Published July 14, 2023 | Version v1
Software Open

Code for "The effect of a short observational record on the statistics of temperature extremes"

Authors/Creators

  • 1. ETH Zurich

Contributors

  • 1. Sebastian
  • 2. Olivier, C.
  • 3. Erich M.

Description

This is the (cleaned) git repository for the "The effect of a short observational record on the statistics of temperature extremes" paper by Zeder et al. (in review) published in Geophysical Research Letters. It is a close of the GitLab repository https://git.iac.ethz.ch/climphys/climate-extremes/retper-evaluation (publically accessible).

File structure:

  • Settings (mostly plotting) in "settings.R" file
  • GEV fitting and manipulation functions in "GEV_FUN.R"
  • Data preparation in folder "1_data_prep" (Most things are hardcoded and can only be reproduced on IAC servers)
  • GEV fitting procedures for large ensemble data in "2_GEVest_LE_fit"
  • GEV evaluation procedures for large ensemble data in "3_GEVest_LE_eval"
  • GEV fitting and evaluation procedures for synthetic data experiments in "4_GEVest_sim"
  • Joint analysis (GEV parameter CI coverage) in "5_Joint_Analysis"

Data: A set of pre-processed data is available on https://doi.org/10.3929/ethz-b-000619286. Aside from fevd objects (R lists), all the data is converted to .csv files for long-term readability (numerical values were rounded to four significant digits). The script "read_csv.R" can be used to read in the .csv file and assemble the data in an .RData file, which is later read by the individual scripts.

R Session: The R code was produced on a Linux machine and in an R environment.

Files

retper-evaluation-main.zip

Files (124.5 kB)

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md5:d3192773a4b3aa961a478ce6f2fd2b8d
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Additional details

Funding

Swiss National Science Foundation
Understanding and quantifying the occurrence of very rare climate extremes in a changing climate 200020_178778
European Commission
XAIDA - EXTREME EVENTS: ARTIFICIAL INTELLIGENCE FOR DETECTION AND ATTRIBUTION 101003469
Swiss National Science Foundation
Graph structures, sparsity and high-dimensional inference for extremes PCEGP2_186858