dwdopen

Python library to access DWD Open Data numerical weather prediction (NWP) data: discover what data is currently offered, define what you want, and download it.

Still in alpha, not everything works yet and the API may change before 1.0. See the Roadmap.

Install

Requires Python 3.11 or newer.

pip install dwdopen

Quickstart

from dwdopen import DWD

with DWD() as dwd:
    icon_eu = dwd.nwp.model("icon-eu")

    query = icon_eu.select(parameters=["T_2M", "PMSL"], steps="6h")
    query.download("forecast.grib2")

Discovery

dwd.nwp.models()                    # every model DWD currently publishes
icon_eu.parameters()                # every parameter of one model
icon_eu.parameter("T").level_types  # (pressure (100), model (150))
icon_eu.levels("T", "pressure")     # available level values

Selecting

Forecast steps

Steps are durations, ICON-D2 publishes precipitation every 15 minutes and ICON-D2-RUC every 5.

from dwdopen import Between, Every, hours, minutes

steps="6h"                        # exactly this step
steps=["0h", "3h", "6h"]          # exactly these
steps=Every("0h", "48h", "3h")    # interval, inclusive at both ends
steps=Between("0h", "48h")        # whatever exists in the interval
steps="all"                       # everything published

steps=hours(0, 6, 12, 18)         # plain numbers, if that is what you have
steps=hours(range(0, 121, 3))     # or any iterable of them
steps=minutes(0, 15, 30)          # for the sub-hourly models

hours() and minutes() turn a single number into a single duration, so they compose with the others too: Every(hours(0), hours(48), hours(3)).

Every names exact steps, so a missing one makes the request incomplete. Between and "all" ask for whatever is there and can never be incomplete.

Note that DWD publishes data incrementally on the server, so a run might just be halfway available. If you use Between, you might just get what's already available, not the full data.

Ensemble members

The -eps models publish each member separately. Members are plain integers; how many there are differs per model (40 for ICON-EPS and ICON-EU-EPS, 20 for ICON-D2-EPS, 10 for ICON-ART-EPS):

eps.select(parameters="T_2M", members=[1, 2, 5])
eps.select(parameters="T_2M", members=Between(1, 10))
eps.select(parameters="T_2M")                       # every member

Leaving members out takes all of them, the same as levels and steps.

Vertical levels

icon_eu.select(parameters="T", level_type="pressure", levels=[850, 500])
icon_eu.select(parameters="T", level_type="model", levels=Between(60, 74))
icon_eu.select(parameters="T_SO", level_type="soil", levels="all")

level_type may be left out only while the selection is unambiguous. T is available on both pressure and model levels, so omitting it raises AmbiguousSelectionError rather than guessing. A 2-D field such as T_2M needs no level type at all. Time-invariant fields need no level type either. These are published under every run with a single step. HHL is the exception, being model half-level heights:

icon_d2.select(parameters="HSURF", steps="0h")                      # no level type
icon_d2.select(parameters="HHL", level_type="model", levels="all")

Investigate before you download

resolve() freezes a query against one run and hands back a plan you can inspect before any download happens:

plan = icon_eu.select(parameters="U", level_type="pressure").resolve()
print(plan)
# ResolvedRequest(run=2026-09-23T06:00:00+00:00, 1860 assets, 1.5 GB, U,
#                 on pressure (100), 20 levels, steps 0h..120h)

print(plan.assets[0])
# Asset(U, 50 hPa, 0h, 723.4 KB)

plan.download("u.grib2")

A plan never switches to a newer run later, so what you inspected is what you get.

Downloading

query.download("forecast.grib2")                    # one combined file
query.download("members/", combine="member")        # one file per ensemble member
query.download("params/", combine="parameter")      # one file per parameter
query.download("forecast/", combine="none")         # one file per message
query.download("f.grib2", temp_dir="/scratch")      # partial downloads before combining elsewhere

combine="all" concatenates the messages into a single GRIB2 file. You can also provide just a directory in this case and the name is generated automatically.

plan = query.resolve()
plan.suggested_name()            # 'icon-eu_2026-09-24T0600_PMSL+T_2M_single_0h-24h_2801ebe5.grib2'
plan.download("/home/weather/")   # writes that name into the directory

Downloads run concurrently, are written to a temporary name and renamed into place, so a partial file is never mistaken for a finished one. Several processes may write the same directory at once. Already downloaded files are skipped. DownloadResult.assets_skipped says how many were already there, which is worth checking in a scheduled job.

Progress

On a terminal or in Jupyter a download shows one line:

━━━━━━━━━━━━━━━━╸━━━━━━━━━━━  57%  312/546 files  2.3 GB/4.0 GB  48.2 MB/s  ETA 0:36

When nobody watches the output (e.g., output redirected to a file), the same numbers are logged every 10 seconds instead. DWD(progress=False) turns progress off.

Caching

Directory listings are cached to keep the number of requests to the server low. The structure of a model (parameters, level types, levels) is kept for the lifetime of the DWD client, as it only changes when DWD changes the model. Runs and steps change constantly as runs are published and deleted, so they are re-read on every call. One resolve() sees one consistent view of the server.

If DWD changes a model while your client is running, dwd.refresh() drops the cache.

Logging

The library logs through the dwdopen logger. You can change the logging level via:

import logging, dwdopen

dwdopen.set_log_level(logging.DEBUG)    # a line per file to download
dwdopen.set_log_level(logging.WARNING)  # only retries and warnings
dwdopen.set_log_level(None)             # disable the dwdopen logging

The handler is connected to the dwdopen logger. It will not override the logging your application sets up for itself. If you do configure your own logger and want to use that logger, call set_log_level(None). That will uninstall the handler.

Roadmap

1.0

• Feedback of download progress.
• ICON-ART wavelengths (the wvl1 segment).

After 1.0

• A command-line interface.

Long term

The goal is a complete Python interface to DWD Open Data:

• Observations: station data (/weather/weather_reports)
• Radar: composites and single-site products (weather/radar).
• Point forecasts: MOSMIX (weather/local_forecasts).
• Weather warnings (weather/alerts).
• Satellite products (weather/satellite).
• Current station reports (weather/weather_reports).

License

MIT. See LICENSE.

Forecast data itself is provided by Deutscher Wetterdienst (DWD) under its own terms of use.