Meteorology - Revision history

BPeat at 00:16, 9 September 2023

2023-09-09T00:16:06Z

BPeat at 12:18, 10 July 2023

2023-07-10T12:18:31Z

BPeat at 10:24, 5 July 2023

2023-07-05T10:24:47Z

BPeat at 12:43, 29 April 2023

2023-04-29T12:43:36Z

BPeat at 10:10, 11 August 2021

2021-08-11T10:10:03Z

BPeat at 10:06, 11 August 2021

2021-08-11T10:06:25Z

BPeat at 10:04, 11 August 2021

2021-08-11T10:04:40Z

BPeat at 10:03, 11 August 2021

2021-08-11T10:03:51Z

BPeat at 14:35, 30 September 2020

2020-09-30T14:35:57Z

BPeat at 12:17, 23 September 2020

2020-09-23T12:17:40Z

@@ Line 24: / Line 24: @@
 * [[Assessing Damage]]
 * [[Backtesting]]
-* [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]]
+* [[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://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]

@@ Line 20: / Line 20: @@
 ** [[Environmental Science]]
 ** [[Life Sciences]]
 * [[Risk, Compliance and Regulation]]
 * [[Assessing Damage]]

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|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
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [http://www.youtube.com/results?search_query=Meteorology+artificial+intelligence+deep+machine+machine+learning Youtube search...]
@@ Line 12: / Line 21: @@
 ** [[Life Sciences]]
 * [[Risk, Compliance and Regulation]]
 * [[Assessing Damage]]
 * [[Backtesting]]
 * [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]

← Older revision		Revision as of 12:43, 29 April 2023
Line 43:		Line 43:
	<youtube>AyHpt8uxwSo</youtube>		<youtube>AyHpt8uxwSo</youtube>
	<youtube>iYVXVA1viPc</youtube>		<youtube>iYVXVA1viPc</youtube>
		+
		+	= 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

← Older revision		Revision as of 10:10, 11 August 2021
Line 40:		Line 40:
	<youtube>ks3fkRj8Iqc</youtube>		<youtube>ks3fkRj8Iqc</youtube>
	<youtube>_R8r4bB2T4w</youtube>		<youtube>_R8r4bB2T4w</youtube>
		+	<youtube>mvUPj2WbuKc</youtube>
		+	<youtube>AyHpt8uxwSo</youtube>
		+	<youtube>iYVXVA1viPc</youtube>

← Older revision		Revision as of 10:06, 11 August 2021
Line 39:		Line 39:
	<youtube>dENh2sazQqw</youtube>		<youtube>dENh2sazQqw</youtube>
	<youtube>ks3fkRj8Iqc</youtube>		<youtube>ks3fkRj8Iqc</youtube>
		+	<youtube>_R8r4bB2T4w</youtube>

@@ Line 23: / Line 23: @@
 * [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]
 http://scx1.b-cdn.net/csz/news/800a/2021/researchers-use-artifi.jpg
 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]