@inproceedings{89ab30e51881497b9a53ffc38bd0eb69,
title = "Machine learning for applied weather prediction",
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{\textregistered}) 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.",
keywords = "Artificial intelligence, Machine learning, Renewable energy, Surface transportation, Weather forecasting",
author = "Haupt, \{Sue Ellen\} and Jim Cowie and Seth Linden and Tyler McCandless and Branko Kosovic and Stefano Alessandrini",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 14th IEEE International Conference on eScience, e-Science 2018 ; Conference date: 29-10-2018 Through 01-11-2018",
year = "2018",
month = dec,
day = "24",
doi = "10.1109/eScience.2018.00047",
language = "English",
series = "Proceedings - IEEE 14th International Conference on eScience, e-Science 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "276--277",
booktitle = "Proceedings - IEEE 14th International Conference on eScience, e-Science 2018",
address = "United States",
}