paper
2018
Explainability, uncertainty & physics
New model or method
RNN / LSTM / ConvLSTMCNN / U-Net6 h–3 daysETH Zurich Journal
2018-06-29
018
Jun 2018
Isabelle Roesch, Tobias Günther
Computer Graphics Forum
ETH Zurich
Interactive visualization system helps users inspect recurrent convolutional network forecasts of temperature, pressure and wind trained on 25 years of climate data.
Explainability, uncertainty & physics
Visualization of Neural Network Predictions for Weather Forecasting
In one sentence. Interactive visualization system helps users inspect recurrent convolutional network forecasts of temperature, pressure and wind trained on 25 years of climate data.
Labels
- Topic
- Explainability, uncertainty & physics
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM, CNN / U-Net
- Time scale
- 6 h–3 days
- Domain
- Global
- Data
- ERA5 / reanalysis
- Status
- Research
- Identifiers
- DOI:10.1111/cgf.13453 OpenAlex:W2809710708
- Added
- 2026-10-22
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