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Improving Wind Forecasts in the Lower Stratosphere by Distilling an Analog Ensemble into a Deep Neural Network
  • Salvatore Candido,
  • Aakanksha Singh,
  • Luca Delle Monache
Salvatore Candido

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Aakanksha Singh
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Luca Delle Monache
University of California San Diego
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We discuss improving forecasts of winds in the lower stratosphere using machine learning to post-process the output of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System. We post-process global three-dimensional predictions, and demonstrate distilling the analog ensemble (AnEn) method into a deep neural network which reduces post-processing 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.
16 Aug 2020Published in Geophysical Research Letters volume 47 issue 15. 10.1029/2020GL089098