diff --git a/.Rbuildignore b/.Rbuildignore
index b2d8e5fcebca62bff5e5380a881580283874cd54..5e7002cde0d3d0c313e16670c354202c777521ab 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -6,3 +6,4 @@
 .*^(?!data)\.RData$
 .*\.gitlab-ci.yml$
 #^tests$
+./.nfs*
diff --git a/.gitignore b/.gitignore
index 35a09175a4a4e587947985914ec082148bc51880..23a560302d2818630fb7428585681dd3b07a2b3a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,4 +17,4 @@ Rplots.pdf
 .nfs*
 *.RData
 !data/*.RData
-
+.\.nfs*
diff --git a/DESCRIPTION b/DESCRIPTION
index dfea00eacdfbaec8857e73d3a2bebc400bb21c1a..57b1ab7161f66500fe9dbd7bb4ed787b194eb98e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -4,7 +4,7 @@ Version: 0.0.1
 Authors@R: c(
     person("Núria", "Pérez-Zanón", , "nuria.perez@bsc.es", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8568-3071")),
     person("Llorenç", "Lledó", , "llorenc.lledo@bsc.es", role = "aut"))
-Description: The package contains the definition-based computation of the sectoral indicators for the Climate Service.
+Description: The definition-based computation of the sectoral indicators for the Climate Service.
 Depends:
     R (>= 3.6.0)
 Imports:
@@ -22,6 +22,5 @@ VignetteBuilder: knitr
 License: Apache License 2.0
 URL: https://earth.bsc.es/gitlab/es/csindicators/
 BugReports: https://earth.bsc.es/gitlab/es/csindicators/-/issues
-LazyData: true
 Encoding: UTF-8
 RoxygenNote: 7.0.1
diff --git a/R/AbsToProbs.R b/R/AbsToProbs.R
index d2bcfbd14bb3067378fe520755647ea87f54624e..c36640d17526a0b000958620cebe2521fdb885fc 100644
--- a/R/AbsToProbs.R
+++ b/R/AbsToProbs.R
@@ -82,15 +82,17 @@ CST_AbsToProbs <- function(data, start = NULL, end = NULL,
 #'@examples
 #'exp <- CSTools::lonlat_prec$data
 #'exp_probs <- AbsToProbs(exp)
-#'data <- array(rnorm(5 * 3 * 214 * 2),
+#'\dontrun{
+#'data <- array(rnorm(5 * 2 * 61 * 2),
 #'                    c(member = 5, sdate = 3, ftime = 214, lon = 2)) 
 #'Dates <- c(seq(as.Date("01-05-2000", format = "%d-%m-%Y"), 
-#'                     as.Date("30-11-2000", format = "%d-%m-%Y"), by = 'day'),
+#'                     as.Date("30-06-2000", format = "%d-%m-%Y"), by = 'day'),
 #'                 seq(as.Date("01-05-2001", format = "%d-%m-%Y"), 
-#'                     as.Date("30-11-2001", format = "%d-%m-%Y"), by = 'day'),
+#'                     as.Date("30-06-2001", format = "%d-%m-%Y"), by = 'day'),
 #'                 seq(as.Date("01-05-2002", format = "%d-%m-%Y"), 
-#'                     as.Date("30-11-2002", format = "%d-%m-%Y"), by = 'day'))
+#'                     as.Date("30-06-2002", format = "%d-%m-%Y"), by = 'day'))
 #'exp_probs <- AbsToProbs(exp, start = list(21, 4), end = list(21, 6))
+#'}
 #'@export
 AbsToProbs <- function(data, dates = NULL, start = NULL, end = NULL, time_dim = 'time',
                        memb_dim = 'member', 
diff --git a/R/AccumulationExceedingThreshold.R b/R/AccumulationExceedingThreshold.R
index 4d3e50e3f2bea78dcf17941496ef2e3dc340d430..e953d32c5f781f6eb2dbe17e717593aa25439d0e 100644
--- a/R/AccumulationExceedingThreshold.R
+++ b/R/AccumulationExceedingThreshold.R
@@ -22,19 +22,8 @@
 #'@import multiApply
 #'@examples
 #'exp <- CSTools::lonlat_data$exp
+#'exp$data <- CSTools::lonlat_data$exp$data[1, 5, 3, 3, 1, 1]
 #'DOT <- CST_AccumulationExceedingThreshold(exp, threshold = 280)
-#'# Assuming exp$data is already (tasmax + tasmin)/2 - 10
-#'exp$data <- array(rnorm(5 * 3 * 214 * 2, mean = 25, sd = 3),
-#'                    c(memb = 5, sdate = 3, ftime = 214, lon = 2)) 
-#'exp$Dates[[1]] <- c(seq(as.Date("01-05-2000", format = "%d-%m-%Y"), 
-#'                     as.Date("30-11-2000", format = "%d-%m-%Y"), by = 'day'),
-#'                 seq(as.Date("01-05-2001", format = "%d-%m-%Y"), 
-#'                     as.Date("30-11-2001", format = "%d-%m-%Y"), by = 'day'),
-#'                 seq(as.Date("01-05-2002", format = "%d-%m-%Y"), 
-#'                     as.Date("30-11-2002", format = "%d-%m-%Y"), by = 'day'))
-#'GDD <- CST_AccumulationExceedingThreshold(exp, threshold = 0,
-#'                                          start = list(1, 4), end = list(31, 10))
-#'
 #'@export
 CST_AccumulationExceedingThreshold <- function(data, threshold, op = '>', 
                                             diff = FALSE,
@@ -99,8 +88,6 @@ CST_AccumulationExceedingThreshold <- function(data, threshold, op = '>',
 #'
 #'@import multiApply
 #'@examples
-#'exp <- CSTools::lonlat_data$exp$data
-#'AET <- AccumulationExceedingThreshold(exp, threshold = 280, time_dim = 'ftime')
 #'# Assuming data is already (tasmax + tasmin)/2 - 10
 #'data <- array(rnorm(5 * 3 * 214 * 2, mean = 25, sd = 3),
 #'                    c(memb = 5, sdate = 3, time = 214, lon = 2)) 
@@ -110,7 +97,8 @@ CST_AccumulationExceedingThreshold <- function(data, threshold, op = '>',
 #'               as.Date("30-11-2001", format = "%d-%m-%Y"), by = 'day'),
 #'           seq(as.Date("01-05-2002", format = "%d-%m-%Y"), 
 #'               as.Date("30-11-2002", format = "%d-%m-%Y"), by = 'day'))
-#'GDD <- AccumulationExceedingThreshold(data, threshold = 0, start = list(1, 4), end = list(31, 10))
+#'GDD <- AccumulationExceedingThreshold(data, threshold = 0, start = list(1, 4),
+#'                                      end = list(31, 10))
 #'@export
 AccumulationExceedingThreshold <- function(data, threshold, op = '>',
                                         diff = FALSE,
@@ -145,14 +133,6 @@ AccumulationExceedingThreshold <- function(data, threshold, op = '>',
   if (is.null(names(dim(threshold))) && length(threshold) > 1) {
     stop("Parameter 'threshold' must have named dimensions.")
   }
-  # This check doen't seem necessary. It limits flexibility.
-  #common_dims <- which(names(dim(data)) %in% names(dim(threshold)))
-  #if (length(threshold) > 1) {    
-  #  if (any(dim(data)[common_dims] != 
-  #          dim(threshold)[which(names(dim(threshold)) %in% names(dim(data)))])) {
-  #  stop("Parameter 'data' and 'threshold' must have the same length on common dimensions.")
-  #  }
-  #} 
   if (!is.null(dates)) {
     if (!is.null(start) && !is.null(end)) {
       if (!any(c(is.list(start), is.list(end)))) {
@@ -186,6 +166,12 @@ AccumulationExceedingThreshold <- function(data, threshold, op = '>',
                    target_dims = list(time_dim, time_dim),
                    fun = .sumexceedthreshold, op = op, na.rm = na.rm,
                    ncores = ncores)$output1
+  } else if (any(time_dim %in% names(dim(threshold)))) {
+    total <- Apply(list(data, threshold),
+                   target_dims = list(time_dim,
+                                      time_dim[time_dim %in% names(dim(threshold))]),
+                   fun = .sumexceedthreshold, op = op, na.rm = na.rm,
+                   ncores = ncores)$output1
   } else {
     total <- Apply(list(data, threshold),
                    target_dims = list(time_dim, NULL),
diff --git a/R/PeriodMean.R b/R/PeriodMean.R
index fa1b4783131560e9de5b5ba7cd5a731a3bf68f2a..c63c5f05dfc19d323c15abad431fffe192670dce 100644
--- a/R/PeriodMean.R
+++ b/R/PeriodMean.R
@@ -21,17 +21,6 @@
 #'exp <- CSTools::lonlat_data$exp
 #'SA <- CST_PeriodMean(exp)
 #'
-#'exp$data <- array(rnorm(5 * 3 * 214 * 2),
-#'                    c(memb = 5, sdate = 3, ftime = 214, lon = 2)) 
-#'exp$Dates[[1]] <- c(seq(as.Date("01-04-2000", format = "%d-%m-%Y"), 
-#'                     as.Date("31-10-2000", format = "%d-%m-%Y"), by = 'day'),
-#'                 seq(as.Date("01-04-2001", format = "%d-%m-%Y"), 
-#'                     as.Date("31-10-2001", format = "%d-%m-%Y"), by = 'day'),
-#'                 seq(as.Date("01-04-2002", format = "%d-%m-%Y"), 
-#'                     as.Date("31-10-2002", format = "%d-%m-%Y"), by = 'day'))
-#'SprTX <- CST_PeriodMean(exp, start = list(1, 4), end = list(31, 5))
-#'dim(SprTX$data)
-#'head(SprTX$Dates)
 #'@export
 CST_PeriodMean <- function(data, start = NULL, end = NULL,
                            time_dim = 'ftime', na.rm = FALSE,
@@ -89,17 +78,6 @@ CST_PeriodMean <- function(data, start = NULL, end = NULL,
 #'@examples
 #'exp <- CSTools::lonlat_prec$data
 #'SA <- PeriodMean(exp, time_dim = 'ftime')
-#'
-#'data <- array(rnorm(5 * 3 * 214 * 2),
-#'                    c(memb = 5, sdate = 3, time = 214, lon = 2)) 
-#'Dates <- c(seq(as.Date("01-04-2000", format = "%d-%m-%Y"), 
-#'                     as.Date("31-10-2000", format = "%d-%m-%Y"), by = 'day'),
-#'                 seq(as.Date("01-04-2001", format = "%d-%m-%Y"), 
-#'                     as.Date("31-10-2001", format = "%d-%m-%Y"), by = 'day'),
-#'                 seq(as.Date("01-04-2002", format = "%d-%m-%Y"), 
-#'                     as.Date("31-10-2002", format = "%d-%m-%Y"), by = 'day'))
-#'dim(Dates) <- c(time = 214, sdate = 3)
-#'SprTX <- PeriodMean(data, Dates, start = list(1, 4), end = list(31, 5))
 #'@export
 PeriodMean <- function(data, dates = NULL, start = NULL, end = NULL,
                        time_dim = 'time', na.rm = FALSE, ncores = NULL) {
diff --git a/R/QThreshold.R b/R/QThreshold.R
index 366cec2f305c5cea402986d246939970505708be..9549b93611f2da690d8c35a214337ad2960bb036 100644
--- a/R/QThreshold.R
+++ b/R/QThreshold.R
@@ -104,15 +104,9 @@ CST_QThreshold <- function(data, threshold, start = NULL, end = NULL,
 #'@importFrom ClimProjDiags Subset
 #'@examples
 #'threshold = 25
-#'data <- array(rnorm(25 * 3 * 214 * 2, mean = 26), 
-#'              c(member = 25, sdate = 3, time = 214, lon = 2)) 
+#'data <- array(rnorm(25 * 3 * 20 * 2, mean = 26), 
+#'              c(member = 25, sdate = 3, time = 20, lon = 2)) 
 #'thres_q <- QThreshold(data, threshold)
-#'data <- array(rnorm(1 * 3 * 214 * 2), c(member = 1, sdate = 3, time = 214, lon = 2))
-#'threshold <- array(1:428, c(time = 214, lon = 2))
-#'res <- QThreshold(data, threshold)
-#'threshold <- array(1:50, c(member = 25, lon = 2)) 
-#'# it can work using the version for separated members.
-#'threshold <- array(1:75, c(member = 25, sdate = 3))
 #'@export
 QThreshold <- function(data, threshold, dates = NULL, start = NULL, end = NULL,
                        time_dim = 'time', memb_dim = 'member', sdate_dim = 'sdate',
diff --git a/R/TotalSpellTimeExceedingThreshold.R b/R/TotalSpellTimeExceedingThreshold.R
index 5997a96bc48f7c1f4a73c22e1ed07a2a14dc2c45..0ecbe9ef97ea4fe6ed57f7c1f08f330a8f42c31a 100644
--- a/R/TotalSpellTimeExceedingThreshold.R
+++ b/R/TotalSpellTimeExceedingThreshold.R
@@ -21,20 +21,9 @@
 #'@import multiApply
 #'@examples
 #'exp <- CSTools::lonlat_data$exp
-#'TTSET <- CST_TotalSpellTimeExceedingThreshold(exp, threshold = 283, spell = 3)
-#'exp$data <- array(rnorm(5 * 3 * 214 * 2, mean = 25, sd = 3),
-#'                    c(member = 5, sdate = 3, ftime = 214, lon = 2)) 
-#'exp$Dates[[1]] <- c(seq(as.Date("01-05-2000", format = "%d-%m-%Y"), 
-#'                     as.Date("30-11-2000", format = "%d-%m-%Y"), by = 'day'),
-#'                 seq(as.Date("01-05-2001", format = "%d-%m-%Y"), 
-#'                     as.Date("30-11-2001", format = "%d-%m-%Y"), by = 'day'),
-#'                 seq(as.Date("01-05-2002", format = "%d-%m-%Y"), 
-#'                     as.Date("30-11-2002", format = "%d-%m-%Y"), by = 'day'))
-#'# compare with the scalar fixed threshold
-#'WSDI <- CST_TotalSpellTimeExceedingThreshold(exp, threshold = 35, spell = 6)
-#'# compare with percentile
-#'thresholdP <- CST_Threshold(exp, threshold = 0.9)
-#'WSDI_P <- CST_TotalSpellTimeExceedingThreshold(exp, threshold = thresholdP, spell = 6) 
+#'exp$data <- array(rnorm(5 * 3 * 20 * 2, mean = 25, sd = 3),
+#'                    c(member = 5, sdate = 3, ftime = 20, lon = 2))  
+#'TTSET <- CST_TotalSpellTimeExceedingThreshold(exp, threshold = 23, spell = 3)
 #'@export
 CST_TotalSpellTimeExceedingThreshold <- function(data, threshold, spell, op = '>',
                                             start = NULL, end = NULL,
@@ -101,9 +90,6 @@ CST_TotalSpellTimeExceedingThreshold <- function(data, threshold, spell, op = '>
 #'data <- array(rnorm(120), c(member = 1, sdate = 2, time = 20, lat = 4))
 #'threshold <- array(rnorm(4), c(lat = 4))
 #'total <- TotalSpellTimeExceedingThreshold(data, threshold, spell = 6)
-#'# compare with percentile
-#'thresholdP <- Threshold(data, threshold = 0.9)
-#'total_P <- TotalSpellTimeExceedingThreshold(data, threshold = thresholdP, spell = 6)
 #'@export
 TotalSpellTimeExceedingThreshold <- function(data, threshold, spell, op = '>', dates = NULL,
                                              start = NULL, end = NULL, time_dim = 'time',
@@ -148,8 +134,10 @@ TotalSpellTimeExceedingThreshold <- function(data, threshold, spell, op = '>', d
                    fun = .totalspellthres,
                    threshold = threshold, spell = spell, op = op,
                    ncores = ncores)$output1
-  } else if (time_dim %in% names(dim(threshold))) {
-    total <- Apply(list(data, threshold), target_dims = list(time_dim, time_dim),
+  } else if (any(time_dim %in% names(dim(threshold)))) {
+    total <- Apply(list(data, threshold),
+                 target_dims = list(time_dim, 
+                                    time_dim[time_dim %in% names(dim(threshold))]),
                  fun = .totalspellthres, spell = spell, op = op,
                  ncores = ncores)$output1
 
diff --git a/R/TotalTimeExceedingThreshold.R b/R/TotalTimeExceedingThreshold.R
index 4c64dbd559f6557d4e846b9c21f2fa5747d91f87..84d829c5e67eef14c76d6a02669e46293a1600b5 100644
--- a/R/TotalTimeExceedingThreshold.R
+++ b/R/TotalTimeExceedingThreshold.R
@@ -27,38 +27,8 @@
 #'@import multiApply
 #'@examples
 #'exp <- CSTools::lonlat_data$exp
+#'exp$data <- CSTools::lonlat_data$exp$data[1, 5, 3, 3, 1, 1]
 #'DOT <- CST_TotalTimeExceedingThreshold(exp, threshold = 280)
-#'exp$data <- array(rnorm(5 * 3 * 214 * 2, mean = 25, sd = 3),
-#'                    c(memb = 5, sdate = 3, ftime = 214, lon = 2)) 
-#'exp$Dates[[1]] <- c(seq(as.Date("01-05-2000", format = "%d-%m-%Y"), 
-#'                     as.Date("30-11-2000", format = "%d-%m-%Y"), by = 'day'),
-#'                 seq(as.Date("01-05-2001", format = "%d-%m-%Y"), 
-#'                     as.Date("30-11-2001", format = "%d-%m-%Y"), by = 'day'),
-#'                 seq(as.Date("01-05-2002", format = "%d-%m-%Y"), 
-#'                     as.Date("30-11-2002", format = "%d-%m-%Y"), by = 'day'))
-#'SU35 <- CST_TotalTimeExceedingThreshold(exp, threshold = 35)
-#'
-#'# For the case of comparing percentiles
-#'exp$data <- array(rnorm(5 * 3 * 214 * 2, mean = 28, sd = 10),
-#'                   c(member = 5, sdate = 3, ftime = 214, lon = 2))
-#'exp$Dates[[1]] <- c(seq(as.Date("01-05-2000", format = "%d-%m-%Y"), 
-#'                     as.Date("30-11-2000", format = "%d-%m-%Y"), by = 'day'),
-#'                 seq(as.Date("01-05-2001", format = "%d-%m-%Y"), 
-#'                     as.Date("30-11-2001", format = "%d-%m-%Y"), by = 'day'),
-#'                 seq(as.Date("01-05-2002", format = "%d-%m-%Y"), 
-#'                     as.Date("30-11-2002", format = "%d-%m-%Y"), by = 'day'))
-#'obs <- exp
-#'obs$data <- array(rnorm(3 * 214 * 2, mean = 27, sd = 9),
-#'                   c(sdate = 3, time = 214, lon = 2))
-#'# convert foreasts to probability
-#'exp_percentile <- CST_AbsToProbs(exp)
-#'# convert the fixed threshold to percentile with given observations
-#'obs_percentile <- CST_QThreshold(obs, threshold = 35) 
-#'
-#' # compare percentiles between forecasts and observations
-#'SU35_percentile <- CST_TotalTimeExceedingThreshold(exp,
-#'   threshold = obs_percentile)
-#'
 #'@export
 CST_TotalTimeExceedingThreshold <- function(data, threshold, op = '>',
                                             start = NULL, end = NULL,
@@ -127,29 +97,8 @@ CST_TotalTimeExceedingThreshold <- function(data, threshold, op = '>',
 #'
 #'@import multiApply
 #'@examples
-#'exp <- CSTools::lonlat_data$exp$data
-#'DOT <- TotalTimeExceedingThreshold(exp, threshold = 280, time_dim = 'ftime')
-#'data <- array(rnorm(5 * 3 * 214 * 2, mean = 25, sd = 3),
-#'                    c(memb = 5, sdate = 3, time = 214, lon = 2)) 
-#'Dates <- c(seq(as.Date("01-05-2000", format = "%d-%m-%Y"), 
-#'               as.Date("30-11-2000", format = "%d-%m-%Y"), by = 'day'),
-#'           seq(as.Date("01-05-2001", format = "%d-%m-%Y"), 
-#'               as.Date("30-11-2001", format = "%d-%m-%Y"), by = 'day'),
-#'           seq(as.Date("01-05-2002", format = "%d-%m-%Y"), 
-#'               as.Date("30-11-2002", format = "%d-%m-%Y"), by = 'day'))
-#'SU35 <- TotalTimeExceedingThreshold(data, threshold = 35)
-#'
-#'# For the case of comparing between percentiles
-#'exp <- array(rnorm(5 * 3 * 214 * 2, mean = 28, sd = 10),
-#'                   c(member = 5, sdate = 3, time = 214, lon = 2))
-#'obs <- array(rnorm(3 * 214 * 2, mean = 27, sd = 9),
-#'                   c(sdate = 3, time = 214, lon = 2))
-#'exp_percentile <- AbsToProbs(exp) # convert foreasts to probability
-#'# convert the fixed threshold to percentile with given observations:
-#'obs_percentile <- QThreshold(obs, threshold = 35)
-#'# compare percentiles between forecasts and observations:
-#'SU35_percentile <- TotalTimeExceedingThreshold(aperm(exp_percentile, c(2,1,3,4)),
-#'                                               obs_percentile)
+#'exp <- CSTools::lonlat_data$exp$data[1, 5, 3, 3, 1, 1]
+#'DOT <- TotalTimeExceedingThreshold(exp, threshold = 300, time_dim = 'ftime')
 #'
 #'@export
 TotalTimeExceedingThreshold <- function(data, threshold, op = '>',
@@ -212,11 +161,17 @@ TotalTimeExceedingThreshold <- function(data, threshold, op = '>',
                    fun = .exceedthreshold,
                    y = threshold, op = op, na.rm = na.rm,
                    ncores = ncores)$output1
-  } else if (time_dim %in% names(dim(threshold))) {
+  } else if (all(time_dim %in% names(dim(threshold)))) {
     total <- Apply(list(data, threshold),
                    target_dims = list(time_dim, time_dim),
                    fun = .exceedthreshold, op = op, na.rm = na.rm,
                    ncores = ncores)$output1
+  } else if (any(time_dim %in% names(dim(threshold)))) {
+    total <- Apply(list(data, threshold),
+                   target_dims = list(time_dim, 
+                                      time_dim[time_dim %in% names(dim(threshold))]),
+                   fun = .exceedthreshold, op = op, na.rm = na.rm,
+                   ncores = ncores)$output1   
   } else {
     total <- Apply(list(data, threshold),
                    target_dims = list(time_dim, NULL),
diff --git a/man/AbsToProbs.Rd b/man/AbsToProbs.Rd
index 7b56a7855d3488714a636acd9c749b77688f8537..bb4dc50de9240f0c7bdfb8244dd11495fad6d3e0 100644
--- a/man/AbsToProbs.Rd
+++ b/man/AbsToProbs.Rd
@@ -41,13 +41,15 @@ The Cumulative Distribution Function of a forecast is used to obtain the probabi
 \examples{
 exp <- CSTools::lonlat_prec$data
 exp_probs <- AbsToProbs(exp)
-data <- array(rnorm(5 * 3 * 214 * 2),
+\dontrun{
+data <- array(rnorm(5 * 2 * 61 * 2),
                    c(member = 5, sdate = 3, ftime = 214, lon = 2)) 
 Dates <- c(seq(as.Date("01-05-2000", format = "\%d-\%m-\%Y"), 
-                    as.Date("30-11-2000", format = "\%d-\%m-\%Y"), by = 'day'),
+                    as.Date("30-06-2000", format = "\%d-\%m-\%Y"), by = 'day'),
                 seq(as.Date("01-05-2001", format = "\%d-\%m-\%Y"), 
-                    as.Date("30-11-2001", format = "\%d-\%m-\%Y"), by = 'day'),
+                    as.Date("30-06-2001", format = "\%d-\%m-\%Y"), by = 'day'),
                 seq(as.Date("01-05-2002", format = "\%d-\%m-\%Y"), 
-                    as.Date("30-11-2002", format = "\%d-\%m-\%Y"), by = 'day'))
+                    as.Date("30-06-2002", format = "\%d-\%m-\%Y"), by = 'day'))
 exp_probs <- AbsToProbs(exp, start = list(21, 4), end = list(21, 6))
 }
+}
diff --git a/man/AccumulationExceedingThreshold.Rd b/man/AccumulationExceedingThreshold.Rd
index 25987809784fee76f624a20884aec708de17eadc..0dd414f7a476a9767de2c8b76da61d1c3d5e147e 100644
--- a/man/AccumulationExceedingThreshold.Rd
+++ b/man/AccumulationExceedingThreshold.Rd
@@ -50,8 +50,6 @@ Providing mean daily temperature data, the following agriculture indices for hea
  \item\code{GDD}{Summation of daily differences between daily average temperatures and 10°C between April 1st and October 31st}}
 }
 \examples{
-exp <- CSTools::lonlat_data$exp$data
-AET <- AccumulationExceedingThreshold(exp, threshold = 280, time_dim = 'ftime')
 # Assuming data is already (tasmax + tasmin)/2 - 10
 data <- array(rnorm(5 * 3 * 214 * 2, mean = 25, sd = 3),
                    c(memb = 5, sdate = 3, time = 214, lon = 2)) 
@@ -61,5 +59,6 @@ Dates <- c(seq(as.Date("01-05-2000", format = "\%d-\%m-\%Y"),
               as.Date("30-11-2001", format = "\%d-\%m-\%Y"), by = 'day'),
           seq(as.Date("01-05-2002", format = "\%d-\%m-\%Y"), 
               as.Date("30-11-2002", format = "\%d-\%m-\%Y"), by = 'day'))
-GDD <- AccumulationExceedingThreshold(data, threshold = 0, start = list(1, 4), end = list(31, 10))
+GDD <- AccumulationExceedingThreshold(data, threshold = 0, start = list(1, 4),
+                                     end = list(31, 10))
 }
diff --git a/man/CST_AccumulationExceedingThreshold.Rd b/man/CST_AccumulationExceedingThreshold.Rd
index 7aa58e060172ceab4a1f03585226c99e9694672f..4fbcebc97ef68a855a66971c331ce974b9072d45 100644
--- a/man/CST_AccumulationExceedingThreshold.Rd
+++ b/man/CST_AccumulationExceedingThreshold.Rd
@@ -48,17 +48,6 @@ Providing mean daily temperature data, the following agriculture indices for hea
 }
 \examples{
 exp <- CSTools::lonlat_data$exp
+exp$data <- CSTools::lonlat_data$exp$data[1, 5, 3, 3, 1, 1]
 DOT <- CST_AccumulationExceedingThreshold(exp, threshold = 280)
-# Assuming exp$data is already (tasmax + tasmin)/2 - 10
-exp$data <- array(rnorm(5 * 3 * 214 * 2, mean = 25, sd = 3),
-                   c(memb = 5, sdate = 3, ftime = 214, lon = 2)) 
-exp$Dates[[1]] <- c(seq(as.Date("01-05-2000", format = "\%d-\%m-\%Y"), 
-                    as.Date("30-11-2000", format = "\%d-\%m-\%Y"), by = 'day'),
-                seq(as.Date("01-05-2001", format = "\%d-\%m-\%Y"), 
-                    as.Date("30-11-2001", format = "\%d-\%m-\%Y"), by = 'day'),
-                seq(as.Date("01-05-2002", format = "\%d-\%m-\%Y"), 
-                    as.Date("30-11-2002", format = "\%d-\%m-\%Y"), by = 'day'))
-GDD <- CST_AccumulationExceedingThreshold(exp, threshold = 0,
-                                         start = list(1, 4), end = list(31, 10))
-
 }
diff --git a/man/CST_PeriodMean.Rd b/man/CST_PeriodMean.Rd
index bbc1fe350f14d11ae56bc1af07431feb8dbba825..95b4a36560f33c5975460872cb09f4d55145371a 100644
--- a/man/CST_PeriodMean.Rd
+++ b/man/CST_PeriodMean.Rd
@@ -40,15 +40,4 @@ Providing temperature data, two agriculture indices can be obtain by using this
 exp <- CSTools::lonlat_data$exp
 SA <- CST_PeriodMean(exp)
 
-exp$data <- array(rnorm(5 * 3 * 214 * 2),
-                   c(memb = 5, sdate = 3, ftime = 214, lon = 2)) 
-exp$Dates[[1]] <- c(seq(as.Date("01-04-2000", format = "\%d-\%m-\%Y"), 
-                    as.Date("31-10-2000", format = "\%d-\%m-\%Y"), by = 'day'),
-                seq(as.Date("01-04-2001", format = "\%d-\%m-\%Y"), 
-                    as.Date("31-10-2001", format = "\%d-\%m-\%Y"), by = 'day'),
-                seq(as.Date("01-04-2002", format = "\%d-\%m-\%Y"), 
-                    as.Date("31-10-2002", format = "\%d-\%m-\%Y"), by = 'day'))
-SprTX <- CST_PeriodMean(exp, start = list(1, 4), end = list(31, 5))
-dim(SprTX$data)
-head(SprTX$Dates)
 }
diff --git a/man/CST_TotalSpellTimeExceedingThreshold.Rd b/man/CST_TotalSpellTimeExceedingThreshold.Rd
index 0038d367b280726c75a32adf546902de09e98ec1..8e65e5827b9e6998ddabefa54420883ffa65dea6 100644
--- a/man/CST_TotalSpellTimeExceedingThreshold.Rd
+++ b/man/CST_TotalSpellTimeExceedingThreshold.Rd
@@ -44,20 +44,9 @@ This function requires the data and the threshold to be in the same units. The 9
 }
 \examples{
 exp <- CSTools::lonlat_data$exp
-TTSET <- CST_TotalSpellTimeExceedingThreshold(exp, threshold = 283, spell = 3)
-exp$data <- array(rnorm(5 * 3 * 214 * 2, mean = 25, sd = 3),
-                   c(member = 5, sdate = 3, ftime = 214, lon = 2)) 
-exp$Dates[[1]] <- c(seq(as.Date("01-05-2000", format = "\%d-\%m-\%Y"), 
-                    as.Date("30-11-2000", format = "\%d-\%m-\%Y"), by = 'day'),
-                seq(as.Date("01-05-2001", format = "\%d-\%m-\%Y"), 
-                    as.Date("30-11-2001", format = "\%d-\%m-\%Y"), by = 'day'),
-                seq(as.Date("01-05-2002", format = "\%d-\%m-\%Y"), 
-                    as.Date("30-11-2002", format = "\%d-\%m-\%Y"), by = 'day'))
-# compare with the scalar fixed threshold
-WSDI <- CST_TotalSpellTimeExceedingThreshold(exp, threshold = 35, spell = 6)
-# compare with percentile
-thresholdP <- CST_Threshold(exp, threshold = 0.9)
-WSDI_P <- CST_TotalSpellTimeExceedingThreshold(exp, threshold = thresholdP, spell = 6) 
+exp$data <- array(rnorm(5 * 3 * 20 * 2, mean = 25, sd = 3),
+                   c(member = 5, sdate = 3, ftime = 20, lon = 2))  
+TTSET <- CST_TotalSpellTimeExceedingThreshold(exp, threshold = 23, spell = 3)
 }
 \seealso{
 [Threshold()] and [AbsToProbs()].
diff --git a/man/CST_TotalTimeExceedingThreshold.Rd b/man/CST_TotalTimeExceedingThreshold.Rd
index 1f4ae842512353e132cbe9f75622676e212e50ff..50124fe72818e7b3c0bf9f5abeca496e4a94ef57 100644
--- a/man/CST_TotalTimeExceedingThreshold.Rd
+++ b/man/CST_TotalTimeExceedingThreshold.Rd
@@ -51,36 +51,6 @@ Providing maximum temperature daily data, the following agriculture indices for
 }
 \examples{
 exp <- CSTools::lonlat_data$exp
+exp$data <- CSTools::lonlat_data$exp$data[1, 5, 3, 3, 1, 1]
 DOT <- CST_TotalTimeExceedingThreshold(exp, threshold = 280)
-exp$data <- array(rnorm(5 * 3 * 214 * 2, mean = 25, sd = 3),
-                   c(memb = 5, sdate = 3, ftime = 214, lon = 2)) 
-exp$Dates[[1]] <- c(seq(as.Date("01-05-2000", format = "\%d-\%m-\%Y"), 
-                    as.Date("30-11-2000", format = "\%d-\%m-\%Y"), by = 'day'),
-                seq(as.Date("01-05-2001", format = "\%d-\%m-\%Y"), 
-                    as.Date("30-11-2001", format = "\%d-\%m-\%Y"), by = 'day'),
-                seq(as.Date("01-05-2002", format = "\%d-\%m-\%Y"), 
-                    as.Date("30-11-2002", format = "\%d-\%m-\%Y"), by = 'day'))
-SU35 <- CST_TotalTimeExceedingThreshold(exp, threshold = 35)
-
-# For the case of comparing percentiles
-exp$data <- array(rnorm(5 * 3 * 214 * 2, mean = 28, sd = 10),
-                  c(member = 5, sdate = 3, ftime = 214, lon = 2))
-exp$Dates[[1]] <- c(seq(as.Date("01-05-2000", format = "\%d-\%m-\%Y"), 
-                    as.Date("30-11-2000", format = "\%d-\%m-\%Y"), by = 'day'),
-                seq(as.Date("01-05-2001", format = "\%d-\%m-\%Y"), 
-                    as.Date("30-11-2001", format = "\%d-\%m-\%Y"), by = 'day'),
-                seq(as.Date("01-05-2002", format = "\%d-\%m-\%Y"), 
-                    as.Date("30-11-2002", format = "\%d-\%m-\%Y"), by = 'day'))
-obs <- exp
-obs$data <- array(rnorm(3 * 214 * 2, mean = 27, sd = 9),
-                  c(sdate = 3, time = 214, lon = 2))
-# convert foreasts to probability
-exp_percentile <- CST_AbsToProbs(exp)
-# convert the fixed threshold to percentile with given observations
-obs_percentile <- CST_QThreshold(obs, threshold = 35) 
-
-# compare percentiles between forecasts and observations
-SU35_percentile <- CST_TotalTimeExceedingThreshold(exp,
-  threshold = obs_percentile)
-
 }
diff --git a/man/PeriodMean.Rd b/man/PeriodMean.Rd
index b0fd0c3bc3bd7930b3e53016761a9c2cab3f04b9..6062daa437dadd3ec1c5cfbc748bb7f53c2853ae 100644
--- a/man/PeriodMean.Rd
+++ b/man/PeriodMean.Rd
@@ -42,15 +42,4 @@ Providing temperature data, two agriculture indices can be obtain by using this
 \examples{
 exp <- CSTools::lonlat_prec$data
 SA <- PeriodMean(exp, time_dim = 'ftime')
-
-data <- array(rnorm(5 * 3 * 214 * 2),
-                   c(memb = 5, sdate = 3, time = 214, lon = 2)) 
-Dates <- c(seq(as.Date("01-04-2000", format = "\%d-\%m-\%Y"), 
-                    as.Date("31-10-2000", format = "\%d-\%m-\%Y"), by = 'day'),
-                seq(as.Date("01-04-2001", format = "\%d-\%m-\%Y"), 
-                    as.Date("31-10-2001", format = "\%d-\%m-\%Y"), by = 'day'),
-                seq(as.Date("01-04-2002", format = "\%d-\%m-\%Y"), 
-                    as.Date("31-10-2002", format = "\%d-\%m-\%Y"), by = 'day'))
-dim(Dates) <- c(time = 214, sdate = 3)
-SprTX <- PeriodMean(data, Dates, start = list(1, 4), end = list(31, 5))
 }
diff --git a/man/QThreshold.Rd b/man/QThreshold.Rd
index 257f21eee90ed7103fc8108cc9353d384613819c..8f55badb94c1120165ccda35cb6dbbd10ce17963 100644
--- a/man/QThreshold.Rd
+++ b/man/QThreshold.Rd
@@ -52,13 +52,7 @@ The sample used will depend on the dimensions of the data provided and the dimen
 }
 \examples{
 threshold = 25
-data <- array(rnorm(25 * 3 * 214 * 2, mean = 26), 
-             c(member = 25, sdate = 3, time = 214, lon = 2)) 
+data <- array(rnorm(25 * 3 * 20 * 2, mean = 26), 
+             c(member = 25, sdate = 3, time = 20, lon = 2)) 
 thres_q <- QThreshold(data, threshold)
-data <- array(rnorm(1 * 3 * 214 * 2), c(member = 1, sdate = 3, time = 214, lon = 2))
-threshold <- array(1:428, c(time = 214, lon = 2))
-res <- QThreshold(data, threshold)
-threshold <- array(1:50, c(member = 25, lon = 2)) 
-# it can work using the version for separated members.
-threshold <- array(1:75, c(member = 25, sdate = 3))
 }
diff --git a/man/TotalSpellTimeExceedingThreshold.Rd b/man/TotalSpellTimeExceedingThreshold.Rd
index 79f3e5d4f16497e59f34deec6d023ad6b99fcb74..bbbfe357c65818e3eff3e50e9ed64609192a50bf 100644
--- a/man/TotalSpellTimeExceedingThreshold.Rd
+++ b/man/TotalSpellTimeExceedingThreshold.Rd
@@ -52,9 +52,6 @@ This function considers NA values as the end of the spell. For a different behav
 data <- array(rnorm(120), c(member = 1, sdate = 2, time = 20, lat = 4))
 threshold <- array(rnorm(4), c(lat = 4))
 total <- TotalSpellTimeExceedingThreshold(data, threshold, spell = 6)
-# compare with percentile
-thresholdP <- Threshold(data, threshold = 0.9)
-total_P <- TotalSpellTimeExceedingThreshold(data, threshold = thresholdP, spell = 6)
 }
 \seealso{
 [Threshold()] and [AbsToProbs()].
diff --git a/man/TotalTimeExceedingThreshold.Rd b/man/TotalTimeExceedingThreshold.Rd
index 213db45b58e83cedbad8bf69418e7b73617bb213..855ef869f4818c6da3e31644c94c36bdc35ed69d 100644
--- a/man/TotalTimeExceedingThreshold.Rd
+++ b/man/TotalTimeExceedingThreshold.Rd
@@ -53,28 +53,7 @@ Providing maximum temperature daily data, the following agriculture indices for
 }
 }
 \examples{
-exp <- CSTools::lonlat_data$exp$data
-DOT <- TotalTimeExceedingThreshold(exp, threshold = 280, time_dim = 'ftime')
-data <- array(rnorm(5 * 3 * 214 * 2, mean = 25, sd = 3),
-                   c(memb = 5, sdate = 3, time = 214, lon = 2)) 
-Dates <- c(seq(as.Date("01-05-2000", format = "\%d-\%m-\%Y"), 
-              as.Date("30-11-2000", format = "\%d-\%m-\%Y"), by = 'day'),
-          seq(as.Date("01-05-2001", format = "\%d-\%m-\%Y"), 
-              as.Date("30-11-2001", format = "\%d-\%m-\%Y"), by = 'day'),
-          seq(as.Date("01-05-2002", format = "\%d-\%m-\%Y"), 
-              as.Date("30-11-2002", format = "\%d-\%m-\%Y"), by = 'day'))
-SU35 <- TotalTimeExceedingThreshold(data, threshold = 35)
-
-# For the case of comparing between percentiles
-exp <- array(rnorm(5 * 3 * 214 * 2, mean = 28, sd = 10),
-                  c(member = 5, sdate = 3, time = 214, lon = 2))
-obs <- array(rnorm(3 * 214 * 2, mean = 27, sd = 9),
-                  c(sdate = 3, time = 214, lon = 2))
-exp_percentile <- AbsToProbs(exp) # convert foreasts to probability
-# convert the fixed threshold to percentile with given observations:
-obs_percentile <- QThreshold(obs, threshold = 35)
-# compare percentiles between forecasts and observations:
-SU35_percentile <- TotalTimeExceedingThreshold(aperm(exp_percentile, c(2,1,3,4)),
-                                              obs_percentile)
+exp <- CSTools::lonlat_data$exp$data[1, 5, 3, 3, 1, 1]
+DOT <- TotalTimeExceedingThreshold(exp, threshold = 300, time_dim = 'ftime')
 
 }
diff --git a/vignettes/EnergyIndicators.Rmd b/vignettes/EnergyIndicators.Rmd
index 3b3d90cfa9f7e61b6cec7e1d27e347530e8f83c6..1399044f2bde3617c56bfeaa56d874fd9a0ad487 100644
--- a/vignettes/EnergyIndicators.Rmd
+++ b/vignettes/EnergyIndicators.Rmd
@@ -32,6 +32,7 @@ The `mean` and `sd` of the WPD can be employed to summarize the wind resource in
 
 ```{r}
 library(CSIndicators)
+set.seed(1)
 wind <- rweibull(n = 1000, shape = 2, scale = 6)
 WPD <- WindPowerDensity(wind)
 mean(WPD)