synoptReg (version 0.2.0)

synoptclas: Synoptic classification

Description

synoptclas establish a synoptic classification based on any atmospheric variable (i.e. mean sea level pressure, geoptential height at 500 hPa, etc.)

Usage

synoptclas(smode_data, ncomp, extreme_scores = 2)

Arguments

smode_data

Data frame. S-mode data frame of the reanalysis variable. I.e. output obtained from tidy_cuttime_nc function.

ncomp

Integer. Number of components to be retained.

extreme_scores

Integer. Definition of extreme score threshold (Esteban et al., 2005). Default is 2.

Value

A list with:

• A data frame containing data grouped by circulation weather types ("grouped_data").

• An integer with the circulation weather types ("clas").

• A data frame containing the number and percentage of days assigned to each weather type ("cwt_freq").

• A data frame containing the number of days assigned to each weather type by month ("monthly_freq").

• A data frame containing the number of days assigned to each weather type by year ("annual_freq").

• The 'rotated' loadings matrix of class "loadings" ("rotated_loadings").

• The scores of the supplied data on the principal components ("scores").

• The coordinates of the scores used to perform the k-means clustering. For more information, read Esteban et al. (2005)("scores_coordinates").

Details

A PCA is applied to a S-mode matrix to reduce the dimension of the variables, in which the grid points are the variables and the days are the observations. These principal components are subsequently rotated by means of a varimax rotation. With the rotated components, the scores are used to apply the extreme scores method (Esteban et al., 2005). The scores show the degree of representativeness associated with the variation modes of each principal component, i.e., the classification of each day to its more representative centroid. Thus, the extreme scores method uses the scores > 2 and < -2, establishing a positive and negative phase for each principal component. The extreme scores procedure establishes the number of groups and their centroids in order to apply the K-means method without iterations.

References

Esteban, P. , Jones, P. D., MartC-nb Atmospheric circulation patterns related to heavy snowfall days in Andorra, Pyrenees Int. J. Climatol. 25: 319-329. doi:10.1002/joc.1103

See Also

pca_decision

Examples

# NOT RUN {
# Load data (mslp)
data(mslp)
# Converting our data into a S-mode, but without modifying time period
smode_mslp <- tidy_cuttime_nc(mslp, only_convert = TRUE)
# classification performance
smode_clas <- synoptclas(smode_mslp$smode_data, ncomp = 6)


# }