Exposing Long-term Characteristics of Surface Air Quality through MERRA-2 Reanalysis Data
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Understanding the current state of the lunar interior is key to evaluating its formation history and evolution. The analyses of seismic waves recorded by 5 years of the Apollo passive seismic network, from 1972-1977, have provided some fundamental constraints on the lunar interior structure. Here, we show that comparisons between (a) calculated seismic velocities of simulated lunar interiors and (b) measured Apollo seismic velocities can resolve new solutions. Specifically, we show that comparison of seismic travel times can be used to constrain the lower bound of possible selenotherms.
Deep convection in the Asian summer monsoon (ASM) rapidly transports boundary layer pollutants including ozone-depleting substances into the upper troposphere and lower stratosphere (UTLS). The confinement of the seasonal anticyclonic circulation in the UTLS over Asia initially traps the pollutants near the tropopause level, but air masses are subsequently transported throughout the global stratosphere and impact Earth’s climate. Characterizing the transport pathways and processes of these pollutants are critical for identifying source regions and quantifying regional contributions to global stratospheric concentrations. The Asian summer monsoon Chemical and Climate Impact Project (ACCLIP) was conducted in summer 2022 using two high-altitude aircraft (NSF NCAR Gulfstream V and the NASA WB-57) to obtain UTLS chemical composition and aerosol distribution for characterizing the impact of the ASM on global chemistry and climate. Backward trajectories from ACCLIP flight tracks were traced through time-varying, satellite-derived global convective cloud top altitude fields to investigate the convective air mass origins of the sampled air parcels. Analyses reveal that air masses lofted by convection within the ASM (South Asian summer monsoon) and near the edge of the of the ASM anticyclone (East Asian summer monsoon) contributed to the airborne observations. In this study, we present a summary of the transport statistics of UTLS air masses sampled during ACCLIP and discuss the implications of these results on our understanding of the role of ASM in global constituent transport.
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As global methane (CH4) continues to grow, accurate quantification of CH4 emission sources has become a high priority for climate management. Urban CH4 emissions in the US could potentially account close to 20% of the national CH4 anthropogenic emissions. While isolated insitu urban CH4 emissions quantification projects have been successful, they require sophisticated planning and prolonged analyses. Such studies are imperative, but they cannot easily operate in every city. An ability to quantify the magnitude of CH4 point sources in a variety of cities with a satellite could support wide scale urban CH4 emissions quantification. Whole-earth scanning satellite instruments such as the TROPOMI and GOSAT are too coarse and noisy to provide meaningful measurements over cities with mid-strength emissions and most point-source emissions in general. It may be possible to address this deficiency utilizing a product with the high spatial resolution of 30-50 m from a private company GHGSat Inc. These sensors are specifically designed to identify and quantify CH4 emissions from diverse types of point sources of varying intensities. In this work we analyze a year (05/2023-05/2024) of GHGSat scans over South Side Landfill in downtown Indianapolis, Indiana and assess the instruments’ accuracy and precision with respect to prior estimations. The results indicate that the data are helpful for estimating landfill’s emissions (~750 kg/hr ± 200 kg/hr) over months-to-a-year time frame but may not be appropriate for identifying daily or even seasonal variations with any trustable degree of certainty, at least not in an urban environment.
Wildfires impose substantial impacts on communities and regions downwind of wildfire smoke. We present a TEMPO‑enabled workflow that generates value‑added Level‑3 smoke‑plume masks and fire‑front maps for large wildfires, such as 2024 Park Fire, using the self‑supervised deep learning system SIT‑FUSE, along with near‑surface NO₂ predictions produced by a foundation model (Microsoft Aurora). We conclude by outlining a roadmap for expanding these capabilities to additional Western U.S. wildfire events and for delivering actionable tools to stakeholders. This open-source, reproducible workflow provides a scalable framework for cross-agency wildfire monitoring to overcome traditional limitations in smoke-cloud discrimination and air-quality forecasting by incorporating TEMPO data and beyond.
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