Improving Wind Forecasts in the Lower Stratosphere by Distilling an Analog Ensemble Into a Deep Neural Network
In one sentence. Distilling an analog ensemble into a deep network postprocesses ECMWF stratospheric wind forecasts, cutting latency to near zero and beating ENS wind-speed error by 20% against balloon data.
Abstract
Abstract We discuss improving forecasts of winds in the lower stratosphere using machine learning to postprocess the output of the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecast System. We postprocess global three‐dimensional predictions and demonstrate distilling the analog ensemble (AnEn) method into a deep neural network, which reduces postprocessing latency to near zero maintaining increased forecast skill. This approach reduces the error with respect to ECMWF high‐resolution deterministic prediction between 2–15% for wind speed and 15–25% for direction and is on par with ECMWF ensemble (ENS) forecast skill to hour 60. Verifying with Loon data from stratospheric balloons, AnEn has 20% lower error than ENS for wind speed and 15% for wind direction, despite significantly lower real‐time computational cost to ENS. Similar performance patterns are reported for probabilistic predictions, with larger improvements of AnEn with respect to ENS. We also demonstrate that AnEn generates a calibrated probabilistic forecast.
Labels
- Topic
- Post-processing & calibration, Probabilistic & ensemble forecasting
- Type
- New model or method
- Method
- Other
- Time scale
- 6 h–3 days
- Domain
- Global
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1029/2020gl089098 OpenAlex:W3044055657
- Added
- 2026-10-22
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