Enhancing Wildfire and Smoke Forecasting by Integrating Fire Observations: A Comparative Analysis of Methods for Integrating Infrared and Satellite Data Into a Coupled Fire‐Atmosphere Model.
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| Title: | Enhancing Wildfire and Smoke Forecasting by Integrating Fire Observations: A Comparative Analysis of Methods for Integrating Infrared and Satellite Data Into a Coupled Fire‐Atmosphere Model. |
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| Authors: | Clough, Kathleen1 (AUTHOR), Farguell, Angel1 (AUTHOR), Mandel, Jan2 (AUTHOR), Hilburn, Kyle3 (AUTHOR), Kochanski, Adam1 (AUTHOR) adam.kochanski@sjsu.edu |
| Source: | Journal of Geophysical Research. Atmospheres. Jun2025, Vol. 130 Issue 12, p1-19. 19p. |
| Subject Terms: | *Wildfires, *Particulate matter, *Air quality, Data assimilation, Remote-sensing images |
| Abstract: | Accurate forecasts of fire spread and smoke impacts using coupled fire‐atmosphere models require advanced methods for fire initialization. This paper proposes and tests three different methods for integrating fire observations. The methods reconstruct the initial fire evolution needed for the atmosphere spin‐up at the beginning of the simulation and identify actively burning fire regions. One method is based solely on IR perimeters, while the other two leverage IR perimeters and satellite detections. One uses only the most recent satellite data, while the other contextualized method uses two consecutive satellite detections to identify which sections of the fire perimeter experienced significant growth and which were inactive. This integration method addresses the problem of inaccurate identification of actively burning regions, thus correcting the previously seen overestimated growth in real‐time forecasts. This problem is fundamental when forecasting multiday fire incidents, which benefit from updating the state of the fire at the beginning of each forecast. The methods were tested within the WRFx fire forecasting system. The entire real‐time forecast for the 2021 Caldor Fire was rerun using the three methods and compared against observations. The analysis of the fire growth, as well as surface PM2.5 concentrations and smoke heights, indicate that the contextual method utilizing both IR perimeters and satellite detections offers significant improvements when compared to the other methods for all variables analyzed, with the most considerable improvement in forecast skill seen in forecasts with 24–48 hr lead time. Plain Language Summary: Predicting how wildfires spread and how their smoke affects air quality is crucial for protecting communities. To improve fire forecasts, we tested three ways of using satellite and aircraft fire observations to track better where fires are actively burning. One method relies only on infrared (IR) fire perimeter data from aircraft, while the other two also use satellite detections of fire activity. One of these satellite‐based methods uses only the latest satellite data, while the other compares two satellite images taken at different times to determine which parts of the fire have grown and which have stopped burning. We applied these methods to the WRFx fire forecasting system and tested them using the observed growth of the 2021 Caldor Fire. Our results show that the contextual method combining IR perimeter data with two consecutive satellite detections provided the most accurate forecasts. This approach reduced errors in identifying actively burning areas, leading to better predictions of fire growth, smoke levels, and air quality impacts, particularly for 24–48‐hr forecasts. These improvements are essential for emergency responders and air quality managers who rely on accurate fire and smoke forecasts to make timely decisions. Key Points: The paper evaluates three real‐time methods integrating infrared perimeters and satellite detections into a coupled fire‐atmosphere modelThe IR + Sat‐Context method improved forecasted fire growth, surface PM2.5, and smoke heights, particularly for longer lead timesBy refining fire initialization, the contextual integration method improves real‐time WRFx forecasts, as seen in the 2021 Caldor Fire [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Geophysical Research. Atmospheres is the property of Wiley-Blackwell 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.) | |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 186224972 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhancing Wildfire and Smoke Forecasting by Integrating Fire Observations: A Comparative Analysis of Methods for Integrating Infrared and Satellite Data Into a Coupled Fire‐Atmosphere Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Clough%2C+Kathleen%22">Clough, Kathleen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Farguell%2C+Angel%22">Farguell, Angel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mandel%2C+Jan%22">Mandel, Jan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hilburn%2C+Kyle%22">Hilburn, Kyle</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kochanski%2C+Adam%22">Kochanski, Adam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> adam.kochanski@sjsu.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Geophysical+Research%2E+Atmospheres%22">Journal of Geophysical Research. Atmospheres</searchLink>. Jun2025, Vol. 130 Issue 12, p1-19. 19p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Wildfires%22">Wildfires</searchLink><br />*<searchLink fieldCode="DE" term="%22Particulate+matter%22">Particulate matter</searchLink><br />*<searchLink fieldCode="DE" term="%22Air+quality%22">Air quality</searchLink><br /><searchLink fieldCode="DE" term="%22Data+assimilation%22">Data assimilation</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate forecasts of fire spread and smoke impacts using coupled fire‐atmosphere models require advanced methods for fire initialization. This paper proposes and tests three different methods for integrating fire observations. The methods reconstruct the initial fire evolution needed for the atmosphere spin‐up at the beginning of the simulation and identify actively burning fire regions. One method is based solely on IR perimeters, while the other two leverage IR perimeters and satellite detections. One uses only the most recent satellite data, while the other contextualized method uses two consecutive satellite detections to identify which sections of the fire perimeter experienced significant growth and which were inactive. This integration method addresses the problem of inaccurate identification of actively burning regions, thus correcting the previously seen overestimated growth in real‐time forecasts. This problem is fundamental when forecasting multiday fire incidents, which benefit from updating the state of the fire at the beginning of each forecast. The methods were tested within the WRFx fire forecasting system. The entire real‐time forecast for the 2021 Caldor Fire was rerun using the three methods and compared against observations. The analysis of the fire growth, as well as surface PM2.5 concentrations and smoke heights, indicate that the contextual method utilizing both IR perimeters and satellite detections offers significant improvements when compared to the other methods for all variables analyzed, with the most considerable improvement in forecast skill seen in forecasts with 24–48 hr lead time. Plain Language Summary: Predicting how wildfires spread and how their smoke affects air quality is crucial for protecting communities. To improve fire forecasts, we tested three ways of using satellite and aircraft fire observations to track better where fires are actively burning. One method relies only on infrared (IR) fire perimeter data from aircraft, while the other two also use satellite detections of fire activity. One of these satellite‐based methods uses only the latest satellite data, while the other compares two satellite images taken at different times to determine which parts of the fire have grown and which have stopped burning. We applied these methods to the WRFx fire forecasting system and tested them using the observed growth of the 2021 Caldor Fire. Our results show that the contextual method combining IR perimeter data with two consecutive satellite detections provided the most accurate forecasts. This approach reduced errors in identifying actively burning areas, leading to better predictions of fire growth, smoke levels, and air quality impacts, particularly for 24–48‐hr forecasts. These improvements are essential for emergency responders and air quality managers who rely on accurate fire and smoke forecasts to make timely decisions. Key Points: The paper evaluates three real‐time methods integrating infrared perimeters and satellite detections into a coupled fire‐atmosphere modelThe IR + Sat‐Context method improved forecasted fire growth, surface PM2.5, and smoke heights, particularly for longer lead timesBy refining fire initialization, the contextual integration method improves real‐time WRFx forecasts, as seen in the 2021 Caldor Fire [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Geophysical Research. Atmospheres is the property of Wiley-Blackwell 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: 10.1029/2024JD042561 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1 Subjects: – SubjectFull: Wildfires Type: general – SubjectFull: Particulate matter Type: general – SubjectFull: Air quality Type: general – SubjectFull: Data assimilation Type: general – SubjectFull: Remote-sensing images Type: general Titles: – TitleFull: Enhancing Wildfire and Smoke Forecasting by Integrating Fire Observations: A Comparative Analysis of Methods for Integrating Infrared and Satellite Data Into a Coupled Fire‐Atmosphere Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Clough, Kathleen – PersonEntity: Name: NameFull: Farguell, Angel – PersonEntity: Name: NameFull: Mandel, Jan – PersonEntity: Name: NameFull: Hilburn, Kyle – PersonEntity: Name: NameFull: Kochanski, Adam IsPartOfRelationships: – BibEntity: Dates: – D: 28 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 2169897X Numbering: – Type: volume Value: 130 – Type: issue Value: 12 Titles: – TitleFull: Journal of Geophysical Research. Atmospheres Type: main |
| ResultId | 1 |
