Picture 1. WCRP 40th Session Image. From WCRP by N. V. Wel, 2017, https://www.wcrp-climate.org/images/WCRP_conferences/JSC40/images/JSC40header_featurebox.jpg
About
The World Climate Research Programme (WCRP) are group of scientists focused on understanding the climate change. This group differs from CliMA in that WCRP chose to enhance the traditional method of prediction and augment it using machine learning instead of replacing the system from scratch. They host annual meetings with global officials at attendance and provide suggestions to the policy makers on the current state of Earth.
References
Wel, N. V. (2019, June 18). 40th Session of the Joint Scientific Committee. Retrieved from https://www.wcrp-climate.org/jsc40-about
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/
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 ...
Origin 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...
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