| Title: | Extended Classification of Weather Types |
|---|---|
| Description: | Provides a gridded classification of weather types by applying the Jenkinson and Collison classification. For a given region (it can be either local region or the whole map),it computes at each grid the 11 weather types during the period considered for the analysis. See Otero et al., (2017) <doi:10.1007/s00382-017-3705-y> for more information. |
| Authors: | Noelia Otero |
| Maintainer: | Noelia Otero <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.1 |
| Built: | 2025-02-11 04:00:28 UTC |
| Source: | https://github.com/cran/jcext |
Applies the rules to obtain the weather types. (more details, see Jones et al. 1997)
calculate_cwt(Z, TF, directionflow, G, thr = 6, Gth = 30)calculate_cwt(Z, TF, directionflow, G, thr = 6, Gth = 30)
Z |
Data frame with the total shear vorticity and dates. |
TF |
Data frame with resultant flow and dates. |
directionflow |
Data frame with the direction of the flow and dates. |
G |
Data frame with Gale days and dates. |
thr |
Numeric threshold used for Unclassified days. |
Gth |
Numeric threshold used for Gale days. |
As defined in the original scheme, the threshold to determine unclassified days is 6. Gale days are estimated by using a threshold of 30. If Gale days with a greater intensity (e.g. 40 or 50) is wanted, Gth must be changed. The airflow indices are used within the following rules to define the appropriate Lamb weather types.
The appropriate direction is calculated on an eight-point compass allowing 45° per sector.
If abs(Z) is less than TF, flow is essentially pure directional type.
If |Z| is greater than 2TF, then the pattern is strongly cyclonic (Z > 0) or anticyclonic (Z < 0).
If |Z| lies between TF and 2TF then the flow is partly (anti-) cyclonic and this corresponds to one of Lamb’s synoptic/direction hybrid types, e.g. AE.
If TF is less than 6 and |Z| is less than 6, there is light indeterminate flow corresponding to Lamb’s unclassified type U.
A list with two objects:
Total_CWT with five groups of weather types: LWT_D, LWT_C, LWT_CH, LWT_U and LWT_G The main four groups contain the total of 27 weather types and the five list LWT_G refers to the gale days.
LWT_D: 8 directional types.
LWT_C: 1 anticyclonic and 1 cyclonic.
LWT_CH: 16 hybrid types.
LWT_U: 1 unclassified type.
Logical values of daily occurrence of each type. The user might want to use these values to get composites with specific atmospheric fields (e.g. pressure, temperature).
Calculates the classification of the main weather types for one central point that is surrounded by 16-points (grid16). Wind-flow characteristics are computed for the daily pressure field according to the rules proposed by the original Jenkinson and Collison classification (see Jones et al. 1993, Jones et al. 2016).
classification_jc(mslp, grid16, centralp, loni, lati, times, gale)classification_jc(mslp, grid16, centralp, loni, lati, times, gale)
mslp |
3-Dimensional multi-array ([loni,lati,time]) with mean sea level pressure in Pa. |
grid16 |
Data frame obtained in the main function (extended_jc) that contains the 16 grid-points defining the scheme. First row is for longitudes, while the second row is for latitudes. |
centralp |
Numeric that refers to the central point for which the JC classification is calculated. |
loni |
Array with longitude values. |
lati |
Array with latitude values. |
times |
Array with the dates used. |
gale |
A logical for deteriming Gale days. |
Daily frequencies of Weather Types and airflow indices.
Jones, P. D., Hulme M., Briffa K. R. (1993) A comparison of Lamb circulation types with an objective classification scheme Int. J. Climatol. 13: 655–663.
Jones, P. D., Harpham C, Briffa K. R. (2013) Lamb weather types derived from Reanalysis products Int. J. Climatol. 33: 1129–1139.
# Load data data(press) mslp <- press$msl loni <- press$loni lati <- press$lati times <- press$dates # Define a central point centralp <- c(10,50) # Get the scheme for the central point grid16 <- get_jcpoints(10,50)[1:16] classification_jc(mslp, grid16, centralp, loni, lati, times, gale=FALSE)# Load data data(press) mslp <- press$msl loni <- press$loni lati <- press$lati times <- press$dates # Define a central point centralp <- c(10,50) # Get the scheme for the central point grid16 <- get_jcpoints(10,50)[1:16] classification_jc(mslp, grid16, centralp, loni, lati, times, gale=FALSE)
Converts 3D arrays with daily frequencies of weather types to NETCDF files.
create_ncdfcwt(cwt_out, times, centralp, onefile = TRUE, path = NULL)create_ncdfcwt(cwt_out, times, centralp, onefile = TRUE, path = NULL)
cwt_out |
List with a list for each grid point that contains two objects: CWT and indices. |
times |
Numeric with the dates. |
centralp |
Numeric with the centralp obtained from the main program. |
onefile |
Logical. If TRUE one single output file with all WT is created, if FALSE one file per type is created. |
path |
Path name to create the output file. |
A netcdf file with all weather types or one file per type.
# This is a long running example cwtGlobal <- extended_jc(press$msl, press$loni, press$lati, press$dates, gale=FALSE, num_cores=2) # Create ncdf file (one file with all types) create_ncdfcwt(cwtGlobal, press$dates, cwtGlobal$centralp, onefile = TRUE, path = NULL)# This is a long running example cwtGlobal <- extended_jc(press$msl, press$loni, press$lati, press$dates, gale=FALSE, num_cores=2) # Create ncdf file (one file with all types) create_ncdfcwt(cwtGlobal, press$dates, cwtGlobal$centralp, onefile = TRUE, path = NULL)
Converts 3D arrays with daily values of airflow indices to NETCDF files.
createindex_ncdf(cwt_out, times, centralp, path = NULL)createindex_ncdf(cwt_out, times, centralp, path = NULL)
cwt_out |
List with a list for each grid point that contains two objects $CWT and $indices. |
times |
Numeric with the dates. |
centralp |
Numeric with the centralp obtained from the main program. |
path |
Path name to create the output file. |
A ncdf file with the airflow indices.
# This is a long running example cwtGlobal <- extended_jc(press$msl, press$loni, press$lati, press$dates, gale=FALSE, num_cores=2) # Create ncdf file (one file with all types) createindex_ncdf(cwtGlobal, press$dates, cwtGlobal$centralp, path = NULL)# This is a long running example cwtGlobal <- extended_jc(press$msl, press$loni, press$lati, press$dates, gale=FALSE, num_cores=2) # Create ncdf file (one file with all types) createindex_ncdf(cwtGlobal, press$dates, cwtGlobal$centralp, path = NULL)
Gets daily classification of weather types at every grid-point over the map or selected area of interest according to the Jenkison and Collison scheme.
extended_jc(mslp, loni, lati, times, gale = FALSE, num_cores = 2)extended_jc(mslp, loni, lati, times, gale = FALSE, num_cores = 2)
mslp |
3-Dimensional array ([loni,lati,time]) with mean sea level pressure in Pa. |
loni |
Array with longitude values. |
lati |
Array with latitude values. |
times |
Array with the dates used. |
gale |
Logial. If TRUE, the function returns also Gale days. |
num_cores |
Number of cores (2 by default). |
A list with two objects:
A list of eleven matrix of daily frequencies of weather types ("wtypes"). Each matrix is a 3D array [loni,lati,times] and it refers to each weather type (N,NE,E,SE,S,SW,W,A,C and U).
A list of six matrix of daily frequency of airflow indices ("indices"). Each matrix is a 3D array [loni,lati,times] and it refers to each airflow index (W,S,TF,ZW,ZS,Z and D).
A list with the central points for which the classification is applied.
Otero, N., Sillmann, J. & Butler, T. Assessment of an extended version of the Jenkinson–Collison classification on CMIP5 models over Europe Climate Dynamics. https://doi.org/10.1007/s00382-017-3705-y
classification_jc
calculate_cwt
# Load data data(press) # Get coordinates longitudes <- press$loni latitudes <- press$lati times <- press$dates # Example when the classification is restricted to an area # Select longitudes and latitudes within the European domain: -10W,40E, 40N,70N ilon <- which(longitudes>(-10)&longitudes<40) loni <- longitudes[ilon] ilat <- which(latitudes>40&latitudes<70) lati <- latitudes[ilat] cwtEU <- extended_jc(press$msl[ilon,ilat,], loni, lati, times, gale=FALSE, num_cores=2) ## Not run: # Not run # This is a long running example # Get the classification for the whole map, all longitudes and latitudes cwtGlobal <- extended_jc(press$msl, longitudes, latitudes, times, gale=FALSE, num_cores=2) ## End(Not run)# Load data data(press) # Get coordinates longitudes <- press$loni latitudes <- press$lati times <- press$dates # Example when the classification is restricted to an area # Select longitudes and latitudes within the European domain: -10W,40E, 40N,70N ilon <- which(longitudes>(-10)&longitudes<40) loni <- longitudes[ilon] ilat <- which(latitudes>40&latitudes<70) lati <- latitudes[ilat] cwtEU <- extended_jc(press$msl[ilon,ilat,], loni, lati, times, gale=FALSE, num_cores=2) ## Not run: # Not run # This is a long running example # Get the classification for the whole map, all longitudes and latitudes cwtGlobal <- extended_jc(press$msl, longitudes, latitudes, times, gale=FALSE, num_cores=2) ## End(Not run)
Computes the 16-grid points that determine the JC-scheme, based on the coordinates of the central point.
get_jcpoints(lon, lat)get_jcpoints(lon, lat)
lon |
Longitude of the central point. |
lat |
Latitude of the central point. |
The function excludes the poles and the equatorial areas between 25S-25N.
A data frame with the coordinates of the 16-grid points and the central point.
Visualises absolute frequencies of the 11 main types from the extended_jc over the period.
plot_freqmap_wtypes(mat, loni, lati, all.types = TRUE, mytype = NULL, center = TRUE)plot_freqmap_wtypes(mat, loni, lati, all.types = TRUE, mytype = NULL, center = TRUE)
mat |
Matrix output from extended_jc [loni,lati,time] |
loni |
Vector with longitude values must be -180, 180 |
lati |
Latitude values |
all.types |
Logical. If TRUE all weather types are plotted in the same plot |
mytype |
Character with the name of the weather type wanted (i.g. N,NE,E,SE,S,SW,W,A,C and U) |
center |
Logical. If TRUE a center map is plotted |
A ggplot2 map
## Not run: library(jcext) # This is a long running example for plotting results for all types globally cwtGlobal <- extended_jc(press$msl, press$loni,press$lati, press$dates, gale=FALSE, num_cores=2) wtypesGlobal <- cwtGlobal$wtypes plot_freqmap_wtypes(wtypesGlobal,press$loni, press$lati, all.types = TRUE,mytype = NULL ,center = T) # Plot the global results only for one type plot_freqmap_wtypes(wtypesGlobal,press$loni, press$lati, all.types = FALSE,mytype = "C" ,center = T) ## End(Not run)## Not run: library(jcext) # This is a long running example for plotting results for all types globally cwtGlobal <- extended_jc(press$msl, press$loni,press$lati, press$dates, gale=FALSE, num_cores=2) wtypesGlobal <- cwtGlobal$wtypes plot_freqmap_wtypes(wtypesGlobal,press$loni, press$lati, all.types = TRUE,mytype = NULL ,center = T) # Plot the global results only for one type plot_freqmap_wtypes(wtypesGlobal,press$loni, press$lati, all.types = FALSE,mytype = "C" ,center = T) ## End(Not run)
Visualises the original Jenkinson & Collison scheme for one given central point surrounded by the 16-points.
plot_jcscheme(centralp, loni, lati, fullmap = TRUE)plot_jcscheme(centralp, loni, lati, fullmap = TRUE)
centralp |
Array with the central point (lonigutde, latitude). |
loni |
Array with longitude values. |
lati |
Array with latitude values. |
fullmap |
Logical. If TRUE a fullmap is plotted, if FALSE, only the region selected is plotted. |
The map shows the scheme over the whole map, or either it shows the scheme over the selected region. For that, the maximum and mininum coordinates are defined as: Maximum longitude, by default defined by the points: x6, x10 or 14. Minimum longitude, by default defined by the points: x3, x7 or 11. Maximum latitude, by default defined by the points: x1 or x2. Minimum latitude, by default defined by the points: x15 or 16.
A plot device
# Visualise the scheme for one point library(jcext) # Define a central point mycentral <- c(10,50) # load the data to get coordinates data(press) # Visualise the whole map plot_jcscheme(mycentral,press$loni,press$lati,fullmap=TRUE) # Visualise the region plot_jcscheme(mycentral,press$loni,press$lati,fullmap=FALSE)# Visualise the scheme for one point library(jcext) # Define a central point mycentral <- c(10,50) # load the data to get coordinates data(press) # Visualise the whole map plot_jcscheme(mycentral,press$loni,press$lati,fullmap=TRUE) # Visualise the region plot_jcscheme(mycentral,press$loni,press$lati,fullmap=FALSE)
Data from a ERA-Interim reanalysis data set downloaded from ECMWF (http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/). This data corresponds to global daily values of mean sea level pressure with 2.5 x 2.5 resolution for January 2000.
data(press)data(press)
A list with values of pressure and coordinates (longitude, latitude, time)
mean sea level pressure values, "Pa"
144
73
366, one year (2000)
Dee et al. (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q.J.R. Meteorol. Soc., 137: 553–597. doi:10.1002/qj.828
data(press) mslp <- press$msl loni <- press$loni lati <- press$lati times <- press$datesdata(press) mslp <- press$msl loni <- press$loni lati <- press$lati times <- press$dates
Reads a ncdf input file to extract the input for the classification: pressure field, longitudes, latitudes and dates. Absolute time is required to read the dates properly.
read_ncdata(ncinput, nam_coor, units)read_ncdata(ncinput, nam_coor, units)
ncinput |
A NETCDF file with pressure field. |
nam_coor |
Names of space and time coordinates |
units |
Units required (Pa or hPa). |
A list with:
A 3D-array of mean sea level (or pressure field) as [lon,lat,times]. The units returned as hPa.
A numeric with longitudes values.
A numeric with latitudes values.
A numeric with dates values.