A Statistical Model for Multisource Remote-Sensing Data Streams of Wildfire Aerosol Optical Depth

Guanzhou Wei (), Venkat Krishnan (), Yu Xie (), Manajit Sengupta (), Yingchen Zhang (), Haitao Liao () and Xiao Liu ()
Additional contact information
Guanzhou Wei: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Venkat Krishnan: PA Consulting, London SW1E 5DN, United Kingdom
Yu Xie: National Renewable Energy Laboratory, Golden, Colorado 80401
Manajit Sengupta: National Renewable Energy Laboratory, Golden, Colorado 80401
Yingchen Zhang: Utilidata, Providence, Rhode Island 02903
Haitao Liao: Department of Industrial Engineering, University of Arkansas, Fayetteville, Arkansas 72701
Xiao Liu: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332

INFORMS Joural on Data Science, 2024, vol. 3, issue 2, 162-178

Abstract: Increasingly frequent wildfires have significant impact on solar energy production as the atmospheric aerosols generated by wildfires diminish the incoming solar radiation. Atmospheric aerosols can be measured by aerosol optical depth (AOD), and multiple spatiotemporal AOD data streams are available from geostationary satellites. Although these multisource remote-sensing data streams are measurements of the same underlying AOD process, they often present heterogeneous characteristics, such as different measurement biases and errors, data missing rates, etc. It is usually not known which data source provides more accurate measurements, and simply averaging multiple data streams is not always the best practice. Hence, this paper proposes a statistical model that is capable of inferring the underlying true AOD process by simultaneously integrating heterogeneous multisource remote-sensing data streams and the advection-diffusion equation that governs the dynamics of the AOD process. A bias correction process is included in the model to account for the bias of the physics model and the truncation error of the Fourier series. The proposed approach is applied to California wildfires AOD data obtained from the GOES East (GOES-16) and GOES West (GOES-17) satellites operated by the National Oceanic and Atmospheric Administration. Comprehensive numerical examples are provided to demonstrate the predictive capabilities and model the interpretability of the proposed approach.

Keywords: physics-informed statistical learning; spatiotemporal models; advection-diffusion processes; aerosol optical depth; wildfires; solar energy; remote sensing; data fusion (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/ijds.2021.0058 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orijds:v:3:y:2024:i:2:p:162-178

Access Statistics for this article

More articles in INFORMS Joural on Data Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().