Web: http://arxiv.org/abs/2111.14844

Jan. 24, 2022, 2:11 a.m. | Maximiliano A. Sacco, Juan J. Ruiz, Manuel Pulido, Pierre Tandeo

cs.LG updates on arXiv.org arxiv.org

Producing an accurate weather forecast and a reliable quantification of its
uncertainty is an open scientific challenge. Ensemble forecasting is, so far,
the most successful approach to produce relevant forecasts along with an
estimation of their uncertainty. The main limitations of ensemble forecasting
are the high computational cost and the difficulty to capture and quantify
different sources of uncertainty, particularly those associated with model
errors. In this work proof-of-concept model experiments are conducted to
examine the performance of ANNs trained …

arxiv evaluation forecast learning machine machine learning machine learning techniques uncertainty

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