DISCLAIMER: This project is a private open source project and doesn't have any connection with Deutscher Wetterdienst.
If you like my work, I would be really happy if you buy me some coffee: Buy Me A Coffee
This is a python package for simple access to hourly forecast data for the next 10 days. The data is updated every six hours and updated when needed. Some stations also have actual reported weather, which you can also retrieve.
Available station-IDs can be found here or you can use the method dwdforecast.get_nearest_station_id(latitude, longitude) which tries to find it for you.
Forecasted weather conditions are evaluated using this table and then converted into these possible weather conditions:
- cloudy
- fog
- lightning-rainy
- partlycloudy
- pouring
- rainy
- snowy
- snowy-rainy
- sunny
The reported weather is delayed (roughly one hour), so have a close look at the time within the presented data.
The weather report for the region which is available on the DWD homepage (see an example here) can also be retrieved via a method which maps the station to the relevant region.
You can also retrieve weather maps from the DWD GeoServer with this package.
python3 -m pip install simple_dwd_weatherforecastfrom simple_dwd_weatherforecast import dwdforecast
from datetime import datetime, timedelta, timezone
#Find nearest Station-ID automatically
#id = dwdforecast.get_nearest_station_id(50.1109221, 8.6821267)
dwd_weather = dwdforecast.Weather("10385") # Station-ID For BERLIN-SCHOENEFELD
time_now = datetime.now(timezone.utc)
temperature_now = dwd_weather.get_forecast_data(dwdforecast.WeatherDataType.TEMPERATURE, time_now)
time_tomorrow = datetime.now(timezone.utc)+timedelta(days=1)
temperature_tomorrow = dwd_weather.get_forecast_data(dwdforecast.WeatherDataType.TEMPERATURE, time_tomorrow)All methods return their values as string. The datetime value has to be in UTC. If no data is available for this datetime, None will be returned. With the optional boolean shouldUpdate an automated check for new updates can be prevented by setting this parameter to False. Otherwise data is updated if new data is available with every function call.
There is also data available which is updated every hour by DWD. Be careful though, as this will download each time roughly 37MB of data. Furthermore, some elements are missing from this data:
- PRECIPITATION_PROBABILITY
- PRECIPITATION_DURATION
You can use this data by using the optional parameter force_hourly=True.
is_valid_station_id(station_id) #Checks if given station_id is valid
get_nearest_station_id(latitude, longitude) #Returns nearest Station-ID for the coordinates. latitude and longitude expect float values.
update(optional bool force_hourly) #Force fetch of new data from the DWD server. With this parameter set to True, there will be missing the precipitation forecast. See above.
class WeatherDataType(Enum):
CONDITION = "condition"
TEMPERATURE = "TTT" # Unit: K
DEWPOINT = "Td" # Unit: K
PRESSURE = "PPPP" # Unit: Pa
WIND_SPEED = "FF" # Unit: m/s
WIND_DIRECTION = "DD" # Unit: Degrees
WIND_GUSTS = "FX1" # Unit: m/s
PRECIPITATION = "RR1c" # Unit: kg/m2
PRECIPITATION_PROBABILITY = "wwP" # Unit: % (0..100)
PRECIPITATION_DURATION = "DRR1" # Unit: s
CLOUD_COVERAGE = "N" # Unit: % (0..100)
VISIBILITY = "VV" # Unit: m
SUN_DURATION = "SunD1" # Unit: s
SUN_IRRADIANCE = "Rad1h" # Unit: W/m2
FOG_PROBABILITY = "wwM" # Unit: % (0..100)
HUMIDITY = "humidity" # Unit: %
EVAPORATION = ("PEvap", "evaporation") # In the last 24h Unit: kg/m2
class Weather:
get_station_name(optional bool shouldUpdate) # Return Station name
get_forecast_data(weatherDataType: see WeatherDataType, datetime, optional bool shouldUpdate) # Returns the requested weather data
get_get_reported_weather(weatherDataType: see WeatherDataType, optional bool shouldUpdate) # Returns the latest actual reported value if available for this station
get_daily_max(weatherDataType: see WeatherDataType, datetime, optional bool shouldUpdate) # Returns the maximum daily value
get_timeframe_max(weatherDataType: see WeatherDataType, datetime, timeframe: hours after datetime as int, optional bool shouldUpdate) # Returns the maximum of that value within the time frame
get_daily_min(weatherDataType: see WeatherDataType, datetime, optional bool shouldUpdate) # Returns the minimum daily value
get_timeframe_min(weatherDataType: see WeatherDataType, datetime, timeframe: hours after datetime as int, optional bool shouldUpdate) # Returns the minimum of that value within the time frame
get_daily_sum(weatherDataType: see WeatherDataType, datetime, optional bool shouldUpdate) # Returns the daily sum of that value
get_timeframe_sum(weatherDataType: see WeatherDataType, datetime, timeframe: hours after datetime as int, optional bool shouldUpdate) # Returns the sum of that value within the time frame
get_daily_avg(weatherDataType: see WeatherDataType, datetime, optional bool shouldUpdate) # Returns the daily average of that value
get_timeframe_avg(weatherDataType: see WeatherDataType, datetime, timeframe: hours after datetime as int, optional bool shouldUpdate) # Returns the average of that value within the time frame
get_forecast_condition(datetime, optional bool shouldUpdate) # Result is condition as text
get_daily_condition(datetime, optional bool shouldUpdate) # Result is an approximate "feeled" condition at this day
get_timeframe_condition(datetime, timeframe: hours after datetime as int, optional bool shouldUpdate) # Result is an approximate "feeled" condition at this time frame
get_weather_report(optional bool shouldUpdate) # Returns the weather report for the geographical region of the station as HTML
get_uv_index(int day_from_today (values: 0-2)) # Returns the UV index for the nearest station available for today, tomorrow or the day after tomorrow
update(self, optional bool force_hourly (default: False), optional bool with_forecast (default: True), optional bool with_measurements (default: False), optional bool with_report (default: False), optional bool with_uv (default: True)) # Updates the weather dataIf you want to access the forecast data for the next 10 days directly for further processing, you can do so. All data is stored in dictonary and can be accessed like this:
from simple_dwd_weatherforecast import dwdforecast
dwd_weather​ ​=​ ​dwdforecast​.​Weather​(​"10385"​) # Station-ID For BERLIN-SCHOENEFELD​
dwd_weather.update() # This has to be done manually to fetch the data from the DWD server
access_forecast_dict = dwd_weather.forecast_data # dwd_weather.forecast_data contains the forecast as a dictKeep in mind that the weather condition is stored as the original digit value as provided by DWD. So if you want to use them, you have to convert these yourself. You can use my simplified conversion from the source code in the variable weather_codes or the original conversion available here.
You can download weather maps from the DWD GeoServer with this package. There are different options for the displayed foreground and background data. See below for further information.
from simple_dwd_weatherforecast import dwdmap
image = dwdmap.get_from_location(51.272, 8.84, radius_km=100, map_type=dwdmap.WeatherMapType.NIEDERSCHLAGSRADAR, background_type=dwdmap.WeatherBackgroundMapType.BUNDESLAENDER)
image.save("niederschlag.png")
image = dwdmap.get_germany(map_type=dwdmap.WeatherMapType.UVINDEX, image_width=520, image_height=580)
image.save("uvindex.png")class WeatherMapType(Enum):
NIEDERSCHLAGSRADAR = "dwd:Niederschlagsradar"
MAXTEMP = "dwd:GefuehlteTempMax"
UVINDEX = "dwd:UVI_CS"
POLLENFLUG = "dwd:Pollenflug"
SATELLITE_RGB = "dwd:Satellite_meteosat_1km_euat_rgb_day_hrv_and_night_ir108_3h"
SATELLITE_IR = "dwd:Satellite_worldmosaic_3km_world_ir108_3h"
WARNUNGEN_GEMEINDEN = "dwd:Warnungen_Gemeinden"
WARNUNGEN_KREISE = "dwd:Warnungen_Landkreise"
class WeatherBackgroundMapType(Enum):
LAENDER = "dwd:Laender"
BUNDESLAENDER = "dwd:Warngebiete_Bundeslaender"
KREISE = "dwd:Warngebiete_Kreise"
GEMEINDEN = "dwd:Warngebiete_Gemeinden"
SATELLIT = "dwd:bluemarble"
class MarkerShape(Enum):
CIRCLE = "circle"
SQUARE = "square"
CROSS = "cross"
class Marker(
latitude: float,
longitude: float,
shape: MarkerShape,
size: int,
colorRGB: tuple[int, int, int],
width: int = 0,
)
get_from_location(longitude, latitude, radius_km, map_type: WeatherMapType, background_type: WeatherBackgroundMapType, optional integer image_width, optional integer image_height, optional markers: list[Marker]) #Returns map as pillow image with given radius from coordinates
get_germany(map_type: WeatherMapType, optional WeatherBackgroundMapType background_type, optional integer image_width, optional integer image_height, optional markers: list[Marker]) #Returns map as pillow image of whole germany
get_map(minx,miny,maxx,maxy, map_type: WeatherMapType, background_type: WeatherBackgroundMapType, optional integer image_width, optional integer image_height, optional markers: list[Marker]) #Returns map as pillow imageThere is also the possibility to retrieve multiple images as a loop. This can be done by the class ImageLoop. This however only works for the precipitation radar.
from simple_dwd_weatherforecast import dwdmap
maploop = dwdmap.ImageLoop(
dwdmap.germany_boundaries.minx,
dwdmap.germany_boundaries.miny,
dwdmap.germany_boundaries.maxx,
dwdmap.germany_boundaries.maxy,
dwdmap.WeatherMapType.NIEDERSCHLAGSRADAR,
dwdmap.WeatherBackgroundMapType.BUNDESLAENDER,
steps=5,
)
for image in enumerate(maploop._images):
image[1].save(f"image{image[0]}.png")ImageLoop(minx: float, miny: float, maxx: float, maxy: float, map_type: WeatherMapType, background_type: WeatherBackgroundMapType,
steps: int = 6, image_width: int = 520,image_height: int = 580, markers: list[Marker] = []) -> ImageLoop
get_images() -> Iterable[ImageFile.ImageFile] # Returns the image loop
update() # Updates the loop to the most recent filesFeel free to open an issue if you find one and I will do my best to help you. If you want to contribute, your help is appreciated! If you want to add a new feature, add a pull request first so we can chat about the details.
This package uses public data from DWD OpenData. The Copyright can be viewed here.
