xskillscore.core.resampling.resample_iterations_idx¶
- xskillscore.core.resampling.resample_iterations_idx(forecast, iterations, dim='member', dim_max=None, replace=True)¶
Resample over
dim
by indexiterations
times.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
iteration
of sizeiterations
. When usingdask
, the number of tasks inresample_iterations()
will scale withiterations
but constant chunksize, whereas the tasks inresample_iterations_idx()
will stay constant with increasing chunksize.- Parameters:
- Returns:
forecast_smp – data resampled along dimension
dim
with 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