Let’s Talk About the Weather
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A while ago I made some plots I really liked, but I never made a blog post about them. Then the data source stopped working and I couldn’t make them again. Now there’s a new data source, so it’s time for a post about some weather data!
Way back in the mid 2010s I was interested in getting my hands on some weather data to answer a question I had. In Australia we have the Bureau of Meteorology (“don’t call it ‘the bom’”) which has been keeping track of weather data for a very long time (on the scale of colonial Australia). They don’t have an official API to get to that data, they have a website that doesn’t use HTTPS – if you have a link to a page on their site and try to visit in in any modern browser which assumes you want to use HTTPS, you’ll see this page
then be redirected to the landing page, regardless of where your link was trying to send you. They are working on improving this, and there’s a new https://beta.bom.gov.au/ which notes it’s a test website.
That rant aside, if you wanted some temperature data, you clicked your way through the interface on the website to specify a station and some settings, and eventually were presented with a tabular output of the data and the ability to export it as a .zip file containing an actual .csv plus a note file.
At the time, I was aware of a BoM-related R package {bomrang} by Adam Sparks,
which began life at the rOpenSci AUUnconf in Brisbane, 2016 where I had the
pleasure of meeting him. It didn’t have access to the weather data the way I
wanted, but it had a lot of other cool functionality such as finding the closest
station and fetching forecasts, radar imagery, and bulletins. I wasn’t able to
use a package to get the data I wanted, so I had to figure something out myself.
I realised that the generated URL to that .zip file was parameterized, involving the station id, and some codes presumably representing what type of data was being fetched (temperature, rainfall, …). The codes themselves weren’t documented on the site, they were most likely only for internal use, but if I knew which one I wanted, I probably had enough information to get the full URL. With a bit of tinkering, I figured out the meaning of one of the codes (rain = 136, min_temp = 123, max_temp = 122, solar = 193) and could specify which one I wanted. There was still one more code in the URL, and I found a way to decode that via an additional URL query, at which point I could build the entire URL.
After that, I could programatically unpack the .zip with utils::unzip() and
read in the .csv data. Thus, bomrang::get_historical_weather() was forged and
introduced via a pull request.
Fast forward to 2021 and this function started failing – the data was available just fine via a browser, but was returning an error in R/RStudio. It turned out that changing the user-agent allowed for a brief period of access, but eventually the official statement was released
The Bureau is monitoring screen scraping activity on the site and will commence interrupting, and eventually blocking, this activity on www.bom.gov.au from Wednesday, 3 March 2021
and there was no way to get to the data nicely. This was extremely frustrating, especially since we (taxpayers) technically pay for this data.
All was not lost, however – Adam continued to develop package infrastructure and found a new resource in the form of SILO. Adam built a new package {weatherOz} and submitted a paper to JOSS including all the original authors as contributors to both – I can’t claim to have contributed to the new package, so I’m somewhat hopeful that a detailed blog post can help repay some of that kindness.
{weatherOz} has now been accepted onto CRAN
(announcement) and so can be installed with
install.packages("weatherOz")
Adam also has a nice blog post exploring some temperature data here.
Now it’s my turn!
library(weatherOz)
There are many weather stations within capital cities, so I need to narrow down to just one. I can get the station information from SILO with
adl_stations <- get_stations_metadata(station_name = "Adelaide", which_api = "silo") adl_stations ## station_code station_name start end ## 1 014092 Adelaide River Post Office 1956-01-01 2024-07-27 ## 2 023034 Adelaide Airport 1955-01-01 2024-07-27 ## 3 023005 Adelaide Glen Osmond 1883-01-01 2024-07-27 ## 4 023096 Adelaide Hope Valley Reservoir 1979-01-01 2024-07-27 ## 5 023732 Adelaide Morphett Vale 1886-01-01 2024-07-27 ## 6 023098 Adelaide Morphettville Racecourse 1947-01-01 2024-07-27 ## 7 023026 Adelaide Pooraka 1876-01-01 2024-07-27 ## 8 023023 Adelaide Salisbury Bowling Club 1870-01-01 2024-07-27 ## 9 023024 Adelaide Seaton 1912-01-01 2024-07-27 ## 10 023000 Adelaide West Terrace Ngayirdapira 1839-01-01 2024-07-27 ## 11 023011 North Adelaide 1883-01-01 2024-07-27 ## latitude longitude state elev_m source status wmo ## 1 -13.2373 131.1042 NT 52.5 Bureau of Meteorology (BOM) open NA ## 2 -34.9524 138.5196 SA 2.0 Bureau of Meteorology (BOM) open 94672 ## 3 -34.9464 138.6519 SA 128.0 Bureau of Meteorology (BOM) open NA ## 4 -34.8564 138.6844 SA 105.0 Bureau of Meteorology (BOM) open NA ## 5 -35.1351 138.5274 SA 92.0 Bureau of Meteorology (BOM) open NA ## 6 -34.9742 138.5425 SA 11.0 Bureau of Meteorology (BOM) open NA ## 7 -34.8324 138.6125 SA 21.0 Bureau of Meteorology (BOM) open NA ## 8 -34.7674 138.6434 SA 32.0 Bureau of Meteorology (BOM) open NA ## 9 -34.8965 138.5103 SA 10.0 Bureau of Meteorology (BOM) open NA ## 10 -34.9257 138.5832 SA 29.3 Bureau of Meteorology (BOM) open 94648 ## 11 -34.9163 138.5950 SA 26.0 Bureau of Meteorology (BOM) open NA
and I can select one of those - the station with code 023000 has a lot of data
ngayirdapira <- adl_stations[adl_stations$station_code == "023000", ] dplyr::glimpse(ngayirdapira) ## Rows: 1 ## Columns: 11 ## $ station_code <fct> 023000 ## $ station_name <chr> "Adelaide West Terrace Ngayirdapira" ## $ start <date> 1839-01-01 ## $ end <date> 2024-07-27 ## $ latitude <dbl> -34.9257 ## $ longitude <dbl> 138.5832 ## $ state <chr> "SA" ## $ elev_m <dbl> 29.3 ## $ source <chr> "Bureau of Meteorology (BOM)" ## $ status <chr> "open" ## $ wmo <dbl> 94648
The listed start date of the data is 1839-01-01 but trying to use that produces an error. I’ll start from the listed year of SILO data (1889).
Getting the actual data from SILO doesn’t technically need an API key, it just wants to know who is fetching the data, so asks that you use an email address as the key. I have that set as an environment variable. I’m also saving the data to disk since I don’t want to fetch it every time I edit this post as I’m writing it.
silo_data <- get_patched_point(
station_code = ngayirdapira$station_code,
start_date = "1889-01-01",
end_date = ngayirdapira$end,
values = "all",
api_key = Sys.getenv("SILO_API_KEY")
)
saveRDS(silo_data, file = "adl_silo_data.rds")
There’s a lot of data here - along with the (air) temperature min and max, there are other measurements that I’m not interested in at the moment.
dplyr::glimpse(silo_data) ## Rows: 49,515 ## Columns: 46 ## $ station_code <fct> 023000, 023000, 023000, 023000, 023000, 023… ## $ station_name <chr> "Adelaide West Terrace Ngayirdapira", "Adel… ## $ year <dbl> 1889, 1889, 1889, 1889, 1889, 1889, 1889, 1… ## $ month <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1… ## $ day <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, … ## $ date <date> 1889-01-01, 1889-01-02, 1889-01-03, 1889-0… ## $ air_tmax <dbl> 34.2, 21.7, 21.6, 21.4, 20.2, 22.4, 24.8, 3… ## $ air_tmax_source <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ air_tmin <dbl> 22.0, 20.4, 13.3, 15.0, 12.8, 14.6, 12.6, 1… ## $ air_tmin_source <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ elev_m <chr> "29.3 m", "29.3 m", "29.3 m", "29.3 m", "29… ## $ et_morton_actual <dbl> 1.9, 3.9, 4.8, 4.3, 4.1, 4.3, 4.0, 2.5, 1.3… ## $ et_morton_actual_source <int> 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26,… ## $ et_morton_potential <dbl> 11.0, 7.1, 6.4, 7.1, 6.5, 7.3, 7.7, 10.4, 1… ## $ et_morton_potential_source <int> 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26,… ## $ et_morton_wet <dbl> 6.5, 5.5, 5.6, 5.7, 5.3, 5.8, 5.8, 6.5, 7.4… ## $ et_morton_wet_source <int> 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26,… ## $ et_short_crop <dbl> 6.9, 4.8, 4.5, 4.8, 4.5, 5.0, 5.3, 6.7, 7.9… ## $ et_short_crop_source <int> 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26,… ## $ et_tall_crop <dbl> 9.0, 5.8, 5.4, 5.9, 5.4, 6.0, 6.6, 8.8, 10.… ## $ et_tall_crop_source <int> 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26,… ## $ evap_comb <dbl> 8.8, 5.9, 6.2, 6.4, 6.1, 6.5, 7.0, 8.7, 10.… ## $ evap_comb_source <int> 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26,… ## $ evap_morton_lake <dbl> 6.6, 5.6, 5.8, 5.9, 5.5, 6.0, 6.0, 6.6, 7.5… ## $ evap_morton_lake_source <int> 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26,… ## $ evap_pan <dbl> 7.9, 8.0, 8.0, 8.1, 8.1, 8.1, 8.2, 8.2, 8.2… ## $ evap_pan_source <int> 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75,… ## $ evap_syn <dbl> 8.8, 5.9, 6.2, 6.4, 6.1, 6.5, 7.0, 8.7, 10.… ## $ evap_syn_source <int> 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26,… ## $ extracted <date> 2024-07-27, 2024-07-27, 2024-07-27, 2024-0… ## $ latitude <dbl> -34.9257, -34.9257, -34.9257, -34.9257, -34… ## $ longitude <dbl> 138.5832, 138.5832, 138.5832, 138.5832, 138… ## $ mslp <dbl> 1013.8, 1013.9, 1014.1, 1014.5, 1014.5, 101… ## $ mslp_source <int> 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75,… ## $ radiation <dbl> 24.1, 23.3, 25.8, 26.1, 25.3, 26.4, 26.5, 2… ## $ radiation_source <int> 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,… ## $ rainfall <dbl> 0.0, 58.4, 10.4, 0.3, 0.0, 0.3, 0.0, 0.0, 0… ## $ rainfall_source <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ rh_tmax <dbl> 28.1, 59.7, 52.7, 49.9, 49.4, 47.3, 38.3, 2… ## $ rh_tmax_source <int> 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26,… ## $ rh_tmin <dbl> 57.1, 64.7, 89.1, 74.5, 79.2, 77.1, 82.3, 6… ## $ rh_tmin_source <int> 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26,… ## $ vp <dbl> 15.1, 15.5, 13.6, 12.7, 11.7, 12.8, 12.0, 1… ## $ vp_deficit <dbl> 30.2, 9.9, 9.1, 10.4, 9.4, 11.2, 14.0, 26.5… ## $ vp_deficit_source <int> 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26,… ## $ vp_source <int> 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,…
That’s 49,515 daily weather observations covering the last 135 or so years!
I’ll add some convenience columns; the day of the year, the decade, and the abbreviation for the month.
silo_data$doy <- lubridate::yday(silo_data$date)
silo_data$decade <- cut(silo_data$year,
breaks = seq(1880, 2030, by = 10),
right = FALSE,
labels = seq(1880, 2020, by = 10))
silo_data$monthabb = factor(month.abb[silo_data$month], levels = month.abb)
My original motivation for getting any of this data was Christmas 2016 - I live in the southern hemisphere so I get to enjoy Christmas in summer (despite being inundated with snowy imagery and having to explain to my kids why none of it is relevant here).
We still partake in some of the northern hemisphere traditions like a roast lunch and warm puddings for dessert, but we complement that with some cold, fresh prawns and some cold beers.
2016 was a particularly hot Christmas day, and I wanted to know how it compared historically. It’s not uncommon, I suppose, for someone in the USA to note that it was 40 degrees on Christmas (40°F = 4°C) but here it was also expected to hit 40… Celsius. 40°C = 104°F.
We can find all the entries in the silo data where it was 40°C or more on Christmas day
silo_data |> dplyr::filter(air_tmax >= 40, month == 12, day == 25) |> dplyr::select(date, air_tmin, air_tmax) ## date air_tmin air_tmax ## 1 1941-12-25 29.7 41.4 ## 2 1945-12-25 29.8 40.1 ## 3 2016-12-25 15.9 40.3
So, those were the hottest, but were they exceptional for that time of year?
I originally produced these plots using the (now superseded) {bomrang} package,
and now that I had a useful replacement, I wanted to recreate and update them with
some new data.
These plots aren’t terribly fancy; they show the min or max temperature for each day of the year, coloured by the decade. They demonstrate the range of temperature variation throughout the year, but aren’t super useful for extracting information necessarily - squint to see the general pattern.
library(ggplot2)
ggplot(silo_data) +
geom_point(aes(x = doy, y = air_tmax, col = decade), alpha = 0.2) +
theme_bw() +
labs(
title = "Daily Maximum Temperatures",
subtitle = "Adelaide, South Australia, 1889-2024",
caption = "source: https://www.longpaddock.qld.gov.au/silo/",
y = "Daily Maximum Temperature [°C]",
x = "Day of Year"
) +
scale_y_continuous(
limits = c(-5, 50),
sec.axis = sec_axis( ~ . * 9 / 5 + 32, name = "Daily Maximum Temperature [°F]")
) +
viridis::scale_color_viridis(discrete = TRUE)
The same for the minimum temperatures:
pmin <- ggplot(silo_data) +
geom_point(aes(x = doy, y = air_tmin, col = decade), alpha = 0.2) +
theme_bw() +
labs(
title = "Daily Minimum Temperatures",
subtitle = "Adelaide, South Australia, 1889-2024",
caption = "source: https://www.longpaddock.qld.gov.au/silo/",
y = "Daily Minimum Temperature [°C]",
x = "Day of Year"
) +
scale_y_continuous(
limits = c(-5, 50),
sec.axis = sec_axis( ~ . * 9 / 5 + 32, name = "Daily Minimum Temperature [°F]")
) +
viridis::scale_color_viridis(discrete = TRUE)
pmin
This winter (middle of the year here) feels like it’s been particularly cold - but
is it colder than usual? I saw someone demonstrate a feature of {ggplot2} that
I haven’t previously made good use of; the data argument to a geom can take a
formula for a function as long as the result of that is another data.frame, so
a filter or head or slice operation works great here.
I’ll add in the points for June onward this year in red:
pmin +
geom_point(
data = ~ dplyr::filter(.x, year == 2024, month > 5),
aes(x = doy, y = air_tmin),
col = "red"
)
Definitely one particularly cold day there - it got as low as -5°C in some places.
In order to see how the temperatures have changed over the years, I also split out
the data for each month and plotted the max/min for every day in the month for each
year - this nicely shows that the spread is a lot tighter in winter and the days in
our summer are a lot more variable. Again I’m using the helpful data = ~f(.x) pattern,
this time with dplyr::slice_max().
ggplot(dplyr::arrange(silo_data, monthabb), aes(x = year, y = air_tmax)) +
geom_point(aes(col = factor(monthabb)), alpha = 0.3, show.legend = FALSE) +
geom_point(
data = ~ dplyr::slice_max(.x, air_tmax, n = 1, by = monthabb),
aes(x = year, y = air_tmax),
col = "red"
) +
geom_text(
data = ~ dplyr::slice_max(.x, air_tmax, n = 1, by = monthabb),
aes(x = year, y = air_tmax + 5, label = year),
col = "red"
) +
facet_wrap( ~ monthabb) +
labs(
title = "Daily Maximum Temperature",
subtitle = "Adelaide, South Australia, 1889-2024",
caption = "source: https://www.longpaddock.qld.gov.au/silo/",
x = "Year",
y = "Daily Maximum Temperature [°C]"
) +
theme_bw() +
scale_x_continuous(breaks = seq(1880, 2030, 20)) +
ggeasy::easy_rotate_x_labels(angle = 45, side = "right")
The same for the minimum temperatures:
ggplot(dplyr::arrange(silo_data, monthabb), aes(x = year, y = air_tmin)) +
geom_point(aes(col = factor(monthabb)), alpha = 0.3, show.legend = FALSE) +
geom_point(
data = ~ dplyr::slice_min(.x, air_tmin, n = 1, by = monthabb),
aes(x = year, y = air_tmin),
col = "blue"
) +
geom_text(
data = ~ dplyr::slice_min(.x, air_tmin, n = 1, by = monthabb),
aes(x = year, y = air_tmin - 5, label = year),
col = "blue"
) +
facet_wrap( ~ monthabb) +
labs(
title = "Daily Minimum Temperature",
subtitle = "Adelaide, South Australia, 1889-2024",
caption = "source: https://www.longpaddock.qld.gov.au/silo/",
x = "Year",
y = "Daily Minimum Temperature [°C]"
) +
theme_bw() +
scale_x_continuous(breaks = seq(1880, 2030, 20)) +
ggeasy::easy_rotate_x_labels(angle = 45, side = "right")
I’m very pleased that I’m once again able to query weather data for my country,
and am deeply grateful to Adam Sparks for building and maintaining {weatherOz}.
If you have comments, suggestions, or improvements, as always, feel free to use the comment section below, or hit me up on Mastodon. Also let me know if you can think of (or make) some remix or other visualization of this data!
Stay cool/warm/comfortable!
devtools::session_info()
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