clidatajp"
In clidatajp: Data from Japan Meteorological Agency

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Japan Meteorological Agency ('JMA') web page

'JMA' web page consists of some layers. You can use different function for download each component.

Area select: download_area_links()
Country select: download_links()
Station select: download_links()
Climate data of a station: download_climate()

Links for climate data and station information

I recommend to use existing data, which are already downloaded and cleaned up.

When you want to download links for climate data, use download_area_links() and download_links(). download_area_links() returns links for 6 areas. download_links() returns links for countries and stations.

For polite scraping, 5 sec interval is set in download_links(), it takes about 15 minutes to get all station links. Please use existing links by "data(station_links)", if you do not need to renew links.

library(clidatajp)
library(magrittr)
library(dplyr)
library(tibble)
library(ggplot2)
library(stringi)

  # existing data
data(station_links)
station_links %>%
  dplyr::mutate("station" := stringi::stri_unescape_unicode(station)) %>%
  print() %>%
  `$`("station") %>%
  clean_station() %>%
  dplyr::bind_cols(station_links["url"])

  # Download new data
  # If you want links for all countries and all sations, remove head().
url <- "https://www.data.jma.go.jp/gmd/cpd/monitor/nrmlist/"
res <- gracefully_fail(url)
if(!is.null(res)){
  area_links <- download_area_links()
  station_links <- NULL
  area_links <- head(area_links)  # for test
  for(i in seq_along(area_links)){
      print(stringr::str_c("area: ", i, " / ", length(area_links)))
      country_links <- download_links(area_links[i])
      country_links <- head(country_links)  # for test
      for(j in seq_along(country_links)){
          print(stringr::str_c("    country: ", j, " / ", length(country_links)))
          station_links <- c(station_links, download_links(country_links[j]))
      }
  }
  station_links <- tibble::tibble(url = station_links)
  station_links
}

Climate data

I recommend to use existing data, which are already downloaded and cleaned up.

When you want to know how to prepare "data(climate_jp)", please check url shown below.

https://github.com/matutosi/clidatajp/blob/main/data-raw/climate_jp.R

For polite scraping, 5 sec interval is set in download_climate(), it takes over 5 hours to get world climate data of all stations because of huge amount of data (3444 stations). Please use existing data by "data(climate_world)", if you do not need to renew climate data.

  # existing data
data(climate_jp)
climate_jp %>%
  dplyr::mutate_if(is.character, stringi::stri_unescape_unicode)

data(climate_world)
climate_world %>%
  dplyr::mutate_if(is.character, stringi::stri_unescape_unicode)

  # Download new data
  # If you want links for all countries and all sations, remove head().
url <- "https://www.data.jma.go.jp/gmd/cpd/monitor/nrmlist/"
res <- gracefully_fail(url)
if(!is.null(res)){
  station_links <-
    station_links %>%
    head() %>%
    `$`("url")
  climate <- list()
  for(i in seq_along(station_links)){
    print(stringr::str_c(i, " / ", length(station_links)))
    climate[[i]] <- download_climate(station_links[i])
  }
  world_climate <- dplyr::bind_rows(climate)
  world_climate
}

Plot

Clean up data before drawing plot.

data(climate_world)
data(climate_jp)
climate <- 
  dplyr::bind_rows(climate_world, climate_jp) %>%
  dplyr::mutate_if(is.character, stringi::stri_unescape_unicode)  %>%
  dplyr::group_by(country, station) %>%
  dplyr::filter(sum(is.na(temperature), is.na(precipitation)) == 0) %>%
  dplyr::filter(period != "1991-2020" | is.na(period))

climate <- 
  climate %>%
  dplyr::summarise(temp = mean(as.numeric(temperature)), prec = sum(as.numeric(precipitation))) %>%
  dplyr::left_join(dplyr::distinct(dplyr::select(climate, station:altitude))) %>%
  dplyr::left_join(tibble::tibble(NS = c("S", "N"), ns = c(-1, 1))) %>%
  dplyr::left_join(tibble::tibble(WE = c("W", "E"), we = c(-1, 1))) %>%
  dplyr::group_by(station) %>%
  dplyr::mutate(lat = latitude * ns, lon = longitude * we)

Draw a world map with temperature.

climate %>%
  ggplot2::ggplot(aes(lon, lat, colour = temp)) +
    scale_colour_gradient2(low = "blue", mid = "gray", high = "red", midpoint = 15) + 
    geom_point() + 
    coord_fixed() + 
    theme_bw() + 
    theme(legend.key.size = unit(0.3, 'cm'))
    # ggsave("temperature.png")

Draw a world map with precipitation except over 5000 mm/yr (to avoid extended legend).

climate %>%
  dplyr::filter(prec < 5000) %>%
  ggplot2::ggplot(aes(lon, lat, colour = prec)) +
    scale_colour_gradient2(low = "yellow", mid = "gray", high = "blue", midpoint = 1500) + 
    geom_point() + 
    coord_fixed() + 
    theme_bw() + 
    theme(legend.key.size = unit(0.3, 'cm'))
  # ggsave("precipitation.png")

Show relationships between temperature and precipitation except Japan.

japan <- stringi::stri_unescape_unicode("\\u65e5\\u672c")
climate %>%
  dplyr::filter(country != japan) %>%
  ggplot2::ggplot(aes(temp, prec)) + 
  geom_point() + 
  theme_bw() + 
  theme(legend.position="none")
  # ggsave("climate_nojp.png")

Show relationships between temperature and precipitation including Japan.

climate %>%
  ggplot2::ggplot(aes(temp, prec)) + 
    geom_point() + 
    theme_bw()
  # ggsave("climate_all.png")

Show relationships between temperature and precipitation. Blue: Japan, red: others.

climate %>%
  dplyr::mutate(jp = (country == japan)) %>%
  ggplot2::ggplot(aes(temp, prec, colour = jp)) + 
    geom_point() + 
    theme_bw() +
    theme(legend.position="none")
  # ggsave("climate_compare_jp.png")