Daily-Scale Meteorological Normalization of Surface Solar Radiation in Varying Pollution Levels: A Statistical Case Study in Beijing (2015–2019).
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| Title: | Daily-Scale Meteorological Normalization of Surface Solar Radiation in Varying Pollution Levels: A Statistical Case Study in Beijing (2015–2019). |
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| Authors: | Wu, Tong1 (AUTHOR), Li, Zhigang2 (AUTHOR) lizg@zzu.edu.cn, Zhou, Xueying1 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1368. 30p. |
| Subjects: | Random forest algorithms, Multiple regression analysis, Solar radiation, Atmospheric aerosols, Air pollution |
| Geographic Terms: | Beijing (China) |
| Abstract: | Highlights: What are the main findings? Meteorological controls on daily surface solar radiation in Beijing vary systematically with pollution level, with cloud cover and RH showing strong associations and diffuse radiation exhibiting the clearest pollution-dependent shift. RF reproduced daily radiation components with strong predictive performance (R2 = 0.83–0.88), and RF- and MLR-derived adjusted anomalies showed broadly consistent temporal variations (r = 0.63–0.78). What are the implications of the main findings? Daily radiation–pollution relationships are strongly confounded by meteorological variability, so meteorological influences should be explicitly accounted for in daily radiation analyses. RF-based meteorological normalization is a practical tool for daily station radiation records and may be extended to multi-site analyses and finer temporal resolution. Surface solar radiation at the ground is affected by aerosols, clouds, and atmospheric moisture, as well as by circulation-related conditions that influence cloud formation and pollutant transport. In daily observations, these influences are mixed, which makes pollution-related variability difficult to interpret. We analyzed data from Beijing station 54511 (2015–2019), including daily integrated radiation components and collocated meteorological and pollution variables. We used wavelet coherence, pollution-stratified association analysis, and gray relational analysis, and compared two meteorological normalization methods: multiple linear regression (MLR) and random forest (RF). The results show that meteorological–radiation relationships vary systematically across pollution levels, indicating substantial meteorological confounding in daily radiation analyses. Among the radiation components, DR shows the clearest pollution-dependent shift in its relationship with RH, while several direct components become less sensitive to cloud cover under heavier pollution. RF reproduced daily radiation components with strong predictive performance (R2 = 0.83–0.88), and the meteorologically adjusted anomalies from RF were consistent with those from MLR (r = 0.63–0.78 across components). These findings suggest that both MLR and RF can be effectively used to normalize meteorological effects in daily station records. The analysis supports routine interpretation of day-to-day surface radiation variability and can be extended to multi-site studies and finer temporal resolution. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI 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: 193715399 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Daily-Scale Meteorological Normalization of Surface Solar Radiation in Varying Pollution Levels: A Statistical Case Study in Beijing (2015–2019). – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wu%2C+Tong%22">Wu, Tong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Zhigang%22">Li, Zhigang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> lizg@zzu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Xueying%22">Zhou, Xueying</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 9, p1368. 30p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+regression+analysis%22">Multiple regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Solar+radiation%22">Solar radiation</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+aerosols%22">Atmospheric aerosols</searchLink><br /><searchLink fieldCode="DE" term="%22Air+pollution%22">Air pollution</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Beijing+%28China%29%22">Beijing (China)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? Meteorological controls on daily surface solar radiation in Beijing vary systematically with pollution level, with cloud cover and RH showing strong associations and diffuse radiation exhibiting the clearest pollution-dependent shift. RF reproduced daily radiation components with strong predictive performance (R2 = 0.83–0.88), and RF- and MLR-derived adjusted anomalies showed broadly consistent temporal variations (r = 0.63–0.78). What are the implications of the main findings? Daily radiation–pollution relationships are strongly confounded by meteorological variability, so meteorological influences should be explicitly accounted for in daily radiation analyses. RF-based meteorological normalization is a practical tool for daily station radiation records and may be extended to multi-site analyses and finer temporal resolution. Surface solar radiation at the ground is affected by aerosols, clouds, and atmospheric moisture, as well as by circulation-related conditions that influence cloud formation and pollutant transport. In daily observations, these influences are mixed, which makes pollution-related variability difficult to interpret. We analyzed data from Beijing station 54511 (2015–2019), including daily integrated radiation components and collocated meteorological and pollution variables. We used wavelet coherence, pollution-stratified association analysis, and gray relational analysis, and compared two meteorological normalization methods: multiple linear regression (MLR) and random forest (RF). The results show that meteorological–radiation relationships vary systematically across pollution levels, indicating substantial meteorological confounding in daily radiation analyses. Among the radiation components, DR shows the clearest pollution-dependent shift in its relationship with RH, while several direct components become less sensitive to cloud cover under heavier pollution. RF reproduced daily radiation components with strong predictive performance (R2 = 0.83–0.88), and the meteorologically adjusted anomalies from RF were consistent with those from MLR (r = 0.63–0.78 across components). These findings suggest that both MLR and RF can be effectively used to normalize meteorological effects in daily station records. The analysis supports routine interpretation of day-to-day surface radiation variability and can be extended to multi-site studies and finer temporal resolution. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI 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: 10.3390/rs18091368 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 30 StartPage: 1368 Subjects: – SubjectFull: Random forest algorithms Type: general – SubjectFull: Multiple regression analysis Type: general – SubjectFull: Solar radiation Type: general – SubjectFull: Atmospheric aerosols Type: general – SubjectFull: Air pollution Type: general – SubjectFull: Beijing (China) Type: general Titles: – TitleFull: Daily-Scale Meteorological Normalization of Surface Solar Radiation in Varying Pollution Levels: A Statistical Case Study in Beijing (2015–2019). Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wu, Tong – PersonEntity: Name: NameFull: Li, Zhigang – PersonEntity: Name: NameFull: Zhou, Xueying IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 9 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
