Difference between revisions of "Weather forecasting"

From Climate Change AI Wiki
Jump to navigation Jump to search
Line 7: Line 7:
 
==ML Application areas==
 
==ML Application areas==
 
*'''[[Storm Tracking|Storm tracking]]''': While climate models can forecast long-term changes in the climate system, separate systems are required to detect specific extreme weather phenomena, like cyclones, atmospheric rivers, and tornadoes. Identifying extreme events in climate model outputs can inform scientific understanding of where and when these events may occur. ML can help classify, detect, and track climate-related extreme events such as hurricanes in climate model outputs.
 
*'''[[Storm Tracking|Storm tracking]]''': While climate models can forecast long-term changes in the climate system, separate systems are required to detect specific extreme weather phenomena, like cyclones, atmospheric rivers, and tornadoes. Identifying extreme events in climate model outputs can inform scientific understanding of where and when these events may occur. ML can help classify, detect, and track climate-related extreme events such as hurricanes in climate model outputs.
  +
 
==Background Readings==
 
==Background Readings==
   

Revision as of 09:40, 15 March 2021

🌎 This article is a stub, and is currently under construction. You can help by adding to it!

This page is part of the Climate Change AI Wiki, which aims provide resources at the intersection of climate change and machine learning.

ML is a suitable tool for making short-term weather predictions[1] based on the observed initial conditions, and post-processing the output from weather models[2].

ML Application areas

  • Storm tracking: While climate models can forecast long-term changes in the climate system, separate systems are required to detect specific extreme weather phenomena, like cyclones, atmospheric rivers, and tornadoes. Identifying extreme events in climate model outputs can inform scientific understanding of where and when these events may occur. ML can help classify, detect, and track climate-related extreme events such as hurricanes in climate model outputs.

Background Readings

Conferences, Journals, and Professional Organizations

Libraries and Tools

Data

Future Directions

Relevant Groups and Organizations

References

  1. Düben, Peter; Modigliani, Umberto; Geer, Alan; Siemen, Stephan; Pappenberger, Florian; Bauer, Peter; Brown, Andy; Palkovic, Martin; Raoult, Baudouin (2021). "Machine learning at ECMWF: A roadmap for the next 10 years". www.ecmwf.int. Retrieved 2021-01-25.
  2. Rasp, Stephan; Lerch, Sebastian (2018-11-01). "Neural Networks for Postprocessing Ensemble Weather Forecasts". Monthly Weather Review. 146 (11): 3885–3900. doi:10.1175/MWR-D-18-0187.1. ISSN 1520-0493.