Explore Seasonal Variability of the Trained Model

[DaniJonesOcean]

Task Description:

Investigate the seasonal variability of the trained DeepSensor model by generating plots of the prediction mean and prediction standard deviation for each season. Train the model using anomalies.

[NOTE: If you start running out of time/bandwith, it's totally fine to convert this issue to the simpler version of "plot some example snapshots from selected days".]

Checklist:

1. Define Seasons:

   • [ ] Define the four seasons for analysis (e.g., Winter: December-February, Spring: March-May, Summer: June-August, Fall: September-November).

2. Extract Seasonal Data:

   • [ ] Extract prediction data for each season from the trained DeepSensor model (see below).

3. Generate Plots:

   • [ ] Create plots for the prediction mean for each season.
   • [ ] Create plots for the prediction standard deviation for each season.
   • [ ] Ensure that the plots are clear, labeled, and include legends for better interpretation.

   Example (This examples assumes that you have a prediction data xarray Dataset, with both a prediction_mean and prediction_std variable. This would have to be created using DeepSensor):

   import matplotlib.pyplot as plt
   import xarray as xr
   # Load prediction data
   prediction_data = xr.open_dataset('path_to_prediction_data.nc')
   
   # Define seasons
   seasons = {
      'Winter': ('12-01', '02-28'),
      'Spring': ('03-01', '05-31'),
      'Summer': ('06-01', '08-31'),
      'Fall': ('09-01', '11-30')
   }
   
   for season, (start_date, end_date) in seasons.items():
      season_data = prediction_data.sel(time=slice(start_date, end_date))
      prediction_mean = season_data['prediction_mean'].mean(dim='time')
      prediction_std = season_data['prediction_std'].mean(dim='time')
   
      # Plot prediction mean
      plt.figure()
      prediction_mean.plot()
      plt.title(f'Prediction Mean - {season}')
      plt.savefig(f'prediction_mean_{season.lower()}.png')
   
      # Plot prediction standard deviation
      plt.figure()
      prediction_std.plot()
      plt.title(f'Prediction Standard Deviation - {season}')
      plt.savefig(f'prediction_std_{season.lower()}.png')

4. Analyze and Interpret Results:

   • [ ] Analyze the generated plots to observe how prediction means and standard deviations differ across seasons.
   • [ ] Document any notable patterns, differences, or insights observed from the plots.

5. Document the Process and Findings:

   • [ ] Create a Jupyter notebook to document the process of exploring seasonal variability, including code for data extraction, plot generation, and analysis.
   • [ ] Save the generated plots as images.
   • [ ] Upload the Jupyter notebook and plot images to the shared GitHub repository under an appropriate directory (e.g., notebooks).

[DaniJonesOcean]

@eredding02 Feel free to put your seasonal plots here and close this issue! : )

[eredding02]

@DaniJonesOcean