climpred
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toy model
s2dverification
, similar to climpred
but written in R, has a toy model to test expected behaviour of skill (dependence of number of inits, members) on synthetic data. nice-to-have
References
- Manubens, Nicolau, Louis-Philippe Caron, Alasdair Hunter, Omar Bellprat, Eleftheria Exarchou, Neven S. Fučkar, Javier Garcia-Serrano, et al. “An R Package for Climate Forecast Verification.” Environmental Modelling & Software 103 (May 1, 2018): 29–42. https://doi.org/10/gc9wzk.
- Weigel, A. P., M. A. Liniger, and C. Appenzeller. “Can Multi-Model Combination Really Enhance the Prediction Skill of Probabilistic Ensemble Forecasts?” Quarterly Journal of the Royal Meteorological Society 134, no. 630 (2008): 241–60. https://doi.org/10/bg2hkm.
Are you supposed to know exactly what correlations, etc. should come out from it for testing functions? Or is it just a way to generate feasible prediction data to plug into functions? (as opposed to doing np.random.rand
for instance).
just a way to generate feasible prediction data to plug into functions
https://xclim.readthedocs.io/en/latest/notebooks/sdba.html