{"id":352414,"date":"2024-11-26T17:28:38","date_gmt":"2024-11-26T16:28:38","guid":{"rendered":"https:\/\/climatescience.press\/?p=352414"},"modified":"2024-11-26T17:28:41","modified_gmt":"2024-11-26T16:28:41","slug":"improving-hurricane-modeling-with-physics-informed-machine-learning","status":"publish","type":"post","link":"https:\/\/climatescience.press\/?p=352414","title":{"rendered":"Improving hurricane modeling with physics-informed machine learning"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

<\/p>\n\n\n\n

From Watts Up With That?<\/a><\/p>\n\n\n\n

By Anthony Watts<\/a><\/p>\n\n\n\n

Algorithm reconstructs wind fields quickly, accurately, and with less observational data.<\/em><\/p>\n\n\n\n

Via Eurekalert, maybe this will work, maybe not. Worth a try.

WASHINGTON, Nov. 19, 2024 \u2013 Hurricanes, or tropical cyclones, can be devastating natural disasters, leveling entire cities and claiming hundreds or thousands of lives. A key aspect of their destructive potential is their unpredictability. Hurricanes are complex weather phenomena, and how strong one will be or where it will make landfall is difficult to estimate.<\/p>\n\n\n\n

In a paper published this week in Physics of Fluids<\/em>, by AIP Publishing, a pair of researchers from the City University of Hong Kong employed machine learning to more accurately model the boundary layer wind field of tropical cyclones.<\/p>\n\n\n\n

In atmospheric science, the boundary layer of the atmosphere is the region closest to the Earth\u2019s surface.<\/p>\n\n\n\n

\u201cWe human beings are living in this boundary layer, so understanding and accurately modeling it is essential for storm forecasting and hazard preparedness,\u201d said author Qiusheng Li.<\/p>\n\n\n\n

However, because air in the boundary layer interacts with land, the ocean, and everything else at surface level, modeling it is especially challenging. Conventional approaches to storm forecasting involve large numerical simulations run on supercomputers incorporating mountains of observational data, and they still often result in inaccurate or incomplete predictions.<\/p>\n\n\n\n

In contrast, the author\u2019s machine learning algorithm is equipped with atmospheric physics equations that can produce more accurate results faster and with less data.<\/p>\n\n\n\n

\u201cUnlike traditional numerical models, our model employs an advanced physics-informed machine learning framework,\u201d said author Feng Hu. \u201cOnly a small amount of real data is required by our model to capture the complex behavior of the wind field of tropical cyclones. The model\u2019s flexibility and ability to integrate sparse observational data result in more accurate and realistic reconstructions.\u201d<\/p>\n\n\n\n

Being able to reconstruct a tropical cyclone\u2019s wind field provides valuable data that experts can use to determine how severe the storm will be.<\/p>\n\n\n

\n
\"\"
Wind fields modeled by the authors\u2019 physics-informed neural network (PINN) produces similar results to a Weather Research & Forecasting (WRF) simulation while using far fewer resources. Credit:
Feng Hu and Qiusheng Li<\/figcaption><\/figure>\n<\/div>\n\n\n

\u201cThe wind field of a tropical cyclone contains the information of the storm\u2019s intensity, structure, and potential impact on coastal regions,\u201d said Li.<\/p>\n\n\n\n

With a more detailed picture of what that wind field looks like, disaster authorities can better prepare for storms before they make landfall.<\/p>\n\n\n\n

\u201cWith more frequent and intense hurricanes due to climate change, our model could significantly improve the accuracy of wind field predictions,\u201d said Hu. \u201cThis advancement can help refine weather forecasts and risk assessments, providing timely warnings and enhancing the resilience of coastal communities and infrastructure. \u201c<\/p>\n\n\n\n

The authors are planning to continue to develop their model and employ it to study different types of storms.<\/p>\n\n\n\n

\u201cWe are planning to incorporate more observational data sources and improve the model\u2019s capability to handle the time evolution of winds,\u201d said Hu. \u201cExpanding the application to more storm events across the world and integrating the model into real-time forecasting systems is also planned to enhance its utility for weather prediction and risk management.\u201d<\/p>\n\n\n\n

###<\/p>\n\n\n\n

The article \u201cReconstruction of tropical cyclone boundary layer wind field using physics-informed machine learning\u201d is authored by Feng Hu and Qiusheng Li. It will appear in Physics of Fluids<\/em> on Nov. 19, 2024 (DOI: 10.1063\/5.0234728). After that date, it can be accessed at https:\/\/doi.org\/10.1063\/5.0234728<\/a>.<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Algorithm reconstructs wind fields quickly, accurately, and with less observational data.<\/p>\n

Via Eurekalert, maybe this will work, maybe not. Worth a try.<\/p>\n","protected":false},"author":121246920,"featured_media":352419,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_crdt_document":"","advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[1],"tags":[691831683,691831682,691818104,691825233],"class_list":{"0":"post-352414","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","6":"hentry","7":"category-uncategorized","8":"tag-city-university-of-hong-kong","9":"tag-hurricane-modeling","10":"tag-hurricanes","11":"tag-tropical-cyclones","13":"fallback-thumbnail"},"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/11\/01200px-Hurricane_Isabel_from_ISS.jpg?fit=1200%2C794&ssl=1","jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paxLW1-1tG6","jetpack-related-posts":[{"id":300680,"url":"https:\/\/climatescience.press\/?p=300680","url_meta":{"origin":352414,"position":0},"title":"Hurricane Category 6?","author":"uwe.roland.gross","date":"08\/02\/2024","format":false,"excerpt":"The\u00a0PNAS\u00a0reasoning was that a few Pacific Hurricanes (aka tropical cyclones) have exceeded SF level 5. 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