usecase.md

# Usecase scripts

In this document, you can link to the example scripts for various demands. For the beginners, it is highly recommended to read the [practical guide](inst/doc/practical_guide.md) carefully first. You can find basic scripts and the configuration for different machines there.

1. **Retrieve data (use `Start()` only)** 
   1. [Interpolation in Start()](inst/doc/usecase/ex1_1_tranform.R)  
      Do the interpolation within Start(), and compare with Load() result. When the Start() parameter `transform_extra_cells = 2`, the two results will be the same.
   2. [Use s2dverification map plotting functions for exp and obs data](inst/doc/usecase/ex1_2_plotmap.R)  
      Use `s2dverification::PlotEquiMap, PlotStereoMap, PlotLayout` to visualize load-in data, and use the experimental data attributes to load in associated observational data. It also shows how to use parameters `xxx_reorder`, `xxx_across`, `merge_across_dims`, `split_multiselected_dims`.  
   3. [Use experimental data attribute to load in oberservational data](inst/doc/usecase/ex1_3_attr_loadin.R)
      Load the experimental data first (with `retrieve = FALSE`), then retreive its dates and time attributes to use in the observational data load-in. It also shows how to use parameters `xxx_tolerance`, `xxx_across`, `merge_across_dims`, `split_multiselected_dims`.  

2. **Execute computation (use `Compute()`)**
   1. [Function working on time dimension](inst/doc/usecase/ex2_1_timedim.R)
   2. [Function using attributes of the data](inst/doc/usecase/ex2_2_attr.R)  
      Using attributes is only available in startR_v0.1.3 or above.
   3. [Use function CDORemap for interpolation](inst/doc/usecase/ex2_3_cdo.R)  
      Using parameter `CDO_module` is only available in startR_v0.1.3 or above. Interpolate data by using `s2dverification::CDORemap` in the workflow. 
   4. [Use two functions in workflow](inst/doc/usecase/ex2_4_two_func.R)
   5.   
   6. [Use external parameters in atomic function](inst/doc/usecase/ex2_6_ext_param_func.R)
   7. [Calculate the ensemble-adjusted Continuous Ranked Probability Score (CRPS)](inst/doc/usecase/ex2_7_seasonal_forecast_crps.R)
      Use `SpecsVerification::EnsCrps` to calculate the ensemble-adjusted Continuous Ranked Probability Score (CRPS) for ECWMF experimental data, and do ensemble mean. Use `s2dverification::PlotEquiMap` to plot the CRPS map.  
   8. [Use CSTools Calibration function](inst/doc/usecase/ex2_8_calibration.R)
      Use `CSTools:::.cal`, the interior function of `CSTools::CST_Calibration`, to do the bias adjustment for ECMWF experimental monthly mean data.