Input/Output¶

Reading the raw data¶

ninolearn.IO.read_raw.hca_mon()[source]¶: heat content anomaly, seasonal variable to the first day of the middle season and upsample the data

ninolearn.IO.read_raw.iod()[source]¶: get IOD index data

ninolearn.IO.read_raw.nino34_anom()[source]¶: Get the Nino3.4 Index anomaly.

ninolearn.IO.read_raw.nino_anom(index='3.4', period='S', detrend=False)[source]¶: read various Nino indeces from the raw directory

ninolearn.IO.read_raw.olr()[source]¶: get v-wind from NCEP/NCAR reanalysis

ninolearn.IO.read_raw.sat(mean='monthly')[source]¶

Get the surface air temperature from NCEP/NCAR Reanalysis

Parameters: mean – Choose between daily and monthly mean fields

ninolearn.IO.read_raw.ssh()[source]¶: Get sea surface height. And change some attirbutes and coordinate names

ninolearn.IO.read_raw.sst_ERSSTv5()[source]¶: get the sea surface temperature from the ERSST-v5 data set

ninolearn.IO.read_raw.sst_HadISST()[source]¶: get the sea surface temperature from the ERSST-v5 data set and directly manipulate the time axis in such a way that the monthly mean values are assigned to the beginning of a month as this is the default for the other data sets

ninolearn.IO.read_raw.ustr()[source]¶: get u-wind stress from ICOADS 1-degree Enhanced

ninolearn.IO.read_raw.uwind()[source]¶: get u-wind from NCEP/NCAR reanalysis

ninolearn.IO.read_raw.vwind()[source]¶: get v-wind from NCEP/NCAR reanalysis

ninolearn.IO.read_raw.wwv_anom(cardinal_direction='')[source]¶: get the warm water volume anomaly

Reading the processed data¶

class ninolearn.IO.read_processed.data_reader(startdate='1980-01', enddate='2018-12', lon_min=120, lon_max=280, lat_min=-30, lat_max=30)[source]¶

read_csv(variable, processed='anom')[source]¶: get data from processed csv

read_forecasts(model_name, lead)[source]¶: Read forecasts from a NinoLearn model.

read_netcdf(variable, dataset='', processed='', chunks=None)[source]¶

wrapper for xarray.open_dataarray.

Parameters

variable – the name of the variable
dataset – the name of the dataset
processed – the postprocessing that was applied
chunks – same as for xarray.open_dataarray

read_other_forecasts(model, lead)[source]¶

Read forecasts from other models.

Parameters: model (str) – Model name.