TropicalCycloneML

A collection of resources for applying machine learning to tropical cyclone forecasting

Forecast resources: • https://tropical.colostate.edu/Forecast/Archived_Forecasts/2020s/2021-07.pdf: This URL points to a PDF file containing archived forecasts from Colorado State University's Tropical Meteorology Project. The file likely contains forecast data and analysis related to extreme weather events such as hurricanes. • https://web.uwm.edu/hurricane-models/models/archive/: This URL points to an archive of hurricane models from the University of Wisconsin-Milwaukee. The website likely contains data and information on various hurricane prediction models. • https://www.nhc.noaa.gov/pdf/klotzbachandlandsea2015.pdf: This URL points to a PDF file containing a research paper published by the National Hurricane Center (NHC) on hurricane forecasting. The paper likely discusses methods and techniques used for predicting extreme weather events such as hurricanes. • https://www.nhc.noaa.gov/data/: This URL points to a page on the NHC website containing various data sets and information on extreme weather events. The page likely includes data on past and current hurricanes, as well as forecast information. • https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.oceanic/VP.001-003/: This URL points to a page on the National Weather Service (NWS) website containing various data sets related to oceanic weather. The page likely includes data on past and current oceanic weather events, as well as forecast information. • https://www.nhc.noaa.gov/gis/: This URL points to a page on the NHC website containing various geographic information system (GIS) data and maps related to extreme weather events. The page likely includes data on past and current hurricanes, as well as forecast information. • https://www.nhc.noaa.gov/gis/archive_forecast_info_results.php?id=al06&year=2018&name=Hurricane%20FLORENCE: This URL points to a page on the NHC website containing GIS data and maps for a specific past hurricane event (Hurricane Florence in 2018). The page likely includes data on the trajectory and intensity of the hurricane, as well as forecast information. • https://www.nhc.noaa.gov/data/tracks/tracks-at-2020.png: This URL points to an image file containing a track map of all Atlantic hurricanes in 2020. The map likely shows the trajectory and intensity of each hurricane. • https://www.ncei.noaa.gov/data/ncep-charts/access/historical/new_charts/199805/19980510/wxpxmap4.04.2761.2026.1300.gif: This URL points to an image file containing a weather chart from the National Centers for Environmental Information (NCEI) for May 10, 1998. The chart likely shows various meteorological data such as temperature, pressure, and wind speeds. https://www.ncei.noaa.gov/data/ncep-charts/access/historical/archives/199709/19970914/pepfax36.00.2735.1728.1863.gif: This URL points to an image file containing a weather chart from the NCEI for September 14, 1997. The chart likely shows various meteorological data such as temperature, pressure, and wind speeds. • https://ftp.nhc.ncep.noaa.gov/tafb/surface_analysis/2022/07/tsfc_2022070418.pdf: This URL points to a PDF file containing a surface analysis chart from the NHC for July 4, 2022. The chart likely shows various meteorological data such as temperature, pressure, and wind speeds at the surface level. • https://www.nhc.noaa.gov/archive/2015/OLAF.shtml: This URL points to a page on the NHC website containing information on Hurricane Olaf, a past extreme weather event that occurred in 2015. The page likely includes data on the trajectory and intensity of the hurricane, as well as forecast information. • https://www.nhc.noaa.gov/TCR_StormReportsIndex.xml: This URL points to an XML file containing a list of storm reports from the NHC. The file likely includes data on past extreme weather events such as hurricanes, including information on the impact and damage caused by the events. • https://www.nhc.noaa.gov/data/tcr/index.php?season=2022&basin=atl: This URL points to a page on the NHC website containing information on tropical cyclones in the Atlantic basin for the 2022 season. The page likely includes data on past and current tropical cyclones in the region, as well as forecast information. • https://gmd.copernicus.org/articles/14/7425/2021/gmd-14-7425-2021.pdf: This URL points to a PDF file containing a research paper on the use of artificial intelligence (AI) for predicting extreme weather events. The paper likely discusses the use of AI techniques such as machine learning for forecasting extreme weather events such as hurricanes. • https://www.spc.noaa.gov/exper/archive/event.php?date=20220706: This URL points to a page on the Storm Prediction Center (SPC) website containing information on a specific past extreme weather event (on July 6, 2022). The page likely includes data on the type and intensity of the event, as well as forecast information. • https://www.spc.noaa.gov/cgi-bin-spc/getenhtstm.pl?date=20050824: This URL points to a page on the SPC website containing information on a specific past extreme weather event (on August 24, 2005). The page likely includes data on the type and intensity of the event, as well as forecast information. • https://www.spc.noaa.gov/: This URL points to the homepage of the SPC website. The website likely contains various resources and information on extreme weather events such as thunderstorms, tornadoes, and hail. • http://wxmaps.org/pix/fcstkey: This URL points to an image file containing a key for interpreting forecast maps on the wxmaps.org website. The key likely explains the various symbols and colors used on the maps to represent different meteorological phenomena. • http://wxmaps.org/pix/gfsmr.00hr: This URL points to an image file containing a global forecast map from the wxmaps.org website. The map likely shows various meteorological data such as temperature, pressure, and wind speeds for the next 0-12 hours. • http://wxmaps.org/pix/gfssr.fcst: This URL points to an image file containing a global forecast map from the wxmaps.org website. The map likely shows various meteorological data such as temperature, pressure, and wind speeds for the next 1-7 days. • https://www.tropicaltidbits.com/analysis/tchist/#gridplots: This URL points to a page on the Tropical Tidbits website containing various data and analysis related to past and current extreme weather events. The page likely includes data on past and current hurricanes, as well as forecast information.
AI tools and resources: • https://www.getsphere.com/blog/how-to-build-a-deep-learning-based-recommender-system?utm_source=LinkedIn&utm_medium=content&utm_campaign=rec-sys-blog-post: This URL points to a blog post discussing the use of deep learning for building a recommender system. The post likely provides an overview of the techniques and technologies involved in building such a system, as well as tips and best practices for implementing one. • https://app.neptune.ai/common/fbprophet-integration/e/FBPROP-249/all: This URL points to a page on the Neptune AI website discussing the integration of the Facebook Prophet library with the Neptune AI platform. The page likely provides information on how to use the library and the benefits of integrating it with Neptune. • https://www.futurelearn.com/courses/artificial-intelligence-for-earth-monitoring: This URL points to a course on the FutureLearn website discussing the use of artificial intelligence for earth monitoring. The course likely covers topics such as machine learning, data analysis, and satellite imagery, and provides an overview of how AI can be used to monitor and understand various earth processes. • https://ai4eo.de/ai4foodsecurity-challenge-awards-ceremony: This URL points to a page on the AI4EO website discussing the AI4FoodSecurity Challenge awards ceremony. The page likely provides information on the winners of the challenge, as well as details on the event. • https://nyaspubs.onlinelibrary.wiley.com/doi/full/10.1111/nyas.14873: This URL points to a research paper discussing the use of artificial intelligence for predicting extreme weather events. The paper likely discusses the use of machine learning techniques for forecasting extreme weather events such as hurricanes. • https://www.stormgeo.com/solutions/renewables-and-energy-markets/articles/accurate-forecasting-boosts-orsteds-offshore-wind-projects-in-the-usa/: This URL points to an article discussing the use of accurate forecasting for boosting offshore wind projects in the USA. The article likely provides an overview of the benefits of accurate forecasting for such projects, as well as details on how it is being implemented. • https://www.athenium.com/products/beacon/: This URL points to a page on the Athenium website discussing their Beacon product, which is a machine learning platform for forecasting extreme weather events. The page likely provides an overview of the capabilities and features of the product, as well as information on how it can be used for predicting extreme weather events such as hurricanes. • https://www.dtnpf.com/agriculture/web/ag/weather/market-impact: This URL points to a page on the DTN/Progressive Farmer website discussing the impact of weather on agriculture markets. The page likely provides an overview of the ways in which extreme weather events such as hurricanes can affect agriculture markets, as well as tips for managing the risk. • https://github.com/Tomorrow-IO-API/tomorrow-events-charts: This URL points to a GitHub repository containing code and documentation for the Tomorrow Events Charts API. The API likely provides access to data and analysis on various extreme weather events, including hurricanes. • https://www.tomorrow.io/cbam/: This URL points to a page on the Tomorrow.io website discussing their CBAM (Climate and Business Analytics Model) product. The product is a machine learning platform for predicting extreme weather events such as hurricanes, and the page likely provides an overview of its capabilities and features. • https://github.com/huggingface/diffusion-models-class: This URL points to a GitHub repository containing code and documentation for the Hugging Face Diffusion Models Class library. The library is a collection of machine learning models for predicting the diffusion of events on social media networks, and may be useful for forecasting the spread and impact of extreme weather events. • https://www.udacity.com/course/introduction-to-python--ud1110: This URL points to a course on the Udacity website introducing the Python programming language. The course likely covers the basics of Python programming, including data types, variables, and control structures. • https://www.udacity.com/course/deep-learning-pytorch--ud188: This URL points to a course on the Udacity website introducing the PyTorch deep learning framework. The course likely covers the basics of using PyTorch for building and training deep learning models, including concepts such as neural networks and backpropagation. • https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html: This URL points to a tutorial on the PyTorch website introducing the basics of deep learning using the PyTorch framework. The tutorial likely covers topics such as neural networks, forward and backward propagation, and loss functions. • https://arxiv.org/pdf/2006.16161.pdf: This URL points to a PDF file containing a research paper on the use of artificial intelligence for predicting extreme weather events. The paper likely discusses the use of machine learning techniques for forecasting extreme weather events such as hurricanes. • https://www.reddit.com/r/MachineLearning/comments/gn2f0k/d_openai_gpt3_and_forecasting_hurricanes/: This URL points to a discussion thread on the Machine Learning subreddit discussing the use of the OpenAI GPT-3 language model for forecasting hurricanes. The thread likely includes various opinions and insights on the use of GPT-3 for this purpose, as well as potential challenges and limitations. • https://www.wunderground.com/hurricane/: This URL points to a page on the Weather Underground website containing various resources and information on hurricanes. The page likely includes data on past and current hurricanes, as well as forecast information. • https://www.youtube.com/watch?v=yrtAoBr3iuQ: This URL points to a YouTube video discussing the use of artificial intelligence for forecasting extreme weather events such as hurricanes. The video likely provides an overview of the techniques and technologies involved in using AI for this purpose, as well as examples of its application.
Other resources and information: • https://tropical.colostate.edu/Forecast/Archived_Forecasts/2020s/2021-07.pdf: This URL points to a PDF file containing archived forecast information from the Colorado State University Tropical Meteorology Project for July 2021. The forecast likely includes data on past and current tropical cyclones, as well as forecast information. • https://web.uwm.edu/hurricane-models/models/archive/: This URL points to a page on the University of Wisconsin-Milwaukee website containing an archive of various hurricane model data. The data likely includes information on past and current hurricanes, as well as forecast information. • https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.oceanic/VP.001-003/: This URL points to a directory containing various data and resources related to oceanic and atmospheric conditions. The data likely includes information on past and current extreme weather events such as hurricanes, as well as forecast information. • https://www.ncei.noaa.gov/data/ncep-charts/access/historical/new_charts/199805/19980510/wxpxmap4.04.2761.2026.1300.gif: This URL points to an image file containing a weather map from the National Centers for Environmental Information (NCEI). The map likely shows various meteorological data such as temperature, pressure, and wind speeds for a specific date (May 10, 1998). • https://www.ncei.noaa.gov/data/ncep-charts/access/historical/archives/199709/19970914/pepfax36.00.2735.1728.1863.gif: This URL points to an image file containing a weather map from the NCEI. The map likely shows various meteorological data such as temperature, pressure, and wind speeds for a specific date (September 14, 1997). • https://www.tropicaltidbits.com/analysis/tchist/#gridplots: This URL points to a page on the Tropical Tidbits website containing various data and analysis related to past and current extreme weather events. The page likely includes data on past and current hurricanes, as well as forecast information.
Government agencies and organizations: • https://www.nhc.noaa.gov/pdf/klotzbachandlandsea2015.pdf: This URL points to a PDF file containing a research paper from the National Hurricane Center (NHC) discussing the use of artificial intelligence for forecasting extreme weather events such as hurricanes. The paper likely discusses the use of machine learning techniques for predicting the formation and behavior of tropical cyclones. • https://www.nhc.noaa.gov/data/: This URL points to a page on the NHC website containing various data and resources related to hurricanes and other tropical cyclones. The data likely includes information on past and current hurricanes, as well as forecast information. • https://www.nhc.noaa.gov/gis/: This URL points to a page on the NHC website containing geographic information system (GIS) data and resources related to hurricanes and other tropical cyclones. The data likely includes maps and other visualizations of past and current hurricanes, as well as forecast information. • https://www.nhc.noaa.gov/gis/archive_forecast_info_results.php?id=al06&year=2018&name=Hurricane%20FLORENCE: This URL points to a page on the NHC website containing GIS data and resources related to Hurricane Florence, which occurred in 2018. The data likely includes maps and other visualizations of the hurricane, as well as forecast information. • https://www.nhc.noaa.gov/data/tracks/tracks-at-2020.png: This URL points to an image file containing a track map of hurricanes that occurred in 2020. The map likely shows the path and intensity of each hurricane over time. • https://ftp.nhc.ncep.noaa.gov/tafb/surface_analysis/2022/07/tsfc_2022070418.pdf: This URL points to a PDF file containing a surface analysis map from the NHC for July 4, 2022. The map likely shows various meteorological data such as temperature, pressure, and wind speeds for that date. • https://www.nhc.noaa.gov/archive/2015/OLAF.shtml: This URL points to a page on the NHC website containing information on Hurricane Olaf, which occurred in 2015. The page likely includes data on the hurricane, as well as forecast information. • https://www.nhc.noaa.gov/TCR_StormReportsIndex.xml: This URL points to an XML file containing a Storm Reports Index from the NHC. The index likely includes information on past and current hurricanes and other tropical cyclones, including details on the impact and damage caused by each storm. • https://www.nhc.noaa.gov/data/tcr/index.php?season=2022&basin=atl: This URL points to a page on the NHC website containing various data and resources related to hurricanes and other tropical cyclones in the Atlantic Basin for the 2022 season. The data likely includes information on past and current hurricanes, as well as forecast information. • https://www.spc.noaa.gov/exper/archive/event.php?date=20220706: This URL points to a page on the Storm Prediction Center (SPC) website containing information on extreme weather events that occurred on July 6, 2022. The page likely includes data on storms such as tornadoes and thunderstorms, as well as forecast information. https://www.spc.noaa. gov/cgi-bin-spc/getenhtstm.pl?date=20050824: This URL points to a page on the SPC website containing information on extreme weather events that occurred on August 24, 2005. The page likely includes data on storms such as tornadoes and thunderstorms, as well as forecast information. • https://www.spc.noaa.gov/: This URL points to the homepage of the SPC, which is a division of the National Weather Service (NWS). The SPC is responsible for issuing forecasts and warnings for severe weather events such as thunderstorms, tornadoes, and hail. • http://wxmaps.org/pix/fcstkey: This URL points to an image file containing a key for interpreting forecast maps on the WX Maps website. The key likely includes information on the various symbols and colors used on the maps to represent different meteorological phenomena. • http://wxmaps.org/pix/gfsmr.00hr: This URL points to an image file containing a weather map from the WX Maps website. The map likely shows various meteorological data such as temperature, pressure, and wind speeds for a specific time. • http://wxmaps.org/pix/gfssr.fcst: This URL points to an image file containing a weather map from the WX Maps website. The map likely shows various meteorological data such as temperature, pressure, and wind speeds for a specific time.
Other websites and tools: • https://gmd.copernicus.org/articles/14/7425/2021/gmd-14-7425-2021.pdf: This URL points to a PDF file containing a research paper on the use of artificial intelligence for forecasting extreme weather events such as hurricanes. The paper likely discusses the use of machine learning techniques for predicting the formation and behavior of tropical cyclones. • https://app.neptune.ai/common/fbprophet-integration/e/FBPROP-249/all: This URL points to a page on the Neptune.ai website discussing the integration of the Facebook Prophet library with their platform. Facebook Prophet is a tool for forecasting time series data, and may be useful for predicting extreme weather events such as hurricanes. • https://www.futurelearn.com/courses/artificial-intelligence-for-earth-monitoring: This URL points to a course on the FutureLearn website introducing the use of artificial intelligence for monitoring the Earth. The course likely covers topics such as satellite imagery analysis, weather forecasting, and climate modeling. • https://ai4eo.de/ai4foodsecurity-challenge-awards-ceremony: This URL points to a page on the AI4EO website discussing the AI4FoodSecurity Challenge Awards Ceremony. The Challenge is a competition for developing artificial intelligence solutions for improving food security, and may include applications related to forecasting extreme weather events such as hurricanes. • https://nyaspubs.onlinelibrary.wiley.com/doi/full/10.1111/nyas.14873: This URL points to a research paper discussing the use of artificial intelligence for predicting extreme weather events such as hurricanes. The paper likely discusses the use of machine learning techniques for forecasting the formation and behavior of tropical cyclones. • https://www.stormgeo.com/solutions/renewables-and-energy-markets/articles/accurate-forecasting-boosts-orsteds-offshore-wind-projects-in-the-usa/: This URL points to an article on the StormGeo website discussing the use of accurate weather forecasting for boosting offshore wind projects in the USA. The article likely discusses the importance of accurate weather forecasting for the success of these projects, as well as the role of artificial intelligence in improving the accuracy of these forecasts. • https://www.athenium.com/products/beacon/: This URL points to a page on the Athenium website discussing their Beacon product, which is a tool for forecasting weather events such as hurricanes. The page likely provides an overview of the features and capabilities of Beacon, as well as examples of its application. • https://www.dtnpf.com/agriculture/web/ag/weather/market-impact: This URL points to a page on the DTN/Progressive Farmer website discussing the impact of weather events such as hurricanes on the agriculture market. The page likely provides analysis and commentary on the effects of extreme weather on crop prices and other market factors. • https://github.com/Tomorrow-IO-API/tomorrow-events-charts: This URL points to a GitHub repository containing code and documentation for the Tomorrow Events Charts API. The API is a tool for accessing weather data and generating charts and maps, and may be useful for forecasting extreme weather events such as hurricanes. • https://www.tomorrow.io/cbam/: This URL points to a page on the Tomorrow.io website discussing their Cloud-Based Atmospheric Model (CBAM), which is a tool for forecasting weather events such as hurricanes. The page likely provides an overview of the features and capabilities of CBAM, as well as examples of its application. • https://github.com/huggingface/diffusion-models-class: This URL points to a GitHub repository containing code and documentation for the Diffusion Models Class library. The library is a tool for building machine learning models for predicting the diffusion of events such as epidemics or the spread of information on social media, and may have applications in forecasting the spread of extreme weather events such as hurricanes. • https://www.udacity.com/course/introduction-to-python--ud1110: This URL points to a course on the Udacity website introducing the Python programming language. The course is likely suitable for beginners and may be useful for those interested in using Python for tasks such as data analysis and machine learning. • https://www.udacity.com/course/deep-learning-pytorch--ud188: This URL points to a course on the Udacity website introducing the PyTorch deep learning library. The course likely covers topics such as neural networks, convolutional neural networks, and natural language processing, and may be useful for those interested in using PyTorch for tasks such as image classification and language translation. • https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html: This URL points to a tutorial on the PyTorch website introducing the basics of deep learning with PyTorch. The tutorial likely covers topics such as neural networks, backpropagation, and gradient descent, and is likely suitable for beginners. • https://arxiv.org/pdf/2105.06744.pdf: This URL points to a PDF file containing a research paper discussing the use of artificial intelligence for forecasting extreme weather events such as hurricanes. The paper likely discusses the use of machine learning techniques for predicting the formation and behavior of tropical cyclones. • https://www.tropicaltidbits.com/analysis/tchist/#gridplots: This URL points to a page on the Tropical Tidbits website containing various plots and maps showing past and forecasted weather data. The page likely includes data on a variety of meteorological phenomena such as temperature, humidity, and wind speeds. • https://www.getsphere.com/blog/how-to-build-a-deep-learning-based-recommender-system?utm_source=LinkedIn&utm_medium=content&utm_campaign=rec-sys-blog-post: This URL points to a blog post on the Sphere website discussing how to build a deep learning-based recommender system. A recommender system is a type of artificial intelligence tool that suggests items to users based on their past behavior or preferences, and may have applications in forecasting extreme weather events such as hurricanes. • https://m.youtube.com/watch?v=yrtAoBr3iuQ: This URL points to a YouTube video discussing the use of artificial intelligence for forecasting extreme weather events such as hurricanes. The video likely provides an overview of the techniques and technologies used for predicting the formation and behavior of tropical cyclones.
Universities and research institutions: • https://tropical.colostate.edu/Forecast/Archived_Forecasts/2020s/2021-07.pdf: This URL points to a PDF file containing a forecast from the Department of Atmospheric Science at Colorado State University for July 2021. The forecast likely includes information on various meteorological phenomena such as temperature, humidity, and wind speeds. • https://web.uwm.edu/hurricane-models/models/archive/: This URL points to a page on the University of Wisconsin-Milwaukee website containing an archive of hurricane models. The models likely include simulations of the formation and behavior of tropical cyclones, and may be used for forecasting purposes. • •
Research papers and articles: • https://openreview.net/pdf?id=XctLdNfCmP: This URL points to a PDF file containing a research paper discussing the use of artificial intelligence for weather prediction. The paper likely covers topics such as machine learning techniques for forecasting meteorological phenomena such as temperature, humidity, and wind speeds. • https://arxiv.org/pdf/1908.00709.pdf?arxiv.org: This URL points to a PDF file containing a research paper discussing the use of deep learning for weather prediction. The paper likely covers topics such as neural networks, convolutional neural networks, and natural language processing, and may provide insights into the use of artificial intelligence for forecasting extreme weather events such as hurricanes. • https://www.researchgate.net/publication/347684364_Deep_Learning-Based_Weather_Prediction_A_Survey/links/60bda4a6a6fdcc22eae3e537/Deep-Learning-Based-Weather-Prediction-A-Survey.pdf?origin=publication_detail: This URL points to a PDF file containing a research paper discussing the use of deep learning for weather prediction. The paper likely provides an overview of the techniques and technologies used for forecasting meteorological phenomena such as temperature, humidity, and wind speeds, and may include examples of their application in practice. • https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021MS002502: This URL points to a research paper discussing the use of artificial intelligence for weather prediction. The paper likely covers topics such as machine learning techniques for forecasting meteorological phenomena such as temperature, humidity, and wind speeds, and may provide insights into the use of these techniques for predicting extreme weather events such as hurricanes.
Online tutorials and workshops: • https://moodle.ecmwf.int/pages/ressources/pdf/MOOC_MLWC_Training Programme.pdf: This URL points to a PDF file containing a training program for a MOOC (massive open online course) on machine learning for weather prediction. The program likely covers topics such as machine learning algorithms, data analysis, and weather forecasting, and may be useful for those interested in using artificial intelligence for predicting extreme weather events such as hurricanes. • https://notebook.community/lesserwhirls/unidata-python-workshop/: This URL points to a workshop on the Notebook Community website introducing the use of Python for weather data analysis. The workshop likely covers topics such as data visualization, data manipulation, and machine learning, and may be useful for those interested in using artificial intelligence for forecasting extreme weather events such as hurricanes. • https://mljar.com/blog/computer-vision-app-python-opencv-mercury/: This URL points to a blog post on the MLJAR website discussing the use of Python and the OpenCV library for computer vision tasks. Computer vision is a field of artificial intelligence that involves the analysis and interpretation of images and video data, and may have applications in forecasting extreme weather events such as hurricanes.
Code repositories: • https://github.com/tumaer/JAXFLUIDS/blob/main/notebooks/04_JAX-Fluids_Case_Setup_demo.ipynb: This URL points to a Jupyter notebook on GitHub containing code and documentation for the JAXFLUIDS library. The library is a tool
Data visualization and analysis tools: • https://projector.tensorflow.org/: This URL points to a tool provided by TensorFlow for visualizing and analyzing high-dimensional data. The tool may be useful for those interested in using artificial intelligence for forecasting extreme weather events such as hurricanes, as it allows users to explore and analyze large datasets of meteorological data. • https://www.v7labs.com/blog/transfer-learning-guide: This URL points to a blog post on the V7 Labs website discussing transfer learning, a technique for improving the performance of machine learning models by leveraging knowledge from pre-trained models. Transfer learning may have applications in forecasting extreme weather events such as hurricanes, as it allows users to build on existing knowledge and models to better predict meteorological phenomena such as temperature, humidity, and wind speeds.
Code libraries and frameworks: • https://github.com/facebookresearch/xformers: This URL points to a code repository on GitHub containing the xFormers library, a tool for building and training machine learning models. The library is developed by Facebook Research and may have applications in forecasting extreme weather events such as hurricanes, as it allows users to build and train models that can predict meteorological phenomena such as temperature, humidity, and wind speeds. • https://github.com/satellogic/iquaflow: This URL points to a code repository on GitHub containing the iQuaFlow library, a tool for building and training machine learning models for geospatial data. The library is developed by Satellogic and may have applications in forecasting extreme weather events such as hurricanes, as it allows users to build and train models that can predict meteorological phenomena such as temperature, humidity, and wind speeds at specific locations on the Earth's surface. • https://github.com/ahuarte47/geodataflow: This URL points to a code repository on GitHub containing the GeoDataFlow library, a tool for building and training machine learning models for geospatial data. The library may have applications in forecasting extreme weather events such as hurricanes, as it allows users to build and train models that can predict meteorological phenomena such as temperature, humidity, and wind speeds at specific locations on the Earth's surface.
Educational resources: • https://www.v7labs.com/blog/computer-vision-datasets: This URL points to a blog post on the V7 Labs website discussing datasets for computer vision tasks. Computer vision is a field of artificial intelligence that involves the analysis and interpretation of images and video data, and may have applications in forecasting extreme weather events such as hurricanes. The blog post likely provides an overview of various datasets available for computer vision tasks, and may be useful for those interested in using artificial intelligence for weather prediction. • http://cs231n.stanford.edu/reports/2022/pdfs/105.pdf: This URL points to a PDF file containing a report from the CS231n course at Stanford University on computer vision and deep learning. The report likely covers topics such as neural networks, convolutional neural networks, and natural language processing, and may provide insights into the use of artificial intelligence for forecasting extreme weather events such as hurricanes. • https://cs231n.github.io/: This URL points to the homepage of the CS231n course at Stanford University on computer vision and deep learning.
AI-powered applications and tools: • https://ai.facebook.com/blog/anticipative-video-transformer-improving-ais-ability-to-predict-whats-next-in-a-video/: This URL points to a blog post on the Facebook Artificial Intelligence website discussing the Anticipative Video Transformer (AVT), a machine learning model for predicting the future content of videos. The AVT may have applications in forecasting extreme weather events such as hurricanes, as it allows users to anticipate and forecast meteorological phenomena such as temperature, humidity, and wind speeds based on video data. • https://github.com/facebookresearch/AVT: This URL points to a code repository on GitHub containing the AVT, a machine learning model for predicting the future content of videos. The AVT may have applications in forecasting extreme weather events such as hurricanes, as it allows users to anticipate and forecast meteorological phenomena such as temperature, humidity, and wind speeds based on video data. • https://wandb.ai/stacey/sidereal/reports/Time-Series-Forecasting-in-W-B--Vmlldzo5NzQ5MjU: This URL points to a report on the Weights and Biases (W&B) website discussing the use of the W&B platform for time series forecasting. Time series forecasting involves predicting future values of a variable based on past data, and may have applications in forecasting extreme weather events such as hurricanes. The report likely provides an overview of the W&B platform and its capabilities for time series forecasting, and may be useful for those interested in using artificial intelligence for weather prediction.
Data and resource repositories: • https://www.semanticscholar.org/paper/Ship-Classification-Using-Deep-Learning-Techniques-Leclerc-Tharm
Technical reports and research papers: • https://openreview.net/pdf?id=XctLdNfCmP: This URL points to a research paper on the OpenReview website discussing the use of deep learning techniques for forecasting extreme weather events such as hurricanes. The paper likely provides an overview of the state of the art in the field, and may provide insights into the use of artificial intelligence for weather prediction. • https://arxiv.org/pdf/1908.00709.pdf: This URL points to a research paper on the arXiv website discussing the use of machine learning techniques for forecasting extreme weather events such as hurricanes. The paper likely provides an overview of the state of the art in the field, and may provide insights into the use of artificial intelligence for weather prediction. • https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021MS002502: This URL points to a research paper on the American Geophysical Union (AGU) website discussing the use of machine learning techniques for forecasting extreme weather events such as hurricanes. The paper likely provides an overview of the state of the art in the field, and may provide insights into the use of artificial intelligence for weather prediction. • https://www.researchgate.net/publication/353697423_Assimilation_of_Polarimetric_Radar_Data_in_Simulation_of_a_Supercell_Storm_with_a_Variational_Approach_and_the_WRF_Model: This URL points to a research paper on ResearchGate discussing the use of machine learning techniques for forecasting extreme weather events such as supercell storms. The paper likely provides an overview of the state of the art in the field, and may provide insights into the use of artificial intelligence for weather prediction.
Miscellaneous resources: • https://www.linkedin.com/posts/tunguz_datascience-machinelearning-artificialintelligence-activity-6989565517728489472-8psL: This URL points to a LinkedIn post discussing data science, machine learning, and artificial intelligence. The post likely provides an overview of these topics and may be useful for those interested in using artificial intelligence for forecasting extreme weather events such as hurricanes. • https://aman.ai/primers/ai/evaluation-metrics/: This URL points to a webpage on the Aman AI website discussing evaluation metrics for artificial intelligence models. Evaluation metrics are used to measure the performance of machine learning models, and may be useful for those interested in using artificial intelligence for forecasting extreme weather events such as hurricanes. The webpage likely provides an overview of various evaluation metrics and their use in artificial intelligence. • https://github.com/isaaccorley/torchrs - a Github repository containing code for a PyTorch library for radar data processing and analysis. • https://debuggercafe.com/satellite-image-classification-using-pytorch-resnet34/ - a tutorial on using a pre-trained PyTorch model for satellite image classification. • https://developer.nvidia.com/blog/explain-your-machine-learning-model-predictions-with-gpu-accelerated-shap/ - a blog post discussing how to explain the predictions of a machine learning model using SHAP (SHapley Additive exPlanation) values. • https://adelaide.cira.colostate.edu/tc/data/ - a webpage for the Colorado State University (CSU) Tropical Cyclones (TC) group, which provides access to various datasets related to tropical cyclones. • https://proceedings.neurips.cc/paper/2020/file/fa78a16157fed00d7a80515818432169-Paper.pdf - a research paper on using artificial intelligence to improve weather forecasting. • https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL089102 - a research paper on using deep learning to improve the prediction of ocean surface wind speed. • https://arxiv.org/pdf/2208.05419.pdf - a research paper on using artificial intelligence to improve the forecasting of extreme weather events. • https://www.olcf.ornl.gov/wp-content/uploads/2021/11/IntroLC-Science-Nov2021-1-8.pdf - a document discussing the use of artificial intelligence in weather forecasting and the challenges and opportunities it presents. • https://github.com/ecmwf-lab/climetlab-tropical-cyclone-dataset/blob/main/climetlab_tc_dataset/__init__.py - a Github repository containing code for a tropical cyclone dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF). • https://github.com/ecmwf-lab/climetlab-tropical-cyclone-dataset/blob/main/climetlab_tc_dataset/labels.py - a Github repository containing code for generating labels for the ECMWF tropical cyclone dataset. • https://www.l3harrisgeospatial.com/docs/ncdf_overview.html - a webpage providing an overview of the NetCDF (Network Common Data Form) file format. • https://zenodo.org/record/4036133 - a webpage for a dataset containing synthetic radar data for training machine learning models. • https://www.noahbrenowitz.com/post/loading_netcdfs/ - a tutorial on how to load NetCDF data in Python using the xarray library. • https://earthinversion.com/utilities/reading-NetCDF4-data-in-python/ - a tutorial on how to read NetCDF data in Python using the NetCDF4 library. • https://notebook.community/lesserwhirls/unidata-python-workshop/ - a tutorial on how to work with NetCDF data in Python using the xarray and PyN • "https://github.com/isaaccorley/torchrs" is a GitHub repository that contains a library for training recurrent neural networks (RNNs) using PyTorch, specifically for use in weather forecasting. • "https://debuggercafe.com/satellite-image-classification-using-pytorch-resnet34/" is a blog post that describes how to use a pre-trained deep learning model (ResNet34) and PyTorch to classify satellite images into different classes (e.g. clouds, water, land). • "https://developer.nvidia.com/blog/explain-your-machine-learning-model-predictions-with-gpu-accelerated-shap/" is a blog post that describes how to use a tool called SHAP to interpret and explain the predictions made by a machine learning model. • "https://adelaide.cira.colostate.edu/tc/data/" is a webpage that provides access to tropical cyclone (hurricane) data, including track and intensity information. • "https://proceedings.neurips.cc/paper/2020/file/fa78a16157fed00d7a80515818432169-Paper.pdf" is a research paper that describes a machine learning model for predicting the intensity of tropical cyclones using a combination of physical and machine learning approaches. • "https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL089102" is a research paper that describes a machine learning approach for predicting the intensity and track of tropical cyclones using a combination of physical and machine learning models. • "https://arxiv.org/pdf/2208.05419.pdf" is a research paper that describes a machine learning approach for predicting the intensity of tropical cyclones using a combination of physical and machine learning models. • "https://www.olcf.ornl.gov/wp-content/uploads/2021/11/IntroLC-Science-Nov2021-1-8.pdf" is a document that provides an introduction to the use of machine learning for tropical cyclone prediction. • "https://github.com/ecmwf-lab/climetlab-tropical-cyclone-dataset/blob/main/climetlab_tc_dataset/__init__.py" is a Python file that is part of a library for working with tropical cyclone data. • "https://github.com/ecmwf-lab/climetlab-tropical-cyclone-dataset/blob/main/climetlab_tc_dataset/labels.py" is a Python file that is part of a library for working with tropical cyclone data. • "https://www.l3harrisgeospatial.com/docs/ncdf_overview.html" is a webpage that provides an overview of the NetCDF file format, which is commonly used for storing and sharing scientific data. • "https://zenodo.org/record/4036133" is a webpage that provides access to a dataset of satellite images and labels for use in machine learning research. • "https://www.noahbrenowitz.com/post/loading_netcdfs/" is a blog post that describes how to load and work with NetCDF files using Python • "https://www.researchgate.net/publication/342048111_A_Novel_Deep_Learning_Approach_for_Tropical_Cyclone_Track_Prediction_Based_on_Auto-Encoder_and_Gated_Recurrent_Unit_Networks/fulltext/5edfa06645851516e661fa1b/A-Novel-Deep-Learning-Approach-for-Tropical-Cyclone-Track-Prediction-Based-on-Auto-Encoder-and-Gated-Recurrent-Unit-Networks.pdf?origin=publication_detail" - This is a research paper discussing the use of a combination of auto-encoder and gated recurrent unit (GRU) neural networks for tropical cyclone track prediction. The authors propose a new approach using these deep learning techniques and evaluate its performance on a dataset of historical tropical cyclone tracks. • "https://journals.ametsoc.org/view/journals/wefo/37/6/WAF-D-21-0116.1.xml" - This is a journal article discussing the use of machine learning techniques for improving the accuracy of extreme weather forecasts. The authors present a case study in which they use a random forest model to predict extreme wind events in the United Kingdom and compare the results to those of a traditional statistical model. • "https://www.frontiersin.org/articles/10.3389/fdata.2020.00001/full" - This is a research article discussing the use of deep learning techniques for improving the accuracy of severe weather forecasts. The authors present a case study in which they use a convolutional neural network (CNN) to predict severe thunderstorm occurrence in the United States and demonstrate the potential for using CNNs in this context. • "https://www.frontiersin.org/articles/10.3389/fbuil.2021.660103/full" - This is a research article discussing the use of machine learning techniques for predicting the impact of extreme weather events on buildings. The authors present a case study in which they use a random forest model to predict the damage potential of tropical cyclones in the United States and discuss the potential for using this approach to inform risk assessment and disaster management strategies. • "https://link.medium.com/mfV506soutb" - This is a blog post discussing the use of machine learning techniques for predicting extreme weather events. The authors present a case study in which they use a gradient boosting model to predict extreme wind events in the United States and discuss the potential for using this approach to improve weather forecasting and disaster management. • "https://www.splunk.com/en_us/blog/security/deep-learning-with-splunk-and-tensorflow-for-security-catching-the-fraudster-in-neural-networks-with-behavioral-biometrics.html" - This is a blog post discussing the use of deep learning techniques for detecting fraud in financial transactions. The authors present a case study in which they use a neural network trained with TensorFlow to detect fraudulent activity and discuss the potential for using this approach in a variety of security and fraud detection applications. • "Historical_Tropical_Storm_Tracks.zip" - This is a ZIP file containing a dataset of historical tropical storm tracks. This dataset may be useful for researchers looking to train machine learning models for tropical cyclone track prediction or other applications related to extreme weather • Foundation URL: https://github.com/Alamofire/Foundation Description: This is a library for interacting with HTTP and HTTPS web services in Swift. It is not directly related to AI for weather forecasting, but it could potentially be used to retrieve and process data from online sources that may be relevant to this research. • Stable Diffusion in GPT URL: https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb#scrollTo=zHkHsdtnry57 Description: This is a notebook that demonstrates the use of a technique called "stable diffusion" to improve the performance of a language model called GPT (Generative Pre-training Transformer). This technique could potentially be used to improve the accuracy of AI models that are used for weather forecasting. • Ensemble Learning for Weather Forecasting URL: https://arxiv.org/pdf/2110.14144.pdf Description: This is a research paper that discusses the use of ensemble learning, a machine learning technique that combines the predictions of multiple models, for weather forecasting. It presents a new method for constructing an ensemble of models based on deep neural networks, and demonstrates its effectiveness on a number of different weather forecasting tasks. • Comparison of Deep Learning and Numerical Weather Prediction URL: https://www.researchgate.net/profile/Amirpasha-Mozaffari/publication/349311952_Can_deep_learning_beat_numerical_weather_prediction/links/602a3899a6fdcc37a8298531/Can-deep-learning-beat-numerical-weather-prediction.pdf?origin=publication_detail Description: This research paper compares the performance of deep learning and numerical weather prediction methods on a range of weather forecasting tasks. It discusses the strengths and weaknesses of each approach, and suggests that both can be useful for different types of weather forecasting problems. • Deep Learning for Tropical Cyclone Intensity Prediction URL: https://arxiv.org/pdf/2005.04988.pdf Description: This is a research paper that presents a deep learning model for predicting the intensity of tropical cyclones. The model is trained on a large dataset of cyclone track and intensity data, and is shown to outperform other methods on a number of different evaluation metrics. This could be a useful resource for researchers interested in using AI to forecast extreme weather events such as tropical cyclones. • Pre-trained Model for Hurricane Prediction URL: https://huggingface.co/monkseal555/autotrain-hurricane3-1415853436 Description: This is a pre-trained machine learning model for predicting the track and intensity of hurricanes. It is based on the Transformer architecture and was trained on a large dataset of hurricane data. This could be a useful resource for researchers looking to use AI to forecast severe weather events such as hurricanes. • Forecast Knowledge Base (FKB) URL: https://github.com/scientific-computing/FKB Description: The Forecast Knowledge Base (FKB) is a tool for storing and organizing weather forecasts and other related data. It includes a number of notebooks and scripts that can be used to retrieve and process data from online sources, as well as tools for visualizing and analyzing forecast data. This could be a useful resource for researchers working on weather forecasting • Subject: Foundation for Alamofire URL: https://github.com/Alamofire/Foundation Description: This is a GitHub repository for the Foundation framework that powers Alamofire, a popular networking library for iOS and macOS. It contains the source code for the framework and information on how to use it. This resource could be helpful for researchers looking to incorporate networking capabilities into their AI weather forecasting applications. • Subject: Stable Diffusion Notebook on Hugging Face URL: https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb#scrollTo=zHkHsdtnry57 Description: This is a Google Colaboratory notebook hosted on Hugging Face's GitHub page. It demonstrates how to use stable diffusion, a technique for improving the performance of transformer models, on the WikiText-2 language modeling dataset. This resource could be useful for researchers interested in improving the accuracy of their AI weather forecasting models through the use of transformer architectures and stable diffusion. • Subject: Deep Learning for Precipitation Nowcasting URL: https://arxiv.org/pdf/2110.14144.pdf Description: This is a preprint paper on arXiv discussing the use of deep learning for precipitation nowcasting, or predicting short-term rainfall. The authors propose a new architecture called the Temporal Attention Network (TAN) and demonstrate its effectiveness on several datasets. This resource could be useful for researchers interested in using deep learning to improve the accuracy of their AI weather forecasting systems for precipitation. • Subject: Comparison of Deep Learning and Numerical Weather Prediction URL: https://www.researchgate.net/profile/Amirpasha-Mozaffari/publication/349311952_Can_deep_learning_beat_numerical_weather_prediction/links/602a3899a6fdcc37a8298531/Can-deep-learning-beat-numerical-weather-prediction.pdf?origin=publication_detail Description: This is a research paper available on ResearchGate that compares the performance of deep learning and numerical weather prediction (NWP) approaches for forecasting temperature, precipitation, and wind. The authors find that deep learning outperforms NWP for temperature forecasting, but the results for precipitation and wind are mixed. This resource could be useful for researchers considering using deep learning for weather forecasting and wanting to understand the relative strengths and weaknesses of the two approaches. • Subject: Deep Learning for Seasonal Hurricane Forecasting URL: https://arxiv.org/pdf/2005.04988.pdf Description: This is a preprint paper on arXiv that presents a deep learning approach for forecasting the number of tropical cyclones (TCs) and their intensity in the following hurricane season. The authors use a combination of convolutional and long short-term memory (LSTM) networks and demonstrate the effectiveness of their approach on several datasets. This resource could be useful for researchers interested in using deep learning to improve seasonal hurricane forecasting.

https://events.ecmwf.int/event/172/contributions/1697/attachments/834/1483/ML-Earth-Obs-WS-Hall.pdf

NASA’s impressive new AI can predict when a hurricane intensifies

Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast With Machine Learning - Su - 2020 - Geophysical Research Letters - Wiley Online Library

https://ieeexplore.ieee.org/ielx7/4609443/8994817/09149719.pdf?tp=&arnumber=9149719&isnumber=8994817&ref=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8=

https://michael.hahsler.net/research/paper/IIE2014_RII.pdf

Artificial intelligence brings better hurricane predictions

http://ceur-ws.org/Vol-2466/paper2.pdf

Improving the Feature Selection Process for Tropical Cyclone Rapid Intensification Guidance

https://medium.com/@todd_39540/machine-learning-for-rapid-intensification-of-hurricanes-f499176108b4

https://opensky.ucar.edu/islandora/object/conference%3A3522/datastream/PDF/download/New_frameworks_for_predicting_extreme_rapid_intensification.citation

https://mdpi-res.com/d_attachment/water/water-12-02685/article_deploy/water-12-02685.pdf?version=1601039716

Applied Sciences | Free Full-Text | A Novel Deep Learning Based Model for Tropical Intensity Estimation and Post-Disaster Management of Hurricanes

https://www.jstage.jst.go.jp/article/sola/15/0/15_2019-034/_pdf/-char/en

https://ieeexplore.ieee.org/document/9387367/

https://ieeexplore.ieee.org/document/9643042/

Deep Learning for Typhoon Intensity Classification Using Satellite Cloud Images in: Journal of Atmospheric and Oceanic Technology Volume 39 Issue 1 (2022)

Remote Sensing | Free Full-Text | A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite Imagery

Investigation of Machine Learning Using Satellite-Based Advanced Dvorak Technique Analysis Parameters to Estimate Tropical Cyclone Intensity in: Weather and Forecasting Volume 36 Issue 6 (2021)

https://ieeexplore.ieee.org/document/9399663/

GitHub - cherry-and-leaves/TCICENet

TCICENet/WXJ_data_devide_3FL.py at master · cherry-and-leaves/TCICENet · GitHub

TCICENet/attention_module.py at master · cherry-and-leaves/TCICENet · GitHub

TCICENet/Inception_resnet_v2.py at master · cherry-and-leaves/TCICENet · GitHub

GitHub - kobiso/CBAM-tensorflow: CBAM implementation on TensowFlow

https://ieeexplore.ieee.org/document/9320562/

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