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.
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.
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.
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.
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!
21 August 2024