Examining the Impact of Different Uncrewed Aerial System (UAS) Sampling Strategies on Fog Forecasts in the Ohio Valley Using Observing System Simulation Experiments (OSSEs).
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| Title: | Examining the Impact of Different Uncrewed Aerial System (UAS) Sampling Strategies on Fog Forecasts in the Ohio Valley Using Observing System Simulation Experiments (OSSEs). |
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| Authors: | Wilson, Matthew B.1 (AUTHOR) mawilson@ucar.edu, Pinto, James1 (AUTHOR), Colavito, Jenny2 (AUTHOR) |
| Source: | Weather & Forecasting. May2026, Vol. 41 Issue 5, p1-25. 25p. |
| Subjects: | Weather forecasting, Data assimilation, Sampling methods, Atmospheric boundary layer, Drone aircraft, Aeronautical safety measures |
| Geographic Terms: | Ohio River Valley |
| Abstract: | Dense fog events can be highly impactful, causing major airport delays, aviation accidents, and highway pileups. However, they are often difficult to predict due in part to a lack of dense spatiotemporal sampling of the nocturnal planetary boundary layer (PBL). Uncrewed Aerial Systems (UAS) represent a potential source of such dense PBL observations; however, the number of UAS needed for a positive forecast impact and the best sampling strategies to pursue with them for the greatest positive impact on a model forecast are still unknown. Using a series of Observing System Simulation Experiments (OSSEs) focusing on three impactful fog events in the Ohio Valley, this study compares the impacts of two different UAS sampling strategies: a 3-D mesonet-like experiment with profiling UAS located at selected surface observing sites and an experiment testing the impact of observations collected by a network of delivery drones. Results indicate that assimilating UAS observations collected with both sampling strategies similarly improves fog forecast skill in the OSSEs. However, the profiling UAS experiment led to larger improvements in analyses and forecasts of temperature and dewpoint aloft, suggesting that this network configuration may be more useful for improving predictions of aviation hazards that may be more dependent on the accuracy of the analyses well above the surface, such as low ceilings, low-level wind shear, precipitation phase, and the initiation and evolution of deep convection. [ABSTRACT FROM AUTHOR] |
| Copyright of Weather & Forecasting is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194578061 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Examining the Impact of Different Uncrewed Aerial System (UAS) Sampling Strategies on Fog Forecasts in the Ohio Valley Using Observing System Simulation Experiments (OSSEs). – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wilson%2C+Matthew+B%2E%22">Wilson, Matthew B.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mawilson@ucar.edu</i><br /><searchLink fieldCode="AR" term="%22Pinto%2C+James%22">Pinto, James</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Colavito%2C+Jenny%22">Colavito, Jenny</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Weather+%26+Forecasting%22">Weather & Forecasting</searchLink>. May2026, Vol. 41 Issue 5, p1-25. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Weather+forecasting%22">Weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Data+assimilation%22">Data assimilation</searchLink><br /><searchLink fieldCode="DE" term="%22Sampling+methods%22">Sampling methods</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+boundary+layer%22">Atmospheric boundary layer</searchLink><br /><searchLink fieldCode="DE" term="%22Drone+aircraft%22">Drone aircraft</searchLink><br /><searchLink fieldCode="DE" term="%22Aeronautical+safety+measures%22">Aeronautical safety measures</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Ohio+River+Valley%22">Ohio River Valley</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Dense fog events can be highly impactful, causing major airport delays, aviation accidents, and highway pileups. However, they are often difficult to predict due in part to a lack of dense spatiotemporal sampling of the nocturnal planetary boundary layer (PBL). Uncrewed Aerial Systems (UAS) represent a potential source of such dense PBL observations; however, the number of UAS needed for a positive forecast impact and the best sampling strategies to pursue with them for the greatest positive impact on a model forecast are still unknown. Using a series of Observing System Simulation Experiments (OSSEs) focusing on three impactful fog events in the Ohio Valley, this study compares the impacts of two different UAS sampling strategies: a 3-D mesonet-like experiment with profiling UAS located at selected surface observing sites and an experiment testing the impact of observations collected by a network of delivery drones. Results indicate that assimilating UAS observations collected with both sampling strategies similarly improves fog forecast skill in the OSSEs. However, the profiling UAS experiment led to larger improvements in analyses and forecasts of temperature and dewpoint aloft, suggesting that this network configuration may be more useful for improving predictions of aviation hazards that may be more dependent on the accuracy of the analyses well above the surface, such as low ceilings, low-level wind shear, precipitation phase, and the initiation and evolution of deep convection. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Weather & Forecasting is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1175/WAF-D-25-0140.1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 1 Subjects: – SubjectFull: Weather forecasting Type: general – SubjectFull: Data assimilation Type: general – SubjectFull: Sampling methods Type: general – SubjectFull: Atmospheric boundary layer Type: general – SubjectFull: Drone aircraft Type: general – SubjectFull: Aeronautical safety measures Type: general – SubjectFull: Ohio River Valley Type: general Titles: – TitleFull: Examining the Impact of Different Uncrewed Aerial System (UAS) Sampling Strategies on Fog Forecasts in the Ohio Valley Using Observing System Simulation Experiments (OSSEs). Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wilson, Matthew B. – PersonEntity: Name: NameFull: Pinto, James – PersonEntity: Name: NameFull: Colavito, Jenny IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08828156 Numbering: – Type: volume Value: 41 – Type: issue Value: 5 Titles: – TitleFull: Weather & Forecasting Type: main |
| ResultId | 1 |
