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 refining the process via the reduction of the computational cost, it has become easier and more scalable to perform. In this research paper, professor Deshpande and his colleagues tackled the challenge of producing a concrete evidence to support the long-predicted hypothesis that links the demise of coral reefs and climate change. The conclusion of the research suggests two very important achievements. Not only does the research successfully supports the hypothesis with stronger evidence, it also suggests the powerful impact the technology would have on the future of the studies, opening new avenue of research and providing powerful tools for scientists.
References
Bue, B. D., Thompson, D. R., Deshpande, S., Eastwood, M., Green, R. O., Mullen, T., . . . Parente, M. (2019). Neural Network Radiative Transfer for Imaging Spectroscopy. Atmospheric Measurement Techniques Discussions, 1-16. doi:10.5194/amt-2018-436
Deshpande, S., Bue, B. D., Thompson, D. R., Natraj, V., & Parente, M. (2019). Learning Radiative Transfer Models for Climate Change Applications in Imaging Spectroscopy. arXiv preprint arXiv:1906.03479.
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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