
- Probability of precipitation (percent)
Appendix 2 - Output From get_coastal_forecast()
The output of get_coastal_forecast() or parse_coastal_forecast() will return a data frame with coastal waters forecast values of each area within the given state with the following fields:
- index
- Forecast index number. 0 = current day
- product_id
- BOM Product ID from which the data are derived
- type
- Forecast Region type e.g. Coastal
- state_code
- State name (postal code abbreviation)
- dist_name
- Name of forecast district
- pt_1_name
- Start of forecast district
- pt_2_name
- End of forecast district
- aac
- AMOC Area Code, _e.g._, WA_MW008, a unique identifier for each location
- start_time_local
- Start of forecast date and time in local TZ
- end_time_local
- End of forecast date and time in local TZ
- UTC_offset
- Hours offset from difference in hours and minutes from Coordinated Universal Time (UTC) for `start_time_local` and `end_time_local`
- start_time_utc
- Start of forecast date and time in UTC
- end_time_utc
- End of forecast date and time in UTC
- forecast_seas
- Forecast sea conditions
- forecast_weather
- Forecast weather summary
- forecast_winds
- Forecast winds summary
- forecast_swell1
- Forecast primary swell summary
- forecast_swell2
- Forecast seondary swell summary (not always provided)
- forecast_caution
- Forecast caution issued (not always provided)
- marine_forecast
- Additional marine forecast warning information (not always provided)
Try the weatherOz package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.