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enjoys the extensive amount of previous work, but it also faces possible short-comings inherit to a design based on outdated technologies.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 the current weather prediction systems to capitalize on the most recent technologies and eliminate some of the design flaws of the old system, such as inaccurate cloud-formation prediction (Perkins, 2018). They are joined by researchers from other institutions and interested parties, such as MIT and US Navy (Voosen, 2019). Due to the uncertain benefit of the project, some expressed concern for loss of funding that may have benefited other more established projects (Voosen, 2019). The future is unknown, but it certainly is very exciting to see such collaborations happening on the new technology to tackle a monumental challenge.
References
Perkins, R. (2018, December 12). New Climate Model to Be Built from the Ground Up. Retrieved June 23, 2019, from https://www.caltech.edu/about/news/new-climate-model-be-built-ground-84636
Pickett, M., Perkins, R., & Perkins, R. (n.d.). Home. Retrieved June 23, 2019, from https://clima.caltech.edu/
Voosen, P. (2019, May 22). Science insurgents plot a climate model driven by artificial intelligence. Retrieved May 26, 2019, from https://www.sciencemag.org/news/2018/07/science-insurgents-plot-climate-model-driven-artificial-intelligence
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 ...
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/
Background: Data Science Before talking about “AI for Earth observation and numerical weather prediction” by Sid-Ahmed Boukabara, I need to talk a little bit about data science. One of interesting distinction in the data science world is the separation of data and information: the former refers to the raw facts without context while the latter is a processed data to give it a meaning. In another words, raw data is not very useful in that state until it is distilled into information. This distinction formed the foundation of the article by Boukabara, the acting deputy director of the NOAA NESDIS Center for Satellite Applications and Research. Bottleneck Boukabara’s article describes a lop-sided advancement in technology: the human infrastructure to collect data, such as an array of satellites, has far surpassed our ability to process the data in a timely manner. This imbalance is so huge that “only 3-5 percent of satellite observations are actually used in preparing numerical we...
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