[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125300-en":3,"doc-seo-125300-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125300,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning for Improvement of Ocean Data Resolution - Weather Forecasting and Climatological Research","Severe weather events such as hurricanes and tornadoes demand more accurate forecasts, yet forecast skill is constrained by insufficient high-resolution initial ocean and atmospheric conditions. Focusing on the Atlantic “Hurricane Alley,” the research integrates robust in-situ measurements with complementary satellite products, highlighting radio occultation as the most accurate 5–25 km atmospheric source while noting a performance gap below 5 km. The work develops a satellite-informed data-driven system and a graph neural network to fuse sparse high-quality ocean data with abundant lower-quality satellite data, producing improved-resolution estimates.","Machine Learning for Improvement of Ocean Data Resolution, for Weather Forecasting and Climatological Research  \nMd Nurul Huda  \nDissertation submitted to the Faculty of the  \nVirginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy  \nin  \nAerospace Engineering  \nEric G Paterson, Chair  \nScott Leslie England  \nStefano Brizzolara  \nScott M Bailey  \nBlacksburg, Virginia  \nKeywords: Multi-fidelity data assimilation, GEE satellite data, ARGO Floats, Numerical weather prediction (NWP), Geo-Informed ML, GNN  \nCopyright 2023, Md Nurul Huda  \nMachine Learning for Improvement of Ocean Data Resolution, for Weather Forecasting and Climatological Research  \nMd Nurul Huda  \n(ABSTRACT)  \nSevere weather events like hurricanes and tornadoes pose major risks globally, underscoring the critical need for accurate forecasts to mitigate impacts. While advanced computational capabilities and climate models have improved predictions, lack of high-resolution initial conditions still limits forecast accuracy. The Atlantic’s ”Hurricane Alley” region sees most storms arise, thus needing robust in-situ ocean data plus atmospheric profiles to enable precise hurricane tracking and intensity forecasts. Examining satellite datasets reveals radio occultation (RO) provides the most accurate 5-25 km altitude atmospheric measurements. However, below 5 km accuracy remains insufficient over oceans versus land areas. Some recent benchmark study e.g. Patil Iiyama (2022), and Wei Guan (2022) in their work proposed the use of deep learning models for sea surface temperature (SST) prediction in the Tohoku region with very low errors ranging from 0.35°C to 0.75°C and the root-mean-square error increases from 0 .27°C to 0 .53°C over the over the China seas respectively. The approach we have developed remains unparalleled in its domain as of this date. This research is divided into two parts and aims to develop a data driven satellite-informed machine learning system to combine high-quality but sparse in-situ ocean data with more readily available low-quality satellite data. In the first part of the work, a novel data-driven satellite-informed machine learning algorithm was implemented that combines High-Quality/Low-Coverage in-situ point ocean data (e.g. ARGO Floats) and Low-Quality/High-Coverage Satellite ocean Data (e.g. HYCOM, MODIS-Aqua, G-COM) and generated high resolution data with a RMSE of  \n0.58◦ C over the Atlantic Ocean.The second part of the work a novel GNN algorithm was implemented on the Gulf of Mexico and showed it can successfully capture the complex interactions between the ocean and mimic the path of a ARGO floats with a RMSE of 1.40◦ C.  \nMachine Learning for Improvement of Ocean Data Resolution, for Weather Forecasting and Climatological Research  \nMd Nurul Huda  \n(GENERAL AUDIENCE ABSTRACT)  \nSevere weather like hurricanes and tornadoes are dangerous across the world. This shows we urgently need good forecasts to reduce harm. New supercomputers and climate models have improved predictions. But lack of detailed starting conditions still limits forecast accuracy. The Atlantic’s ”Hurricane Alley” has most storms. So we need lots of ocean data plus atmospheric profiles there to track hurricanes precisely and forecast strength. Studying satellite data shows radio occultation gives the most accurate 5-25 km high air measurements. But under 5 km accuracy is still poor over oceans versus land. Some recent studies like Patil Iiyama (2022) and Wei Guan (2022) used deep learning for sea surface temperature prediction with very low errors of 0 .35-0.75°C in certain regions. Our approach is unmatched so far. This research has two parts. First we made a data-driven satellite-informed machine learning system. It combines sparse high-quality ocean data with more available low-quality satellite data. We got high resolution data with 0 .58◦ C error over the Atlantic. Second we used a novel","cbCainsuioJrRI03","https://ap.wps.com/l/cbCainsuioJrRI03","pdf",22942054,1,148,"English","en",105,"# Introduction\n# Literature Review\n## Traditional Approaches for Weather Prediction\n## Machine Learning for Weather Prediction\n## GNN for Weather Prediction\n# Data Assimilation\n## Argo\n## HYCOM\n## MODIS Aqua\n## GCOM-C\n## Data Extraction\n## Statistical Insights\n## Feature Selection\n# Research Methodology\n## Combining Satellite Image and Argo Floats\n## Spatio-Temporal Graph Neural Networks\n# Results","[{\"question\":\"Why are high-resolution initial conditions important for weather forecasting in this research?\",\"answer\":\"Accurate hurricane tracking and intensity forecasting depend on detailed starting conditions. Limited high-resolution ocean and atmospheric inputs reduce forecast accuracy even with advanced computing and climate models.\"},{\"question\":\"What data sources does the study combine to improve ocean data resolution?\",\"answer\":\"The approach fuses sparse high-quality in-situ point ocean data (e.g., ARGO floats) with more available lower-quality satellite ocean data such as HYCOM, MODIS-Aqua, and GCOM-C.\"},{\"question\":\"What is the role of the graph neural network (GNN) in the second part of the research?\",\"answer\":\"The GNN is applied to the Gulf of Mexico to learn complex ocean–atmosphere interactions. It is designed to mimic ARGO float trajectories and reduce prediction error.\"}]","Machine Learning for Improvement of Ocean Data Resolution - Weather Forecasting and Climatological Research | PDF",1785898055,373,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-improvement-of-ocean-data-resolution-weather-forecasting-and-climatological-research","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-improvement-of-ocean-data-resolution-weather-forecasting-and-climatological-research/125300/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are high-resolution initial conditions important for weather forecasting in this research?","Question",{"text":75,"@type":76},"Accurate hurricane tracking and intensity forecasting depend on detailed starting conditions. Limited high-resolution ocean and atmospheric inputs reduce forecast accuracy even with advanced computing and climate models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data sources does the study combine to improve ocean data resolution?",{"text":80,"@type":76},"The approach fuses sparse high-quality in-situ point ocean data (e.g., ARGO floats) with more available lower-quality satellite ocean data such as HYCOM, MODIS-Aqua, and GCOM-C.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the role of the graph neural network (GNN) in the second part of the research?",{"text":84,"@type":76},"The GNN is applied to the Gulf of Mexico to learn complex ocean–atmosphere interactions. 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