home.md

@@ -40,7 +40,9 @@ Analysis:
   Variables:
     # name: variable name(s) in the /esarchive (Mandatory, str)
     # freq: 'monthly_mean', 'daily' or 'daily_mean' (Mandatory, str)
-    - {name: 'tas', freq: 'monthly_mean'}
+    # units: desired data units for each variable. Only available for temperature,
+    #   precipitation, and pressure variables.
+    - {name: 'tas', freq: 'monthly_mean', units: 'C'}
   Datasets:
     System:
       # name: System name (Mandatory, str)
@@ -194,6 +196,16 @@ The Verification Suite uses [startR](https://CRAN.R-project.org/package=startR)
 
 The startR documentation has [a guide to explain how to select your longitude range appropriately](https://earth.bsc.es/gitlab/es/startR/-/blob/master/inst/doc/faq.md#11-select-the-longitudelatitude-region). The Loading module handles the `Sort()` and `CircularSort()` automatically from the ranges in your recipe.
 
+## Units module
+
+The Units module must be used after the Loading module to ensure consistency between system and reference datasets. It checks the metadata of the hindcast, forecast and reference and compares them against each other to make sure that all of the datasets have consistent units. If a specific unit has been requested in the recipe, all datasets will be transformed according to that information, if needed. If no unit was chosen, all the datasets will be returned in the same units as the experiment data.
+
+For the time being, unit transformation is only available for temperature, precipitation and pressure. For the precipitation units, the user can also decide whether to obtain the total amount or the flux by setting parameter`flux` to true/false in the Variable section of the recipe.
+
+The output of the function, `Units()`, is a list containing the hindcast, observations and forecast, named hcst, obs and fcst respectively. fcst will be `NULL` if no forecast years have been requested.
+
+
+
 ## Calibration module
 
 The Calibration module performs bias correction on the loaded data. It accepts the output of the Loading module as input, and also requires the recipe. It applies the selected bias correction method to the hindcast and forecast data using the observations as a reference, and returns the calibrated data and its metadata as an `s2dv_cube` object.