updated_processings_v20231204 #77
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…, enso pattern and enso life cycle
…d era (1901-2010) and mix (any period available for obs and model)
…e arrays are averaged (lat or lon) afterwards new threshold to mask land or ocean
Compute: N3 PR and N3.4 SST mean, skewness, variance Goal: find out if there is a general relationship between epoch length and ensemble variance
…tats' I'm afraid that N3.4 SST skewness may swing between positive and negative in some model so the noise-to-signal ratio (used for the accuracy on the ensemble-mean) may not be a good measure
collections created: 'ENSO_eq_stat2' and 'ENSO_eq_stat3' EnsoMetricsLib.py metrics created: 'dcorr' (same as nstar but here the decorrelation time is saved), 'enso_wait_time' (number of months between ENSO events) metrics modified: 'nstar' and 'stat_box' (correction of supplementary diagnostics saved in netCDF, ability to compute of gradient) EnsoUvcdatToolsLib.py function created: 'enso_time_interval' used by metric 'enso_wait_time' to count the number of months between ENSO events function modified: 'DetectEvents' so that it is similar as 'enso_time_interval'
for the keyword 'statistic', the value 'time mean' has been replaced by 'average'
EnsoMetricsLib.py
keyword 'event_definition' has been replaced by 'enso_definition'
metric enso_wait_time:
1) a statistic (string) needs to be defined in the metrics collection to define the metric ('average', 'skewness', 'standard deviation', 'variance')
2) 'wait_definition' (dictionary) must be defined in the metrics collection to define how the time interval between events are computed ('detect': intervals between witch enso events, 'method': just interval between event years or from peak to peak, 'smoothing': smoothing method/window to detect peaks, if applicable)
KeyArgLib.py
keywords 'event_definition' and 'wait_definition' have now a default value
EnsoUvcdatToolsLib.py
function enso_time_interval:
user now defines intervals between witch enso events are computed ('detect') and the smoothing method/window to detect peaks, if applicable ('smoothing')
function ReadLandmaskSelectRegion:
1) simplified (loop on both variable file and landmask file, loop on possible variables)
2) calls EstimateLandmask with a global grid if needed
EnsoToolsLib.py
function simple_stats created, returns mean (numpy), skewness (scipy), standard deviation (numpy) and variance (numpy) of given array along given axis
metric enso_wait_time:
1) enso_method corrected (the string was replaced instead of added)
2) metric name & method, 'ENSO' replaced by given keyword 'detect'
3) keyword 'compute_anom' added when calling the function enso_time_interval
EnsoUvcdatToolsLib.py
function enso_time_interval:
1) keyword 'compute_anom' is added (as in DetectEvents) to detect the peak using interannual anomalies
2) description corrected (the string was replaced instead of added)
metrics collection ENSO_eq_stats2:
1) stat_box_ave_pr_global: the quantification of the global mean precipitation is added to this collection
EnsoMetricsLib.py
metrics dcorr, enso_wait_time, nstar, stat_box:
1) minor correction of the value given to 'method_var' (variable describing the computation done for the metric)
create file 'enso_xarray_lib.py' with first functions to convert from cdat to xcdat (read, add, multiply, mask,...)
trying to figure out how to loop on them after reading files
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