bluuebunny/crossref_metadata_2025 huggingface dataset:
Studying the icy moons of Jupiter using a database framework
Authors: Cornet Thomas , Cruz-Mermy Guillaume , Belgacem Ines , Andrieu Francois , Schmidt Frederic
Published: March 2024

The NASA Galileo spacecraft explored the Jupiter system between 1995 and 2003. The spacecraft was equipped with the Near-Infrared Mapping Spectrometer instrument (NIMS), able to probe Jupiter’s atmosphere and the icy moons’ surface composition in the near-infrared with its 17 detectors operating between 0.7 to 5.2 microns [1]. The Galileo NIMS dataset was collected during flybys, which resulted in a series of very diverse data cubes, viewing geometries and spatial resolutions. In addition, depending on the instrument mode used to collect the data, and on the instrument’s own health status, the NIMS infrared spectra were collected with a varying spectral sampling (between 15 and 408 wavelengths), and an evolving absolute wavelength calibration over the course of the mission. Despite its heterogeneity and complexity of use, the Galileo/NIMS dataset is one of the most valuable resource to model and map the surface properties (composition, grain size, roughness, phase function) of Jupiter’s moons, which are the prime targets of the Europa Clipper [2] and ESA JUICE [3] missions in this decade.We converted the Galileo/NIMS calibrated dataset publicly available on the PDS Imaging Node (as g-cubes) into a MySQL relational database, which allows to quickly select and extract radiance factors (I/F), geometry data, and metadata from the entire NIMS data set. The smallest element in the database is a spectrum (i.e. one pixel). Using SQL queries relying on the pixel viewing geometry (incidence, emission, phase, and azimuth) and the geographic pixel location (latitudes and longitudes on a target), phase curves and/or collections of spectra can be easily retrieved. Individual g-cubes data can also be accessed upon request. We used this database framework, together with Bayesian inversion methods and the Hapke model [4,5], to perform detailed compositional studies on Europa’s dark lineaments [6,7], and spectro-photometric modeling of broader regions of interest located in different hemispheres [8]. References[1] Carlson et al., Space Science Reviews, 60, 457-502, 1992.[2] Howell and Pappalardo, Nat Commun 11, 1311, 2020.[3] Grasset et al., Plan Spac Sci 78, 1-21, 2013.[4] Hapke, Icarus 221, 1079-1083, 2012.[5] Hapke, Cambridge University Press, 1993.[6] Cruz Mermy et al., Icarus 394, 115379, 2023.[7] Andrieu et al., EPSC 2022.[8] Belgacem et al., EPSC, 2022.
Viability of satellite derived irradiance data for ML-based nowcasts
Authors: Gfäller Pascal , Schicker Irene , Papazek Petrina
Published: March 2024

Photovoltaic (PV) power production is increasingly becoming a central pillar in the shift to renewable power sources. The use of solar irradiance has great potential, as it is practically limitless and globally provides magnitudes more energy to the Earth than currently or foreseeable required. Solar irradiance as a power source does, however come with certain downsides. Besides the effects of seasonality and day-night-cycles on its usable potential, it´s broad use suffers mostly from uncertainty through its volatility. The actual extent of solar irradiance at the surface of the Earth is strongly influenced by a variety of atmospheric phenomena, most prominently clouds and atmospheric turbidity. The forecasting of near-future solar irradiance can thereby be beneficial in the estimation of PV power production in itself and with the goal of maintaining a stable equilibrium in electrical grids.To achieve nowcasts on a larger grid scope, forecasting of solar irradiance from satellite data can substitute forecasting of power output for individual sites. Satellite data, in contrast to ground-based data sources or NWP model estimates, is less reliant on the proper workings of a wide range of externalities. General-purpose spatiotemporal neural networks can be adapted to this task and provide predictions within a very short timeframe, with no requirement of HPC-infrastructure. A sparse model relying on a single satellite-based data source has less points of failure that could affect its forecasting performance and can be very efficient, but this sparsity could also reduce the achievable predictive accuracy. Benefits of smaller and simpler forecasting pipelines therefore may need to be balanced with requirements in terms of accuracy.To gather more meaningful and reliable results, a variety of spatiotemporal neural networks is implemented and tested to provide a more meaningful foundation. The models were selected and evaluated with respect to their different architectural patterns and designs, to get a notion of architectures beneficial to this task and achieve a more generalizable argument concerning the use satellite data as the sole basis of solar irradiance nowcasting.In an attempt of improving the viability of satellite-based nowcasting a commonly occurring flaw in near-real-time satellite data sources, missing or skipped frames, solutions to mitigate issues in operational nowcasting are considered. In place of ad-hoc preprocessing such as interpolation of missing data frames, an attempt to condition the models to missing frames is made.
An improved dataset of ASTER elevation time series in High Mountain Asia to study surge dynamics
Authors: Béraud Luc , Brun Fanny , Dehecq Amaury , Charrier Laurane , Hugonnet Romain
Published: March 2024

Some glaciers display flow instabilities, among which surge events particularly stand out. Surges are quasi-periodic flow perturbations with an abnormally fast flow over a few months to years. It can result in surface elevation changes of more than 100 m in a few months.The estimation of the mass transfer and the flow variation can be inferred from the glacier surface elevation and velocities. It is critical data to better understand the dynamics of a surge. While satellite-based DEMs provide useful information for studying surges, their use in previous studies was generally limited to a few DEM differences extending over periods of several years. To date, very few studies have leveraged the full time series of elevation data available since ~2000 which could help quantify the variations of mass transfer during the very short surge phases.Here, we exploited the high temporal and spatial coverage of the ASTER optical satellite sensor to compute a dense time series of elevation suited for the study of surges. Our case study area is the Karakoram range, in High Mountain Asia. We used non-filtered ASTER digital elevation models (DEMs) of 100 m resolution from Hugonnet et al. (2021). The time series range from about 2001 to 2019, with a median of 56 observations per on-glacier pixel over the whole period. We developed a specific method for filtering the elevation time series that preserves surge signals, as opposed to the original method that tends to reject this behaviour as outliers. A LOWESS method – locally weighted polynomial regression (Derkacheva et al., 2020; Cleveland, 1979) is at the core of this workflow. Then, we predicted the elevation over a regular temporal and spatial grid from filtered data, with the B-spline method ALPS-REML (Shekhar et al., 2021).In this presentation, we will present the results of this method applied to more than 1000 DEMs covering the Karakoram region to derive elevation time series at 100 m resolution. The filter and the prediction performances will be discussed. The results will be compared with those of other studies, in terms of surge onset and end dates, location or volume transported. Finally, the  elevation data set will be analysed with regard to velocities extracted from ITS_LIVE (Gardner et al., 2024) to validate the approach and highlight the complementarity of both types of observations.
Geochemical composition of sandstone samples from drill cores of the former uranium ore mine Königstein (Saxony) and the determination of hydrochemical equilibrium concentrations of uranium by means of batch tests
Authors: Schramm Sebastian , Schiperski Ferry , Hübschmann Mathias , Horna Frank , Scheytt Traugott
Published: March 2024

Geochemical background values are commonly used for the authorization of remediation measures. However, in the case of the former Königstein uranium ore mine, located in Saxony (Germany), natural uranium concentrations in groundwater cannot be directly assessed, due to active mining activities. The Königstein uranium ore depositis located within the 4th aquifer and consists of the Oberhäslich Formation and 'Wurm'-Sandstone.This study aimed at deriving natural uranium content using batch shake test, assuming chemical equilibrium with regard to the speciation of uranium in solution and the binding to the rock matrix. For that purpose, representative subsamples from core material were taken for batch experiments and geochemical analyses. As uranium solubility strongly dependents on the redox state, the pH value, and the hydrochemistry of the target fluid, experiments were performed under various conditions.The rock samples were analyzed with respect to geochemical and mineralogical compositions, while uranium concentration in fluid samples was measured using inductively coupled plasma - mass spectrometry. Concentrations of major cations were analyzed using cation chromatography techniques. The PHREEQC software was used to analyses the species distribution of uranium under the hydrochemical conditions of the the unaffected inflowing water from the inflow area.Results show, that the sandstones in the middle of the 4th aquifer consist mainly of SiO2 with more than 98 wt.-%. Two-layer clay minerals and iron oxides were identified in another sample with fractions more than 50 wt.-% kaolinite. Uranium was found in the anaerobic zone in one rock sample at 1820 ppm and once at only 25.6 ppm.Equilibrium modeling revealed, that at a pH of 5.5 to 6 and under oxidizing conditions, uranium mainly occurs as UO22+, UO2OH+, and UO2CO3. It can therefore be expected that the uranium species present as cations will likely be adsorbed by the solid matrix. Initial shaking tests showed that combining reduced rock materials with oxidizing water led to excessive uranium fractionation into the fluid phase. Batch tests using deionised water showed uranium concentrations between 2 and 2.5 mg/l, contrasting expectation of natural uranium concentrations in the lower µg/l range.
Learning phytoplankton bloom patterns - A long and rocky road from data to equations 
Authors: Nieters Pascal , Berthold Maximilian , Vortmeyer-Kley Rahel
Published: March 2024

Non-linear, dynamic patterns are the rule rather than the exception in ecosystems. Predicting such patterns would allow an improved understanding of energy and nutrient flows in such systems. The Scientific Machine Learning approach Universal Differential Equation (UDE) by Rackauckas et al. (2020) tries to extract the underlying dynamical relations of state variables directly from their time series in combination with some knowledge on the dynamics of the system. This approach makes this kind of tool a promising approach to support classical modeling when precise knowledge of dynamical relationships is lacking, but measurement data of the phenomenon to be modeled is available.We applied the UDE approach to a 22-year data set of the southern Baltic Sea coast, which constituted six different phytoplankton bloom types. The data set contained the state variables chlorophyll and different dissolved and total nutrients. We learned the chlorophyll:nutrient interactions from the data with additional forcing of external temperature, salinity and light attenuation dynamics as drivers. We used a neural network as a universal function approximator that provided time series of the state variables and their derivatives.Finally, we recovered algebraic relationships between the variables chlorophyll, dissolved and total nutrients and the external drivers temperature, salinity and light attenuation using Sparse Identification of nonlinear Dynamics (SinDy) by Brunton et al. (2016).The gained algebraic relationships differed in their importance of the different state variables and drivers for the six phytoplankton bloom types in accordance with general mechanisms reported in literature for the southern Baltic Sea coast. Our approach may be a viable option to guide ecosystem management decisions based on those algebraic relationships.Rackauckas et al. (2020), arXiv preprint arXiv:2001.04385.Brunton et al. (2016), PNAS 113.15: 3932-3937.
Effect of supercritical CO2/water interactions on geomechanical behavior of quartz-rich sandstone for CO2 geological storage
Authors: Fujii Takashi , Shiga Masashige , Oikawa Yasuki , Lei Xinglin
Published: March 2024

In the course of CO2 injection into a storage reservoir, understanding of a volumetric change (e.g., swelling), induced by interacting among CO2, water and rock into pores of rocks should be a critical step for modeling of hydro-mechanical response relevant to CO2 capture and storage (CCS) technology. For the majority of ongoing and planning CCS sites in the globe, hard sedimentary rocks, which is main component of quartz and feldspars with less clay minerals (e.g., smectite, illite), is a representative reservoir rock. It is well-known that caprocks (i.e., mudstone and shale) occur the swelling behavior of a rock matrix in the presence of water and/or CO2 due to intercalation and exchange reactions between layers of clay minerals. However, such volumetric change effect for quartz-rich rocks is not yet being investigated enough. In this study, we investigate geomechanical behavior of quartz-rich sandstone (Berea sandstone) in supercritical CO2 (scCO2)-water system under effective pressure of 10 MPa for up to approximately 1 week, the condition of which assumes that CO2 is injected into a storage reservoir at 1 km depth. Our results demonstrated that quartz-rich sandstone had a significant potential for changes in geomechanical properties (i.e., axial stress, displacement, volumetric strain) in scCO2-water system, like that do clay-rich caprocks, although little the change being observed for only water-saturation under the same effective stresses, and its maximum value was approximately 0.3 % for scCO2/water system. Also, increasing axial stress induced by the change in volumetric strain of the rock sample tested were more than 1 MPa for all experimental runs. A comparison results suggested that the obtained volumetric strains for this system could not be explained fully by change in bulk modulus before and after introducing scCO2 into the rock sample. The findings of our study might provide a significant contribution for the coupled hydro-mechanical behavior in storing CO2 into hard sedimentary rocks.
Intention to apply Artificial Intelligence using fact checking tools in disaster management
Authors: Zobeidi Tahere , Komendantova Nadejda
Published: March 2024

The daily dissemination of a substantial amount of information concerning to disasters and crises on social media platforms, including Facebook, Instagram and Twitter in one side, and the sensitivity of this information, on the other hand, underscores the importance of evaluating the credibility of online information in this area. Fact-checking tools employing artificial intelligence represent a novel approach to verifying the validity of online information across various fields, including disaster management. The inclination of individuals to utilize fact-checking tools in such circumstances is influenced by their perceptions. Although there is a limited studies on the impact of perceptions and information processing on the intention to employ fact-checking tools in disaster-related contexts, it is anticipated that factors like critical thinking, as a concept that involves meticulous assessment of unclear or requiring careful consideration, heuristic processing, a concept indicating acceptance of news content without filtering, and the new-source tracking a concept demonstrating openness and positivity towards social media information, play pivotal roles in predicting this intention. Consequently, a conceptual framework was formulated wherein critical thinking, aside from its direct impact on the intention to use fact-checking tools, also exerts influence through two mediators of information processing and the new source tracking variables. This study's framework was examined using data from 202 respondents across various European countries, collected through an online survey. The conceptual framework analysised utilizing AMOS software. Descriptive findings indicate a moderate level of familiarity with misinformation detection tools among respondents (M=2.65; sd=1.04). Respondents exhibited close knowledge levels regarding fact-checking tools such as Rbutr, Foller, me and Botometer, Fakespot, NewsGuard, and Greek Hoaxes Detector, ranging between approximately (1.57-1.70). Contrary to initial expectations, the study's results reveal that critical thinking, was unable to directly predict the intention to use fact-checking tools. However, the indirect effect of critical thinking was confirmed through the two mediators of new source tracking and information processing (heuristic processing). Critical thinking significantly influenced the new source tracking (β=.49; p
The pulse-like dynamics of large earthquakes illuminated by a minimal elastodynamic model
Authors: Barras Fabian , Aharonov Einat , Renard François
Published: March 2024

Observations suggest that large earthquakes often propagate as self-healing slip pulses but the mechanical reason of this ubiquity remains debated. Pulse-like ruptures differ from the classical crack-like dynamics by the fact that the slipping portion of the fault is limited to the immediate vicinity of the propagating tip. In this work, we first propose a minimal model describing the dynamics of large earthquakes. In its simplest form, the model contains only two free parameters: a dimensionless stress parameter characterizing the initial state of stress along the fault and a ratio of elastic moduli. The model illuminates how self-healing slip pulses can be produced by the paucity of elastic strain energy that arises once the rupture dynamics interplays with the finite geometry of fault zones—even in the absence of additional mechanisms such as rate-dependent friction.Next, we discuss the example of faults surrounded by a damage zone whose reduction in elastic wave velocity restricts the flow of strain energy to the rupture tip and promotes pulse-like rupture. Using the proposed model, we demonstrate how the contrast in wave velocities and the initial stress level in the fault zone mediate the propagation mode of the earthquake.
Increasing the grid flexibility with unconventional operations in one of the largest Pumped Storage Units in EU: The tests of XFLEX Hydro in Frades II
Authors: Presas Alexandre , Valentin David , Diogo Pedro , Jung Alexander , Moraga Greco , Egusquiza Monica , Egusquiza Eduard
Published: March 2024

Pumped Storage Hydro (PSH) serves as a significant contributor to the transition toward net zero emissions, primarily due to its capacity to store large amounts of energy with very high round trip efficiency (RTE). This is especially important given the volatility and unpredictability associated with the current energy mix, largely influenced by sources like wind and solar. Managing these fluctuations poses operational challenges for pumped storage schemes. Within the EU's XFLEX Hydro project, various developments and operational strategies have been tested in one of the largest Pumped Storage Units globally, aiming to enhance actual Pumped Storage flexibility services. This paper provides an overview of the effects of some of the most demanding operations, such as fast ramps, variations in power as pumps, and hydraulic short-circuit operations in the hydraulic and mechanical components of the Pumped Storage Plant.
Exploring the capacity of climate art to communicate climate change from a cultural perspective 
Authors: Radionovskaya Svetlana
Published: March 2024

The word «art» can be interpreted in very different ways. Institutions such as museums, galleries and art schools, and art critics are the pinnacle of contemporary art and set certain «trends», which are easily spotted in contemporary galleries. Conceptual artworks presented in these exhibitions often go on to generate thousands-to-millions of pounds at art auctions therefore creating an elitist art world.How can contemporary visual arts (and can they) offer different ways of imag(in)ing the problem of climate change, which would create an experience beyond visual (i.e. emotive and behavioral)? Here, I review «historical» climate art and propose that climate art can be split into three categories: representations (emphasizing visualization and communication of climate change); performance and conceptual art (engagement through immersion and experience); and interventions such as public engagement and activism (invoking motivation to take action). What are the challenges associated with these categories?According to the art world, the good art, i.e. the conceptual art, or “art for art’s sake” as originally described by Clement Greenberg in 1940, appeals and creates a desired experience for a limited amount of people with an attained cultural capital, and thus, I argue that it ultimately fails to communicate climate change to the wider public; yet it’s the preferred category of the art institutions. In the meantime, the bad art, which is more illustrative and communicates the climate message clearly, disinterests artistic institutions and critics. This slowed the development, and led to a decline of climate art produced, or displayed in galleries in the late 2000s and early 2010s; however an explosion of climate art popularity has since occurred in the last five years, likely because the issue of climate change has been brought to the forefront of political debate and contemporary culture. Therefore, it remains an open question how to best portray, communicate and create an “ultimate” experience of climate change through art and how to assess the success of these pieces (eg. art critics opinions, public opinions/ interpretations, amount of income generated); but it is clear that collaboration between scientists and artists is desperately needed to develop climate art.
Numerical assessment of effective bulk moduli of porous rocks using high-performance computing on GPUs
Authors: Yakovlev Maxim , Yarushina Victoriya
Published: March 2024

The coupled process models incorporate fluid flow, chemical transport, and mechanical deformation equations. These equations adhere to the thermodynamic principles ensuring the preservation of mass and energy within the system. However, for accurate predictions, it is crucial to establish closure equations that provide additional information and ensure the model's completeness. Closure equations are derived either from extrapolating experimental data or from micromechanical models that consider processes at the scale of individual grains or particles. Microscale models are often based on simplified analytical solutions obtained for idealized conditions, which may not fully capture the complexity of real-world situations. For instance, these models may assume a dilute concentration of voids or pores, neglecting interactions between the pores. While this assumption may be suitable for very small porosities below 1%, it may not accurately reflect interactions at porosities around 10%, influencing the compaction process. One approach to address this challenge is to derive more sophisticated analytical solutions, which may sometimes be impractical. Alternatively, a common strategy is to retain simplified solutions and validate them against numerical simulations that include multiple interacting voids. Effective bulk modulus, frequently employed to describe compaction-driven fluid flow in porous rocks, relies on effective media models. We propose a new effective media model based on a Representative Volume Element consisting of multiple interacting pores. To address stress and strain field interactions caused by multiple pores in an elastoplastic matrix, we utilize the numerical simulator CAE Fidesys, implementing classical associated plastic flow laws with von Mises and Tresca yield criteria. For viscoplastic rocks, the correspondence principle is applied. We derive 2D effective stress-strain relations for porous viscoelastoplastic rocks under a general non-hydrostatic stress field and compare the results with existing and novel analytical solutions.
Strength variability of the Mediterranean Outflow Water during late Quaternary: Preliminary results from IODP Site U1588
Authors: Wu Jiawang , Chen Xinyang , He Zongxian , Deng Qin , Zhong Lifeng , Pang Xiaolei , Hodell David , Abrantes Fatima , Zarikian Carlos Alvarez ,   Expedition 397 Scientific Party
Published: March 2024

The Mediterranean Sea is thought to play a role in changing past ocean circulation and North Atlantic climate, through the outflow of warm, saline intermediate waters (Mediterranean Outflow Water; MOW) into the North Atlantic. Previous studies mostly focused on the Gulf of Cádiz, immediately after the MOW existing the Mediterranean, but how the MOW varied along the northward transport is still unclear. Fine grain-size parameters have been widely employed to infer paleo-flow speeds of near-bottom currents in the deep sea, in particular the terrigenous non-cohesive “sortable silt” (denoted as SS) controlled by selective deposition. Here we present terrigenous sediment grain size results on IODP Site U1588 (37°57.61′N, 9°30.99′W, 1339 m water depth), which was retrieved from the Iberian Margin during the IODP Expedition 397. Our aim is to reconstruct strength variations in the lower branch of the MOW over the past ~250,000 years. After removing organic matter (leached with 10% H2O2 at 85 ℃) and marine carbonates (leached with 0.5 M HCl), the terrigenous detrital component of about 100 samples were measured on a Malvern Mastersizer 3000 instrument. Our grain-size results show a bimodal distribution, with a small peak near 1 μm and the main mode between 5–8 μm. The correlation between the percentage and mean of the sortable silt fraction (10–63 μm) is significant (R2=0.43, P
Learning from the past to shape the future. Harnessing multi-scale human data and earth observations to foster sustainable water usage and societal adaptation to climate change
Authors: Cominola Andrea
Published: March 2024

Human-water feedbacks have been increasingly studied in the last decades, motivating the foundation of new disciplines such as socio-hydrology and, in general, enhanced interest toward conceptualization and modelling of the spatial and temporal dynamics of human-water systems. With anthropogenic activities being widely recognized as a major driver of global change and the human population being increasingly exposed to hydroclimatic extreme events, human systems are now at the forefront of the water cycle. Yet, human preferences, behaviors, and decisions in relation to water systems - including water usage dynamics, adoption of precautionary measures against climate extremes, and adaptation of urban landscapes - are often modelled based on behavioral or economic theories, or derived from small-scale samples. This often leads to heterogeneous results, which are often case-specific, or lack validation against real-world observations.The availability of increasingly fine-resolution data from distributed sensors and databases (e.g., water consumption data from intelligent meters, flood insurance adoption records at the household level, and socio-demographic data) and earth observations (e.g., aerial and satellite imagery) provides us with an empirical basis to model heterogeneous individual and societal behavioral patterns, along with their determinants.In my research, I strive to develop multi-disciplinary data-driven behavioral modelling approaches that bridge hydrologic/hydraulic sciences, informatics, economics, and systems engineering and harness information from multi-scale human data and earth observations and the power of data analytics and machine learning to better understand, model, and characterize human behaviors in coupled human-water systems. In this talk, I will first provide an overview of recent advances in descriptive behavioral modelling in human-water systems, with a focus on household-to-continental scale modelling of residential water consumption patterns and adoption of household flood insurance. Second, I will elaborate on modelling challenges that are motivating ongoing research related to machine learning-based behavioral models, including model explainability, data and computational requirements, generalization and scalability, and the influence of data resolution in time and space. Finally, I will discuss how developing descriptive models that learn human behaviors retrospectively can be used to inform forecasting tasks and formulate policy-relevant recommendations to shape future societal adaptation to climate change. Implications span from informing the design of feedback-based digital user engagement in pursuit of water conservation, to fostering proactive climate adaptation, addressing societal inequalities and heterogeneous water access and affordability conditions, or evaluating incentive programs and policies for sustainable urban development.
The barystatic contribution to multi-decadal sea-level change in the 19th century.
Authors: Jackson Luke , Williams Sophie , Hibbert Fiona , Dangendorf Sönke , Garrett Ed , Sole Andrew , Gehrels Roland
Published: March 2024

Understanding long-term trends in mass loss is vital for assessing the (in)stability of ice sheets and glaciers and their subsequent contribution to global mean sea level. Observational estimates of mass loss from the Greenland and Antarctic Ice Sheets are scarce before the satellite era (i.e., 1990s), and from glaciers before the 1950s. A variety of modelling techniques (process-driven and statistical) have been employed to synthesise and extend observational estimates, so that much of the 20th century sea-level budget is closed within uncertainty. Despite this work, uncertainty remains, particularly for contributions prior to ~1940 and the 19th century. 

Sea-level fingerprinting exploits the fact that the geometry of land-based water masses (i.e., ice sheets, glaciers, hydrological storage) and any changes (via loss or gain) will generate a unique gravitational equipotential surface (fingerprint). We apply this technique in a Monte-Carlo-based linear inversion model to isolate the globally averaged barystatic contribution from Greenland, Antarctica and glaciers over pentadal periods since 1813. We use a selection of long-duration tide gauges and high-resolution proxy-based sea-level reconstructions, with model-based glacio-isostatic adjustment (GIA), stero-dynamic, and terrestrial water storage corrections. 

Our initial findings confirm the validity of the approach when comparing barystatic contributions to observed estimates for the last 50 years. Whilst uncertainty is significant for the 19th century, the barystatic contribution deviates from zero in different pentads. We also conduct a sensitivity analysis to evaluate the idealised locations/corrections required to enhance confidence in the inversion procedure.
Earth Observation Applications through Neural Embedding Compression from Foundation Models
Authors: Gomes Carlos , Brunschwiler Thomas
Published: March 2024

Earth observation (EO) repositories comprise Petabytes of data. Due to their widespread use, these repositories experience extremely large volumes of data transfers. For example, users of the Sentinel Data Access System downloaded 78.6 PiB of data in 2022 alone. The transfer of such data volumes between data producers and consumers causes substantial latency and requires significant amounts of energy and vast storage capacities. This work introduces Neural Embedding Compression (NEC), a method that transmits compressed embeddings to users instead of raw data, greatly reducing transfer and storage costs. The approach uses general purpose embeddings from Foundation Models (FM), which can serve multiple downstream tasks and neural compression, which balances between compression rate and the utility of the embeddings. We implemented the method by updating a minor portion of the FM’s parameters (approximately 10%) for a short training period of about 1% of the original pre-training iterations. NEC’s effectiveness is assessed through two EO tasks: scene classification and semantic segmentation. When compared to traditional compression methods applied to raw data, NEC maintains similar accuracy levels while reducing data by 75% to 90%. Notably, even with a compression rate of 99.7%, there’s only a 5% decrease in accuracy for scene classification. In summary, NEC offers a resource-efficient yet effective solution for multi-task EO modeling with minimal transfer of data volumes.
Combining scanning-DOAS and SO2 camera observations leads to more robust volcanic SO2 flux records.
Authors: Lo Bue Trisciuzzi Giovanni , Aiuppa Alessandro , Salerno Giuseppe , Delle Donne Dario , Bitetto Marcello , Curcio Luciano , Vitale Angelo , Nogueira Lages Joao Pedro , Lo Forte Francesco Maria , Murè Filippo , Maugeri Roberto , Principato Paolo
Published: March 2024

The volcanic SO2 flux is key indicator of magma influx into the shallower portions of magmatic plumbing systems, and as such is central to volcano monitoring. However, observations are challenged by a variety of technical and methodological caveats and limitations, requiring the use of a multiple-technique approach as a key to acquiring more robust SO2 flux time-series. Here, we compare ~9 years (2014 to 2022) of SO2 flux measurements at Stromboli obtained through (i) a near-vent SO2 camera (UV1 system, managed by UNIPA) and (ii) a network of scanning-DOAS spectrometers (FLAME network, managed by INGV-OE). The UV1 system operates in close proximity to the summit craters (~500 m), while the FLAME network intercepts the volcanic plume from a greater distance (~2 km).We find remarkable differences in the SO2 flux time-series streamed by the two observational techniques, with the FLAME daily averages fluxes being up to ~200% higher than those seen by the SO2 camera. By examining the individual components involved in the flux calculation, we find the SO2 integrated column amounts (ICAs) to match one each other within a factor 30%. Hence, the large mismatch between the two SO2 fluxes is primarily caused by large differences in the used plume speeds. By applying a simple dispersion model, we find the Stromboli’s plume to exhibit a non-Gaussian dispersion behaviour, in which in-plume SO2 concentrations are non-linearly diluted (upon atmospheric dispersion) as a function of wind intensity. In the “puffing” plume conditions our model suggests for Stromboli, use of wind speed as a proxy for real gas velocity may not be appropriate, as it can lead to a net overestimation of SO2 fluxes when observed by distally operating scanning-DOAS. In contrast, SO2 camera observations can provide more accurate plume transport results, but are challenged by radiative transfer issues in the optically opaque, proximal plume.Ultimately, we propose a new SO2 flux record for Stromboli that combines DOAS and SO2 camera and takes advantage of the specific advantages of both techniques. Our combined SO2 fluxes is obtained by combining the SO2 camera-derived plume velocities (obtained from the optical flow algorithm applied to high-frequency UV camera images in near-vent conditions) with the FLAME-derived ICAs, recalculated at source conditions using an experimentally derived plume dilution function. The resulting SO2 flux exhibits a stronger correlation with volcanic activity than obtained using any of the two techniques alone. We emphasize that integrating near-vent measurements through UV cameras with distal scanning-DOAS measurements may significantly improve our understanding of volcano degassing dynamics and behaviour.
 Systematic detection of abrupt change and tipping points in TIPMIP 
Authors: Loriani Sina , Dennis Donovan , Donges Jonathan , Sakschewski Boris , Winkelmann Ricarda
Published: March 2024

With ongoing anthropogenic emissions and ensuing accelerated climate change, the planet is increasingly leaving its long-stable Holocene state. In fact, recent assessments have shown that a range of climate tipping points are at risk of being crossed at warming levels well within temperature projections of the 21st century. However, such assessments have been largely based on expert judgement of scattered literature, with corresponding large uncertainties in critical thresholds and potential tipping dynamics. The Tipping Point Modelling Intercomparison Project (TIPMIP, www.tipmip.org) aims to close this research gap through a standardised framework for numerical experiments exploring tipping across systems and models. Built on precursory experiments, we here introduce the Tipping and Other Abrupt Events Detector (TOAD) method, to automatically identify spatial clusters of dynamically connected regions exhibiting tipping dynamics. This will serve as an evaluation scheme for the suite of experiments generated within the TIPMIP protocol. Overall, this systematic approach to tipping point risks at different levels of human pressures can inform quantification of planetary or Earth system boundaries to map out the safe and just operating space for humanity in the Anthropocene.
What information can be gathered from patterns in turbulent free-surface flows?
Authors: Babiker Omer , Aanes Jørgen , Xuan Anqing , Shen Lian , Ellingsen Simen A
Published: March 2024

Gas transfer between ocean and atmosphere is largely governed by the turbulence in the topmost centimetres beneath the free surface. It has been frequently observed that areas of strong positive divergence of the surface-tangential velocity field correspond to efficient surface renewal and consequently increased transfer of gas across the interface. Patches of strong positive surface divergence occur through intermittent upwelling events visible as ``boils'' on the surface.It has been qualitatively observed that surface-attached ``bathtub'' vortices tend to appear at the edges of upwelling boils, as well as sharp valleys, sometimes referred to as ``scars”. Surface-attached vortices and scars leave imprints on the surface which are particularly simple to detect: the vortices are circular and live for a long time, while scars are sharp and elongated structures.From direct numerical simulations, we show that a very high correlation exists between the time-dependent number of surface-attached vortices and the mean square of the surface divergence. We use a newly developed method whereby the surface-attached vortices are identified with high precision and accuracy from their surface imprint only.We also looked at the turbulent structures just beneath the surface-attached vortices and the scars, noting how far under the surface these structures propagate and, thus, how far into the flow subsurface features can be read from patterns on the surface only.The main application is in remote sensing, as these patterns on the surface can be easily detected using camera footage, for example. These patterns would give estimates of the subsurface quantities without the need for expensive measurement.
Analysis of the spatial distribution of perceived flood damage in Italy
Authors: Zambrini Federica , Menduni Giovanni , D'Alessandro Sara
Published: March 2024

Being able to quantify the possible flood damage in a territory is a pivotal matter while designing strategies to mitigate risk. In this context, data driven quick assessment can be an useful tool to better understand the territory needs and to establish priorities.With our work we have been working on the spatial distribution of perceived flood damage in Italy, analyzing data coming from more than 45000 citizens’ declarations registered after events occurred in Italy between 2013 and 2021. We focused on events which required the national state of emergency, which activated the procedure of claims' collection carried out by regions. Working on such material, we’ve been able to identify the subset of data which are specifically associated to flood events and to geolocalize them. Once the definitive dataset was ready we had a framework of the perceived damage at square meter in the country, which has been analyzed at different spatial scales. The work highlighted a great variability and inhomogeinity within different areas in the country. A further step of the analysis tried to link the perceived damage to the characteristic of the society and the territory, explaining what drives the perception of damage.
Towards automated rapid earthquake dynamics characterization:  a proof of concept based on the 2024 Mw7.4 Noto Peninsula, Japan, earthquake
Authors: Ulrich Thomas , Gabriel Alice-Agnes , Kutschera Fabian
Published: March 2024

Rapid earthquake source characterization following an earthquake is crucial for effective emergency response and risk management. It may help for example in warning of potential secondary hazards like tsunamis or aftershocks or may guide resource allocation towards efficient coordination of rescue operations. The United States Geological Survey (USGS) provides routinely generated kinematic models based on teleseismic body and surface wave data, CMT solutions, and scaling relationships (Hayes, 2017). For the most significant earthquakes, models can be iteratively updated based on available data, such as strong motion or geodetic data (Goldberg, 2022). Nevertheless, in most cases, such automatically derived source characterization is limited to static slip. Yet, a characterization of rupture kinematics or dynamics would be beneficial as well. We here propose a workflow for the automated generation of earthquake dynamic rupture scenarios based on the fault slip distribution of a given kinematic model, and we present a proof of concept based on the 2024 Mw7.4 Noto Peninsula, Japan, earthquake. Our workflow, here based on the USGS model,  consists of retrieving the fault slip associated with the kinematic model, automatically generating a mesh and the input files, and running a set of dynamic rupture scenarios, informed by the stress change of the kinematic model. We plan to explore a limited parameter set, from which a preferred scenario could be selected, based on the fit to the input slip distribution, the fit to routinely inferred moment rate release, or to other available datasets, such as teleseismic, geodetic, or strong motion data. We expect such routinely derived dynamic rupture scenarios to be beneficial in the emergency response phase, for example, to better assess the potential damage to structures. More generally, such systematic source characterization could feed earthquake source databases, and therefore contribute to improving earthquake hazard assessment and the overall understanding of tectonic processes.
Uncertainty estimation of conductive thin plates parameters through a Bayesian approach
Authors: Vinciguerra Alessandro , Marquis Guy , Girard Jean-François , Harrison Grant , Williard Elodie
Published: March 2024

The uranium deposits of the Athabasca basin (Canada) represent one of the world’s highest-grade uranium resources. They are unconformity-related type at the base of relatively flat-lying sequences, where faults acted as circulation paths for hydrothermal fluids. The fault zones often contain graphitic mineralization and hence represent a valuable exploration guide of small lateral extension but detectable by electromagnetic (EM) surveys. Time-domain EM (TEM) is the method of choice for uranium exploration in the Athabasca, and taking into account the frequencies involved we can approximate the graphitization along the fault as a conductive thin plate.To better determine the geometry of the deposit, it might be crucial to recover the subsurface resistivity and the geometric parameters of the plate (position, dip, depth, azimuth etc.). Moreover, the assessment of the uncertainty associated to the parameters can help to evaluate the reliability of geological models and to guide the subsequent drilling activities.A quantitative approach consists of employing Bayesian inversion algorithms, which allows to exploit the prior information available. Indeed, Bayesian inversion algorithms aim to solve the inverse problem statistically returning the posterior probability density function (ppdf). In particular, they are based on the Bayes theorem, which relates the prior information (e.g. from geological and petrophysical models) with the likelihood function to assess the posterior probability density function and thus the uncertainty. We implement the Differential Evolution Markov Chain algorithm (DEMC), a multi-chain approach that integrates the Metropolis selection rule with population evolution to sample the ppdf. The chains run in parallel and each current model is updated drawing two other chains and exploiting the models at the previous iteration. After an initial stage of burn-in, the algorithm reaches the stationary regime where the chains start sampling the ppdf resulting at the end in an ensemble of models. From these models the moments of first and second order (mean and variance) are computed obtaining the uncertainty of the inverse problem solution. As forward operator we employ the LEROI forward code developed by CSIRO (AMIRA), which computes the TEM response of one or more conductive 3D thin plates embedded in a horizontally layered earth.In this work we propose the DEMC inversion of TEM data as a tool to assess thin plates parameters and uncertainty in the context of uranium exploration. 
Slow-Slip Earthquakes observations in Costa Rica and their Potential Impact on Seismic Hazard Assessments
Authors: Arroyo Solórzano Mario , Cotton Fabrice , Weatherill Graeme , Jara Jorge , González Álvaro
Published: March 2024

Costa Rica is located at a subduction margin in a complex tectonic setting where four tectonic plates (Caribbean, Coco, Nazca, and Panama) interact, and large earthquakes are generated. Slow-Slip earthquakes (SSEs) are defined as a seismic activity that involves the gradual and aseismic release of tectonic stress. Therefore, SSEs play a very complex role in the seismic cycle, representing a crucial element to be considered in seismic hazard assessment. These events are a common feature in subduction regimes and have been reported in most of the well geodetically instrumented subduction zones worldwide. In northern Costa Rica, shallow and deep SSEs have been identified at the Nicoya peninsula, and recently, shallow SSEs were also documented in the southern part of the country at the Osa peninsula. Here, we present a synthesis and compilation of SSEs observations in Costa Rica based on an in-depth review of previous studies, aiming to delve into potential implications and explore possible viable ways to incorporate it in seismic hazard assessments. We accomplished this by identifying differences among patches inside the subduction segments where occur or not SSEs, evaluating the coupling factors, and considering the observations of recurrence intervals to infer slip deficit values. Based on the previous analysis, we summarized the main findings regarding possible implications of the SSEs occurrence in Costa Rica for seismic hazard purposes. A significant result from the comparison with the 2022 Costa Rica seismic hazard model is that the non-quantification of SSEs in PSHA may be conducting to overestimations, particularly in subduction margins near the coast, as in the case of Costa Rica.