paper
2023
Downscaling & super-resolution
Explainability, uncertainty & physics New model or method
RNN / LSTM / ConvLSTMClassical ML (trees, GP, linear, kernels)Climate projections / centennial Journal
2023-07-17
012
Jul 2023
Vahid Nourani, Kasra Khodkar, Aida Hosseini Baghanam et al.
Journal of Applied Meteorology and Climatology
Downscales CanESM5 temperature and precipitation at two Iranian stations with feedforward and LSTM networks, adding LUBE prediction intervals; LSTM gains about 55% higher NSE.
Downscaling & super-resolution
Uncertainty Quantification of Deep Learning–Based Statistical Downscaling of Climatic Parameters
In one sentence. Downscales CanESM5 temperature and precipitation at two Iranian stations with feedforward and LSTM networks, adding LUBE prediction intervals; LSTM gains about 55% higher NSE.
Labels
- Topic
- Downscaling & super-resolution, Explainability, uncertainty & physics
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM, Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Local / station
- Data
- CMIP / climate model output, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1175/jamc-d-23-0057.1 OpenAlex:W4384524063
- Added
- 2026-10-22
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