Climate Change Machine Learning and Data Science Links

• From DataScience Programs.com: How Data Science can help us fight climate change. These pages give an overview of how data science/machine learning/data analytics tools can offer many effective methods for understanding aspects of climate change and predicting its future evolution. Included are links and videos. Acknowledgements to Amanda Green and her student Ada informing us of this link. The DataScience main webpage has many other helpful links.

  Some quotations: "Data science is a powerful tool in the fight against global warming. It uses different methods, such as machine learning (where computers learn from data) and data visualization (turning data into pictures), to analyze the details and understand the most challenging components of climate change."

  "Data science works with vast amounts of information about the climate, helping people understand all the different ways climate change is affecting our world. Scientists use data science methods to look at how climate change changes things like life in the oceans, how we use land, our food supplies, and the spread of diseases."

  "Data science is vital in our fight against climate change. It's involved in everything from making models and analyzing data to assessing risks, reducing dangers, adapting to changes, and guiding policies. By tapping into the power of data science, we can better understand the complex nature of climate issues. This helps us devise smart ways to lessen the impact of climate change and keep our planet safe for future generations. In short, data science equips us with the tools we need to tackle climate challenges effectively and work towards a sustainable future."

  The topics covered include understanding climate change through data, the role of data in comprehending climate patterns and anomalies, how historical data has been used to predict climate trends, data driven solutions to climate change, sustainable agriculture and resource management, enhancing climate models with machine learning, and the future of data science in climate change mitigation.

• Prof Steve Brunton, Intro to Data Science: Overview. This is the first of a series of Youtube videos providing an introduction to data science in general. The sub titles of other videos in Prof Brunton's series include Historical Context, What is Data Science?, Answering questions with data science, The nature of data, Machine learning overview, Machine learning and cross-validation, Types of machine learning 1, Types of machine learning 2, Artificial intelligence, Neural network overview, Neural network architectures & deep learning, Neural networks and deep learning, Neural networks: caveats, Digital twins.

• Prof Bing Wen Brunton, Data visualization. These Youtube videos are a continuation of the Data Science series of Steve Brunton. This first provides an overview for data visualization. Other subtitles are Types of data, Storytelling with data, and Buyer beware.

• R. J. Chase, D. R. Harrison, G. M. Lackmann and A. McGovern, A machine learning tutorial for operational meteorology, Part I: traditional machine learning. Journal of the American Meteorological Society (2022), 1509-1529.

  This introductory tutorial provides a survey of some of the most common machine learning methods. These are linear regression, logistic regression, decision trees, random forests, vector processing and Bayse statistics. There are clues on how the reader might apply machine learning to their own questions. Methods are illustrated with application to the recognition problem for thunderstorms and the counting of lightning flashes in an image. The methods used are supported with a github repository.

• R. J. Chase, D. R. Harrison, G. M. Lackmann and A. McGovern, A machine learning tutorial for operational meteorology, Part II: neural networks and deep learning. Journal of the American Meteorological Society (2023), 1271-1293.

  In this the second to two tutorial articles, a broad spectrum of neual network and deep learning methods of use in meteorology are covered. In particular, perceptrons, artificial networks, convolution networks, and U-networks are included. The same two applications are used as in the first tutorial, namely thunderstorm and lightning flash recognition. Again, the applications are supported with a github repository.

• Catherine O. de Burgh-Day and Tennessee Leeuwenburg, Machine learning for numerical weather and climate modelling: a review. Geoscientific Model Development, vol 16 (2023), 6422-6477.

  This review contains very extensive and in depth information and time structured references regarding machine learning applications to meteorology. It also has an overview of machine learning terminology and a glossary. The goal of the authors is to provide a primer for researchers and model developers, but it is more of a reference work than a tutorial.

• Prof Steve Brunton, Introduction to Fluid Machanics. This is the first of a series of Youtube videos leading up to the use of machine learning to close a set of deterministic equations by providing a so-called "turbulence closure". Since some readers will need background material, the related preliminary videos (also by Steve Brunton) are entitled Introduction to machine learning methods, Machine learning for fluid dynamics: patterns, Introduction to turbulence, Turbulence is everywhere!, Machine learning for computational fluid dynamics, Turbulence closure models, Reynolds averaged Navier Stokes (RANS) and large eddy simulation (LES).

  This might not be for the faint-hearted, but given the complex nature of the atmosphere, oceans, land and ice and their interactions, the reader might consider him/herself encouraged!

• Athul Sudheesh, Introduction to Julia Machine Learning. An online book with a scientific orientation. See also the article linked here entitled "Machine learning with Julia - How to build and deploy a trained AI model as a web service" by Andrey Germanov.

• Editorial Staff of AIFWD, an article entitled "Our guide to online machine learning courses" linked here. An excellent introduction and list of some mostly free online courses presented by major institutions. It has a business applications orientation. Thank you to another student of Amanda Green, Sohana, for pointing this out.

  30 October 2024