Subseasonal to Seasonal (S2S) Prediction Algorithms using Hybrid Machine Learning Techniques

1.ECMWF observations/hindcast realizations

hindcast-like-observations_2000-2019_biweekly_deterministic.zarr
forecast-like-observations_2020_biweekly_deterministic.zarr
ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr
ecmwf_forecast-input_2020_biweekly_deterministic.zarr
hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc
2. External variables

"nino" folder -> nino12.long.anom.data, nino34.long.anom.data : El Niño data
"Oscillation" folder

-> ersst.v5.pdo.dat.text : PDO (Pacific Decadal Oscillation)
-> norm.nao.monthly.b5001.current.ascii.table.txt : NAO (North Atlantic Oscillation)
-> qbo.dat : QBO (Quasi Biennial Oscillation)
"great_lake" folder -> N_seaice_extent_daily_v3.0 : Great lakes ice cover 
observed-solar-cycle-indices.json : Sunspot cycles (two variables: original value and smoothed value)
3. Region.txt : Region and its bound
4. Biweekly historical statistics data

biw_stat_w34 folder -> data (mean, standard deviation, median, skewness, kurtosis) for Week 3-4
biw_stat_w56 folder -> data (mean, standard deviation, median, skewness, kurtosis) for Week 5-6