Generate a climogram using bomrang to download station data for 2018/19 summer growing season in Australia

bomrang_climogram.R

library(bomrang)
library(tidyverse)
library(theme.usq)

# Find stations ----------------------------------------------------------------

# Find nearest stations for each area
# search https://www.latlong.net/ for the lat lon values of each location
# clermont
head(capella <-
       sweep_for_stations(latlon = c(-23.083333, 148.0166666)))
head(clermont <-
       sweep_for_stations(latlon = c(-22.824930, 147.639380)))

# SQ
head(dalby <-
       sweep_for_stations(latlon = c(-27.183270, 151.263733)))
head(toowoomba <-
       sweep_for_stations(latlon = c(-27.563830, 151.953970)))
head(millmerran <-
       sweep_for_stations(latlon = c(-27.873250, 151.271110)))

# NNSW
head(inverell <-
       sweep_for_stations(latlon = c(-29.776279, 151.113434)))
head(mullalley <-
       sweep_for_stations(latlon = c(-31.099090, 149.909210)))
head(gunnedah <-
       sweep_for_stations(latlon = c(-30.976900, 150.251820)))

# for Capella and Clermont we can use the Clermont Airport station
# for the Darling Downs we will just use the Dalby Airport
# for NNSW we can use both the Inverell Research Centre and Gunnedah Airport

# Download ---------------------------------------------------------------------

clermont_rain <-
  get_historical(stationid = clermont[1, 1], type = "rain")[, -c(1, 8)]
clermont_max <-
  get_historical(stationid = clermont[1, 1], type = "max")[, -c(1, 8)]
clermont_min <-
  get_historical(stationid = clermont[1, 1], type = "min")[, -c(1, 8)]

dalby_rain <-
  get_historical(stationid = dalby[1, 1], type = "rain")[, -c(1, 8)]
dalby_max <-
  get_historical(stationid = dalby[1, 1], type = "max")[, -c(1, 8)]
dalby_min <-
  get_historical(stationid = dalby[1, 1], type = "min")[, -c(1, 8)]

inverell_rain <-
  get_historical(stationid = inverell[1, 1], type = "rain")[, -c(1, 8)]
inverell_max <-
  get_historical(stationid = inverell[1, 1], type = "max")[, -c(1, 8)]
inverell_min <-
  get_historical(stationid = inverell[1, 1], type = "min")[, -c(1, 8)]

gunnedah_rain <-
  get_historical(stationid = gunnedah[1, 1], type = "rain")[, -c(1, 8)]
gunnedah_max <-
  get_historical(stationid = gunnedah[1, 1], type = "max")[, -c(1, 8)]
gunnedah_min <-
  get_historical(stationid = gunnedah[1, 1], type = "min")[, -c(1, 8)]

# Munge for summary statistics for reporting 2018/19 season --------------------
clermont_avgs <- left_join(clermont_rain, clermont_max) %>%
  left_join(., clermont_min) %>%
  mutate(YYYYMMDD = ISOdate(year, month, day)) %>%
  filter(YYYYMMDD >= "2019-02-28" &
           YYYYMMDD <= "2019-05-01") %>%
  summarise(
    min = min(min_temperature, na.rm = TRUE),
    max = max(max_temperature, na.rm = TRUE),
    avg_tmp = round(mean(
      c(min_temperature, max_temperature), na.rm = TRUE
    ), 2),
    rainfall = sum(rainfall, na.rm = TRUE),
    start = min(YYYYMMDD),
    end = max(YYYYMMDD)
  ) %>%
  add_column(Location = "Clermont") %>%
  select(c("Location", "min", "max", "avg_tmp", "rainfall", "start", "end"))

dalby_avgs <- left_join(dalby_rain, dalby_max) %>%
  left_join(., dalby_min) %>%
  mutate(YYYYMMDD = ISOdate(year, month, day)) %>%
  filter(YYYYMMDD >= "2018-12-31" &
           YYYYMMDD <= "2019-03-01") %>%
  mutate(
    tmp = mean(c(min_temperature, max_temperature), na.rm = TRUE),
    tmp = na_if(min_temperature, NA),
    tmp = na_if(max_temperature, NA)
  ) %>%
  summarise(
    min = min(min_temperature, na.rm = TRUE),
    max = max(max_temperature, na.rm = TRUE),
    avg_tmp = round(mean(
      c(min_temperature, max_temperature), na.rm = TRUE
    ), 2),
    rainfall = sum(rainfall, na.rm = TRUE),
    start = min(YYYYMMDD),
    end = max(YYYYMMDD)
  ) %>%
  add_column(Location = "Dalby") %>%
  select(c("Location", "min", "max", "avg_tmp", "rainfall", "start", "end"))


inverell_avgs <- left_join(inverell_rain, inverell_max) %>%
  left_join(., inverell_min) %>%
  mutate(YYYYMMDD = ISOdate(year, month, day)) %>%
  filter(YYYYMMDD >= "2018-12-31" &
           YYYYMMDD <= "2019-03-01") %>%
  summarise(
    min = min(min_temperature, na.rm = TRUE),
    max = max(max_temperature, na.rm = TRUE),
    avg_tmp = round(mean(
      c(min_temperature, max_temperature), na.rm = TRUE
    ), 2),
    rainfall = sum(rainfall, na.rm = TRUE),
    start = min(YYYYMMDD),
    end = max(YYYYMMDD)
  ) %>%
  add_column(Location = "Inverell") %>%
  select(c("Location", "min", "max", "avg_tmp", "rainfall", "start", "end"))

gunnedah_avgs <- left_join(gunnedah_rain, gunnedah_max) %>%
  left_join(., gunnedah_min) %>%
  mutate(YYYYMMDD = ISOdate(year, month, day)) %>%
  filter(YYYYMMDD >= "2018-12-31" &
           YYYYMMDD <= "2019-03-01") %>%
  summarise(
    min = min(min_temperature, na.rm = TRUE),
    max = max(max_temperature, na.rm = TRUE),
    avg_tmp = round(mean(
      c(min_temperature, max_temperature), na.rm = TRUE
    ), 2),
    rainfall = sum(rainfall, na.rm = TRUE),
    start = min(YYYYMMDD),
    end = max(YYYYMMDD)
  ) %>%
  add_column(Location = "Gunnedah") %>%
  select(c("Location", "min", "max", "avg_tmp", "rainfall", "start", "end"))

# Averages and summary for growing season --------------------------------------
(bind_rows(clermont_avgs, dalby_avgs, inverell_avgs, gunnedah_avgs))

# Munge for graph --------------------------------------------------------------
clermont <- left_join(clermont_rain, clermont_max) %>%
  left_join(., clermont_min) %>%
  mutate(YYYYMMDD = ISOdate(year, month, day)) %>%
  filter(YYYYMMDD >= "2018-12-01" &
           YYYYMMDD <= "2019-05-31") %>%
  mutate(
    tmp = mean(c(min_temperature, max_temperature), na.rm = TRUE),
    tmp = na_if(min_temperature, NA),
    tmp = na_if(max_temperature, NA)
  ) %>%
  add_column(Location = "Clermont") %>%
  select(c("Location", "YYYYMMDD", "tmp", "rainfall"))


dalby <- left_join(dalby_rain, dalby_max) %>%
  left_join(., dalby_min) %>%
  mutate(YYYYMMDD = ISOdate(year, month, day)) %>%
  filter(YYYYMMDD >= "2018-09-01" &
           YYYYMMDD <= "2019-04-30") %>%
  mutate(
    tmp = mean(c(min_temperature, max_temperature), na.rm = TRUE),
    tmp = na_if(min_temperature, NA),
    tmp = na_if(max_temperature, NA)
  ) %>%
  add_column(Location = "Dalby") %>%
  select(c("Location", "YYYYMMDD", "tmp", "rainfall"))

inverell <- left_join(inverell_rain, inverell_max) %>%
  left_join(., inverell_min) %>%
  mutate(YYYYMMDD = ISOdate(year, month, day)) %>%
  filter(YYYYMMDD >= "2018-09-01" &
           YYYYMMDD <= "2019-03-31") %>%
  mutate(
    tmp = mean(c(min_temperature, max_temperature), na.rm = TRUE),
    tmp = na_if(min_temperature, NA),
    tmp = na_if(max_temperature, NA)
  ) %>%
  add_column(Location = "Inverell") %>%
  select(c("Location", "YYYYMMDD", "tmp", "rainfall"))

gunnedah <- left_join(gunnedah_rain, gunnedah_max) %>%
  left_join(., gunnedah_min) %>%
  mutate(YYYYMMDD = ISOdate(year, month, day)) %>%
  filter(YYYYMMDD >= "2018-09-01" &
           YYYYMMDD <= "2019-03-31") %>%
  mutate(
    tmp = mean(c(min_temperature, max_temperature), na.rm = TRUE),
    tmp = na_if(min_temperature, NA),
    tmp = na_if(max_temperature, NA)
  ) %>%
  add_column(Location = "Gunnedah") %>%
  select(c("Location", "YYYYMMDD", "tmp", "rainfall"))

# nnsw <- left_join(inverell, gunnedah, by = "YYYYMMDD") %>%
#   mutate(tmp = mean(c(tmp.x, tmp.y), na.rm = TRUE)) %>%
#   mutate(rainfall = mean(c(rainfall.x, rainfall.y), na.rm = TRUE)) %>%
#   add_column(Location = "NNSW") %>%
#   select(c("Location", "YYYYMMDD", "tmp", "rainfall"))


weather <- bind_rows(clermont, dalby, inverell, gunnedah)
weather$Location <- factor(weather$Location,
                           levels = c("Clermont",
                                      "Dalby",
                                      "Inverell",
                                      "Gunnedah"))

# Graph ------------------------------------------------------------------------

# max rainfall set from max clermont rainfall in Apr 2019
ylim.prim <- c(0, 160)
ylim.sec <- c(10, 50)

b <- diff(ylim.prim) / diff(ylim.sec)
a <- b * (ylim.prim[1] - ylim.sec[1])
png(filename = "Climograms/Image_1.png", width = 1024, height = 557, units = "px")
ggplot(weather, aes(x = as.Date(YYYYMMDD), y = rainfall)) +
  geom_col() +
  geom_line(aes(y = a + tmp * b),
            color = usq_cols("support red"),
            size = 1.25) +
  scale_y_continuous("Precipitation (mm)",
                     sec.axis = sec_axis( ~ (. - a) / b,
                                          name = "Mean Temperature (˚C)")) +
  labs(
    title = paste0("Climogram for ", bYYYY, "-", eYYYY, " Summer Cropping Season"),
    subtitle = "Daily data sourced from Australia Bureau of Meteorology (BOM)",
    caption = "Clermont data is from Clermont Airport, Dalby data is from the Dalby Airport,
          Inverell data is from the Inverell Research Centre and Gunnedah is from the Gunnedah Airport AWS"
  ) +
  scale_x_date(date_breaks = "1 month", date_labels =  "%b\n%Y") +
  xlab("Date") +
  theme_usq() +
  theme(
    axis.ticks.y.right = element_line(color = usq_cols("support red")),
    axis.text.y.right = element_text(color = usq_cols("support red")),
    axis.title.y.right = element_text(color = usq_cols("support red"))
  ) +
  facet_grid(Location ~ .)
dev.off()