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 "pre-alpha", not everything works yet. See Not yet implemented.

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("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_0h-24h.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.

Logging

The library logs through the standard dwdopen logger. It never prompts or reads from stdin as the library is meant to run unattended.

import logging
logging.basicConfig(level=logging.INFO)

INFO reports what is about to be downloaded and how large it is. WARNING covers retries and mixed level types.

Not yet implemented

• combine="parameter".
• Listing cache. Every call re-reads the catalogue today.
• Feedback of download progress (bar, text, visual?)
• ICON-ART wavelengths (the wvl1 segment).
• A command-line interface.
• Automatic parameter choice for Model.latest_run(), which needs parameter= at the moment.

License

MIT. See LICENSE.

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