Data-Driven Sparse Sensor Selection for Observing-Network Optimization and Its Impact on Data Assimilation.
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| Title: | Data-Driven Sparse Sensor Selection for Observing-Network Optimization and Its Impact on Data Assimilation. |
|---|---|
| Authors: | Ikuta, Yasutaka1 (AUTHOR) yasutaka.ikuta@mri-jma.go.jp, Seko, Hiromu1 (AUTHOR) |
| Source: | Monthly Weather Review. May2026, Vol. 154 Issue 5, p1-15. 15p. |
| Subjects: | Data assimilation, Sensor placement, Meteorological observations, Numerical weather forecasting, Precipitable water |
| Geographic Terms: | Japan |
| Abstract: | This study primarily focuses on the optimized planning of observational networks, using data assimilation and numerical weather prediction (NWP) as evaluation frameworks. The target observations are ground-based Global Navigation Satellite System (GNSS) precipitable water vapor (PWV) retrievals currently operated in Japan. Using a greedy sparse sensor placement algorithm, 500 operational GNSS observation sites were ranked according to their contribution to minimizing the domain-averaged PWV reconstruction error over a ten-year period, with respect to the NWP analysis fields used as initial condition in Japan's operational NWP system. The ranked observation sites were divided into three groups—top, middle, and bottom 100—and each group was assimilated separately into the data assimilation system to evaluate its impact. Assimilating the top 100 sites yielded better results than assimilating the middle or bottom 100 sites. Significant improvements were found in the PWV analysis field, mid- to lower-tropospheric humidity, and temperature throughout the troposphere. Although the magnitude of improvement decreased with forecast lead time, assimilation of the top-ranked sites led to statistically significant gains in PWV forecasts and maintained significant improvements in humidity and temperature at multiple vertical levels. These results demonstrate that assimilation of objectively selected, high-ranked observations consistently enhances forecast skill compared with lower-ranked observations. Previous studies have rarely examined whether objectively ranked sparse subsets of observations can produce meaningful impacts within operational systems. The novelty of this study lies in linking climatology-based sparse sensor ranking with an operational data assimilation framework. [ABSTRACT FROM AUTHOR] |
| Copyright of Monthly Weather Review 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194578197 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Data-Driven Sparse Sensor Selection for Observing-Network Optimization and Its Impact on Data Assimilation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ikuta%2C+Yasutaka%22">Ikuta, Yasutaka</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yasutaka.ikuta@mri-jma.go.jp</i><br /><searchLink fieldCode="AR" term="%22Seko%2C+Hiromu%22">Seko, Hiromu</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Monthly+Weather+Review%22">Monthly Weather Review</searchLink>. May2026, Vol. 154 Issue 5, p1-15. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Data+assimilation%22">Data assimilation</searchLink><br /><searchLink fieldCode="DE" term="%22Sensor+placement%22">Sensor placement</searchLink><br /><searchLink fieldCode="DE" term="%22Meteorological+observations%22">Meteorological observations</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+weather+forecasting%22">Numerical weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Precipitable+water%22">Precipitable water</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Japan%22">Japan</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study primarily focuses on the optimized planning of observational networks, using data assimilation and numerical weather prediction (NWP) as evaluation frameworks. The target observations are ground-based Global Navigation Satellite System (GNSS) precipitable water vapor (PWV) retrievals currently operated in Japan. Using a greedy sparse sensor placement algorithm, 500 operational GNSS observation sites were ranked according to their contribution to minimizing the domain-averaged PWV reconstruction error over a ten-year period, with respect to the NWP analysis fields used as initial condition in Japan's operational NWP system. The ranked observation sites were divided into three groups—top, middle, and bottom 100—and each group was assimilated separately into the data assimilation system to evaluate its impact. Assimilating the top 100 sites yielded better results than assimilating the middle or bottom 100 sites. Significant improvements were found in the PWV analysis field, mid- to lower-tropospheric humidity, and temperature throughout the troposphere. Although the magnitude of improvement decreased with forecast lead time, assimilation of the top-ranked sites led to statistically significant gains in PWV forecasts and maintained significant improvements in humidity and temperature at multiple vertical levels. These results demonstrate that assimilation of objectively selected, high-ranked observations consistently enhances forecast skill compared with lower-ranked observations. Previous studies have rarely examined whether objectively ranked sparse subsets of observations can produce meaningful impacts within operational systems. The novelty of this study lies in linking climatology-based sparse sensor ranking with an operational data assimilation framework. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Monthly Weather Review 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194578197 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1175/MWR-D-25-0267.1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1 Subjects: – SubjectFull: Data assimilation Type: general – SubjectFull: Sensor placement Type: general – SubjectFull: Meteorological observations Type: general – SubjectFull: Numerical weather forecasting Type: general – SubjectFull: Precipitable water Type: general – SubjectFull: Japan Type: general Titles: – TitleFull: Data-Driven Sparse Sensor Selection for Observing-Network Optimization and Its Impact on Data Assimilation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ikuta, Yasutaka – PersonEntity: Name: NameFull: Seko, Hiromu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00270644 Numbering: – Type: volume Value: 154 – Type: issue Value: 5 Titles: – TitleFull: Monthly Weather Review Type: main |
| ResultId | 1 |
