Daily Weather Data

In GHCNr: Download Weather Station Data from GHCNd

# rmarkdown::render("vignettes/daily.Rmd")
# rmarkdown::render("vignettes/daily.Rmd", rmarkdown::github_document())
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(GHCNr)
library(terra)  # for handling countries geometries

Select GHCNd stations

The station inventory file of GHCNd is stored at https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily. The function stations() can read from this source or from a local file, specified with filename. The inventory can also be downloaded to a file using download_inventory().

inventory_file <- download_inventory("~/Downloads/ghcn-inventory.txt")
s <- stations(
  inventory_file,
  variables = "TMAX",
  first_year = 1990,
  last_year = 2000
)

s <- stations(variables = "TMAX", first_year = 1990, last_year = 2000)
s
# A tibble: 16,763 × 6
   station     latitude longitude variable firstYear lastYear
   <chr>          <dbl>     <dbl> <chr>        <dbl>    <dbl>
 1 AE000041196     25.3     55.5  TMAX          1944     2024
 2 AEM00041194     25.3     55.4  TMAX          1983     2024
 3 AEM00041217     24.4     54.7  TMAX          1983     2024
 4 AFM00040938     34.2     62.2  TMAX          1973     2020
 5 AFM00040948     34.6     69.2  TMAX          1966     2021
 6 AFM00040990     31.5     65.8  TMAX          1973     2020
 7 AG000060390     36.7      3.25 TMAX          1940     2024
 8 AG000060590     30.6      2.87 TMAX          1940     2024
 9 AG000060611     28.0      9.63 TMAX          1958     2024
10 AG000060680     22.8      5.43 TMAX          1940     2004
# ℹ 16,753 more rows
# ℹ Use `print(n = ...)` to see more rows

By specifying variables = "TMAX" only the stations that recorded that variable are kept. Available variables implemented at the moment are precipitation ("PRCP"), minimum temperature ("TMIN"), and maximum temperature ("TMAX"). The arguments first_year and last_year specify the minimum time period required for the stations. Here, stations that are not sampled at least from 1990 until at least 2000 are dropped.

Spatial filters can also be easily applied.
Spatial boundaries of countries can be downloaded from <https://www.geoboundaries.org/> using the `get_countr(couuntry_code = ...)` function, where `country_code` is the ISO3 code.
```r
italy <- get_country("ITA")

get_countries() can take several ISO3 codes to return a geometry of multiple countries.

s <- filter_stations(s, italy)
s
# A tibble: 41 × 6
   station     latitude longitude variable firstYear lastYear
   <chr>          <dbl>     <dbl> <chr>        <dbl>    <dbl>
 1 IT000016090     45.4     10.9  TMAX          1951     2024
 2 IT000016134     44.2     10.7  TMAX          1951     2024
 3 IT000016232     42       15    TMAX          1975     2024
 4 IT000016239     41.8     12.6  TMAX          1951     2024
 5 IT000016320     40.6     17.9  TMAX          1951     2024
 6 IT000016560     39.2      9.05 TMAX          1951     2024
 7 IT000160220     46.2     11.0  TMAX          1951     2024
 8 IT000162240     42.1     12.2  TMAX          1954     2024
 9 IT000162580     41.7     16.0  TMAX          1951     2024
10 ITE00100554     45.5      9.19 TMAX          1763     2008
# ℹ 31 more rows
# ℹ Use `print(n = ...)` to see more rows

Download daily timeseries

Daily timeseries for a station can be downloaded using the daily() function. In addition to the station ID, daily() needs start and end dates of the timeseries. These should be provided as strings with the format "YYYY-mm-dd", e.g., "1990-01-01".

daily_ts <- daily(
  station_id = "CA003076680",
  start_date = paste("2002", "11", "01", sep = "-"),
  end_date = paste("2024", "04", "22", sep = "-"),
  variables = "tmax"
)
daily_ts

daily_ts <- CA003076680[, c("date", "station", "tmax", "tmax_flag")]
daily_ts

Multiple stations can also be downloaded at once. Too many stations will cause the API to fail.

daily_ts <- daily(
  station_id = c("CA003076680", "USC00010655"),
  start_date = paste("2002", "11", "01", sep = "-"),
  end_date = paste("2024", "04", "22", sep = "-"),
  variables = "tmax"
)
plot(daily_ts, "tmax")

daily_ts <- rbind(CA003076680, USC00010655)[, c("date", "station", "tmax", "tmax_flag")]
plot(daily_ts, "tmax")

Implmented variables are "tmin", "tmax", and "prcp". daily() returns a table with the value of the variable chosen and associated flags.

Remove flagged records

Flagged records can be removed using remove_flagged(). In remove_flagged() the argument strict (dafault = TRUE) specifies which flags to include. The flags removed are:

as.list(GHCNr:::.flags(strict = TRUE))

Setting strict = FALSE will only remove the flags:

as.list(GHCNr:::.flags(strict = FALSE))

This will also remove the "*_flag=" column.

daily_ts <- remove_flagged(daily_ts)
plot(daily_ts, "tmax")

Temporal coverage

Coverage of the timeseries can be calculated using coverage().

station_coverage <- coverage(daily_ts)
station_coverage

period_coverage_* calculates the coverage across the whole period, including missing years.

The output is a table with coverage by month and year (monthly_coverage), by year (annual_coverage), and for the whole time period (period_coverage). annual_coverage is constant within the same year and year is always a constant. This table is useful to inspect stations that may have problematic timeseries, such as

unique(station_coverage[
  station_coverage$annual_coverage_tmax < .95,
  c("station", "year", "annual_coverage_tmax")
])

Monthly and annual timeseries, climatological normals

The functions monthly(), quarterly(), and annual() summarized the weather time series to monthly, quarterly, and annual time series, respectively. Summaries are calculated as follows:

• $T_{min}$ is the minimum daily temperature recorded in the month or the year.
• $T_{max}$ is the maximum daily temperature recorded in the month or the year.
• $Prcp$ is the cumulative precipitation during the month or year.

NAs are removed during calculation.

monthly_ts <- monthly(daily_ts)
monthly_ts
plot(monthly_ts, "tmax")

quarterly_ts <- quarterly(daily_ts)
quarterly_ts
plot(quarterly_ts, "tmax")

annual_ts <- annual(daily_ts)
annual_ts
plot(annual_ts, "tmax")

GHCNr documentation built on April 3, 2025, 11:16 p.m.