Difference between revisions of "Meteorology"
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| − | |keywords=artificial, intelligence, machine, learning, models | + | |keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools |
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[http://www.youtube.com/results?search_query=Meteorology+artificial+intelligence+deep+machine+machine+learning Youtube search...] | [http://www.youtube.com/results?search_query=Meteorology+artificial+intelligence+deep+machine+machine+learning Youtube search...] | ||
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** [[Environmental Science]] | ** [[Environmental Science]] | ||
** [[Life Sciences]] | ** [[Life Sciences]] | ||
| + | ** [[Satellite#Satellite Imagery|Satellite Imagery]] | ||
* [[Risk, Compliance and Regulation]] | * [[Risk, Compliance and Regulation]] | ||
| − | * [[ | + | * [[Assessing Damage]] |
| + | * [[Backtesting]] | ||
| + | * [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]] | ||
* [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://nbviewer.jupyter.org/github/srnghn/ml_example_notebooks/blob/master/Predicting%20Yacht%20Resistance%20with%20Decision%20Trees%20%26%20Random%20Forests.ipynb?source=post_page--------------------------- Predicting Yacht Resistance with Decision Trees & Random Forests | D. Dua and E. Karra Taniskidou] | * [http://nbviewer.jupyter.org/github/srnghn/ml_example_notebooks/blob/master/Predicting%20Yacht%20Resistance%20with%20Decision%20Trees%20%26%20Random%20Forests.ipynb?source=post_page--------------------------- Predicting Yacht Resistance with Decision Trees & Random Forests | D. Dua and E. Karra Taniskidou] | ||
* [http://stackabuse.com/using-machine-learning-to-predict-the-weather-part-1/ Using Machine Learning to Predict the Weather: Part 1 | Adam McQuistan - Stack Abuse] | * [http://stackabuse.com/using-machine-learning-to-predict-the-weather-part-1/ Using Machine Learning to Predict the Weather: Part 1 | Adam McQuistan - Stack Abuse] | ||
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* [http://aditya-grover.github.io/files/publications/kdd15.pdf A Deep Hybrid Model for Weather Forecasting | A. Grover, A. Kapoor, and E. Horvitz] | * [http://aditya-grover.github.io/files/publications/kdd15.pdf A Deep Hybrid Model for Weather Forecasting | A. Grover, A. Kapoor, and E. Horvitz] | ||
* [http://news.psu.edu/story/627330/2020/08/03/research/application-machine-learning-can-optimize-hurricane-track-forecast Application of machine learning can optimize hurricane track forecast | Matt Swayne - Penn State News] | * [http://news.psu.edu/story/627330/2020/08/03/research/application-machine-learning-can-optimize-hurricane-track-forecast Application of machine learning can optimize hurricane track forecast | Matt Swayne - Penn State News] | ||
| + | * [http://phys.org/news/2021-08-artificial-intelligence-extreme-weather-mysteries.html Artificial intelligence unlocks extreme weather mysteries | Rob Jordan, Stanford University - Phys.org] | ||
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| + | http://scx1.b-cdn.net/csz/news/800a/2021/researchers-use-artifi.jpg | ||
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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 [http://ral.ucar.edu/ 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."[http://www.hpe.com/us/en/insights/articles/why-ai-is-an-increasingly-important-tool-in-weather-prediction-2007.html?jumpid=in_510385102_aiweatherprediction_SN072720 Why AI is an increasingly important tool in weather prediction | Sharon Gaudin - Hewlett Packard Enterprise] | 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 [http://ral.ucar.edu/ 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."[http://www.hpe.com/us/en/insights/articles/why-ai-is-an-increasingly-important-tool-in-weather-prediction-2007.html?jumpid=in_510385102_aiweatherprediction_SN072720 Why AI is an increasingly important tool in weather prediction | Sharon Gaudin - Hewlett Packard Enterprise] | ||
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<youtube>dENh2sazQqw</youtube> | <youtube>dENh2sazQqw</youtube> | ||
<youtube>ks3fkRj8Iqc</youtube> | <youtube>ks3fkRj8Iqc</youtube> | ||
| + | <youtube>_R8r4bB2T4w</youtube> | ||
| + | <youtube>mvUPj2WbuKc</youtube> | ||
| + | <youtube>AyHpt8uxwSo</youtube> | ||
| + | <youtube>iYVXVA1viPc</youtube> | ||
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| + | = A Study on Various Deep Learning-based Weather Forecasting Models = | ||
| + | The past few years have seen the development of deep learning-based Weather Forecasting Models like MetNet-2, WF-UNet, ClimaX, GraphCast, Pangu-Weather, and more. This article briefly discusses these models to get an insight into how these models are quickly beating traditional Meteorological Simulators by large margins. [https://www.marktechpost.com/2023/03/01/a-study-on-various-deep-learning-based-weather-forecasting-models | Tanushree Shenwai - MarkTecPost] | ||
| + | |||
| + | # Pangu-Weather For Global Weather Forecasting | ||
| + | # A Multi-Resolution Deep Learning Framework | ||
| + | # Real-time Bias Correction of Wind Field Forecasts | ||
| + | # Predicting Wind Farm Power And Downstream Wakes Using Weather Patterns | ||
| + | # GraphCast: Providing Efficient Medium-Range Global Weather Forecasting | ||
| + | # WeatherFusionNet For Predicting Precipitation from Satellite Data | ||
| + | # WF-UNet: Weather Fusion UNet for Precipitation Nowcasting | ||
| + | # ClimaX: Foundation Model For Weather & Climate | ||
Latest revision as of 19:16, 8 September 2023
Youtube search... ...Google search
- Case Studies
- Risk, Compliance and Regulation
- Assessing Damage
- Backtesting
- Artificial General Intelligence (AGI) to Singularity ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
- 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
- Artificial intelligence unlocks extreme weather mysteries | Rob Jordan, Stanford University - Phys.org
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
A Study on Various Deep Learning-based Weather Forecasting Models
The past few years have seen the development of deep learning-based Weather Forecasting Models like MetNet-2, WF-UNet, ClimaX, GraphCast, Pangu-Weather, and more. This article briefly discusses these models to get an insight into how these models are quickly beating traditional Meteorological Simulators by large margins. | Tanushree Shenwai - MarkTecPost
- Pangu-Weather For Global Weather Forecasting
- A Multi-Resolution Deep Learning Framework
- Real-time Bias Correction of Wind Field Forecasts
- Predicting Wind Farm Power And Downstream Wakes Using Weather Patterns
- GraphCast: Providing Efficient Medium-Range Global Weather Forecasting
- WeatherFusionNet For Predicting Precipitation from Satellite Data
- WF-UNet: Weather Fusion UNet for Precipitation Nowcasting
- ClimaX: Foundation Model For Weather & Climate
