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 grid is abstracted into a set of numbers called parameters (Voosen, 2019). Smaller grids create high-resolution information while larger grids create coarse-resolution information (Voosen, 2019). It is not too difficult to understand that higher resolution provides more accurate information at the cost of greater number of grids and therefore computation challenge while the lower resolution information provides more feasible computational requirement at the cost of accuracy. The cloud becomes a problem because the cloud formation often happens within a single grid, rendering prediction of location and amount difficult to make due to the abstraction of the information (Voosen, 2019). This problem can be solved by using higher resolution information, but the cost of computation makes it difficult.
Proposed Solution
Professor Gentine and his colleagues decided to take advantage of the accuracy of high-resolution information while using the computationally-feasible coarse-resolution information. The team took advantage of the machine-learning technology that is relatively more inexpensive than observing the high-resolution information. First, they trained a machine-learning model, called Cloud Brain (CBRAIN), to predict cloud-formation using specialized high-resolution information to provide similar level of precision and accuracy. The team then used CBRAIN on the coarse-resolution model to predict cloud formation. This technique provided a powerful method to supplement the current technology by reducing cost and increasing scalability that ultimately increased accuracy. This is an incredible breakthrough as scientists are now equipped with a potential to resolve a challenge that was once through impossible to solve.
References
Evarts, H. (2018, June 19). Machine Learning May Be a Game-Changer for Climate Prediction. Retrieved May 26, 2019, from https://engineering.columbia.edu/press-releases/machine-learning-climate-prediction
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., & Yacalis, G. ( 2018). Could machine learning break the convection parameterization deadlock? Geophysical Research Letters, 45, 5742– 5751. https://doi.org/10.1029/2018GL078202
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
Social Social aspect of the topic covers events or communication aimed towards the general public. The posts in this category consists of various social and political events that pertains to climate change and climate informatics.
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 ...
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