Climate Informatics

About In this 2018 paper, “Predicting weather forecast uncertainty with machine learning” by professor Scher and professor Messori, shows an early effort to test the feasibility of the technology as a potential replacement to the popular ensemble weather model approach. The ensemble weather model approach, as the name suggest, is similar to having a panel of experts instead of just one expert; the approach produces information that is a composite of different information generated by multiple models given the same input data. Each model caters to different specialization and has their own strength and weaknesses, and by combining information from each model, the approach attempts to generate more accurate information. As such, while it increases the accuracy and precision, it became computationally very expensive to perform in a timely manner. The researchers’ goal was to provide a competitive solution to the computationally expensive ensemble weather model approach by using machine ...

Work of Many The paper “Tackling Climate Change with Machine Learning” is a comprehensive, cross-discipline overview of the research in climate change and other related fields. This paper sets its scope beyond simply predicting the future and defines a comprehensive role scientists and society should play to address the current and future challenges using machine learning. It divides the current researches and efforts into two categories: mitigation and adaptation (Rolnick, et al. 2019). In mitigation, the efforts are focused on lessening the effects of the climate change, such as changing the energy industry to consume less fossil fuel or building more sustainable cities (Rolnick, et al. 2019). The adaptation, on the other hand, accepts that certain outcomes are inevitable and focuses on technologies that would allow humans to survive, including weather prediction, social infrastructure, and education (Rolnick, et al. 2019). The paper also provides some guidance to prioritizing d...

Figure 1. Coral Reef From Coral Reef by Jürgen Freund, http://d2ouvy59p0dg6k.cloudfront.net/img/web_289583_4_528276.jpg Follow-up Research This is a follow-up experiment to the research , “Neural Network Radiative Transfer for Imaging Spectroscopy”. This time lead by professor Deshpande, many of the previous members joins the research to continue their work on the algorithm that may potentially replace the expensive Radiative Transfer Model (RTM) method. Figure 2. Coral Reef Spread. From WWF by Hugo Ahlenius, UNEP/GRID-Arendal,http://d2ouvy59p0dg6k.cloudfront.net/img/coraldistribution_001_362390.png Context The cause of the rapid and on-going demise of coral reef has long been suspected to be caused by the climate change but drawing a direct link between the greenhouse gases and the phenomena has been difficult (Deshpande, et al, 2019). The paper illustrates that the root cause is has been the inability to process necessary and large scale of data into information. By...

Figure 1. The study zone: the India Peninsula. From Predictability assessment of northeast monsoon rainfall in India using sea surface temperature anomaly through statistical and machine learning techniques, by Y. Dash, 2019. Purpose Professor Dash and his colleagues investigated correlation between northeast monsoon rainfall (NEMR) and sea surface temperature (SST) in hopes of finding an effective way to predict flood and draughts in the Indian peninsular (Dash, et al. 2019). In their approach, they compared three different methods of machine learning: linear regression, artificial neural network, and extreme learning machine (Dash, et al. 2019). Focus This research was had multiple focuses. As with many other researches currently studying the feasibility of using machine learning in the study of climate change, the research aimed to compare the efficiency and scalability of various machine learning algorithms to study weather patterns. Not only that, it also was poised ...

Previously In the article , “AI for Earth observation and numerical weather prediction”, by Boukabara, the community identified the need to boost the growth of data refinement technology. Furthermore, based on the trend and performances observed in other disciplines with similar challenges, Boukabara suggested a strong candidate for the solution: AI. The research by Professor Bue and his colleagues support Boukabara’s argument. Spectroscopy The spectroscopy is essentially the study of light crossed with chemistry. When a chemical is treated with certain stress, such as heat, it emits light. This is like a finger print to a person: each chemical emits light with a specific wavelength. The spectroscopy is a branch of science that studies the light wave to the chemical that is associated with it. It is widely used to identify the chemical constituent of an unknown compound or a distant star system, among other things. It is also used to understand the chemical composition of a ...

Technology If the general science forms the basis of our understanding, technology is the application of our understanding. This series compiles information on some of the latest technological advancements on climate informatics.