metobs_toolkit.station.Station#

class Station(stationname: str, site: Site, all_sensor_data: list)[source]#

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

Parameters:

stationname (str) – Name of the station.
site (Site) – Site instance containing metadata and location.
all_sensor_data (list) – List of SensorData instances for the station.

__init__(stationname: str, site: Site, all_sensor_data: list)[source]#

Initialize a Station with its name, site metadata and sensor data.

Parameters:

stationname (str) – Unique name of the station.
site (Site) – Site instance carrying spatial metadata for this station.
all_sensor_data (list of SensorData) – List of SensorData instances (one per observed variable).

Methods

`__init__`(stationname, site, all_sensor_data)	Initialize a Station with its name, site metadata and sensor data.
`add_to_modeldata`(new_modeltimeseries[, ...])	Add a new ModelTimeSeries to the Station.
`add_to_sensordata`(new_sensordata[, force_update])	Add a new SensorData to the Station.
`convert_outliers_to_gaps`([all_observations, ...])	Convert outlier values in the observation data to gaps.
`copy`([deep])	Return a copy of the Station.
`fill_gaps_with_debiased_modeldata`(obstype[, ...])	Fill the gaps using model data corrected for the bias.
`fill_gaps_with_diurnal_debiased_modeldata`(obstype)	Fill the gaps using model data corrected for the diurnal bias.
`fill_gaps_with_raw_modeldata`(obstype[, ...])	Fill the gap(s) using model data without correction.
`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.
`gap_overview_df`()	Create gap status overview DataFrame with one row per gap period.
`get_LCZ`([update_metadata, initialize_gee, ...])	Retrieve Local Climate Zone (LCZ) for the station using Google Earth Engine (GEE).
`get_altitude`([update_metadata, initialize_gee])	Retrieve altitude for the station using Google Earth Engine (GEE).
`get_gee_timeseries_data`(gee_manager[, ...])	Extract time series data from GEE.
`get_info`([printout])	Retrieve and optionally print detailed information about the station.
`get_landcover_fractions`([buffers, ...])	Get landcover fractions for a circular buffer at the station using GEE.
`get_modeltimeseries`(obstype[, modelname, ...])	Get the ModelTimeSeries instance for a specific observation type.
`get_qc_stats`([obstype, make_plot])	Summarize QC label frequencies for one station and optionally plot pies.
`get_sensor`(obstype)	Get the SensorData instance for a specific observation type.
`get_static_gee_buffer_fraction_data`(gee_manager)	Extract circular buffer fractions of a GEE dataset at Station locations.
`get_static_gee_point_data`(gee_manager[, ...])	Extract static data from GEE dataset at Station locations.
`gross_value_check`([obstype, ...])	Identify outliers based on thresholds.
`interpolate_gaps`(obstype[, method, ...])	Fill the gap(s) using interpolation of SensorData.
`make_plot`([obstype, colorby, ...])	Generate a time series plot for observational data.
`make_plot_of_modeldata`([obstype, modelname, ...])	Generate a time series plot of model data for a specific observation type.
`persistence_check`([obstype, timewindow, ...])	Check if values are not constant in a moving time window.
`qc_overview_df`([subset_obstypes])	Build a QC overview DataFrame for all sensors of a Station.
`repetitions_check`([obstype, ...])	Test if an observation changes after a number of repetitions.
`resample`(target_freq[, obstype, ...])	Resample observation data to a specified frequency.
`step_check`([obstype, ...])	Check for 'spikes' and 'dips' in a time series.
`to_csv`([filepath, overwrite])	Save the station observations to a CSV file.
`to_netcdf`([filepath, overwrite])	Save the Station as a netCDF file.
`to_parquet`([filepath, overwrite])	Save the station observations to a parquet file.
`to_xr`()	Merge all sensor and model data of a station into a single Dataset.
`window_variation_check`([obstype, ...])	Test if the increase/decrease in a time window exceeds a threshold.

Attributes

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