CST_WeatherRegimes {CSTools} | R Documentation |

This function computes the weather regimes from a cluster analysis.
It is applied on the array `data`

in a 's2dv_cube' object. The dimensionality of this object can be also reduced
by using PCs obtained from the application of the #'EOFs analysis to filter the dataset.
The cluster analysis can be performed with the traditional k-means or those methods
included in the hclust (stats package).

CST_WeatherRegimes( data, ncenters = NULL, EOFs = TRUE, neofs = 30, varThreshold = NULL, method = "kmeans", iter.max = 100, nstart = 30, ncores = NULL )

`data` |
a 's2dv_cube' object |

`ncenters` |
Number of clusters to be calculated with the clustering function. |

`EOFs` |
Whether to compute the EOFs (default = 'TRUE') or not (FALSE) to filter the data. |

`neofs` |
number of modes to be kept (default = 30). |

`varThreshold` |
Value with the percentage of variance to be explained by the PCs. Only sufficient PCs to explain this much variance will be used in the clustering. |

`method` |
Different options to estimate the clusters. The most traditional approach is the k-means analysis (default=’kmeans’) but the function also support the different methods included in the hclust . These methods are: "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC). For more details about these methods see the hclust function documentation included in the stats package. |

`iter.max` |
Parameter to select the maximum number of iterations allowed (Only if method='kmeans' is selected). |

`nstart` |
Parameter for the cluster analysis determining how many random sets to choose (Only if method='kmeans' is selected). |

`ncores` |
The number of multicore threads to use for parallel computation. |

A list with two elements `$data`

(a 's2dv_cube' object containing the composites cluster=1,..,K for case (*1)
`$pvalue`

(array with the same structure as `$data`

containing the pvalue of the composites obtained through a t-test that accounts for the serial dependence.),
`cluster`

(A matrix or vector with integers (from 1:k) indicating the cluster to which each time step is allocated.),
`persistence`

(Percentage of days in a month/season before a cluster is replaced for a new one (only if method=’kmeans’ has been selected.)),
`frequency`

(Percentage of days in a month/season belonging to each cluster (only if method=’kmeans’ has been selected).),

Verónica Torralba - BSC, veronica.torralba@bsc.es

Cortesi, N., V., Torralba, N., González-Reviriego, A., Soret, and F.J., Doblas-Reyes (2019). Characterization of European wind speed variability using weather regimes. Climate Dynamics,53, 4961–4976, doi:10.1007/s00382-019-04839-5.

Torralba, V. (2019) Seasonal climate prediction for the wind energy sector: methods and tools for the development of a climate service. Thesis. Available online: https://eprints.ucm.es/56841/

## Not run: res1 <- CST_WeatherRegimes(data = lonlat_data$obs, EOFs = FALSE, ncenters = 4) res2 <- CST_WeatherRegimes(data = lonlat_data$obs, EOFs = TRUE, ncenters = 3) ## End(Not run)

[Package *CSTools* version 4.0.1 Index]