Climate Informatics

Personalization The Faceapp is a controversial phone app that manipulates a person’s picture to show how the person would look many years from now (Koetsier, 2019). Controversies aside, it is an interesting app that has sparked an explosive interest from the general population by showing them a personal and likely future of its user. In many ways, Schmidt and his colleagues had a very similar idea in their effort to gain public interest on climate change: show a personal and likely future of its user (Schmidt, et al., 2019). Instead of face, the team used house, and instead of just few years of aging, the team showed the future after 50 years with likely climate change and the related natural disaster in mind (Schmidt, et al., 2019). Figure 1: "Before" and "After" Pictures. From Visualizing the consequences of climate change using cycle-consistent adversarial networks by V. Schmidt, et al., 2019 Conclusion This is a refreshing effort by scientists to nud...

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...

AI for Earth In the Microsoft blog called “Researchers turn to AI in a bid to improve weather forecasts” by Roach, the author highlights some of the company's contribution to the research that benefits mankind. Through the AI for Earth initiative, the company funds various challenging research projects that studies Earth. The blog also highlights the social activities, such as hackathons, that illustrates some of the current trends of using machine learning to solve difficult problems. References Roach, J. (2019, May 20). Researchers turn to AI in a bid to improve weather forecasts. Retrieved May 26, 2019, from https://blogs.microsoft.com/ai/ai-subseasonal-weather-forecast/

Upgrade or Replace There are two approaches when upgrading an existing system: improve the current system or replace the system. The former enjoys the extensive amount of previous work, but it also faces possible short-comings inherit to a design based on outdated technologies. The latter option provides an opportunity to design a solution based on the latest technologies, it but may prove costly to replace old system. Most of the researchers deemed replacement too expensive and opted to supplement the existing system by solving the existing problems. Professor Tapio Schneider and his colleagues decided otherwise (Perkins, 2018). They created a team called CliMA (Perkins, 2018). Picture 1. Professor Tapio Schneider. From New Climate Model to Be Built from the Ground Up by S. Diani, 2019, https://www.sciencemag.org/sites/default/files/styles/inline__699w__no_aspect/public/350cs_80727X_Tapio_0.jpg?itok=6HRPTU8g New Climate Model This young but ambitious project aims to replace ...

Cloud formation and parameterization (Voosen, 2019); T. SCHNEIDER ET AL., GEOPHYSICAL RESEARCH LETTERS 44, 12,396 (2017), ADAPTED BY N. DESAI/SCIENCE Clouds The clouds in the story had been associated with many things. It is often seen as soft, fluffy, and harmless object floating around in the sky. It provides many imaginations to the children, world-wide, while also providing life-giving water to the plant. It is difficult, therefore, to imagine clouds as the most difficult and confounding problem in scientific world of weather prediction (Gentine, et al, 2018). Clouds play a significant role in predicting rain falls, and not being able to provide accurate prediction results in inaccurate prediction of the location and amount of rainfall (Voosen, 2019). Parameterization To better understand the cloud problem, we need to discuss about parameterization in weather prediction. To process data into manageable set of information, the globe is sectioned off into grids, and each...