back
Dataset
Open Access
Creative Commons Attribution Share Alike 4.0 International License
Data for "Can artificial intelligence-based weather prediction models simulate the butterfly effect?"
Tobias Selz1 and George Craig2
1German Aerospace Center
2Ludwig-Maximilians-Universität München
First published:
Sept. 18, 2023
DOI: 10.57970/e61hw-rrz34
Keywords:
Butterfly effect
Error growth
Ensemble prediction
Artificial intelligence

Selz, T. and Craig, G. (2023): Data for "Can artificial intelligence-based weather prediction models simulate the butterfly effect?". LMU Munich, Faculty of Physics. (Dataset). DOI: 10.57970/e61hw-rrz34

wget and curl are the two standard tools that are available on most Linux and macOS computers. wget contains a feature for downloading a list of files: 
wget -x -nH -i 'https://opendata.physik.lmu.de/e61hw-rrz34/?list'
curl is missing a feature like that, but the same functionality can be created by combining curl and xargs: 
curl 'https://opendata.physik.lmu.de/e61hw-rrz34/?list' | xargs -I URL -n1 bash -c 'curl --create-dirs -o ${1:31} ${1}' -- URL
Abstract
Data for "Selz and Craig, 2023: Can artificial intelligence-based weather prediction models simulate the butterfly effect? Geophysical Research Letters."
README.md

Data for "Can artificial intelligence-based weather prediction models simulate the butterfly effect?"

This folder contains the data used by the publication:

Selz, T. and G. Craig, 2023: Can artificial intelligence-based models simulate the butterfly effect? Geophysical Research Letters.

The paper considers 5 experiments, with the data from each experiment consolidated into a single netCDF file.

Experiment File
Pangu-100% pangu1000.nc
Pangu-0.1% pangu0001.nc
ICON-LR-100% iconlr1000.nc
ICON-LR-0.1% iconlr0001.nc
ICON-HR-0.1% iconhr0001.nc

The content of each file is organized as follows.

Dimensions:  (ens: 5, time: 73, plev: 1, lat: 721, lon: 1440, nsp: 259560, nc2: 2)

Coordinates:
  * time     (time) datetime64[ns] 2021-06-26 2021-06-26T01:00:00 ... 2021-06-29
  * lon      (lon) float64 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.25 359.50 359.75
  * lat      (lat) float64 -90.0 -89.75 -89.5 -89.25 ... 89.25 89.5 89.75 90.0
  * plev     (plev) float64 300.0
  * ens      (ens) int64 1 2 3 4 5
Dimensions without coordinates: nsp, nc2

Data variables:
    u        (ens, time, plev, lat, lon) float32 ...
    v        (ens, time, plev, lat, lon) float32 ...
    geopot   (ens, time, plev, lat, lon) float32 ...
    vo       (ens, time, plev, nsp, nc2) float32 ...
    div      (ens, time, plev, nsp, nc2) float32 ...

Spherical harmonics (T719) are ordered according tho the GRIB convention (dimension nsp), i.e.,

(l,m) = (0,0), (1,0), ..., (T,0); (1,1), (2,1), ..., (T,1); (2,2), (3,2), ..., (T,2); ...

and dimension nc2 denotes real and imaginary part, respectively.

In case of problems or questions please contact
tobias.selz@lmu.de

Files