# s2dverification

s2dverification (seasonal to decadal verification) is an R framework that aids in the analysis of forecasts from the data retrieval stage, through computation of statistics and skill scores against observations, to visualisation of data and results. While some of its components are only targeted to verification of seasonal to decadal climate forecasts, it provides with tools that can be useful for verification of forecasts in any field.

Find out more in the overview below, on the wiki page at <https://earth.bsc.es/gitlab/es/s2dverification/wikis/home> or on the CRAN website at <https://cran.r-project.org/web/packages/s2dverification/index.html>. You can also sign up to the s2dverification mailing list by sending a message with the subject 'subscribe' to s2dverification-request@bsc.es if you want to keep abreast of internal discussons or latest development releases.

## Installation

s2dverification has a system dependency, the CDO libraries, for interpolation of grid data. Make sure you have these libraries installed in the system or download and install from <https://code.zmaw.de/projects/cdo>

You can then install the released version of s2dverification from CRAN:
```R
install.packages("s2dverification")
```

Or the development version from the GitLab repository:
```R
# install.packages("devtools")
devtools::install_git("https://earth.bsc.es/gitlab/es/s2dverification.git")
```

## Overview

The following diagram depicts the modules of s2dverification and how they interact:

![s2dverification module diagram](vignettes/s2dv_modules.png)

The [Data retrieval](https://earth.bsc.es/gitlab/es/s2dverification/wikis/data_retrieval.md) module allows you to gather and homogenize NetCDF data files stored in a local or remote file system. A previous step is required, however, to set up some configuration parameters so that the module can locate the source files and recognize their format.

Once the data has been loaded into an R object, [Basic statistics](https://earth.bsc.es/gitlab/es/s2dverification/wikis/basic_statistics.md) can be computed, such as climatologies, trends, bias correction, smoothing, ...

Either after computing basic statistics or directly from the original data, the functions in the [Verification](https://earth.bsc.es/gitlab/es/s2dverification/wikis/verification.md) module allow you to compute deterministic and probabilistic scores and skill scores, such as root mean square error, time or spatial correlation or brier score.

[Visualisation](https://earth.bsc.es/gitlab/es/s2dverification/wikis/visualisation.md) functions are also provided to plot the results obtained from any of the modules above. 

Next you can see an example of usage of s2dverification spanning its four modules.

### Data retrieval

```R
library(s2dverification)
data <- Load('tas', c('ExperimentID_A', 'ExperimentID_B'), 
                    c('ObservationID_X', 'ObservationID_Y'),
                    sdates = c('19901101', '19951101', '20001101'),
                    lonmin = 100, lonmax = 250, latmin = -10, latmax = 60,
                    leadtimemin = 1, leadtimemax = 30,
                    output = 'lonlat', grid = 't106grid', 
                    method = 'distance-weighted',
                    configfile = '/example/path/to/configfile.conf')
# * The load call you issued is:
# *   Load(var = "tas", exp = c("ExperimentID_A", "ExperimentID_B"), 
# *                     obs = c("ObservationID_X", "ObservationID_Y"), 
# *                     sdates = c("19901101", "19951101", "20001101"), 
# *                     grid = "t106grid", output = "lonlat", 
# *                     storefreq = "monthly", ...)
# * See the full call in '$load_parameters' after Load() finishes.
# * Reading configuration file: /example/path/to/configfile.conf 
# * Config file read successfully.
# * All pairs (var, exp) and (var, obs) have matching entries.
# * Fetching first experimental files to work out 'var_exp' size...
# * Exploring dimensions... /path/to/experimentA/monthly_mean/tas/tas_19901101.nc
# * Success. Detected dimensions of experimental data: 2, 5, 3, 30, 63, 134
# * Fetching first observational files to work out 'var_obs' size...
# * Exploring dimensions... /path/to/observationX/monthly_mean/tas/tas_199011.nc
# * Success. Detected dimensions of observational data: 2, 1, 3, 30, 63, 134
# * Will now proceed to read and process 96 data files:
# * The list is long. You can check after Load() finishes in '$source_files'.
# * Total size of requested data:  72938880 bytes.
# *   - Experimental data:  ( 2 x 5 x 3 x 30 x 63 x 134 ) x 8 bytes = 60782400 bytes.
# *   - Observational data: ( 2 x 1 x 3 x 30 x 63 x 134 ) x 8 bytes = 12156480 bytes.
# * If size of requested data is close to or above the free shared RAM memory, R will crash.
# * Loading... This may take several minutes...
# * Progress: 0% + 10% + 70% + 10% + 10%
str(data)
# List of 11
#  $ mod            : num [1:2, 1:5, 1:3, 1:30, 1:63, 1:134] 273 273 273 273 273 ...
#  $ obs            : num [1:2, 1, 1:3, 1:30, 1:63, 1:130] 273 273 273 NA 273 ...
#  $ lat            : num [1:63(1d)] 60 58.9 57.8 56.6 55.5 ...
#  $ lon            : num [1:134(1d)] 100 101 102 104 105 ...
#  $ source_files   : chr [1:96] "/path/to/experimentA/monthly_mean/tas/tas_19901101.nc"
#                                "/path/to/experimentA/monthly_mean/tas/tas_19951101.nc"
#                                "/path/to/experimentA/monthly_mean/tas/tas_20001101.nc"
#                                ...
#  $ not_found_files: NULL
#  $ load_parameters:List of 29
#   ..$ var         : chr "tas"
#   ..$ exp         : chr "ExperimentID_A" "ExperimentID_B"
#   ..$ obs         : chr "ObservationID_X" "ObservationID_Y"
#   ..$ sdates      : chr [1:3] "19901101" "19951101" "20001101"
#   ..$ grid        : chr "t106grid"
#   ..$ output      : chr "lonlat"
#   ..$ storefreq   : chr "monthly"
#   ..$ configfile  : /example/path/to/configfile.conf
#   ..$ dimnames    : NULL
#   ..$ latmax      : num 60
#   ..$ latmin      : num -10
#   ..$ leadtimemax : num 30
#   ..$ leadtimemin : num 1
#   ..$ lonmax      : num 250
#   ..$ lonmin      : num 100
#   ..$ maskmod     :List of 15
#   .. ..$ : NULL
#   .. ..$ : NULL
#   ...
#   ..$ maskobs     :List of 15
#   .. ..$ : NULL
#   .. ..$ : NULL
#   ...
#   ..$ method      : chr "distance-weighted"
#   ..$ nleadtime   : NULL
#   ..$ nmember     : NULL
#   ..$ nmemberobs  : NULL
#   ..$ nprocs      : NULL
#   ..$ remapcells  : num 2
#   ..$ sampleperiod: num 1
#   ..$ silent      : logi FALSE
#   ..$ suffixexp   : NULL
#   ..$ suffixobs   : NULL
#   ..$ varmax      : NULL
#   ..$ varmin      : NULL
#  $ when           : POSIXct[1:1], format: "2015-11-09 14:49:11"
#  $ dimnames       : chr [1:6] "dataset" "member" "sdate" "time" ...
#  $ units          : chr "K"
#  $ var_long_name  : chr "Sea surface temperature"
```

### Basic statistics
```R
```

### Verification
```R
```

### Visualisation
```R
```
