From a402a2fe9e548ffe97536cc7a7b758c2fb37ea56 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Mon, 2 Nov 2020 18:08:55 +0100
Subject: [PATCH 01/43] Upload New File

---
 vignettes/Figures/MostLikelyTercile_fig1.png | Bin 0 -> 103054 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 vignettes/Figures/MostLikelyTercile_fig1.png

diff --git a/vignettes/Figures/MostLikelyTercile_fig1.png b/vignettes/Figures/MostLikelyTercile_fig1.png
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-- 
GitLab


From e7777618fe5972343d16a8af80cc0f880f72f555 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Mon, 2 Nov 2020 18:09:36 +0100
Subject: [PATCH 02/43] Upload New File

---
 vignettes/Figures/MostLikelyTercile_fig2.png | Bin 0 -> 64438 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 vignettes/Figures/MostLikelyTercile_fig2.png

diff --git a/vignettes/Figures/MostLikelyTercile_fig2.png b/vignettes/Figures/MostLikelyTercile_fig2.png
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GitLab


From 5beefe8cedef1049a4df9b3210338f34104865d5 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Mon, 2 Nov 2020 18:09:56 +0100
Subject: [PATCH 03/43] Upload New File

---
 vignettes/Figures/MostLikelyTercile_fig3.png | Bin 0 -> 103146 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 vignettes/Figures/MostLikelyTercile_fig3.png

diff --git a/vignettes/Figures/MostLikelyTercile_fig3.png b/vignettes/Figures/MostLikelyTercile_fig3.png
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-- 
GitLab


From bc9663d7e28941fc507cd5b04e1712fc06ab1cc3 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Mon, 2 Nov 2020 18:10:36 +0100
Subject: [PATCH 04/43] Upload New File

---
 vignettes/example_mostlikelytercile.md | 178 +++++++++++++++++++++++++
 1 file changed, 178 insertions(+)
 create mode 100644 vignettes/example_mostlikelytercile.md

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
new file mode 100644
index 00000000..e89541d9
--- /dev/null
+++ b/vignettes/example_mostlikelytercile.md
@@ -0,0 +1,178 @@
+---
+author: "Louis-Philippe Caron and Núria Pérez-Zanón"
+date: "`r Sys.Date()`"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteEngine{knitr::knitr}
+  %\VignetteIndexEntry{Most Likely Tercile}
+  %\usepackage[utf8]{inputenc}
+---
+
+
+Most Likely Tercile
+========================
+
+This vignette aims to illustrate a step-by-step example of how to determine the most likely tercile of a forecast using CSTools functionalities.
+
+### 1- Required packages 
+
+The functions to compute the Weather Regimes are part of CSTools while plotting functions are included in s2dverification package:
+
+```r
+library(CSTools)
+library(s2dverification)
+library(easyVerification)
+library(multiApply)
+library(CSTools)
+require(zeallot)
+```
+
+### 2 - We define 2 functions
+
+#### A function to produce our terciles
+```r
+quantile_aux = function(data, n_categories = 3){
+  data = as.vector(data)
+  q = quantile(x = data, probs = 1:(n_categories - 1) / n_categories, type = 8, na.rm = TRUE)
+  return(q)
+}
+```
+
+#### A function to convert the continuous ensemble forecast to counts of ensemble members per category
+```r
+c2p <- function(forecast, t) {
+  colMeans(easyVerification::convert2prob(as.vector(forecast), threshold = as.vector(t)))
+}
+```
+
+### 3 - We define the region to look at
+```r
+lat_min = 25
+lat_max = 52
+lon_min = -10  
+lon_max = 40
+```
+
+### 4 - We define the start dates for the hindcasts/forecast (in this case, May 1st 1981-2020) and create a sequence of dates.
+```r
+ini <- 1981
+fin <- 2020
+numyears <- fin - ini +1
+mth = '05'
+start <- as.Date(paste(ini, mth, "01", sep = ""), "%Y%m%d")
+end <- as.Date(paste(fin, mth, "01", sep = ""), "%Y%m%d")
+dateseq <- format(seq(start, end, by = "year"), "%Y%m%d")
+```
+
+###  5 - We define a target season: JJA.
+```r
+mon1 <- 2
+monf <- 4
+```
+
+### 6 - Finally, we define the model, the reference, the variable of interest and the common grid. 
+```r
+forecastsys <- 'system5c3s'
+obs <- 'erainterim'
+grid <- "256x128"
+clim_var = 'tas'
+```
+
+### 7 - We load the data.
+```r
+c(exp,obs) %<-% CST_Load(var = clim_var, exp = forecastsys,
+                        obs = obs, sdates = dateseq, leadtimemin = mon1, leadtimemax = monf,
+                        lonmin = lon_min, lonmax = lon_max, latmin = lat_min, latmax = lat_max, 
+                        storefreq = "monthly", sampleperiod = 1,  nmember = 10, 
+                        output = "lonlat", method = "bilinear", 
+                        grid = paste("r", grid, sep = ""))
+```
+
+### 8 - We extract the latitude and longitude of the data.
+```r
+Lat <- exp$lat
+Lon <- exp$lon
+```
+#
+### 9 - We extract the anomalies from the forecasts and the observations.
+```r
+c(ano_exp, ano_obs) %<-% CST_Anomaly(exp = exp, obs = obs, cross = TRUE, memb = TRUE)
+```
+
+### 10 - We compute the seasonal mean of forecasts and observations.
+```r
+ano_exp$data <- Mean1Dim(ano_exp$data,4)
+
+ano_obs$data <- Mean1Dim(ano_obs$data,4)
+```
+
+
+### 11 - We create our terciles using Apply.
+```r
+quantiles <- multiApply::Apply(data = ano_exp$data, target_dims = c('sdate','member'), 
+                               quantile_aux, output_dims = 'bin', ncores = 4)$output1
+```
+
+
+### 12 - We calculate the probability using Apply and c2p.
+```r
+probs <- Apply(data = list(forecast = ano_exp$data, t = quantiles),
+                        target_dims = list('member', c('bin')), #, 'member')),
+                        fun = c2p, output_dims = 'bin', ncores = 4)$output1
+```
+
+### 13 - We extract the data for the latest forecast.
+```r
+prob_map <- drop(probs)[,numyears,,]
+names(dim(prob_map)) <- c('bin','lat','lon')
+```
+
+### 14 - We plot the most likely quantile.
+```
+CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_width=1.5, legend_scale = 0.8, 
+                                   toptitle = paste0('Most likely tercile - ',variable,' - ECMWF System5 - JJA 2020'),
+                                   width = 12, height = 7, fileout = paste0(clim_var, '_most_likely_tercile.png'))
+```
+<img src="./Figures/MostLikelyTercile_fig1.png"/>
+
+#
+### We can also mask the regions for which the model doesn't have skill. For this, we will calculate the RPSS and exclude the region for which the value is smaller or equal to 0.
+
+### 15 - First, we evaluate the RPSS, i.e. the skill at predicting the right tercile.
+```r
+v2 <- aperm(drop(ano_obs$data),c(3,2,1))
+v2 <- v2[,,1:(numyears-2)]
+v1 <- aperm(drop(ano_exp$data),c(4,3,2,1))
+v1 <- v1[,,1:(numyears-2),]
+
+RPSS <- veriApply('FairRpss',fcst=v1,obs=v2,ensdim=4,tdim=3,prob =c(1/3,2/3))
+```
+
+### 16 - We can plot the RPSS
+```r
+PlotEquiMap(RPSS[[1]], lat=Lat,lon=Lon, brks=seq(-1,1,by=0.1),filled.continents=F, 
+            fileout = paste0(figures_path, clim_var, '_RPSS.png'))
+```
+<img src="./Figures/MostLikelyTercile_fig2.png"/>
+
+### 17 - From the RPSS, we create a mask. Regions with RPSS<=0 will be masked. 
+```r
+mask_rpss <- RPSS[[1]]
+mask_rpss[RPSS[[1]] <= 0] <- 1
+mask_rpss[is.na(RPSS[[1]])] <- 1
+mask_rpss[RPSS[[1]] > 0] <- 0
+```
+
+### 18 - Finally, we plot the latest forecast using the mask
+```r
+CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_width=1.5, legend_scale = 0.8, 
+                                   mask = t(mask_rpss),
+                                   toptitle = paste0('Most likely tercile - ',variable,' - ECMWF System5 - JJA 2020'),
+                                   width = 12, height = 7, 
+                                   fileout = paste0(clim_var, '_most_likely_tercile_mask.png'))
+```
+<img src="./Figures/MostLikelyTercile_fig3.png"/>  
+
+
+
+
-- 
GitLab


From ba0e89fa3758529b9dcaf5704a22e849f8bf677a Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Mon, 2 Nov 2020 18:14:05 +0100
Subject: [PATCH 05/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index e89541d9..62ae1de4 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -148,7 +148,7 @@ v1 <- v1[,,1:(numyears-2),]
 RPSS <- veriApply('FairRpss',fcst=v1,obs=v2,ensdim=4,tdim=3,prob =c(1/3,2/3))
 ```
 
-### 16 - We can plot the RPSS
+### 16 - We can plot the RPSS.
 ```r
 PlotEquiMap(RPSS[[1]], lat=Lat,lon=Lon, brks=seq(-1,1,by=0.1),filled.continents=F, 
             fileout = paste0(figures_path, clim_var, '_RPSS.png'))
@@ -163,7 +163,7 @@ mask_rpss[is.na(RPSS[[1]])] <- 1
 mask_rpss[RPSS[[1]] > 0] <- 0
 ```
 
-### 18 - Finally, we plot the latest forecast using the mask
+### 18 - Finally, we plot the latest forecast using the mask.
 ```r
 CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_width=1.5, legend_scale = 0.8, 
                                    mask = t(mask_rpss),
-- 
GitLab


From 93d8927eef9e9337aac8d6c3d2e2103d1b3f4700 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Tue, 3 Nov 2020 16:35:09 +0100
Subject: [PATCH 06/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index 62ae1de4..e091c15c 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -12,24 +12,23 @@ vignette: >
 Most Likely Tercile
 ========================
 
-This vignette aims to illustrate a step-by-step example of how to determine the most likely tercile of a forecast using CSTools functionalities.
+In this example, we will use CSTools to visualize a probabilistic forecast of summer near-surface temperature produced by ECMWF System5. The (re-)forecasts used are initilized on May 1st for the period 1981-2020. The target for the forecast is June-August (JJA) 2020. 
 
 ### 1- Required packages 
 
-The functions to compute the Weather Regimes are part of CSTools while plotting functions are included in s2dverification package:
+To run this vignette, the following R packages should be installed and loaded:
 
 ```r
 library(CSTools)
 library(s2dverification)
 library(easyVerification)
 library(multiApply)
-library(CSTools)
 require(zeallot)
 ```
 
-### 2 - We define 2 functions
+### 2 - We define 2 functions:
 
-#### A function to produce our terciles
+#### A function to produce our terciles.
 ```r
 quantile_aux = function(data, n_categories = 3){
   data = as.vector(data)
@@ -38,14 +37,14 @@ quantile_aux = function(data, n_categories = 3){
 }
 ```
 
-#### A function to convert the continuous ensemble forecast to counts of ensemble members per category
+#### A function to convert the continuous ensemble forecast to counts of ensemble members per category.
 ```r
 c2p <- function(forecast, t) {
   colMeans(easyVerification::convert2prob(as.vector(forecast), threshold = as.vector(t)))
 }
 ```
 
-### 3 - We define the region to look at
+### 3 - We define the region for the forecast. In this example, we focus on the Mediterranean region.
 ```r
 lat_min = 25
 lat_max = 52
@@ -53,6 +52,8 @@ lon_min = -10
 lon_max = 40
 ```
 
+If the boundaries are not specified, the domain will be the entire globe.
+
 ### 4 - We define the start dates for the hindcasts/forecast (in this case, May 1st 1981-2020) and create a sequence of dates.
 ```r
 ini <- 1981
@@ -70,7 +71,7 @@ mon1 <- 2
 monf <- 4
 ```
 
-### 6 - Finally, we define the model, the reference, the variable of interest and the common grid. 
+### 6 - Finally, we define the model, the reference, the variable of interest and the common grid onto which to interpolate. 
 ```r
 forecastsys <- 'system5c3s'
 obs <- 'erainterim'
-- 
GitLab


From 873a2cb9405e6904e834f0b06b5d653af9dce29a Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Tue, 3 Nov 2020 17:20:50 +0100
Subject: [PATCH 07/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index e091c15c..b97dd830 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -44,7 +44,9 @@ c2p <- function(forecast, t) {
 }
 ```
 
-### 3 - We define the region for the forecast. In this example, we focus on the Mediterranean region.
+### 3 - We then define a few parameters.
+
+We first define the region for the forecast. In this example, we focus on the Mediterranean region.
 ```r
 lat_min = 25
 lat_max = 52
@@ -54,7 +56,7 @@ lon_max = 40
 
 If the boundaries are not specified, the domain will be the entire globe.
 
-### 4 - We define the start dates for the hindcasts/forecast (in this case, May 1st 1981-2020) and create a sequence of dates.
+We define the start dates for the hindcasts/forecast (in this case, May 1st 1981-2020) and create a sequence of dates.
 ```r
 ini <- 1981
 fin <- 2020
-- 
GitLab


From 291fa1d46854376fb311f14c2c6cc931ac92c36c Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Tue, 3 Nov 2020 17:42:37 +0100
Subject: [PATCH 08/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 34 +++++++++++++-------------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index b97dd830..84b45074 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -28,7 +28,7 @@ require(zeallot)
 
 ### 2 - We define 2 functions:
 
-#### A function to produce our terciles.
+A function to produce our terciles.
 ```r
 quantile_aux = function(data, n_categories = 3){
   data = as.vector(data)
@@ -37,7 +37,7 @@ quantile_aux = function(data, n_categories = 3){
 }
 ```
 
-#### A function to convert the continuous ensemble forecast to counts of ensemble members per category.
+A function to convert the continuous ensemble forecast to counts of ensemble members per category.
 ```r
 c2p <- function(forecast, t) {
   colMeans(easyVerification::convert2prob(as.vector(forecast), threshold = as.vector(t)))
@@ -67,13 +67,13 @@ end <- as.Date(paste(fin, mth, "01", sep = ""), "%Y%m%d")
 dateseq <- format(seq(start, end, by = "year"), "%Y%m%d")
 ```
 
-###  5 - We define a target season: JJA.
+We define a target season: JJA.
 ```r
 mon1 <- 2
 monf <- 4
 ```
 
-### 6 - Finally, we define the model, the reference, the variable of interest and the common grid onto which to interpolate. 
+Finally, we define the forecast system, the reference, the variable of interest and the common grid onto which to interpolate. 
 ```r
 forecastsys <- 'system5c3s'
 obs <- 'erainterim'
@@ -81,7 +81,7 @@ grid <- "256x128"
 clim_var = 'tas'
 ```
 
-### 7 - We load the data.
+### 4 - We load the data.
 ```r
 c(exp,obs) %<-% CST_Load(var = clim_var, exp = forecastsys,
                         obs = obs, sdates = dateseq, leadtimemin = mon1, leadtimemax = monf,
@@ -91,18 +91,18 @@ c(exp,obs) %<-% CST_Load(var = clim_var, exp = forecastsys,
                         grid = paste("r", grid, sep = ""))
 ```
 
-### 8 - We extract the latitude and longitude of the data.
+### 5 - We extract the latitude and longitude of the data.
 ```r
 Lat <- exp$lat
 Lon <- exp$lon
 ```
 #
-### 9 - We extract the anomalies from the forecasts and the observations.
+### 6 - We extract the anomalies from the forecasts and the observations.
 ```r
 c(ano_exp, ano_obs) %<-% CST_Anomaly(exp = exp, obs = obs, cross = TRUE, memb = TRUE)
 ```
 
-### 10 - We compute the seasonal mean of forecasts and observations.
+### 7 - We compute the seasonal mean of forecasts and observations.
 ```r
 ano_exp$data <- Mean1Dim(ano_exp$data,4)
 
@@ -110,27 +110,27 @@ ano_obs$data <- Mean1Dim(ano_obs$data,4)
 ```
 
 
-### 11 - We create our terciles using Apply.
+### 8 - We create our terciles using Apply.
 ```r
 quantiles <- multiApply::Apply(data = ano_exp$data, target_dims = c('sdate','member'), 
                                quantile_aux, output_dims = 'bin', ncores = 4)$output1
 ```
 
 
-### 12 - We calculate the probability using Apply and c2p.
+### 9 - We calculate the probability using Apply and c2p.
 ```r
 probs <- Apply(data = list(forecast = ano_exp$data, t = quantiles),
                         target_dims = list('member', c('bin')), #, 'member')),
                         fun = c2p, output_dims = 'bin', ncores = 4)$output1
 ```
 
-### 13 - We extract the data for the latest forecast.
+### 10 - We extract the data for the latest forecast.
 ```r
 prob_map <- drop(probs)[,numyears,,]
 names(dim(prob_map)) <- c('bin','lat','lon')
 ```
 
-### 14 - We plot the most likely quantile.
+### 11 - We plot the most likely quantile.
 ```
 CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_width=1.5, legend_scale = 0.8, 
                                    toptitle = paste0('Most likely tercile - ',variable,' - ECMWF System5 - JJA 2020'),
@@ -139,9 +139,9 @@ CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_
 <img src="./Figures/MostLikelyTercile_fig1.png"/>
 
 #
-### We can also mask the regions for which the model doesn't have skill. For this, we will calculate the RPSS and exclude the region for which the value is smaller or equal to 0.
+We can also mask the regions for which the model doesn't have skill. For this, we will calculate the RPSS and exclude the region for which the value is smaller or equal to 0.
 
-### 15 - First, we evaluate the RPSS, i.e. the skill at predicting the right tercile.
+### 12 - First, we evaluate the RPSS, i.e. the skill at predicting the right tercile.
 ```r
 v2 <- aperm(drop(ano_obs$data),c(3,2,1))
 v2 <- v2[,,1:(numyears-2)]
@@ -151,14 +151,14 @@ v1 <- v1[,,1:(numyears-2),]
 RPSS <- veriApply('FairRpss',fcst=v1,obs=v2,ensdim=4,tdim=3,prob =c(1/3,2/3))
 ```
 
-### 16 - We can plot the RPSS.
+### 13 - We can plot the RPSS.
 ```r
 PlotEquiMap(RPSS[[1]], lat=Lat,lon=Lon, brks=seq(-1,1,by=0.1),filled.continents=F, 
             fileout = paste0(figures_path, clim_var, '_RPSS.png'))
 ```
 <img src="./Figures/MostLikelyTercile_fig2.png"/>
 
-### 17 - From the RPSS, we create a mask. Regions with RPSS<=0 will be masked. 
+### 14 - From the RPSS, we create a mask. Regions with RPSS<=0 will be masked. 
 ```r
 mask_rpss <- RPSS[[1]]
 mask_rpss[RPSS[[1]] <= 0] <- 1
@@ -166,7 +166,7 @@ mask_rpss[is.na(RPSS[[1]])] <- 1
 mask_rpss[RPSS[[1]] > 0] <- 0
 ```
 
-### 18 - Finally, we plot the latest forecast using the mask.
+### 15 - Finally, we plot the latest forecast using the mask.
 ```r
 CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_width=1.5, legend_scale = 0.8, 
                                    mask = t(mask_rpss),
-- 
GitLab


From 55c8ee2eeaf8b376ed1272a5852c7b290549f00f Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Tue, 3 Nov 2020 18:10:06 +0100
Subject: [PATCH 09/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index 84b45074..7191e07a 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -67,7 +67,7 @@ end <- as.Date(paste(fin, mth, "01", sep = ""), "%Y%m%d")
 dateseq <- format(seq(start, end, by = "year"), "%Y%m%d")
 ```
 
-We define a target season: JJA.
+We define the forecast time. For the target season JJA we have 
 ```r
 mon1 <- 2
 monf <- 4
-- 
GitLab


From 6ebcfdb5e28224655f5816232e16300ed1474134 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Tue, 3 Nov 2020 18:18:18 +0100
Subject: [PATCH 10/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index 7191e07a..d3bf282e 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -105,7 +105,6 @@ c(ano_exp, ano_obs) %<-% CST_Anomaly(exp = exp, obs = obs, cross = TRUE, memb =
 ### 7 - We compute the seasonal mean of forecasts and observations.
 ```r
 ano_exp$data <- Mean1Dim(ano_exp$data,4)
-
 ano_obs$data <- Mean1Dim(ano_obs$data,4)
 ```
 
@@ -138,8 +137,10 @@ CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_
 ```
 <img src="./Figures/MostLikelyTercile_fig1.png"/>
 
-#
-We can also mask the regions for which the model doesn't have skill. For this, we will calculate the RPSS and exclude the region for which the value is smaller or equal to 0.
+The forecast calls for above average temperature over most of the Mediterranean basin and near average temperature for some smaller regions as well. But can we trust this forecast? 
+For this, it is useful evaluate the skill of the system at forecasting near surface temperature over the period for which hindcasts are available. We can then use this information to mask the regions for which the system doesn't have skill. 
+
+In order to do this, we will first calculate the RPSS and then exclude/mask from the forecasts the regions for which the RPSS is smaller or equal to 0 (no improvement with respect to climatology).
 
 ### 12 - First, we evaluate the RPSS, i.e. the skill at predicting the right tercile.
 ```r
-- 
GitLab


From 19fe3367534394e9d9cc52971b9a9f8449b84aa1 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Tue, 3 Nov 2020 18:20:34 +0100
Subject: [PATCH 11/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index d3bf282e..0315363c 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -140,7 +140,7 @@ CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_
 The forecast calls for above average temperature over most of the Mediterranean basin and near average temperature for some smaller regions as well. But can we trust this forecast? 
 For this, it is useful evaluate the skill of the system at forecasting near surface temperature over the period for which hindcasts are available. We can then use this information to mask the regions for which the system doesn't have skill. 
 
-In order to do this, we will first calculate the RPSS and then exclude/mask from the forecasts the regions for which the RPSS is smaller or equal to 0 (no improvement with respect to climatology).
+In order to do this, we will first calculate the ranked probability skill score (RPSS) and then exclude/mask from the forecasts the regions for which the RPSS is smaller or equal to 0 (no improvement with respect to climatology).
 
 ### 12 - First, we evaluate the RPSS, i.e. the skill at predicting the right tercile.
 ```r
-- 
GitLab


From cf245682d1bd7db5f95479794e6fbafc26a1ae21 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Tue, 3 Nov 2020 18:24:07 +0100
Subject: [PATCH 12/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index 0315363c..ae511454 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -142,7 +142,7 @@ For this, it is useful evaluate the skill of the system at forecasting near surf
 
 In order to do this, we will first calculate the ranked probability skill score (RPSS) and then exclude/mask from the forecasts the regions for which the RPSS is smaller or equal to 0 (no improvement with respect to climatology).
 
-### 12 - First, we evaluate the RPSS, i.e. the skill at predicting the right tercile.
+### 12 - First, we evaluate and plot the RPSS.
 ```r
 v2 <- aperm(drop(ano_obs$data),c(3,2,1))
 v2 <- v2[,,1:(numyears-2)]
@@ -150,16 +150,15 @@ v1 <- aperm(drop(ano_exp$data),c(4,3,2,1))
 v1 <- v1[,,1:(numyears-2),]
 
 RPSS <- veriApply('FairRpss',fcst=v1,obs=v2,ensdim=4,tdim=3,prob =c(1/3,2/3))
-```
 
-### 13 - We can plot the RPSS.
-```r
 PlotEquiMap(RPSS[[1]], lat=Lat,lon=Lon, brks=seq(-1,1,by=0.1),filled.continents=F, 
             fileout = paste0(figures_path, clim_var, '_RPSS.png'))
 ```
 <img src="./Figures/MostLikelyTercile_fig2.png"/>
 
-### 14 - From the RPSS, we create a mask. Regions with RPSS<=0 will be masked. 
+Areas displayed in red (RPSS>0) are areas for which the forecast system shows skill above climatology whereas areas in blue (such as a large part of the Iberian peninsula) are areas for which the model does not.
+
+### 13 - From the RPSS, we create a mask. Regions with RPSS<=0 will be masked. 
 ```r
 mask_rpss <- RPSS[[1]]
 mask_rpss[RPSS[[1]] <= 0] <- 1
-- 
GitLab


From 8679811e6573f53325a3f8c74a5657aa1aa3d467 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Tue, 3 Nov 2020 18:28:29 +0100
Subject: [PATCH 13/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index ae511454..32b88b05 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -156,7 +156,7 @@ PlotEquiMap(RPSS[[1]], lat=Lat,lon=Lon, brks=seq(-1,1,by=0.1),filled.continents=
 ```
 <img src="./Figures/MostLikelyTercile_fig2.png"/>
 
-Areas displayed in red (RPSS>0) are areas for which the forecast system shows skill above climatology whereas areas in blue (such as a large part of the Iberian peninsula) are areas for which the model does not.
+Areas displayed in red (RPSS>0) are areas for which the forecast system shows skill above climatology whereas areas in blue (such as a large part of the Iberian peninsula) are areas for which the model does not. We will mask the areas in blue.
 
 ### 13 - From the RPSS, we create a mask. Regions with RPSS<=0 will be masked. 
 ```r
@@ -166,7 +166,7 @@ mask_rpss[is.na(RPSS[[1]])] <- 1
 mask_rpss[RPSS[[1]] > 0] <- 0
 ```
 
-### 15 - Finally, we plot the latest forecast using the mask.
+### 14 - Finally, we plot the latest forecast, as in the previous step, but add the mask we just created.
 ```r
 CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_width=1.5, legend_scale = 0.8, 
                                    mask = t(mask_rpss),
@@ -176,6 +176,6 @@ CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_
 ```
 <img src="./Figures/MostLikelyTercile_fig3.png"/>  
 
-
+We obtain the same figure as before, but this time, we only display the areas for which the model has skill at forecasting the right tercile.
 
 
-- 
GitLab


From 1a202450200fe824893bbd7bffe8ee4995aea2de Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Tue, 3 Nov 2020 18:30:08 +0100
Subject: [PATCH 14/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index 32b88b05..b801e0bb 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -12,7 +12,7 @@ vignette: >
 Most Likely Tercile
 ========================
 
-In this example, we will use CSTools to visualize a probabilistic forecast of summer near-surface temperature produced by ECMWF System5. The (re-)forecasts used are initilized on May 1st for the period 1981-2020. The target for the forecast is June-August (JJA) 2020. 
+In this example, we will use CSTools to visualize a probabilistic forecast (most likely tercile) of summer near-surface temperature produced by ECMWF System5. The (re-)forecasts used are initilized on May 1st for the period 1981-2020. The target for the forecast is June-August (JJA) 2020. 
 
 ### 1- Required packages 
 
-- 
GitLab


From 9527abce3a0169ca068211cbaf311e8d0d26a4bd Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Wed, 4 Nov 2020 11:39:18 +0100
Subject: [PATCH 15/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 41 ++++----------------------
 1 file changed, 6 insertions(+), 35 deletions(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index b801e0bb..861dcef1 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -21,28 +21,11 @@ To run this vignette, the following R packages should be installed and loaded:
 ```r
 library(CSTools)
 library(s2dverification)
-library(easyVerification)
 library(multiApply)
 require(zeallot)
 ```
 
-### 2 - We define 2 functions:
 
-A function to produce our terciles.
-```r
-quantile_aux = function(data, n_categories = 3){
-  data = as.vector(data)
-  q = quantile(x = data, probs = 1:(n_categories - 1) / n_categories, type = 8, na.rm = TRUE)
-  return(q)
-}
-```
-
-A function to convert the continuous ensemble forecast to counts of ensemble members per category.
-```r
-c2p <- function(forecast, t) {
-  colMeans(easyVerification::convert2prob(as.vector(forecast), threshold = as.vector(t)))
-}
-```
 
 ### 3 - We then define a few parameters.
 
@@ -109,35 +92,23 @@ ano_obs$data <- Mean1Dim(ano_obs$data,4)
 ```
 
 
-### 8 - We create our terciles using Apply.
+### 9 - We calculate the probability of each tercile for the latest forecast (2020) using the function ProbBins in s2dv.
 ```r
-quantiles <- multiApply::Apply(data = ano_exp$data, target_dims = c('sdate','member'), 
-                               quantile_aux, output_dims = 'bin', ncores = 4)$output1
+PB <- ProbBins(ano_exp$data, fcyr = numyears, thr = c(1/3, 2/3), quantile = TRUE, 
+               posdates = 3, posdim = 2, compPeriod = "Without fcyr")
 ```
 
 
-### 9 - We calculate the probability using Apply and c2p.
-```r
-probs <- Apply(data = list(forecast = ano_exp$data, t = quantiles),
-                        target_dims = list('member', c('bin')), #, 'member')),
-                        fun = c2p, output_dims = 'bin', ncores = 4)$output1
-```
-
-### 10 - We extract the data for the latest forecast.
-```r
-prob_map <- drop(probs)[,numyears,,]
-names(dim(prob_map)) <- c('bin','lat','lon')
-```
-
 ### 11 - We plot the most likely quantile.
 ```
+prob_map <- Mean1Dim(Mean1Dim(Mean1Dim(PB,3),2),2)
 CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_width=1.5, legend_scale = 0.8, 
                                    toptitle = paste0('Most likely tercile - ',variable,' - ECMWF System5 - JJA 2020'),
                                    width = 12, height = 7, fileout = paste0(clim_var, '_most_likely_tercile.png'))
 ```
 <img src="./Figures/MostLikelyTercile_fig1.png"/>
 
-The forecast calls for above average temperature over most of the Mediterranean basin and near average temperature for some smaller regions as well. But can we trust this forecast? 
+The forecast calls for above average temperature over most of the Mediterranean basin and near average temperature for some smaller regions as well. But can this forecast be trusted? 
 For this, it is useful evaluate the skill of the system at forecasting near surface temperature over the period for which hindcasts are available. We can then use this information to mask the regions for which the system doesn't have skill. 
 
 In order to do this, we will first calculate the ranked probability skill score (RPSS) and then exclude/mask from the forecasts the regions for which the RPSS is smaller or equal to 0 (no improvement with respect to climatology).
@@ -176,6 +147,6 @@ CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_
 ```
 <img src="./Figures/MostLikelyTercile_fig3.png"/>  
 
-We obtain the same figure as before, but this time, we only display the areas for which the model has skill at forecasting the right tercile.
+We obtain the same figure as before, but this time, we only display the areas for which the model has skill at forecasting the right tercile. The gray regions represents area where the system doesn't have sufficient skill over the verification period.
 
 
-- 
GitLab


From b992565ebb5a0ba4ab512516f627f0e90fc655e0 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Wed, 4 Nov 2020 11:40:19 +0100
Subject: [PATCH 16/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 22 ++++++++++------------
 1 file changed, 10 insertions(+), 12 deletions(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index 861dcef1..71e3f864 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -25,9 +25,7 @@ library(multiApply)
 require(zeallot)
 ```
 
-
-
-### 3 - We then define a few parameters.
+### 2 - We then define a few parameters.
 
 We first define the region for the forecast. In this example, we focus on the Mediterranean region.
 ```r
@@ -64,7 +62,7 @@ grid <- "256x128"
 clim_var = 'tas'
 ```
 
-### 4 - We load the data.
+### 3 - We load the data.
 ```r
 c(exp,obs) %<-% CST_Load(var = clim_var, exp = forecastsys,
                         obs = obs, sdates = dateseq, leadtimemin = mon1, leadtimemax = monf,
@@ -74,32 +72,32 @@ c(exp,obs) %<-% CST_Load(var = clim_var, exp = forecastsys,
                         grid = paste("r", grid, sep = ""))
 ```
 
-### 5 - We extract the latitude and longitude of the data.
+### 4 - We extract the latitude and longitude of the data.
 ```r
 Lat <- exp$lat
 Lon <- exp$lon
 ```
 #
-### 6 - We extract the anomalies from the forecasts and the observations.
+### 5 - We extract the anomalies from the forecasts and the observations.
 ```r
 c(ano_exp, ano_obs) %<-% CST_Anomaly(exp = exp, obs = obs, cross = TRUE, memb = TRUE)
 ```
 
-### 7 - We compute the seasonal mean of forecasts and observations.
+### 6 - We compute the seasonal mean of forecasts and observations.
 ```r
 ano_exp$data <- Mean1Dim(ano_exp$data,4)
 ano_obs$data <- Mean1Dim(ano_obs$data,4)
 ```
 
 
-### 9 - We calculate the probability of each tercile for the latest forecast (2020) using the function ProbBins in s2dv.
+### 7 - We calculate the probability of each tercile for the latest forecast (2020) using the function ProbBins in s2dv.
 ```r
 PB <- ProbBins(ano_exp$data, fcyr = numyears, thr = c(1/3, 2/3), quantile = TRUE, 
                posdates = 3, posdim = 2, compPeriod = "Without fcyr")
 ```
 
 
-### 11 - We plot the most likely quantile.
+### 8 - We plot the most likely quantile.
 ```
 prob_map <- Mean1Dim(Mean1Dim(Mean1Dim(PB,3),2),2)
 CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_width=1.5, legend_scale = 0.8, 
@@ -113,7 +111,7 @@ For this, it is useful evaluate the skill of the system at forecasting near surf
 
 In order to do this, we will first calculate the ranked probability skill score (RPSS) and then exclude/mask from the forecasts the regions for which the RPSS is smaller or equal to 0 (no improvement with respect to climatology).
 
-### 12 - First, we evaluate and plot the RPSS.
+### 9 - First, we evaluate and plot the RPSS.
 ```r
 v2 <- aperm(drop(ano_obs$data),c(3,2,1))
 v2 <- v2[,,1:(numyears-2)]
@@ -129,7 +127,7 @@ PlotEquiMap(RPSS[[1]], lat=Lat,lon=Lon, brks=seq(-1,1,by=0.1),filled.continents=
 
 Areas displayed in red (RPSS>0) are areas for which the forecast system shows skill above climatology whereas areas in blue (such as a large part of the Iberian peninsula) are areas for which the model does not. We will mask the areas in blue.
 
-### 13 - From the RPSS, we create a mask. Regions with RPSS<=0 will be masked. 
+### 10 - From the RPSS, we create a mask. Regions with RPSS<=0 will be masked. 
 ```r
 mask_rpss <- RPSS[[1]]
 mask_rpss[RPSS[[1]] <= 0] <- 1
@@ -137,7 +135,7 @@ mask_rpss[is.na(RPSS[[1]])] <- 1
 mask_rpss[RPSS[[1]] > 0] <- 0
 ```
 
-### 14 - Finally, we plot the latest forecast, as in the previous step, but add the mask we just created.
+### 11 - Finally, we plot the latest forecast, as in the previous step, but add the mask we just created.
 ```r
 CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_width=1.5, legend_scale = 0.8, 
                                    mask = t(mask_rpss),
-- 
GitLab


From 768da8ebec14222fd79542b8167512c44c3fb68e Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Wed, 4 Nov 2020 12:09:01 +0100
Subject: [PATCH 17/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index 71e3f864..14057268 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -9,7 +9,7 @@ vignette: >
 ---
 
 
-Most Likely Tercile
+Computing and displaying the most likely tercile of a seasonal forecast
 ========================
 
 In this example, we will use CSTools to visualize a probabilistic forecast (most likely tercile) of summer near-surface temperature produced by ECMWF System5. The (re-)forecasts used are initilized on May 1st for the period 1981-2020. The target for the forecast is June-August (JJA) 2020. 
-- 
GitLab


From 56105e6e7d42cf1ae2426757abacb7262bf18601 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Wed, 4 Nov 2020 12:10:54 +0100
Subject: [PATCH 19/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index 14057268..99d376b2 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -22,7 +22,7 @@ To run this vignette, the following R packages should be installed and loaded:
 library(CSTools)
 library(s2dverification)
 library(multiApply)
-require(zeallot)
+library(zeallot)
 ```
 
 ### 2 - We then define a few parameters.
-- 
GitLab


From 5b8ccf80ebc2aa3c2c2f327ead4cb677c2cfa102 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Wed, 4 Nov 2020 12:11:43 +0100
Subject: [PATCH 20/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index 99d376b2..57674657 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -37,7 +37,7 @@ lon_max = 40
 
 If the boundaries are not specified, the domain will be the entire globe.
 
-We define the start dates for the hindcasts/forecast (in this case, May 1st 1981-2020) and create a sequence of dates.
+We also define the start dates for the hindcasts/forecast (in this case, May 1st 1981-2020) and create a sequence of dates that will be required by the load function.
 ```r
 ini <- 1981
 fin <- 2020
-- 
GitLab


From 1358a5f50a900edb2ae38dc73d81b426a559b4ff Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Wed, 4 Nov 2020 12:13:37 +0100
Subject: [PATCH 21/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index 57674657..a92fd08c 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -48,7 +48,7 @@ end <- as.Date(paste(fin, mth, "01", sep = ""), "%Y%m%d")
 dateseq <- format(seq(start, end, by = "year"), "%Y%m%d")
 ```
 
-We define the forecast time. For the target season JJA we have 
+We then define the target months for the forecast (i.e. JJA). The months are given relative to the start date (May in this case).
 ```r
 mon1 <- 2
 monf <- 4
-- 
GitLab


From c64032247be69ece0ee5d2ab5e789411b2c529d7 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Wed, 4 Nov 2020 12:14:30 +0100
Subject: [PATCH 22/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index a92fd08c..5085db8c 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -54,7 +54,7 @@ mon1 <- 2
 monf <- 4
 ```
 
-Finally, we define the forecast system, the reference, the variable of interest and the common grid onto which to interpolate. 
+Finally, we define the forecast system, an observational reference, the variable of interest and the common grid onto which to interpolate. 
 ```r
 forecastsys <- 'system5c3s'
 obs <- 'erainterim'
-- 
GitLab


From e437808a7ba7fddcc0e9ef6ab3eef61ecd433f41 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Wed, 4 Nov 2020 12:18:47 +0100
Subject: [PATCH 23/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 29 ++++++++++++++++----------
 1 file changed, 18 insertions(+), 11 deletions(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index 5085db8c..a0961ac7 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -72,32 +72,39 @@ c(exp,obs) %<-% CST_Load(var = clim_var, exp = forecastsys,
                         grid = paste("r", grid, sep = ""))
 ```
 
-### 4 - We extract the latitude and longitude of the data.
+Loading the data using CST_Load allows to obtain two lists, one for the experimental data and another for the observe data, with the same elements and compatible dimensions of the data element:
 ```r
-Lat <- exp$lat
-Lon <- exp$lon
+> dim(exp$data)
+dataset  member   sdate   ftime     lat     lon 
+      1      10      40       3      19      36 
+> dim(obs$data)
+dataset  member   sdate   ftime     lat     lon 
+      1       1      40       3      19      36 
 ```
-#
-### 5 - We extract the anomalies from the forecasts and the observations.
+
+### 4 - We extract the latitude and longitude and the anomalies from the forecasts and the observations.
 ```r
+Lat <- exp$lat
+Lon <- exp$lon
+
 c(ano_exp, ano_obs) %<-% CST_Anomaly(exp = exp, obs = obs, cross = TRUE, memb = TRUE)
 ```
 
-### 6 - We compute the seasonal mean of forecasts and observations.
+### 5 - We compute the seasonal mean of both forecasts and observations by averaging over the ftime dimension.
 ```r
 ano_exp$data <- Mean1Dim(ano_exp$data,4)
 ano_obs$data <- Mean1Dim(ano_obs$data,4)
 ```
 
 
-### 7 - We calculate the probability of each tercile for the latest forecast (2020) using the function ProbBins in s2dv.
+### 6 - We calculate the probability of each tercile for the latest forecast (2020) using the function ProbBins in s2dv.
 ```r
 PB <- ProbBins(ano_exp$data, fcyr = numyears, thr = c(1/3, 2/3), quantile = TRUE, 
                posdates = 3, posdim = 2, compPeriod = "Without fcyr")
 ```
 
 
-### 8 - We plot the most likely quantile.
+### 7 - We plot the most likely quantile.
 ```
 prob_map <- Mean1Dim(Mean1Dim(Mean1Dim(PB,3),2),2)
 CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_width=1.5, legend_scale = 0.8, 
@@ -111,7 +118,7 @@ For this, it is useful evaluate the skill of the system at forecasting near surf
 
 In order to do this, we will first calculate the ranked probability skill score (RPSS) and then exclude/mask from the forecasts the regions for which the RPSS is smaller or equal to 0 (no improvement with respect to climatology).
 
-### 9 - First, we evaluate and plot the RPSS.
+### 8 - First, we evaluate and plot the RPSS.
 ```r
 v2 <- aperm(drop(ano_obs$data),c(3,2,1))
 v2 <- v2[,,1:(numyears-2)]
@@ -127,7 +134,7 @@ PlotEquiMap(RPSS[[1]], lat=Lat,lon=Lon, brks=seq(-1,1,by=0.1),filled.continents=
 
 Areas displayed in red (RPSS>0) are areas for which the forecast system shows skill above climatology whereas areas in blue (such as a large part of the Iberian peninsula) are areas for which the model does not. We will mask the areas in blue.
 
-### 10 - From the RPSS, we create a mask. Regions with RPSS<=0 will be masked. 
+### 9 - From the RPSS, we create a mask. Regions with RPSS<=0 will be masked. 
 ```r
 mask_rpss <- RPSS[[1]]
 mask_rpss[RPSS[[1]] <= 0] <- 1
@@ -135,7 +142,7 @@ mask_rpss[is.na(RPSS[[1]])] <- 1
 mask_rpss[RPSS[[1]] > 0] <- 0
 ```
 
-### 11 - Finally, we plot the latest forecast, as in the previous step, but add the mask we just created.
+### 10 - Finally, we plot the latest forecast, as in the previous step, but add the mask we just created.
 ```r
 CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_width=1.5, legend_scale = 0.8, 
                                    mask = t(mask_rpss),
-- 
GitLab


From a16247313a28a017bbca3b03e19279c01791901e Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Wed, 4 Nov 2020 12:24:32 +0100
Subject: [PATCH 24/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index a0961ac7..52d2855c 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -97,16 +97,16 @@ ano_obs$data <- Mean1Dim(ano_obs$data,4)
 ```
 
 
-### 6 - We calculate the probability of each tercile for the latest forecast (2020) using the function ProbBins in s2dv.
+### 6 - We evaluate which tercile is forecasted by each ensemble member for the latest forecast (2020) using the function ProbBins in s2dv and then average the results along the member dimension to obtain the probability of each tercile.
 ```r
-PB <- ProbBins(ano_exp$data, fcyr = numyears, thr = c(1/3, 2/3), quantile = TRUE, 
-               posdates = 3, posdim = 2, compPeriod = "Without fcyr")
+PB <- ProbBins(ano_exp$data, fcyr = numyears, thr = c(1/3, 2/3), quantile = TRUE, posdates = 3, posdim = 2, compPeriod = "Without fcyr")
+prob_map <- Mean1Dim(Mean1Dim(Mean1Dim(PB,3),2),2)
+
 ```
 
 
-### 7 - We plot the most likely quantile.
+### 7 - We then plot the most likely quantile using the CSTools function PlotMostLikelyQuantileMap. 
 ```
-prob_map <- Mean1Dim(Mean1Dim(Mean1Dim(PB,3),2),2)
 CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_width=1.5, legend_scale = 0.8, 
                                    toptitle = paste0('Most likely tercile - ',variable,' - ECMWF System5 - JJA 2020'),
                                    width = 12, height = 7, fileout = paste0(clim_var, '_most_likely_tercile.png'))
-- 
GitLab


From 2f6d3b21887cc5795bfc38b556a5a9e73fbde6b6 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Wed, 4 Nov 2020 12:25:17 +0100
Subject: [PATCH 25/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index 52d2855c..36ca257b 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -25,9 +25,9 @@ library(multiApply)
 library(zeallot)
 ```
 
-### 2 - We then define a few parameters.
+### 2 - We first define a few parameters.
 
-We first define the region for the forecast. In this example, we focus on the Mediterranean region.
+We start by defining the region we are interested in. In this example, we focus on the Mediterranean region.
 ```r
 lat_min = 25
 lat_max = 52
-- 
GitLab


From 110a84390630483e59c143e8f20b1dec22d51dde Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Wed, 4 Nov 2020 12:30:22 +0100
Subject: [PATCH 26/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index 36ca257b..ffb5ed2f 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -97,7 +97,9 @@ ano_obs$data <- Mean1Dim(ano_obs$data,4)
 ```
 
 
-### 6 - We evaluate which tercile is forecasted by each ensemble member for the latest forecast (2020) using the function ProbBins in s2dv and then average the results along the member dimension to obtain the probability of each tercile.
+### 6 - We compute the probabilities of each tercile
+
+We evaluate which tercile is forecasted by each ensemble member for the latest forecast (2020) using the function ProbBins in s2dv and then average the results along the member dimension to obtain the probability of each tercile.
 ```r
 PB <- ProbBins(ano_exp$data, fcyr = numyears, thr = c(1/3, 2/3), quantile = TRUE, posdates = 3, posdim = 2, compPeriod = "Without fcyr")
 prob_map <- Mean1Dim(Mean1Dim(Mean1Dim(PB,3),2),2)
-- 
GitLab


From bbeee7598eaf6cec2851828ff70922da3445a464 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Wed, 4 Nov 2020 12:33:30 +0100
Subject: [PATCH 27/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index ffb5ed2f..2b07108d 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -154,6 +154,6 @@ CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_
 ```
 <img src="./Figures/MostLikelyTercile_fig3.png"/>  
 
-We obtain the same figure as before, but this time, we only display the areas for which the model has skill at forecasting the right tercile. The gray regions represents area where the system doesn't have sufficient skill over the verification period.
+We obtain the same figure as before, but this time, we only display the areas for which the model has skill at forecasting the right tercile. The gray regions represents areas where the system doesn't have sufficient skill over the verification period.
 
 
-- 
GitLab


From 565609e639ad799e4fa140b4c7201d4b931a93dc Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Wed, 4 Nov 2020 17:57:48 +0100
Subject: [PATCH 28/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index 2b07108d..40dbb0a6 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -110,7 +110,7 @@ prob_map <- Mean1Dim(Mean1Dim(Mean1Dim(PB,3),2),2)
 ### 7 - We then plot the most likely quantile using the CSTools function PlotMostLikelyQuantileMap. 
 ```
 CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_width=1.5, legend_scale = 0.8, 
-                                   toptitle = paste0('Most likely tercile - ',variable,' - ECMWF System5 - JJA 2020'),
+                                   toptitle = paste0('Most likely tercile - ',clim_var,' - ECMWF System5 - JJA 2020'),
                                    width = 12, height = 7, fileout = paste0(clim_var, '_most_likely_tercile.png'))
 ```
 <img src="./Figures/MostLikelyTercile_fig1.png"/>
-- 
GitLab


From 2d219de18d4c956352d4355a38f881ae28987c03 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Wed, 4 Nov 2020 18:02:12 +0100
Subject: [PATCH 29/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index 40dbb0a6..0d1bb18f 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -23,6 +23,7 @@ library(CSTools)
 library(s2dverification)
 library(multiApply)
 library(zeallot)
+library(easyVerification)
 ```
 
 ### 2 - We first define a few parameters.
@@ -92,8 +93,8 @@ c(ano_exp, ano_obs) %<-% CST_Anomaly(exp = exp, obs = obs, cross = TRUE, memb =
 
 ### 5 - We compute the seasonal mean of both forecasts and observations by averaging over the ftime dimension.
 ```r
-ano_exp$data <- Mean1Dim(ano_exp$data,4)
-ano_obs$data <- Mean1Dim(ano_obs$data,4)
+ano_exp$data <- MeanDims(ano_exp$data, 'ftime')
+ano_obs$data <- MeanDims(ano_obs$data, 'ftime')
 ```
 
 
@@ -102,8 +103,7 @@ ano_obs$data <- Mean1Dim(ano_obs$data,4)
 We evaluate which tercile is forecasted by each ensemble member for the latest forecast (2020) using the function ProbBins in s2dv and then average the results along the member dimension to obtain the probability of each tercile.
 ```r
 PB <- ProbBins(ano_exp$data, fcyr = numyears, thr = c(1/3, 2/3), quantile = TRUE, posdates = 3, posdim = 2, compPeriod = "Without fcyr")
-prob_map <- Mean1Dim(Mean1Dim(Mean1Dim(PB,3),2),2)
-
+prob_map <- MeanDims(PB, c('sdate', 'member', 'dataset'))
 ```
 
 
-- 
GitLab


From 0dda0a05039e4f5a751543ffdc2c2251b1208efe Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Wed, 4 Nov 2020 18:04:54 +0100
Subject: [PATCH 30/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index 0d1bb18f..31e264d6 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -111,7 +111,7 @@ prob_map <- MeanDims(PB, c('sdate', 'member', 'dataset'))
 ```
 CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_width=1.5, legend_scale = 0.8, 
                                    toptitle = paste0('Most likely tercile - ',clim_var,' - ECMWF System5 - JJA 2020'),
-                                   width = 12, height = 7, fileout = paste0(clim_var, '_most_likely_tercile.png'))
+                                   width = 12, height = 7)
 ```
 <img src="./Figures/MostLikelyTercile_fig1.png"/>
 
@@ -129,8 +129,7 @@ v1 <- v1[,,1:(numyears-2),]
 
 RPSS <- veriApply('FairRpss',fcst=v1,obs=v2,ensdim=4,tdim=3,prob =c(1/3,2/3))
 
-PlotEquiMap(RPSS[[1]], lat=Lat,lon=Lon, brks=seq(-1,1,by=0.1),filled.continents=F, 
-            fileout = paste0(figures_path, clim_var, '_RPSS.png'))
+PlotEquiMap(RPSS[[1]], lat=Lat,lon=Lon, brks=seq(-1,1,by=0.1),filled.continents=F)
 ```
 <img src="./Figures/MostLikelyTercile_fig2.png"/>
 
@@ -149,8 +148,7 @@ mask_rpss[RPSS[[1]] > 0] <- 0
 CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_width=1.5, legend_scale = 0.8, 
                                    mask = t(mask_rpss),
                                    toptitle = paste0('Most likely tercile - ',variable,' - ECMWF System5 - JJA 2020'),
-                                   width = 12, height = 7, 
-                                   fileout = paste0(clim_var, '_most_likely_tercile_mask.png'))
+                                   width = 12, height = 7)
 ```
 <img src="./Figures/MostLikelyTercile_fig3.png"/>  
 
-- 
GitLab


From d0eed517ffb81cbb79b616959eda5a3ac4135820 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Wed, 4 Nov 2020 18:12:26 +0100
Subject: [PATCH 31/43] Update example_mostlikelytercile.md

---
 vignettes/example_mostlikelytercile.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index 31e264d6..c7ba198b 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -12,7 +12,7 @@ vignette: >
 Computing and displaying the most likely tercile of a seasonal forecast
 ========================
 
-In this example, we will use CSTools to visualize a probabilistic forecast (most likely tercile) of summer near-surface temperature produced by ECMWF System5. The (re-)forecasts used are initilized on May 1st for the period 1981-2020. The target for the forecast is June-August (JJA) 2020. 
+In this example, we will use CSTools to visualize a probabilistic forecast (most likely tercile) of summer near-surface temperature produced by ECMWF System5. The (re-)forecasts used are initilized on May 1st for the period 1981-2020. The target for the forecast is June-August (JJA) 2020. The forecast data are taken from the Copernicus Climate Data Store.
 
 ### 1- Required packages 
 
-- 
GitLab


From 26df390ecc706cf76347a80c848449e5fa249641 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Thu, 5 Nov 2020 09:39:58 +0100
Subject: [PATCH 32/43] Titles, code length, using Reorder and fixes

---
 vignettes/example_mostlikelytercile.md | 122 ++++++++++++++++++-------
 1 file changed, 87 insertions(+), 35 deletions(-)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/example_mostlikelytercile.md
index c7ba198b..929f6932 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/example_mostlikelytercile.md
@@ -14,21 +14,23 @@ Computing and displaying the most likely tercile of a seasonal forecast
 
 In this example, we will use CSTools to visualize a probabilistic forecast (most likely tercile) of summer near-surface temperature produced by ECMWF System5. The (re-)forecasts used are initilized on May 1st for the period 1981-2020. The target for the forecast is June-August (JJA) 2020. The forecast data are taken from the Copernicus Climate Data Store.
 
-### 1- Required packages 
+### 1. Preliminary setup 
 
 To run this vignette, the following R packages should be installed and loaded:
 
 ```r
 library(CSTools)
+library(s2dv)
 library(s2dverification)
 library(multiApply)
 library(zeallot)
 library(easyVerification)
 ```
 
-### 2 - We first define a few parameters.
+### 2. Loading the data
+
+We first define a few parameters. We start by defining the region we are interested in. In this example, we focus on the Mediterranean region.
 
-We start by defining the region we are interested in. In this example, we focus on the Mediterranean region.
 ```r
 lat_min = 25
 lat_max = 52
@@ -39,6 +41,8 @@ lon_max = 40
 If the boundaries are not specified, the domain will be the entire globe.
 
 We also define the start dates for the hindcasts/forecast (in this case, May 1st 1981-2020) and create a sequence of dates that will be required by the load function.
+
+
 ```r
 ini <- 1981
 fin <- 2020
@@ -49,13 +53,18 @@ end <- as.Date(paste(fin, mth, "01", sep = ""), "%Y%m%d")
 dateseq <- format(seq(start, end, by = "year"), "%Y%m%d")
 ```
 
-We then define the target months for the forecast (i.e. JJA). The months are given relative to the start date (May in this case).
+We then define the target months for the forecast (i.e. JJA). The months are given relative to the start date (May in this case) considering that monthly simulations are being analyzed.
+
+
 ```r
 mon1 <- 2
 monf <- 4
 ```
 
+
 Finally, we define the forecast system, an observational reference, the variable of interest and the common grid onto which to interpolate. 
+
+
 ```r
 forecastsys <- 'system5c3s'
 obs <- 'erainterim'
@@ -63,17 +72,21 @@ grid <- "256x128"
 clim_var = 'tas'
 ```
 
-### 3 - We load the data.
+Finally, the data is loaded using `CST_Load`: 
+
+
 ```r
-c(exp,obs) %<-% CST_Load(var = clim_var, exp = forecastsys,
-                        obs = obs, sdates = dateseq, leadtimemin = mon1, leadtimemax = monf,
-                        lonmin = lon_min, lonmax = lon_max, latmin = lat_min, latmax = lat_max, 
-                        storefreq = "monthly", sampleperiod = 1,  nmember = 10, 
-                        output = "lonlat", method = "bilinear", 
-                        grid = paste("r", grid, sep = ""))
+c(exp,obs) %<-% CST_Load(var = clim_var, exp = forecastsys, obs = obs,
+                         sdates = dateseq, leadtimemin = mon1, leadtimemax = monf,
+                         lonmin = lon_min, lonmax = lon_max,
+                         latmin = lat_min, latmax = lat_max, 
+                         storefreq = "monthly", sampleperiod = 1,  nmember = 10, 
+                         output = "lonlat", method = "bilinear", 
+                         grid = paste("r", grid, sep = ""))
 ```
 
-Loading the data using CST_Load allows to obtain two lists, one for the experimental data and another for the observe data, with the same elements and compatible dimensions of the data element:
+Loading the data using CST_Load allows to obtain two objects, one for the experimental data and another for the observe data, with the same elements and compatible dimensions of the data element:
+
 ```r
 > dim(exp$data)
 dataset  member   sdate   ftime     lat     lon 
@@ -83,59 +96,95 @@ dataset  member   sdate   ftime     lat     lon
       1       1      40       3      19      36 
 ```
 
-### 4 - We extract the latitude and longitude and the anomalies from the forecasts and the observations.
+The latitude and longitude and the anomalies from the forecasts and the observations are saved for later used:
+
+
 ```r
 Lat <- exp$lat
 Lon <- exp$lon
+```
+
+### 3. Computing probabilities
+
+First, anomalies of forecast and observations are computed using cross-validation on individual members:
 
+
+```
 c(ano_exp, ano_obs) %<-% CST_Anomaly(exp = exp, obs = obs, cross = TRUE, memb = TRUE)
 ```
 
-### 5 - We compute the seasonal mean of both forecasts and observations by averaging over the ftime dimension.
+
+The seasonal mean of both forecasts and observations are computed by averaging over the ftime dimension.
+
+
 ```r
 ano_exp$data <- MeanDims(ano_exp$data, 'ftime')
 ano_obs$data <- MeanDims(ano_obs$data, 'ftime')
 ```
 
 
-### 6 - We compute the probabilities of each tercile
+Finally, the probabilities of each tercile are computed by evaluating which tercile is forecasted by each ensemble member for the latest forecast (2020) using the function `ProbBins` in **s2dverification** and then average the results along the member dimension to obtain the probability of each tercile.
+
 
-We evaluate which tercile is forecasted by each ensemble member for the latest forecast (2020) using the function ProbBins in s2dv and then average the results along the member dimension to obtain the probability of each tercile.
 ```r
-PB <- ProbBins(ano_exp$data, fcyr = numyears, thr = c(1/3, 2/3), quantile = TRUE, posdates = 3, posdim = 2, compPeriod = "Without fcyr")
+PB <- ProbBins(ano_exp$data, fcyr = numyears, thr = c(1/3, 2/3), quantile = TRUE,
+               posdates = 3, posdim = 2, compPeriod = "Without fcyr")
 prob_map <- MeanDims(PB, c('sdate', 'member', 'dataset'))
 ```
 
 
-### 7 - We then plot the most likely quantile using the CSTools function PlotMostLikelyQuantileMap. 
+### 4. Visualization with PlotMostLikelyQuantileMap
+
+
+We then plot the most likely quantile using the **CSTools** function `PlotMostLikelyQuantileMap. 
+
+
 ```
-CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_width=1.5, legend_scale = 0.8, 
-                                   toptitle = paste0('Most likely tercile - ',clim_var,' - ECMWF System5 - JJA 2020'),
-                                   width = 12, height = 7)
+PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,
+                          coast_width=1.5, legend_scale = 0.8, 
+                          toptitle = paste0('Most likely tercile - ', clim_var,
+                                            ' - ECMWF System5 - JJA 2020'))
 ```
+
 <img src="./Figures/MostLikelyTercile_fig1.png"/>
 
 The forecast calls for above average temperature over most of the Mediterranean basin and near average temperature for some smaller regions as well. But can this forecast be trusted? 
+
 For this, it is useful evaluate the skill of the system at forecasting near surface temperature over the period for which hindcasts are available. We can then use this information to mask the regions for which the system doesn't have skill. 
 
 In order to do this, we will first calculate the ranked probability skill score (RPSS) and then exclude/mask from the forecasts the regions for which the RPSS is smaller or equal to 0 (no improvement with respect to climatology).
 
-### 8 - First, we evaluate and plot the RPSS.
+
+### 5. Computing Skill Score
+
+
+First, we evaluate and plot the RPSS. Therefore, we use `RPSS` function from **easyVerification** package which requires little preformating of the anomalies
+
+
 ```r
-v2 <- aperm(drop(ano_obs$data),c(3,2,1))
-v2 <- v2[,,1:(numyears-2)]
-v1 <- aperm(drop(ano_exp$data),c(4,3,2,1))
-v1 <- v1[,,1:(numyears-2),]
+reshaped_obs <- Reorder(drop(ano_obs$data), c('lon', 'lat', 'sdate'))
+reshaped_obs <- reshaped_obs[,, 1:(numyears - 2)]
+reshaped_exp <- Reorder(drop(ano_exp$data), c('lon', 'lat', 'sdate', 'member'))
+reshaped_exp <- reshaped_exp[,, 1:(numyears - 2),]
 
-RPSS <- veriApply('FairRpss',fcst=v1,obs=v2,ensdim=4,tdim=3,prob =c(1/3,2/3))
+RPSS <- veriApply('FairRpss', fcst = reshaped_exp, obs = reshaped_obs, ensdim = 4,
+                  tdim = 3, prob = c(1/3, 2/3))
 
-PlotEquiMap(RPSS[[1]], lat=Lat,lon=Lon, brks=seq(-1,1,by=0.1),filled.continents=F)
+PlotEquiMap(RPSS[[1]], lat = Lat, lon = Lon, brks = seq(-1, 1, by = 0.1),
+            filled.continents = FALSE)
 ```
+
+
 <img src="./Figures/MostLikelyTercile_fig2.png"/>
 
-Areas displayed in red (RPSS>0) are areas for which the forecast system shows skill above climatology whereas areas in blue (such as a large part of the Iberian peninsula) are areas for which the model does not. We will mask the areas in blue.
 
-### 9 - From the RPSS, we create a mask. Regions with RPSS<=0 will be masked. 
+Areas displayed in red (RPSS > 0) are areas for which the forecast system shows skill above climatology whereas areas in blue (such as a large part of the Iberian peninsula) are areas for which the model does not. We will mask the areas in blue.
+
+### 6. Simultaneous isualization of probabilities and skill scores
+
+From the RPSS, we create a mask: regions with RPSS <= 0 will be masked. 
+
+
 ```r
 mask_rpss <- RPSS[[1]]
 mask_rpss[RPSS[[1]] <= 0] <- 1
@@ -143,13 +192,16 @@ mask_rpss[is.na(RPSS[[1]])] <- 1
 mask_rpss[RPSS[[1]] > 0] <- 0
 ```
 
-### 10 - Finally, we plot the latest forecast, as in the previous step, but add the mask we just created.
+Finally, we plot the latest forecast, as in the previous step, but add the mask we just created.
+
+
 ```r
-CSTools::PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat,coast_width=1.5, legend_scale = 0.8, 
-                                   mask = t(mask_rpss),
-                                   toptitle = paste0('Most likely tercile - ',variable,' - ECMWF System5 - JJA 2020'),
-                                   width = 12, height = 7)
+PlotMostLikelyQuantileMap(probs = prob_map, lon = Lon, lat = Lat, coast_width = 1.5,
+                          legend_scale = 0.8, mask = t(mask_rpss),
+                          toptitle = paste('Most likely tercile -', clim_var,
+                                           '- ECMWF System5 - JJA 2020'))
 ```
+
 <img src="./Figures/MostLikelyTercile_fig3.png"/>  
 
 We obtain the same figure as before, but this time, we only display the areas for which the model has skill at forecasting the right tercile. The gray regions represents areas where the system doesn't have sufficient skill over the verification period.
-- 
GitLab


From a453bc9ecaa628628093ea716d04a579d4e7b388 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Thu, 5 Nov 2020 09:44:06 +0100
Subject: [PATCH 33/43] Change file names and index name

---
 ...ample_mostlikelytercile.md => MostLikelyTercile_vignette.md} | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename vignettes/{example_mostlikelytercile.md => MostLikelyTercile_vignette.md} (99%)

diff --git a/vignettes/example_mostlikelytercile.md b/vignettes/MostLikelyTercile_vignette.md
similarity index 99%
rename from vignettes/example_mostlikelytercile.md
rename to vignettes/MostLikelyTercile_vignette.md
index 929f6932..c429c57b 100644
--- a/vignettes/example_mostlikelytercile.md
+++ b/vignettes/MostLikelyTercile_vignette.md
@@ -4,7 +4,7 @@ date: "`r Sys.Date()`"
 output: rmarkdown::html_vignette
 vignette: >
   %\VignetteEngine{knitr::knitr}
-  %\VignetteIndexEntry{Most Likely Tercile}
+  %\VignetteIndexEntry{Most Likely Terciles}
   %\usepackage[utf8]{inputenc}
 ---
 
-- 
GitLab


From 2ae7d7fbb0500202ed38881e1b64586257350a78 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Thu, 5 Nov 2020 09:49:36 +0100
Subject: [PATCH 34/43] fixing misspelling

---
 vignettes/MostLikelyTercile_vignette.md | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/vignettes/MostLikelyTercile_vignette.md b/vignettes/MostLikelyTercile_vignette.md
index c429c57b..1f53a832 100644
--- a/vignettes/MostLikelyTercile_vignette.md
+++ b/vignettes/MostLikelyTercile_vignette.md
@@ -14,10 +14,13 @@ Computing and displaying the most likely tercile of a seasonal forecast
 
 In this example, we will use CSTools to visualize a probabilistic forecast (most likely tercile) of summer near-surface temperature produced by ECMWF System5. The (re-)forecasts used are initilized on May 1st for the period 1981-2020. The target for the forecast is June-August (JJA) 2020. The forecast data are taken from the Copernicus Climate Data Store.
 
+
 ### 1. Preliminary setup 
 
+
 To run this vignette, the following R packages should be installed and loaded:
 
+
 ```r
 library(CSTools)
 library(s2dv)
@@ -27,10 +30,13 @@ library(zeallot)
 library(easyVerification)
 ```
 
+
 ### 2. Loading the data
 
+
 We first define a few parameters. We start by defining the region we are interested in. In this example, we focus on the Mediterranean region.
 
+
 ```r
 lat_min = 25
 lat_max = 52
@@ -87,6 +93,7 @@ c(exp,obs) %<-% CST_Load(var = clim_var, exp = forecastsys, obs = obs,
 
 Loading the data using CST_Load allows to obtain two objects, one for the experimental data and another for the observe data, with the same elements and compatible dimensions of the data element:
 
+
 ```r
 > dim(exp$data)
 dataset  member   sdate   ftime     lat     lon 
@@ -96,7 +103,8 @@ dataset  member   sdate   ftime     lat     lon
       1       1      40       3      19      36 
 ```
 
-The latitude and longitude and the anomalies from the forecasts and the observations are saved for later used:
+
+The latitude and longitude are saved for later use:
 
 
 ```r
@@ -136,7 +144,7 @@ prob_map <- MeanDims(PB, c('sdate', 'member', 'dataset'))
 ### 4. Visualization with PlotMostLikelyQuantileMap
 
 
-We then plot the most likely quantile using the **CSTools** function `PlotMostLikelyQuantileMap. 
+We then plot the most likely quantile using the **CSTools** function `PlotMostLikelyQuantileMap`. 
 
 
 ```
@@ -180,7 +188,7 @@ PlotEquiMap(RPSS[[1]], lat = Lat, lon = Lon, brks = seq(-1, 1, by = 0.1),
 
 Areas displayed in red (RPSS > 0) are areas for which the forecast system shows skill above climatology whereas areas in blue (such as a large part of the Iberian peninsula) are areas for which the model does not. We will mask the areas in blue.
 
-### 6. Simultaneous isualization of probabilities and skill scores
+### 6. Simultaneous Visualization of probabilities and skill scores
 
 From the RPSS, we create a mask: regions with RPSS <= 0 will be masked. 
 
-- 
GitLab


From da96cab6d8b3b753f7b5bfd52521805d3a507328 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Thu, 5 Nov 2020 09:51:40 +0100
Subject: [PATCH 35/43] remove capital letter

---
 vignettes/MostLikelyTercile_vignette.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/MostLikelyTercile_vignette.md b/vignettes/MostLikelyTercile_vignette.md
index 1f53a832..804fd8a6 100644
--- a/vignettes/MostLikelyTercile_vignette.md
+++ b/vignettes/MostLikelyTercile_vignette.md
@@ -188,7 +188,7 @@ PlotEquiMap(RPSS[[1]], lat = Lat, lon = Lon, brks = seq(-1, 1, by = 0.1),
 
 Areas displayed in red (RPSS > 0) are areas for which the forecast system shows skill above climatology whereas areas in blue (such as a large part of the Iberian peninsula) are areas for which the model does not. We will mask the areas in blue.
 
-### 6. Simultaneous Visualization of probabilities and skill scores
+### 6. Simultaneous visualization of probabilities and skill scores
 
 From the RPSS, we create a mask: regions with RPSS <= 0 will be masked. 
 
-- 
GitLab


From cecb4fd2caa95c73b0b32de5a2f32474356ef90d Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Thu, 5 Nov 2020 11:12:13 +0100
Subject: [PATCH 36/43] Update MostLikelyTercile_vignette.md

---
 vignettes/MostLikelyTercile_vignette.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/MostLikelyTercile_vignette.md b/vignettes/MostLikelyTercile_vignette.md
index 804fd8a6..b3b8e788 100644
--- a/vignettes/MostLikelyTercile_vignette.md
+++ b/vignettes/MostLikelyTercile_vignette.md
@@ -78,7 +78,7 @@ grid <- "256x128"
 clim_var = 'tas'
 ```
 
-Finally, the data is loaded using `CST_Load`: 
+Finally, the data are loaded using `CST_Load`: 
 
 
 ```r
-- 
GitLab


From 86203423a32a74f74809c02402e1d7ba8523cf34 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Thu, 5 Nov 2020 11:13:21 +0100
Subject: [PATCH 37/43] Update MostLikelyTercile_vignette.md

---
 vignettes/MostLikelyTercile_vignette.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/MostLikelyTercile_vignette.md b/vignettes/MostLikelyTercile_vignette.md
index b3b8e788..eb5cdfc5 100644
--- a/vignettes/MostLikelyTercile_vignette.md
+++ b/vignettes/MostLikelyTercile_vignette.md
@@ -91,7 +91,7 @@ c(exp,obs) %<-% CST_Load(var = clim_var, exp = forecastsys, obs = obs,
                          grid = paste("r", grid, sep = ""))
 ```
 
-Loading the data using CST_Load allows to obtain two objects, one for the experimental data and another for the observe data, with the same elements and compatible dimensions of the data element:
+Loading the data using CST_Load returns two objects, one for the experimental data and another one for the observe data, with the same elements and compatible dimensions of the data element:
 
 
 ```r
-- 
GitLab


From 3f65c733f040dffa62113a49710beff2df96bdb1 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Thu, 5 Nov 2020 11:20:06 +0100
Subject: [PATCH 38/43] Update MostLikelyTercile_vignette.md

---
 vignettes/MostLikelyTercile_vignette.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/MostLikelyTercile_vignette.md b/vignettes/MostLikelyTercile_vignette.md
index eb5cdfc5..231646bc 100644
--- a/vignettes/MostLikelyTercile_vignette.md
+++ b/vignettes/MostLikelyTercile_vignette.md
@@ -131,7 +131,7 @@ ano_obs$data <- MeanDims(ano_obs$data, 'ftime')
 ```
 
 
-Finally, the probabilities of each tercile are computed by evaluating which tercile is forecasted by each ensemble member for the latest forecast (2020) using the function `ProbBins` in **s2dverification** and then average the results along the member dimension to obtain the probability of each tercile.
+Finally, the probabilities of each tercile are computed by evaluating which tercile is forecasted by each ensemble member for the latest forecast (2020) using the function `ProbBins` in **s2dverification** and then averaging the results along the member dimension to obtain the probability of each tercile.
 
 
 ```r
-- 
GitLab


From 48a958fee5be616c8eb75d2f0c8acc80fc228760 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Thu, 5 Nov 2020 11:21:33 +0100
Subject: [PATCH 39/43] Update MostLikelyTercile_vignette.md

---
 vignettes/MostLikelyTercile_vignette.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/MostLikelyTercile_vignette.md b/vignettes/MostLikelyTercile_vignette.md
index 231646bc..25d47208 100644
--- a/vignettes/MostLikelyTercile_vignette.md
+++ b/vignettes/MostLikelyTercile_vignette.md
@@ -166,7 +166,7 @@ In order to do this, we will first calculate the ranked probability skill score
 ### 5. Computing Skill Score
 
 
-First, we evaluate and plot the RPSS. Therefore, we use `RPSS` function from **easyVerification** package which requires little preformating of the anomalies
+First, we evaluate and plot the RPSS. Therefore, we use `RPSS` function from **easyVerification** package which requires a little preformating of the anomalies
 
 
 ```r
-- 
GitLab


From 52f13924af322c52d506904708ad19e5e6da0b10 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Thu, 5 Nov 2020 11:23:31 +0100
Subject: [PATCH 40/43] Update MostLikelyTercile_vignette.md

---
 vignettes/MostLikelyTercile_vignette.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/MostLikelyTercile_vignette.md b/vignettes/MostLikelyTercile_vignette.md
index 25d47208..600829cb 100644
--- a/vignettes/MostLikelyTercile_vignette.md
+++ b/vignettes/MostLikelyTercile_vignette.md
@@ -186,7 +186,7 @@ PlotEquiMap(RPSS[[1]], lat = Lat, lon = Lon, brks = seq(-1, 1, by = 0.1),
 <img src="./Figures/MostLikelyTercile_fig2.png"/>
 
 
-Areas displayed in red (RPSS > 0) are areas for which the forecast system shows skill above climatology whereas areas in blue (such as a large part of the Iberian peninsula) are areas for which the model does not. We will mask the areas in blue.
+Areas displayed in red (RPSS > 0) are areas for which the forecast system shows skill above climatology whereas areas in blue (such as a large part of the Iberian Peninsula) are areas for which the model does not. We will mask these areas in blue.
 
 ### 6. Simultaneous visualization of probabilities and skill scores
 
-- 
GitLab


From c2a02f59bf122febf430ee9e00d9b1080a405001 Mon Sep 17 00:00:00 2001
From: Louis-Philippe Caron <louis-philippe.caron@bsc.es>
Date: Thu, 5 Nov 2020 11:24:24 +0100
Subject: [PATCH 41/43] Update MostLikelyTercile_vignette.md

---
 vignettes/MostLikelyTercile_vignette.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/MostLikelyTercile_vignette.md b/vignettes/MostLikelyTercile_vignette.md
index 600829cb..b6beac94 100644
--- a/vignettes/MostLikelyTercile_vignette.md
+++ b/vignettes/MostLikelyTercile_vignette.md
@@ -186,7 +186,7 @@ PlotEquiMap(RPSS[[1]], lat = Lat, lon = Lon, brks = seq(-1, 1, by = 0.1),
 <img src="./Figures/MostLikelyTercile_fig2.png"/>
 
 
-Areas displayed in red (RPSS > 0) are areas for which the forecast system shows skill above climatology whereas areas in blue (such as a large part of the Iberian Peninsula) are areas for which the model does not. We will mask these areas in blue.
+Areas displayed in red (RPSS > 0) are areas for which the forecast system shows skill above climatology whereas areas in blue (such as a large part of the Iberian Peninsula) are areas for which the model does not. We thus want to mask the areas currently displayed in blue.
 
 ### 6. Simultaneous visualization of probabilities and skill scores
 
-- 
GitLab


From fe69991c32bd76b24996d0467b849ff7944ef587 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Tue, 10 Nov 2020 09:39:36 +0100
Subject: [PATCH 42/43] fix tests

---
 tests/testthat/test-CST_MultiMetric.R | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tests/testthat/test-CST_MultiMetric.R b/tests/testthat/test-CST_MultiMetric.R
index b08b68ba..309cc4e9 100644
--- a/tests/testthat/test-CST_MultiMetric.R
+++ b/tests/testthat/test-CST_MultiMetric.R
@@ -11,11 +11,11 @@ test_that("basic use case", {
   attr(exp, 'class') <- 's2dv_cube'
   attr(obs, 'class') <- 's2dv_cube'
   
-  result <- list(data = array(rep(c(rep(1, 9), 
-rep(0.89999999999999991118215802998747676610950, 3)), 48), 
-                dim = c(dataset = 3, dataset = 1, statistics = 4, 
+  result <- list(data = array(rep(c(rep(1, 9), rep(0, 3)), 48), 
+                 dim = c(dataset = 3, dataset = 1, statistics = 4, 
                        lat = 6, lon = 8)),
-                lat = lat, lon = lon) 
+                 lat = lat, lon = lon) 
+
   attr(result, 'class') <- 's2dv_cube'
   expect_equal(CST_MultiMetric(exp = exp, obs = obs), result)
   
-- 
GitLab


From 054d2a9c1e4752e5fdc22a09558de35786c990d3 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Mon, 30 Nov 2020 18:47:27 +0100
Subject: [PATCH 43/43] update NEWS

---
 NEWS.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/NEWS.md b/NEWS.md
index 8889b807..a4e4394a 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -7,6 +7,7 @@
     + RFWeights is exposed to users
     + CST_RainFARM accepts multi-dimensional slopes and weights and handless missing values in sample dimensions.
     + CST_MultiMetric includes 'rpss' metric and it is addapted to s2dv.
+    + PlotMostLikelyQuantileMap vignette
 
 - Fixes:
     + PlotForecastPDF correctly displays terciles labels 
-- 
GitLab