Select and Download Climate Data from 'DWD' (German Weather Service)

Handle climate data from the 'DWD' ('Deutscher Wetterdienst', see < https://www.dwd.de/EN/climate_environment/cdc/cdc_node_en.html> for more information). Choose observational time series from meteorological stations with 'selectDWD()'. Find raster data from radar and interpolation according to < https://brry.github.io/rdwd/raster-data.html>. Download (multiple) data sets with progress bars and no re-downloads through 'dataDWD()'. Read both tabular observational data and binary gridded datasets with 'readDWD()'.

rdwd

rdwd is an R package to select, download and read climate data from the German Weather Service (Deutscher Wetterdienst, DWD).
The DWD provides thousands of datasets with weather observations online at opendata.dwd.de.
Since May 2019, rdwd also supports reading the Radolan (binary) raster data at grids_germany.

rdwd is available on CRAN:

It has been presented at FOSDEM 2017 and UseR!2017 in Brussels and with a 5 Minute video at e-Rum2020, featured in Rstudio's data package list, written about in OSOR and used e.g. for NDR: Starkregen im Norden. Development of rdwd was triggered 2016 by flash flood research in Braunsbach (1, 2, 3, 4).

Documentation

A website with more information, examples, use cases and an interactive map of the DWD stations can be found at https://brry.github.io/rdwd/

Usage

Usage for observational weather data from the measuring stations usually looks something like the following:

# Download and install (once only):
install.packages("rdwd")
# update to the most recent fileIndex (ca 3x/year):
rdwd::updateRdwd() 
# Load the package into library (needed in every R session):
library(rdwd)

# Select a dataset (e.g. last year's daily climate data from Potsdam city):
link <- selectDWD("Potsdam", res="daily", var="kl", per="recent")

# Download (if needed) and read it:
clim <- dataDWD(link, varnames=TRUE)

# Inspect the data.frame:
str(clim)
# Quick time series graphic:
plotDWD(clim, "FM.Windgeschwindigkeit")

For data interpolated onto a 1 km raster, including radar data up to the last hour, see the corresponding chapter on the website.

To automatically merge historical and recent data, see this use case.

App

Since April 2023, there is an interactive app to compare weather periods:

With rdwd::app(), you can run this locally with cached data, i.e. faster responses.

New to R

If you're new to R, these links might help you to get started:

back to rdwd:

Installation

Normal

install.packages("rdwd")

Latest version

rdwd::updateRdwd()
# checks version and (if needed) calls  remotes::install_github("brry/rdwd", build_vignettes=TRUE)

Full

Suggested (not mandatory) dependencies:

install.packages("rdwd", dependencies="Suggests")

RCurl for indexFTP and selectDWD(..., current=TRUE)
data.table, bit64 for readDWD(..., fread=TRUE)
terra, stars, R.utils, ncdf4, dwdradar for readDWD with gridded data
readr for readDWD.stand(..., fast=TRUE)
knitr, rmarkdown, testthat, roxygen2, devtools, remotes, XML, gsheet for local testing, development and documentation
leaflet, OSMscale for interactive/static maps, see rJava installation tips
shiny for the interactive weather comparison app