美国国家航空航天局

We present g4chargeit, a kinetic Monte Carlo framework built on Geant4 for simulation of time-dependent electrostatic charging in dielectric materials. The model explicitly incorporates stochastic particle transport and scattering processes using validated Geant4 cross-sections, while self-consistently evolving the electric potential and field. As a representative application, we simulate the charging of lunar regolith grains under average dayside conditions. The surface of the Moon, in addition to other airless planetary bodies, is regularly exposed to solar ultraviolet photons and solar-wind plasma, creating a radiation environment in which electrostatic interactions among regolith grains become significant. Until now, simulations of regolith charging have often relied on analytical approximations that oversimplify grain geometry and interaction mechanisms. Our Geant4-based simulations reveal charge accumulation within intergrain micro-cavities, leading to repulsive electrostatic forces consistent with experimental observations. The framework establishes a multiscale approach that links microscopic scattering events to the continuity equation of surface charge density and to the formation of macroscopic surface charge patches in complex grain geometries. Although demonstrated here for planetary regolith, the method is general and applicable to a broad range of dielectric charging problems. The code is openly available at https://github.com/kgandhi63/g4chargeit.git.

Accurate quantification of terrestrial gross primary production (GPP) is fundamental to understanding plant productivity, ecosystem function, and carbon-climate feedbacks. Existing approaches typically rely on meteorological, soil, and other ancillary inputs, which introduce substantial uncertainties at regional to global scales. NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, carrying the Ocean Color Instrument (OCI), provides the first contiguous global hyperspectral observations from space. Using 122 spectral bands from PACE, we developed a data-driven framework based on partial least squares regression to generate an 8-day global GPP product. The model achieves a robust performance (R2 = 0.71, RMSE = 1.93 gC m−2 d−1), comparable to models that incorporate meteorological drivers, and outperforms approaches based on broadband reflectance or vegetation indices. PACE-derived GPP showed broad spatial correspondence with solar-induced fluorescence (SIF), including hotspots across high productivity regions such as the U.S. Corn Belt, the Amazon and Congo basins, and the Indo-Gangetic and North China Plains. This strong spatial coherence supports the physiological relevance of the PACE retrievals. Our results demonstrate that hyperspectral observations provide a direct, observation-driven framework for estimating plant productivity at the global scale, opening new opportunities for monitoring vegetation responses to environmental change.

Frequent surface reflectance measurements at fine spatial resolutions are crucial for land surface monitoring. The NASA Harmonized Landsat and Sentinel-2 (HLS) project addresses this need by combining data from Landsat and Sentinel-2 missions to provide 30-m surface reflectance products with a global average revisit frequency of 2-3 days. This study evaluates the consistency of the HLS Version 2 (V2.0) product against the established Moderate Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance (NBAR) dataset. We found a high degree of consistency between HLS and MODIS NBAR, with strong positive correlations across all six common spectral bands (R2 values: 0.939–0.986). Globally, mean absolute differences (MADs) were generally low, typically under 0.03 for visible bands and below 0.05 for NIR and SWIR bands. However, we observed band-dependent discrepancies, with higher discrepancies in the blue and SWIR1 bands. Spatially, substantial differences were found in polar regions, largely due to isolated snow, ice, and water which are fractional and hard to detect within the coarser MODIS pixels but are filtered out in the HLS data. This finding suggests a potential explanation for past disagreements in vegetation trend analyses involving the use of satellite data of different spatial resolutions, as coarser-resolution datasets may mischaracterize greening trends in areas with partial snow and ice cover. These insights are vital for helping users interpret HLS data more effectively and for refining multi-sensor data fusion methods. Our research also provides a valuable baseline for ongoing quality assessment of the HLS product for applications such as vegetation monitoring, land management, and disaster response.

Planets orbiting M-dwarf stars are prime targets in the search for rocky exoplanet atmospheres. The small size of M dwarfs renders their planets exceptional targets for transmission spectroscopy, facilitating atmospheric characterization. However, it remains unknown whether their host stars' highly variable extreme-UV radiation environments allow atmospheres to persist. With JWST, we have begun to determine whether or not the most favorable rocky worlds orbiting M dwarfs have detectable atmospheres. Here, we present a 2.8-5.2 micron JWST NIRSpec/G395H transmission spectrum of the warm (700 K, 40.3x Earth's insolation) super-Earth GJ 486b (1.3 R$_{\oplus}$ and 3.0 M$_{\oplus}$). The measured spectrum from our two transits of GJ 486b deviates from a flat line at 2.2 - 3.3 $\sigma$, based on three independent reductions. Through a combination of forward and retrieval models, we determine that GJ 486b either has a water-rich atmosphere (with the most stringent constraint on the retrieved water abundance of H2O > 10% to 2$\sigma$) or the transmission spectrum is contaminated by water present in cool unocculted starspots. We also find that the measured stellar spectrum is best fit by a stellar model with cool starspots and hot faculae. While both retrieval scenarios provide equal quality fits ($\chi^2_\nu$ = 1.0) to our NIRSpec/G395H observations, shorter wavelength observations can break this degeneracy and reveal if GJ 486b sustains a water-rich atmosphere.

Cloud water serves as a chemical reactor where gases and particles undergo chemical transformations and thus is a key component in aerosol-cloud interactions. A total of 535 cloud water samples collected aboard the NASA ACTIVATE HU-25 Falcon (2020-2022) over the northwest Atlantic (U.S. East Coast to Bermuda) are analyzed, and a set of 31 dissolved species spanning major inorganic ions, selected organic acids, and trace elements are used here for source apportionment analysis. The EPA positive matrix factorization (PMF) 5.0 model yields a six-factor solution that explains considerable variability in dissolved solute mass (r = 0.77), with the factors being sea salt (81.3% of reconstructed mass), secondary aerosol (12.2%), traffic/combustion (5.2%), metal-enriched dust (0.9%), aged dust (0.3%), and an industrial/metallurgical factor (0.07%). Seasonal and spatial patterns showed strong sea salt mass contributions in all months, but especially in winter enhanced aged dust influence near Bermuda in June, and larger relative contributions of metal and traffic/combustion-related factors near the coast. Concentration-weighted trajectory maps link the dust factor to trans-Atlantic transport from North Africa and most other factors to export from the eastern United States. Overall, the results show that mass-based cloud water chemistry is strongly weighted toward larger, highly hygroscopic particles, while still retaining clear signatures of continental pollution and long-range dust transport.