From 80d5ff85d8847221d80dff72613ac63f7ca85d0a Mon Sep 17 00:00:00 2001
From: Eva Rifa <eva.rifarovira@bsc.es>
Date: Mon, 16 Oct 2023 11:30:24 +0200
Subject: [PATCH 01/13] Add Hands-on 2 for PATC 2023

---
 .../PATC2023/handson_2-data-assesment.md      | 260 ++++++++++++++++++
 1 file changed, 260 insertions(+)
 create mode 100644 inst/doc/tutorial/PATC2023/handson_2-data-assesment.md

diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
new file mode 100644
index 00000000..7de88529
--- /dev/null
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
@@ -0,0 +1,260 @@
+# Hands-on 2: Data assesment
+
+**Goal**
+
+Use CSTools to calibrate, compute anomalies and skill of a climate dataset over a period.
+
+**Load packages**
+```r
+# Clean the session
+rm(list = ls())
+
+library(CSTools) # CST_Calibration
+library(startR) # Start
+# TODO: remove startR if new CSTools is installed
+library(s2dv) # 
+```
+
+## 1. Load the data
+
+In this section we will use the function **Start** to load the data. Then, we will transfrom the output `startR_array` to an `s2dv_cube` object in order that the data is easy to use within CSTools functions.
+
+The `s2dv_cube` object is a structured list that contains the information needed to work with multidimensional arrays of data. Coordinates, dimensions, and metadata are neatly combined to allow for a more intuitive, concise, and less error-prone experience.
+
+**Note:** If you have already loaded the data with Start, go directly to section 1b).
+
+### 1a) Load the data
+TODO: If CSTools new version can be installed, use CST_Start
+
+The following section is taken from ( TODO: cite handson1 ). 
+
+```r
+#TODO: update the path
+# Use this one if on workstation or nord3 (have access to /esarchive)
+path_exp <- "/esarchive/exp/meteofrance/system7c3s/monthly_mean/$var$_f6h/$var$_$syear$.nc"
+
+# Use this one if on Marenostrum4 and log in with PATC2021 account
+path_exp <- paste0('/gpfs/scratch/nct01/nct01127/d3_R_handson/esarchive/',
+                'exp/ecmwf/system5c3s/daily_mean/',
+                '$var$_s0-24h/$var$_$sdate$.nc')
+
+var <- 'tas'
+sdate_hcst <- paste0(1993:2016, '1101')
+sdate_fcst <- '20201101'
+lon.min <- -20
+lon.max <- 40
+lat.min <- 20
+lat.max <- 80
+```
+
+```r
+hcst <- Start(dat = path_exp,
+              var = var,
+              syear = sdate_hcst,
+              ensemble = 'all',
+              time = 1:2,
+              latitude = values(list(lat.min, lat.max)),
+              latitude_reorder = Sort(),
+              longitude = values(list(lon.min, lon.max)),
+              longitude_reorder = CircularSort(-180, 180),
+              synonims = list(syear = c('syear', 'sdate'),
+                              latitude = c('lat', 'latitude'),
+                              longitude = c('lon', 'longitude')),
+              return_vars = list(time = 'syear',
+                                  longitude = NULL, latitude = NULL),
+              retrieve = TRUE)
+```
+
+```r
+fcst <- Start(dat = path_exp,
+              var = var,
+              syear = sdate_fcst,
+              ensemble = 'all',
+              time = 1:2, 
+              latitude = values(list(lat.min, lat.max)),
+              latitude_reorder = Sort(),
+              longitude = values(list(lon.min, lon.max)),
+              longitude_reorder = CircularSort(-180, 180),
+              synonims = list(syear = c('syear', 'sdate'),
+                              latitude = c('lat', 'latitude'),
+                              longitude = c('lon', 'longitude')),
+              return_vars = list(time = 'syear',
+                                  longitude = NULL, latitude = NULL),
+              retrieve = TRUE)
+```
+
+```r
+# Adjust the day to the correct month
+attributes(hcst)$Variables$common$time <- attributes(hcst)$Variables$common$time - lubridate::days(1)
+
+date_string <- format(attributes(hcst)$Variables$common$time, '%Y%m')
+sdate_obs <- array(date_string, dim = c(syear = 24, time = 2))
+```
+
+```r
+path_obs <- '/esarchive/recon/ecmwf/era5/monthly_mean/$var$_f1h-r1440x721cds/$var$_$syear$.nc'
+
+obs <- Start(dat = path_obs,
+              var = var,
+              syear = sdate_obs,
+              split_multiselected_dims = TRUE,
+              latitude = values(list(lat.min, lat.max)),
+              latitude_reorder = Sort(),
+              longitude = values(list(lon.min, lon.max)),
+              longitude_reorder = CircularSort(-180, 180),
+              transform = CDORemapper,
+#TODO: Change to relative path
+#               transform_params = list(grid = './griddes_system7c3s.txt',
+              transform_params = list(grid = '/esarchive/scratch/aho/git/startR/inst/doc/tutorial/PATC2023/griddes_system7c3s.txt',
+                                      method = 'bilinear'),
+              transform_vars = c('latitude', 'longitude'),
+              synonims = list(syear = c('syear', 'sdate'),
+                              latitude = c('lat', 'latitude'),
+                              longitude = c('lon', 'longitude')),
+              return_vars = list(time = 'syear',
+                                longitude = NULL, latitude = NULL),
+              retrieve = TRUE)
+```
+
+### 1b) Create `s2dv_cube`:
+
+Now we convert the data (`startR_array`) into an 's2dv_cube' object with the function **as.s2dv_cube**:
+
+```r
+> str(hcst)
+ 'startR_array' num [1, 1, 1:24, 1:25, 1:2, 1:60, 1:60] 295 296 297 297 297 ...
+ - attr(*, "Variables")=List of 2
+  ..$ common:List of 4
+  .. ..$ time     : POSIXct[1:48], format: "1993-12-01" "1994-12-01" ...
+  .. ..$ longitude: num [1:60(1d)] -19.5 -18.5 -17.5 -16.5 -15.5 -14.5 -13.5 -12.5 -11.5 -10.5 ...
+  .. .. ..- attr(*, "variables")=List of 1
+  .. .. .. ..$ longitude:List of 7
+  .. .. .. .. ..$ ndims        : num 1
+  .. .. .. .. ..$ size         : int 360
+  .. .. .. .. ..$ units        : chr "degrees_east"
+  .. .. .. .. ..$ dim          :List of 1
+  .. .. .. .. .. ..$ :List of 10
+
+```
+Now, we convert the startR object into 's2dv_cube' object.
+```r
+hcst <- as.s2dv_cube(hcst)
+fcst <- as.s2dv_cube(fcst)
+obs <- as.s2dv_cube(obs)
+```
+**Questions 1:**   
+
+Find `s2dv_cube` information:  
+
+1. Can you tell what type of object is an **s2dv_cube** in R structures?  
+2. What are the time dimensions of the object?  
+3. In which region we have loaded the data?  
+4. How many coordinates have the object hcst?  
+5. How many ensemble members have the observation dataset?  
+6. What is the full variable name of the loaded data?  
+7. What season is the data loaded from?  
+
+**Exercise 1**:   
+1. Find the mean, the maximum and the minimum of the fcst, hcst and obs data and compare it.
+2. Explore the data (TODO: complete it)
+
+
+## 2. Calibrate the data
+
+Calibrate the hindcast:
+```r
+hcst_cal <- CST_Calibration(exp = hcst, obs = obs,
+                            cal.method = "evmos",
+                            eval.method = "leave-one-out",
+                            multi.model = FALSE,
+                            na.fill = TRUE,
+                            na.rm = TRUE,
+                            apply_to = NULL,
+                            alpha = NULL,
+                            memb_dim = "ensemble",
+                            sdate_dim = "syear",
+                            ncores = 10)
+```
+
+Calibrate the forecast: 
+```r
+fcst_cal <- CST_Calibration(exp = hcst, obs = obs, exp_cor = fcst,
+                            cal.method = "evmos",
+                            eval.method = "leave-one-out",
+                            multi.model = FALSE,
+                            na.fill = TRUE,
+                            na.rm = TRUE,
+                            apply_to = NULL,
+                            alpha = NULL,
+                            memb_dim = "ensemble",
+                            sdate_dim = "syear",
+                            ncores = 10)
+
+```
+
+
+
+## 2. Compute Anomalies
+
+
+```r
+hcst_anom <- CST_Anomaly(exp = hcst_cal, obs = obs,
+                         cross = TRUE,
+                         memb = TRUE,
+                         memb_dim = 'ensemble',
+                         dim_anom = 'syear',
+                         dat_dim = c('dat', 'ensemble'),
+                         ftime_dim = 'time',
+                         ncores = 10)
+```
+Compute forecast anomaly field:
+```r
+clim <- s2dv::Clim(exp = hcst_cal$data, obs = obs$data,
+                   time_dim = "syear",
+                   dat_dim = c("dat", "ensemble"),
+                   memb = FALSE,
+                   memb_dim = "ensemble",
+                   ftime_dim = "time",
+                   ncores = 10)
+clim_hcst <- InsertDim(clim$clim_exp, posdim = 1, lendim = 1, name = "syear")
+dims <- dim(clim_hcst)
+clim_hcst <- rep(clim_hcst, fcst$dim[['ensemble']])
+dim(clim_hcst) <- c(dims, ensemble = fcst$dim[['ensemble']])
+clim_hcst <- Reorder(clim_hcst, order = names(fcst$dim))
+fcst_anom <- fcst_cal$data - clim_hcst
+```
+
+## 3. Compute skill: RPSS
+
+Compute Ranked Probability Skill Score: 
+```r
+skill_data <- RPSS(exp = hcst_cal$data, obs = obs$data, memb_dim = 'ensemble', 
+                   time_dim = 'syear', Fair = FALSE, cross.val = TRUE, 
+                   ncores = 10) 
+
+skill_anom <- RPSS(exp = hcst_anom$exp$data, obs = hcst_anom$obs$data,
+                   time_dim = 'syear',
+                   memb_dim = 'ensemble',
+                   Fair = FALSE,
+                   cross.val = TRUE,
+                   ncores = 10)
+```
+Explore the result:  
+
+```r
+> summary(skill_data$rpss)
+    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
+-0.54095 -0.11060 -0.03200 -0.03599  0.04465  0.41849 
+> summary(skill_data$sign)
+   Mode   FALSE    TRUE 
+logical    6813     387 
+> summary(skill_anom$rpss)
+    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
+-0.54005 -0.11225 -0.03170 -0.03722  0.04480  0.44376 
+> summary(skill_anom$sign)
+   Mode   FALSE    TRUE 
+logical    6798     402 
+```
+
+
+## References
\ No newline at end of file
-- 
GitLab


From 38047ebccdbccec9df71cec2f6e1d0908b4aea77 Mon Sep 17 00:00:00 2001
From: Eva Rifa <eva.rifarovira@bsc.es>
Date: Mon, 16 Oct 2023 12:47:23 +0200
Subject: [PATCH 02/13] Compute only RPSS from Anomalies

---
 .../PATC2023/handson_2-data-assesment.md      | 29 +++++++------------
 1 file changed, 10 insertions(+), 19 deletions(-)

diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
index 7de88529..290da549 100644
--- a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
@@ -226,32 +226,23 @@ fcst_anom <- fcst_cal$data - clim_hcst
 
 ## 3. Compute skill: RPSS
 
-Compute Ranked Probability Skill Score: 
+Compute Ranked Probability Skill Score for anomalies: 
 ```r
-skill_data <- RPSS(exp = hcst_cal$data, obs = obs$data, memb_dim = 'ensemble', 
-                   time_dim = 'syear', Fair = FALSE, cross.val = TRUE, 
-                   ncores = 10) 
-
-skill_anom <- RPSS(exp = hcst_anom$exp$data, obs = hcst_anom$obs$data,
-                   time_dim = 'syear',
-                   memb_dim = 'ensemble',
-                   Fair = FALSE,
-                   cross.val = TRUE,
-                   ncores = 10)
+
+skill <- RPSS(exp = hcst_anom$exp$data, obs = hcst_anom$obs$data,
+              time_dim = 'syear',
+              memb_dim = 'ensemble',
+              Fair = FALSE,
+              cross.val = TRUE,
+              ncores = 10)
 ```
 Explore the result:  
 
 ```r
-> summary(skill_data$rpss)
-    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
--0.54095 -0.11060 -0.03200 -0.03599  0.04465  0.41849 
-> summary(skill_data$sign)
-   Mode   FALSE    TRUE 
-logical    6813     387 
-> summary(skill_anom$rpss)
+> summary(skill$rpss)
     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
 -0.54005 -0.11225 -0.03170 -0.03722  0.04480  0.44376 
-> summary(skill_anom$sign)
+> summary(skill$sign)
    Mode   FALSE    TRUE 
 logical    6798     402 
 ```
-- 
GitLab


From d1ee5ea70becb043cea77253e913a637b655955c Mon Sep 17 00:00:00 2001
From: Eva Rifa <eva.rifarovira@bsc.es>
Date: Mon, 16 Oct 2023 16:24:45 +0200
Subject: [PATCH 03/13] Remove rm()

---
 inst/doc/tutorial/PATC2023/handson_2-data-assesment.md | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
index 290da549..6fdcff13 100644
--- a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
@@ -6,9 +6,6 @@ Use CSTools to calibrate, compute anomalies and skill of a climate dataset over
 
 **Load packages**
 ```r
-# Clean the session
-rm(list = ls())
-
 library(CSTools) # CST_Calibration
 library(startR) # Start
 # TODO: remove startR if new CSTools is installed
-- 
GitLab


From 051c38061a8818f2df0425ea555d2c71db830c5e Mon Sep 17 00:00:00 2001
From: Eva Rifa <eva.rifarovira@bsc.es>
Date: Wed, 25 Oct 2023 18:29:57 +0200
Subject: [PATCH 04/13] Improve hands-on 2

---
 .../PATC2023/handson_2-data-assesment.md      | 345 +++++++++-----
 .../PATC2023/handson_2-data-assesment_ans.md  | 437 ++++++++++++++++++
 2 files changed, 674 insertions(+), 108 deletions(-)
 create mode 100644 inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md

diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
index 6fdcff13..74ce3eb2 100644
--- a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
@@ -2,14 +2,12 @@
 
 **Goal**
 
-Use CSTools to calibrate, compute anomalies and skill of a climate dataset over a period.
+Use CSTools and s2dv to calibrate, compute anomalies and skill of a climate dataset over a period.
 
 **Load packages**
 ```r
-library(CSTools) # CST_Calibration
-library(startR) # Start
-# TODO: remove startR if new CSTools is installed
-library(s2dv) # 
+library(CSTools)
+library(s2dv)
 ```
 
 ## 1. Load the data
@@ -21,96 +19,98 @@ The `s2dv_cube` object is a structured list that contains the information needed
 **Note:** If you have already loaded the data with Start, go directly to section 1b).
 
 ### 1a) Load the data
-TODO: If CSTools new version can be installed, use CST_Start
 
-The following section is taken from ( TODO: cite handson1 ). 
+The following section is taken from [PATC 2023 startR tutorial](https://earth.bsc.es/gitlab/es/startR/-/blob/doc-bsc_training_2023/inst/doc/tutorial/PATC2023/handson_1-data-loading.md?ref_type=heads). The experiment data are Meteo-France System 7 from ECMWF, and the observation ones are ERA5 from ECMWF. We're going to analyze the near-surface temperature (short name: tas) for seasonal forecast. We will focus on the Europe region (roughly 20W-40E, 20N-80N). The hindcast years are 1993 to 2016, and the forecast year is 2020. The initial month is November. 
 
 ```r
-#TODO: update the path
 # Use this one if on workstation or nord3 (have access to /esarchive)
 path_exp <- "/esarchive/exp/meteofrance/system7c3s/monthly_mean/$var$_f6h/$var$_$syear$.nc"
+#----------------------------------------------------------------------
+# Run these two lines if you're on Marenostrum4 and log in with training account
+prefix <- '/gpfs/scratch/bsc32/bsc32734/bsc_training_2023/R_handson/'
+path_exp <- paste0(prefix, path_exp)
+#----------------------------------------------------------------------
 
-# Use this one if on Marenostrum4 and log in with PATC2021 account
-path_exp <- paste0('/gpfs/scratch/nct01/nct01127/d3_R_handson/esarchive/',
-                'exp/ecmwf/system5c3s/daily_mean/',
-                '$var$_s0-24h/$var$_$sdate$.nc')
-
-var <- 'tas'
 sdate_hcst <- paste0(1993:2016, '1101')
+
+hcst <- CST_Start(dat = path_exp,
+                  var = 'tas',
+                  syear = sdate_hcst,
+                  ensemble = 'all',
+                  time = 1:2,
+                  latitude = startR::values(list(20, 80)),
+                  latitude_reorder = startR::Sort(),
+                  longitude = startR::values(list(-20, 40)),
+                  longitude_reorder = startR::CircularSort(-180, 180),
+                  transform = startR::CDORemapper,
+                  transform_params = list(grid = 'r360x181', method = 'bilinear'),
+                  transform_vars = c('latitude', 'longitude'),
+                  synonims = list(latitude = c('lat', 'latitude'),
+                                  longitude = c('lon', 'longitude')),
+                  return_vars = list(time = 'syear',
+                                     longitude = NULL, latitude = NULL),
+                  retrieve = TRUE)
+
+```
+
+```r
 sdate_fcst <- '20201101'
-lon.min <- -20
-lon.max <- 40
-lat.min <- 20
-lat.max <- 80
-```
-
-```r
-hcst <- Start(dat = path_exp,
-              var = var,
-              syear = sdate_hcst,
-              ensemble = 'all',
-              time = 1:2,
-              latitude = values(list(lat.min, lat.max)),
-              latitude_reorder = Sort(),
-              longitude = values(list(lon.min, lon.max)),
-              longitude_reorder = CircularSort(-180, 180),
-              synonims = list(syear = c('syear', 'sdate'),
-                              latitude = c('lat', 'latitude'),
-                              longitude = c('lon', 'longitude')),
-              return_vars = list(time = 'syear',
-                                  longitude = NULL, latitude = NULL),
-              retrieve = TRUE)
-```
-
-```r
-fcst <- Start(dat = path_exp,
-              var = var,
-              syear = sdate_fcst,
-              ensemble = 'all',
-              time = 1:2, 
-              latitude = values(list(lat.min, lat.max)),
-              latitude_reorder = Sort(),
-              longitude = values(list(lon.min, lon.max)),
-              longitude_reorder = CircularSort(-180, 180),
-              synonims = list(syear = c('syear', 'sdate'),
-                              latitude = c('lat', 'latitude'),
-                              longitude = c('lon', 'longitude')),
-              return_vars = list(time = 'syear',
-                                  longitude = NULL, latitude = NULL),
-              retrieve = TRUE)
+
+fcst <- CST_Start(dat = path_exp,
+                  var = 'tas',
+                  syear = sdate_fcst,
+                  ensemble = 'all',
+                  time = 1:2, 
+                  latitude = startR::values(list(20, 80)),
+                  latitude_reorder = startR::Sort(),
+                  longitude = startR::values(list(-20, 40)),
+                  longitude_reorder = startR::CircularSort(-180, 180),
+                  transform = startR::CDORemapper,
+                  transform_params = list(grid = 'r360x181', method = 'bilinear'),
+                  transform_vars = c('latitude', 'longitude'),
+                  synonims = list(syear = c('syear', 'sdate'),
+                                  latitude = c('lat', 'latitude'),
+                                  longitude = c('lon', 'longitude')),
+                  return_vars = list(time = 'syear',
+                                    longitude = NULL, latitude = NULL),
+                  retrieve = TRUE)
+
 ```
 
 ```r
 # Adjust the day to the correct month
-attributes(hcst)$Variables$common$time <- attributes(hcst)$Variables$common$time - lubridate::days(1)
+hcst$attrs$Dates <- hcst$attrs$Dates - lubridate::days(1)
 
-date_string <- format(attributes(hcst)$Variables$common$time, '%Y%m')
+date_string <- format(hcst$attrs$Dates, '%Y%m')
 sdate_obs <- array(date_string, dim = c(syear = 24, time = 2))
 ```
 
 ```r
 path_obs <- '/esarchive/recon/ecmwf/era5/monthly_mean/$var$_f1h-r1440x721cds/$var$_$syear$.nc'
+#----------------------------------------------------------------------
+# Run these two lines if you're on Marenostrum4 and log in with training account
+prefix <- '/gpfs/scratch/bsc32/bsc32734/bsc_training_2023/R_handson/'
+path_obs <- paste0(prefix, path_obs)
+#----------------------------------------------------------------------
+
+obs <- CST_Start(dat = path_obs,
+                 var = 'tas',
+                 syear = sdate_obs,
+                 split_multiselected_dims = TRUE,
+                 latitude = startR::values(list(20, 80)),
+                 latitude_reorder = startR::Sort(),
+                 longitude = startR::values(list(-20, 40)),
+                 longitude_reorder = startR::CircularSort(-180, 180),
+                 transform = startR::CDORemapper,
+                 transform_params = list(grid = 'r360x181', method = 'bilinear'),
+                 transform_vars = c('latitude', 'longitude'),
+                 synonims = list(syear = c('syear', 'sdate'),
+                                 latitude = c('lat', 'latitude'),
+                                 longitude = c('lon', 'longitude')),
+                 return_vars = list(time = 'syear',
+                                    longitude = NULL, latitude = NULL),
+                 retrieve = TRUE)
 
-obs <- Start(dat = path_obs,
-              var = var,
-              syear = sdate_obs,
-              split_multiselected_dims = TRUE,
-              latitude = values(list(lat.min, lat.max)),
-              latitude_reorder = Sort(),
-              longitude = values(list(lon.min, lon.max)),
-              longitude_reorder = CircularSort(-180, 180),
-              transform = CDORemapper,
-#TODO: Change to relative path
-#               transform_params = list(grid = './griddes_system7c3s.txt',
-              transform_params = list(grid = '/esarchive/scratch/aho/git/startR/inst/doc/tutorial/PATC2023/griddes_system7c3s.txt',
-                                      method = 'bilinear'),
-              transform_vars = c('latitude', 'longitude'),
-              synonims = list(syear = c('syear', 'sdate'),
-                              latitude = c('lat', 'latitude'),
-                              longitude = c('lon', 'longitude')),
-              return_vars = list(time = 'syear',
-                                longitude = NULL, latitude = NULL),
-              retrieve = TRUE)
 ```
 
 ### 1b) Create `s2dv_cube`:
@@ -139,26 +139,121 @@ hcst <- as.s2dv_cube(hcst)
 fcst <- as.s2dv_cube(fcst)
 obs <- as.s2dv_cube(obs)
 ```
+
+```r
+> str(hcst)
+List of 4
+ $ data  : num [1, 1, 1:24, 1:25, 1:2, 1:60, 1:60] 295 296 297 297 297 ...
+ $ dims  : Named int [1:7] 1 1 24 25 2 60 60
+  ..- attr(*, "names")= chr [1:7] "dat" "var" "syear" "ensemble" ...
+ $ coords:List of 7
+  ..$ dat      : chr "dat1"
+  .. ..- attr(*, "indices")= logi FALSE
+  ..$ var      : chr "tas"
+  .. ..- attr(*, "values")= logi TRUE
+  .. ..- attr(*, "indices")= logi FALSE
+  ..$ syear    : chr [1:24] "19931101" "19941101" "19951101" "19961101" ...
+  .. ..- attr(*, "values")= logi TRUE
+  .. ..- attr(*, "indices")= logi FALSE
+  ..$ ensemble : int [1:25] 1 2 3 4 5 6 7 8 9 10 ...
+  .. ..- attr(*, "indices")= logi TRUE
+  ..$ time     : int [1:2] 1 2
+  .. ..- attr(*, "indices")= logi TRUE
+  ..$ latitude : num [1:60] 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5 ...
+  .. ..- attr(*, "variables")=List of 1
+  .. .. ..$ latitude:List of 7
+  .. .. .. ..$ ndims        : num 1
+  [...]
+```
 **Questions 1:**   
 
-Find `s2dv_cube` information:  
+Find `s2dv_cube` information of `hcst` object. Here there are some useful functions:
+```r
+str()
+typeof()
+class()
+dim()
+names()
+summary()
+```
+```r
+`s2dv_cube` overview
+$ data: [data array]
+$ dims: [dimensions vector]
+$ coords: [List of coordinates vectors]
+  [...]
+$ attrs: [List of the attributes]
+  $ Dates 
+  $ Variable:
+    $ varName
+    $ metadata 
+  [...]
+```
 
 1. Can you tell what type of object is an **s2dv_cube** in R structures?  
-2. What are the time dimensions of the object?  
-3. In which region we have loaded the data?  
-4. How many coordinates have the object hcst?  
-5. How many ensemble members have the observation dataset?  
-6. What is the full variable name of the loaded data?  
-7. What season is the data loaded from?  
+```r
+class(hcst)
+# "s2dv_cube"
+typeof(___)
+
+```
+1. Can you tell what type of object is the element data in common language? Use the function `dim()` and `typeof()` to check `hcst$data`
+```r
+dim(____)
+
+typeof(hcst$data) 
+
+```
+2. The Dates of an `s2dv_cube` can be found in element: `hcst$attrs$Dates`. 
+Could you tell what are the time dimensions of the object?  
+```r
+
+```
+3. The coordinates in the  `s2dv_cube` are stored in element `hcst$coords`. What are the coordinates names in the object `hcst`? Use the function `names()` to check:
+```r
+names(____)
+
+```
+4.  In which region we have loaded the data?  
+```r
+
+```
+5. What is the start date dimension name? What is the ensemble member dimension name?
+```r
+hcst$dims
+# syear
+# ensemble
+```
+
+6. How many ensemble members have the `hcst`, `fcst` and `obs` datasets? 
+```r
+
+```
+7. The metadata of the `s2dv_cube` is stored in `hcst$attrs$Variable` What is the full variable name of the loaded data? Find out the information in `hcst$attrs$Variable$metadata` with the function `str()`:
+```r
+
+```
+8. What season is the data loaded from? You can use the function `month()`
+```r
+
+```
+9. What are the units of the data?
+```r
+
+```
 
 **Exercise 1**:   
-1. Find the mean, the maximum and the minimum of the fcst, hcst and obs data and compare it.
-2. Explore the data (TODO: complete it)
+1. Find the mean, the maximum and the minimum of the `fcst`, `hcst` and obs data and compare it:
+
+```r
 
+```
 
 ## 2. Calibrate the data
+Now, we are going to use the function CST_Calibration from CSTools to calibrate the hindcast. The parameter `cal.method` alllows us to chose the calibration method. In this exercise we are going to chose the `evmos`.  
 
-Calibrate the hindcast:
+The "evmos" method applies a variance inflation technique to ensure the correction of
+the bias and the correspondence of variance between forecast and observation (Van Schaeybroeck and Vannitsem, 2011).
 ```r
 hcst_cal <- CST_Calibration(exp = hcst, obs = obs,
                             cal.method = "evmos",
@@ -173,8 +268,9 @@ hcst_cal <- CST_Calibration(exp = hcst, obs = obs,
                             ncores = 10)
 ```
 
-Calibrate the forecast: 
+Now we are going to calibrate also the forecast:
 ```r
+
 fcst_cal <- CST_Calibration(exp = hcst, obs = obs, exp_cor = fcst,
                             cal.method = "evmos",
                             eval.method = "leave-one-out",
@@ -186,13 +282,18 @@ fcst_cal <- CST_Calibration(exp = hcst, obs = obs, exp_cor = fcst,
                             memb_dim = "ensemble",
                             sdate_dim = "syear",
                             ncores = 10)
-
+```
+**Exercise 2**: 
+1. Test the hcst with a different calibration methods available and compare the results. You can use the function `summary()` to see the differences.
+```r
+hcst_cal_bias <- CST_Calibration(____)
 ```
 
+## 3. Compute Anomalies
 
+Now, we will compute the hindcast anomalies with the calibrated `hcst`. We are going to use the function CST_Anomaly from CSTools.
 
-## 2. Compute Anomalies
-
+This function computes the anomalies relative to a climatology computed along the selected dimension (usually starting dates or forecast time) allowing the application or not of crossvalidated climatologies. The computation is carried out independently for experimental and observational data products.
 
 ```r
 hcst_anom <- CST_Anomaly(exp = hcst_cal, obs = obs,
@@ -203,29 +304,23 @@ hcst_anom <- CST_Anomaly(exp = hcst_cal, obs = obs,
                          dat_dim = c('dat', 'ensemble'),
                          ftime_dim = 'time',
                          ncores = 10)
+
 ```
-Compute forecast anomaly field:
+**Exercise 3**: 
+1. Calculate the hcst anomalies with the raw hindcast but change the number of ncores.
 ```r
-clim <- s2dv::Clim(exp = hcst_cal$data, obs = obs$data,
-                   time_dim = "syear",
-                   dat_dim = c("dat", "ensemble"),
-                   memb = FALSE,
-                   memb_dim = "ensemble",
-                   ftime_dim = "time",
-                   ncores = 10)
-clim_hcst <- InsertDim(clim$clim_exp, posdim = 1, lendim = 1, name = "syear")
-dims <- dim(clim_hcst)
-clim_hcst <- rep(clim_hcst, fcst$dim[['ensemble']])
-dim(clim_hcst) <- c(dims, ensemble = fcst$dim[['ensemble']])
-clim_hcst <- Reorder(clim_hcst, order = names(fcst$dim))
-fcst_anom <- fcst_cal$data - clim_hcst
+t1 <- Sys.time()
+hcst_anom_raw <- CST_Anomaly(____)
+t2 <- Sys.time()
+t2-t1
 ```
 
-## 3. Compute skill: RPSS
+
+## 4. Compute skill: RPSS
+In order to trust the results we need to evaluate the skill of the system. 
 
 Compute Ranked Probability Skill Score for anomalies: 
 ```r
-
 skill <- RPSS(exp = hcst_anom$exp$data, obs = hcst_anom$obs$data,
               time_dim = 'syear',
               memb_dim = 'ensemble',
@@ -233,16 +328,50 @@ skill <- RPSS(exp = hcst_anom$exp$data, obs = hcst_anom$obs$data,
               cross.val = TRUE,
               ncores = 10)
 ```
-Explore the result:  
+**Exercise 4**: 
+1. Compare the result with the raw results.
 
 ```r
+skill_raw <- RPSS(exp = hcst_anom_raw$exp$data, obs = hcst_anom_raw$obs$data,
+                  time_dim = 'syear',
+                  memb_dim = 'ensemble',
+                  Fair = FALSE,
+                  cross.val = TRUE,
+                  ncores = 10)
 > summary(skill$rpss)
     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
 -0.54005 -0.11225 -0.03170 -0.03722  0.04480  0.44376 
 > summary(skill$sign)
    Mode   FALSE    TRUE 
 logical    6798     402 
+> summary(skill_raw$rpss)
+    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
+-0.59113 -0.11045 -0.03069 -0.03585  0.04637  0.44305 
+> summary(skill_raw$sign)
+   Mode   FALSE    TRUE 
+logical    7055     387 
 ```
 
+## 5. Additional Exercise: Visualization
+
+We can use the function from s2dv PlotEquiMap to visualize the data. 
 
-## References
\ No newline at end of file
+**Example**
+
+With the following code, we will plot a map to compare the hindcast raw and calibrated data. Can we appreciate the differences? 
+
+We are going to plot the last year of the hindcast period (2016) for the last timestep (December). Also, we are going to use the last ensemble member (arbirtrary choice).
+```r
+dim(hcst$data)
+    #   dat       var     syear  ensemble      time  latitude longitude 
+    #     1         1        24        25         2        60        60 
+```
+```r
+# Visualization: compare raw and calibrated data
+PlotEquiMap(hcst$data[,,24,1,1,,], lat = lat, lon = lon, 
+            filled.continents = FALSE, fileout = "/esarchive/scratch/erifarov/rpackages/PATC2023/figs_res/raw_hcst_24.png")
+
+PlotEquiMap(hcst_cal$data[,,24,1,1,,], lat = lat, lon = lon, 
+            filled.continents = FALSE, fileout = "/esarchive/scratch/erifarov/rpackages/PATC2023/figs_res/cal_hcst_24.png")
+
+```
diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
new file mode 100644
index 00000000..d21c5834
--- /dev/null
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
@@ -0,0 +1,437 @@
+# Hands-on 2: Data assesment
+
+**Goal**
+
+Use CSTools and s2dv to calibrate, compute anomalies and skill of a climate dataset over a period.
+
+**Load packages**
+```r
+library(CSTools)
+library(s2dv)
+```
+
+## 1. Load the data
+
+In this section we will use the function **Start** to load the data. Then, we will transfrom the output `startR_array` to an `s2dv_cube` object in order that the data is easy to use within CSTools functions.
+
+The `s2dv_cube` object is a structured list that contains the information needed to work with multidimensional arrays of data. Coordinates, dimensions, and metadata are neatly combined to allow for a more intuitive, concise, and less error-prone experience.
+
+**Note:** If you have already loaded the data with Start, go directly to section 1b).
+
+### 1a) Load the data
+
+The following section is taken from [PATC 2023 startR tutorial](https://earth.bsc.es/gitlab/es/startR/-/blob/doc-bsc_training_2023/inst/doc/tutorial/PATC2023/handson_1-data-loading.md?ref_type=heads). The experiment data are Meteo-France System 7 from ECMWF, and the observation ones are ERA5 from ECMWF. We're going to analyze the near-surface temperature (short name: tas) for seasonal forecast. We will focus on the Europe region (roughly 20W-40E, 20N-80N). The hindcast years are 1993 to 2016, and the forecast year is 2020. The initial month is November. 
+
+```r
+# Use this one if on workstation or nord3 (have access to /esarchive)
+path_exp <- "/esarchive/exp/meteofrance/system7c3s/monthly_mean/$var$_f6h/$var$_$syear$.nc"
+#----------------------------------------------------------------------
+# Run these two lines if you're on Marenostrum4 and log in with training account
+prefix <- '/gpfs/scratch/bsc32/bsc32734/bsc_training_2023/R_handson/'
+path_exp <- paste0(prefix, path_exp)
+#----------------------------------------------------------------------
+
+sdate_hcst <- paste0(1993:2016, '1101')
+
+hcst <- CST_Start(dat = path_exp,
+                  var = 'tas',
+                  syear = sdate_hcst,
+                  ensemble = 'all',
+                  time = 1:2,
+                  latitude = startR::values(list(20, 80)),
+                  latitude_reorder = startR::Sort(),
+                  longitude = startR::values(list(-20, 40)),
+                  longitude_reorder = startR::CircularSort(-180, 180),
+                  transform = startR::CDORemapper,
+                  transform_params = list(grid = 'r360x181', method = 'bilinear'),
+                  transform_vars = c('latitude', 'longitude'),
+                  synonims = list(latitude = c('lat', 'latitude'),
+                                  longitude = c('lon', 'longitude')),
+                  return_vars = list(time = 'syear',
+                                     longitude = NULL, latitude = NULL),
+                  retrieve = TRUE)
+
+```
+
+```r
+sdate_fcst <- '20201101'
+
+fcst <- CST_Start(dat = path_exp,
+                  var = 'tas',
+                  syear = sdate_fcst,
+                  ensemble = 'all',
+                  time = 1:2, 
+                  latitude = startR::values(list(20, 80)),
+                  latitude_reorder = startR::Sort(),
+                  longitude = startR::values(list(-20, 40)),
+                  longitude_reorder = startR::CircularSort(-180, 180),
+                  transform = startR::CDORemapper,
+                  transform_params = list(grid = 'r360x181', method = 'bilinear'),
+                  transform_vars = c('latitude', 'longitude'),
+                  synonims = list(syear = c('syear', 'sdate'),
+                                  latitude = c('lat', 'latitude'),
+                                  longitude = c('lon', 'longitude')),
+                  return_vars = list(time = 'syear',
+                                    longitude = NULL, latitude = NULL),
+                  retrieve = TRUE)
+
+```
+
+```r
+# Adjust the day to the correct month
+hcst$attrs$Dates <- hcst$attrs$Dates - lubridate::days(1)
+
+date_string <- format(hcst$attrs$Dates, '%Y%m')
+sdate_obs <- array(date_string, dim = c(syear = 24, time = 2))
+```
+
+```r
+path_obs <- '/esarchive/recon/ecmwf/era5/monthly_mean/$var$_f1h-r1440x721cds/$var$_$syear$.nc'
+#----------------------------------------------------------------------
+# Run these two lines if you're on Marenostrum4 and log in with training account
+prefix <- '/gpfs/scratch/bsc32/bsc32734/bsc_training_2023/R_handson/'
+path_obs <- paste0(prefix, path_obs)
+#----------------------------------------------------------------------
+
+obs <- CST_Start(dat = path_obs,
+                 var = 'tas',
+                 syear = sdate_obs,
+                 split_multiselected_dims = TRUE,
+                 latitude = startR::values(list(20, 80)),
+                 latitude_reorder = startR::Sort(),
+                 longitude = startR::values(list(-20, 40)),
+                 longitude_reorder = startR::CircularSort(-180, 180),
+                 transform = startR::CDORemapper,
+                 transform_params = list(grid = 'r360x181', method = 'bilinear'),
+                 transform_vars = c('latitude', 'longitude'),
+                 synonims = list(syear = c('syear', 'sdate'),
+                                 latitude = c('lat', 'latitude'),
+                                 longitude = c('lon', 'longitude')),
+                 return_vars = list(time = 'syear',
+                                    longitude = NULL, latitude = NULL),
+                 retrieve = TRUE)
+
+```
+
+### 1b) Create `s2dv_cube`:
+
+Now we convert the data (`startR_array`) into an 's2dv_cube' object with the function **as.s2dv_cube**:
+
+```r
+> str(hcst)
+ 'startR_array' num [1, 1, 1:24, 1:25, 1:2, 1:60, 1:60] 295 296 297 297 297 ...
+ - attr(*, "Variables")=List of 2
+  ..$ common:List of 4
+  .. ..$ time     : POSIXct[1:48], format: "1993-12-01" "1994-12-01" ...
+  .. ..$ longitude: num [1:60(1d)] -19.5 -18.5 -17.5 -16.5 -15.5 -14.5 -13.5 -12.5 -11.5 -10.5 ...
+  .. .. ..- attr(*, "variables")=List of 1
+  .. .. .. ..$ longitude:List of 7
+  .. .. .. .. ..$ ndims        : num 1
+  .. .. .. .. ..$ size         : int 360
+  .. .. .. .. ..$ units        : chr "degrees_east"
+  .. .. .. .. ..$ dim          :List of 1
+  .. .. .. .. .. ..$ :List of 10
+
+```
+Now, we convert the startR object into 's2dv_cube' object.
+```r
+hcst <- as.s2dv_cube(hcst)
+fcst <- as.s2dv_cube(fcst)
+obs <- as.s2dv_cube(obs)
+```
+
+```r
+> str(hcst)
+List of 4
+ $ data  : num [1, 1, 1:24, 1:25, 1:2, 1:60, 1:60] 295 296 297 297 297 ...
+ $ dims  : Named int [1:7] 1 1 24 25 2 60 60
+  ..- attr(*, "names")= chr [1:7] "dat" "var" "syear" "ensemble" ...
+ $ coords:List of 7
+  ..$ dat      : chr "dat1"
+  .. ..- attr(*, "indices")= logi FALSE
+  ..$ var      : chr "tas"
+  .. ..- attr(*, "values")= logi TRUE
+  .. ..- attr(*, "indices")= logi FALSE
+  ..$ syear    : chr [1:24] "19931101" "19941101" "19951101" "19961101" ...
+  .. ..- attr(*, "values")= logi TRUE
+  .. ..- attr(*, "indices")= logi FALSE
+  ..$ ensemble : int [1:25] 1 2 3 4 5 6 7 8 9 10 ...
+  .. ..- attr(*, "indices")= logi TRUE
+  ..$ time     : int [1:2] 1 2
+  .. ..- attr(*, "indices")= logi TRUE
+  ..$ latitude : num [1:60] 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5 ...
+  .. ..- attr(*, "variables")=List of 1
+  .. .. ..$ latitude:List of 7
+  .. .. .. ..$ ndims        : num 1
+  [...]
+```
+**Questions 1:**   
+
+Find `s2dv_cube` information of `hcst` object. Here there are some useful functions:
+```r
+str()
+typeof()
+class()
+dim()
+names()
+summary()
+```
+```r
+`s2dv_cube` overview
+$ data: [data array]
+$ dims: [dimensions vector]
+$ coords: [List of coordinates vectors]
+  [...]
+$ attrs: [List of the attributes]
+  $ Dates 
+  $ Variable:
+    $ varName
+    $ metadata 
+  [...]
+```
+
+1. Can you tell what type of object is an **s2dv_cube** in R structures?  
+```r
+class(hcst)
+# "s2dv_cube"
+typeof(hcst)
+# "list"
+```
+1. Can you tell what type of object is the element data in common language?  
+```r
+dim(hcst$data)
+#   dat       var     syear  ensemble      time  latitude longitude 
+#     1         1        24        25         2        61        61
+typeof(hcst$data) # base type
+# [1] "double"
+
+# Answer: Multi-dimensional array / N-dimensional array /tensor.
+```
+2. The Dates of an `s2dv_cube` can be found in element: `hcst$attrs$Dates`. 
+Could you tell what are the time dimensions of the object?  
+```r
+dim(hcst$attrs$Dates)
+# syear  time 
+#   24     2 
+```
+3. The coordinates in the  `s2dv_cube` are stored in element `hcst$coords`. What are the coordinates names in the object `hcst`?  
+```r
+names(hcst$coords)
+# [1] "dat"       "var"       "syear"     "ensemble"  "time"      "latitude" 
+# [7] "longitude"
+```
+4.  In which region we have loaded the data?  
+```r
+hcst$coords$lat
+# latitude: 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
+# [26] 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69
+# [51] 70 71 72 73 74 75 76 77 78 79 80
+
+hcst$coords$lon
+#  [1] -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10  -9  -8  -7  -6  -5  -4  -3  -2
+# [20]  -1   0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17
+# [39]  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
+# [58]  37  38  39  40
+```
+5. What is the start date dimension name? What is the ensemble member dimension name?
+```r
+hcst$dims
+# syear
+# ensemble
+```
+
+6. How many ensemble members have the `hcst`, `fcst` and `obs` datasets? 
+```r
+hcst$dims[['ensemble']]
+# [1] 25
+fcst$dims[['ensemble']]
+# [1] 51
+obs$dims
+# No ensemble member in obs
+```
+7. The metadata of the `s2dv_cube` is stored in `hcst$attrs$Variable` What is the full variable name of the loaded data?  
+```r
+str(hcst$attrs$Variable)
+str(hcst$attrs$Variable$metadata$tas$long_name)
+#  chr "2 metre temperature"
+```
+8. What season is the data loaded from?  
+```r
+dim(hcst$attrs$Dates)
+hcst$attrs$Dates[1,]
+months(hcst$attrs$Dates[1,])
+#  "December" "January" 
+```
+9. What are the units of the data?
+```r
+hcst$attrs$Variable$metadata$tas$units
+# K
+```
+
+**Exercise 1**:   
+1. Find the mean, the maximum and the minimum of the `fcst`, `hcst` and obs data and compare it:
+
+```r
+summary(hcst$data)
+#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+#   237.3   274.1   280.5   280.1   287.8   303.5 
+summary(hcst$data)
+#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+#   237.3   274.1   280.5   280.1   287.8   303.5 
+summary(fcst$data)
+#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+#   240.6   274.9   280.8   280.8   288.2   303.4 
+summary(obs$data)
+#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+#   239.8   272.9   279.8   279.6   287.7   303.0 
+```
+
+## 2. Calibrate the data
+
+Calibrate the hindcast:
+```r
+hcst_cal <- CST_Calibration(exp = hcst, obs = obs,
+                            cal.method = "evmos",
+                            eval.method = "leave-one-out",
+                            multi.model = FALSE,
+                            na.fill = TRUE,
+                            na.rm = TRUE,
+                            apply_to = NULL,
+                            alpha = NULL,
+                            memb_dim = "ensemble",
+                            sdate_dim = "syear",
+                            ncores = 10)
+```
+
+Calibrate the forecast: 
+```r
+
+fcst_cal <- CST_Calibration(exp = hcst, obs = obs, exp_cor = fcst,
+                            cal.method = "evmos",
+                            eval.method = "leave-one-out",
+                            multi.model = FALSE,
+                            na.fill = TRUE,
+                            na.rm = TRUE,
+                            apply_to = NULL,
+                            alpha = NULL,
+                            memb_dim = "ensemble",
+                            sdate_dim = "syear",
+                            ncores = 10)
+```
+**Exercise 2**: 
+1. Test the hcst with a different calibration methods available and compare the results.
+```r
+hcst_cal_bias <- CST_Calibration(exp = hcst, obs = obs, exp_cor = fcst,
+                                 cal.method = "bias",
+                                 eval.method = "leave-one-out",
+                                 multi.model = FALSE,
+                                 na.fill = TRUE,
+                                 na.rm = TRUE,
+                                 apply_to = NULL,
+                                 alpha = NULL,
+                                 memb_dim = "ensemble",
+                                 sdate_dim = "syear",
+                                 ncores = 10)
+summary(hcst_cal_bias$data)
+   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+  240.4   273.6   280.0   280.3   288.1   302.7 
+summary(hcst_cal$data)
+   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+  237.9   272.9   279.8   279.6   287.6   303.5 
+```
+
+## 3. Compute Anomalies
+
+Now, we will compute the hindcast anomalies with the calibrated `hcst`.
+```r
+hcst_anom <- CST_Anomaly(exp = hcst_cal, obs = obs,
+                         cross = TRUE,
+                         memb = TRUE,
+                         memb_dim = 'ensemble',
+                         dim_anom = 'syear',
+                         dat_dim = c('dat', 'ensemble'),
+                         ftime_dim = 'time',
+                         ncores = 10)
+
+```
+**Exercise 3**: 
+1. Calculate the hcst anomalies with the raw hindcast but change the number of ncores.
+```r
+t1 <- Sys.time()
+hcst_anom_raw <- CST_Anomaly(exp = hcst, obs = obs,
+                             cross = TRUE,
+                             memb = TRUE,
+                             memb_dim = 'ensemble',
+                             dim_anom = 'syear',
+                             dat_dim = c('dat', 'ensemble'),
+                             ftime_dim = 'time',
+                             ncores = 5)
+t2 <- Sys.time()
+t2-t1
+```
+
+
+## 4. Compute skill: RPSS
+
+Compute Ranked Probability Skill Score for anomalies: 
+```r
+skill <- RPSS(exp = hcst_anom$exp$data, obs = hcst_anom$obs$data,
+              time_dim = 'syear',
+              memb_dim = 'ensemble',
+              Fair = FALSE,
+              cross.val = TRUE,
+              ncores = 10)
+```
+**Exercise 4**: 
+1. Compare the result with the raw results.
+
+```r
+skill_raw <- RPSS(exp = hcst_anom_raw$exp$data, obs = hcst_anom_raw$obs$data,
+                  time_dim = 'syear',
+                  memb_dim = 'ensemble',
+                  Fair = FALSE,
+                  cross.val = TRUE,
+                  ncores = 10)
+> summary(skill$rpss)
+    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
+-0.54005 -0.11225 -0.03170 -0.03722  0.04480  0.44376 
+> summary(skill$sign)
+   Mode   FALSE    TRUE 
+logical    6798     402 
+> summary(skill_raw$rpss)
+    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
+-0.59113 -0.11045 -0.03069 -0.03585  0.04637  0.44305 
+> summary(skill_raw$sign)
+   Mode   FALSE    TRUE 
+logical    7055     387 
+```
+
+## 5. Additional Exercise: Visualization
+
+We can use the function from s2dv PlotEquiMap to visualize the data. 
+
+**Example**
+
+With the following code, we will plot a map to compare the hindcast raw and calibrated data. Can we appreciate the differences? 
+
+We are going to plot the last year of the hindcast period (2016) for the last timestep (December). Also, we are going to use the last ensemble member (arbirtrary choice).
+```r
+dim(hcst$data)
+    #   dat       var     syear  ensemble      time  latitude longitude 
+    #     1         1        24        25         2        60        60 
+```
+```r
+lat <- hcst$coords$lat
+lon <- hcst$coords$lon
+# Visualization: compare raw and calibrated data
+PlotEquiMap(hcst$data[,,24,1,1,,], lat = lat, lon = lon, 
+            filled.continents = FALSE, fileout = "/esarchive/scratch/erifarov/rpackages/PATC2023/figs_res/raw_hcst_24.png")
+
+PlotEquiMap(hcst_cal$data[,,24,1,1,,], lat = lat, lon = lon, 
+            filled.continents = FALSE, fileout = "/esarchive/scratch/erifarov/rpackages/PATC2023/figs_res/cal_hcst_24.png")
+            
+# Visualization of RPSS
+PlotEquiMap(skill$rpss[,,1,,], lat = lat, lon = lon, brks = seq(-1, 1, by = 0.1),
+            filled.continents = FALSE, fileout = "/esarchive/scratch/erifarov/rpackages/PATC2023/figs_res/skill.png")
+
+```
-- 
GitLab


From e95380995b83fe259f09a06a1f2415ee1018a9b8 Mon Sep 17 00:00:00 2001
From: Eva Rifa <eva.rifarovira@bsc.es>
Date: Thu, 26 Oct 2023 11:01:37 +0200
Subject: [PATCH 05/13] Few corrections

---
 .../PATC2023/handson_2-data-assesment_ans.md  | 22 +++++++++++++++++--
 1 file changed, 20 insertions(+), 2 deletions(-)

diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
index d21c5834..2ff5e31e 100644
--- a/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
@@ -316,7 +316,7 @@ fcst_cal <- CST_Calibration(exp = hcst, obs = obs, exp_cor = fcst,
                             alpha = NULL,
                             memb_dim = "ensemble",
                             sdate_dim = "syear",
-                            ncores = 10)
+                            ncores = 14)
 ```
 **Exercise 2**: 
 1. Test the hcst with a different calibration methods available and compare the results.
@@ -353,6 +353,22 @@ hcst_anom <- CST_Anomaly(exp = hcst_cal, obs = obs,
                          ftime_dim = 'time',
                          ncores = 10)
 
+```
+**Compute forecast anomaly field**
+```r
+clim <- s2dv::Clim(exp = hcst_cal$data, obs = obs$data,
+                         time_dim = "syear",
+                         dat_dim = c("dat", "ensemble"),
+                         memb = FALSE,
+                         memb_dim = "ensemble",
+                         ftime_dim = "time",
+                         ncores = 10)
+clim_hcst <- InsertDim(clim$clim_exp, posdim = 1, lendim = 1, name = "syear")
+dims <- dim(clim_hcst)
+clim_hcst <- rep(clim_hcst, fcst$dim[['ensemble']])
+dim(clim_hcst) <- c(dims, ensemble = fcst$dim[['ensemble']])
+clim_hcst <- Reorder(clim_hcst, order = names(fcst$dim))
+fcst_anom <- fcst_cal$data - clim_hcst
 ```
 **Exercise 3**: 
 1. Calculate the hcst anomalies with the raw hindcast but change the number of ncores.
@@ -429,7 +445,9 @@ PlotEquiMap(hcst$data[,,24,1,1,,], lat = lat, lon = lon,
 
 PlotEquiMap(hcst_cal$data[,,24,1,1,,], lat = lat, lon = lon, 
             filled.continents = FALSE, fileout = "/esarchive/scratch/erifarov/rpackages/PATC2023/figs_res/cal_hcst_24.png")
-            
+```
+
+```r        
 # Visualization of RPSS
 PlotEquiMap(skill$rpss[,,1,,], lat = lat, lon = lon, brks = seq(-1, 1, by = 0.1),
             filled.continents = FALSE, fileout = "/esarchive/scratch/erifarov/rpackages/PATC2023/figs_res/skill.png")
-- 
GitLab


From aa48128b97a6b5dffd91fd0cc57ee22918f7f062 Mon Sep 17 00:00:00 2001
From: Eva Rifa <eva.rifarovira@bsc.es>
Date: Thu, 26 Oct 2023 15:09:32 +0200
Subject: [PATCH 06/13] Clean text

---
 .../PATC2023/handson_2-data-assesment.md      | 182 ++++---------
 .../PATC2023/handson_2-data-assesment_ans.md  | 250 ++++++------------
 2 files changed, 140 insertions(+), 292 deletions(-)

diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
index 74ce3eb2..548a1dcb 100644
--- a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
@@ -2,7 +2,7 @@
 
 **Goal**
 
-Use CSTools and s2dv to calibrate, compute anomalies and skill of a climate dataset over a period.
+Use CSTools and s2dv to perform a quality assesment of a climate model.
 
 **Load packages**
 ```r
@@ -16,11 +16,11 @@ In this section we will use the function **Start** to load the data. Then, we wi
 
 The `s2dv_cube` object is a structured list that contains the information needed to work with multidimensional arrays of data. Coordinates, dimensions, and metadata are neatly combined to allow for a more intuitive, concise, and less error-prone experience.
 
-**Note:** If you have already loaded the data with Start, go directly to section 1b).
+**Note:** If you have already loaded the data with Start, go directly to section **b)**.
 
-### 1a) Load the data
+### a) Load the data
 
-The following section is taken from [PATC 2023 startR tutorial](https://earth.bsc.es/gitlab/es/startR/-/blob/doc-bsc_training_2023/inst/doc/tutorial/PATC2023/handson_1-data-loading.md?ref_type=heads). The experiment data are Meteo-France System 7 from ECMWF, and the observation ones are ERA5 from ECMWF. We're going to analyze the near-surface temperature (short name: tas) for seasonal forecast. We will focus on the Europe region (roughly 20W-40E, 20N-80N). The hindcast years are 1993 to 2016, and the forecast year is 2020. The initial month is November. 
+The following section is taken from [PATC 2023 startR tutorial](https://earth.bsc.es/gitlab/es/startR/-/blob/doc-bsc_training_2023/inst/doc/tutorial/PATC2023/handson_1-data-loading.md?ref_type=heads). The experiment data are Meteo-France System 7 from ECMWF, and the observation ones are ERA5 from ECMWF for near-surface temperature (short name: tas). We will focus on the Europe region (roughly 20W-40E, 20N-80N). The hindcast years are 1993 to 2016. 
 
 ```r
 # Use this one if on workstation or nord3 (have access to /esarchive)
@@ -53,30 +53,6 @@ hcst <- CST_Start(dat = path_exp,
 
 ```
 
-```r
-sdate_fcst <- '20201101'
-
-fcst <- CST_Start(dat = path_exp,
-                  var = 'tas',
-                  syear = sdate_fcst,
-                  ensemble = 'all',
-                  time = 1:2, 
-                  latitude = startR::values(list(20, 80)),
-                  latitude_reorder = startR::Sort(),
-                  longitude = startR::values(list(-20, 40)),
-                  longitude_reorder = startR::CircularSort(-180, 180),
-                  transform = startR::CDORemapper,
-                  transform_params = list(grid = 'r360x181', method = 'bilinear'),
-                  transform_vars = c('latitude', 'longitude'),
-                  synonims = list(syear = c('syear', 'sdate'),
-                                  latitude = c('lat', 'latitude'),
-                                  longitude = c('lon', 'longitude')),
-                  return_vars = list(time = 'syear',
-                                    longitude = NULL, latitude = NULL),
-                  retrieve = TRUE)
-
-```
-
 ```r
 # Adjust the day to the correct month
 hcst$attrs$Dates <- hcst$attrs$Dates - lubridate::days(1)
@@ -115,132 +91,82 @@ obs <- CST_Start(dat = path_obs,
 
 ### 1b) Create `s2dv_cube`:
 
-Now we convert the data (`startR_array`) into an 's2dv_cube' object with the function **as.s2dv_cube**:
+Now we convert the data (`startR_array`) into an 's2dv_cube' object with the function **as.s2dv_cube** from **CSTools** package:
 
-```r
-> str(hcst)
- 'startR_array' num [1, 1, 1:24, 1:25, 1:2, 1:60, 1:60] 295 296 297 297 297 ...
- - attr(*, "Variables")=List of 2
-  ..$ common:List of 4
-  .. ..$ time     : POSIXct[1:48], format: "1993-12-01" "1994-12-01" ...
-  .. ..$ longitude: num [1:60(1d)] -19.5 -18.5 -17.5 -16.5 -15.5 -14.5 -13.5 -12.5 -11.5 -10.5 ...
-  .. .. ..- attr(*, "variables")=List of 1
-  .. .. .. ..$ longitude:List of 7
-  .. .. .. .. ..$ ndims        : num 1
-  .. .. .. .. ..$ size         : int 360
-  .. .. .. .. ..$ units        : chr "degrees_east"
-  .. .. .. .. ..$ dim          :List of 1
-  .. .. .. .. .. ..$ :List of 10
-
-```
-Now, we convert the startR object into 's2dv_cube' object.
 ```r
 hcst <- as.s2dv_cube(hcst)
-fcst <- as.s2dv_cube(fcst)
 obs <- as.s2dv_cube(obs)
 ```
-
-```r
-> str(hcst)
-List of 4
- $ data  : num [1, 1, 1:24, 1:25, 1:2, 1:60, 1:60] 295 296 297 297 297 ...
- $ dims  : Named int [1:7] 1 1 24 25 2 60 60
-  ..- attr(*, "names")= chr [1:7] "dat" "var" "syear" "ensemble" ...
- $ coords:List of 7
-  ..$ dat      : chr "dat1"
-  .. ..- attr(*, "indices")= logi FALSE
-  ..$ var      : chr "tas"
-  .. ..- attr(*, "values")= logi TRUE
-  .. ..- attr(*, "indices")= logi FALSE
-  ..$ syear    : chr [1:24] "19931101" "19941101" "19951101" "19961101" ...
-  .. ..- attr(*, "values")= logi TRUE
-  .. ..- attr(*, "indices")= logi FALSE
-  ..$ ensemble : int [1:25] 1 2 3 4 5 6 7 8 9 10 ...
-  .. ..- attr(*, "indices")= logi TRUE
-  ..$ time     : int [1:2] 1 2
-  .. ..- attr(*, "indices")= logi TRUE
-  ..$ latitude : num [1:60] 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5 ...
-  .. ..- attr(*, "variables")=List of 1
-  .. .. ..$ latitude:List of 7
-  .. .. .. ..$ ndims        : num 1
-  [...]
-```
-**Questions 1:**   
-
-Find `s2dv_cube` information of `hcst` object. Here there are some useful functions:
+By printing the object, we see that it has been organized following an order. 
 ```r
-str()
-typeof()
-class()
-dim()
-names()
-summary()
+hcst
 ```
 ```r
-`s2dv_cube` overview
-$ data: [data array]
-$ dims: [dimensions vector]
-$ coords: [List of coordinates vectors]
-  [...]
-$ attrs: [List of the attributes]
-  $ Dates 
-  $ Variable:
-    $ varName
-    $ metadata 
-  [...]
+'s2dv_cube'
+Data          [ 294.975204467773, 295.99658203125, 296.999153137207, 296.874618530273, 297.662521362305, 297.113525390625, 296.145011901855, 295.981201171875 ... ] 
+Dimensions    ( dat = 1, var = 1, syear = 24, ensemble = 25, time = 2, latitude = 61, longitude = 61 ) 
+Coordinates  
+ * dat : dat1 
+ * var : tas 
+ * syear : 19931101, 19941101, 19951101, 19961101, 19971101, 19981101, 19991101, 20001101, 20011101, 20021101, 20031101, 20041101, 20051101, 20061101, 20071101, 20081101, 20091101, 20101101, 20111101, 20121101, 20131101, 20141101, 20151101, 20161101 
+   ensemble : 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 
+   time : 1, 2 
+ * latitude : 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80 
+ * longitude : -20, -19, -18, -17, -16, -15, -14, -13, -12, -11, -10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 
+Attributes   
+   Dates  : 1993-11-30 1994-11-30 1995-11-30 1996-11-30 1997-11-30 ... 
+   varName  : tas 
+   metadata :  
+      time 
+        other : class, tzone 
+      longitude 
+        other : dim 
+      latitude 
+        other : dim 
+      tas 
+        units : K 
+        long name : 2 metre temperature 
+        other : prec, dim, unlim, make_missing_value, missval, hasAddOffset, hasScaleFact, code, table 
+   Datasets  : dat1 
+   when  : 2023-10-26 14:43:30 
+   source_files  : /esarchive/exp/meteofrance/system7c3s/monthly_mean/tas_f6h/tas_19931101.nc ... 
+   load_parameters  : 
+       ( dat1 )  : dat = dat1, var = tas, syear = 19931101 ... 
+       ... 
 ```
+> **Note:** An `s2dv_cube` object is an structured list in base R. To acces the elements, you need to use the `$` operator (e.g. `hcst$data`, `hcst$coords`, `hcst$attrs`, ...)
 
-1. Can you tell what type of object is an **s2dv_cube** in R structures?  
+#### Questions 1: 
+**Goal:** To find `s2dv_cube` information of `hcst` object. 
+
+1. What type of object is an **s2dv_cube** in base R?  
 ```r
-class(hcst)
-# "s2dv_cube"
+class(____)
 typeof(___)
-
 ```
-1. Can you tell what type of object is the element data in common language? Use the function `dim()` and `typeof()` to check `hcst$data`
+2. What type of object is the element `hcst$data` (common language)? Use the function `dim()` and `typeof()` to check `hcst$data`:
 ```r
+typeof(____) 
 dim(____)
-
-typeof(hcst$data) 
-
 ```
-2. The Dates of an `s2dv_cube` can be found in element: `hcst$attrs$Dates`. 
-Could you tell what are the time dimensions of the object?  
-```r
+3. What are the **time dimensions** of the `hcst` object? The Dates of an `s2dv_cube` can be found in element: `hcst$attrs$Dates`.  
 
-```
-3. The coordinates in the  `s2dv_cube` are stored in element `hcst$coords`. What are the coordinates names in the object `hcst`? Use the function `names()` to check:
+4. What are the coordinates names in the object `hcst`? Use the function `names()` to check. The coordinates in the  `s2dv_cube` are stored in element `hcst$coords`.
 ```r
 names(____)
 
 ```
-4.  In which region we have loaded the data?  
-```r
-
-```
-5. What is the start date dimension name? What is the ensemble member dimension name?
-```r
-hcst$dims
-# syear
-# ensemble
-```
+5. In which **latitude** and **longitude** we have loaded the data? 
 
-6. How many ensemble members have the `hcst`, `fcst` and `obs` datasets? 
-```r
+6. What is the **start date** dimension name of the `hcst`? What is the **ensemble member** dimension name?
 
-```
-7. The metadata of the `s2dv_cube` is stored in `hcst$attrs$Variable` What is the full variable name of the loaded data? Find out the information in `hcst$attrs$Variable$metadata` with the function `str()`:
-```r
+7. How many **ensemble members** have the `hcst`, `fcst` and `obs` datasets? 
 
-```
-8. What season is the data loaded from? You can use the function `month()`
-```r
+8. What is the full variable name of the loaded data? Find out the information in `hcst$attrs$Variable$metadata` with the function `str()`.
 
-```
-9. What are the units of the data?
-```r
+9. From what season is the data loaded from? You can use the function `months()`.
 
-```
+10. What are the **units** of the data?
 
 **Exercise 1**:   
 1. Find the mean, the maximum and the minimum of the `fcst`, `hcst` and obs data and compare it:
diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
index 2ff5e31e..7362c980 100644
--- a/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
@@ -2,7 +2,7 @@
 
 **Goal**
 
-Use CSTools and s2dv to calibrate, compute anomalies and skill of a climate dataset over a period.
+Use CSTools and s2dv to perform a quality assesment of a climate model.
 
 **Load packages**
 ```r
@@ -16,11 +16,11 @@ In this section we will use the function **Start** to load the data. Then, we wi
 
 The `s2dv_cube` object is a structured list that contains the information needed to work with multidimensional arrays of data. Coordinates, dimensions, and metadata are neatly combined to allow for a more intuitive, concise, and less error-prone experience.
 
-**Note:** If you have already loaded the data with Start, go directly to section 1b).
+> **Note:** If you have already loaded the data with Start, go directly to section **b)**.
 
-### 1a) Load the data
+### a) Load the data
 
-The following section is taken from [PATC 2023 startR tutorial](https://earth.bsc.es/gitlab/es/startR/-/blob/doc-bsc_training_2023/inst/doc/tutorial/PATC2023/handson_1-data-loading.md?ref_type=heads). The experiment data are Meteo-France System 7 from ECMWF, and the observation ones are ERA5 from ECMWF. We're going to analyze the near-surface temperature (short name: tas) for seasonal forecast. We will focus on the Europe region (roughly 20W-40E, 20N-80N). The hindcast years are 1993 to 2016, and the forecast year is 2020. The initial month is November. 
+The following section is taken from [PATC 2023 startR tutorial](https://earth.bsc.es/gitlab/es/startR/-/blob/doc-bsc_training_2023/inst/doc/tutorial/PATC2023/handson_1-data-loading.md?ref_type=heads). The experiment data are Meteo-France System 7 from ECMWF, and the observation ones are ERA5 from ECMWF for near-surface temperature (short name: tas). We will focus on the Europe region (roughly 20W-40E, 20N-80N). The hindcast years are 1993 to 2016. 
 
 ```r
 # Use this one if on workstation or nord3 (have access to /esarchive)
@@ -53,30 +53,6 @@ hcst <- CST_Start(dat = path_exp,
 
 ```
 
-```r
-sdate_fcst <- '20201101'
-
-fcst <- CST_Start(dat = path_exp,
-                  var = 'tas',
-                  syear = sdate_fcst,
-                  ensemble = 'all',
-                  time = 1:2, 
-                  latitude = startR::values(list(20, 80)),
-                  latitude_reorder = startR::Sort(),
-                  longitude = startR::values(list(-20, 40)),
-                  longitude_reorder = startR::CircularSort(-180, 180),
-                  transform = startR::CDORemapper,
-                  transform_params = list(grid = 'r360x181', method = 'bilinear'),
-                  transform_vars = c('latitude', 'longitude'),
-                  synonims = list(syear = c('syear', 'sdate'),
-                                  latitude = c('lat', 'latitude'),
-                                  longitude = c('lon', 'longitude')),
-                  return_vars = list(time = 'syear',
-                                    longitude = NULL, latitude = NULL),
-                  retrieve = TRUE)
-
-```
-
 ```r
 # Adjust the day to the correct month
 hcst$attrs$Dates <- hcst$attrs$Dates - lubridate::days(1)
@@ -113,114 +89,86 @@ obs <- CST_Start(dat = path_obs,
 
 ```
 
-### 1b) Create `s2dv_cube`:
+### b) Create `s2dv_cube`:
 
-Now we convert the data (`startR_array`) into an 's2dv_cube' object with the function **as.s2dv_cube**:
+Now we convert the data (`startR_array`) into an 's2dv_cube' object with the function **as.s2dv_cube** from **CSTools** package:
 
-```r
-> str(hcst)
- 'startR_array' num [1, 1, 1:24, 1:25, 1:2, 1:60, 1:60] 295 296 297 297 297 ...
- - attr(*, "Variables")=List of 2
-  ..$ common:List of 4
-  .. ..$ time     : POSIXct[1:48], format: "1993-12-01" "1994-12-01" ...
-  .. ..$ longitude: num [1:60(1d)] -19.5 -18.5 -17.5 -16.5 -15.5 -14.5 -13.5 -12.5 -11.5 -10.5 ...
-  .. .. ..- attr(*, "variables")=List of 1
-  .. .. .. ..$ longitude:List of 7
-  .. .. .. .. ..$ ndims        : num 1
-  .. .. .. .. ..$ size         : int 360
-  .. .. .. .. ..$ units        : chr "degrees_east"
-  .. .. .. .. ..$ dim          :List of 1
-  .. .. .. .. .. ..$ :List of 10
-
-```
-Now, we convert the startR object into 's2dv_cube' object.
 ```r
 hcst <- as.s2dv_cube(hcst)
-fcst <- as.s2dv_cube(fcst)
 obs <- as.s2dv_cube(obs)
 ```
-
-```r
-> str(hcst)
-List of 4
- $ data  : num [1, 1, 1:24, 1:25, 1:2, 1:60, 1:60] 295 296 297 297 297 ...
- $ dims  : Named int [1:7] 1 1 24 25 2 60 60
-  ..- attr(*, "names")= chr [1:7] "dat" "var" "syear" "ensemble" ...
- $ coords:List of 7
-  ..$ dat      : chr "dat1"
-  .. ..- attr(*, "indices")= logi FALSE
-  ..$ var      : chr "tas"
-  .. ..- attr(*, "values")= logi TRUE
-  .. ..- attr(*, "indices")= logi FALSE
-  ..$ syear    : chr [1:24] "19931101" "19941101" "19951101" "19961101" ...
-  .. ..- attr(*, "values")= logi TRUE
-  .. ..- attr(*, "indices")= logi FALSE
-  ..$ ensemble : int [1:25] 1 2 3 4 5 6 7 8 9 10 ...
-  .. ..- attr(*, "indices")= logi TRUE
-  ..$ time     : int [1:2] 1 2
-  .. ..- attr(*, "indices")= logi TRUE
-  ..$ latitude : num [1:60] 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5 ...
-  .. ..- attr(*, "variables")=List of 1
-  .. .. ..$ latitude:List of 7
-  .. .. .. ..$ ndims        : num 1
-  [...]
-```
-**Questions 1:**   
-
-Find `s2dv_cube` information of `hcst` object. Here there are some useful functions:
+By printing the object, we see that it has been organized following an order. 
 ```r
-str()
-typeof()
-class()
-dim()
-names()
-summary()
+hcst
 ```
 ```r
-`s2dv_cube` overview
-$ data: [data array]
-$ dims: [dimensions vector]
-$ coords: [List of coordinates vectors]
-  [...]
-$ attrs: [List of the attributes]
-  $ Dates 
-  $ Variable:
-    $ varName
-    $ metadata 
-  [...]
-```
-
-1. Can you tell what type of object is an **s2dv_cube** in R structures?  
+'s2dv_cube'
+Data          [ 294.975204467773, 295.99658203125, 296.999153137207, 296.874618530273, 297.662521362305, 297.113525390625, 296.145011901855, 295.981201171875 ... ] 
+Dimensions    ( dat = 1, var = 1, syear = 24, ensemble = 25, time = 2, latitude = 61, longitude = 61 ) 
+Coordinates  
+ * dat : dat1 
+ * var : tas 
+ * syear : 19931101, 19941101, 19951101, 19961101, 19971101, 19981101, 19991101, 20001101, 20011101, 20021101, 20031101, 20041101, 20051101, 20061101, 20071101, 20081101, 20091101, 20101101, 20111101, 20121101, 20131101, 20141101, 20151101, 20161101 
+   ensemble : 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 
+   time : 1, 2 
+ * latitude : 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80 
+ * longitude : -20, -19, -18, -17, -16, -15, -14, -13, -12, -11, -10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 
+Attributes   
+   Dates  : 1993-11-30 1994-11-30 1995-11-30 1996-11-30 1997-11-30 ... 
+   varName  : tas 
+   metadata :  
+      time 
+        other : class, tzone 
+      longitude 
+        other : dim 
+      latitude 
+        other : dim 
+      tas 
+        units : K 
+        long name : 2 metre temperature 
+        other : prec, dim, unlim, make_missing_value, missval, hasAddOffset, hasScaleFact, code, table 
+   Datasets  : dat1 
+   when  : 2023-10-26 14:43:30 
+   source_files  : /esarchive/exp/meteofrance/system7c3s/monthly_mean/tas_f6h/tas_19931101.nc ... 
+   load_parameters  : 
+       ( dat1 )  : dat = dat1, var = tas, syear = 19931101 ... 
+       ... 
+```
+> **Note:** An `s2dv_cube` object is an structured list in base R. To acces the elements, you need to use the `$` operator (e.g. `hcst$data`, `hcst$coords`, `hcst$attrs`, ...)
+
+#### Questions 1: 
+**Goal:** To find `s2dv_cube` information of `hcst` object.
+
+1. What type of object is an **s2dv_cube** in base R?  
 ```r
 class(hcst)
 # "s2dv_cube"
 typeof(hcst)
 # "list"
 ```
-1. Can you tell what type of object is the element data in common language?  
+2. What type of object is the element `hcst$data` (common language)? Use the function `dim()` and `typeof()` to check `hcst$data`:
 ```r
-dim(hcst$data)
-#   dat       var     syear  ensemble      time  latitude longitude 
-#     1         1        24        25         2        61        61
 typeof(hcst$data) # base type
 # [1] "double"
+dim(hcst$data) # dimensions
+#   dat       var     syear  ensemble      time  latitude longitude 
+#     1         1        24        25         2        61        61
 
-# Answer: Multi-dimensional array / N-dimensional array /tensor.
+# Answer: Multi-dimensional array / N-dimensional array / Tensor.
 ```
-2. The Dates of an `s2dv_cube` can be found in element: `hcst$attrs$Dates`. 
-Could you tell what are the time dimensions of the object?  
+3. What are the **time dimensions** of the `hcst` object? The Dates of an `s2dv_cube` can be found in element: `hcst$attrs$Dates`. 
 ```r
 dim(hcst$attrs$Dates)
 # syear  time 
 #   24     2 
 ```
-3. The coordinates in the  `s2dv_cube` are stored in element `hcst$coords`. What are the coordinates names in the object `hcst`?  
+4. What are the coordinates names in the object `hcst`? Use the function `names()` to check. The coordinates in the  `s2dv_cube` are stored in element `hcst$coords`.
 ```r
 names(hcst$coords)
 # [1] "dat"       "var"       "syear"     "ensemble"  "time"      "latitude" 
 # [7] "longitude"
 ```
-4.  In which region we have loaded the data?  
+5. In which **latitude** and **longitude** we have loaded the data? 
 ```r
 hcst$coords$lat
 # latitude: 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
@@ -233,14 +181,14 @@ hcst$coords$lon
 # [39]  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
 # [58]  37  38  39  40
 ```
-5. What is the start date dimension name? What is the ensemble member dimension name?
+6. What is the **start date** dimension name of the `hcst`? What is the **ensemble member** dimension name?
 ```r
 hcst$dims
 # syear
 # ensemble
 ```
 
-6. How many ensemble members have the `hcst`, `fcst` and `obs` datasets? 
+7. How many **ensemble members** have the `hcst`, `fcst` and `obs` datasets?
 ```r
 hcst$dims[['ensemble']]
 # [1] 25
@@ -249,20 +197,20 @@ fcst$dims[['ensemble']]
 obs$dims
 # No ensemble member in obs
 ```
-7. The metadata of the `s2dv_cube` is stored in `hcst$attrs$Variable` What is the full variable name of the loaded data?  
+8. What is the full variable name of the loaded data? Find out the information in `hcst$attrs$Variable$metadata` with the function `str()`.
 ```r
 str(hcst$attrs$Variable)
 str(hcst$attrs$Variable$metadata$tas$long_name)
 #  chr "2 metre temperature"
 ```
-8. What season is the data loaded from?  
+9. From what season is the data loaded from? You can use the function `months()`.
 ```r
 dim(hcst$attrs$Dates)
 hcst$attrs$Dates[1,]
 months(hcst$attrs$Dates[1,])
 #  "December" "January" 
 ```
-9. What are the units of the data?
+10. What are the **units** of the data?
 ```r
 hcst$attrs$Variable$metadata$tas$units
 # K
@@ -303,42 +251,6 @@ hcst_cal <- CST_Calibration(exp = hcst, obs = obs,
                             ncores = 10)
 ```
 
-Calibrate the forecast: 
-```r
-
-fcst_cal <- CST_Calibration(exp = hcst, obs = obs, exp_cor = fcst,
-                            cal.method = "evmos",
-                            eval.method = "leave-one-out",
-                            multi.model = FALSE,
-                            na.fill = TRUE,
-                            na.rm = TRUE,
-                            apply_to = NULL,
-                            alpha = NULL,
-                            memb_dim = "ensemble",
-                            sdate_dim = "syear",
-                            ncores = 14)
-```
-**Exercise 2**: 
-1. Test the hcst with a different calibration methods available and compare the results.
-```r
-hcst_cal_bias <- CST_Calibration(exp = hcst, obs = obs, exp_cor = fcst,
-                                 cal.method = "bias",
-                                 eval.method = "leave-one-out",
-                                 multi.model = FALSE,
-                                 na.fill = TRUE,
-                                 na.rm = TRUE,
-                                 apply_to = NULL,
-                                 alpha = NULL,
-                                 memb_dim = "ensemble",
-                                 sdate_dim = "syear",
-                                 ncores = 10)
-summary(hcst_cal_bias$data)
-   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-  240.4   273.6   280.0   280.3   288.1   302.7 
-summary(hcst_cal$data)
-   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-  237.9   272.9   279.8   279.6   287.6   303.5 
-```
 
 ## 3. Compute Anomalies
 
@@ -354,22 +266,7 @@ hcst_anom <- CST_Anomaly(exp = hcst_cal, obs = obs,
                          ncores = 10)
 
 ```
-**Compute forecast anomaly field**
-```r
-clim <- s2dv::Clim(exp = hcst_cal$data, obs = obs$data,
-                         time_dim = "syear",
-                         dat_dim = c("dat", "ensemble"),
-                         memb = FALSE,
-                         memb_dim = "ensemble",
-                         ftime_dim = "time",
-                         ncores = 10)
-clim_hcst <- InsertDim(clim$clim_exp, posdim = 1, lendim = 1, name = "syear")
-dims <- dim(clim_hcst)
-clim_hcst <- rep(clim_hcst, fcst$dim[['ensemble']])
-dim(clim_hcst) <- c(dims, ensemble = fcst$dim[['ensemble']])
-clim_hcst <- Reorder(clim_hcst, order = names(fcst$dim))
-fcst_anom <- fcst_cal$data - clim_hcst
-```
+
 **Exercise 3**: 
 1. Calculate the hcst anomalies with the raw hindcast but change the number of ncores.
 ```r
@@ -422,9 +319,10 @@ logical    6798     402
 logical    7055     387 
 ```
 
-## 5. Additional Exercise: Visualization
+## 5. Additional Exercises: Visualization
 
 We can use the function from s2dv PlotEquiMap to visualize the data. 
+**Exercise 1: Visualization**
 
 **Example**
 
@@ -436,6 +334,7 @@ dim(hcst$data)
     #   dat       var     syear  ensemble      time  latitude longitude 
     #     1         1        24        25         2        60        60 
 ```
+
 ```r
 lat <- hcst$coords$lat
 lon <- hcst$coords$lon
@@ -453,3 +352,26 @@ PlotEquiMap(skill$rpss[,,1,,], lat = lat, lon = lon, brks = seq(-1, 1, by = 0.1)
             filled.continents = FALSE, fileout = "/esarchive/scratch/erifarov/rpackages/PATC2023/figs_res/skill.png")
 
 ```
+
+**Exercise 2: Change Calibration method and compare results**
+
+1. Test the hcst with a different calibration methods available and compare the results.
+```r
+hcst_cal_bias <- CST_Calibration(exp = hcst, obs = obs, 
+                                 cal.method = "bias",
+                                 eval.method = "leave-one-out",
+                                 multi.model = FALSE,
+                                 na.fill = TRUE,
+                                 na.rm = TRUE,
+                                 apply_to = NULL,
+                                 alpha = NULL,
+                                 memb_dim = "ensemble",
+                                 sdate_dim = "syear",
+                                 ncores = 10)
+summary(hcst_cal_bias$data)
+   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+  240.4   273.6   280.0   280.3   288.1   302.7 
+summary(hcst_cal$data)
+   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+  237.9   272.9   279.8   279.6   287.6   303.5 
+```
\ No newline at end of file
-- 
GitLab


From 2b1396761dcb8dad08e0cb730a5d8b3f461d94a8 Mon Sep 17 00:00:00 2001
From: Eva Rifa <eva.rifarovira@bsc.es>
Date: Thu, 26 Oct 2023 15:12:44 +0200
Subject: [PATCH 07/13] Improve text

---
 inst/doc/tutorial/PATC2023/handson_2-data-assesment.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
index 548a1dcb..9e81ec98 100644
--- a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
@@ -16,7 +16,7 @@ In this section we will use the function **Start** to load the data. Then, we wi
 
 The `s2dv_cube` object is a structured list that contains the information needed to work with multidimensional arrays of data. Coordinates, dimensions, and metadata are neatly combined to allow for a more intuitive, concise, and less error-prone experience.
 
-**Note:** If you have already loaded the data with Start, go directly to section **b)**.
+> **Note:** If you have already loaded the data with Start, go directly to section **b)**.
 
 ### a) Load the data
 
@@ -89,7 +89,7 @@ obs <- CST_Start(dat = path_obs,
 
 ```
 
-### 1b) Create `s2dv_cube`:
+### b) Create `s2dv_cube`:
 
 Now we convert the data (`startR_array`) into an 's2dv_cube' object with the function **as.s2dv_cube** from **CSTools** package:
 
-- 
GitLab


From 987f8bad96c5d0479a5722468dceb3f00a3b878c Mon Sep 17 00:00:00 2001
From: Eva Rifa <eva.rifarovira@bsc.es>
Date: Thu, 26 Oct 2023 17:34:15 +0200
Subject: [PATCH 08/13] Improve Handson

---
 .../PATC2023/Figures/hcst_anom_cal.png        | Bin 0 -> 12792 bytes
 .../PATC2023/Figures/hcst_anom_raw.png        | Bin 0 -> 12676 bytes
 .../tutorial/PATC2023/Figures/skill_cal.png   | Bin 0 -> 14520 bytes
 .../tutorial/PATC2023/Figures/skill_raw.png   | Bin 0 -> 14718 bytes
 .../PATC2023/handson_2-data-assesment.md      | 191 +++++++++---------
 .../PATC2023/handson_2-data-assesment_ans.md  | 187 ++++++++---------
 6 files changed, 178 insertions(+), 200 deletions(-)
 create mode 100644 inst/doc/tutorial/PATC2023/Figures/hcst_anom_cal.png
 create mode 100644 inst/doc/tutorial/PATC2023/Figures/hcst_anom_raw.png
 create mode 100644 inst/doc/tutorial/PATC2023/Figures/skill_cal.png
 create mode 100644 inst/doc/tutorial/PATC2023/Figures/skill_raw.png

diff --git a/inst/doc/tutorial/PATC2023/Figures/hcst_anom_cal.png b/inst/doc/tutorial/PATC2023/Figures/hcst_anom_cal.png
new file mode 100644
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diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
index 9e81ec98..c32a2c67 100644
--- a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
@@ -12,9 +12,9 @@ library(s2dv)
 
 ## 1. Load the data
 
-In this section we will use the function **Start** to load the data. Then, we will transfrom the output `startR_array` to an `s2dv_cube` object in order that the data is easy to use within CSTools functions.
+In this section we will use the function **Start** to load the data. Then, we will transfrom the output **startR_array** to an **s2dv_cube** object in order that the data is easy to use within CSTools functions.
 
-The `s2dv_cube` object is a structured list that contains the information needed to work with multidimensional arrays of data. Coordinates, dimensions, and metadata are neatly combined to allow for a more intuitive, concise, and less error-prone experience.
+The **s2dv_cube** object is a structured list that contains the information needed to work with multidimensional arrays of data. Coordinates, dimensions, and metadata are neatly combined to allow for a more intuitive, concise, and less error-prone experience.
 
 > **Note:** If you have already loaded the data with Start, go directly to section **b)**.
 
@@ -91,17 +91,16 @@ obs <- CST_Start(dat = path_obs,
 
 ### b) Create `s2dv_cube`:
 
-Now we convert the data (`startR_array`) into an 's2dv_cube' object with the function **as.s2dv_cube** from **CSTools** package:
+Now we convert the **hindcast and observations data** (**startR_array**) into an **s2dv_cube** object with the function **as.s2dv_cube** from **CSTools** package:
 
 ```r
 hcst <- as.s2dv_cube(hcst)
 obs <- as.s2dv_cube(obs)
 ```
 By printing the object, we see that it has been organized following an order. 
+
 ```r
-hcst
-```
-```r
+> hcst
 's2dv_cube'
 Data          [ 294.975204467773, 295.99658203125, 296.999153137207, 296.874618530273, 297.662521362305, 297.113525390625, 296.145011901855, 295.981201171875 ... ] 
 Dimensions    ( dat = 1, var = 1, syear = 24, ensemble = 25, time = 2, latitude = 61, longitude = 61 ) 
@@ -134,24 +133,24 @@ Attributes
        ( dat1 )  : dat = dat1, var = tas, syear = 19931101 ... 
        ... 
 ```
-> **Note:** An `s2dv_cube` object is an structured list in base R. To acces the elements, you need to use the `$` operator (e.g. `hcst$data`, `hcst$coords`, `hcst$attrs`, ...)
+> **Note:** An **s2dv_cube** object is an structured list in base R. To acces the elements, you need to use the `$` operator (e.g. `hcst$data`, `hcst$coords`, `hcst$attrs`, ...)
 
-#### Questions 1: 
-**Goal:** To find `s2dv_cube` information of `hcst` object. 
+#### Exercise 1
+**Goal:** To find **s2dv_cube** information of **hindcast** data.
 
 1. What type of object is an **s2dv_cube** in base R?  
 ```r
 class(____)
-typeof(___)
+typeof(____)
 ```
-2. What type of object is the element `hcst$data` (common language)? Use the function `dim()` and `typeof()` to check `hcst$data`:
+2. What type of object is the element `hcst$data` (common language)? Use the function **dim()** and **typeof()** to check `hcst$data`:
 ```r
 typeof(____) 
 dim(____)
 ```
-3. What are the **time dimensions** of the `hcst` object? The Dates of an `s2dv_cube` can be found in element: `hcst$attrs$Dates`.  
+3. What are the **time dimensions** of the **hindcast** data? The Dates of an **s2dv_cube** can be found in element: `hcst$attrs$Dates`.
 
-4. What are the coordinates names in the object `hcst`? Use the function `names()` to check. The coordinates in the  `s2dv_cube` are stored in element `hcst$coords`.
+4. What are the coordinates names in the **hindcast**? Use the function **names()** to check. The coordinates in the  **s2dv_cube** are stored in element `hcst$coords`.
 ```r
 names(____)
 
@@ -160,44 +159,31 @@ names(____)
 
 6. What is the **start date** dimension name of the `hcst`? What is the **ensemble member** dimension name?
 
-7. How many **ensemble members** have the `hcst`, `fcst` and `obs` datasets? 
+7. How many **ensemble members** have the **hindcast** and **observations** datasets?
 
-8. What is the full variable name of the loaded data? Find out the information in `hcst$attrs$Variable$metadata` with the function `str()`.
+8. What is the full variable name of the loaded data? Find out the information in `hcst$attrs$Variable$metadata` with the function **str()**.
 
-9. From what season is the data loaded from? You can use the function `months()`.
+9. From what season is the data loaded from? You can use the function **months()**.
 
 10. What are the **units** of the data?
 
-**Exercise 1**:   
-1. Find the mean, the maximum and the minimum of the `fcst`, `hcst` and obs data and compare it:
 
-```r
+## 2. Calibrate the data
 
-```
+The first step to perform a quality assesment is to correct biases as well as dispersion errors of the model. The function **Calibration** from **CSTools** allows us to chose from different calibration member-by-member techniques. 
 
-## 2. Calibrate the data
-Now, we are going to use the function CST_Calibration from CSTools to calibrate the hindcast. The parameter `cal.method` alllows us to chose the calibration method. In this exercise we are going to chose the `evmos`.  
+In this case, we are going to chose a method called **"evmos"** which applies a variance inflation technique to ensure the correction of the bias and the correspondence of variance between forecast and observation (Van Schaeybroeck and Vannitsem, 2011). 
 
-The "evmos" method applies a variance inflation technique to ensure the correction of
-the bias and the correspondence of variance between forecast and observation (Van Schaeybroeck and Vannitsem, 2011).
-```r
-hcst_cal <- CST_Calibration(exp = hcst, obs = obs,
-                            cal.method = "evmos",
-                            eval.method = "leave-one-out",
-                            multi.model = FALSE,
-                            na.fill = TRUE,
-                            na.rm = TRUE,
-                            apply_to = NULL,
-                            alpha = NULL,
-                            memb_dim = "ensemble",
-                            sdate_dim = "syear",
-                            ncores = 10)
-```
+> **Note:** The functions of **CSTools** whose name starts with the prefix **CST** work directly with **s2dv_cube** objects. If we are not using **s2dv_cube** we can use the standard version without the prefix that work with arrays (e.g. use **Calibration**, **Anomaly** instead of **CST_Calibration**, **CST_Anomaly**...).
 
-Now we are going to calibrate also the forecast:
-```r
+To calibrate the hindcast, we need to use also the observations and to specify some parameters, such as the adjustment specifications and the dimension names.  
+
+#### Exercise 2:
 
-fcst_cal <- CST_Calibration(exp = hcst, obs = obs, exp_cor = fcst,
+**Goal:** Calibrate the hindcast with completing the ensemble member dimension names and the start date dimension names of data.
+
+```r
+hcst_cal <- CST_Calibration(exp = hcst, obs = obs,
                             cal.method = "evmos",
                             eval.method = "leave-one-out",
                             multi.model = FALSE,
@@ -205,47 +191,42 @@ fcst_cal <- CST_Calibration(exp = hcst, obs = obs, exp_cor = fcst,
                             na.rm = TRUE,
                             apply_to = NULL,
                             alpha = NULL,
-                            memb_dim = "ensemble",
-                            sdate_dim = "syear",
+                            memb_dim = ____,
+                            sdate_dim = ____,
                             ncores = 10)
 ```
-**Exercise 2**: 
-1. Test the hcst with a different calibration methods available and compare the results. You can use the function `summary()` to see the differences.
-```r
-hcst_cal_bias <- CST_Calibration(____)
-```
 
 ## 3. Compute Anomalies
+In this section we will compute the hindcast anomalies from the calibrated data in the previous step. 
 
-Now, we will compute the hindcast anomalies with the calibrated `hcst`. We are going to use the function CST_Anomaly from CSTools.
+Anomalies are deviations from the average weather conditions over a long period. A positive anomaly indicates that the conditions are higher than the average while negative indicates that is lower. Calculating anomalies is an important step in the model quality assesment for several reasons such as removing seasonal variations,  visualization and for policy and decision-making among others.
 
-This function computes the anomalies relative to a climatology computed along the selected dimension (usually starting dates or forecast time) allowing the application or not of crossvalidated climatologies. The computation is carried out independently for experimental and observational data products.
+We are going to use the function **CST_Anomaly** from **CSTools**. This function computes the anomalies relative to a climatology computed along the selected dimension (in our case starting dates). The computation is carried out independently for experimental and observational datasets.
+
+#### Exercise 3:
+**Goal:** Calculate the hindcast anomalies from the calibrated hindcast and observations dataset. You can take a look on the [CSTools package documentation](https://cran.r-project.org/web/packages/CSTools/CSTools.pdf) on page 40.
 
 ```r
-hcst_anom <- CST_Anomaly(exp = hcst_cal, obs = obs,
+hcst_anom <- CST_Anomaly(exp = ____, 
+                         obs = ____,
                          cross = TRUE,
                          memb = TRUE,
-                         memb_dim = 'ensemble',
-                         dim_anom = 'syear',
+                         memb_dim = ____,
+                         dim_anom = ____,
                          dat_dim = c('dat', 'ensemble'),
-                         ftime_dim = 'time',
+                         ftime_dim = ____,
                          ncores = 10)
 
 ```
-**Exercise 3**: 
-1. Calculate the hcst anomalies with the raw hindcast but change the number of ncores.
-```r
-t1 <- Sys.time()
-hcst_anom_raw <- CST_Anomaly(____)
-t2 <- Sys.time()
-t2-t1
-```
 
 
 ## 4. Compute skill: RPSS
-In order to trust the results we need to evaluate the skill of the system. 
+To trust the climate models we need to evaluate its skill. To do it, we are going to use the Ranked Probability Skill Score (RPSS; Wilks, 2011). Is the skill score based on the Ranked Probability Score (RPS; Wilks, 2011).  It can be used to assess whether a forecast presents an improvement or worsening with respect to a reference forecast.
 
-Compute Ranked Probability Skill Score for anomalies: 
+The RPSS ranges between minus infinite and 1. If the RPSS is positive, it indicates that the forecast has higher skill than the reference forecast, while a negative value means that it has a lower skill. It is computed as `RPSS = 1 - RPS_exp / RPS_ref`. The statistical significance is obtained based on a Random Walk test at
+the specified confidence level (DelSole and Tippett, 2016). 
+
+Next, we compute the RPSS for anomalies: 
 ```r
 skill <- RPSS(exp = hcst_anom$exp$data, obs = hcst_anom$obs$data,
               time_dim = 'syear',
@@ -254,50 +235,62 @@ skill <- RPSS(exp = hcst_anom$exp$data, obs = hcst_anom$obs$data,
               cross.val = TRUE,
               ncores = 10)
 ```
-**Exercise 4**: 
-1. Compare the result with the raw results.
+
+The output of the **RPSS** function is a list of two elements. The first element is the RPSS; the second element, `sign` is a logical array of the statistical significance of the RPSS with the same dimensions as `rpss`.
+
+#### Exercise 4:
+
+**Goal:** Compare the **RPSS** results with calibrated and raw anomalies.
 
 ```r
-skill_raw <- RPSS(exp = hcst_anom_raw$exp$data, obs = hcst_anom_raw$obs$data,
-                  time_dim = 'syear',
-                  memb_dim = 'ensemble',
-                  Fair = FALSE,
-                  cross.val = TRUE,
-                  ncores = 10)
-> summary(skill$rpss)
-    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
--0.54005 -0.11225 -0.03170 -0.03722  0.04480  0.44376 
-> summary(skill$sign)
-   Mode   FALSE    TRUE 
-logical    6798     402 
-> summary(skill_raw$rpss)
-    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
--0.59113 -0.11045 -0.03069 -0.03585  0.04637  0.44305 
-> summary(skill_raw$sign)
-   Mode   FALSE    TRUE 
-logical    7055     387 
-```
+hcst_anom <- CST_Anomaly(exp = ____, 
+                         obs = ____,
+                         cross = ____,
+                         memb = ____,
+                         memb_dim = ____,
+                         dim_anom = ____,
+                         dat_dim = ____,
+                         ftime_dim = ____,
+                         ncores = ____)
+skill_raw <- RPSS(exp = ____, 
+                  obs = ____,
+                  time_dim = ____,
+                  memb_dim = ____,
+                  Fair = ____,
+                  cross.val = ____,
+                  ncores = ____)
+
+summary(____)
 
-## 5. Additional Exercise: Visualization
+```
 
-We can use the function from s2dv PlotEquiMap to visualize the data. 
+## 5. Additional Exercises: Visualization
 
-**Example**
+#### Exercise 5
+**Goal:** Use the function **PlotEquiMap** from **s2dv** to compare the raw and calibrated data.
 
-With the following code, we will plot a map to compare the hindcast raw and calibrated data. Can we appreciate the differences? 
+We are going to plot the **last year** of the hindcast period (**2016**) for the last timestep (**December**). Also, we are going to use the **last ensemble member** (arbirtrary choice).
 
-We are going to plot the last year of the hindcast period (2016) for the last timestep (December). Also, we are going to use the last ensemble member (arbirtrary choice).
 ```r
-dim(hcst$data)
-    #   dat       var     syear  ensemble      time  latitude longitude 
-    #     1         1        24        25         2        60        60 
-```
-```r
-# Visualization: compare raw and calibrated data
-PlotEquiMap(hcst$data[,,24,1,1,,], lat = lat, lon = lon, 
-            filled.continents = FALSE, fileout = "/esarchive/scratch/erifarov/rpackages/PATC2023/figs_res/raw_hcst_24.png")
 
-PlotEquiMap(hcst_cal$data[,,24,1,1,,], lat = lat, lon = lon, 
-            filled.continents = FALSE, fileout = "/esarchive/scratch/erifarov/rpackages/PATC2023/figs_res/cal_hcst_24.png")
+lat <- hcst$coords$lat
+lon <- hcst$coords$lon
+
+PlotEquiMap(var = hcst_anom$exp$data[24, , 25, , 2, , ], lat = lat, lon = lon, 
+            filled.continents = FALSE,
+            fileout = ____)
 
+PlotEquiMap(var = ____, lat = ____, lon = ____, 
+            filled.continents = ____,
+            fileout = ____)
 ```
+
+#### Exercise 6
+**Goal:** Use the function **PlotEquiMap** from **s2dv** to compare the RPSS results with calibrated and raw data.
+
+```r        
+PlotEquiMap(var = ____, lat = ____, lon = ____, 
+            brks = seq(-1, 1, by = 0.1),
+            filled.continents = ____, 
+            fileout = ____)
+```
\ No newline at end of file
diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
index 7362c980..33e9bed9 100644
--- a/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
@@ -12,9 +12,9 @@ library(s2dv)
 
 ## 1. Load the data
 
-In this section we will use the function **Start** to load the data. Then, we will transfrom the output `startR_array` to an `s2dv_cube` object in order that the data is easy to use within CSTools functions.
+In this section we will use the function **Start** to load the data. Then, we will transfrom the output **startR_array** to an **s2dv_cube** object in order that the data is easy to use within CSTools functions.
 
-The `s2dv_cube` object is a structured list that contains the information needed to work with multidimensional arrays of data. Coordinates, dimensions, and metadata are neatly combined to allow for a more intuitive, concise, and less error-prone experience.
+The **s2dv_cube** object is a structured list that contains the information needed to work with multidimensional arrays of data. Coordinates, dimensions, and metadata are neatly combined to allow for a more intuitive, concise, and less error-prone experience.
 
 > **Note:** If you have already loaded the data with Start, go directly to section **b)**.
 
@@ -91,17 +91,16 @@ obs <- CST_Start(dat = path_obs,
 
 ### b) Create `s2dv_cube`:
 
-Now we convert the data (`startR_array`) into an 's2dv_cube' object with the function **as.s2dv_cube** from **CSTools** package:
+Now we convert the **hindcast and observations data** (**startR_array**) into an **s2dv_cube** object with the function **as.s2dv_cube** from **CSTools** package:
 
 ```r
 hcst <- as.s2dv_cube(hcst)
 obs <- as.s2dv_cube(obs)
 ```
 By printing the object, we see that it has been organized following an order. 
+
 ```r
-hcst
-```
-```r
+> hcst
 's2dv_cube'
 Data          [ 294.975204467773, 295.99658203125, 296.999153137207, 296.874618530273, 297.662521362305, 297.113525390625, 296.145011901855, 295.981201171875 ... ] 
 Dimensions    ( dat = 1, var = 1, syear = 24, ensemble = 25, time = 2, latitude = 61, longitude = 61 ) 
@@ -134,10 +133,10 @@ Attributes
        ( dat1 )  : dat = dat1, var = tas, syear = 19931101 ... 
        ... 
 ```
-> **Note:** An `s2dv_cube` object is an structured list in base R. To acces the elements, you need to use the `$` operator (e.g. `hcst$data`, `hcst$coords`, `hcst$attrs`, ...)
+> **Note:** An **s2dv_cube** object is an structured list in base R. To acces the elements, you need to use the `$` operator (e.g. `hcst$data`, `hcst$coords`, `hcst$attrs`, ...)
 
-#### Questions 1: 
-**Goal:** To find `s2dv_cube` information of `hcst` object.
+#### Exercise 1
+**Goal:** To find **s2dv_cube** information of **hindcast** data.
 
 1. What type of object is an **s2dv_cube** in base R?  
 ```r
@@ -146,7 +145,7 @@ class(hcst)
 typeof(hcst)
 # "list"
 ```
-2. What type of object is the element `hcst$data` (common language)? Use the function `dim()` and `typeof()` to check `hcst$data`:
+2. What type of object is the element `hcst$data` (common language)? Use the function **dim()** and **typeof()** to check `hcst$data`:
 ```r
 typeof(hcst$data) # base type
 # [1] "double"
@@ -156,13 +155,13 @@ dim(hcst$data) # dimensions
 
 # Answer: Multi-dimensional array / N-dimensional array / Tensor.
 ```
-3. What are the **time dimensions** of the `hcst` object? The Dates of an `s2dv_cube` can be found in element: `hcst$attrs$Dates`. 
+3. What are the **time dimensions** of the **hindcast** data? The Dates of an **s2dv_cube** can be found in element: `hcst$attrs$Dates`. 
 ```r
 dim(hcst$attrs$Dates)
 # syear  time 
 #   24     2 
 ```
-4. What are the coordinates names in the object `hcst`? Use the function `names()` to check. The coordinates in the  `s2dv_cube` are stored in element `hcst$coords`.
+4. What are the coordinates names in the **hindcast**? Use the function **names()** to check. The coordinates in the  **s2dv_cube** are stored in element `hcst$coords`.
 ```r
 names(hcst$coords)
 # [1] "dat"       "var"       "syear"     "ensemble"  "time"      "latitude" 
@@ -181,14 +180,14 @@ hcst$coords$lon
 # [39]  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
 # [58]  37  38  39  40
 ```
-6. What is the **start date** dimension name of the `hcst`? What is the **ensemble member** dimension name?
+6. What is the **start date** dimension name of the **hindcast**? What is the **ensemble member** dimension name?
 ```r
 hcst$dims
 # syear
 # ensemble
 ```
 
-7. How many **ensemble members** have the `hcst`, `fcst` and `obs` datasets?
+7. How many **ensemble members** have the **hindcast** and **observations** datasets?
 ```r
 hcst$dims[['ensemble']]
 # [1] 25
@@ -197,13 +196,13 @@ fcst$dims[['ensemble']]
 obs$dims
 # No ensemble member in obs
 ```
-8. What is the full variable name of the loaded data? Find out the information in `hcst$attrs$Variable$metadata` with the function `str()`.
+8. What is the full variable name of the loaded data? Find out the information in `hcst$attrs$Variable$metadata` with the function **str()**.
 ```r
 str(hcst$attrs$Variable)
 str(hcst$attrs$Variable$metadata$tas$long_name)
 #  chr "2 metre temperature"
 ```
-9. From what season is the data loaded from? You can use the function `months()`.
+9. From what season is the data loaded from? You can use the function **months()**.
 ```r
 dim(hcst$attrs$Dates)
 hcst$attrs$Dates[1,]
@@ -216,27 +215,20 @@ hcst$attrs$Variable$metadata$tas$units
 # K
 ```
 
-**Exercise 1**:   
-1. Find the mean, the maximum and the minimum of the `fcst`, `hcst` and obs data and compare it:
+## 2. Calibrate the data
 
-```r
-summary(hcst$data)
-#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#   237.3   274.1   280.5   280.1   287.8   303.5 
-summary(hcst$data)
-#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#   237.3   274.1   280.5   280.1   287.8   303.5 
-summary(fcst$data)
-#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#   240.6   274.9   280.8   280.8   288.2   303.4 
-summary(obs$data)
-#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#   239.8   272.9   279.8   279.6   287.7   303.0 
-```
+The first step to perform a quality assesment is to correct biases as well as dispersion errors of the model. The function **Calibration** from **CSTools** allows us to chose from different calibration member-by-member techniques. 
 
-## 2. Calibrate the data
+In this case, we are going to chose a method called **"evmos"** which applies a variance inflation technique to ensure the correction of the bias and the correspondence of variance between forecast and observation (Van Schaeybroeck and Vannitsem, 2011).
+
+> **Note:** The functions of **CSTools** whose name starts with the prefix **CST** work directly with **s2dv_cube** objects. If we are not using **s2dv_cube** we can use the standard version without the prefix that work with arrays (e.g. use **Calibration**, **Anomaly** instead of **CST_Calibration**, **CST_Anomaly**...).
+
+To calibrate the hindcast, we need to use also the observations and to specify some parameters, such as the adjustment specifications and the dimension names.  
+
+#### Exercise 2:
+
+**Goal:** Calibrate the hindcast with completing the ensemble member dimension names and the start date dimension names of data.
 
-Calibrate the hindcast:
 ```r
 hcst_cal <- CST_Calibration(exp = hcst, obs = obs,
                             cal.method = "evmos",
@@ -251,12 +243,20 @@ hcst_cal <- CST_Calibration(exp = hcst, obs = obs,
                             ncores = 10)
 ```
 
-
 ## 3. Compute Anomalies
 
-Now, we will compute the hindcast anomalies with the calibrated `hcst`.
+In this section we will compute the hindcast anomalies from the calibrated data in the previous step. 
+
+Anomalies are deviations from the average weather conditions over a long period. A positive anomaly indicates that the conditions are higher than the average while negative indicates that is lower. Calculating anomalies is an important step in the model quality assesment for several reasons such as removing seasonal variations,  visualization and for policy and decision-making among others.
+
+We are going to use the function **CST_Anomaly** from **CSTools**. This function computes the anomalies relative to a climatology computed along the selected dimension (in our case starting dates). The computation is carried out independently for experimental and observational datasets.
+
+#### Exercise 3:
+**Goal:** Calculate the hindcast anomalies from the calibrated hindcast and observations dataset. You can take a look on the [CSTools package documentation](https://cran.r-project.org/web/packages/CSTools/CSTools.pdf) on page 40.
+
 ```r
-hcst_anom <- CST_Anomaly(exp = hcst_cal, obs = obs,
+hcst_anom <- CST_Anomaly(exp = hcst_cal, 
+                         obs = obs,
                          cross = TRUE,
                          memb = TRUE,
                          memb_dim = 'ensemble',
@@ -267,39 +267,42 @@ hcst_anom <- CST_Anomaly(exp = hcst_cal, obs = obs,
 
 ```
 
-**Exercise 3**: 
-1. Calculate the hcst anomalies with the raw hindcast but change the number of ncores.
-```r
-t1 <- Sys.time()
-hcst_anom_raw <- CST_Anomaly(exp = hcst, obs = obs,
-                             cross = TRUE,
-                             memb = TRUE,
-                             memb_dim = 'ensemble',
-                             dim_anom = 'syear',
-                             dat_dim = c('dat', 'ensemble'),
-                             ftime_dim = 'time',
-                             ncores = 5)
-t2 <- Sys.time()
-t2-t1
-```
-
 
 ## 4. Compute skill: RPSS
 
-Compute Ranked Probability Skill Score for anomalies: 
+To trust the climate models we need to evaluate its skill. To do it, we are going to use the Ranked Probability Skill Score (RPSS; Wilks, 2011). Is the skill score based on the Ranked Probability Score (RPS; Wilks, 2011).  It can be used to assess whether a forecast presents an improvement or worsening with respect to a reference forecast.
+
+The RPSS ranges between minus infinite and 1. If the RPSS is positive, it indicates that the forecast has higher skill than the reference forecast, while a negative value means that it has a lower skill. It is computed as `RPSS = 1 - RPS_exp / RPS_ref`. The statistical significance is obtained based on a Random Walk test at
+the specified confidence level (DelSole and Tippett, 2016). 
+
+Next, we compute the RPSS for anomalies: 
 ```r
-skill <- RPSS(exp = hcst_anom$exp$data, obs = hcst_anom$obs$data,
+skill <- RPSS(exp = hcst_anom$exp$data, 
+              obs = hcst_anom$obs$data,
               time_dim = 'syear',
               memb_dim = 'ensemble',
               Fair = FALSE,
               cross.val = TRUE,
               ncores = 10)
 ```
-**Exercise 4**: 
-1. Compare the result with the raw results.
+
+The output of the **RPSS** function is a list of two elements. The first element is the RPSS; the second element, `sign` is a logical array of the statistical significance of the RPSS with the same dimensions as `rpss`.
+
+#### Exercise 4:
+**Goal:** Compare the **RPSS** results with calibrated and raw anomalies.
 
 ```r
-skill_raw <- RPSS(exp = hcst_anom_raw$exp$data, obs = hcst_anom_raw$obs$data,
+hcst_anom_raw <- CST_Anomaly(exp = hcst, 
+                             obs = obs,
+                             cross = TRUE,
+                             memb = TRUE,
+                             memb_dim = 'ensemble',
+                             dim_anom = 'syear',
+                             dat_dim = c('dat', 'ensemble'),
+                             ftime_dim = 'time',
+                             ncores = 10)
+skill_raw <- RPSS(exp = hcst_anom_raw$exp$data, 
+                  obs = hcst_anom_raw$obs$data,
                   time_dim = 'syear',
                   memb_dim = 'ensemble',
                   Fair = FALSE,
@@ -321,57 +324,39 @@ logical    7055     387
 
 ## 5. Additional Exercises: Visualization
 
-We can use the function from s2dv PlotEquiMap to visualize the data. 
-**Exercise 1: Visualization**
-
-**Example**
+#### Exercise 5
+**Goal:** Use the function **PlotEquiMap** from **s2dv** to compare the raw and calibrated data.
 
-With the following code, we will plot a map to compare the hindcast raw and calibrated data. Can we appreciate the differences? 
+We are going to plot the **last year** of the hindcast period (**2016**) for the last timestep (**December**). Also, we are going to use the **last ensemble member** (arbirtrary choice).
 
-We are going to plot the last year of the hindcast period (2016) for the last timestep (December). Also, we are going to use the last ensemble member (arbirtrary choice).
 ```r
-dim(hcst$data)
-    #   dat       var     syear  ensemble      time  latitude longitude 
-    #     1         1        24        25         2        60        60 
-```
 
-```r
 lat <- hcst$coords$lat
 lon <- hcst$coords$lon
-# Visualization: compare raw and calibrated data
-PlotEquiMap(hcst$data[,,24,1,1,,], lat = lat, lon = lon, 
-            filled.continents = FALSE, fileout = "/esarchive/scratch/erifarov/rpackages/PATC2023/figs_res/raw_hcst_24.png")
 
-PlotEquiMap(hcst_cal$data[,,24,1,1,,], lat = lat, lon = lon, 
-            filled.continents = FALSE, fileout = "/esarchive/scratch/erifarov/rpackages/PATC2023/figs_res/cal_hcst_24.png")
-```
-
-```r        
-# Visualization of RPSS
-PlotEquiMap(skill$rpss[,,1,,], lat = lat, lon = lon, brks = seq(-1, 1, by = 0.1),
-            filled.continents = FALSE, fileout = "/esarchive/scratch/erifarov/rpackages/PATC2023/figs_res/skill.png")
+PlotEquiMap(hcst_anom$exp$data[24, , 25, , 2, , ], lat = lat, lon = lon, 
+            filled.continents = FALSE,
+            fileout = "/esarchive/scratch/erifarov/git/cstools/inst/doc/tutorial/PATC2023/Figures/hcst_anom_cal.png")
 
+PlotEquiMap(hcst_anom_raw$exp$data[24, , 25, , 2, , ], lat = lat, lon = lon, 
+            filled.continents = FALSE,
+            fileout = "/esarchive/scratch/erifarov/git/cstools/inst/doc/tutorial/PATC2023/Figures/hcst_anom_raw.png")
 ```
+![](./Figures/hcst_anom_cal.png) 
+![](./Figures/hcst_anom_raw.png) 
 
-**Exercise 2: Change Calibration method and compare results**
+#### Exercise 6
+**Goal:** Use the function **PlotEquiMap** from **s2dv** to compare the RPSS results with calibrated and raw data.
 
-1. Test the hcst with a different calibration methods available and compare the results.
-```r
-hcst_cal_bias <- CST_Calibration(exp = hcst, obs = obs, 
-                                 cal.method = "bias",
-                                 eval.method = "leave-one-out",
-                                 multi.model = FALSE,
-                                 na.fill = TRUE,
-                                 na.rm = TRUE,
-                                 apply_to = NULL,
-                                 alpha = NULL,
-                                 memb_dim = "ensemble",
-                                 sdate_dim = "syear",
-                                 ncores = 10)
-summary(hcst_cal_bias$data)
-   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-  240.4   273.6   280.0   280.3   288.1   302.7 
-summary(hcst_cal$data)
-   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-  237.9   272.9   279.8   279.6   287.6   303.5 
-```
\ No newline at end of file
+```r        
+PlotEquiMap(skill$rpss[ , , 2, , ], lat = lat, lon = lon, 
+            brks = seq(-1, 1, by = 0.1),
+            filled.continents = FALSE, 
+            fileout = "/esarchive/scratch/erifarov/git/cstools/inst/doc/tutorial/PATC2023/Figures/skill_cal.png")
+PlotEquiMap(skill_raw$rpss[ , , 2, , ], lat = lat, lon = lon, 
+            brks = seq(-1, 1, by = 0.1),
+            filled.continents = FALSE, 
+            fileout = "/esarchive/scratch/erifarov/git/cstools/inst/doc/tutorial/PATC2023/Figures/skill_raw.png")
+```
+![](./Figures/skill_cal.png) 
+![](./Figures/skill_raw.png) 
\ No newline at end of file
-- 
GitLab


From f887980d71e6d37c30cc364d77a745af6cc92e57 Mon Sep 17 00:00:00 2001
From: Eva Rifa <eva.rifarovira@bsc.es>
Date: Thu, 26 Oct 2023 17:58:02 +0200
Subject: [PATCH 09/13] Improve figures from PATC 2023

---
 .../PATC2023/Figures/hcst_anom_cal.png        | Bin 12792 -> 12464 bytes
 .../PATC2023/Figures/hcst_anom_raw.png        | Bin 12676 -> 12089 bytes
 .../tutorial/PATC2023/Figures/skill_cal.png   | Bin 14520 -> 14180 bytes
 .../tutorial/PATC2023/Figures/skill_raw.png   | Bin 14718 -> 14152 bytes
 .../PATC2023/handson_2-data-assesment.md      |  46 ++++++++----------
 .../PATC2023/handson_2-data-assesment_ans.md  |  32 ++++++------
 6 files changed, 37 insertions(+), 41 deletions(-)

diff --git a/inst/doc/tutorial/PATC2023/Figures/hcst_anom_cal.png b/inst/doc/tutorial/PATC2023/Figures/hcst_anom_cal.png
index 749830c4f39d8f610efb4dbd01339491a35fe516..90e676fddac41bae2331599c93c99edd3bb4a344 100644
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diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
index c32a2c67..f775a506 100644
--- a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
@@ -1,8 +1,6 @@
 # Hands-on 2: Data assesment
 
-**Goal**
-
-Use CSTools and s2dv to perform a quality assesment of a climate model.
+**Goal:** Use CSTools and s2dv to perform a quality assesment of a climate model.
 
 **Load packages**
 ```r
@@ -12,11 +10,11 @@ library(s2dv)
 
 ## 1. Load the data
 
-In this section we will use the function **Start** to load the data. Then, we will transfrom the output **startR_array** to an **s2dv_cube** object in order that the data is easy to use within CSTools functions.
+In this section we will use the function **Start** to load the data. Then, we will transfrom the output **startR_array** to an **s2dv_cube** object in order that the data is easy to use within **CSTools** functions.
 
 The **s2dv_cube** object is a structured list that contains the information needed to work with multidimensional arrays of data. Coordinates, dimensions, and metadata are neatly combined to allow for a more intuitive, concise, and less error-prone experience.
 
-> **Note:** If you have already loaded the data with Start, go directly to section **b)**.
+> **Note:** If you have already loaded the data with **Start**, go directly to section **b)**.
 
 ### a) Load the data
 
@@ -97,7 +95,11 @@ Now we convert the **hindcast and observations data** (**startR_array**) into an
 hcst <- as.s2dv_cube(hcst)
 obs <- as.s2dv_cube(obs)
 ```
-By printing the object, we see that it has been organized following an order. 
+By printing the object, we can see the object structure. The first level elements are: 
+- **Data**: A multidimensional array containing the data
+- **Dimensions**: A vector with the data dimensions.
+- **Coordinates**: A list with vector coordinates.
+- **Attributes**: A list containing the metadata.
 
 ```r
 > hcst
@@ -133,28 +135,19 @@ Attributes
        ( dat1 )  : dat = dat1, var = tas, syear = 19931101 ... 
        ... 
 ```
-> **Note:** An **s2dv_cube** object is an structured list in base R. To acces the elements, you need to use the `$` operator (e.g. `hcst$data`, `hcst$coords`, `hcst$attrs`, ...)
+> **Note:** An **s2dv_cube** object is an structured list in base R. To acces the elements, you need to use the `$` operator (e.g. `hcst$data`, `hcst$dims`, `hcst$coords`, `hcst$attrs`, ...)
 
 #### Exercise 1
 **Goal:** To find **s2dv_cube** information of **hindcast** data.
 
-1. What type of object is an **s2dv_cube** in base R?  
-```r
-class(____)
-typeof(____)
-```
+1. What type of object is an **s2dv_cube** in base R? Use the function **class()** and **typeof()**:
+
 2. What type of object is the element `hcst$data` (common language)? Use the function **dim()** and **typeof()** to check `hcst$data`:
-```r
-typeof(____) 
-dim(____)
-```
+
 3. What are the **time dimensions** of the **hindcast** data? The Dates of an **s2dv_cube** can be found in element: `hcst$attrs$Dates`.
 
 4. What are the coordinates names in the **hindcast**? Use the function **names()** to check. The coordinates in the  **s2dv_cube** are stored in element `hcst$coords`.
-```r
-names(____)
 
-```
 5. In which **latitude** and **longitude** we have loaded the data? 
 
 6. What is the **start date** dimension name of the `hcst`? What is the **ensemble member** dimension name?
@@ -167,7 +160,6 @@ names(____)
 
 10. What are the **units** of the data?
 
-
 ## 2. Calibrate the data
 
 The first step to perform a quality assesment is to correct biases as well as dispersion errors of the model. The function **Calibration** from **CSTools** allows us to chose from different calibration member-by-member techniques. 
@@ -204,7 +196,7 @@ Anomalies are deviations from the average weather conditions over a long period.
 We are going to use the function **CST_Anomaly** from **CSTools**. This function computes the anomalies relative to a climatology computed along the selected dimension (in our case starting dates). The computation is carried out independently for experimental and observational datasets.
 
 #### Exercise 3:
-**Goal:** Calculate the hindcast anomalies from the calibrated hindcast and observations dataset. You can take a look on the [CSTools package documentation](https://cran.r-project.org/web/packages/CSTools/CSTools.pdf) on page 40.
+**Goal:** Calculate the hindcast anomalies from the calibrated hindcast and observations dataset. You can take a look on the [CSTools package documentation](https://cran.r-project.org/web/packages/CSTools/CSTools.pdf) on page 40 to find the missing parameters.
 
 ```r
 hcst_anom <- CST_Anomaly(exp = ____, 
@@ -212,7 +204,7 @@ hcst_anom <- CST_Anomaly(exp = ____,
                          cross = TRUE,
                          memb = TRUE,
                          memb_dim = ____,
-                         dim_anom = ____,
+                         dim_anom = 'syear',
                          dat_dim = c('dat', 'ensemble'),
                          ftime_dim = ____,
                          ncores = 10)
@@ -221,14 +213,14 @@ hcst_anom <- CST_Anomaly(exp = ____,
 
 
 ## 4. Compute skill: RPSS
-To trust the climate models we need to evaluate its skill. To do it, we are going to use the Ranked Probability Skill Score (RPSS; Wilks, 2011). Is the skill score based on the Ranked Probability Score (RPS; Wilks, 2011).  It can be used to assess whether a forecast presents an improvement or worsening with respect to a reference forecast.
+To trust the climate models we need to evaluate its skill. To do it, we are going to use the **Ranked Probability Skill Score** (RPSS; Wilks, 2011). Is the skill score based on the **Ranked Probability Score** (RPS; Wilks, 2011).  It can be used to assess whether a forecast presents an improvement or worsening with respect to a reference forecast.
 
-The RPSS ranges between minus infinite and 1. If the RPSS is positive, it indicates that the forecast has higher skill than the reference forecast, while a negative value means that it has a lower skill. It is computed as `RPSS = 1 - RPS_exp / RPS_ref`. The statistical significance is obtained based on a Random Walk test at
-the specified confidence level (DelSole and Tippett, 2016). 
+The **RPSS** ranges between minus infinite and 1. If the **RPSS is positive**, it indicates that the **forecast has higher skill than the reference forecast**, while a **negative** value means that it has a **lower skill**. It is computed as `RPSS = 1 - RPS_exp / RPS_ref`. The statistical significance is obtained based on a Random Walk test at the specified confidence level (DelSole and Tippett, 2016). 
 
 Next, we compute the RPSS for anomalies: 
 ```r
-skill <- RPSS(exp = hcst_anom$exp$data, obs = hcst_anom$obs$data,
+skill <- RPSS(exp = hcst_anom$exp$data, 
+              obs = hcst_anom$obs$data,
               time_dim = 'syear',
               memb_dim = 'ensemble',
               Fair = FALSE,
@@ -240,7 +232,7 @@ The output of the **RPSS** function is a list of two elements. The first element
 
 #### Exercise 4:
 
-**Goal:** Compare the **RPSS** results with calibrated and raw anomalies.
+**Goal:** Compare the RPSS results with **calibrated** and **raw anomalies**.
 
 ```r
 hcst_anom <- CST_Anomaly(exp = ____, 
diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
index 33e9bed9..74c31352 100644
--- a/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
@@ -1,8 +1,6 @@
 # Hands-on 2: Data assesment
 
-**Goal**
-
-Use CSTools and s2dv to perform a quality assesment of a climate model.
+**Goal:** Use CSTools and s2dv to perform a quality assesment of a climate model.
 
 **Load packages**
 ```r
@@ -12,11 +10,11 @@ library(s2dv)
 
 ## 1. Load the data
 
-In this section we will use the function **Start** to load the data. Then, we will transfrom the output **startR_array** to an **s2dv_cube** object in order that the data is easy to use within CSTools functions.
+In this section we will use the function **Start** to load the data. Then, we will transfrom the output **startR_array** to an **s2dv_cube** object in order that the data is easy to use within **CSTools** functions.
 
 The **s2dv_cube** object is a structured list that contains the information needed to work with multidimensional arrays of data. Coordinates, dimensions, and metadata are neatly combined to allow for a more intuitive, concise, and less error-prone experience.
 
-> **Note:** If you have already loaded the data with Start, go directly to section **b)**.
+> **Note:** If you have already loaded the data with **Start**, go directly to section **b)**.
 
 ### a) Load the data
 
@@ -97,7 +95,11 @@ Now we convert the **hindcast and observations data** (**startR_array**) into an
 hcst <- as.s2dv_cube(hcst)
 obs <- as.s2dv_cube(obs)
 ```
-By printing the object, we see that it has been organized following an order. 
+By printing the object, we can see the object structure. The first level elements are: 
+- **Data**: A multidimensional array containing the data
+- **Dimensions**: A vector with the data dimensions.
+- **Coordinates**: A list with vector coordinates.
+- **Attributes**: A list containing the metadata. 
 
 ```r
 > hcst
@@ -133,7 +135,7 @@ Attributes
        ( dat1 )  : dat = dat1, var = tas, syear = 19931101 ... 
        ... 
 ```
-> **Note:** An **s2dv_cube** object is an structured list in base R. To acces the elements, you need to use the `$` operator (e.g. `hcst$data`, `hcst$coords`, `hcst$attrs`, ...)
+> **Note:** An **s2dv_cube** object is an structured list in base R. To acces the elements, you need to use the `$` operator (e.g. `hcst$data`, `hcst$dims`, `hcst$coords`, `hcst$attrs`, ...)
 
 #### Exercise 1
 **Goal:** To find **s2dv_cube** information of **hindcast** data.
@@ -252,7 +254,7 @@ Anomalies are deviations from the average weather conditions over a long period.
 We are going to use the function **CST_Anomaly** from **CSTools**. This function computes the anomalies relative to a climatology computed along the selected dimension (in our case starting dates). The computation is carried out independently for experimental and observational datasets.
 
 #### Exercise 3:
-**Goal:** Calculate the hindcast anomalies from the calibrated hindcast and observations dataset. You can take a look on the [CSTools package documentation](https://cran.r-project.org/web/packages/CSTools/CSTools.pdf) on page 40.
+**Goal:** Calculate the hindcast anomalies from the calibrated hindcast and observations dataset. You can take a look on the [CSTools package documentation](https://cran.r-project.org/web/packages/CSTools/CSTools.pdf) on page 40 to find the missing parameters.
 
 ```r
 hcst_anom <- CST_Anomaly(exp = hcst_cal, 
@@ -270,10 +272,9 @@ hcst_anom <- CST_Anomaly(exp = hcst_cal,
 
 ## 4. Compute skill: RPSS
 
-To trust the climate models we need to evaluate its skill. To do it, we are going to use the Ranked Probability Skill Score (RPSS; Wilks, 2011). Is the skill score based on the Ranked Probability Score (RPS; Wilks, 2011).  It can be used to assess whether a forecast presents an improvement or worsening with respect to a reference forecast.
+To trust the climate models we need to evaluate its skill. To do it, we are going to use the **Ranked Probability Skill Score** (RPSS; Wilks, 2011). Is the skill score based on the **Ranked Probability Score** (RPS; Wilks, 2011).  It can be used to assess whether a forecast presents an improvement or worsening with respect to a reference forecast.
 
-The RPSS ranges between minus infinite and 1. If the RPSS is positive, it indicates that the forecast has higher skill than the reference forecast, while a negative value means that it has a lower skill. It is computed as `RPSS = 1 - RPS_exp / RPS_ref`. The statistical significance is obtained based on a Random Walk test at
-the specified confidence level (DelSole and Tippett, 2016). 
+The **RPSS** ranges between minus infinite and 1. If the **RPSS is positive**, it indicates that the **forecast has higher skill than the reference forecast**, while a **negative** value means that it has a **lower skill**. It is computed as `RPSS = 1 - RPS_exp / RPS_ref`. The statistical significance is obtained based on a Random Walk test at the specified confidence level (DelSole and Tippett, 2016). 
 
 Next, we compute the RPSS for anomalies: 
 ```r
@@ -289,7 +290,7 @@ skill <- RPSS(exp = hcst_anom$exp$data,
 The output of the **RPSS** function is a list of two elements. The first element is the RPSS; the second element, `sign` is a logical array of the statistical significance of the RPSS with the same dimensions as `rpss`.
 
 #### Exercise 4:
-**Goal:** Compare the **RPSS** results with calibrated and raw anomalies.
+**Goal:** Compare the RPSS results with **calibrated** and **raw anomalies**.
 
 ```r
 hcst_anom_raw <- CST_Anomaly(exp = hcst, 
@@ -330,16 +331,17 @@ logical    7055     387
 We are going to plot the **last year** of the hindcast period (**2016**) for the last timestep (**December**). Also, we are going to use the **last ensemble member** (arbirtrary choice).
 
 ```r
-
 lat <- hcst$coords$lat
 lon <- hcst$coords$lon
 
 PlotEquiMap(hcst_anom$exp$data[24, , 25, , 2, , ], lat = lat, lon = lon, 
-            filled.continents = FALSE,
+            filled.continents = FALSE, 
+            toptitle = "Calibrated Hindcast Anomalies", 
             fileout = "/esarchive/scratch/erifarov/git/cstools/inst/doc/tutorial/PATC2023/Figures/hcst_anom_cal.png")
 
 PlotEquiMap(hcst_anom_raw$exp$data[24, , 25, , 2, , ], lat = lat, lon = lon, 
             filled.continents = FALSE,
+            toptitle = "Raw Hindcast Anomalies", 
             fileout = "/esarchive/scratch/erifarov/git/cstools/inst/doc/tutorial/PATC2023/Figures/hcst_anom_raw.png")
 ```
 ![](./Figures/hcst_anom_cal.png) 
@@ -352,10 +354,12 @@ PlotEquiMap(hcst_anom_raw$exp$data[24, , 25, , 2, , ], lat = lat, lon = lon,
 PlotEquiMap(skill$rpss[ , , 2, , ], lat = lat, lon = lon, 
             brks = seq(-1, 1, by = 0.1),
             filled.continents = FALSE, 
+            toptitle = "RPSS from Calibrated Hindcast Anomalies", 
             fileout = "/esarchive/scratch/erifarov/git/cstools/inst/doc/tutorial/PATC2023/Figures/skill_cal.png")
 PlotEquiMap(skill_raw$rpss[ , , 2, , ], lat = lat, lon = lon, 
             brks = seq(-1, 1, by = 0.1),
             filled.continents = FALSE, 
+            toptitle = "RPSS from Raw Hindcast Anomalies", 
             fileout = "/esarchive/scratch/erifarov/git/cstools/inst/doc/tutorial/PATC2023/Figures/skill_raw.png")
 ```
 ![](./Figures/skill_cal.png) 
-- 
GitLab


From 9681f7dfedcdef3a64c8731270057ed6fb3417ef Mon Sep 17 00:00:00 2001
From: Eva Rifa <eva.rifarovira@bsc.es>
Date: Thu, 26 Oct 2023 18:01:48 +0200
Subject: [PATCH 10/13] Remove figure path

---
 .../PATC2023/handson_2-data-assesment_ans.md         | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
index 74c31352..09f29d53 100644
--- a/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
@@ -336,13 +336,11 @@ lon <- hcst$coords$lon
 
 PlotEquiMap(hcst_anom$exp$data[24, , 25, , 2, , ], lat = lat, lon = lon, 
             filled.continents = FALSE, 
-            toptitle = "Calibrated Hindcast Anomalies", 
-            fileout = "/esarchive/scratch/erifarov/git/cstools/inst/doc/tutorial/PATC2023/Figures/hcst_anom_cal.png")
+            toptitle = "Calibrated Hindcast Anomalies")
 
 PlotEquiMap(hcst_anom_raw$exp$data[24, , 25, , 2, , ], lat = lat, lon = lon, 
             filled.continents = FALSE,
-            toptitle = "Raw Hindcast Anomalies", 
-            fileout = "/esarchive/scratch/erifarov/git/cstools/inst/doc/tutorial/PATC2023/Figures/hcst_anom_raw.png")
+            toptitle = "Raw Hindcast Anomalies")
 ```
 ![](./Figures/hcst_anom_cal.png) 
 ![](./Figures/hcst_anom_raw.png) 
@@ -354,13 +352,11 @@ PlotEquiMap(hcst_anom_raw$exp$data[24, , 25, , 2, , ], lat = lat, lon = lon,
 PlotEquiMap(skill$rpss[ , , 2, , ], lat = lat, lon = lon, 
             brks = seq(-1, 1, by = 0.1),
             filled.continents = FALSE, 
-            toptitle = "RPSS from Calibrated Hindcast Anomalies", 
-            fileout = "/esarchive/scratch/erifarov/git/cstools/inst/doc/tutorial/PATC2023/Figures/skill_cal.png")
+            toptitle = "RPSS from Calibrated Hindcast Anomalies")
 PlotEquiMap(skill_raw$rpss[ , , 2, , ], lat = lat, lon = lon, 
             brks = seq(-1, 1, by = 0.1),
             filled.continents = FALSE, 
-            toptitle = "RPSS from Raw Hindcast Anomalies", 
-            fileout = "/esarchive/scratch/erifarov/git/cstools/inst/doc/tutorial/PATC2023/Figures/skill_raw.png")
+            toptitle = "RPSS from Raw Hindcast Anomalies")
 ```
 ![](./Figures/skill_cal.png) 
 ![](./Figures/skill_raw.png) 
\ No newline at end of file
-- 
GitLab


From 28c6c79e96222e22f880b468552a26d7c1240e0d Mon Sep 17 00:00:00 2001
From: Eva Rifa <eva.rifarovira@bsc.es>
Date: Mon, 30 Oct 2023 10:10:57 +0100
Subject: [PATCH 11/13] Update path

---
 inst/doc/tutorial/PATC2023/handson_2-data-assesment.md     | 5 ++---
 inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md | 4 ++--
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
index f775a506..a193bb6e 100644
--- a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
@@ -25,7 +25,7 @@ The following section is taken from [PATC 2023 startR tutorial](https://earth.bs
 path_exp <- "/esarchive/exp/meteofrance/system7c3s/monthly_mean/$var$_f6h/$var$_$syear$.nc"
 #----------------------------------------------------------------------
 # Run these two lines if you're on Marenostrum4 and log in with training account
-prefix <- '/gpfs/scratch/bsc32/bsc32734/bsc_training_2023/R_handson/'
+prefix <- '/gpfs/scratch/nct01/nct01001/d2_handson_R/'
 path_exp <- paste0(prefix, path_exp)
 #----------------------------------------------------------------------
 
@@ -63,7 +63,7 @@ sdate_obs <- array(date_string, dim = c(syear = 24, time = 2))
 path_obs <- '/esarchive/recon/ecmwf/era5/monthly_mean/$var$_f1h-r1440x721cds/$var$_$syear$.nc'
 #----------------------------------------------------------------------
 # Run these two lines if you're on Marenostrum4 and log in with training account
-prefix <- '/gpfs/scratch/bsc32/bsc32734/bsc_training_2023/R_handson/'
+prefix <- '/gpfs/scratch/nct01/nct01001/d2_handson_R/'
 path_obs <- paste0(prefix, path_obs)
 #----------------------------------------------------------------------
 
@@ -264,7 +264,6 @@ summary(____)
 We are going to plot the **last year** of the hindcast period (**2016**) for the last timestep (**December**). Also, we are going to use the **last ensemble member** (arbirtrary choice).
 
 ```r
-
 lat <- hcst$coords$lat
 lon <- hcst$coords$lon
 
diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
index 09f29d53..fad95ccb 100644
--- a/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
@@ -25,7 +25,7 @@ The following section is taken from [PATC 2023 startR tutorial](https://earth.bs
 path_exp <- "/esarchive/exp/meteofrance/system7c3s/monthly_mean/$var$_f6h/$var$_$syear$.nc"
 #----------------------------------------------------------------------
 # Run these two lines if you're on Marenostrum4 and log in with training account
-prefix <- '/gpfs/scratch/bsc32/bsc32734/bsc_training_2023/R_handson/'
+prefix <- '/gpfs/scratch/nct01/nct01001/d2_handson_R/'
 path_exp <- paste0(prefix, path_exp)
 #----------------------------------------------------------------------
 
@@ -63,7 +63,7 @@ sdate_obs <- array(date_string, dim = c(syear = 24, time = 2))
 path_obs <- '/esarchive/recon/ecmwf/era5/monthly_mean/$var$_f1h-r1440x721cds/$var$_$syear$.nc'
 #----------------------------------------------------------------------
 # Run these two lines if you're on Marenostrum4 and log in with training account
-prefix <- '/gpfs/scratch/bsc32/bsc32734/bsc_training_2023/R_handson/'
+prefix <- '/gpfs/scratch/nct01/nct01001/d2_handson_R/'
 path_obs <- paste0(prefix, path_obs)
 #----------------------------------------------------------------------
 
-- 
GitLab


From b31ef4857898cefc3d60016c8d6ca1dfeff0c726 Mon Sep 17 00:00:00 2001
From: Eva Rifa <eva.rifarovira@bsc.es>
Date: Tue, 31 Oct 2023 17:08:27 +0100
Subject: [PATCH 12/13] Correct dates dimensions in s2dv_cube

---
 inst/doc/tutorial/PATC2023/handson_2-data-assesment.md     | 3 ++-
 inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md | 4 ++++
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
index a193bb6e..02061f08 100644
--- a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
@@ -53,8 +53,9 @@ hcst <- CST_Start(dat = path_exp,
 
 ```r
 # Adjust the day to the correct month
+dates_dim <- dim(hcst$attrs$Dates)
 hcst$attrs$Dates <- hcst$attrs$Dates - lubridate::days(1)
-
+dim(hcst$attrs$Dates) <- dates_dim
 date_string <- format(hcst$attrs$Dates, '%Y%m')
 sdate_obs <- array(date_string, dim = c(syear = 24, time = 2))
 ```
diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
index fad95ccb..48b679ff 100644
--- a/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment_ans.md
@@ -52,8 +52,12 @@ hcst <- CST_Start(dat = path_exp,
 ```
 
 ```r
+# Save Dates dimensions
+dates_dim <- dim(hcst$attrs$Dates)
 # Adjust the day to the correct month
 hcst$attrs$Dates <- hcst$attrs$Dates - lubridate::days(1)
+# Add again Dates dimensions
+dim(hcst$attrs$Dates) <- dates_dim
 
 date_string <- format(hcst$attrs$Dates, '%Y%m')
 sdate_obs <- array(date_string, dim = c(syear = 24, time = 2))
-- 
GitLab


From 9fc9658dea29b7506c09f21409a0a6da77c629f5 Mon Sep 17 00:00:00 2001
From: Eva Rifa <eva.rifarovira@bsc.es>
Date: Tue, 31 Oct 2023 17:09:56 +0100
Subject: [PATCH 13/13] Minor correction

---
 inst/doc/tutorial/PATC2023/handson_2-data-assesment.md | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
index 02061f08..f02ed138 100644
--- a/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
+++ b/inst/doc/tutorial/PATC2023/handson_2-data-assesment.md
@@ -52,10 +52,13 @@ hcst <- CST_Start(dat = path_exp,
 ```
 
 ```r
-# Adjust the day to the correct month
+# Save Dates dimensions
 dates_dim <- dim(hcst$attrs$Dates)
+# Adjust the day to the correct month
 hcst$attrs$Dates <- hcst$attrs$Dates - lubridate::days(1)
+# Add again Dates dimensions
 dim(hcst$attrs$Dates) <- dates_dim
+
 date_string <- format(hcst$attrs$Dates, '%Y%m')
 sdate_obs <- array(date_string, dim = c(syear = 24, time = 2))
 ```
-- 
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