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.
Methods
__init__(stationname, site, all_sensor_data)add_to_modeldata(new_modeltimeseries[, ...])Add a new ModelTimeSeries to the Station.
convert_outliers_to_gaps([all_observations, ...])Convert outlier values in the observation data to gaps.
fill_gaps_with_debiased_modeldata(target_obstype)Fill the gaps using model data corrected for the bias.
Fill the gaps using model data corrected for the diurnal bias.
fill_gaps_with_raw_modeldata(target_obstype)Fill the gap(s) using model data without correction.
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.
get_LCZ([overwrite, initialize_gee])Retrieve Local Climate Zone (LCZ) for the station using Google Earth Engine (GEE).
get_altitude([overwrite, initialize_gee])Retrieve altitude for the station using Google Earth Engine (GEE).
get_gee_timeseries_data(geedynamicdatasetmanager)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_qc_stats([target_obstype, make_plot])Generate quality control (QC) frequency statistics.
get_sensor(obstype)Get the SensorData instance for a specific observation type.
get_static_gee_buffer_fraction_data(...[, ...])Extract circular buffer fractions of a GEE dataset at Station locations.
get_static_gee_point_data(...[, overwrite, ...])Extract static data from GEE dataset at Station locations.
gross_value_check([target_obstype, ...])Identify outliers based on thresholds.
interpolate_gaps(target_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, linecolor, ...])Generate a time series plot of model data for a specific observation type.
persistence_check([target_obstype, ...])Check if values are not constant in a moving time window.
repetitions_check([target_obstype, ...])Test if an observation changes after a number of repetitions.
resample(target_freq[, target_obstype, ...])Resample observation data to a specified frequency.
step_check([target_obstype, ...])Check for 'spikes' and 'dips' in a time series.
window_variation_check([target_obstype, ...])Test if the increase/decrease in a time window exceeds a threshold.
Attributes
Construct a DataFrame representation of the observations.
end_datetimeGet the latest end datetime from the observation data.
Construct a DataFrame representation of all the gaps.
Construct a DataFrame representation of metadata.
modeldataRetrieve the model data associated with the station.
Construct a DataFrame representation of all the present model data.
nameThe name of the station.
Construct a DataFrame representation of all the outliers.
Get a list of all the present observation types.
The SensorData related to the station, as a dictionary.
siteThe Site instance of the station.
start_datetimeGet the earliest start datetime from the observation data.