Difference between revisions of "Meteorology"
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* [[Risk, Compliance and Regulation]] | * [[Risk, Compliance and Regulation]] | ||
* [[Explainable Artificial Intelligence (XAI)]] | * [[Explainable Artificial Intelligence (XAI)]] | ||
| + | * [[Assessing Damage]] | ||
* [http://weather.rap.ucar.edu/model/ Real-Time Weather Data] | [http://ral.ucar.edu/ Research Applications Laboratory at the U.S. National Center for Atmospheric Research (NCAR)] | * [http://weather.rap.ucar.edu/model/ Real-Time Weather Data] | [http://ral.ucar.edu/ Research Applications Laboratory at the U.S. National Center for Atmospheric Research (NCAR)] | ||
* [http://www.sciencedirect.com/science/article/pii/S0960077920305336 Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches | Z. Malki, E. Atlam, A. Ella Hassanien, G. Dagnew, M. Elhosseini, and I. Gad - ScienceDirect] | * [http://www.sciencedirect.com/science/article/pii/S0960077920305336 Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches | Z. Malki, E. Atlam, A. Ella Hassanien, G. Dagnew, M. Elhosseini, and I. Gad - ScienceDirect] | ||
Revision as of 07:50, 11 September 2020
Youtube search... ...Google search
- Case Studies
- Risk, Compliance and Regulation
- Explainable Artificial Intelligence (XAI)
- Assessing Damage
- Real-Time Weather Data | Research Applications Laboratory at the U.S. National Center for Atmospheric Research (NCAR)
- Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches | Z. Malki, E. Atlam, A. Ella Hassanien, G. Dagnew, M. Elhosseini, and I. Gad - ScienceDirect
- Predicting Yacht Resistance with Decision Trees & Random Forests | D. Dua and E. Karra Taniskidou
- Using Machine Learning to Predict the Weather: Part 1 | Adam McQuistan - Stack Abuse
- Machine Learning−based Weather Support for the 2022 Winter Olympics | J. Xia, H. Li, Y. Kang, C. Yu, L. Ji, L. Wu, X. Lou, G. Zhu, Z. Wang, Z. Yan, L. Wang, J. Zhu, P. Zhang, M. Chen, Y. Zhang, L. Gao & J. Han
- A Deep Hybrid Model for Weather Forecasting | A. Grover, A. Kapoor, and E. Horvitz
- Application of machine learning can optimize hurricane track forecast | Matt Swayne - Penn State News
AI, massive datasets, and high-performance computing are helping to produce big changes in predictive abilities. Artificial intelligence has been used to analyze data about weather and climate for years. Today, though, with a boost from increasingly powerful high-performance computers (HPC) and massive loads of data, scientists are beginning to apply AI to create forecasts that are more accurate, more granular, and further reaching. That means technology is coming together to provide better climate predictions for the next 100 years as well as more pinpointed weather forecasts offering more warning for people to take shelter from events such as tornadoes and hurricanes. And this powerful combination of predictive technology, already being tested, could be working in the next three to five years. "I think we're about to see real breakthroughs," says Sue Ellen Haupt, senior scientist and deputy director of the Research Applications Laboratory at the U.S. National Center for Atmospheric Research (NCAR) in Boulder, Colorado. "Using AI for forecasting isn't new, but the push to use it more and to use it differently is new. We're beginning to use AI to determine what storms will have extreme events, like hail or tornadoes. We might be able to get more than a few minutes' warning. We hope to maybe even get hours. AI is going to be the key to better forecasting."Why AI is an increasingly important tool in weather prediction | Sharon Gaudin - Hewlett Packard Enterprise
