paper
2023
Sub-seasonal to seasonal
New model or method
Other2 weeks–2 months Preprint
2023-05-01
008
May 2023
Philip Chmielowiec
Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)
Autoencoder models predict US subseasonal temperature anomalies using CESM2 S2S variables to prepare transfer learning; skill varies by region and season.
INVESTIGATING THE LIMITED SAMPLE SIZE OF SUBSEASONAL CLIMATE FORECASTING DATASETS IN DEEP LEARNING APPLICATIONS
In one sentence. Autoencoder models predict US subseasonal temperature anomalies using CESM2 S2S variables to prepare transfer learning; skill varies by region and season.
Labels
- Topic
- Sub-seasonal to seasonal
- Type
- New model or method
- Method
- Other
- Time scale
- 2 weeks–2 months
- Domain
- Regional
- Data
- CMIP / climate model output, ERA5 / reanalysis
- Status
- Research
- Identifiers
- OpenAlex:W7220797187
- Added
- 2026-10-22
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