wetterdienst - A simple example¶

pip install wetterdienst

Import modules necessary for general functioning¶

In [1]:

import warnings
warnings.filterwarnings("ignore")

from wetterdienst.dwd.observations import DWDObservationMetadata, DWDObservationSites, \
    DWDObservationData, DWDObservationPeriod, DWDObservationResolution, \
    DWDObservationParameter, DWDObservationParameterSet

%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 combinations")
print(
    DWDObservationMetadata().discover_parameters()
)
# selection
print("Selection of daily historical data")
print(
    DWDObservationMetadata(
        resolution=DWDObservationResolution.DAILY,
        period=DWDObservationPeriod.HISTORICAL
    ).discover_parameters()
)

All available combinations
{'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', '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 historical 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]:

sites_hdp = DWDObservationSites(
    parameter_set=DWDObservationParameterSet.PRECIPITATION_MORE,
    resolution=DWDObservationResolution.DAILY,
    period=DWDObservationPeriod.HISTORICAL
)
print("Number of stations with available data: ", sites_hdp.all().sum())
print("Some of the stations:")
sites_hdp.all().head()

Number of stations with available data:  STATION_ID                                                 19184225
STATION_HEIGHT                                          1.61503e+06
LAT                                                          285450
LON                                                         58636.5
STATION_NAME      AachAachen (Kläranlage)AachenAachen-BrandAalen...
STATE             Baden-WürttembergNordrhein-WestfalenNordrhein-...
dtype: object
Some of the stations:

Out[3]:

	STATION_ID	FROM_DATE	TO_DATE	STATION_HEIGHT	LAT	LON	STATION_NAME	STATE
0	1	1912-01-01	1986-06-30	478.0	47.8413	8.8493	Aach	Baden-Württemberg
1	2	1951-01-01	2006-12-31	138.0	50.8066	6.0996	Aachen (Kläranlage)	Nordrhein-Westfalen
2	3	1891-01-01	2011-03-31	202.0	50.7827	6.0941	Aachen	Nordrhein-Westfalen
3	4	1951-01-01	1979-10-31	243.0	50.7683	6.1207	Aachen-Brand	Nordrhein-Westfalen
4	6	1982-11-01	2020-11-30	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 = sites_hdp.all().STATION_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 = sites_hdp.all().plot.scatter(
    x="LON", y="LAT", c="STATION_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¶

Usually there are three steps to follow:

select indexed files based on
- its station_id
  - "1048" for Dresden, Germany
- its parameter
  - "kl" for climate
- its time_resolution
  - "daily" for daily data
- its period_type
  - "historical" for data up to the end of the last year
download the resulting list of files
parse it into pandas.DataFrames

We have summarized those steps into one:

collect_dwd_data

Let's try it out for the above selection:

In [5]:

print("Receiving historical daily climate data for Dresden-Klotzsche (1048)")
station_data = DWDObservationData(
    station_ids=[1048],
    parameters=[DWDObservationParameterSet.CLIMATE_SUMMARY],
    resolution=DWDObservationResolution.DAILY,
    periods=[DWDObservationPeriod.HISTORICAL],
    tidy_data=False
).collect_safe()

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

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

Out[5]:

	STATION_ID	DATE	QN_3	FX	FM	QN_4	RSK	RSKF	SDK	NM	VPM	PM	TMK	UPM	TXK	TNK	TGK
8826	1048	1973-01-01	5	15.0	6.0	5	0.0	0.0	7.0	0.4	3.4	1001.8	1.1	51.0	4.0	-1.1	-2.9
8827	1048	1973-01-02	5	12.0	2.8	5	0.0	0.0	6.5	0.0	3.7	1001.5	0.4	59.0	5.3	-0.8	-2.6
8830	1048	1973-01-05	5	6.0	1.6	5	0.4	1.0	0.0	8.0	5.4	1010.0	-1.0	94.0	0.1	-1.9	-2.2
8835	1048	1973-01-10	5	6.0	1.9	5	0.1	1.0	0.0	7.9	6.2	1002.4	0.7	97.0	1.5	0.4	0.3
8836	1048	1973-01-11	5	7.0	2.5	5	0.4	1.0	0.0	8.0	6.6	1002.4	1.6	96.0	2.1	0.1	0.0

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 may as well also use the "tidy_data" option to receive a nicer dataframe and "humanize_column_names" to get meaningful parameters:

In [6]:

print("Receiving historical daily climate data for Dresden-Klotzsche (1048), this time tidied.")
station_data = DWDObservationData(
    station_ids=[1048],
    parameters=[DWDObservationParameterSet.CLIMATE_SUMMARY],
    resolution=DWDObservationResolution.DAILY,
    periods=[DWDObservationPeriod.HISTORICAL],
    tidy_data=True,
    humanize_column_names=True
).collect_safe()

station_data.dropna(axis=0).groupby("ELEMENT").first()

Receiving historical daily climate data for Dresden-Klotzsche (1048), this time tidied.

Out[6]:

	STATION_ID	DATE	PARAMETER	VALUE	QUALITY
ELEMENT
WIND_SPEED	1048	1973-01-01	CLIMATE_SUMMARY	6.0	5
WIND_GUST_MAX	1048	1973-01-01	CLIMATE_SUMMARY	15.0	5
CLOUD_COVER_TOTAL	1048	1934-01-01	CLIMATE_SUMMARY	8.0	1
PRESSURE_AIR	1048	1934-01-01	CLIMATE_SUMMARY	1008.6	1
PRECIPITATION_HEIGHT	1048	1934-01-01	CLIMATE_SUMMARY	0.2	1
PRECIPITATION_FORM	1048	1934-01-01	CLIMATE_SUMMARY	8.0	1
SUNSHINE_DURATION	1048	1935-02-01	CLIMATE_SUMMARY	0.6	1
SNOW_DEPTH	1048	1934-01-01	CLIMATE_SUMMARY	0.0	1
TEMPERATURE_AIR_MIN_005	1048	1935-09-01	CLIMATE_SUMMARY	12.9	1
TEMPERATURE_AIR_200	1048	1934-01-01	CLIMATE_SUMMARY	0.5	1
TEMPERATURE_AIR_MIN_200	1048	1934-01-01	CLIMATE_SUMMARY	0.2	1
TEMPERATURE_AIR_MAX_200	1048	1934-01-01	CLIMATE_SUMMARY	0.7	1
HUMIDITY	1048	1934-01-01	CLIMATE_SUMMARY	97.0	1
PRESSURE_VAPOR	1048	1934-01-01	CLIMATE_SUMMARY	6.4	1

With the latest update we can now also request direct parameters from the given 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 [7]:

print("Receiving historical daily temperature and precipitation for Dresden-Klotzsche "
      "(1048).")
station_data = DWDObservationData(
    station_ids=[1048],
    parameters=[DWDObservationParameter.DAILY.TEMPERATURE_AIR_200,
                DWDObservationParameter.DAILY.TEMPERATURE_AIR_MAX_200,
                DWDObservationParameter.DAILY.TEMPERATURE_AIR_MIN_200,
                DWDObservationParameter.DAILY.PRECIPITATION_HEIGHT],
    resolution=DWDObservationResolution.DAILY,
    periods=[DWDObservationPeriod.HISTORICAL],
    humanize_column_names=False
).collect_safe()

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

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

Out[7]:

	STATION_ID	DATE	PARAMETER	ELEMENT	VALUE	QUALITY
233928	1048	1934-01-01	CLIMATE_SUMMARY	TMK	0.5	1
233929	1048	1934-01-02	CLIMATE_SUMMARY	TMK	-0.1	1
233930	1048	1934-01-03	CLIMATE_SUMMARY	TMK	-0.7	1
233931	1048	1934-01-04	CLIMATE_SUMMARY	TMK	-1.6	1
233932	1048	1934-01-05	CLIMATE_SUMMARY	TMK	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 [8]:

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

PARAMETERS = ["TNK", "TMK", "TXK", "RSK"]

station_data_grouped = station_data.groupby(station_data["ELEMENT"], 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 [9]:

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

for (year, parameter), group in station_data.groupby(
        [station_data["DATE"].dt.year, "ELEMENT"], 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[9]:

VALUE     10.645479
VALUE     14.985479
VALUE      5.540548
VALUE    675.600000
VALUE      9.053425
            ...    
VALUE    421.100000
VALUE     11.229041
VALUE     15.583836
VALUE      6.808493
VALUE    503.300000
Length: 288, dtype: float64

5. Find a station¶

We may want to find a station near to a certain area. Therefor simply call get_nearest_station

In [10]:

DWDObservationSites(
    parameter_set=DWDObservationParameterSet.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
)

Out[10]:

	STATION_ID	FROM_DATE	TO_DATE	STATION_HEIGHT	LAT	LON	STATION_NAME	STATE	DISTANCE_TO_LOCATION
0	1051	1936-01-01	2020-11-30	120.0	51.0248	13.7750	Dresden-Strehlen	Sachsen	5.005144
1	1048	1934-01-01	2020-11-30	227.0	51.1280	13.7543	Dresden-Klotzsche	Sachsen	8.756424
2	1050	1949-01-01	2020-11-30	114.0	51.0221	13.8470	Dresden-Hosterwitz	Sachsen	12.501498
3	991	1954-09-01	2020-11-30	359.0	50.9116	13.7087	Dippoldiswalde-Reinberg	Sachsen	15.834661
4	3234	1956-06-01	2020-11-30	158.0	51.1294	13.4328	Garsebach bei Meißen	Sachsen	35.076014