Weather regime analysis
Weather regimes are a set of patterns able to characterize the different circulation structures occurring each month/season. This vignette aims to illustrate a step-by-step example of how to perform a weather regime assessment using CSTools functionalities.
1- Required packages
The functions to compute the Weather Regimes are part of CSTools while plotting functions are included in s2dv package:
2- Retrive data from files
The data employed in this example are described below.
- Sea level pressure (psl): this has been selected as the circulation
variable, however other variables such as geopotential at 500 hPa can be
also used. - Region: Euro-Atlantic domain [85.5ºW-45ºE; 27-81ºN]. -
Datasets: seasonal predictions from ECMWF System 4 (Molteni
et al. 2011) and ERA-Interim reanalysis (Dee et
al. 2011) as a reference dataset. - Period: 1991-2010. Only
20 years have been selected for illustrative purposes, but the full
hindcast period could be used for the analysis.
repos_exp <- paste0('/esarchive/exp/ecmwf/system4_m1/daily_mean/$var$_f6h/',
'$var$_$sdate$.nc')
sdates <- paste0(1991:2010, '1201')
latmin = 27
latmax = 81
lonmin = 274.5
lonmax = 45
exp <- CST_Start(dataset = repos_exp,
var = 'psl',
member = "all",
sdate = sdates,
ftime = startR::indices(1:31),
lat = startR::values(list(latmin, latmax)),
lat_reorder = startR::Sort(decreasing = TRUE),
lon = startR::values(list(lonmin, lonmax)),
lon_reorder = startR::CircularSort(0, 360),
synonims = list(lon = c('lon', 'longitude'),
lat = c('lat', 'latitude'),
member = c('member', 'ensemble'),
ftime = c('ftime', 'time')),
return_vars = list(lat = NULL,
lon = NULL, ftime = 'sdate'),
retrieve = TRUE)
dates0 <- exp$attrs$Dates
repos_obs <-"/esarchive/recon/ecmwf/erainterim/daily_mean/$var$_f6h/$var$_$date$.nc"
obs <- CST_Start(dataset = repos_obs,
var = 'psl',
date = unique(format(dates0, '%Y%m')),
ftime = startR::values(dates0),
ftime_across = 'date',
ftime_var = 'ftime',
merge_across_dims = TRUE,
split_multiselected_dims = TRUE,
lat = startR::values(list(latmin, latmax)),
lat_reorder = startR::Sort(decreasing = TRUE),
lon = startR::values(list(lonmin, lonmax)),
lon_reorder = startR::CircularSort(0, 360),
synonims = list(lon = c('lon', 'longitude'),
lat = c('lat', 'latitude'),
ftime = c('ftime', 'time')),
return_vars = list(lon = NULL,
lat = NULL,
ftime = 'date'),
retrieve = TRUE)Notice that you need the files to be stored locally in your computer or server with correct configuration file. If you are interested into run this vignette, contact nuria.perez at bsc.es to get a data sample.
The objects returned by CST_Start() are
s2v_cube class. They contains among others, the array with
the requested data.
3- Daily anomalies based on a smoothed climatology
The weather regimes classification is based on daily anomalies, which have been computed by following these steps:
c(ano_exp, ano_obs) %<-% CST_Anomaly(exp = exp, obs = obs, filter_span = 1,
dat_dim = c('dataset', 'member'))The LOESS filter has been applied to the climatology to remove the
short-term variability and retain the annual cycle. The parameter
filter_span should be adapted to the length of the period
used to compute the climatology (e.g. season, month, week,…). In this
example we are using daily data, so we have selected
filter_span = 1.
4- Weather regimes in observations
CST_WeatherRegimes() function is used to define the
clusters based on the sea level pressure anomalies from ERA-Interim.
This function is based on the kmeans
function from the stats R package. In this example we have made
different assumptions: four clusters (ncenters=4) will be
produced and the Empirical orthogonal functions are not used to filter
the data (EOFS=FALSE) just to take into account the extreme
values. More details about the methodology can be found in Cortesi et
al. 2018 (submitted).
CST_WeatherRegime() provides a s2dv_cube
object with several elements. $data the 4 weather regimes
composites are stored while $statistics contains extra
information ($pvalue, $cluster,
$persistence and $frequency) which are the
needed parameters for the weather regimes assessment. Further details
about the outputs provided by the CST_WeatherRegime()
function can be found in the package documentation or typing
?CST_WeatherRegimes in the R session.
5- Visualisation of the observed weather regimes
To plot the composite maps of each regime and the mean frequencies of
each cluster, we have employed the PlotLayout() and
PlotEquiMap() functions available in s2dv. The object
WR_obs$data is divided by 100 to change from Pa to hPa. As
the WR_obs$statistics$frequency provides the monthly
frequencies, the climatological frequencies are obtained as the average
across the 20 years of the monthly frequencies. Note that these
frequencies could slightly change as a consequence of the randomness
inherent to the iterative processes involved in the k-means.
clim_frequencies <- paste0('freq = ', round(MeanDims(WR_obs$statistics$frequency, 1), 1), '%')
PlotLayout(PlotEquiMap, c(1, 2), lon = obs$coords$lon, lat = obs$coords$lat,
var = WR_obs$data / 100,
titles = paste0(paste0('Cluster ', 1:4), ' (', clim_frequencies,' )'),
filled.continents = FALSE,
axelab = FALSE, draw_separators = TRUE, subsampleg = 1,
brks = seq(-16, 16, by = 2),
bar_extra_labels = c(2, 0, 0, 0))
6- Visualisation of the observed regime persistence
Persistence measures the average number of days a regime lasts before
evolving to a different regime. To plot regime persistence for each
start date, we have employed the PlotTriangles4Categories()
function available in CSTools.
freq_obs <- WR_obs$statistics$persistence
freq_obs[is.na(freq_obs)] <- 0
dim(freq_obs) <- c(dimy = 20, dimcat = 4, dimx = 1)
PlotTriangles4Categories(freq_obs, toptitle = 'Persistence',
xtitle = 'Start Dates', ytitle = '', xlab = FALSE,
ylabels = substr(sdates, 1, 4), cex_leg = 0.6,
lab_legend = c('AR', 'NAO-', 'BL', 'NAO+'), figure.width = .7)
7- Weather regimes in the predictions
Predicted anomalies for each day, month, member and lead time are matched with the observed clusters (obtained in step 4). The assignment of the anomalies to a pre-defined set of clusters guarantees that the predicted weather regimes have very similar spatial structures to the observed regimes, which is an essential requirement for the verification of weather regimes. This is an example of how to produce a set of weather regimes based on the predictions that can be verified with the observational dataset, but this approach can be also used in an operational context for which the probability of occurence of each cluster could be estimated.
The matching is based on the minimization of Eucledian distance
method='distance', but it can also be also done in terms of
spatial correlation method='ACC'. However the computational
efficiency is superior for the distance method.
WR_exp <- CST_RegimesAssign(data = ano_exp, ref_maps = WR_obs,
method = 'distance', composite = TRUE, memb = TRUE)CST_RegimesAssign() provides a object of ‘s2dv_cube’
class which lists several elements. The composites are stored in the
$data element which in the case of `memb =
TRUE` corresponds to the mean composites for all member.
`$pvalue,$cluster` and
`$frequencyare stored in element$statistics. This elements contain similar information to that provided by theWeatherRegime()`
function. Further details about the output can be found in the package
documentation.
8- Visualisation of the predicted weather regimes
The outputs of RegimesAssign() have been represented to
be compared with those obtained for the observational classification
(step 5).
PlotLayout(PlotEquiMap, c(1, 2), lon = exp$coords$lon, lat = exp$coords$lat,
var = WR_exp$data/100,
titles = paste0(paste0('Cluster ',1:4), ' (',paste0('freq = ',
round(WR_exp$statistics$frequency,1),'%'),' )'),
filled.continents = FALSE,
axelab = FALSE, draw_separators = TRUE,
subsampleg = 1, brks = seq(-16, 16, by = 2),
bar_extra_labels = c(2, 0, 0, 0))
Observed and predicted weather regimes are very similar although their frequencies are slightly different. Cluster 4 is the Atlantic Ridge and cluster 2 the Blocking pattern, while cluster 3 and 1 are the positive and negative phases of the NAO. This patterns can change depending on the period analyzed.