Enhanced Weathering (EW) of silicate rocks is viewed an essential methodology for scaling carbon dioxide removal (CDR) to reach goals of negative global CO2 emissions. EW has an equally important potential for improving agricultural soil, increasing crop yields, and reducing traditional fertilizer use and associated waste. Of all potential silicate raw materials for EW basaltic (mafic) rocks have perhaps the greatest potential, even though the total amount of CDR per unit is less than pure minerals like olivine. Basaltic rocks are widespread compared to other lithologies, are proven to benefit degraded agricultural soils, have significant CDR potential, and minimal negative environmental impacts including heavy metals. Systematic approaches to select basaltic raw material for agricultural and CDR benefits include petrographic and XRD analyses of minerals, XRF and ICP-MS analyses of major and trace elements and grainsize analysis of primary milled material or byproduct material from other aggregate uses. Analysis of commercially available “basalt” shows a wide range in relative quality in terms of primary mineralogy, chemical composition, degree of alteration and metamorphism, achievable grain size, proximity to low carbon transportation and application sites, climate, soil type, and other economic factors. Some products advertised as basalt are dark colored felsic rocks (andesite, dacite) or metamorphosed mafic rocks all of which have lower potential for CDR. These factors limit potential sources of basalt to a small percentage of quarry locations. Mafic, basaltic rocks have SiO2 contents between 45 and 52 percent and vary widely in percentages of mineral phenocrysts– e.g. olivine may range from 0 to >5%, which are also factors in selection of raw material. From an agricultural perspective, analyses of water extractable and Mehlich-3 nutrients reveal values less than the totals of primary unweathered basaltic material; these values increase with degree of EW. Widespread adoption of EW methods for improving soil health will require local and regional crop specific trials and realistic guidelines for application rates and grain sizes that are practical and economic for producers. Characterizing raw material is paramount to instilling confidence in CDR potential and benefits for crops and soil health.

The retrieval of sun-induced fluorescence (SIF) from hyperspectral imagery requires accurate atmospheric compensation to correctly disentangle its small contribution to the at-sensor radiance from other confounding factors. In spectral fitting SIF retrieval approaches this compensation is estimated in a joint optimization of free variables when fitting the measured at-sensor signal. Due to the computational complexity of Radiative Transfer Models (RTMs) that satisfy the level of precision required for accurate SIF retrieval, fully joint estimations are practically inachievable with exact physical simulation. We present in this contribution an emulator-based spectral fitting method neural network (EmSFMNN) approach integrating RTM emulation and self-supervised training for computationally efficient and accurate SIF retrieval in the O2-A absorption band of HyPlant imagery. In a validation study with insitu top-of-canopy SIF measurements we find improved performance over traditional spectral fitting methods. Furthermore, we show that the model predicts plausible SIF emission in topographically variable terrain without scene-specific adaptations. Since EmSFMNN can be adapted to different hyperspectral imaging sensors in a straightforward fashion, it may prove an interesting SIF retrieval method for other sensors on airborne and spaceborne platforms.

This study aims to develop a framework for identifying local circulation-dominated pollution hotspots using large eddy simulations (LES). The top-down nitrogen oxides (NOx) emissions based on satellite data provide improved accuracy, enabling clearer identification of pollution hotspots. A comparison between the top-down NOx emissions and government emission inventories is conducted to highlight the significance of the new emission method in shaping pollution hotspots. The framework utilized the Taiwan Vector Vorticity equation cloud-resolving Model (TaiwanVVM), which better resolves turbulence structures in local circulations and boundary layer processes. Coupled with a chemical module, the formation and evolution of pollution hotspots at the local scale can be closely examined in the high-resolution LES of TaiwanVVM. The composite results show similar daytime pollution pattern even under different emission inventories. However, there is a significant difference in the nighttime pollution hotspots. Detailed analysis revealed that the lee vortices can influence hotspot locations and facilitate offshore pollution transport, effectively reducing onshore concentrations even when boundary layer heights are low at midnight. Through the case study, the differences in emission source inventories directly contribute to nighttime concentration differences, while variations in chemical processes also influenced the formation of pollution hotspots. This framework emphasizes the importance of satellite-based emission inventories in identifying new pollution hotspots and highlights the role of lee vortices in shaping these hotspots, providing valuable insights for emission regulation through semi-realistic simulations and potential adaptation to future climate scenarios.

Magnetic flux ropes are an important area of research in space physics. Among the various magnetic field enhancement events in the solar wind, some remain puzzling due to their multiple possible origins, with interlinked flux ropes being a key candidate. In this study, we analyzed two distinct events to develop a classification framework. Using high-resolution Hall MHD models of interlinked flux ropes, we reproduced the observed magnetic field and velocity components. Our findings show that one event involved flux ropes with a chirality combination that favors magnetic reconnection, while the other had a configuration that suppressed reconnection, consistent with observations. By applying standard data analysis methods, we identified chirality combinations consistent with our models. This study provides direct evidence of 3D flux rope interactions and offers data products to facilitate future event classification.

The Longitudinal Valley Fault (LVF) and the Central Range Fault (CRF) in eastern Taiwan consist of a head-to-head double-vergence structure hosting disastrous earthquakes. It was previously proposed that the fault slip on one of these faults suppresses the earthquake generation on the other. Nonetheless, the 2022 Chihshang earthquake (Mw 7.0) on the CRF occurred soon after the 2022 Yuli earthquake (Mw 6.7) on the LVF. Here, we provide a comprehensive framework of the fault interaction consistently explaining these contradictory findings. First, we estimated the coseismic slip distribution of the Yuli earthquake from Global Navigation Satellite System (GNSS) and L-band satellite interferograms. The results indicate almost pure reverse faulting. Second, with the estimated slip distribution, we calculated changes in Coulomb failure function (ΔCFF) on the CRF due to the Yuli earthquake on the LVF. The ΔCFF reaches +0.25 MPa around the main rupture area of the Chihshang earthquake, which is equivalent to the clock advance of ~50 years, suggesting a large impact on the earthquake generation cycles. Finally, we found that a rake angle of fault slip has a significant effect on the ΔCFF on the other fault in the double-vergence structure: it takes large positive values when 90° like the Yuli earthquake, but almost negative when 45° or less, which comprehensively explains the seismic quiescence previously reported and the positive ΔCFF on the CRF caused by the Yuli earthquake. The strong impact of the rake angle is also supported by the temporal distribution of historical earthquakes in eastern Taiwan.

In this work, we explore embodied data interaction with the empirical space weather datasets in a mixed reality (MR) environment using extended reality (XR) technologies. Three empirical variables related to the study of solar storms over an 18-year period are shown: SYM-H index data, sunspot number, and ground-based total electron content from the northwest sector of the continental United States. We aim to showcase correlations among these data variables in a novel MR environment and use audio and visual analytics to explore using the XR capabilities to detect correlations. We provide sonification (the use of non-speech audio to convey information) and visual analytics techniques in an MR tool deployed on an MR headset. A human research study involving 54 participants tested the usability and efficacy of the newly developed MR tool on users with no space-science expertise or prior knowledge of the datasets. The results show that even with minimal knowledge of space weather and limited experience with MR environments, the participants were able to identify correlations and begin to understand the scientific context of space weather phenomena. At the CEDAR 2023 conference, we broadened the demographics of the study by studying the responses of space-weather experts in order to more rigorously quantify and assess the potential impact of XR technologies on educational and analysis tools and techniques. Forty-two space science experts gave their feedback about the usability and the potential of this emerging technology as a scientific education and analysis methodology. At the NASA 4th Eddy Cross-Disciplinary Symposium in 2023, we gained a global perspective and feedback from the heliophysics community around the world. At AGU 2024, we intend to present the results collected from space weather experts worldwide, discuss the next steps, and highlight the strengths and challenges of this novel approach to data analysis.

A document by Saman Aryana. Click on the document to view its contents.

We have recently argued that a simple nonlinear law could be important for the origin of the Universe resulting in fractal or multifractal features [1]. Various fractal scaling models of the large-scale mass distribution have already been proposed. The expected universal multifractal function for galaxies is similar to that identified by NASA's Voyager mission in the Solar System [2]. Now we apply the similar method to determine a reliable multifractal spectrum of distribution of galaxies on cosmological scales, based on selected observations from a million of galaxies in the Redshift Catalog (June 2008) [3]. We show that the observed spectrum is consistent with the weighted oneor two-scale Cantor set models characteristic for turbulence in laboratory and inside the Sun's heliosphere immersed in the very local interstellar medium. However, the total degree of multifractality ∆ ≈ 0.2 is much smaller then that inside the heliosphere. This would be characteristic for a simple linear fractal scaling of galaxy distribution, but somewhat varying for nearby (∆ ≈ 0.1) and the most remote galaxies (∆ ≈ 0.2) receding from our Solar System. The parameters p ≈ 0.45 and λ ≤ 1/2 for one-scale model are apparently related to some voids in the large-scale distribution of matter. A possible asymmetry (A ≈ 0.75) of the total spectrum for two-scale weighted Cantor set (A ̸ = 1) could admittedly be attributed to some deviations from the Hubble's law for the ideal uniform expansion of the Universe.

Framed by Connecticut’s rocky shore and New York’s outstretched Long Island, the 180 km tidal estuary known as the Long Island Sound makes up one of many major estuary systems across North America’s eastern coastline. Since the Last Glacial Maximum roughly 21,000 years ago, the formation of the Long Island Sound tells of an intricate geologic history—of a thawing Laurentide Ice Sheet, a draining glacial lake, river-sculpted lakebeds, and the inpour of a rising ocean. The rise of the Atlantic has turned what was once the exposed lakebed of Glacial Lake Connecticut into a flooded estuary, burying away the Sound’s chronologic record held in submerged paleo-features and remnant glacial lake deposits. In an effort to elucidate the glacial to post-glacial history of the Sound, we use high-resolution 2-D CHIRP sonar data to image sub-bottom strata in the Sound’s northeastern region between Westbrook, CT, and Fisher’s Island, where Glacial Lake Connecticut once drained toward a farther Atlantic. Subsurface facies interpretations of digitized profiles were created using Kingdom Suite (Seismic Micro-Technology, Inc.) and then interpolated using ArcGIS Pro, reconstructing a detailed mapping of the region’s sub-bottom. Subsurface facies interpretations were created using Procreate. Substantial erosion from ongoing marine action has exposed considerable portions of lakebed deposits at the sound floor alongside outcropping bedrock, buried promontories, and paleo-shore remnants. Our study revealed preserved glacial lake deposits channelized by 4-6 newly identified southward flowing paleo-rivers, potentially joining at the confluence of a greater, previously mapped paleo-river. Interpreted unconformities and depositional shifts in glacial lake sediments added complexities to Glacial Lake Connecticut’s formative chronology and the dynamics of its meltwater sources, drainage, and estuarine transition. 2-D CHIRP sub-bottom profiles proved to be an effective tool for the preliminary identification of submerged and buried features within a complex, geodiverse region of the Long Island Sound.

Reliable Flood Inundation Mapping (FIM) is a key component in the flood forecasting and analysis framework. The selection of FIM prediction solvers is a trade-off between computational and data costs and accuracy. Physically based (hydraulic) models are considered more accurate but computationally and data expensive, while low complexity terrain-based solvers offer efficiency and scalability at the expense of accuracy. The NOAA Office of Water Predictions (OWP) operational FIM framework is based on the Height Above Nearest Drainage (HAND) approach. In this study, a deep Convolutional neural network (CNN) technique is explored to enhance the OWP HAND-FIM using a surrogate modeling approach. Among various architectures, an encoder-decoder U-Net architecture performs well to classify the flooded area precisely. U-Net's skip connections help to maintain high-resolution spatial information by combining characteristics from multiple Convolutional layers, which improves prediction accuracy, whereas skip connections help reduce vanishing gradient concerns, which improves the model's generalization capabilities. The model is trained using physics-based HEC-RAS simulations and a suite of other input features (digital elevation model, topographical wetness index, land use, land cover, flow accumulations, etc.) over three HUC8-scale watersheds. The results show a considerable improvement in FIM accuracy when using a surrogate CNN model compared to the original OWP HAND-FIM. The transferability of the model is tested in a fourth HUC8 region, showing considerable enhancement in the OWP HAND-FIM after the trained surrogate model is applied. The enhanced FIM was also evaluated using building footprints, resulting in a significant improvement in the surrogate model impact detection accuracy. This demonstrates that CNN holds a generalizing capability by capturing the spatial correlation between pixels. Introducing physical laws to attain behaviors during modeling and increasing the size and diversity of its training data is expected to further enhance the model's transferability and predicting capability.

The recent advances in computer modeling have spurred the production of several global storm-resolving models (GSRMs), which resolve scales from deep convection to large-scale and global circulations. As a result, GSRMs may simulate the formation and evolution of tropical cirrus clouds more physically than typical GCMs which use parameterizations to represent deep convection. We use nine GSRMs from the DYnamics of the Atmospheric general circulation Modelled On Non-hydrostatic Domains (DYAMOND) initiative (Stevens et al., 2019), with a focus on the second phase of DYAMOND that simulated a period in Jan.-Feb. 2020. In the tropics, models capture the mean OLR within -5 to 14 W/m2 of observed climatology, though most models have a higher OLR and more convective precipitation over the 40-day simulation period than observed. While the models represent large-scale tropical convection with some fidelity, large regional differences in cloud properties and TOA radiation fluxes exist. We focus on a region within the Tropical Western Pacific (TWP) to study the local and small-scale features available with the high spatiotemporal resolution of GSRMs. When including models that participated in both phases of DYAMOND and simulated boreal summer and winter, most models capture the seasonal differences between the two phases. Each model simulates unique cloud characteristics that are persistent across seasons. GSRMs even simulate the notoriously difficult to observe thin TTL cirrus so GSRMs may be a useful tool to enhance our knowledge of TTL cirrus even though the TTL varies greatly between models over the short 40-day simulation.

Creep events are millimeter-sized, aseismic movements that produce slip along portions of certain active faults \citep{Tymofyeyeva_2019}. These movements were first observed on the San Andreas through cultural offsets and have been measured with creepmeters since the 1970s. Despite having knowledge of the events for over 40 years, little is known about the mechanisms that produce creep events. There are two popular mechanisms which have been proposed to drive creep events: the ‘kick’ hypothesis (e.g., \citealp*{Kanu_2011}; \citealp*{Helmstetter_2009}) and the self-driven hypothesis (e.g., \citealp{Rubin_2008}; \citealp{Skarbek_2012};\citealp{Wei_2013}). The self-driven hypothesis asserts that creep events are triggered at moderate depths by local frictional weakening patches and then propagate to the surface. In contrast, the ‘kick’ hypothesis suggests that slip events originate very near the surface from perturbations in stress or pore-pressure \citep*{Gittins_2022}, which in turn may be linked to rainfall, atmospheric tides, or other environmental changes. To investigate the ‘kick’ hypothesis, we used a newly generated catalog of creep events synthesized from a global network of creepmeters first established on the central portion of the San Andreas Fault in the 1970s. This catalog of over 5000 creep events spanning over 40 years allows for a more complete analysis compared to earlier work \citep{Roeloffs_2001}. We used this catalog to perform statistical analyses to investigate the relationship between the creep events and rainfall. Our analysis has uncovered that even within the same region, some creepmeters' creep events are more correlated with rain than others. Thus, our results favor the conclusion that creep events are not explained by one of the two theories alone, but instead by a combination of both hypotheses.

A document by Ryan Joseph M. Felicidario. Click on the document to view its contents.

Global warming has induced more frequent and intense heavy rainfall, significantly affecting dryland productivity, which dominates the interannual trend and variability of the terrestrial global carbon cycle. However, the lasting impact of such extreme wet years, termed as ’legacy effects,’ is poorly understood. We used satellite NDVI and GPP derived from both the LUE model and eddy covariance as productivity proxies to examine legacy effects on productivity after extreme wet years in Australia, where 75% of the continent is drylands, and the climate is highly variable due to ENSO events. After excluding random responses using Superposed Epoch Analysis (SEA), we confirmed that only the 2010-2011 wet year, caused by the La Niña event, indicated a strong and significant legacy effects in the subsequent first year (over 50% of the area showed significant positive legacy effects (> 90% significance threshold) in the first year following). We then conducted a further analysis of the legacy effect and found that the arid areas in Australia show significant sensitivity (p<0.05) to the productivity of the previous year. In these antecedent-sensitive areas, the legacy effects can contribute up to 25% of the current year’s productivity. Furthermore, the productivity of wet years is the main factor (r > 0.9, p < 0.01) driving the legacy effects, rather than the severity of the wet conditions, i.e., it is the plant growth during wet years that caused legacy effect in following year rather than the extreme rainfall itself. Our results highlight that the wet year’s legacy effect should be considered in carbon-climate models in drylands. It is crucial to revisit the legacy effects of wet years on dryland productivity from an ecogeographic perspective: investigating the coupling of abnormal increases in productivity during wet years and their occurrence in antecedent-sensitive regions, as these ecogeographic characteristics are key determinants of the magnitude of legacy effects.

The Martian magnetosheath acts as a conduit for mass and energy transfer between the upstream solar wind and its induced magnetosphere. However, our understanding of its global properties remains limited. Using nine years of data from NASA’s Mars Atmosphere and Volatile EvolutioN (MAVEN) mission, we performed a quantitative statistical analysis to explore the spatial distribution of the magnetic fields, solar wind and planetary ions in the magnetosheath. We discovered significant asymmetries in the magnetic field, solar wind protons, and planetary ions between the quasi-perpendicular and quasi-parallel magnetosheaths. The asymmetries in the Martian magnetosheath exhibit both similarities and differences compared to those in the Earth’s and Venus’s magnetosheaths. These results indicate that the Martian magnetosheath is shaped by both shock geometry and planetary ions, making it an ideal natural laboratory for exploring different plasma physics processes.

Sediments on Arctic continental shelves are impacted by sea ice and ice-related processes for up to nine months per year. As a result, seabed morphology in cold regions can exhibit features such as ice scours which are absent on lower-latitude shelves. Previous literature has provided rich qualitative and some quantitative assessments of keel scours and strudel scours (i.e., feature density) in pan-Arctic settings. However, based on a recent multibeam survey along >600 km of tracklines in Harrison Bay, Alaska, Arctic shelves can exhibit a much greater diversity of interesting morphologic features. In this paper we present an atlas of seabed features observed across a 60x90 km portion of the Alaskan Beaufort Shelf, with the goal of highlighting the great diversity of Arctic continental-shelf seabed morphologies. We applied a combined approach of modern geomorphologic and geotechnical analysis of Arctic seabed environments, including: (1) machine learning algorithms applied to keel scour data, in order to better describe densities, geometries, and orientations at different water depths; and (2) portable free-fall penetrometer measurements collected at high spatial densities, in order to better characterize the geotechnical seabed properties associated with keel scour. Together this atlas of Arctic shelf seabed morphologies and applications of emerging quantitative tools to Arctic settings provides an advance in our understanding of Arctic seascape from a geotechnical and textural perspective.

• We present a deep learning framework that accurately predicts the evolution of cycloneinduced water levels across multiple domains. • An attention mechanism enhances the framework's recognition of extreme water level patterns within and beyond training locations. • It effectively identifies unseen water level patterns, different from those in training, thus enhancing model's transfer learning capability.

Despite extensive implementation of best management practices to mitigate external nutrient runoff, numerous New Hampshire lakes are continuing to experience diminished water quality, likely due to internal phosphorus loading (IPL). This remobilization of legacy nutrients can lead to more frequent and prolonged cyanobacteria blooms and exacerbate the process of eutrophication. This is the case for Lake Kanasatka, Moultonborough, NH, where IPL contributes to nearly 25% of its total phosphorus budget, with that percentage increasing towards 50% by the end of summer as the hypolimnion reaches anoxia, accelerating dissolutive release. Cyanobacteria blooms at Lake Kanasatka have become more prevalent in recent years, heightened around fall turnover when bioavailable phosphate is upwelled into the epilimnion, with blooms lasting up to 83 days. With Lake Kanasatka receiving an aluminum sulfate treatment in early 2024, we are compiling a comprehensive biogeochemical profile of the water column from April to October with a focus on the fractionation of phosphorus into its organic, inorganic, and reactive forms to understand how treatment affects the different forms and bioavailability of this critical nutrient. In addition, we are conducting a comparison to the interconnected upstream waterbody, Wakondah Pond, to better understand the influence of IPL on the efficacy of treatment, and establish if Wakondah Pond is a possible source of external nutrients. Wakondah Pond has exhibited similar seasonal patterns and serves as a control to compare the Lake Kanasatka post-treatment results against. Evaluating both waterbodies will provide broader insight on the water quality and biogeochemical dynamics of the watershed and determine what future management and conservation efforts are needed. With only two other New Hampshire lakes receiving an aluminum sulfate treatment since 1984, it is important to analyze seasonal trends in nutrient loading along with consistent monitoring to better understand the longevity of treatment and its impact on water quality.