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***
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A public repository to store materials and hands-on exercises for R-related BSC trainings
# Open the recipe with a text editor such as vim or emacs
vim /gpfs/scratch/nct01/<your_account>/recipe_PATC_2023-<your_name>.yml
```
Once you have opened your recipe, it's time to edit the contents. For this example, we will evaluate the temperature-at-surface (tas) monthly means, using MeteoFrance System 7 data as our experiment and ERA5 as our reference dataset, for the initialization month of November.
```yaml
Description:
Author:<___>
Description:Calibration and skill assessment of MeteoFrance System 7 surface temperature
Analysis:
Horizon:Seasonal
Variables:
name:tas
freq:monthly_mean
units:<___># Choose your units: C or K
Datasets:
System:
name:Meteo-France-System7
Multimodel:no
Reference:
name:ERA5
Time:
sdate:'1101'
fcst_year:'2020'
hcst_start:'1993'
hcst_end:'2016'
ftime_min:<___># Choose the first time step! A number from 1 to 6
ftime_max:<___># Choose the last time step! A number from 1 to 6
## 1. Load the required SUNSET modules and read the recipe
Open an R session again, by simply typing `R` on the terminal.
To run SUNSET, we must set up the directory where the code is as our working directory. Then, each SUNSET module should be sourced in order to have access to the module functions in within our R session.
The function `prepare_outputs()` creates a unique folder for the logs, data files and plots that result from the execution of your recipe, inside the directory you specified. It also runs a check over the recipe to detect any potential errors, misspellings or missing arguments. At the end of the check, a message is displayed indicating whether or not the recipe passed the check, along with the list of errors and warnings.
**Questions**
Read the logs!
(1) Did your recipe pass the check? Did you get any warnings?
(2) Where will your outputs be saved? Copy and paste this directory somewhere, so that you can check it later!
*Tip*: The recipe is now stored as a `list` containing all the information of the original YAML file, plus some extra things! If you want to see any particular element of the recipe from the R session, you can simply access that element in the list. For example:
```r
# Checking the variable name
recipe$Analysis$Variables$name
# Checking the output directory
recipe$Run$output_dir
```
## 2. Load the data and change the units
The **Loading** module retrieves the information from the recipe to load the data that has been requested it in. It loads the experiment data for the hindcast period, the reference data for the corresponding period, and the experiment forecast if a forecast year has been requested.
For certain variables like temperature, precipitation or sea level pressure, the user can request for specific units to load the data in. The **Units** module will read the original units as stored in the netCDF files and perform any necessary unit converstions to match the request in the recipe. It also verifies that all of the loaded datasets share the same units, even if no specific unit has been requested. For this reason, users are strongly encouraged to run it even if they did not request any unit conversion.
```r
# Load datasets
data<-Loading(recipe)
# Change units
data<-Units(recipe,data)
```
**Questions**
(1) What is the structure of `data`? What is the class of the objects in `data`? *Tip*: you can use functions like `class()`, `names()` or `str()` to gain information about the structure of the object and its contents.
```r
class(data)
names(data)
str(data,max.level=2)
# You can access any of the three objects with the `$` operator:
class(data$____)
```
(2) Pay attention to the log messages: Did your units get converted? Are the new units what you expect? You can check the metadata of any of the objects in data. SUNSET also provides the `data_summary()` function, which lets you have a quick look at your objects:
```r
# Check the new units and data of hcst, fcst and/or obs. Are they all the same?
data$____$attrs$Variable$metadata$tas$units
data_summary(data$____,recipe)
```
(3) What are the dimensions of the datasets? Are they consistent with what is requested in the recipe? *Tip*: Check the data summary!
## 3. Calibrate the data and compute the anomalies
SUNSET has a few modules to perform post-processing on the experimental and the reference datasets. The **Calibration** module performs the bias correction method indicated in the recipe, using the `CSTools::CST_Calibration()` function.
The **Anomalies** module removes the climatologies using functions like `CSTools::CST_Anomaly()` and `s2dv::Clim()`, and also returns the full fields in case they are needed for any future computations.
```r
# Calibrate the data
data<-Calibration(recipe,data)
# Compute anomalies
data<-Anomalies(recipe,data)
```
**Questions**
(1) Verify that you now have anomaly values instead of the original full field. *Tip*: Use `data_summary()` like in the previous example and pay attention to the new values.
## 4. Evaluate the model skill and compute the probability thresholds
The **Skill** module returns a list of all the evaluation metrics requested in the recipe, in the shape of multi-dimensional arrays. In this case, we will compute three metrics:
-**RPSS (Ranked Probability Skill Score)**: This skill score measures how well a forecast predicts the probability of the tercile categories (below normal, normal and above-normal), compared to the climatology.
-**BSS10 and BSS90 (Brier Skill Score):** This skill score measures how well a forecast predicts the probability of the 10th percentile and 90th percentile extreme events, compared to the climatology.
The `Probabilities()` function returns the probability values for each requested category for the hindcast and forecast data, as well as the hindcast percentile values corresponding to each threshold.
```
# Compute skill metrics
skill_metrics <- Skill(recipe, data)
# Compute percentiles and probability bins
probabilities <- Probabilities(recipe, data)
```
**Questions**
(1) What is the structure of `skill_metrics`? Which metrics were computed? What dimensions do they have? *Tip*: use `str()` and `names()`.
(2) What is the structure of `probabilities`? Can you identify the probability categories and the percentiles? *Tip*: use `str()`.
## 5. Plotting the results
Now, let's visualize the information that was computed!
The **Visualization** module will generate the three types of maps we requested in the recipe:
- Skill maps to visualize the skill distribution of the model, for each metric.
- The ensemble mean of the calibrated forecast anomalies.
- A map showing the most likely tercile category for each point in the grid.
With the significance option in the `Visualization()` function, you can choose whether or not to shade the grid points that are statistically significant in each skill metric plot.
```r
# Plot data
Visualization(recipe,data,
skill_metrics=skill_metrics,
probabilities=probabilities,
significance=TRUE)
```
Now, you can `cd` to the the output directory and inspect the contents of the `plots/` subdirectory. The plots are png files that can be visualized with the `display` command. They have a descriptive name including the content of the plot, the date and the forecast time.
**Questions**
(1) Let's take a look at the forecast ensemble mean. What is the sign of the anomalies over Spain? In what regions are the anomalous temperatures strongest?
(2) Let's take a look at the skill metrics RPSS, BSS10 and BSS90. In what regions and for which metrics is the forecast most skillful? *Tip*: Positive values indicate that the model is a better predictor than the climatology, with 1 being the perfect score.
(3) Let's take a look at the Most Likely Terciles plots. This plot indicates the probability of the temperature being below normal, near-normal or above normal. What is the most likely category for Spain?
