Earth and Space Science最新文献

The longest known Aletai meteorite belt presents a unique phenomenon in meteoroid dynamics. To investigate its formation mechanism, this study introduces a bilobate-shaped meteoroid model, emphasizing aerodynamic interactions and structure evolution. The sintered bond model is applied to simulate the tensile, compressive, and shear strengths of the bilobate-shaped meteoroid. Its disintegration is analyzed under the combined effects of aerodynamic forces and self-rotation. After disintegration, the transverse velocity of the sub-spherical fragments is applied to track their dispersal trajectories and calculate the resulting strewn field of meteorites. The influence of aerodynamical shock wave and mass ablation is considered throughout the descent process. Numerical simulations are conducted with varying initial entry conditions, particularly focusing on the initial rotation of the bilobate-shaped meteoroid. The study focuses on the mechanism of the skipping trajectory and the associated strewn field during the meteoroid's dynamical evolution. The results highlight the critical role of bilobate-shaped meteoroids in generating skipping trajectories and provide new insights into the formation of Aletai-like ultra-long meteorite belt.

With the increasing demands for global development, deep space exploration missions are taking place more frequently, rendering the traditional 24-hr Polar Motion (PM) predictions inadequate for supporting the frequent operations of spacecraft. Therefore, this study utilized the mainstream EOP C04 and EOP C04 spliced with the final.daily series to evaluate the performance of interpolating PM to 6-hr and then forecasting, under both ideal and real-world environments. Interpolation is performed using the Stable Ultra-high-Precision-Radial-Basis-Function (SURBF) and the Seasonal-Trend-Residual decomposition with adaptive interpolation (STR). Prediction is carried out using the Least Squares and Autoregressive (LS + AR). Our findings indicate that the two input formats, namely interpolating prior to column assignment in the idealized case and direct column assignment before interpolation in the real-world environments, greatly affect prediction results. The former takes full advantage of the disclosed subsequent information, achieving a first-day MAE of less than 0.1mas. The latter adheres to the actual conditions, resulting in a first-day MAE above 0.2mas and performing worse than traditional forecasting. Additionally, we evaluated the forecasting performance using 6-hr International GNSS Service Ultra-rapid (IGU) inputs and found that it not only surpasses traditional methods but also outperforms USNO forecasts over the 1–10 days. Therefore, we do not recommend interpolating 24-hr to 6-hr, as interpolation is essentially a guess and does not provide meaningful information. This study only recommends using IGU-6h derived from real observational data to obtain 6-hr PM predictions. These findings can assist practical deep space exploration projects in making informed decisions when selecting 6-hr PM predictions.

Context. The Electron Proton Helium INstrument (EPHIN) aboard SOlar and Heliospheric Observatory is a particle telescope that measures energetic protons and helium above 4 MeV/nuc and electrons above 150 keV. While a calibration of EPHIN has been performed before launch, it has only partially been assessed reducing the accuracy of the detector and dead layer thicknesses of each detector. However, these parameters are crucial for the correct representation of the instrument in simulation runs which are necessary for detailed analysis of the instruments measurements in space. Aims. An accurate representation of EPHINs detector geometry has been derived from the calibration results. These improvements compared to previous detector models will allow for lower systematic uncertainties as well as new data products for the EPHIN data at high energies. Methods. EPHIN was calibrated using a 4He beam aimed at a gold target. The various particle populations produced in the target were then filtered using a rigidity filter. The measured energy loss distributions in each individual detector have been compared to GEANT4 simulations. The thicknesses and dead layer thicknesses of each detector have been varied in the simulation setup to achieve best agreements between simulation and calibration. Results. Good agreements between simulation and calibration have been reached. Most energy loss distribution fall within a 3 % margin, compared to differences of more than 10 % in earlier models.

Oxychlorines (i.e., perchlorates (ClO₄⁻) and chlorates (ClO₃⁻)) have been detected by several landed missions on Mars at various locations. These missions have provided crucial information about the geographic distribution and abundances of oxychlorines on Mars but have not definitively identified the cation and anion type of in situ, solid oxychlorines. By speciating and precisely locating oxychlorines in Martian rocks, we may be able to better interpret the aqueous history of the rocks, understand the chlorine cycle on Mars, understand the chlorine isotope systematics on Mars, identify the potential for liquid brines on the surface, and advance in situ resource utilization activities for future robotic or landed missions. The Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) instrument on the Mars 2020 Perseverance rover may have the ability to identify oxychlorine species (i.e., cation and anion) in solid surface targets due to their characteristic Raman and fluorescence bands. Additionally, the location and distribution of oxychlorines within rocks can be determined using the Autofocus and Context Imager (ACI) or Wide Angle Topographic Sensor for Operations and eNgineering (WATSON) camera, a subsystem of SHERLOC that provides high-resolution, close-up images of targets analyzed by the SHERLOC Deep-Ultraviolet Raman spectrometer. The overarching goal of this work is to test SHERLOC's ability to identify and differentiate oxychlorine species in synthetic pure and natural mixed samples using a laboratory analog to the SHERLOC Raman and fluorescence spectrometer, identify instrumental limitations, and to further constrain potential detections made within Jezero crater, Mars.

Radio occultation is a well-established remote sensing method that provides reliable estimates of atmospheric profiles of diverse variables, including temperature and pressure. However, as with all indirect methods, radio occultation has some inherent systematic and random error effects, which lead to observational uncertainties. While propagation of uncertainties along the processing chain for individual radio occultation profiles was described in recent studies, this uncertainty information has not yet been carried forward to climatological fields. We close this gap and present an uncertainty propagation procedure that provides uncertainty estimates for aggregated means for climate applications. Estimated random uncertainties, basic and apparent systematic uncertainties and sampling uncertainties (due to the discrete sampling by profiles) are propagated through the aggregation process, resulting in uncertainty estimates for gridded fields. We demonstrate the new procedure for two test months and representative variables, inspecting monthly mean profiles for refractivity, dry temperature and physical temperature measurements. Results show that estimated random uncertainties and residual sampling uncertainties (after sampling bias correction) have similar magnitudes, both decreasing with increasing spatial aggregation sizes and corresponding increasing number of aggregated observations. At small aggregation they are the main contributors to uncertainty in refractivity, and important contributors to uncertainty of temperature. Systematic uncertainty, whose magnitude is independent of the number of profiles, is for refractivity the main source of uncertainty for larger aggregation sizes, and for pressure and dry temperature at all commonly used aggregation sizes. All uncertainty components exhibit pronounced spatial variation over the globe, with polar regions showing the greatest uncertainty.

The active channel of alluvial rivers delineates areas of geomorphic activity over a defined time window. While increasing satellite data availability enables monthly active channel delineations, multi-year analyses often rely on temporal aggregates (e.g., annual medians) to reduce computational costs and intra-annual variability. The potential of monthly information to improve active channels delineation and geomorphic interpretation remains largely unexplored. In this work, we delineated active channels for the Po River (Italy) by aggregating monthly Sentinel-2 classifications of river water and sediment bars into annual frequency maps at 10 m resolution. Annual aggregation mitigated monthly sediment underestimation (12%) but also amplified model overestimation biases (15%). Monthly classification persistence (e.g., classified as active channel for more than N months/year) was then used to reduce these errors and produce active channel areas that closely match those manually delineated from 30 cm orthophotos. The spatiotemporal variability of monthly classifications also show that the active channel area of dynamic reaches can vary ∼50% over the year. These changes revealed areas most prone to water-stage fluctuations, sediment transport, as well as zones seasonally or progressively colonized by vegetation—patterns hidden in single orthophotos or annual medians. Less dynamic reaches, by contrast, showed minimal differences between annual and monthly-based delineation methods. These findings emphasize the importance of adapting temporal aggregation to the river type and process analysed, with sub-annual resolutions better capturing, in dynamic rivers, seasonal and progressive active channel reconfigurations, along with their interaction with sediment and vegetation dynamics.

In the widely adopted description of seismic occurrence, earthquakes are categorized as either background or triggered events. In this work, we present a fully automated, non-parametric algorithm for distinguishing between these two categories, a process known as seismic declustering, based on the widely used nearest-neighbor (NN) metric. We introduce a new measure, the susceptibility index, which identifies an optimal threshold to discriminate between background and triggered events within the NN metric. Through statistical testing on simulated epidemic type aftershock sequence catalogs, we demonstrate that our method yields classification metrics exceeding 90%, outperforming state-of-the art algorithms. Notably, we show that a single threshold is sufficient for reliable discrimination within a given data set. The identification of this threshold requires memory capacity and computational time that scale linearly and quadratically with the data set size, respectively, making the method particurarly suited for large earthquake catalogs. We also apply our method to the relocated Southern California catalog and the GeoNet catalog of New Zealand (NZ). Our method effectively adapts across the different tectonic settings, capturing the variability of background seismicity rates between the shallow crustal events of Southern California and the tectonically diverse seismicity of NZ.

The accurate co-registration of geospatial data is necessary to answer questions that cross-cut disciplines and are key to understanding fundamental questions about our Solar System. To address this need and provide an updated product for Mars that is tied to a common reference frame, we have photogrammetrically controlled Thermal Emission Imaging System (THEMIS) daytime and nighttime infrared IR images. Using this improved image position knowledge, we generated orthorectified daytime and nighttime IR mosaics of Mars at 100 m per pixel for the ±65° latitude region of Mars. The updated spacecraft position and pointing information for the images is also released as SPICE kernels. The co-registration between individual THEMIS images achieves sub-pixel precision, and the average accuracy with which we know the position of any feature within the THEMIS controlled products is approximately 200 m horizontally. A globally controlled image set, with quantified accuracy and precision, is necessary to facilitate exploration and discovery for all bodies in the Solar System. Controlling THEMIS data allows multi-instrument science to be performed with significantly higher confidence as precise co-registration, and the accuracy knowledge of that registration, is necessary for analyses designed to extract information from the subtle differences between multiple images. A global image mosaic of Mars where uncertainties in the absolute image position are well characterized serves a wide range of purposes, including landing site evaluations, providing an accurate base to which high-resolution images (e.g., CTX and HiRISE) can be tied, and enables the fusion of multiple data types within a single framework.

The NASA Soil Moisture Active Passive Mission (SMAP) satellite passive microwave radiometry (PMR) capability is demonstrated for measurements of river stage, river discharge, and lake level with in situ gauging data in the Lower Mekong Basin (LMB). Five river gauging locations with distinct characteristics in the Mekong River system and a location for the Tonle Sap Lake were selected. The SMAP PMR method was validated with forward-split, backward-split, and full-record approaches. Results from the three different validations were consistent and well compared with in situ gauging data at all the locations. Both the narrowest (42-m width, Songkhram River) and the widest river (1,735-m width, Mekong River) achieved high correlation values ≥0.9 and Nash-Sutcliffe Efficiencies >0.8. The SMAP PMR observations of rivers and lake captured seasonal and interannual patterns of river change corresponding to flood and drought conditions. The synergy of SMAP with satellite Ka-band PMR and Soil Moisture and Ocean Salinity (SMOS) data over multiple decades identified flood and drought events, and abrupt changes in river flows in the LMB corresponding to the operations of the two largest dams, Xiaowan and Nuozhadu, on the Lancang (upper Mekong) River. After these two dams went into operation, wet-season flow stage in the lower Mekong River did not again reach the 2.33-year flood stage, and dry-season water level dropped below the lowest stage recorded in the 2015 exceptional drought year. The PMR method enables river and lake monitoring with global coverage on a daily to nearly daily basis over decades.

Accurate aerosol optical depth (AOD) prediction remains challenging due to complex aerosol-radiation interactions and highly variable spatio-temporal patterns. Three critical scientific issues motivate this work: understanding whether and how physical principles can enhance deep learning predictions, identifying which aerosol properties most strongly govern AOD variations, and improving the prediction of extreme AOD events critical for air quality management. Herein, utilizing MERRA-2 reanalysis data (1980–2024) over the Huaihe River Basin in eastern China, a Physics-Guided deep learning framework is presented for Aerosol Optical Depth (AOD) prediction. The model proposed integrates Convolutional Neural Networks (CNN), Long Short-TermMemory (LSTM) networks, and multi-head attention mechanisms to capture both spatio-temporal features and physical relationships of aerosol properties. Three key aspects are involved: First, a hybrid deep learning model is developed and evaluated, which combines CNNs for spatial correlation extraction, bidirectional LSTM for temporal dependency modeling, and multi-head attention for feature interaction learning. Second, a comprehensive feature importance analysis is conducted by examining the relationships between different aerosol properties (mass concentration, scattering coefficient, and Ångström exponent) and AOD prediction, offering physical insights into the model's decision-making process. Third, a specialized approach is proposed for extreme AOD event prediction, focusing on early detection and accurate forecasting of high-AOD episodes. Overall, the results demonstrate the model's efficacy in capturing both regular AOD variations and extreme events, with the Physics-Guided architecture showing superior performance compared to traditional methods. This integrated approach enhances AOD prediction accuracy and deepens insights into aerosol-radiation interactions, thereby improving atmospheric monitoring and air quality forecasting. While MERRA-2 has inherent temporal delays, this framework provides valuable capabilities for historical trend analysis, numerical model validation, and can be readily adapted for real-time applications through transfer learning with satellite observations.