Introduction to the climate package

Meteorological data

• meteo_ogimet() - Downloading hourly and daily meteorological data from the SYNOP stations available in the ogimet.com collection. Any meteorological (aka SYNOP) station working under the World Meteorological Organization (WMO) framework after year 2000 should be accessible. Two backends are available and selected automatically: raw SYNOP decoding (source = "synop", default for interval = "hourly") and HTML scraping (source = "html", default for interval = "daily"). Country-level bulk downloads are supported via the country_name argument (SYNOP backend only).

• meteo_imgw() - Downloading hourly, daily, and monthly meteorological data from the SYNOP/CLIMATE/PRECIP stations available in the danepubliczne.imgw.pl collection. It is a wrapper for meteo_monthly(), meteo_daily(), meteo_hourly() and meteo_imgw_datastore() which gives access from monthly to even 10-min datasets.

• meteo_noaa_hourly() - Downloading hourly NCEI/NOAA Integrated Surface Hourly (ISH) meteorological data - some stations have > 100 years of observations.

• meteo_noaa_co2() - Downloading monthly CO2 measurements from Mauna Loa Observatory.

• sounding_wyoming() - Downloading measurements of the vertical profile of atmosphere (aka rawinsonde data).

• synop_parser() - Decoding raw FM-12 SYNOP meteorological messages into structured R lists or data frames. For a full walkthrough see the SYNOP Messages vignette.

Hydrological data

• hydro_imgw() - Downloading daily and monthly hydrological data from stations available in the danepubliczne.imgw.pl collection. It is a wrapper for hydro_monthly() and hydro_daily().

• hydro_imgw_datastore() - Downloading hourly and sub-hourly hydrological data from the IMGW-PIB hydro telemetry stations.

Auxiliary functions and datasets

• stations_ogimet() - Downloading information about all stations available in the selected country in the Ogimet repository
• nearest_stations_ogimet() - Downloading information about nearest stations to the selected point using the Ogimet repository
• nearest_stations_noaa() - Downloading information about nearest stations to the selected point in the NOAA ISH meteorological repository
• nearest_stations_imgw() - List of nearby meteorological or hydrological IMGW-PIB stations in Poland
• imgw_meteo_stations - Built-in metadata from the IMGW-PIB repository for meteorological stations, their geographical coordinates, and ID numbers
• imgw_hydro_stations - Built-in metadata from the IMGW-PIB repository for hydrological stations, their geographical coordinates, and ID numbers
• stations_meteo_imgw_telemetry - Downloading complete and up-to-date coordinates for IMGW-PIB telemetry meteorological stations
• stations_hydro_imgw_telemetry - Downloading complete and up-to-date coordinates for IMGW-PIB telemetry hydrological stations
• imgw_meteo_abbrev - Dictionary explaining variables available for meteorological stations (from the IMGW-PIB repository)
• imgw_hydro_abbrev - Dictionary explaining variables available for hydrological stations (from the IMGW-PIB repository)

Example 1

Finding a 50 nearest meteorological stations for a given coordinates in a given country(ies):

library(climate)
ns = nearest_stations_ogimet(country = c("United Kingdom"),
                             point = c(-3, 50),
                             no_of_stations = 50, 
                             add_map = TRUE)

if (is.data.frame(ns)) {
 knitr::kable(head(ns, 15))
}

Example 2

Summary of stations available in Ogimet repository for a selected country:

library(climate)
PL = stations_ogimet(country = "Poland", add_map = TRUE)

if (is.data.frame(PL)) {
    knitr::kable(head(PL))
}

Example 3

Downlading hourly meteorological data from Svalbard (Norway) for year 2016 using NOAA service

# downloading data with NOAA service:
df = meteo_noaa_hourly(station = "010080-99999", year = 2016)

# You can also download the same (but more granular) data with Ogimet.com (example for year 2016):
# df = meteo_ogimet(interval = "hourly",
#                   date = c("2016-01-01", "2016-12-31"),
#                   station = "01008")

knitr::kable(head(df))

Example 4

Downloading atmospheric vertical profile (sounding) for Łeba, PL station:

profile_demo = sounding_wyoming(wmo_id = 12120,
                                 yy = 2000,
                                 mm = 3,
                                 dd = 23,
                                 hh = 0)
df2 = profile_demo[[1]] 
colnames(df2)[c(1, 3:4)] = c("PRESS", "TEMP", "DEWPT") # changing column names

Example 5

Preparing an annual summary of air temperature and precipitation using dplyr syntax for 10-years period (1991-2000)

library(climate)
df = meteo_imgw(interval = "monthly", rank = "synop", year = 1991:2000, station = "ŁEBA") 
# please note that sometimes 2 names are used for the same station in different years

suppressMessages(library(dplyr))
df2 = dplyr::select(df, station:t2m_mean_mon, rr_monthly)

monthly_summary = df2 %>% 
  dplyr::group_by(mm) %>% 
  dplyr::summarise(tmax = mean(tmax_abs, na.rm = TRUE), 
                   tmin = mean(tmin_abs, na.rm = TRUE),
                   tavg = mean(t2m_mean_mon, na.rm = TRUE), 
                   precip = sum(rr_monthly) / n_distinct(yy))            

monthly_summary = as.data.frame(t(monthly_summary[, c(5, 2, 3, 4)])) 
monthly_summary = round(monthly_summary, 1)
colnames(monthly_summary) = month.abb

Example 6

Calculate the mean maximum value of the flow on the stations in each year with dplyr’s summarise(), and spread data by year using tidyr’s spread() to get the annual means of maximum flow in the consecutive columns.

library(climate)
library(dplyr)
library(tidyr)
h = hydro_imgw(interval = "monthly", year = 2001:2002)

knitr::kable(head(h))

h2 = h %>%
  dplyr::filter(idex == 3) %>%
  dplyr::select(id, station, X, Y, hyy, Q) %>%
  dplyr::group_by(hyy, id, station, X, Y) %>%
  dplyr::summarise(annual_mean_Q = round(mean(Q, na.rm = TRUE), 1)) %>% 
  tidyr::pivot_wider(names_from = hyy, values_from = annual_mean_Q)
#> `summarise()` has regrouped the output.
#> ℹ Summaries were computed grouped by hyy, id, station, X, and Y.
#> ℹ Output is grouped by hyy, id, station, and X.
#> ℹ Use `summarise(.groups = "drop_last")` to silence this message.
#> ℹ Use `summarise(.by = c(hyy, id, station, X, Y))` for per-operation grouping
#>   (`?dplyr::dplyr_by`) instead.

knitr::kable(head(h2))

Example 7

Downloading monthly CO2 measurements from Mauna Loa Observatory and plotting the Keeling Curve:

library(climate)
library(ggplot2)

co2 = meteo_noaa_co2()
co2$date = ISOdate(co2$yy, co2$mm, 1)

ggplot(co2, aes(date, co2_avg)) +
  geom_line() +
  geom_smooth() +
  theme_bw() +
  labs(title = "Carbon Dioxide (CO2)",
       subtitle = "Mauna Loa Observatory",
       x = "", y = "ppm")

Example 8

Decoding raw SYNOP meteorological messages with synop_parser():

library(climate)

synop_code = "AAXX 01004 88889 12782 61506 10094 20047 30111 40197 53007 60001 81541"

# Decode a single message — returns a named list
result = synop_parser(synop_code)
result$air_temperature$value   # 9.4°C
#> [1] 9.4
result$wind_speed$value        # 6 kt
#> NULL
result$sea_level_pressure$value # 1019.7 hPa
#> [1] 1019.7

# Return a tidy data frame with one row per message
df_parser = synop_parser(synop_code, as_data_frame = TRUE)
knitr::kable(df_parser[, 1:8])