Fire Alarm: Science Data Platform for Wildfire and Air Quality

Wildfires have become a grim indicator of the severity of the weather and climate extremes our planet is increasingly facing. The latest IPCC report describes the unprecedented rate at which the global climate has warmed in the last 200 years (UN News 2021), resulting in ocean temperature increases, rising sea levels, intense rains and floods, new records for heatwaves and droughts, and ever-growing stress on freshwater availability. The enhanced variability in precipitation and freshwater can cause vegetation drying that leads to larger and more frequent wildfire occurrence (Salguero et al, 2020). For example in 2017, after nearly a month of burning, California's Dixie fire consumed more than 600K acres and damaged over 1000 structures. The Thomas Fire, also in 2017, destroyed enough natural vegetation to cause mudslides that claimed 23 lives. In addition to destruction of property and ecosystems, wildfire has a direct impact on human health through the hazardous and toxic chemicals it releases into the air (Knorr et al, 2021). Inhalation of smoke from wildfires can be extremely hazardous, especially to children and seniors (Garcia et al, 2021). The cascading impacts of wildfires highlight the connected nature of the Earth System. For these types of disasters, early warning is the best way to save lives and livelihoods. To that end, we need an integrated solution using the latest observations to predict the likelihood of wildfire, burnt area, and pollutants, and to provide multifaceted monitoring and in-depth analysis for mitigation. Here we propose to advance the emerging AIST Air Quality Analytics Collaborative Framework (AQACF) (Huang et al, 2021) effort to establish a wildfire and air quality ACF, called Fire Alarm, focusing on the prediction and analysis of wildfire, burned area and the air quality as an integrated platform to guide our decision-makers, science researchers, and our response when they occur. In addition to improving the support on the existing air quality measurements and ML-predictions (Co-I Pourhomayoun), we will expand support to the relevant data from ECOSTRESS, GRACE-FO, SMAP, and to prepare for future observations from MAIA and SBG missions. The goal is to provide integrated analysis into fire emissions including black carbon, CO2, SO2, organic carbon, CH4, CO, NO, PM10 and PM2.5. We will improve and integrate long lead-time wildfire danger assessment by Co-I Reager (Jensen et al, 2017; Farhamand et al,, 2020a; Farhamand et al, 2020b) that produces wildfire risk predictions based on hydrological indicators from GRACE/GRACE-FO SMAP, and AIRS missions. Hyperspectral data presents scalable analytic challenges for interactive and on-demand computing. The current state of the art is mainly focusing on imagery and proprietary data formats. As a proxy to the future SBG mission, the JPL and Boston University's Land Cover Data Science pilot developed an Analysis Ready Data (ARD) solution for multiband data (Loubrieu et al, 2021). We will expand and formalize ARD support for hyperspectral data products for future analysis support for SBG data. In Year 3, we will leverage the NOS capability developed through the current Ocean Carbon Cycle NOS Study (Tsontos et al, 2021) for dynamic retasking and analysis of burned area and air quality through acquisition of in situ observations and instrument retasking (such as UAVSAR and NISAR). We will integrate File Alarm with the AIST Integrated Digital Earth Analysis System (IDEAS) (Huang et al, 2021), a digital twin project to provide a more complete digital representation of our Earth System for air quality, drought, wildfire, water cycle, and flood prediction and analysis. We also plan to add post-fire analysis such as mudslide prediction and monitoring. The work being accomplished through this proposal is relevant to the several NASA Programs including the Applied Science, Earth Science Disasters, Earth Science Data Systems, and wildfire-related programs.

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