DUBSTEP Activity on Conditional Skill Analysis
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Scripts for analysing skill in S2S dataset based on large scale modes of variability (ENSO and MJO)
Author: Amulya Chevuturi ([email protected])
Adapted from work of Matt Young and Nick Klingaman (NCAS, University of Reading, UK)
Reference: https://github.com/nick-klingaman/dubstep
Each folder (ENSO or MJO) contains scripts to calculate conditional analysis on the respective large-scale modes of variability
Script read_enso_index.py or read_mjo_index.py reads in the index from the txt file that can be downloaded online; oni.txt or rmm.txt respectivey.
date_str.py provides the date and time structure and grab_data.py reads the data in a particular format for the read_hindcast_analysis.py script. Script read_hindcast_analysis.py reads in all the data for analysis using the analyse_skill_extseason.py. analyse_skill_extseason.py writes out the composites based on phases of ENSO or MJO.
Bootstrapping for calculating significance is done using enso_boot.py (mjo_boot.py) script. Scripts enso_ano.py, enso_ccdiff.py and enso_ccreg.py (mjo_ano.py, mjo_ccdiff.py and mjo_ccreg.py) are plotting scripts.
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