1 Intro

This vignette is build with source code and files available here.
For further details on the data, please consult the DWD FTP server documentation.
Any feedback on rdwd (or this vignette) is very welcome via github or berry-b@gmx.de!
The remainder of this intro chapter is a copy of the github README file.

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, featured in Rstudios data package list and written about in OSOR.

1.1 Installation

Normal:

# download and install the package:
install.packages("rdwd")

Latest version:

# latest development version (incl. vignettes), if wanted:
if(!requireNamespace("remotes", quietly=TRUE)) install.packages("remotes")
remotes::install_github("brry/rdwd", build_opts="--no-manual")

Note: on Windows, you need to have Rtools installed directly at C:/Rtools
(Compiler paths may not have spaces, as there would be with C:/Program Files/R/Rtools/).

Full:

# For full usage, as needed in indexFTP and selectDWD(..., current=TRUE):
install.packages("RCurl") # is only suggested, not mandatory dependency

Note: on Linux (Ubuntu), instead call via the terminal (CTRL+ALT+T, note lowercase rcurl):

sudo apt install r-cran-rcurl

1.2 Usage

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

# 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")

# Actually download that dataset, returning the local storage file name:
file <- dataDWD(link, read=FALSE)

# Read the file from the zip folder:
clim <- readDWD(file, varnames=TRUE)

# Inspect the data.frame:
str(clim)

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