Machine Learning for Applied Weather Prediction

Sue Ellen Haupt; Jim Cowie; Seth Linden; Tyler McCandless; Branko Kosovic; Stefano Alessandrini

doi:10.1109/eScience.2018.00047

2018 IEEE 14th International Conference on e-Science (e-Science)

Machine Learning for Applied Weather Prediction

Year: 2018, Pages: 276-277

DOI Bookmark: 10.1109/eScience.2018.00047

Authors

Sue Ellen Haupt, Res. Applic. Lab., Nat. Center for Atmopsheric Res., Boulder, CO, USA
Jim Cowie, Res. Applic. Lab., Nat. Center for Atmopsheric Res., Boulder, CO, USA
Seth Linden, Res. Applic. Lab., Nat. Center for Atmopsheric Res., Boulder, CO, USA
Tyler McCandless, Res. Applic. Lab., Nat. Center for Atmopsheric Res., Boulder, CO, USA
Branko Kosovic, Res. Applic. Lab., Nat. Center for Atmopsheric Res., Boulder, CO, USA
Stefano Alessandrini, Res. Applic. Lab., Nat. Center for Atmopsheric Res., Boulder, CO, USA

Abstract

The National Center for Atmospheric Research (NCAR) has a long history of applying machine learning to weather forecasting challenges. The Dynamic Integrated foreCasting (DICast®) System was one of the first automated weather forecasting engines. It is now in use in quite a few companies with many applications. Some applications being accomplished at NCAR that include DICast and other artificial intelligence technologies include renewable energy, surface transportation, and wildland fire forecasting.

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Machine Learning for Applied Weather Prediction

Authors

Abstract

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