Marshall Space Flight Center

Abstract We present observations of and a magnetic reconnection scenario for a twin pair of “atypical flares” that occurred on 2022 April 22 in a quadrupolar magnetic configuration formed by two active regions. The spatiotemporal evolution of the two flares is examined using images from the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory and from the ground-based Multi-Application Solar Telescope in Udaipur, India. Characteristic of atypical flares and indicative of slipping reconnection, the ribbons of each of our atypical flares: (1) do not spread apart; and (2) grow longer by the sequential brightening of new flare kernels. The two atypical flares are homologous and plausibly have homologous triggers. There are four additional pairs of flare ribbons, each from a different flaring event releasing much less energy than the atypical flares. Two of these four pairs are each made by a precursor, each possibly triggering one of the two atypical flares. The remaining two pairs accompany a filament’s activation, occurring twice within the span of the two atypical flares. Using a nonlinear force-free-field extrapolation model, we approximate the coronal magnetic field and find two quasi-separatrix layers (QSLs) that are nearly rooted in the flare ribbons of the atypical flares. The observations and the extrapolated field together suggest a scenario in which the nearly simultaneous occurrence of many reconnections between magnetic field lines crossing each other at small angles (slipping reconnection) within each of the two QSLs makes the observed pair of atypical flares.

Saline permafrost exists beneath shallow shelf seas, coastal plains shaped by past marine transgressions, and post-glacially uplifted landscapes that were once submerged. Salinity influences the freezing point and mechanical strength of permafrost; it is, therefore, a critical parameter for assessing its stability. On Svalbard, the Kvadehuksletta region northwest of Ny-Ålesund features a diverse landscape comprising raised beach terraces, lagoons, paleo-lagoons (now lakes), and surface seeps. Our research aims to decipher how marine sediments transform after emergence. We hypothesize that ice formation during permafrost aggradation produces a porewater salinity gradient that triggers the downwards migration of salt in slowly uplifting sediments that are weakly susceptible to groundwater flushing. Sufficient salt build-up may lead to the formation of cryopegs. Cryopegs, a type of talik, are unfrozen layers or pockets within permafrost that persist at subzero temperatures due to their elevated salt content. In summer 2024 and 2025, we carried out several electrical resistivity tomography (ERT) profiles, including three profiles (ranging from 800 to 2300 m in length) perpendicular to the coastline. The westernmost profile (collected in 2025) intersected a dynamic lagoon that was connected to the sea in 2024 but became completely cut off in 2025 by storm-surge deposits. To help delineate frozen and unfrozen permafrost conditions, electrical resistivity-temperature analyses of field samples collected from shallow cores (down to 2.5 m) are currently underway. Laboratory tests indicate that the near-surface marine clays adjacent to the lagoon have low resistivities (< 10 Ωm) when thawed and freezing point temperatures down to -1.6 °C. The field samples are also being analysed for porewater chemistry (electrical conductivity, cations & anions, pH, stable water isotopes) and basic sedimentological properties like grain size. At two coring sites (1 paleo-lagoon, 1 beach setting), an annual ground temperature time series was also collected between field seasons. While the physical and electrical properties of the marine sediments are important to establish, so is their thickness. To potentially provide additional information on the depth to bedrock along selected ERT profile segments, we conducted multiple seismic refraction tomography (SRT) surveys (115 m length) in 2025 using a sledgehammer as an energy source. The synthesis of all datasets to describe uplifted permafrost is work in progress, but preliminary conclusions suggest that cryopeg occurrence is most likely in low-lying coastal areas characterized by warm permafrost, occasional seawater submergence, and saline marine clays with low hydraulic conductivity.

In this study, Al/Zr multilayer thin films were synthesized using pulsed laser deposition (PLD) with a 248 nm KrF excimer laser to investigate their structural and surface properties for extreme ultraviolet (EUV) optical and protective coatings. Alternating Al and Zr layers were deposited on Si substrates under high vacuum, with nanometer-scale thickness control achieved via spectroscopic ellipsometry. X-ray diffraction revealed enhanced crystallinity at 400 °C, notably with strong Zr(002) orientation. SEM and AFM showed a morphological transition from smooth 2D to rough 3D growth at 500 °C, attributed to increased adatom mobility and interfacial strain. SEM images of Al at 400 °C exhibited a uniform nanocrystalline surface with compact grains and low porosity, suggesting stable 2D growth. Computational modeling based on free energy minimization supported the 2D–3D transition, predicting a critical film thickness of 1–2 nm, consistent with observations. These results demonstrate PLD’s capability to produce high-quality Al/Zr multilayers for EUV mirrors and advanced coatings.

Abstract Wildfires can drastically alter land surface characteristics, leading to the formation of heat islands that influence local weather and atmospheric processes. This study presents a comprehensive satellite-based and numerical modeling analysis of the 2018 Camp Fire in northern California. Key land surface variables—vegetation cover, albedo, land surface temperature, and roughness length—were significantly altered following the fire. Weather Research and Forecasting (WRF) Model simulations show that while these changes led to a reduction in net radiation at the surface, they were accompanied by an increase in sensible heat flux. Simulations also show the burn scar causing increases in surface air temperature, decreases in dewpoint, strengthening of terrain-generated circulations, and local changes in clouds and rainfall. These findings highlight the need for postwildfire response and planning to consider changes in land–atmosphere interactions resulting from wildfire-driven alterations in land cover. Significance Statement This study shows that large wildfire events result in the formation of long-lasting burn scar heat islands. This occurs because changes in land surface properties alter the exchange of heat and moisture between the surface and the atmosphere, leading to increased heating and reduced moistening of the lower atmosphere. In turn, these changes affect local wind, cloud, and precipitation patterns that can persist well after the wildfire event. These effects can be especially long-lasting in regions with complex terrain, where wildfires more commonly occur in the United States. The study’s methods and findings are critical for improving postfire planning and hazard mitigation, as well as for advancing understanding of the feedbacks between land-use and land-cover changes and local weather in fire-prone regions.

The online version contains supplementary material available at 10.1007/s44279-026-00498-3.

Abstract The use of low-cost sensors (LCS) in air quality monitoring has been gaining interest across all walks of society, including community and citizen scientists, academic research groups, environmental agencies, and the private sector. Traditional air monitoring, performed by regulatory agencies, involves expensive regulatory-grade equipment and requires ongoing maintenance and quality control checks. The low-price tag, minimal operating cost, ease of use, and open data access are the primary driving factors behind the popularity of LCS. This study discusses the role and associated challenges of PM 2.5 sensors in monitoring air quality. We present the results of evaluations of the PurpleAir (PA.) PA-II LCS against regulatory-grade PM 2.5 federal equivalent methods (FEM) and the development of sensor calibration algorithms. The LCS calibration was performed for 2 to 4 weeks during December 2019-January 2020 in Raleigh, NC, and Delhi, India, to evaluate the data quality under different aerosols loadings and environmental conditions. This exercise aims to develop a robust calibration model that uses PA measured parameters (i.e., PM 2.5 , temperature, relative humidity) as input and provides bias-corrected PM 2.5 output at an hourly scale. Thus, the calibration model relies on simultaneous measurements of PM 2.5 by FEM as target output during the calibration model development process. We applied various statistical and machine learning methods to achieve a regional calibration model. The results from our study indicate that, with proper calibration, we can achieve bias-corrected PM 2.5 data using PA sensors within 12% percentage mean absolute bias at hourly and within 6% for a daily average. Our study also suggests that pre-deployment calibrations developed at local or regional scales should be performed for the PA sensors to correct data from the field for scientific data analysis.

Attributing deforestation to key natural and anthropogenic drivers is critical for aiding reliable, transparent, and effective forest loss mitigation and management strategies. In recent years, the rapid evolution of Geospatial Foundation Models (GFMs) provides a unique opportunity for a fast, large-scale, and computationally efficient detection and attribution of forest loss. In this study, we evaluated the capability of the Prithvi-EO-2.0 300M and 600M GFMs for attributing primary deforestation events to four major drivers – grassland-shrubland expansion, plantation, smallholder agriculture and others (mining, fishpond, logging roads, secondary forests, etc.). We adapted the ForestNet dataset using a Stratified Spatial Shuffling for the training, validation, and test splits and a Scene Data Augmentation strategy for developing a geographically robust and well-balanced dataset. Using the Prithvi-EO-2.0 models, we compared three finetuning strategies – linear probing, low rank adaptation (LoRA, with ~2% of trainable parameters), and full fine-tuning with different fractional data inputs against a baseline ResNet Feature Pyramid Network trained end-to-end with 1.) imagery inputs only; and 2.) imagery and auxiliary inputs. Across all data fractions, Prithvi-EO-2.0 with low rank adaptation and the full-finetuning adaptation strategies outperforms the baseline model, however the linear probing underperforms in certain cases (by macro-averaged accuracy). The best performing models for the imagery only set up were the Prithvi-EO-2.0 600M model using a LoRA strategy (80.40%), followed by the full finetuning strategy (80.22%) for the same model, marginally surpassing the baseline (80.21%). However, when trained with additional auxiliary datasets, the Prithvi-EO-2.0 300M and 600M adapted with a full finetuning strategy reached an 82.75% and 82.94% accuracy, with a +3.8-percentage point accuracy gain over the baseline. The LoRA strategy maintains ≥75 % accuracy at full data for both the 300M and 600M models, while updating just 11.3 million trainable parameters, demonstrating its low‑resource efficiency. These results show the potential of the Prithvi EO models to deliver accurate, computationally efficient deforestation attribution, useful for large-scale forest change attribution.

The first time-resolved leader spectra associated with a downward Terrestrial Gamma-ray Flash (TGF) observed by the Telescope Array Surface Detector (TASD) were recently reported by \citeA{kieu2024first}. Building on that study, this paper presents th