Paleointensity observations from meteorites provide insights into planetary formation and evolution. Meteoritic samples are usually dominated by Fe-rich kamacite, that is capable of faithfully recording the ancient dynamo behavior of meteorites’ parent body. To retrieve paleointensity estimates, most experimental protocols assume that samples are dominated by uniformly magnetized particles. However, most magnetic carriers observed in extraterrestrial samples are non-uniformly magnetized. This inconsistency represents a major impediment in reliably reconstructing paleointensities from meteorites. Here we present the State Group Algorithm (SGA); a micromagnetic based model capable of efficiently simulating thermoremance acquisition of magnetic particles with a single-vortex domain state. The results show that iron particles can acquire thermoremance that is linear with the external field up to ∼100 μT. Single-vortex cooling rate effects are generally weaker than those of single-domain particles, providing more accurate paleointensity estimates. A small number of particles exhibit inverse cooling rate effects, leading to underestimates in paleointensity.

A document by Freya M E Muir. Click on the document to view its contents.

This study presents WaterCROPv2, an advanced agro-hydrological model designed to estimate irrigation water demand at a national scale by effectively balancing hydrological accuracy with manageable data requirements. Building on the original WaterCROPv1 model, WaterCROPv2 incorporates several significant enhancements, including hourly computation, rainwater canopy interception, soil-dependent leakage dynamics, and daily evapotranspiration trends based on localized meteorological data. A comparative analysis of both model versions demonstrates that WaterCROPv2 offers improved reliability in estimating irrigation water demand across various climatic regions, including both dry and wet areas.   When applied to maize cultivation in Italy during 2010, WaterCROPv2 aligned well with independent data from the Italian National Institute of Statistics (ISTAT), showing potentials for expansion to other countries. Notably, the relationship between estimation errors and cumulative precipitation reveals spatial variability: in wetter northern regions, the model slightly underestimates irrigation needs, while in the drier southern areas, it tends to overestimate demand.   Furthermore, WaterCROPv2 was used to assess mean irrigation water demand for maize from 2005 to 2015, illustrating its potential as a decision-support tool for policymakers. This analysis identifies optimal areas for maize cultivation and highlights necessary crop shifts. It also indicates where changes in irrigation systems could yield significant benefits and where investments are most feasible. Model simulations predict that transitioning to micro-irrigation could reduce national water demand by 21%, with reductions of 30-40% in regions with historically abundant water resources where inefficient irrigation practices are prevalent. However, only regions lacking existing irrigation infrastructure are likely to benefit from such system transitions.

Remote Sensing-derived Flood Inundation Maps (RS-FIM) are an attractive and commonly used source of evaluation benchmarks. Errors in model-predicted FIM (M-FIM) evaluation results due to biases in RS-FIM benchmarking are quantified by introducing a high-confidence benchmark FIM, which was manually delineated from ultra-resolution imagery, as Ground Truth. The evaluation results show considerable differences in M-FIM accuracy assessment when using lower-quality benchmarks. A RS-FIM enhancement (gap-filling) procedure is presented and its effect on FIM evaluation results is analyzed. The results show that the enhancement is insufficient for significantly improving the robustness of the evaluation. The impact of including/excluding Permanent Water Bodies (PWB) on FIM evaluation results is analyzed. The results show that including PWB in FIM evaluation can significantly inflate the model accuracy. A novel evaluation strategy is proposed and analyzed. The proposed evaluation strategy is based on excluding low-confidence grid cells and PWB from the M-FIM evaluation analysis. Low-confidence grid cells are those that were estimated to be flooded by the gap-filling procedure, but were not classified as such by the remote sensing analysis. The results show that the proposed evaluation strategy can dramatically improve the robustness of the evaluation, except when a considerable number of false positives exist in the RS-FIM. The analyses showcase the many challenges in FIM evaluation. We provide an in-depth discussion about the need for standards, user-centric evaluation, the use of secondary sources, and qualitative evaluation.

A document by Noya Vainbrand. Click on the document to view its contents.

This study focuses on the production of Sustainable Aviation Fuel (SBAF) within the framework of biorefineries, emphasizing the critical role of catalysis in enabling efficient and sustainable conversion of biomass feedstocks. The research explores various production pathways, including Hydroprocessed Esters and Fatty Acids (HEFA), Fischer-Tropsch (FT) synthesis, and Alcohol-to-Jet (ATJ), and discusses their associated challenges, such as feedstock availability, high production costs, and technological limitations. A comprehensive overview of different catalyst types, including zeolites, metal oxides, and novel catalysts like MOFs and bifunctional catalysts, is presented, along with their catalytic mechanisms, deactivation pathways, and regeneration strategies. The paper also delves into the importance of catalyst characterization techniques in understanding catalyst performance and optimizing reaction conditions. Finally, the environmental and economic aspects of biorefinery-based SBAF production are discussed, highlighting the need for sustainable feedstock sourcing, efficient process design, and supportive policies to ensure the commercial viability and environmental sustainability of this emerging technology. This review aims to provide a comprehensive understanding of the catalytic challenges and opportunities in the production of SBAF within integrated biorefinery systems, paving the way for future research and development efforts.

The enhanced severity and frequency of extreme weather events, such as combined heat waves and heavy rainfall, pose significant concerns due to their devastating regional impacts. This research quantifies the association between heatwaves and heavy rainfall events using Event Coincidence Analysis during 1951-2020. We compute both the Precursor Coincidence Rate (PCR) and the Trigger Coincidence Rate (TCR), which are key metrics in understanding the physical link between heatwaves and subsequent heavy rainfall events. The PCR indicates the proportion of heatwave events that precede heavy rainfall events, providing insight into the reliability of the precursor (heatwave events) as an indicator of the trigger event (heavy rainfall events). TCR represents the proportion of heavy rainfall events that follow heatwave events, useful for evaluating the predictive power of the precursor. Our results reveal TCR decreased in the recent period as compared to the base period for shorter time frame (temporal windows ΔT=2 and Delta ΔT=5), however, exhibited a notable rise (50%) observed in approximately 70% of the grid points for longer temporal windows ( ΔT=7). The observation is very well in congruence that the moisture holding capacity of the atmosphere has enhanced and convective activity and latent heat release (heatwave producing extreme rainfall) take longer than usual. Moreover, PCR increased for all temporal windows, indicating an increased probability of heatwaves triggering precipitation events within 7 days of their end and predictive potential in the recent period compared to the base period. Approximately 27% of grid points experienced one heat-wave event within 7 days prior to heavy rainfall occurrence in the recent period, compared to 13% in the base period, for every 5 precipitation events. Results indicate an increased coupling between the occurrence of heat waves and heavy rainfall within a very short period, attributed to moisture convergence and atmospheric convection.

Airborne research and observations continue to be a part of a robust and resilient Earth Science Division program as defined in NASA’s decadal survey. As part of this, planning of the suborbital research flights involves much thought, time and effort in order to translate scientific sampling objectives to aircraft waypoints. We present the planning process and tools that have been used in 2 field campaigns operated by NASA in 2024; ARCSIX (Arctic Radiation-Cloud-Aerosol-Surface Interaction Experiment) and PACE-PAX (The Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment). Combined these campaigns have different guiding principles, regions, and 5 aircraft. To enable more efficient science operations using distributed observations, we developed an efficient tool for flight planning, which achieved numerous science objectives.

The flux of galactic cosmic rays (GCRs) is modulated by solar activity on different time scales. This is often described with the force-field approximation via the single variable parameters of the modulation potential, which formally corresponds to the average rigidity loss of GCR particles in the heliosphere. The force-field approximation is usually assumed to work properly only for periods of a solar rotation (27 days) or longer. However, daily values of the modulation potential have been constructed based on ground-based neutron monitor (NM) data suggesting that this approach might work reasonably well also on shorter time scales. Here we check this by simulating the daily GCR proton fluxes using the force-field approach and the daily modulation potential reconstructions, and confronting them with the daily proton-flux measurements by the AMS-02 onboard the International Space Station. A polynomial regression between the simulated and measured fluxes is proposed for longer-term analyses and interpolations. For mid-rigidities from 3\,–\,11 GV, the agreement is accurate within $\pm 1\%$ and slightly worsens towards higher rigidities, likely because of smaller statistics. We also found a lingering solar-cycle trend at lower energies and the effect of the solar magnetic field polarity. These findings can be used for the utilization of the force-field approach for short-term variations.

A new signal simulator for Doppler velocity, derived from W-band cloud profiling radar onboard EarthCARE, is developed and implemented into the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package version 2 (COSP2). The simulator converts the vertical motion of hydrometeors and cumulus mass flux in global climate models (GCMs) into Doppler velocity signals, providing statistics on Doppler velocity and its spectrum width in the form of Contoured Frequency by tEmperature Diagram (CFED) or by Altitude Diagram (CFAD). To account for the different treatments of vertical air motion in stratiform and convective clouds within GCMs, their statistics are processed separately. The simulator was tested on the MIROC6 GCM and compared with ground-based radar measurements. The results showed consistency in ice particle growth and melting between the model and the observations. However, the droplet fall speed in the model was quantitatively underestimated, revealing a bias in the cloud microphysics of MIROC6. The combined use of this simulator with another in COSP2 also allows for the investigation of Doppler velocity characteristics as a function of cloud type. The developed simulator enabled COSP2 to generate model diagnostics of cloud particles and cumulus vertical air motions, facilitating future global comparisons with EarthCARE data. The enhanced capabilities of COSP2 thus will add value to model evaluation through the combined use of multiple simulators and multi-sensor synergistic observations provided by EarthCARE.

Globally gridded climate products are often used as substitutes for ground-based station data in health studies. However, each product has unique strengths and limitations that require careful evaluation before use in health applications. Given its high spatiotemporal variability, this is especially true for precipitation, which is often a key driver of disease burden. It is therefore important to systematically assess global gridded precipitation products (GGPPs) for their utility in models of health impacts. Here, we compared and evaluated six GGPPs with different characteristics and spatial resolutions, including reanalysis (ERA5/-Land), satellite-based (CHIRPS, PERSIANN-CDR), and interpolated gauge-based products (CRUTS, GPCC). We provide the first comprehensive analysis specifically evaluating the appropriateness of different precipitation datasets for health research across South America. Our analysis focuses on Brazil and Colombia, two diverse countries differing e.g., in orography, climate, and size. We explored how spatial deviations and uncertainties in these GGPPs affect area-level precipitation estimates typically used in health studies, where epidemiological data are usually attributed to administrative units. Emphasis was placed on analyzing seasonal patterns and deriving extreme precipitation indices, along with disease-related bioclimatic variables. Each dataset was evaluated against national weather station data. The findings revealed substantial variation in the accuracy of local and aggregated precipitation estimates across GGPPs and indicates that estimating tropical precipitation was especially challenging for reanalysis products. Conversely, CHIRPS consistently demonstrated the best overall performance. These results underscore the importance of careful dataset selection for health impact studies.

Using an automated novel approach we conduct a reproducible systematic survey of electromagnetic ion cyclotron wave activity detected by Van Allen Probe B during the time period 2013 January 1 – 2019 July 15. We identify approximately 500 hours of EMIC wave activity, an occurrence rate of ∼ 0.85%. Accounting for satellite dwell time, we find that EMIC waves preferentially occur on the dayside, between 9 and 15 magnetic local time. This is true for both the H+ and He+ wavebands. Higher amplitude waves are found at higher values of L shell, while weaker waves occur at low L. The highest amplitudes are concentrated at high L near dawn and dusk. It is also found that EMIC wave occurrence is enhanced during periods of strong geomagnetic activity, with an occurrence rate of 2.7%. During storm times, waves preferentially occur in the afternoon and early evening sectors. The full list of electromagnetic ion cyclotron wave detection times and their properties is made publicly available to the community. This provides a reference catalog for comparison with other magnetospheric phenomena and other wave databases.

The development of remote sensing of discharge algorithms has enabled us to estimate discharge without in-situ observations, yet their applicability of spatially continuous estimates of discharge is not adequately examined. This study calculated discharge for 668 continual reaches along the mainstem of the Yellow River using remotely sensed width from Landsat images and a discharge estimation algorithm based on the At-Many stations Hydraulic Geometry. Our result confirms the discharge decrease/increase at the dam/confluence points as well as the discharge reduction along the heavily irrigated areas, indicating the potential of capturing both natural and artificial discharge changes from the satellite. Moreover, the prediction fed by prior discharge, which gives the prior probability of the possible discharge values, that virtually considers the effect of water withdrawals improved the prediction accuracy, especially at downstream stations where the influence of irrigation is accumulated.

This study presents the first regional-scale analysis to quantify decadal trends and drivers of surface ocean acidification (OA) across the highly sensitive Pacific-Arctic Region (PAR) using a consistent trend methodology. From 1993 to 2021, the Southern PAR acidified at rates comparable to the global average, with pHT declining by 0.018 units dec−1 and aragonite saturation state (ΩAr) decreasing by 0.063 units dec−1, primarily driven by anthropogenic CO2 uptake. In contrast, the Bering Strait exhibited slower acidification, with pHT declining by 0.011 units dec−1 and ΩAr decreasing by 0.020 units dec−1 — substantially lower than previously reported — likely due to increased primary productivity. The Northern PAR experienced the most rapid acidification: pHT decreased by 0.028 units dec−1 and ΩAr by 0.078 units dec−1, with the Beaufort Gyre acidifying 2–4 times faster than the global mean. This rapid change was driven by rising atmospheric CO2 and significant freshening linked to sea ice melt and increased riverine input, which reduced the ocean’s buffering capacity. Continued warming will likely exacerbate acidification in regions transitioning from multi-year to seasonal ice. While local processes such as primary productivity can temporarily counteract OA, whether they can offset rising anthropogenic CO2 levels remains unclear. This underscores the importance of biogeochemical models that integrate climatic and biological feedbacks, enabling accurate forecasts of OA changes and their impacts on marine ecosystems. These findings highlight the urgent need for sustained monitoring in the PAR, where accelerating changes threaten critical ecosystems.

Within the last 20 years, the alpaca (Lama pacos), a camelid native to South America's mountainous regions, has been introduced to diverse biomes in China. While their numbers are still relatively low (around 2000 in 2020), their presence in community farms/petting zoos and as privately held animals on smallholdings provides an environmental correlation acquired from the environments to which they have been introduced. In this project, bacterial isolates from alpaca faecal samples from six sites worldwide (four public petting zoos and two private smallholdings in Guangdong, Shandong, Zhejiang province and California) were examined for their antimicrobial resistance profiles.

Seismic attenuation tomography is used to map fluids and fractures beneath the Aluto volcano geothermal field of the Main Ethiopian Rift. We present 3D models of peak delay ($\Delta \log_{10}(T_{pd})$) and inverse coda quality factor ($Q_c^{-1}$) which are proxies for seismic scattering and absorption. High $Q_c^{-1}$ anomalies are observed near productive geothermal wells with high-temperature gradients and in areas of hydrothermal activity. High scattering attenuation anomalies are spatially associated with faults and fractures that serve as pathways for fluid flow. These variations correlate well with production variations in boreholes across the geothermal field. Furthermore, a prominent high-scattering (low-absorption) anomaly is observed at a depth of approximately 0-3 km bsl beneath Aluto, which is interpreted as the signature of an intrusive igneous body. Together, these methods for measuring seismic attenuation serve as valuable tools in geothermal exploration, offering constraints on fluid distribution, as well as structural and lithological variations.

There is a growing call to increase collaboration, inclusion, and engagement with end-users in applications such as remote sensing, Earth observation, artificial intelligence, and fire sciences. In response, Canada is making significant investments in cultivating collaboration and user engagement in fire and fire monitoring. Initiatives include the Canadian Forest Service’s Wildfire Resilient Futures Initiative and the upcoming WildFireSat mission. The fire monitoring community relies heavily on geospatial technologies, Earth observation, and remote sensing, benefiting greatly from researchers with diverse expertise and experiences. To advance inclusion and collaboration in fire monitoring, it is important to understand who is working in the field, their networks, and perceived barriers to research and collaboration. This presentation will overview the multi-year “Underserved Actors in Canadian Fire Monitoring” program and its early findings. The program is divided into three phases: 1) a bibliometric and initial network analysis to identify fire monitoring actors in Canada, 2) a survey of fire monitoring actors and enhanced social network analysis using identity-based information, 3) interviews with key actors to identify opportunities for improving collaboration and inclusion in fire monitoring. We will present outcomes from Phase 1, including an overview of the Canadian fire monitoring actors database and the results from a spatial social network analysis. Data on Canadian fire monitoring actors was collected using a systematic search of publication databases in combination with purposive sampling of research consortiums and fire organizations. Our results characterize the types and levels of connectivity within the Canadian fire monitoring community, which will guide subsequent survey and interview questions for the second phase of the study. This research has contributed to broader international efforts to characterize global fire monitoring users as part of the Committee on Earth Observation Satellite Working Group on Disasters Wildfire Pilot program. Additionally, outcomes from this research can be used to tailor resources from future collaboration and capacity-building efforts in fire monitoring.

Verifying model performance is essential for identifying strengths, weaknesses, and areas for improvement. This paper assesses the performance of four ionospheric models, IRI-2020, TIEGCM, WAM-IPE, and GITM in estimating the critical frequency of the F2 layer (foF2). The reference dataset was compiled from 40-50 ionosondes and COSMIC-2 satellite data. Model performance was quantified using Root-Mean-Square-Error (RMSE), Prediction Efficiency (PE), the Ratio of the Range (RR), and the Ratio of the maximum Amplitude (RA). The investigation period, from March 8 to April 30, 2023, featured diverse space weather conditions. Our findings indicate that IRI-2020 demonstrated superior RMSE and PE scores compared to the other models under conditions with maximum Kp < 7 in low- and mid-latitude regions. IRI-2020 was weakest in the low-latitude post-sunset sector however still outperformed the other models. WAM-IPE performed better than TIEGCM and GITM, generally underestimating foF2 peaks in low- and mid-latitude regions however excelled in estimating foF2 ranges as indicated by a superior RR score. TIEGCM exhibited unusually large foF2 ranges compared to observations in the low- and mid-latitude pre-sunrise and pre-sunset sectors. GITM consistently underestimated foF2 values in low- and mid-latitudes. For maximum Kp > 7, WAM-IPE showed the highest sensitivity to storm conditions however incorrectly estimated the absolute timing, location, and magnitude of foF2 changes. TIEGCM and GITM responses during disturbed periods were complex due to inherent model issues. In high latitudes, all models performed similarly for maximum Kp < 7, except WAM-IPE which maintained consistent levels of performance for Kp > 7.