wetterdienst - A basic notebook example¶

pip install wetterdienst

Import modules necessary for general functioning¶

In [1]:

import warnings
warnings.filterwarnings("ignore")

from wetterdienst.provider.dwd.observation import DwdObservationRequest, \
    DwdObservationPeriod, DwdObservationResolution, DwdObservationParameter, DwdObservationDataset

%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import cm

Which parameters are available?

In [2]:

# all
print("All available parameters")
print(
    DwdObservationRequest.discover()
)
# selection
print("Selection of daily data")
print(
    DwdObservationRequest.discover(
        filter_=DwdObservationResolution.DAILY
    )
)

All available parameters
{
    "MINUTE_1": [
        "PRECIPITATION_HEIGHT",
        "PRECIPITATION_HEIGHT_DROPLET",
        "PRECIPITATION_HEIGHT_ROCKER",
        "PRECIPITATION_FORM"
    ],
    "MINUTE_10": [
        "PRESSURE_AIR_STATION_HEIGHT",
        "TEMPERATURE_AIR_200",
        "TEMPERATURE_AIR_005",
        "HUMIDITY",
        "TEMPERATURE_DEW_POINT_200",
        "TEMPERATURE_AIR_MAX_200",
        "TEMPERATURE_AIR_MAX_005",
        "TEMPERATURE_AIR_MIN_200",
        "TEMPERATURE_AIR_MIN_005",
        "WIND_GUST_MAX",
        "WIND_SPEED_MIN",
        "WIND_SPEED_ROLLING_MEAN_MAX",
        "WIND_DIRECTION_MAX_VELOCITY",
        "PRECIPITATION_DURATION",
        "PRECIPITATION_HEIGHT",
        "PRECIPITATION_INDICATOR_WR",
        "RADIATION_SKY_DIFFUSE",
        "RADIATION_GLOBAL",
        "SUNSHINE_DURATION",
        "RADIATION_SKY_LONG_WAVE",
        "WIND_SPEED",
        "WIND_DIRECTION"
    ],
    "HOURLY": [
        "TEMPERATURE_AIR_200",
        "HUMIDITY",
        "CLOUD_COVER_TOTAL",
        "CLOUD_COVER_TOTAL_INDICATOR",
        "CLOUD_TYPE_LAYER1",
        "CLOUD_TYPE_LAYER1_ABBREVIATION",
        "CLOUD_HEIGHT_LAYER1",
        "CLOUD_COVER_LAYER1",
        "CLOUD_TYPE_LAYER2",
        "CLOUD_TYPE_LAYER2_ABBREVIATION",
        "CLOUD_HEIGHT_LAYER2",
        "CLOUD_COVER_LAYER2",
        "CLOUD_TYPE_LAYER3",
        "CLOUD_TYPE_LAYER3_ABBREVIATION",
        "CLOUD_HEIGHT_LAYER3",
        "CLOUD_COVER_LAYER3",
        "CLOUD_TYPE_LAYER4",
        "CLOUD_TYPE_LAYER4_ABBREVIATION",
        "CLOUD_HEIGHT_LAYER4",
        "CLOUD_COVER_LAYER4",
        "TEMPERATURE_DEW_POINT_200",
        "PRECIPITATION_HEIGHT",
        "PRECIPITATION_INDICATOR",
        "PRECIPITATION_FORM",
        "PRESSURE_AIR_SEA_LEVEL",
        "PRESSURE_AIR_STATION_HEIGHT",
        "TEMPERATURE_SOIL_002",
        "TEMPERATURE_SOIL_005",
        "TEMPERATURE_SOIL_010",
        "TEMPERATURE_SOIL_020",
        "TEMPERATURE_SOIL_050",
        "TEMPERATURE_SOIL_100",
        "END_OF_INTERVAL",
        "RADIATION_SKY_LONG_WAVE",
        "RADIATION_SKY_SHORT_WAVE_DIFFUSE",
        "RADIATION_GLOBAL",
        "SUNSHINE_DURATION",
        "SUN_ZENITH",
        "TRUE_LOCAL_TIME",
        "VISIBILITY_INDICATOR",
        "VISIBILITY",
        "WIND_SPEED",
        "WIND_DIRECTION"
    ],
    "SUBDAILY": [
        "TEMPERATURE_AIR_200",
        "HUMIDITY",
        "CLOUD_COVER_TOTAL",
        "CLOUD_DENSITY",
        "PRESSURE_VAPOR",
        "TEMPERATURE_AIR_005",
        "PRESSURE_AIR",
        "TEMPERATURE_SOIL_005",
        "VISIBILITY",
        "WIND_DIRECTION",
        "WIND_FORCE_BEAUFORT"
    ],
    "DAILY": [
        "WIND_GUST_MAX",
        "WIND_SPEED",
        "PRECIPITATION_HEIGHT",
        "PRECIPITATION_FORM",
        "SUNSHINE_DURATION",
        "SNOW_DEPTH",
        "CLOUD_COVER_TOTAL",
        "PRESSURE_VAPOR",
        "PRESSURE_AIR",
        "TEMPERATURE_AIR_200",
        "HUMIDITY",
        "TEMPERATURE_AIR_MAX_200",
        "TEMPERATURE_AIR_MIN_200",
        "TEMPERATURE_AIR_MIN_005",
        "SNOW_DEPTH_NEW",
        "TEMPERATURE_SOIL_002",
        "TEMPERATURE_SOIL_005",
        "TEMPERATURE_SOIL_010",
        "TEMPERATURE_SOIL_020",
        "TEMPERATURE_SOIL_050",
        "RADIATION_SKY_LONG_WAVE",
        "RADIATION_SKY_SHORT_WAVE_DIFFUSE",
        "RADIATION_SKY_SHORT_WAVE_DIRECT",
        "SNOW_DEPTH_EXCELLED",
        "WATER_EQUIVALENT_TOTAL_SNOW_DEPTH",
        "WATER_EQUIVALENT_SNOW",
        "FOG",
        "THUNDER",
        "STORM_STRONG_WIND",
        "STORM_STORMIER_WIND",
        "DEW",
        "GLAZE",
        "RIPE",
        "SLEET",
        "HAIL"
    ],
    "MONTHLY": [
        "CLOUD_COVER_TOTAL",
        "TEMPERATURE_AIR_200",
        "TEMPERATURE_AIR_MAX_MEAN_200",
        "TEMPERATURE_AIR_MIN_MEAN_200",
        "WIND_FORCE_BEAUFORT",
        "TEMPERATURE_AIR_MAX_200",
        "WIND_GUST_MAX",
        "TEMPERATURE_AIR_MIN_200",
        "SUNSHINE_DURATION",
        "PRECIPITATION_HEIGHT",
        "PRECIPITATION_HEIGHT_MAX",
        "SNOW_DEPTH_NEW",
        "SNOW_DEPTH",
        "STORM_STRONG_WIND",
        "STORM_STORMIER_WIND",
        "THUNDER",
        "GLAZE",
        "SLEET",
        "HAIL",
        "FOG",
        "DEW"
    ],
    "ANNUAL": [
        "CLOUD_COVER_TOTAL",
        "TEMPERATURE_AIR_200",
        "TEMPERATURE_AIR_MAX_MEAN_200",
        "TEMPERATURE_AIR_MIN_MEAN_200",
        "WIND_FORCE_BEAUFORT",
        "SUNSHINE_DURATION",
        "WIND_GUST_MAX",
        "TEMPERATURE_AIR_MAX_200",
        "TEMPERATURE_AIR_MIN_200",
        "PRECIPITATION_HEIGHT",
        "PRECIPITATION_HEIGHT_MAX",
        "SNOW_DEPTH_NEW",
        "SNOW_DEPTH",
        "STORM_STRONG_WIND",
        "STORM_STORMIER_WIND",
        "THUNDER",
        "GLAZE",
        "SLEET",
        "HAIL",
        "FOG",
        "DEW"
    ]
}
Selection of daily data
{
    "DAILY": [
        "WIND_GUST_MAX",
        "WIND_SPEED",
        "PRECIPITATION_HEIGHT",
        "PRECIPITATION_FORM",
        "SUNSHINE_DURATION",
        "SNOW_DEPTH",
        "CLOUD_COVER_TOTAL",
        "PRESSURE_VAPOR",
        "PRESSURE_AIR",
        "TEMPERATURE_AIR_200",
        "HUMIDITY",
        "TEMPERATURE_AIR_MAX_200",
        "TEMPERATURE_AIR_MIN_200",
        "TEMPERATURE_AIR_MIN_005",
        "SNOW_DEPTH_NEW",
        "TEMPERATURE_SOIL_002",
        "TEMPERATURE_SOIL_005",
        "TEMPERATURE_SOIL_010",
        "TEMPERATURE_SOIL_020",
        "TEMPERATURE_SOIL_050",
        "RADIATION_SKY_LONG_WAVE",
        "RADIATION_SKY_SHORT_WAVE_DIFFUSE",
        "RADIATION_SKY_SHORT_WAVE_DIRECT",
        "SNOW_DEPTH_EXCELLED",
        "WATER_EQUIVALENT_TOTAL_SNOW_DEPTH",
        "WATER_EQUIVALENT_SNOW",
        "FOG",
        "THUNDER",
        "STORM_STRONG_WIND",
        "STORM_STORMIER_WIND",
        "DEW",
        "GLAZE",
        "RIPE",
        "SLEET",
        "HAIL"
    ]
}

1. First check the metadata to inform yourself of available stations¶

(here we pick historical daily precipitation - hdp)

In [3]:

request = DwdObservationRequest(
    parameter=DwdObservationDataset.PRECIPITATION_MORE,
    resolution=DwdObservationResolution.DAILY,
    period=DwdObservationPeriod.HISTORICAL
)
print("Number of stations with available data: ", request.all().df.sum())
print("Some of the stations:")
request.all().df.head()

Number of stations with available data:  station_id      0000100002000030000400006000070000800009000100...
height                                                  1614425.0
latitude                                              285344.8849
longitude                                              58614.3066
station_name    AachAachen (Kläranlage)AachenAachen-BrandAalen...
state           Baden-WürttembergNordrhein-WestfalenNordrhein-...
dtype: object
Some of the stations:

Out[3]:

	station_id	from_date	to_date	height	latitude	longitude	station_name	state
0	00001	1912-01-01 00:00:00+00:00	1986-06-30 00:00:00+00:00	478.0	47.8413	8.8493	Aach	Baden-Württemberg
1	00002	1951-01-01 00:00:00+00:00	2006-12-31 00:00:00+00:00	138.0	50.8066	6.0996	Aachen (Kläranlage)	Nordrhein-Westfalen
2	00003	1891-01-01 00:00:00+00:00	2011-03-31 00:00:00+00:00	202.0	50.7827	6.0941	Aachen	Nordrhein-Westfalen
3	00004	1951-01-01 00:00:00+00:00	1979-10-31 00:00:00+00:00	243.0	50.7683	6.1207	Aachen-Brand	Nordrhein-Westfalen
4	00006	1982-11-01 00:00:00+00:00	2021-03-26 00:00:00+00:00	455.0	48.8361	10.0598	Aalen-Unterrombach	Baden-Württemberg

The metadata includes an id, the range of the measurements, the position (including height) as well as place and state of it and if it has a file. With the following plot we want to show a map of those stations:

In [4]:

cmap = cm.get_cmap('viridis')
bounds = request.all().df.height.quantile([0, 0.25, 0.5, 0.75, 1]).values
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)

fig, ax = plt.subplots(figsize=(10, 10))
plot = request.all().df.plot.scatter(
    x="longitude", y="latitude", c="height", cmap=cmap, norm=norm, ax=ax)
plot.set_title("Map of daily precipitation stations in Germany\n"
               "Color refers to height of station")
plt.show()

2. The usual way of retrieving data¶

Let's get some data for the above request

In [5]:

print("Receiving historical daily climate data for Dresden-Klotzsche (1048)")
station_data = request.filter(station_id=[1048]).values.all().df

station_data.dropna(axis=0).head()

100%|██████████| 1/1 [00:00<00:00,  1.41it/s]

Receiving historical daily climate data for Dresden-Klotzsche (1048)

Out[5]:

	station_id	date	dataset	parameter	quality
0	01048	1926-04-25 00:00:00+00:00	precipitation_more	rs	1
1	01048	1926-04-26 00:00:00+00:00	precipitation_more	rs	1
2	01048	1926-04-27 00:00:00+00:00	precipitation_more	rs	1
3	01048	1926-04-28 00:00:00+00:00	precipitation_more	rs	1
4	01048	1926-04-29 00:00:00+00:00	precipitation_more	rs	1

See that DATE is already parsed, so we can easily get some nice graphs with matplotlib, which we will do in the next part. We can also request direct parameters from the given parameter sets. We know that CLIMATE_SUMMARY contains TMK, TNK, TXK and RSK, so let's request those. This option will automatically tidy the data.

In [6]:

print("Receiving historical daily temperature and precipitation for Dresden-Klotzsche "
      "(1048).")

request = DwdObservationRequest(
    parameter=[
        DwdObservationParameter.DAILY.TEMPERATURE_AIR_200,
        DwdObservationParameter.DAILY.TEMPERATURE_AIR_MAX_200,
        DwdObservationParameter.DAILY.TEMPERATURE_AIR_MIN_200,
        DwdObservationParameter.DAILY.PRECIPITATION_HEIGHT
    ],
    resolution=DwdObservationResolution.DAILY,
    period=DwdObservationPeriod.HISTORICAL
).filter(station_id=(1048, ))

station_data = request.values.all().df

station_data.dropna(axis=0).head()

Receiving historical daily temperature and precipitation for Dresden-Klotzsche (1048).

100%|██████████| 1/1 [00:02<00:00,  2.62s/it]

Out[6]:

	station_id	date	dataset	parameter	value	quality
0	01048	1934-01-01 00:00:00+00:00	climate_summary	temperature_air_200	0.5	1
1	01048	1934-01-02 00:00:00+00:00	climate_summary	temperature_air_200	-0.1	1
2	01048	1934-01-03 00:00:00+00:00	climate_summary	temperature_air_200	-0.7	1
3	01048	1934-01-04 00:00:00+00:00	climate_summary	temperature_air_200	-1.6	1
4	01048	1934-01-05 00:00:00+00:00	climate_summary	temperature_air_200	0.9	1

3. Let's create some plots¶

Now that we have data, let's create some plots! We can create a time series/histogram of the temperatures and precipitation

In [7]:

cmap = plt.get_cmap('viridis', 4)
colors = cmap.colors

PARAMETERS = ["tnk", "tmk", "txk", "rsk"]

station_data_grouped = station_data.groupby(station_data["parameter"], observed=True)

fig, axes = plt.subplots(nrows=len(PARAMETERS), tight_layout=True, figsize=(10, 40))

for (parameter, group), ax, color in zip(station_data_grouped, axes, colors):
    group.plot(x="date", y="value", label=parameter, alpha=.75, ax=ax, c=color)

plt.tight_layout()
plt.subplots_adjust(top=0.9)
plt.suptitle("Temperature and precipitation time series of Dresden, Germany")

plt.show()

4. Create yearly values¶

In [8]:

import numpy as np
import pandas as pd
station_data_yearly = []

for (year, parameter), group in station_data.groupby(
        [station_data["date"].dt.year, "parameter"], as_index=False, observed=True):
    if parameter == "rsk":
        station_data_yearly.append(group.dropna().agg({"value": np.sum}))
    else:
        station_data_yearly.append(group.dropna().agg({"value": np.mean}))

station_data_yearly = pd.concat(station_data_yearly)

station_data_yearly

Out[8]:

value     2.705976
value     2.040000
value     1.729235
value     1.808493
value     2.591233
           ...    
value     1.153699
value    11.229041
value    15.583836
value     6.808493
value     1.378904
Length: 296, dtype: float64

5. Find a station¶

To find a station near to a certain area, call nearby_number().

In [9]:

DwdObservationRequest(
    parameter=DwdObservationDataset.CLIMATE_SUMMARY,
    resolution=DwdObservationResolution.DAILY,
    period=DwdObservationPeriod.HISTORICAL,
    start_date="2000-01-01",
    end_date="2010-01-01"
).nearby_number(
    51.05089,
    13.73832,
    5
).df

start_date and end_date filtering limited to defined periods [<Period.HISTORICAL: 'historical'>]

Out[9]:

	station_id	from_date	to_date	height	latitude	longitude	station_name	state	distance_to_location
0	01047	1828-01-01 00:00:00+00:00	1915-12-31 00:00:00+00:00	112.0	51.0557	13.7274	Dresden (Mitte)	Sachsen	1.326824
1	01051	1936-01-01 00:00:00+00:00	2021-03-26 00:00:00+00:00	120.0	51.0248	13.7750	Dresden-Strehlen	Sachsen	5.005144
2	01048	1934-01-01 00:00:00+00:00	2021-03-26 00:00:00+00:00	227.0	51.1280	13.7543	Dresden-Klotzsche	Sachsen	8.756424
3	05282	1917-01-01 00:00:00+00:00	1974-06-30 00:00:00+00:00	246.0	51.1197	13.6744	Wahnsdorf bei Dresden	Sachsen	10.443200
4	01050	1949-01-01 00:00:00+00:00	2021-03-26 00:00:00+00:00	112.0	51.0221	13.8470	Dresden-Hosterwitz	Sachsen	12.501498