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

• Main Authors: Kim, Hee-Seung, Zhou, Shanglin, Bienkowski, Adam, Pattipati, Krishna R.
• Format: Dataset
• Language: unknown
• Published: Zenodo 2023
• Subjects: Pacific; North Atlantic; North Atlantic oscillation
• Online Access: https://dx.doi.org/10.5281/zenodo.10267869; https://zenodo.org/doi/10.5281/zenodo.10267869

< S2S dataset.zip > 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, ...