RG-CPD is a framework to process 3-dimensional climate data, such that relationships based on correlation can be tested for conditional dependence, i.e. causality. These causal teleconnections can be used to forecast a target variable of interest.
Causal inference frameworks have been proven valuable by going beyond defining a relationship based upon correlation. Autocorrelation, common drivers and indirect drivers are very common in the climate system, and they lead to spurious (significant) correlations. Tigramite has been successfully applied to 1 dimensional time series in climate science (Kretschmer et al. 2016), in order to filter out these spurious correlations using conditional indepence tests (Runge et al. 2017).
Within RG-CPD, the 1-d precursor time series are obtained by creating point-wise correlation maps and subsequently grouping adjacent significantly correlating gridcells together into precursor regions. These precursor regions are then converted into 1-d time series by taking a spatial mean (Kretschmer et al. 2017).
The final step is the same, where the 1-d time series are processed by Tigramite to extract the causal relationships. This requires thorough understanding of the method, see Runge et al. 2017). These 1d time series contain more information since they are spatially aggregated. The 1d time series of different precursor regions are subsequently tested for causality using the Tigramite package. One has to have good knowledge about the assumptions needed for causal inference (Runge et al., 2018).
- basic pre-processing steps (removing climatology and linear detrending)
- time-aggregation handling (for subseasonal and seasonal user-case)
- set of cross-validations types (random k-fold, stratified k-fold, leave-n-out)
- extracting precursors (from netcdf4):
- Correlation maps (corr. maps -> spatial clustering -> precursor timeseries).
- Correlation maps while regressing out (1) influence of third timeseries or autocorrelation of (2) target and/or (3) precursor.
- Empirical Orthogonal Functions (EOFs, or PCA)
- some climate indices and/or directly loading precursor timeseries
- Tigramite (Causal discovery and inference)
- Scikit-learn models + optional GridSearch for tuning
- flexible forecast verification metrics
- (basic) plotting functions with Cartopy
- ECMWF_retrieval with a download python wrapper to get data from the Climate Data Store ERA-5 dataset
Have a look at subseasonal.ipynb and seasonal.ipynb for an overview of the core functionality.
You commit to cite RG-CPD in your reports or publications if used:
Dr. Marlene Kretschmer, who developed the method and used it for studying Polar Vortex dynamics. Please cite:
Kretschmer, M., Runge, J., & Coumou, D. (2017). Early prediction of extreme stratospheric polar vortex states based on causal precursors. Geophysical Research Letters, 44(16), 8592–8600. https://doi.org/10.1002/2017GL074696
PhD. Sem Vijverberg, who expanded Kretschmer's original python code into a python code that can be applied in a versatile manner.
Vijverberg, S.P., Kretschmer, M. (2018). Python code for applying the Response Guided - Causal Precursor Detection scheme. https://doi.org/10.5281/zenodo.1486739
Dr. Jakob Runge, who developed the causal inference python package Tigramite (https://github.com/jakobrunge/tigramite). Depending on what you implement from Tigramite, please cite accordingly.
If depencies are correct, then all scripts should work. Please use the .yml file to create a new conda environment. Afterwards, Tigramite is installed from github.
conda env create -f RGCPD_no_build.yml
conda activate RGCPD
Git clone https://github.com/jakobrunge/tigramite.git (you can clone this repo into any folder, e.g. your Download folder)
python setup.py install
If you did not have ecmwfapi installed before, you need to create an ecmwf account and copy your key into the file .ecmwfapirc in your home directory. See https://confluence-test.ecmwf.int/display/WEBAPI/Access+MARS#AccessMARS-downloadmars. This will look like this:
{
"url" : "https://api.ecmwf.int/v1",
"key" : ,
"email" :
}
cdo -V
Climate Data Operators version 1.9.4 (http://mpimet.mpg.de/cdo)
System: x86_64-apple-darwin17.6.0
CXX Compiler: /usr/bin/clang++ -std=gnu++11 -pipe -Os -stdlib=libc++ -isysroot/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.13.sdk -arch x86_64 -D_THREAD_SAFE -pthread
CXX version : unknown
C Compiler: /usr/bin/clang -pipe -Os -isysroot/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.13.sdk -arch x86_64 -D_THREAD_SAFE -pthread -D_THREAD_SAFE -D_THREAD_SAFE -pthread
C version : unknown
F77 Compiler: -pipe -Os
Features: 16GB C++11 DATA PTHREADS HDF5 NC4/HDF5 OPeNDAP UDUNITS2 PROJ.4 CURL FFTW3 SSE4_1
Libraries: HDF5/1.10.2 proj/5.1 curl/7.60.0
Filetypes: srv ext ieg grb1 grb2 nc1 nc2 nc4 nc4c nc5
CDI library version : 1.9.4
GRIB_API library version : 2.7.0
NetCDF library version : 4.4.1.1 of Jun 8 2018 03:07:16 $
HDF5 library version : 1.10.2
EXSE library version : 1.4.0
FILE library version : 1.8.3 \
Copyright (c) 2018, VU Amsterdam
GNU General Public License v3