dailymeteo
Python client for meteorological data from DailyMeteo:
get_meteo: daily, monthly or annual values for a location, World, 1961 to the present, from the DailyMeteo API (API key required).meteoeurope1km: download of the MeteoEurope1km gridded dataset from Zenodo. It covers Europe at 1 km for 1961–2020: TMAX, TMIN, TMEAN, PRCP and SLP, with aggregates and climate normals.
It is the Python counterpart of get_meteo, get_coordinates and get_meteoeurope1km in the R package meteo. Spatial interpolation (RFSI, STRK) is available only in R.
Installation
pip install dailymeteo
Point data from the DailyMeteo API
import dailymeteo as dm
lat, lon = dm.get_coordinates("Belgrade")
df = dm.get_meteo(lat, lon, api_key="...", # API key from https://app.dailymeteo.com/
var="tmean", # "tmax", "tmin", "prcp"
agg_level="agg", # "ltm" for long-term means
time_scale="day", # "mon", "ann"
start="2020-01-01", end="2020-01-31")
# or time=["2020-01-01", "2020-01-15"]
The result is a pandas.DataFrame with columns timestamp and value.
MeteoEurope1km gridded dataset
from dailymeteo import meteoeurope1km as meu
# list archives: one ZIP per year with daily Cloud-Optimized GeoTIFFs
meu.files(var="tmean", product="day", years=[2001, 2002])
# download, verify MD5, unzip; interrupted downloads are resumed
meu.download(var="tmean", product="day", years=[2001, 2002], dest_dir="meteoeurope1km", unzip=True)
# aggregates and climate normals
meu.download(var="prcp", product="ltm_diff", dest_dir="meteoeurope1km")
product |
Content | Archives |
|---|---|---|
day |
daily data | one per year (about 1–3.5 GB) |
mon |
monthly aggregates | one per decade |
ann |
annual aggregates, 1961–2020 | one |
ltm_day |
daily climate normals | one per period (period="1961-1990" / "1991-2020") |
ltm_mon_ann |
monthly and annual climate normals | one per period |
ltm_diff |
difference of normals, 1991–2020 minus 1961–1990 | one |
Aggregates are means for temperatures and sums for precipitation. For SLP only daily data are available.
Rasters are in the Equi7 Europe projection (EPSG:27704) at 1000 m. Values are Int16 in tenths of the unit, so divide by 10 to get °C, mm or hPa. NoData is −9999.
meu.catalog() lists all Zenodo records of the dataset.
Citation
Sekulić, A., & Kilibarda, M. (2026). MeteoEurope1km (1961–2020): daily gridded meteorological dataset for Europe at a 1-km spatial resolution. Zenodo. https://doi.org/10.5281/zenodo.23264155
License
GPL-2.0-or-later, the same as the R package meteo.
Metadata
Release files for dailymeteo 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| dailymeteo-0.1.0.tar.gz | 15.8 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| dailymeteo-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 31.6 kB
Release files / dailymeteo-0.1.0.tar.gz
| Download URL | dailymeteo-0.1.0.tar.gz |
|---|---|
| Size | 15.8 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / dailymeteo-0.1.0-py3-none-any.whl
| Download URL | dailymeteo-0.1.0-py3-none-any.whl |
|---|---|
| Size | 15.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.14
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