Abstract
Recent persistent droughts and extreme heatwave events over the Western states of the US and Canada are creating highly favorable conditions for mega wildfires. The International Program of Climate Change AR6 report suggests that such extreme events will continue occurring with increasing frequency and intensity over forested regions, globally. While humangenerated fires for farming in the Amazon are at a potential tipping point, wildfires in the Northern Hemisphere are comparably generating broad regions of deforestation. The smoke from recent mega wildfires in California, driven by atmospheric and fuel conditions controlling their intensity, has been observed to penetrate the planetary boundary layer, stay in the atmosphere for a long time, and travel long distances. The wildfire smoke from such events has the potential to reach distant cities and towns over the Eastern US, significantly reducing the air quality of these distant communities, and to adversely impact human health by increasing Covid-19 morbidity as well as the number of respiratory and smoke-related heart diseases.
In this paper, we will apply the concepts of a dynamical data-driven wildfire system to implement a real-time Wildfire Digital Twin (WDT) simulation at sub-km resolution to enable the study of mega wildfire smoke impact scenarios at various distant locations from the occurring wildfires over western N. America. WDT provides a valuable planning tool to implement parameter impact scenarios by season, location, intensity, and atmospheric state. We augment the NASA Unified WRF (NUWRF) model with a dynamic fire spread parameterization (SFIRE) coupled to GOCART, CHEM, and HRRR5 physics. We implement a data-driven, near-time continuous assimilation scheme for ingesting and assimilating observations from the NOAA satellite instruments, VIIRS, and ABI and from a streaming sensor web of radars, ceilometers, and satellite lidar observational systems into the nested regional NUWRF model. We accelerate the high-resolution nested NUWRF model performance to make it suitable for forecasting applications by emulating the WRF microphysics and GOCART parameterizations with a deep dense transform machine learning neural net architecture, FourCastNet, that can maintain a simulated hourly atmospheric forecast in seconds. The WDT can also model the development of data-driven smoke from plumes and track the smoke across the US as it penetrates the planetary boundary layer, subsequently increasing the surface PM2.5. The SFIRE model spread and plume interaction with the atmosphere is a unique contribution by the WDT, fully enabling the interaction of smoke aerosols with observed clouds, the microphysics precipitation, convection, and the GOCART Chem, currently unavailable in other fire and smoke forecasting models.
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Acknowledgements
The work presented in this paper was mainly supported in part by the NASA ESTO FIRET-QRS-22-0001 Program Office and partly by the NSF funded Center for Accelerated Real Time Analytics at UMBC. We wish to thank the AIST program office manager Dr. J. LeMoigne for initially awarding the AIST and M. Seablom for selecting this proposal as the initial Fire Tech grant. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the NASA Fire-Tech Program or the U.S. Government, or any other funding entities.
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Halem, M. et al. (2024). Towards a Dynamic Data Driven Wildfire Digital Twin (WDT): Impacts on Deforestation, Air Quality and Cardiopulmonary Disease. In: Blasch, E., Darema, F., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2022. Lecture Notes in Computer Science, vol 13984. Springer, Cham. https://doi.org/10.1007/978-3-031-52670-1_40
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