Weather Regimes
+Weather regimes can be calculated using the k-means clustering algorithm, typically applied to the PCs of anomalies from selected variables, after smoothing and detrending.
+The work flow will be something like:
+-
+
- Load data:
Load().
+ - Calculate the anomalies:
Ano().
+ - Smooth the anomalies:
Smooth().
+ - Detrend the smoothed anomalies:
Trend().
+ - Calculate the PCs, the kmeans clusters and reconstruct the field as Cluster center x EOFs:
WeatherRegime().
+ - Visualize the results:
PlotEquiMap().
+
The loading, detrending and calculation of anomalies needs to be done before applying the WeatherRegime() function. The calculation of the EOFs/PC (including the latitudinal area weighting) and the reconstruction of the field (Cluster centers * EOFs) is done within the WeatherRegime() function.
+North Atlantic Weather Regimes
+Below is a simple example of calculating the weather regimes for the North Atlantic from JRA55 SLP for January (with detrending disabled to reduce the computation time).
+
```r
+library(s2dverification)
+library(reshape2)
+JRA55 <- '/esnas/recon/jma/jra55/$STORE_FREQ$_mean/$VAR_NAME$_f6h/$VAR_NAME$_$YEAR$$MONTH$.nc'
+psl <- "psl"
+year.start <- 1981
+year.end <- 2016
+#Euro-Atlantic region
+lat.max <- 81
+lat.min <- 27
+lon.max <- 45
+lon.min <- 274.5
+firstmonth <- 1
+leadmonth <- 0
+psl <- Load(var = psl, exp = NULL, obs = list(list(path=JRA55)), paste0(year.start:year.end,'0101'),
+ storefreq = 'daily', leadtimemax = 366, output = 'lonlat', latmin = lat.min,
+ latmax = lat.max, lonmin = lon.min, lonmax = lon.max, nprocs=8)
+lon <- psl$lon
+lat <- psl$lat
+psl <- psl$obs
+#psl <- Trend(psl, posTR = 3)$detrended
+psl <- psl[1, 1, , , , ] # Drop the dataset and members dimensions
+psl <- Subset(psl, 2, 1 : 31) # Select the month of January
+psl <- psl / 100 #(Convert to hPa)
+psl_ano <- psl - InsertDim(Mean1Dim(psl, 1), 1, 36)
+psl_ano_smoothed <- Smoothing(psl_ano, 7, 2)
+psl_ano_smoothed <- psl_ano_smoothed[, 4 : 28, , ]
+names(dim(psl_ano_smoothed)) <- c("sdate", "ftime", "lat", "lon")
+WR <- WeatherRegime(psl_ano_smoothed, lon = lon, lat = lat, ncenters = 4)
+PlotLayout(fun = c('PlotEquiMap', 'PlotEquiMap', 'PlotEquiMap', 'PlotEquiMap'),
+ plot_dims = c('lat', 'lon'),
+ var = list(WR$field[1, , ], WR$field[2, , ],
+ WR$field[3, , ], WR$field[4, , ]),
+ lon = lon, lat = lat,
+ titles = paste0("Frequency of occurence ", round(unlist(WR$frequency)[1 : 4], digits = 1), "%"),
+ drawleg = 'E', units = 'hPa', units_scale = 2,
+ width = 12, height = 10, res = 200, fileout = 'weather_regimes.png')
+
+```Hierarchical clustering
+Hierarchical clustering methods can also be used through the method option. For example the hierarchical clustering method with mean linkage can be applied via method = average on the same dataset provided above:
+
r WR <- WeatherRegime(psl_ano, lon = lon, lat = lat, ncenters = 4, method = "average") PlotEquiMap(WR$field$composite[,,1], lon = lon, lat = lat, dots = WR$field$pvalue[,,4 ] < 0.05) #Plot the 1st regime
+
+ ```r
+ library(s2dverification)
+ library(reshape2)
+ JRA55 <- '/esnas/recon/jma/jra55/$STORE_FREQ$_mean/$VAR_NAME$_f6h/$VAR_NAME$_$YEAR$$MONTH$.nc'
+ psl <- "psl"
+ year.start <- 1981
+ year.end <- 2016
+ #Euro-Atlantic region
+ lat.max <- 81
+ lat.min <- 27
+ lon.max <- 45
+ lon.min <- 274.5
+ firstmonth <- 1
+ leadmonth <- 0
+ psl <- Load(var = psl, exp = NULL, obs = list(list(path=JRA55)), paste0(year.start:year.end,'0101'),
+ storefreq = 'daily', leadtimemax = 366, output = 'lonlat', latmin = lat.min,
+ latmax = lat.max, lonmin = lon.min, lonmax = lon.max, nprocs=8)
+ lon <- psl$lon
+ lat <- psl$lat
+ psl <- psl$obs
+ #psl <- Trend(psl, posTR = 3)$detrended
+ psl <- psl[1, 1, , , , ] # Drop the dataset and members dimensions
+ psl <- Subset(psl, 2, 1 : 31) # Select the month of January
+ psl <- psl / 100 #(Convert to hPa)
+ psl_ano <- psl - InsertDim(Mean1Dim(psl, 1), 1, 36)
+ psl_ano_smoothed <- Smoothing(psl_ano, 7, 2)
+ psl_ano_smoothed <- psl_ano_smoothed[, 4 : 28, , ]
+ names(dim(psl_ano_smoothed)) <- c("sdate", "ftime", "lat", "lon")
+ WR <- WeatherRegime(psl_ano_smoothed, lon = lon, lat = lat, ncenters = 4)
+ PlotLayout(fun = c('PlotEquiMap', 'PlotEquiMap', 'PlotEquiMap', 'PlotEquiMap'),
+ plot_dims = c('lat', 'lon'),
+ var = list(WR$field[1, , ], WR$field[2, , ],
+ WR$field[3, , ], WR$field[4, , ]),
+ lon = lon, lat = lat,
+ titles = paste0("Frequency of occurence ", round(unlist(WR$frequency)[1 : 4], digits = 1), "%"),
+ drawleg = 'E', units = 'hPa', units_scale = 2,
+ width = 12, height = 10, res = 200, fileout = 'weather_regimes.png')
+
+ ```
+
+
+Hierarchical clustering
+------------------
+
+Hierarchical clustering methods can also be used through the method option. For example the hierarchical clustering method with mean linkage can be applied via method = average on the same dataset provided above:
+
+ 
+
+ ```r
+ WR <- WeatherRegime(psl_ano, lon = lon, lat = lat, ncenters = 4, method = "average")
+ PlotEquiMap(WR$field$composite[,,1], lon = lon, lat = lat, dots = WR$field$pvalue[,,4 ] < 0.05) #Plot the 1st regime
+ ```
+
+
+
+
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diff --git a/man/SeaIceModes.Rd b/man/SeaIceModes.Rd
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+++ b/man/SeaIceModes.Rd
@@ -0,0 +1,88 @@
+\name{SeaIceModes}
+\alias{SeaIceModes}
+\title{
+ Computes sea ice variability modes or clusters.
+}
+\description{
+ This function computes sea ice variability modes based on the K-means clustering
+ method applied to sea ice variable such as area, extent, volume or thickness averages
+ over the selected regions with sea ice in the Northern or Southern Hemisphere.
+}
+\usage{
+ SeaIceModes(var, exp, obs, sdates, seasoninf, seasonsup, nclusters=NULL, nleadtime=NULL, nmember=NULL, leadtimemin=1, leadtimemax=NULL, storefreq="monthly", sampleperiod=1, pldgr=0, listregs=NULL, weights=NULL, listindx=NULL)
+}
+\arguments{
+ \item{var}{
+ For example: sia, sie, sit, .. (sea ice area, extent, thickness, ..)
+ exp, obs, sdates, nleadtime, leadtimemin, leadtimemax, storefreq and sampleperiod
+ are the same as in Load: only var, exp, obs and sdates are mandatory, and only
+ 1 experiment or only 1 observational dataset can be loaded.
+ }
+ \item{nmember}{
+ Selected ensemble member or mean between selected ensemble members, e.g. c(1,3)
+ (default is NULL which produce ensemble mean of all available ensemble members).
+ }
+ \item{seasoninf}{
+ Month of the year when to start computing the extended seasonal means: 1 to 12.
+ }
+ \item{seasonsup}{
+ Month of the year when to stop computing the extended seasonal means: 1 to 12.
+ The extended seasonal means can overlap the 31s December: e.g., DJF average.
+ For monthly means put the same vales for saesoninf and seasonsup.
+ }
+ \item{nclusters}{
+ K number of clusters to be computed, or K initial cluster centers (default is NULL
+ and then Clusters.R will determine the optimal number of clusters and their centers).
+ }
+ \item{pldgr}{
+ pldgr<0 for using the full values, 0 for removing the long-term mean, and 0