Introduction to the climate package

library(climate)
ns = nearest_stations_ogimet(country = c("United Kingdom", "France"),
                             point = c(-3, 50),
                             no_of_stations = 50, 
                             add_map = TRUE)
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpekyspS/file9a146d3eaea0
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpekyspS/file9a142e3ac268

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

library(climate)
PL = stations_ogimet(country = "Poland", add_map = TRUE)
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpekyspS/file9a1469583cdc

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

# 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 = c("01008"))

knitr::kable(head(df))

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

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

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

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 grouped output by 'hyy', 'id', 'station', 'X'. You can
#> override using the `.groups` argument.

knitr::kable(head(h2))