Station

The Station holds all the data related to a single station. This includes:

• observational data, stored as SensorData,

• metadata, stored as a Site,

• and timeseries of a model source, stored as ModelTimeSeries.

Constructor

Station(stationname, site, all_sensor_data)

Represents a weather station, holding metadata, sensor data, and model data.

Data attributes

A summary of all the attributes that hold or return data.

Station.name	The name of the station.
Station.site	The Site instance of the station.
Station.sensordata	The SensorData related to the station, as a dictionary.
Station.df	Station DataFrame constructor.
Station.metadf	Construct a DataFrame representation of metadata.
Station.outliersdf	Construct a DataFrame representation of all the outliers.
Station.gapsdf	Construct a DataFrame representation of all the gaps.
Station.modeldatadf	Construct a DataFrame representation of all the present model data.
Station.present_observations	Get a list of all the present observation types.
Station.start_datetime	Get the earliest start datetime from the observation data.
Station.end_datetime	Get the latest end datetime from the observation data.
Station.modeldata	Retrieve the model data associated with the station.

General methods and attributes

Station.get_sensor(obstype)	Get the SensorData instance for a specific observation type.
Station.add_to_sensordata(new_sensordata[, ...])	Add a new SensorData to the Station.
Station.get_modeltimeseries(obstype[, ...])	Get the ModelTimeSeries instance for a specific observation type.
Station.add_to_modeldata(new_modeltimeseries)	Add a new ModelTimeSeries to the Station.
Station.get_info([printout])	Retrieve and optionally print detailed information about the station.
Station.resample(target_freq[, obstype, ...])	Resample observation data to a specified frequency.

Exporting and converting methods

Station.to_csv([filepath, overwrite])	Save the station observations to a CSV file.
Station.to_parquet([filepath, overwrite])	Save the station observations to a parquet file.
Station.to_xr()	Merge all sensor and model data of a station into a single Dataset.
Station.to_netcdf([filepath, overwrite])	Save the Station as a netCDF file.

GEE related methods

Station.modeldatadf	Construct a DataFrame representation of all the present model data.
Station.get_static_gee_point_data(gee_manager)	Extract static data from GEE dataset at Station locations.
Station.get_static_gee_buffer_fraction_data(...)	Extract circular buffer fractions of a GEE dataset at Station locations.
Station.get_LCZ([update_metadata, ...])	Retrieve Local Climate Zone (LCZ) for the station using Google Earth Engine (GEE).
Station.get_altitude([update_metadata, ...])	Retrieve altitude for the station using Google Earth Engine (GEE).
Station.get_landcover_fractions([buffers, ...])	Get landcover fractions for a circular buffer at the station using GEE.
Station.get_gee_timeseries_data(gee_manager)	Extract time series data from GEE.

Gaps related methods

Station.gapsdf	Construct a DataFrame representation of all the gaps.
Station.gap_overview_df()	Create gap status overview DataFrame with one row per gap period.
Station.convert_outliers_to_gaps([...])	Convert outlier values in the observation data to gaps.
Station.interpolate_gaps(obstype[, method, ...])	Fill the gap(s) using interpolation of SensorData.
Station.fill_gaps_with_raw_modeldata(obstype)	Fill the gap(s) using model data without correction.
Station.fill_gaps_with_debiased_modeldata(obstype)	Fill the gaps using model data corrected for the bias.
Station.fill_gaps_with_diurnal_debiased_modeldata(obstype)	Fill the gaps using model data corrected for the diurnal bias.
Station.fill_gaps_with_weighted_diurnal_debiased_modeldata(obstype)	Fill the gaps using a weighted sum of model data corrected for the diurnal bias and weights with respect to the start of the gap.

Quality control related methods

Station.outliersdf	Construct a DataFrame representation of all the outliers.
Station.gross_value_check([obstype, ...])	Identify outliers based on thresholds.
Station.persistence_check([obstype, ...])	Check if values are not constant in a moving time window.
Station.repetitions_check([obstype, ...])	Test if an observation changes after a number of repetitions.
Station.step_check([obstype, ...])	Check for 'spikes' and 'dips' in a time series.
Station.window_variation_check([obstype, ...])	Test if the increase/decrease in a time window exceeds a threshold.
Station.get_qc_stats([obstype, make_plot])	Summarize QC label frequencies for one station and optionally plot pies.
Station.convert_outliers_to_gaps([...])	Convert outlier values in the observation data to gaps.

Visualisations

Station.make_plot_of_modeldata([obstype, ...])	Generate a time series plot of model data for a specific observation type.
Station.make_plot([obstype, colorby, ...])	Generate a time series plot for observational data.

Special methods

The Station class implements several Python special methods for convenience:

• __add__: Combine two Station objects, merging sensordata, site and modeltimeseries.

• __eq__: Test equality between two Station objects.

• __str__ and __repr__: String representations for printing and debugging.

• copy: Create a (deep) copy of the Station.

Example usage:

from metobs_toolkit.dataset import Dataset

# Assume sta1 and sta2 are Station instances
sta_extend = sta1 + sta2  # Merge Stations

# Equality check
if sta1 == sta2:
    print("Stations are equal")

# Copying
copy_of_1 = sta1.copy(deep=True)

# String representation
print(str(sta1))