Do an empirical extremes trend detection study by fitting a time series to a Gaussian, Gumbel, GEV or GPD with the position parameter and/or scale parameter linearly dependent on a covariate and studying the return level and return time differences in the current climate and a previous climate. When no covariate is given, this tool produces extreme event statistics assuming a stationary climate. Please refer to the climexp documentation. for more details
input (netcdf) – Input time series to analyze. Daily or monthly time series on a single location.
ref_year (int) – Reference point of the analysis, year to compare the event probability and intensity with. For a non-stationary analysis, this will define the past block in the output.
evt_year (int) – Current event year. For a non-stationary analysis, this will define the present block in the output.
covariate (netcdf, optional) – Input covariate for non-stationary analyses. If none given, the analysis is assumed stationary.
distribution (str, optional) – Fitted distribution to the block maxima constructed from the input time series. Choices are gev (default), gumbel, gpd, gauss.
method (str, optional) – Assumption about the correlation of the extreme value distribution with the covariate. Choices are shift (default), scale, denoting a linear relation between between covariate and resp. location and scale.
month (int, optional) – Block maxima are based upon data in the season starting with this argument, default = 1.
num_months (int, optional) – Number of months defining the season over which the extremes are calculated, default= 12.
start_year (int, optional) – Starting year of the analysis. If none given, the start of the input time series.
end_year (int, optional) – Ending year of the analysis. If none given, the end of the input time series.
return_time (int, optional) – Restrict analysis to input return time. If none given, the event value determines the return time.
restrain_shape (real, optional) – Width of input gaussian added to the cost function to restrain the shape parameter during the extreme values fit. If none given, no restraining term is added.
confidence_level (int, optional) – Target confidence level for the bootstrapping procedure (in percent), default = 95.
block_year (int, optional) – Number of years that define the block maxima (for GEV and Gumbel dist). default = 1.
block_ens (int, optional) – Number of ensemble members that define the block maxima (for GEV and Gumbel dist). default = 1.
detrend (int, optional) – If positive, perform de-trending on both input series and covariate before analysis. default = 0.
anomalies (int, optional) – If positive, perform analysis on anomalies of input series and covariate. default = 0.
flip_sign (int, optional) – If positive, flip sign of input series to study minima. default = 0.
include_evt (int, optional) – If positive, include the studied event in the extreme value analysis. default = 0.
Dictionary containing fit parameters, probability ratio, intensity changes and return times.