Add leaderboard component

docs/make.jl

@@ -55,6 +55,7 @@ pages = Any[
     "Tutorials" => tutorials,
     "Standalone models" => standalone_models,
     "Diagnostics" => diagnostics,
+    "Leaderboard" => "leaderboard/leaderboard.md"
     "Contribution guide" => "Contributing.md",
     "Repository structure" => "folderstructure.md",
     "APIs" => apis,

docs/src/leaderboard/leaderboard.md

@@ -0,0 +1,123 @@
+# Leaderboard
+
+## Long run
+
+### Add a new variable to compare against observations
+Computing errors against observations are all contained in the `leaderboard` folder. The
+files in the leaderboard folder are `data_sources.jl` and `leaderboard.jl`. Loading and
+preprocessing variables of interest are done in `data_sources.jl` and computing the errors
+and plotting are done in `leaderboard.jl`. To add a new variable, you ideally only need to
+modify `data_sources.jl`.
+
+### Computation
+As of now, the leaderboard produces bias plots with the global bias and global root mean
+squared error (RMSE). These quantities are computed for each month with the first year of
+the simulation not considered as that is the spinup time. The start date of the simulation
+is 2012 which means that only the year 2013 is used to compare against observational data.
+See the plots below for what this look like.
+
+![bias_with_custom_mask_plot](./leaderboard/images/global_rmse_and_bias_graphs.png)
+![gpp_bias_plot](./leaderboard/images/gpp_bias_plot.png)
+
+### Add a new variable to the bias plots
+There are four dictionaries that you need to modify to add a new variable which are
+`sim_var_dict`, `obs_var_dict`, `mask_dict`, and `compare_vars_biases_plot_extrema`.
+
+To add a variable for the leaderboard, add a key-value pair to the dictionary `sim_var_dict`
+whose key is the short name of the variable and the value is a function that returns a
+[`OutputVar`](https://clima.github.io/ClimaAnalysis.jl/dev/var/). Any preprocessing is done
+in the function which includes unit conversion and shifting the dates.
+
+```julia
+sim_var_dict["et"] =
+        () -> begin
+            # Load in variable
+            sim_var = get(
+                ClimaAnalysis.SimDir(diagnostics_folder_path),
+                short_name = "et",
+            )
+            # Shift to the first day and subtract one month as preprocessing
+            sim_var =
+                ClimaAnalysis.shift_to_start_of_previous_month(sim_var)
+            return sim_var
+        end
+```
+
+Then, add a key-value pair to the dictionary `obs_var_dict` whose key is the same short name
+as before and the value is a function that takes in a start date and returns a `OutputVar`.
+Any preprocessing is done in the function.
+
+```julia
+obs_var_dict["et"] =
+    (start_date) -> begin
+    # We use ClimaArtifacts to use a dataset from ILAMB
+        obs_var = ClimaAnalysis.OutputVar(
+            ClimaLand.Artifacts.ilamb_dataset_path(;
+                context = "evspsbl_MODIS_et_0.5x0.5.nc",
+            ),
+            "et",
+            # start_date is used to align the dates in the observational data
+            # with the simulation data
+            new_start_date = start_date,
+            # Shift dates to the first day of the month before aligning the dates
+            shift_by = Dates.firstdayofmonth,
+        )
+        # More preprocessing to match the units with the simulation data
+        ClimaAnalysis.units(obs_var) == "kg/m2/s" &&
+            (obs_var = ClimaAnalysis.set_units(obs_var, "kg m^-2 s^-1"))
+        # ClimaAnalysis cannot handle `missing` values, but does support handling NaNs
+        obs_var = ClimaAnalysis.replace(obs_var, missing => NaN)
+        return obs_var
+    end
+```
+
+!!! tip "Preprocessing"
+    Observational and simulational data should be preprocessed for dates and units. For
+    simulation data, monthly averages correspond to the first day following the month.
+    For instance, the monthly average corresponding to January 2010 is on the date
+    2/1/2010. Preprocessing is done to shift this date to 1/1/2010. When preprocessing
+    data, we follow the convention that the first day corresponds to the monthly average
+    for that month. For observational data, you should check the convention being followed
+    and preprocess the dates if necessary.
+
+    For `obs_var_dict`, the anonymous function must take in a start date. The start date is
+    used in `leaderboard.jl` to adjust the seconds in the `OutputVar` to match between start
+    date in the simulation data.
+
+    Units should be the same between the simulation and observational data.
+
+Next, add a key-value pair to the dictionary `mask_dict` whose key is the same short name
+as before and the value is a function that takes in a `OutputVar` representing simulation
+data and a `OutputVar` representing observational data and returns a masking function or
+`nothing` if no masking function is needed. The masking function is used to correctly
+normalize the global bias and global RMSE. See the example below where a mask is made using
+the observational data.
+
+```julia
+mask_dict["et"] =
+    (sim_var, obs_var) -> begin
+        return ClimaAnalysis.make_lonlat_mask(
+            # We do this to get a `OutputVar` with only two dimensions:
+            # longitude and latitude
+            ClimaAnalysis.slice(
+                obs_var,
+                time = ClimaAnalysis.times(obs_var) |> first,
+            );
+            # Any values that are NaN should be 0.0
+            set_to_val = isnan,
+            true_val = 0.0
+        )
+    end
+```
+
+Finally, add a key-value pair to the dictionary `compare_vars_biases_plot_extrema` whose
+key is the same short name as before and the value is a tuple of floats which determine
+the range of the bias plots.
+
+```julia
+compare_vars_biases_plot_extrema = Dict(
+    "et" => (-0.00001, 0.00001),
+    "gpp" => (-8.0, 8.0),
+    "lwu" => (-40.0, 40.0),
+)
+```