Weather forecasting: Difference between revisions

From Climate Change AI Wiki
(→‎Relevant Groups and Organizations: add s2s-ai-challenge and context)
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*'''[[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.
 
*[[Dust storm Prediction]]: Dust storms affect people, their properties, and their activities. For this reason, it is crucial to adopt automatic systems by using machine learning to predict or at least enable early detection of dust storms to reduce their deleterious impacts.
 
*[[Dust storm Prediction]]: Dust storms affect people, their properties, and their activities. For this reason, it is crucial to adopt automatic systems by using machine learning to predict or at least enable early detection of dust storms to reduce their deleterious impacts.
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*[[Postprocessing of the output of weather/climate models]]: The ML model gets climate/weather model as inputs and learn patterns to improve these predictions.
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*[[Forward prediction model]]: The ML model integrates initial conditions into the future. Iterate the ML prediction to predict further into the future.
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==Background Readings==
 
==Background Readings==
   
 
==Conferences, Journals, and Professional Organizations==
 
==Conferences, Journals, and Professional Organizations==
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* Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI<ref>{{Cite web|url=https://royalsocietypublishing.org/toc/rsta/2021/379/2194|title=Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI|last=Chantry|first=Matthew|last2=Christensen|first2=Hannah|date=2021|last3=Dueben|first3=Peter|last4=Palmer|first4=Tim}}</ref>
   
 
==Libraries and Tools==
 
==Libraries and Tools==
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* [https://xskillscore.readthedocs.io/en/stable/ forecast verification metrics for gridded climate data]
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* [https://climpred.readthedocs.io/en/stable/ forecast verification for gridded climate data]
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* [https://github.com/jweyn/DLWP-CS Deep learning models for global weather prediction on a cubed sphere]
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* [https://github.com/slerch/ppnn post-processing experiments with neural networks]
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* [https://github.com/pangeo-data/WeatherBench A benchmark dataset for data-driven weather forecasting]
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==Data==
 
==Data==
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* [http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/ SubX data on IRIDL]
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* [https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/ S2S output on IRIDL]
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* [https://climetlab.readthedocs.io/en/latest/guide/pluginlist.html climetlab]
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* [https://github.com/ecmwf-lab/climetlab-s2s-ai-challenge S2S output on European Weather Cloud]
   
 
==Future Directions==
 
==Future Directions==
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* [https://s2s-ai-challenge.github.io/ Challenge: How to improve subseasonal to seasonal predictions with ML/AI?]
   
 
==Relevant Groups and Organizations==
 
==Relevant Groups and Organizations==
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* [http://s2sprediction.net/ S2S Project]
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* [http://cola.gmu.edu/subx/ SubX Project]
   
 
==References==
 
==References==

Revision as of 12:11, 28 June 2021

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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.
  • Dust storm Prediction: Dust storms affect people, their properties, and their activities. For this reason, it is crucial to adopt automatic systems by using machine learning to predict or at least enable early detection of dust storms to reduce their deleterious impacts.
  • Postprocessing of the output of weather/climate models: The ML model gets climate/weather model as inputs and learn patterns to improve these predictions.
  • Forward prediction model: The ML model integrates initial conditions into the future. Iterate the ML prediction to predict further into the future.

Background Readings

Conferences, Journals, and Professional Organizations

  • Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI[3]

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.
  3. Chantry, Matthew; Christensen, Hannah; Dueben, Peter; Palmer, Tim (2021). "Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI".