paper
2023
Post-processing & calibration
Applications & impacts New model or method
RNN / LSTM / ConvLSTMClassical ML (trees, GP, linear, kernels)0–6 h6 h–3 days Journal
2023-08-15
012
Aug 2023
Kyle K. Hugeback, William A. Gallus, Hugo Nestor Villegas Pico
Weather and Forecasting
Machine learning corrects WRF hub-height wind forecasts for black-start operations, cutting day-2 wind MSE by over 90% with a multioutput network and nowcasting with LSTM.
Machine Learning–Adjusted WRF Forecasts to Support Wind Energy Needs in Black Start Operations
In one sentence. Machine learning corrects WRF hub-height wind forecasts for black-start operations, cutting day-2 wind MSE by over 90% with a multioutput network and nowcasting with LSTM.
Labels
- Topic
- Post-processing & calibration, Applications & impacts
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM, Classical ML (trees, GP, linear, kernels)
- Time scale
- 0–6 h, 6 h–3 days
- Domain
- Local / station
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1175/waf-d-23-0023.1 OpenAlex:W4385847250
- Added
- 2026-10-22
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