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<p>An extreme forest fire event occurred in the Uttarakhand region of northern India, impacting air quality from April 23–27, 2024. This study examines the impact of assimilating aerosol optical depth (AOD) data, retrieved from the Earth Observation Satellite (EOS-06) Ocean Color Monitor (OCM-3) sensor, into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The assimilation of OCM-3 related AOD data significantly enhances the accuracy and reliability of fire detection and prediction. Statistical analyses show a notable reduction in bias (~ 61%) and root mean square deviation (RMSD, ~ 33%), and high correlation (0.74), indicating the successful integration of OCM-3 AOD into the WRF-Chem model. Spatial analyses demonstrate the effectiveness of variational assimilation, with the AOD-assimilated analysis experiment (OCM3DA) accurately capturing the spatial distribution of AOD over fire-affected regions, particularly in Uttarakhand and Nepal. Temporal variations in AOD show closer alignment between OCM3DA forecasted values and the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-2) reanalysis product, highlighting the importance of continuous assimilation for improved forecast accuracy. The model’s simulation of organic carbon (OC) effectively tracked the fire spread, suggesting its potential as a marker for detecting forest fires. Overall, the successful assimilation of satellite-derived AOD data into the WRF-Chem model represents a significant advancement in forest fire monitoring and prediction capabilities. These findings underscore the value of integrating high-resolution satellite observations with advanced numerical modeling techniques to enhance our understanding and management of fire events, contributing to improved public safety, environmental protection, and decision-making in fire management strategies.</p>

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Quantitative Insights into Uttarakhand’s Forest Fires: Impact of EOS-06 AOD Assimilation

  • 期刊论文

  • 发表日期：

    2025年5月14日

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  • Aman W. Khan,
  • Prashant Kumar,
  • Manoj Kumar Mishra,
  • P. K. Thapliyal

摘要

An extreme forest fire event occurred in the Uttarakhand region of northern India, impacting air quality from April 23–27, 2024. This study examines the impact of assimilating aerosol optical depth (AOD) data, retrieved from the Earth Observation Satellite (EOS-06) Ocean Color Monitor (OCM-3) sensor, into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The assimilation of OCM-3 related AOD data significantly enhances the accuracy and reliability of fire detection and prediction. Statistical analyses show a notable reduction in bias (~ 61%) and root mean square deviation (RMSD, ~ 33%), and high correlation (0.74), indicating the successful integration of OCM-3 AOD into the WRF-Chem model. Spatial analyses demonstrate the effectiveness of variational assimilation, with the AOD-assimilated analysis experiment (OCM3DA) accurately capturing the spatial distribution of AOD over fire-affected regions, particularly in Uttarakhand and Nepal. Temporal variations in AOD show closer alignment between OCM3DA forecasted values and the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-2) reanalysis product, highlighting the importance of continuous assimilation for improved forecast accuracy. The model’s simulation of organic carbon (OC) effectively tracked the fire spread, suggesting its potential as a marker for detecting forest fires. Overall, the successful assimilation of satellite-derived AOD data into the WRF-Chem model represents a significant advancement in forest fire monitoring and prediction capabilities. These findings underscore the value of integrating high-resolution satellite observations with advanced numerical modeling techniques to enhance our understanding and management of fire events, contributing to improved public safety, environmental protection, and decision-making in fire management strategies.