xskillscore.core.resampling.resample_iterations_idx
- xskillscore.core.resampling.resample_iterations_idx(forecast, iterations, dim='member', replace=True, dim_max=None)
Resample over
dimby indexiterationstimes.Note
This is a much faster way to bootstrap/resample each iteration individually and applying the function to it. This will create a DataArray with dimension
iterationof sizeiterations. When usingdask, the number of tasks inresample_iterations()will scale withiterationsbut constant chunksize, whereas the tasks inresample_iterations_idx()will stay constant with increasing chunksize.- Parameters
- Returns
forecast_smp – data resampled along dimension
dimwith additionaldim='iteration'.- Return type
xr.DataArray, xr.Dataset
Examples
>>> a = xr.DataArray(np.random.rand(1000, 3, 3), ... coords=[("time", np.arange(1000)), ... ("x", np.arange(3)), ... ("y", np.arange(3))]) >>> xs.resample_iterations_idx(a, 500, 'time') <xarray.DataArray (time: 1000, x: 3, y: 3, iteration: 500)>
See also
resample_iterations()References
Mason, S. J., & Mimmack, G. M. (1992). The use of bootstrap confidence intervals for the correlation coefficient in climatology. Theoretical and Applied Climatology, 45(4), 229–233. https://doi.org/10/b6fnsv
Mason, S. J. (2008). Understanding forecast verification statistics. Meteorological Applications, 15(1), 31–40. https://doi.org/10/bgvgnz
Goddard, L., Kumar, A., Solomon, A., Smith, D., Boer, G., Gonzalez, P., Kharin, V., Merryfield, W., Deser, C., Mason, S. J., Kirtman, B. P., Msadek, R., Sutton, R., Hawkins, E., Fricker, T., Hegerl, G., Ferro, C. a. T., Stephenson, D. B., Meehl, G. A., … Delworth, T. (2013). A verification framework for interannual-to-decadal predictions experiments. Climate Dynamics, 40(1–2), 245–272. https://doi.org/10/f4jjvf