Zobrazeno 1 - 10
of 277
pro vyhledávání: '"Krasnopolsky, Vladimir"'
Autor:
Agrawal, Shreya, Carver, Rob, Gazen, Cenk, Maddy, Eric, Krasnopolsky, Vladimir, Bromberg, Carla, Ontiveros, Zack, Russell, Tyler, Hickey, Jason, Boukabara, Sid
Post-processing typically takes the outputs of a Numerical Weather Prediction (NWP) model and applies linear statistical techniques to produce improve localized forecasts, by including additional observations, or determining systematic errors at a fi
Externí odkaz:
http://arxiv.org/abs/2303.16301
The ability of Machine-Learning (ML) based model components to generalize to the previously unseen inputs, and the resulting stability of the models that use these components, has been receiving a lot of recent attention, especially when it comes to
Externí odkaz:
http://arxiv.org/abs/2103.07024
There has been a lot of recent interest in developing hybrid models that couple deterministic numerical model components to statistical model components derived using machine learning techniques. One approach that we follow in this pilot study is to
Externí odkaz:
http://arxiv.org/abs/2103.07028
In the overview, a generic mathematical object (mapping) is introduced, and its relation to model physics parameterization is explained. Machine learning (ML) tools that can be used to emulate and/or approximate mappings are introduced. Applications
Externí odkaz:
http://arxiv.org/abs/2002.00416
Autor:
Boukabara, Sid-Ahmed, Krasnopolsky, Vladimir, Penny, Stephen G., Stewart, Jebb Q., McGovern, Amy, Hall, David, Ten Hoeve, John E., Hickey, Jason, Huang, Hung-Lung Allen, Williams, John K., Ide, Kayo, Tissot, Philippe, Haupt, Sue Ellen, Casey, Kenneth S., Oza, Nikunj, Geer, Alan J., Maddy, Eric S., Hoffman, Ross N.
Publikováno v:
Bulletin of the American Meteorological Society, 2021 May 01. 102(5), E1016-E1032.
Externí odkaz:
https://www.jstor.org/stable/27215177
