Meteo Data

Meteo Data#

Weather data is essential for energy data analysis — outdoor temperature drives heating demand, solar radiation affects PV production, and humidity influences cooling loads. In Switzerland, two main open data sources provide free weather station data:

Provider	Description	Data Access	Resolution
MeteoSwiss	Federal Office of Meteorology and Climatology, official network	Station lookup + data download (open data)	10 min, hourly, daily
Agrometeo	Agricultural weather network	Station lookup + data download	Hourly

The pyedautils package provides a unified interface to both providers. This chapter shows how to:

Find the nearest weather station to any Swiss location
Download hourly weather data
Compare both station networks

Note

Both providers require an internet connection. All API calls go to public endpoints — no API key needed.

import pandas as pd

Station Lookup#

Both providers let you find the nearest weather station to a given location. The typical workflow is:

Convert a postal code (PLZ) to GPS coordinates
Find the nearest station that has the sensor you need

Helper: PLZ to Coordinates#

The geopy module converts any Swiss postal code to latitude/longitude and altitude.

from pyedautils.geopy import get_coordindates_ch_plz, get_altitude_lat_long

plz = 6048  # Horw
coord = get_coordindates_ch_plz(plz)
altitude = get_altitude_lat_long(coord[0], coord[1])

print(f"PLZ {plz}: lat={coord[0]:.4f}, lon={coord[1]:.4f}, altitude={altitude:.0f} m")

PLZ 6048: lat=47.0108, lon=8.3039, altitude=455 m

MeteoSwiss#

MeteoSwiss operates Switzerland’s official automatic weather stations. The find_nearest_station() function returns the station abbreviation (e.g. "LUZ" for Luzern) of the closest station that has the requested sensor and is within ±150 m altitude.

from pyedautils.weather.meteo_swiss import find_nearest_station as find_meteoswiss

station_ms = find_meteoswiss(coord[0], coord[1], altitude, sensor="temp")
print(f"Nearest MeteoSwiss station for temperature: {station_ms}")

Nearest MeteoSwiss station for temperature: LUZ

You can search for different sensor types: "temp", "globrad" (global radiation), "relhum" (relative humidity), "rain".

for sensor in ["temp", "globrad", "relhum", "rain"]:
    station = find_meteoswiss(coord[0], coord[1], altitude, sensor=sensor)
    print(f"  {sensor:8s} → {station}")

  temp     → LUZ
  globrad  → LUZ
  relhum   → LUZ
  rain     → LUZ

Agrometeo#

Agrometeo is a Swiss agricultural weather network with a public API for station lookup and data download.

from pyedautils.weather.agroweather import find_nearest_station as find_agro

station_agro = find_agro(coord[0], coord[1], sensor="temp")
print(f"Nearest Agrometeo station for temperature: {station_agro}")

Nearest Agrometeo station for temperature: 239

Station Networks#

Both providers expose a function to list all available stations. This is useful for understanding coverage and available sensors.

from pyedautils.weather.meteo_swiss import get_current_station_data

stations_ms = get_current_station_data()
print(f"MeteoSwiss: {len(stations_ms)} stations")
stations_ms[["Abk.", "Station", "Stationshöhe m ü. M."]].head()

MeteoSwiss: 300 stations

	Abk.	Station	Stationshöhe m ü. M.
0	TAE	Aadorf / Tänikon	539.0
1	ABE	Aarberg	444.0
2	COM	Acquarossa / Comprovasco	575.0
3	ABO	Adelboden	1321.0
4	AIE	Affoltern i. E.	755.0

from pyedautils.weather.agroweather import get_station_data

stations_agro = get_station_data()
print(f"Agrometeo: {len(stations_agro)} stations")
stations_agro[["id", "name", "lat", "lon", "altitude"]].head()

Agrometeo: 218 stations

	id	name	lat	lon	altitude
0	189	AESCH-BL	47.466863	7.575196	382
1	360	AGNO-Progetto ViSo-KS	45.997000	8.893944	364
2	94	AIGLE	46.314537	6.979451	442
3	78	AMBURNEX	46.536925	6.215849	1321
4	79	AMBURNEX-COMBE	46.535419	6.218099	1285

Downloading Weather Data#

Both providers offer hourly weather data through public interfaces. MeteoSwiss publishes its measurements as open data; download_data() takes the station abbreviation and returns the data in UTC (or the time zone given with tz).

The examples use fixed stations and a fixed past week, so the results stay the same even if a new station opens closer to Horw. Here: MeteoSwiss station Luzern (LUZ) and Agrometeo station Meggen (ID 239).

from pyedautils.weather import meteo_swiss

df_luz = meteo_swiss.download_data("LUZ", "2026-06-01", "2026-06-07", tz="Europe/Zurich")
df_luz.head()

	temp	globrad	relhum	rain
datetime
2026-06-01 00:00:00+02:00	16.1	0.0	91.4	0.0
2026-06-01 01:00:00+02:00	15.6	0.0	94.6	0.0
2026-06-01 02:00:00+02:00	15.2	0.0	95.8	0.0
2026-06-01 03:00:00+02:00	15.2	0.0	95.8	0.0
2026-06-01 04:00:00+02:00	15.5	0.0	93.7	0.0

Agrometeo can be queried in two ways:

By station ID — if you already know the station
By postal code (PLZ) — automatically finds the nearest station(s)

Download by Station ID#

Use download_data() when you know the station ID, e.g. from find_nearest_station() above or from get_station_data().

from pyedautils.weather.agroweather import download_data

df_agro = download_data(
    station_id=239,  # Meggen, nearest Agrometeo station to Horw
    start_date="2026-06-01",
    end_date="2026-06-07",
    sensors=["temp", "globrad", "relhum", "rain"]
)

print(f"Shape: {df_agro.shape}")
print(f"Period: {df_agro.index.min()} to {df_agro.index.max()}")
df_agro.head()

Shape: (168, 4)
Period: 2026-06-01 00:00:00 to 2026-06-07 23:00:00

	temp	globrad	relhum	rain
datetime
2026-06-01 00:00:00	17.1	0.1	84.0	0.1
2026-06-01 01:00:00	16.9	0.4	82.0	0.0
2026-06-01 02:00:00	16.9	NaN	83.0	0.0
2026-06-01 03:00:00	16.9	NaN	84.0	0.0
2026-06-01 04:00:00	17.2	NaN	83.0	0.0

Download by Postal Code#

The convenience function download_data_by_plz() handles the full workflow: PLZ → coordinates → nearest station(s) → download. If different sensors are closest to different stations, it automatically merges the results. Because the nearest station can change over time, use it for quick looks and pin the station ID for reproducible analyses:

from pyedautils.weather.agroweather import download_data_by_plz

df_plz = download_data_by_plz(plz=6048, start_date="2026-06-01", end_date="2026-06-07")

df_agro.describe()

	temp	globrad	relhum	rain
count	168.000000	125.000000	168.000000	168.000000
mean	17.729167	273.253600	67.273810	0.244643
std	3.573344	272.144148	12.144688	0.779664
min	11.400000	0.000000	44.000000	0.000000
25%	15.100000	25.500000	56.000000	0.000000
50%	17.250000	189.600000	69.000000	0.000000
75%	20.375000	474.700000	77.250000	0.000000
max	25.100000	931.600000	85.000000	4.600000

Quick Visualization#

Let’s plot the downloaded data to verify it looks reasonable.

from pyedautils.plots import plot_timeseries

fig = plot_timeseries(
    df_agro,
    columns=["temp"],
    title="Outdoor Temperature — Meggen (Agrometeo 239)",
    ylab="Temperature [°C]",
)
fig.show()

fig = plot_timeseries(
    df_agro,
    columns=["globrad"],
    title="Global Radiation — Meggen (Agrometeo 239)",
    ylab="Radiation [W/m²]",
)
fig.show()

Available Sensors#

Both providers support the same four sensor types:

Sensor key	Description	Unit
`"temp"`	Air temperature	°C
`"globrad"`	Global radiation	W/m²
`"relhum"`	Relative humidity	%
`"rain"`	Precipitation	mm

Which provider should I use?

Criteria	MeteoSwiss	Agrometeo
Data download	Yes, open data (10 min, hourly, daily, back to the start of the measurements)	Yes, via API (hourly)
Altitude matching	Yes (±150 m filter)	No
Best for	Official reference data, long time series	A station close to rural sites

Recommendation: Start with the nearest MeteoSwiss station at a similar altitude; use Agrometeo when it is much closer to your building.

Summary#

Task	MeteoSwiss	Agrometeo
Find nearest station	`weather.meteo_swiss.find_nearest_station(lat, lon, alt, sensor)`	`weather.agroweather.find_nearest_station(lat, lon, sensor)`
List all stations	`weather.meteo_swiss.get_current_station_data()`	`weather.agroweather.get_station_data()`
Download data by station	`weather.meteo_swiss.download_data(station, start, end)`	`weather.agroweather.download_data(station_id, start, end)`
Download data by PLZ	`weather.meteo_swiss.download_data_by_plz(plz, start, end)`	`weather.agroweather.download_data_by_plz(plz, start, end)`
PLZ → coordinates	`geopy.get_coordindates_ch_plz(plz)`	(built into `download_data_by_plz`)
Get altitude	`geopy.get_altitude_lat_long(lat, lon)`	—