In earth science, we must often move and store tremendous amounts of data from remote locations. Present options are typically limited to costly proprietary devices which are rigid in structure and require numerous ongoing expenses. The Openly Published Environmental Sensing (OPEnS) Lab at Oregon State University developed the OPEnS Hub, a new approach using low-power, open-source hardware and software to achieve near real-time data logging from the field to the web. The Hub is two orders of magnitude less expensive than commercial products, inherently modular and flexible, and aims to reduce technical barriers for users with little programming experience. Data can be collected remotely from nearly anywhere on Earth using a host of transmission protocols to relay data from distributed in-situ monitoring devices. Telemetry options include 900 MHz Long Range Radio (LoRa) with up to 25 km range and Nordic Radio Frequency (nRF) for higher data rates. Internet gateways include the established cell network infrastructure, Wi-Fi for high bandwidth applications, and Ethernet where available. The OPEnS Hub is capable of mesh networking with several nodes and backs up to an onboard microSD card. The Hub engages a dynamic, low-latency portal to Google Sheets via the free Application Programming Interface (API), PushingBox, and an adaptable Google Apps Script. This framework was tested on 12 unique sensor suites at remote sites in Oregon. This manuscript details our methods and evaluates PushingBox, Google Apps Script, Adafruit Industries’ open-hardware Feather development boards, the Hypertext Transfer Protocol (HTTP), and the foregoing modes of data transfer.

Chang’E-4, the first soft landing mission on the far side of the Moon, provided high-resolution close-range images up to 2.3 cm/pixel, especially suitable for the study of the degradation in the population of small-sized impact craters around the landing site. This paper presents a preliminary analysis of the population degradation mechanism of the small-sized impact craters. From mosaicked descent camera image, 6316 impact craters in the landing area were extracted, identified, and classified into four types according to their morphology: complete, slightly degraded, moderately degraded, and severely degraded craters. In-situ verification using the lander topographic camera image supported the crater degradation analysis around the landing site. The small-sized impact crater population equilibrium analysis was also carried out. Over 99% of the impact craters in the study area are in degradation, which suggesting the area is severely degraded. Our results suggest that similar to the findings for impact crater populations above the meter scale, the size of meter to centimeter scale impact craters is also strongly correlated with degradation, with smaller impact craters being more likely to degrade. Images from topographic cameras also confirm the severe degradation of small-scale craters. The crater populations in equilibrium of different resolutions areas shows that the cumulative SFD slope is contrary to previous research results, the smaller the size of the impact crater, the more difficult for them to achieve impact equilibrium, which due to secondary craters and the ground resurfacing caused by neighboring craters’ ejecta.

Recently, many studies have demonstrated the use of teleseismic P wave coda autocorrelation for imaging lithosphere structures. However, the reliability of the extracted reflections remains uncertain and a means of evaluation is lacking. In this paper, we propose a velocity analysis method that conveniently solves this problem in place of a synthetic experiment. This method considers the average velocity used for the horizontal slowness correction as an unknown quantity, and then uses the continuously varying average velocity for the horizontal slowness correction. Finally, this method obtains a stacked result that varies with the average velocity and the vertical two-way travel time to produce a va−t0 diagram. This method is similar to the velocity analysis method used in exploration geophysics. In this diagram, reliable reflections correspond to focused energy clusters, while noise lacks this feature. Therefore, this method helps determine which reflections are reliable, while also finding the appropriate parameters for data processing. Synthetic data tests were performed to demonstrate the validity of this method, as well as a test of field data for station BOSA, which illustrates the successful application of the method in the case of a sharp and flat Moho discontinuity. Finally, we applied the method to the NCISP-6 dense array, and observed obvious energy clusters representing reflections from the Moho discontinuity in the results of most stations. The depth and shape of the Moho discontinuity determined by this test is consistent with receiver function results, which verifies the robustness of this method in relatively complex applications.

• Neural networks can learn predictable signals of internal sea surface temperature variability at 1-3, 1-5, and 3-7 year lead times • Neural networks trained on climate model output can skillfully predict sea surface temperature variability in reconstructed observations • Neural network skill in predicting observed sea surface temperature variability depends on the climate model used for training

The existing theory for alongshore rhythmic bars relies on morphodynamic instabilities involving the wave-driven longshore current and rip currents. Transverse finger bars are common on coasts with a beach profile above the equilibrium profile (something not related to those currents). Here we show that under these conditions, the cross-shore transport can induce an instability which is triggered by the onshore transport together with wave refraction by the emerging bars. It is a finite amplitude instability, something not previously found in coastal geomorphology. We use a numerical model that filters out the dynamics associated to those currents. The alongshore spacing scales with the wavelength of the incident waves and the cross-shore extent is about the distance from shore to the depth of closure. The modelled bars compare qualitatively well with observations at El Trabucador back-barrier beach (Ebro delta, Western Mediterranean Sea).

A data-driven model of Earth’s magnetosheath is developed by training a Bayesian recurrent neural network to reproduce Magnetospheric MultiScale (MMS) measurements of the magnetosheath plasma and magnetic field using measurements from the Wind spacecraft upstream of Earth at the first Earth-Sun Lagrange point (L1). This model, called PRIME-SH in reference to its progenitor algorithm PRIME (Probabilistic Regressor for Input to the Magnetosphere Estimation), is shown to predict spacecraft observations of magnetosheath conditions accurately in a statistical sense with a continuous rank probability score (CRPS) of $0.227\sigma$ and more accurately than current analytical models of the magnetosheath. Furthermore, PRIME-SH is shown to reproduce physics not explicitly enforced during training, such as field line draping, the dayside plasma depletion layer, the magnetosheath flow stagnation point, and the Rankine-Hugoniot MHD shock jump conditions. PRIME-SH has the additional benefits of being computationally inexpensive relative to global MHD simulations, being capable of reproducing difficult-to-model physics such as temperature anisotropy, and being capable of reliably estimating its own uncertainty to within $3.5\%$.

Nine different ANN models are developed for the Kangsabati reservoir, India with a various sets of input and target variables. A three-layer fully connected feed forward multilayer network is used for all models. As for the input and target dataset required for training of each model, optimal solutions obtained from the deterministic DP model with generated inflow sequences are used. Back propagation algorithm is used during training. Regression parameters obtained from training under each model, are used to identify the better model. For this purpose, a new parameter $\phi$ is defined to select the best model performed. Simulation is done using the observed inflow record of 32 years, resulting nine monthly release policies which are compared with the release policy obtained from conventional DP and through error analysis in terms of statistical parameters. Finally, the best model is selected. The performance of the selected model is analysed by Monthly surplus/deficit plot in Kharif \& Rabi season. Overall, the performance of the selected ANN Model is satisfactory and acceptable as an optimising model for reservoir operation.

Comprehensive climate models fail to capture historical sea surface temperature (SST) trends in the Southern Ocean, where multidecadal cooling occurred despite global warming. Efforts to explain this discrepancy have invoked underestimated mixed-layer freshening, which produces cold SST anomalies through enhanced stratification. However, single-model freshwater forcing experiments have produced a wide range of SST responses, and the relative importance of different freshwater sources remains unclear. To address these ambiguities, this study quantifies linear SST response functions for standardized freshwater flux increases across CMIP6 models and estimates the 1990-2021 SST cooling from observed freshwater inputs. We attribute the lack of SST cooling trends in CMIP6 to missing Antarctic Ice Sheet meltwater and underestimated precipitation increases, each producing similar 1990-2021 cooling tendencies. Adjusting SST trends for these missing sources improves model-observation agreement, quantifying, for the first time, the impact of missing freshwater forcing on Southern Ocean SST trends across a multi-model ensemble.

Deep low-frequency earthquakes (LFEs) in Northeast Japan occur beneath active volcanoes at depths of 20-40 km. LFEs radiate low-frequency seismic waves, with most energy at 2-8 Hz, despite their low magnitudes (M < 2). Although many previous studies have obtained various focal mechanisms with non-double-couple components and suggested physical processes related to magma, the universal physical process is poorly understood. Therefore, we comprehensively determined the focal mechanisms of 264 LFEs for 26 volcanic regions in Northeast Japan using S/P amplitude ratios. Many of the obtained solutions have large double-couple components with small compensated linear vector dipole components. Such source mechanisms can be explained by shear slip on the bending fault. We also find that the plunge of the null axis is as small as 20-30º at depths of 15-25 km, whereas it becomes larger and more various at deeper depths. We interpret that the regional stress field is relatively homogeneous in the middle of the crust, whereas it may be altered near the Moho discontinuity by thermal stress or other effects related to potential magmatic process. In addition, we quantitatively investigated the possible triggering of LFEs due to static stress change of both the 2008 Iwate-Miyagi earthquake and the 2011 Tohoku earthquake. The weak correlation between triggering potentials and activity change suggests that the activity of LFEs is more or less sensitive to temporal stress changes.

Seven profiling floats equipped with oxygen sensors deployed in the Sea of Okhotsk provide time series data for 33 cases of spring phytoplankton bloom, including nine cases in which sea ice existed just before the bloom (prior-ice case). As an index of biological productivity, we calculated Net Community Production (NCP) based on the increasing oxygen rate using the Redfield ratio. The total NCP in the euphotic layer averaged for prior-ice cases is 31.3 mmolCm-2day-1), ~4 times higher than that of non-ice cases. In addition to intensification of surface stratification, other factors of sea-ice melt likely enhance the bloom. The influence of sea-ice melt is particularly large in the southwestern region, where the iron availability likely limits phytoplankton growth. A suggested scenario is that when the sea ice containing sediment/iron is transported from the northern shelves, a prominent bloom is induced via the iron supply by sea-ice melt.

Atmospheric aerosol affects climate, ecology, visibility, and public health and has attracted widespread attention. The fraction of inorganic aerosols in the atmosphere is significant and constitute about one-third of fine aerosol mass. The sources and formation processes of inorganic nitrogen aerosols in the atmosphere are with great uncertainty and therefore their long-term field measurement is critical. This study conducted a year-round field measurement of TSP, PM2.5 and PM1.0 in five different sites (ZJK, CP, CY DX, and TJ) in the Beijing-Tianjin-Hebei region to determine the concentrations of water-soluble inorganic ions (WSIIs) and also the isotopic composition of inorganic nitrogen (δ15N-NH4+, δ15N-NO3-, and δ18O-NO3-) to explore the sources and formation processes. The seasonal variation of δ15N-NO3- and δ15N-NH4+ were reflective of the relative contributions of the main source and also the effects of meteorological conditions. The source apportionment identified fossil fuel combustion (38.2 ~ 50.6%), agricultural emissions (18 ~ 24.7%), biomass burning (16.3 ~ 22.7%), and road dust/soil (8.7 ~ 23.4%) as the main sources of inorganic aerosols in Beijing-Tianjin-Hebei region. The located sources and regional migration (or transboundary pollution) contribute to the level of inorganic aerosol pollution. In winter, the aerosol of this region was affected by the air mass coming from the northwest. While in spring and summer, the air mass was mainly from the South China. The low temperature and high relative humidity favored the formation of inorganic nitrogen aerosol, while solar radiation affected the formation processes of the inorganic aerosols by changing the oxidation pathway of NO3- and also accelerating the volatilization and dissociation of NH4NO3. This study highlighted the main source contributions of inorganic nitrogen aerosol by utilizing N and O isotopes composition. Also, this study attached great importance in understanding the effects of meteorological conditions on formation and the contribution of regional transport.

Persistent dry and wet spells can arise from stationary weather situations or recurrent flow patterns and result in significant socio-economic impacts. Here, we study the effects of recurrent synoptic-scale transient Rossby wave packets (RRWPs) on the persistence of dry and wet spells using the ERA-Interim reanalysis data. RRWPs significantly alter (decrease and increase) dry and wet spell persistence across the globe. Spatial patterns of statistically significant links between RRWPs and spell durations arise from the superposition of a zonally symmetric component and a wave-like component that is modulated by local factors such as orography and the position relative to major moisture sources. The zonally symmetric component is apparent during the Northern Hemisphere winter and dominates the Southern Hemisphere signal in winter and summer. The wave-like component appears primarily in the Northern Hemisphere, changes its wavenumber with the season and is thus, conceivably related to stationary wave dynamics.

Land surface temperature (LST) serves as an important climate variable which is relevant to a number of studies related to energy and water exchanges, vegetation growth and urban heat island effects. Although LST can be derived from satellite observations, these approaches rely on cloud-free acquisitions. This represents a significant obstacle in regions which are prone to cloud cover. In this paper, a graph-based propagation method, referred to as GraphProp, is introduced. This method can accurately obtain LST values which would otherwise have been missing due to cloud cover. To validate this approach, a series of experiments are presented using synthetically-obscured Landsat acquisitions. The validation takes place over scenarios ranging from between 10% and 90% cloud cover across three urban locations. In presented experiments, GraphProp recovers missing LST values with a mean absolute error of less than 1.1C, 1.0C and 1.8C in 90% cloud cover scenarios across the studied locations respectively.

Forecasting the arrival time of Earth-directed coronal mass ejections (CMEs) via physics-based simulations is an essential but challenging task in space weather research due to the complexity of the underlying physics and limited remote and in-situ observations of these events. Data assimilation (DA) techniques can assist in constraining free model parameters and reduce the uncertainty in subsequent model predictions. In this study, we show that CME simulations conducted with the Space Weather Modeling Framework (SWMF) can be assimilated with SOHO LASCO white-light (WL) observations and solar wind observations at L1 prior to the CME eruption to improve the prediction of CME arrival time. The L1 observations are used to constrain the model of the solar wind background into which the CME is launched. Average speed of CME shock front over propagation angles are extracted from both synthetic WL images from the Alfv\’en Wave Solar atmosphere Model (AWSoM) and the WL observations. We observe a strong rank correlation between the average WL speed and CME arrival time, with the Spearman’s rank correlation coefficients larger than 0.90 for three events occurring during different phases of the solar cycle. This enables us to develop a Bayesian framework to filter ensemble simulations using WL observations, which is found to reduce the mean absolute error of CME arrival time prediction from about 13.4$\pm$3.8 hours to 5.1$\pm$3.0 hours. The results show the potential of assimilating readily available L1 and WL observations within hours of the CME eruption to construct optimal ensembles of Sun-to-Earth CME simulations.

Radiocarbon (14C) ages of acetogenic lipid biomarkers such as n-alkanes are a powerful tool to track carbon-cycle turnover times. In sediments, biomarker ages are almost always older than the depositional age due to reservoir effects. Recently, Lane et al. [2021, Anomalously low radiocarbon content of modern n-alkanes, Organic Geochemistry 152, 104170] reported 14C ages up to ≈1500 yr for n-alkanes extracted from leaf tissue of living plants; they attributed this apparent “pre-aging” to biosynthetic fractionation against 14C. However, reported 14C ages are always corrected for mass-dependent fractionation using a 14C/13C mass law, b, of 2.0. Lane et al.’s interpretation therefore requires that lipid biosynthesis follows large, anomalous deviations from mass-dependent fractionation, with b reaching values as high as ≈ 124. Here, I test this assumption by estimating kinetic and equilibrium mass laws for various processes involved in acetogenic lipid biosynthesis using simple approximations and more robust computational chemistry methods. I find that kinetic b values range from 1.880 to 1.995 and that equilibrium b values for several chain elongation steps range from 1.856 to 1.880, consistent with previous results for other chemical and biological processes. In contrast, complex reaction networks may lead to large expressed b values, but only when net ln(13α) → 0. Combined, these results imply maximum 14C age offsets due to biosynthetic fractionation of ∼ 20 to 40 yr. Biomarker 14C ages are therefore robust to biosynthetic isotope fractionation and can be confidently interpreted to reflect carbon-cycle turnover times.

The data from the CLUSTER FGM magnetometer, recorded for 20 years at ESA’s Cluster Science Archive, have been used to form a database aligned in time. It allows the calculation of curl(B) over all the life of the mission. The ̵⃗B and J⃗ data are then averaged, as a function of the dipole tilt angle, to form a 3D grid of spatial extend of 20 RE, and for any spatial resolution. From these data grids, maps of the direction of the magnetic field and of the current density are produced, allowing the observation of the average behavior of the magnetic field and the current density on a large scale. The validity of the calculation of J⃗ is discussed. By means of spatial interpolation, the grids are used to provide a measurement of the magnetic field at any point in space where the grid is filled. This allows possibility of ray tracing to obtain empirical plots of the magnetic field lines, i.e. not theoreti-cal, but from experimental data. Field lines near the cusp are visualized, even if they are very smoothed by the averaging of IMF and solar wind parameters. In a future work it would be possible to add other classification than just the dipole tilt angle, such as various activity indicese and solar wind parameters. The prospect of adding data from other missions would extend the regions that have been covered by Cluster, and in-crease the spatial extent of the 3D grid and its resolution.

Non-Newtonian fluid flows in porous media are critical in various subsurface and geotechnical engineering applications. However, accurately predicting such flows remains challenging due to complex fluid rheology and intricate pore structures. This study focuses on polymer fluids with shear-thinning rheology. To address the limitations of existing models, we derive a theoretical conductance model for polymer flow in a capillary tube, based on which a customized pore-network method is developed. Simulations reveals three distinct flow regimes, highlighting the impact of complex rheology on flow dynamics. Notably, flow heterogeneity amplifies as the shear-thinning feature directs more flows through wider pores, where the effective viscosity decreases more significantly compared to narrower ones. The conductance model also enables formulating a generalized Darcy's law for non-Newtonian fluids, validated through Pore-network modeling. The proposed framework is adaptable to a broad range of non-Newtonian fluids, offering valuable insights for scaling up to field-scale applications.

This study evaluated the forecast performance of neural network (NN)-based radiation emulators with 300 and 56 neurons developed under the cloud-resolving simulation. These emulators are 20–100 times cheaper to employ than the original parameterization and express evolutionary features well for 6 hrs. The results suggest that the frequent use of an NN emulator can improve not only computational speed but also forecasting accuracy in comparison to the infrequent use of original radiation parameterization, which is commonly used for speedup but can induce numerical instability as a result of imbalance with other processes. The forecast error of the emulator results was much improved in comparison with that for infrequent radiation runs with similar computational cost. The 56-neuron emulator results were even more accurate than the infrequent runs, which had a computational cost five times higher. The speed and accuracy advantages of radiation emulators can be utilized for weather forecasting.