Towards a Dynamic Data Driven Wildfire Digital Twin (WDT): Impacts on Deforestation, Air Quality and Cardiopulmonary Disease
Publication Date
1-1-2024
Document Type
Conference Proceeding
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
13984 LNCS
DOI
10.1007/978-3-031-52670-1_40
First Page
403
Last Page
410
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.
Keywords
Deforestation, Digital Twin, SFIRE, Smoke Impacts, Wildfire
Department
Meteorology and Climate Science
Recommended Citation
M. Halem, A. K. Kochanski, J. Mandel, J. Sleeman, B. Demoz, A. Bargteil, S. Patil, S.Shivadekar, A. Iorga, J. Dorband, J. Mackinnon, S. Chiao, Z. Yang, Ya Yesha, J. Sorkin, E. Kalnay, S. Safa, and C. Da. "Towards a Dynamic Data Driven Wildfire Digital Twin (WDT): Impacts on Deforestation, Air Quality and Cardiopulmonary Disease" Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2024): 403-410. https://doi.org/10.1007/978-3-031-52670-1_40
