The “AI and climate: On the bleeding edge with a pioneering researcher” by journalist Crowder is a dialogue between the journalist and professor Monteleoni. The professor is credited to coining the term, ‘climate informatics’ in 2012 (Crowder, 2018). Crowder interviewed the professor after 6 years to find out more about the new-born field of research and just how much the field has grown since.
Picture 1. Professor Monteleoni. From Predictability assessment of northeast monsoon rainfall in India using sea surface temperature anomaly through statistical and machine learning techniques by L. Crowder, 2018. https://thebulletin.org/wp-content/uploads/2018/02/cmontel-680x1024.jpg
In the conversation, the professor described climate informatics as “innovation at the intersection of data science and climate science” (Crowder 2018). This is similar to bioinformatics that became popularized more than a decade ago which combined biological data with data science, and professor recently saw tell-tale sign of climate informatics taking similar path in contemporary scientific society and educational institutions (Crowder 2018).
References
Crowder, L. (2018, June 28). AI and climate: On the "bleeding edge" with a pioneering researcher. Retrieved May 26, 2019, from https://thebulletin.org/2018/02/ai-and-climate-on-the-bleeding-edge-with-a-pioneering-researcher/
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/
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 ...
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