Earth and Space Science最新文献

Previous papers have outlined nowcasting methods to track the current state of earthquake hazard using only observed seismic catalogs. The basis for one of these methods, the “counting method,” is the Gutenberg-Richter (GR) magnitude-frequency relation. The GR relation states that for every large earthquake of magnitude greater than M_T, there are on average N_GR small earthquakes of magnitude M_S. In this paper we use this basic relation, combined with the Receiver Operating Characteristic (ROC) formalism from machine learning, to compute the probability of a large earthquake. The probability is conditioned on the number of small earthquakes n(t) that have occurred since the last large earthquake. We work in natural time, which is defined as the count of small earthquakes between large earthquakes. We do not need to assume a probability model, which is a major advantage. Instead, the probability is computed as the Positive Predictive Value (PPV) associated with the ROC curve. We find that the PPV following the last large earthquake initially decreases as more small earthquakes occur, indicating the property of temporal clustering of large earthquakes as is observed. As the number of small earthquakes continues to accumulate, the PPV subsequently begins to increase. Eventually a point is reached beyond which the rate of increase becomes much larger and more dramatic. Here we describe and illustrate the method by applying it to a local region around Los Angeles, California, following the 17 January 1994 magnitude M6.7 Northridge earthquake.

The use of CCD-array spectrometers has substantially increased in recent years in many different fields. Although they have numerous advantages over conventional scanning spectrometers, they need to be thoroughly characterized to correct for various sources of error. This study focuses on the experimental characterization of the dark signal of Avantes AvaSpec-2048 CCD-array spectrometers, used to measure solar UV and VIS-NIR radiation. In order to have a large number of measurements of the dark signal at different integration times and temperatures, a ramp methodology has been followed and validated against stabilized-temperature experiments. These data have allowed the analysis of the individual dependencies of the dark signal with integration time and temperature, as well as the proposal of a final multivariate model including both variables. This is one of the first multivariate models proposed for the dark signal of a detector used by a CCD-array spectrometer to measure solar UV radiation. The dependence of the dark signal with integration time and temperature is found to be linear and nonlinear, respectively. The model performs remarkably well, with R² values above 0.99 and relative root mean squared error around 0.1 and 0.05 for the UV and VIS-NIR spectrometers, respectively. The improvement achieved by using an individual model for each pixel is discussed, obtaining notably better results with this model than when using an average model, as suggested by other authors. This study presents a positive contribution to the characterization of the dark signal from CCD-array spectrometers, and the proposed methodology can be extended to other instruments.

The Gravity Recovery and Climate Experiment (GRACE) and its successor, GRACE-Follow On, play an important role in monitoring mass transport across the Earth. Compared to spherical harmonic solutions, mass concentration (mascon) solutions offer less signal leakage and a “higher” spatial resolution. How the shapes, sizes, and positions of mascon are parameterized influences the accuracy of the solutions. In this study, we derive a variable-sized mascon solution that enhances spatial resolution in polar regions by considering orbital coverage of satellites. To this end, we present a numerical simulation aimed at evaluating the performance of different parameterizations in the mascon solutions. We demonstrate that using variable-sized mascons reduce parameterization error by up to 17% and improve goodness of fit by up to 34%. The accuracy of signal recovery improves by about 23%, 34%, and 42% for basin scales, respectively, in low-latitude, mid-latitude, and high-latitude zones. When applied to the GRACE (-FO) data, we see the optimized parameterization scheme reduces noise by up to 1.84 cm in the surface mass change time series. Additionally, the optimally parameterized mascon solution help to enhance signal recovery in mid-to-high latitude regions. We discuss and quantify benefits of variable-sized mason parameterizations for surface mass change recovery and suggest the optimal scheme based on the simulation and real data processing. Overall, the optimized parameterization scheme will benefit finer-scale mass change signal recovery for mascon solution.

The Mohorovičić discontinuity (Moho) marks the boundary between Earth's crust and the underlying mantle, serving as a critical interface for understanding Earth's structure, composition, and geodynamic processes. This study introduces a novel iterative and stable algorithm for global Moho depth inversion. We first derive the gravity disturbance of the Moho interface in the spherical harmonic domain, expressed as a series of spherical harmonic coefficients. These forward expressions are then reformulated into an iterative scheme for Moho depth estimation. To ensure convergence, a damping factor is applied to suppress high-frequency noise, and the process is constrained by observed gravity data to minimize residuals. The algorithm is validated using a synthetic Airy–Heiskanen interface in a closed-loop test. Results show stable convergence within approximately three iterations, yielding minimal gravity residuals (∼0.05 mGal) and small depth errors (standard deviation: 0.07 km), demonstrating the method's high accuracy. A sensitivity analysis of constant and variable Moho density contrasts further shows that when density varies from 450 to 600, the mean difference is less than 1.0 km and the standard deviation is only 1.1 km, indicating that the solution is largely insensitive to density changes. Importantly, incorporating a variable density contrast significantly improves Moho depth recovery along mid-ocean ridges. Finally, the method is applied to refined gravity disturbances that are maximally correlated with Moho depth, successfully recovering global Moho topography. Comparison with the CRUST1.0 seismic Moho model shows strong consistency in both spatial distribution and statistical measures, with depth residuals (standard deviation: 4.23 km) and gravity residuals (∼1.89 mGal), further confirming the robustness of the method. Notably, the use of variable Moho density contrast again provides substantial improvements along mid-ocean ridges.

High-speed solar wind streams (HSS), originating from coronal holes (CH), are key drivers of space weather disturbances and heliospheric dynamics. However, forecasting HSS remains challenging due to the evolving morphology of CH. In this study, we present a deep learning-based framework that models the spatiotemporal relationship between CH and HSS.We applied preprocessing techniques that included the Stonyhurst projection, removal of off-limb structures, transient events, and background noise, thus isolating persistent CH features. We developed two convolutional neural network|convolutional neural networks (CNN) models: one using full-disk extreme ultraviolet images of the sun at 193 Å, 171 Å, and 304 Å wavelengths; the other using binary CH maps derived from 193 Å wavelength. Both models are trained and evaluated across different solar cycle phases using a meta-learning strategy to retain optimal checkpoints based on validation loss. We find that, over the entire solar cycle (SC) period, our model outperforms the benchmark models, achieving a best correlation of $��0.68�\pm �0.03�$, a root mean square error of $��71.4�$ Km/s, and a threat score of 0.71 with the observed solar wind (SW) at a 3-day lead time. The explainability attribution method utilized confirms the model's ability to focus on CH regions and track their evolution over time. Both models learn physically consistent features, highlighting CH structures near the central meridian as key HSS sources. SpeedNet-BM further demonstrates stable and coherent activation responses, reinforcing its predictive capability and the importance of domain-specific preprocessing, phase-aware training, and interpretable deep learning to improve HSS forecasting.

This study evaluates the integration of an explicit electrification module in the Weather Research and Forecasting model to predict lightning over North-Eastern India, using inductive and non-inductive charging mechanisms for hydrometeors and charge exchange during collisions. A multigrid solver computes electric field components, while a bulk discharge scheme reduces charge in regions where the field exceeds the breakdown threshold. Simulations employed nested domains (27 km, 9 km, and 3 km), allowing detailed storm analysis via explicit microphysics. Pre-monsoon simulations included lightning data assimilation (LDA) from Earth Networks Total Lightning Network sensors, adjusting moisture fields through nudging within the 3 km grid. Over 43 pre-monsoon days, including 3 April 2017, have been chosen for this study, LDA improved model accuracy, particularly in high-density regions like Assam and Meghalaya, achieving high Probability of Detection (POD) and Equitable Threat Score (ETS) with low False Alarm Ratio (FAR). LDA enhanced 3–6 hr forecasts (91%–100% accuracy) by refining water vapor fields but struggled beyond 6 hr, where POD dropped and FAR rose. Challenges emerged in low-density areas like Mizoram, where overprediction increased FAR. The model captured core lightning zones but performance decreases surrounding areas, reducing spatial accuracy. A positive bias in flash density over central Assam and Bangladesh suggests adjusting module parameters to improve performance across varied lightning conditions.

In an attempt to improve the model-derived coastal circulation around India, we explore three different frameworks to assimilate surface currents from HF-Radars into an ocean model for the Indian Ocean. The first approach involves correcting the winds through Ekman Theory using the discrepancy between the model surface currents and the HF-Radar observations. The second approach entails direct assimilation of the HF-Radar surface currents into the ocean model. The third approach combines the first two frameworks. We show that all three approaches improve coastal circulation but in varying degrees. The improvements in analysis are least in the wind-correction approach and most prominent in the combined approach. We show that the combined approach of wind correction and assimilation of HF-Radar currents significantly improves the coastal circulation around India with root-mean-squared error at par with the precision of the HF-Radar measurements. This approach yields a correlation of ∼0.8–0.9 between the surface current analysis and the observation. We also show that this approach improves the subsurface currents. The surface current forecasts from the combined approach with a lead time of a day outperforms the free model run by a large margin thereby proving its mettle for operational adoption.

Surface wave dispersion analysis is crucial for investigating crustal and mantle structures on Earth and other celestial bodies. On Mars, Rayleigh and Love waves have been detected, with Rayleigh waves exhibiting vertical and radial motion and Love waves polarized transversely to the source-receiver azimuth. Accurate dispersion measurements require rotating three-component seismic data with an accurate back azimuth. However, large back azimuth (BAZ) uncertainties of marsquakes, due to single-station recordings and low signal-to-noise ratios, can cause rotation errors. These errors result in Rayleigh wave energy leaking into the transverse component, complicating dispersion extraction. In addition, the surface wave signal may be disturbed by multipath waves. To address these challenges, we develop a two-stage framework for measuring marsquake surface wave group velocity dispersion, which has not been utilized in previous studies. First, we perform a back azimuth correction based on surface wave analysis to optimize the rotation angle by scanning back-azimuths in a given range relative to the cataloged value. Second, we apply continuous wavelet transform (CWT) and phase-matched filter (PMF) to suppress noise and enhance the weak surface wave signals in the dispersion spectrogram. We validate this approach using synthetic and observed data on Earth and the S1222a marsquake.

The dominant external forcing to Earth's climate system is solar radiation, referred to as the solar spectral irradiance (SSI) and the total solar irradiance (TSI), which is the integral of SSI over all wavelengths. Accurate measurements of TSI and SSI are made from space because solar radiation is absorbed within Earth's atmosphere and that absorption would otherwise need to be corrected for, with added uncertainty, in ground or airborne solar irradiance observations. Composite solar irradiance records, developed from multiple instruments, can represent solar forcing over timescales longer than the lifetime of any individual instrument. We develop a new TSI composite record from pairs of overlapping individual measurement records with the highest relative stability over the longest timescales as determined from Allan Deviation analysis. Allan Deviation is a time-domain measurement standard analysis technique that was originally established by the metrology community to quantify the stability of atomic clocks. Version 0 of the Time Variance TSI (TV TSI) Composite begins in early 2003 and includes all Total Irradiance Monitor (TIM) instrument records beginning with the NASA Solar Radiation and Climate Experiment (SORCE) mission and continuing through today with the Total and Spectral Solar Irradiance Sensor (TSIS-1) mission. In future work, we plan to use Allan Deviation analysis to guide the incorporation of additional TSI measurement records and their time-dependent expression of uncertainties in order to develop a new version of the TV TSI composite that spans an even longer period of time for climate studies.

Global Climate Models (GCMs) are useful tools for simulating the dynamics of Mars atmosphere on a planetary scale. However their coarse resolution ($��\sim �$100 km) does not allow to capture small-scale phenomena such as slope winds which can occur over scales of just a few kilometers. These local slope winds can be crucial in Mars climate modeling, since they can significantly contribute to dust lifting affecting the amount of dust in the atmosphere, which is the major factor that governs the variability in Mars atmosphere. These winds can also affect the sublimation of water and $��{\text{CO}}_2�$ ice on the surface, both playing a key role in the Martian climate. In this study, we aim to develop a simple and efficient numerical model to simulate slope winds on Mars in single column and global climate models. We compare the model results with those from a mesoscale model with a resolution of a few kilometers capable of resolving these small scale winds (but only for a few days and with a high computational cost). This simple model of slope winds has been included in the Mars Climate Database (MCD v6.1) tool.