The hydrodynamics of river mouths are the result of a complex interaction between river flow, tidal conditions and outlet geometry. This complex interaction of factors shapes the jet that flows onto the continental shelf and influences the dynamics of these areas. To gain insight into the response of the jet to different outlet and nearshore geometries and changing river discharge and tidal conditions, the hydrodynamics of idealised river mouths are simulated numerically. Compared to previous work, the model includes transient river discharge and tidal conditions and more realistic nearshore geometries. Comparison with classical jet theory indicates that the model adequately simulates jet hydrodynamics. The results show that both the outlet geometry and the transient river discharge and tidal conditions have a significant influence on the jet structure and evolution along the nearshore. For constant river discharge and water level conditions, the results indicate that the nearshore profile plays a key role in determining the expansion or contraction of the jet. The momentum balance shows that the jet behaviour is related to the momentum transport and the barotropic terms. In cases where the river discharge and tidal conditions are transient, the jet alternates between a structure with two velocity maxima at the edges or a single peak in the centre during the tidal cycle. This alternation occurs as a function of the time along the tidal phase and the time lag between the tidal conditions and the river hydrograph. The morphodynamic consequences of these two different jet structures are also discussed.

Oxygen Deficient Zones (ODZs) are key areas of N loss, a process dependent on organic matter. Understanding the sources of organic matter to the ODZ is necessary to predict how biogeochemical cycles will respond to ocean changes. Size fractionated (5-20, 20-53, 53-180, 180-500, >500 µm) particulate organic C and N (POM) concentration and isotopic composition depth profiles from three stations in the offshore Eastern Tropical North Pacific (ETNP) ODZ were used to create previously missing ETNP specific particle size to carbon and nitrogen relationships for models, while gaining insights into the origins of POM in the ODZ. Since the within-ODZ Prochlorococcus assimilate nitrite, we used the resulting highly depleted d15N signal to trace organic matter of cyanobacterial origin to medium sized particles at the secondary chlorophyll maximum (SCM), and to >500 µm particles directly below the SCM. This organic nitrogen was consumed in the upper ODZ. Other POM maxima were seen at the zooplankton vertical migration maxima with the increase in POM marked in the 5-20 µm fraction. In the deep ODZ, below the zooplankton migration depth, POM concentrations in the 5-20 µm fraction were unusually small, the C:N ratios were extremely high (>20), and d15N was enriched (8-12‰), indicating degraded material. In deep samples, d13C was more depleted in larger particle size and correlated with enriched d15N, indicating increased degradation in 53-500 µm particles. This trend suggests an additional source of small particles, such as from in situ production, rather than just the fragmentation of large particles.

Dilute pyroclastic density currents (PDCs) present significant hazards due to their high temperatures and dynamic pressures. Accurate estimation of dynamic pressure—- vital for assessing potential damage, requires knowledge of the vertical variations of velocity and particle concentration within the dilute PDC, particularly in the first few meters of the flow. Existing approaches to dynamic pressure calculations used in hazard assessment are often based on average values for velocity and particle concentration. These average values may misrepresent the flow dynamics, especially near the base of the flow where the gradients of flow variables are larger. Here, we present a new, physically based approach that allows for the calculation of the vertical profiles of velocity and concentration from a combination of depth-averaged values for these properties and non-dimensional flow parameters. Finally, we demonstrate the use of these profiles within an existing shallow-water model and show its potential applications toward probabilistic hazard assessment.

Solar flares are major manifestations of solar activity, characterized by intense radiation and particle emissions that affect the entire solar system. High-accurate prediction of solar flare events for solar cycle 25 is crucial for understanding and mitigating these effects. In this work, we used soft X-ray flux from the GOES satellites and the chromospheric flare index to predict solar flare activity for solar cycle 25. Our results show that: (1) There is a temporal and intensity asymmetry between the northern and southern hemispheres, with the southern hemisphere showing greater flare activity during solar cycle 25. (2) Flare activity exhibits Gnevyshev peaks, and the double-peak structures are more prominent than other atmospheric layers. The Gnevyshev peaks may be related to the different timescales of the solar flare responses to the geomagnetic field and the interplanetary magnetic field. (3) Chromospheric flare activity lags behind photospheric sunspot activity. This phase lag phenomenon may be related to the magnetic energy storage in the corona, indicating that changes in sunspot activity occur before flare activity. (4) The activity level of solar cycle 25 will be more pronounced than that of solar cycle 24. This phenomenon may be related to the modulation effect of the Gleissberg cycle, which has been validated by geomagnetic precursor indices. Our results provide valuable insights into the temporal and spatial distribution of solar flare activity during solar cycle 25.

Early meteorological in-situ observation is obtained by manual timing recording, and the temporal resolution is low. The above leads to limitations in the quality of historical data produced by land-surface data assimilation systems. Aiming at the problem of low temporal resolution of in-situ observation in the past, we propose a deep learning-based method to improve the resolution of observation time series, Deep Interpolation. DI introduces a training strategy using uncertainty to weight loss functions to solve the "seesaw phenomenon" in multi-task learning and realize collaborative temporal downscaling of multiple observation variables, including temperature, surface pressure, relative humidity, and wind speed. The experimental results in China show that the DI has achieved significant performance improvement compared with the linear interpolation method and ERA5-land, and the average correlation coefficient is as high as 0.934. DI can accurately capture the hourly fluctuation characteristics of various meteorological variables, especially in high latitude, grassland, and cultivated land regions, showing more vital correction ability and more stable model performance. This study provides an effective solution for improving the temporal resolution of historical meteorological observation data. It lays a foundation for further enhancing the accuracy and reliability of climate models and land surface assimilation systems. Improving the temporal resolution of meteorological observation data can better support the research on climate change, the construction of disaster early warning systems, and decision-making in many fields, such as agriculture and energy.

Precursory spurious arrivals, commonly observed in ambient noise correlations, are generated by near-field noise sources. We have developed an inversion method to evaluate the noise source distribution based on the precursory waves. This method is applied to the BASIN experiment in Los Angeles, revealing that the noise sources show coherent patterns with features like faults and structure boundaries. Our spectral analysis indicates that the energy of the noise source in generating the precursory signal is predominant at higher frequencies, suggesting a shallow source(¡200m). We conclude that near-field noise is primarily produced by scattering from geological structures with significant velocity contrasts, such as faults at shallow depths. This method offers a new way to map faults using ambient noise correlations.

The seasonality of Arctic sea ice cover significantly influences heat, salt, buoyancy fluxes, ocean-ice stresses, and the potential and kinetic energy stored in the ocean mixed layer. This study examines the seasonal variability of oceanic scales and cross-scale flux of kinetic energy in the seasonally ice-covered Arctic, using a high-resolution, idealized coupled ocean-sea ice model. Our simulations demonstrate pronounced seasonality in the scales of oceanic motion within the mixed layer, governed by distinct mechanisms during summer and winter. In summer, an inverse energy cascade sustains mesoscale dynamics and enhances kinetic energy. In winter, ice-induced dissipation suppresses kinetic energy and mesoscale, allowing only the persistence of submesoscale processes. These results underscore the critical role of sea ice in modulating the seasonal dynamics of oceanic motion and their dominant scales, a behavior markedly different from that in the open ocean. Thus, understanding these coupled processes is essential for improving predictions of the ocean's energy evolution as the Arctic transitions toward a summer ice-free regime.

11 12 Corresponding author: Niko Fedkin ([email protected]) 13 Key Points: 14 • Satellite NO2 records and trends of urban, coastal and deep water areas from 2005 to 15 2022, are presented 16 • Classifying NO2 over the Gulf of Mexico (GOM) under various wind conditions 17 highlights typical patterns in average NO2 values 18 • GOM NO2 hotspots from deepwater platforms were identified by TROPOMI under calm 19 wind conditions, the largest of which is over Mars/Olympus 20 21 Abstract 22 The Outer Continental Shelf of the Gulf of Mexico (GOM) is populated with numerous 23 oil and natural gas (ONG) platforms which produce NOx (NOx = NO + NO2), a major component 24 of air pollution. The Bureau of Ocean Energy Management (BOEM) is mandated to ensure that 25 the air quality of coastal states is not degraded by these emissions. As part of a NASA-BOEM 26 collaboration, we conducted a satellite data-based analysis of nitrogen dioxide (NO2) patterns 27 and trends in the GOM. Data from the OMI and TROPOMI sensors were used to obtain 18+ year 28 records of tropospheric column (TrC) NO2 in three GOM regions: 1) Houston urban area, 2) near 29 shore area off the Louisiana coast, and a 3) deepwater area off the Louisiana coast. The 2004-30 2022 time series show a decreasing trend for the urban (-0.027 DU/decade) and near shore (-31 0.0022 DU/decade) areas, and an increasing trend (0.0019 DU/decade) for the deepwater area. 32 MERRA-2 wind and TROPOMI NO2 data were used to reveal several NO2 hotspots (up to 25% 33 above background values) under calm wind conditions near individual platforms. The NO2 34 signals from these deepwater platforms and the high density of shallow water platforms closer to 35 shore were confirmed by TrC NO2 anomalies of up to 10%, taking into account the monthly TrC 36 NO2 climatology over the GOM. The results presented in this study establish a baseline for future 37 estimates of emissions from the ONG hotspots and provide a methodology for analyzing NO2 38 measurements from the new geostationary TEMPO instrument. 39 40 Plain Language Summary 41 Oil and natural gas operations emit nitrogen oxides (NOx), which are major air pollutants and 42 precursors to ground-level ozone. The Bureau of Ocean Energy Management (BOEM) agency is 43 responsible for managing planned oil and natural gas (ONG) activity on the outer continental 44

Accurate modeling of dependencies between variables of interest is imperative for understanding biophysical processes and mechanisms relevant to Earth Science research. This study presents a methodology to model the temporal or spatial distributions of variables using copula-based cosimulation (CopCoSim). This technique models and simulates the joint distribution of multiple variables by capturing their dependencies and correlations. We compared CopCoSim with the traditional Sequential Gaussian CoSimulation (SGCoSim) technique through two applications relevant to Earth Science representing one (i.e., time) and two dimensions (i.e., space). We present an application for soil CO2 efflux, which is a major flux in the global carbon budget, using two case studies: (1) temporal distribution of soil CO2 efflux and temperature, and (2) spatial distribution of soil CO2 efflux and temperature across the conterminous United States (CONUS). The methodology involves three steps: data input, application of stochastic simulation methods, and comparison of simulation outputs. The results indicate that CopCoSim provides a better model with higher precision and accuracy for representing variables of interest. CopCoSim better reproduces the univariate probability distribution, temporal or spatial autocorrelation, and dependency relationship between the predictor and response variables. We propose that CopCoSim is useful for research in Earth Science, where variables of interest (e.g., soil CO2 efflux and temperature) are often interdependent and exhibit complex temporal or spatial patterns.

Marine cold air outbreaks (MCAOs) drive significant evolutions in marine boundary layer clouds and play a crucial role in high-latitude climate systems. This study examines the variability of cloud particle size distributions (PSDs) and the representation of the Gamma shape parameter μ for high-latitude MCAO clouds in the Northern Hemisphere, focusing on the initial stratocumulus stages and cumulus regime transitions. Aircraft in-situ measurements from 20 flights with identified MCAO conditions were collected during two recent field campaigns: M-Phase conducted over the Labrador Sea in March 2022, and Arctic Cold-Air Outbreak (ACAO) conducted over the Nordic Seas in October to November 2022. Results show that high-latitude MCAO clouds in the Northern Hemisphere exhibit narrow PSDs, characterized by higher μ values (mean μ=20) that imply more reflective clouds compared to the fixed μ=2.5 assumption in some bulk microphysics schemes. Cloud PSDs narrow and μ values increase with height in near-adiabatic stratocumulus clouds, while their patterns are more variable in broken cumulus clouds. Liquid water content correlates more strongly with μ variability than cloud number concentrations, suggesting its better predictability as a prognostic variable for PSD variability in these cloud systems. Both the μ=20 and its derived relation with cloud liquid water content can be applied in bulk microphysics schemes to better represent the microphysical and radiative properties of high-latitude MCAO clouds. The proposed high μ values for MCAO clouds are applicable mainly to typical horizontal resolutions of numerical weather prediction and regional climate models.

In this paper in an elegant way will be present the formulas of the Dimensionless Universe. All these equations are simple,elegant and symmetrical in a great physical meaning. These equations are applicable for all energy scales. We will propose the Dimensionless unification of the fundamental interactions and the Dimensionless unification of atomic physics with cosmology. We find the formulas for the cosmological constant and we will prove that the shape of the Universe is Poincaré dodecahedral space. From the dimensionless unification of the fundamental interactions will propose a possible solution for the density parameter of baryonic matter,dark matter and dark energy.

The present study investigated the shear strain energy changes in a shear zone associated with interseismic plastic deformation and strike-slip earthquakes. A model of a very long strike-slip fault system was developed in which deep plastic flow is represented by an inelastic strain distribution. We derived analytical solutions for displacement and stress to estimate the changes in the shear strain energy. Applying this model to the San-in shear zone, Japan, we reproduced the observed surface velocity distribution by assuming a deep plastic flow with a horizontal thickness of T = 30 km, vertical thickness of 17 km, and strain rate of where 9 mm/year. We also found that the deep plastic flow roughly obeys a simple flow law. By using the stress changes induced by the deep plastic flow and the background stress estimated by a stress tensor inversion, we evaluated the changes in shear strain energy density in the shear zone. The energy at the seismogenic zone increases by J/m3/year when we assumed a differential stress of 100 MPa. We also evaluated the energy drop after two large earthquakes (MW 7.0 and 6.6). The estimated energy drop corresponded to an energy accumulation period of ~700 to ~1000 years, which is shorter than the period expected from geological observations.

This paper uses energy flux data from the DMSP F6 thru F19 satellites to construct a new equatorward auroral boundary model. This method of processing the data allows one to easily extend boundary detections to different instrument generations because it is based on a standard deviation value instead of hard thresholds. Using this method, the paper provides statistics from just under 1 million auroral boundaries between 1986 and 2014. These statistics are based on normal distribution fits for each Kp/Hp and MLT bin, which allows one to specify the boundary for an arbitrary distribution percentile. The paper compares how the model performs for driving by Hp30, Hp60, and Kp and finds that Hp30 is the best representation of the data. Finally, the paper provides specifications to recreate the model for the 50th and 95th percentiles for Kp and Hp30.

We have carried out a statistical study of neutral atmospheric parameters in the mesosphere lower thermosphere (MLT) region, by utilizing simultaneous measurements from the EISCAT VHF radar and sodium LIDAR collocated at  Tromsø , Norway. This study focuses on the incoherent scatter (IS) spectral width, which is a function of the ion-neutral collision frequency, ion temperature, (equal to neutral temperatures in the D-region), and ion mass. Using the neutral temperatures obtained from LIDAR, and ion mass estimated using a chemistry model, we have measured the ion-neutral collision frequency in the 80-100 km altitudes by fitting the spectral width. The study shows that the current widely used formulae underestimate the ion-neutral collision frequency on average by 1.53\(\pm\)0.24 in comparison to the measurements. Also, the measured collision frequencies showed large temporal variations due to neutral density fluctuations, indicating the presence of atmospheric waves. The amplitudes of these waves are found to be as large as 50% of the background densities. This suggests that individual spectral width measurements are likely influenced by these random neutral density fluctuations, which can have a significant impact on the IS temperature fits. In addition, for altitudes below 85 km, the ion mass increases drastically indicating the presence of heavy cluster ions. The dominance of heavy ions makes it further challenging to extract the temperature values from the spectral width at these altitudes. In light of these observations, the inherent limitations of inferring temperatures from IS spectral width in the MLT altitudes are studied.

C. Yang1,2 and J. Xu31CAMS & China Re CRM · Joint Open Lab on Meteorological Risk and Insurance, Beijing, China.2Faculty of Geographical Science, Beijing Normal University, Beijing, China.3Qingdao Joint Institute of Marine Meteorology, Chinese Academy of Meteorological Sciences, Beijing, China.Corresponding author: C. Yang ([email protected]), J. Xu ([email protected])Key Points:A modified Rankine vortex model with four free parameters is adopted to reconstruct wind fields along the tropical cyclone tracksA Bayesian hierarchical model with a latent neural network is developed to estimate the parameters based on the best-track data setBayesian model averaging is used to predict wind field parameters for synthetic tropical cyclones over the western North Pacific basinAbstractTropical cyclones (TCs) are one of the biggest threats to life and property around the world. Accurate estimation of TC wind hazard requires estimation of catastrophic TCs having a very long return period spanning up to thousands of years. Since reliable TC data are available only for recently decades, stochastic modeling and simulation turned out to be an effective approach to achieve more stable hazard estimates. In common practice, hundreds of thousands of synthetic TCs are generated first, then wind fields are reconstructed along synthetic TC tracks for hazard estimation. A Bayesian hierarchical modeling approach to the reconstruction of TC wind field is proposed. A modified Rankine vortex is adopted as the wind field model, of which the four free parameters are modeled simultaneously through a multi-output neural network as a latent process of the wind field. The four parameters are finally represented, spatially and temporally, by a set of neural network weights, The Bayesian model averaging technique is used for parameter estimation and wind field reconstruction, based on a ensemble of maximum a posteriori estimates of the set of weights. Together with previously proposed algorithm for synthetic TC simulation, a two-stage scheme for TC wind hazard estimation has been formed, which is based on best-track data only and thus is highly consistent. Application of this scheme to the offshore waters in the western North Pacific basin shows inspiring performance and great flexibility for various purposes of TC wind hazard estimation.

The reliable operation of transformers is critical to power system stability, and early fault detection in transformers can significantly reduce the risk of catastrophic failures. Dissolved gas analysis (DGA) is a commonly used method to assess transformer health, as gas concentrations correlate with fault conditions inside the transformer. Methane (CH4), a gas indicative of thermal faults, serves as a key parameter in detecting early signs of fault. This study proposes a linear epsilon support vector regression (SVR) model fine-tuned with Grid Search for predicting methane concentration based on transformer temperature, oil temperature, winding resistance, load factor, service life, and transformer age using grey relational analysis. By predicting methane concentration in ppm, this model supports proactive fault management, optimizing maintenance schedules, and effectively reducing transformer downtime. Comparative results suggest that the proposed SVR model is an effective baseline for improving dissolved gas analysis accuracy, contributing to enhanced transformer diagnostics and preventive maintenance strategies.

Understanding the fluid flow and solute transport in karst conduits is of great significance to the prevention of pollutants in karst areas and to reveal the mechanism of non-Fickian transport. Firstly, the evolution of eddies in semicircular rough conduits under different hydrodynamic conditions and their effects on solute transport were evaluated. Secondly, we find that the solute exchange between the main flow stream and the eddy zone is intensified with the gradual increase of the inlet flow velocity, weakening the non-Fickian effect. Finally, the transformation mechanism process of advection and hydrodynamic diffusion of solute into karst conduit was revealed. The relationship between the equilibrium concentration, corresponding time and flow velocity at different stages was quantified. This study can provide technical support for groundwater pollution control in karst area.

Air-sea sensible and latent heat fluxes are fundamental to tropical cyclone (TC) energetics, but the impacts of seastate-dependent sea spray heat fluxes on TC structure and intensity are poorly understood. We explore these impacts herein by implementing a recently-developed parameterization for air-sea heat fluxes with seastate-dependent spray physics into a fully coupled atmosphere-wave-ocean model, the Unified Wave INterface–Coupled Model. We conduct model experiments with and without spray for four TCs covering wide ranges of intensity and structure. The effects of spray on model TCs in the open ocean can be summarized in three stages. 1) Spray evaporative cooling dominates in boundary layers (BLs) of tropical storms and weak hurricanes (i.e., with 10-m windspeeds ≲ 30–40 m s-1 and relatively small waves), which hinders intensification. 2) Further TC intensification increases spray generation, producing positive spray sensible heat fluxes (i.e., warming) under the eyewall. This warming is favorable for intensification, but inefficiency caused by cooler BL inflow continues to inhibit eyewall deep convection, and spray continues to oppose intensification overall. 3) Further increase of spray production from continued TC intensification (i.e., past Category 3) ultimately allows spray to promote intensification by warming the BL and enhancing eyewall deep convection. Spray’s tendency to oppose intensification of weak TCs is consistent with the relatively rare occurrence of major hurricanes. However, if a TC intensifies beyond stage 2, spray can support rapid intensification. We also find that enhanced spray generation by wave dissipation in the coastal zone may strengthen landfalling TCs.