Earth and Space Science最新文献

Typical ionosondes operate with >5 min time intervals, which is enough to obtain regular parameters of the ionosphere, but insufficient to observe short-term processes in the Earth's ionosphere. The key point for this study is to increase the ionosondes data time resolution by automatization of ionogram scaling routine. In this study we show the results of implementation of deep learning approach for ionogram parameters scaling. We trained the model on 13 years ionogram data set of Sodankyla ionosonde at high latitude region (67°N). We tested our autoscaling program tool on 2021 years data set and evaluate errors between operator and automatic parameters scaling. The root mean square errors for critical frequencies foF2, foF1, foE, foEs, fmin, fbEs and virtual heights h′F, h′E, h′Es are estimated as 0.12 MHz (2 pixels), 0.07 MHz (1.16 pixels), 0.15 MHz (2.5 pixels), 0.33 MHz (5.5 pixels), 0.15 MHz (2.5 pixels), 0.17 MHz (2.83 pixels), 7.7 km (1.34 pixels), 7.0 km (1.22 pixels), 7.1 km (1.24 pixels), respectively.

Synthetic aperture radar (SAR) has emerged as a key instrument in oceanography due to its high spatial resolution and sensitivity to ocean surface dynamics. The main limitation of a single spaceborne SAR is the long repeat cycle (e.g., 12 days for Sentinel-1), which hinders its capability to monitor the temporal evolution of oceanic processes. The principal objective of this study is to demonstrate the potential of spaceborne SAR to monitor the temporal variation of ocean surface circulation. This is assessed using the Baltic Sea flow through the Danish strait Fehmarn Belt as a case study. In order to overcome the temporal sampling limitation, data from three satellites are combined, namely Sentinel-1A, Sentinel-1B and TanDEM-X. The average revisit time achieved by combining the three satellites is 1.2 days. Two months of opportunistic SAR data (June and July 2020) covering the Fehmarn Belt are used. The radial surface current derived from SAR is compared to ocean model and in situ data. It is shown that the dominant processes that govern the circulation in the Fehmarn Belt exhibit time scales larger than 2 days. Subsequently, it is demonstrated that SAR effectively captures the synoptic-scale features (time scales larger than 2 days) of the Baltic Sea circulation, thereby enabling monitoring the temporal variations of flow dynamics. Comparison of the SAR-derived radial surface current against in situ measurements yields comparable bias ($��\le �$0.08 m/s) and correlation coefficient (R $��\approx �$ 0.75) but lower standard deviations and rms errors (0.15 m/s) than those exhibited by the ocean model (0.31 m/s).

In situ spacecraft observations are critical to our study and understanding of the various phenomena that couple mass, momentum, and energy throughout near-Earth space and beyond. However, on-orbit telemetry constraints can severely limit the capability of spacecraft to transmit high-cadence data, and missions are often only able to telemeter a small percentage of their captured data at full rate. This presents a programmatic need to prioritize intervals with the highest probability of enabling the mission's science goals. Larger missions such as the Magnetospheric Multiscale mission (MMS) aim to solve this problem with a Scientist-In-The-Loop (SITL), where a domain expert flags intervals of time with potentially interesting data for high-cadence data downlink and subsequent study. Although suitable for some missions, the SITL solution is not always feasible, especially for low-cost missions such as CubeSats and NanoSats. This manuscript presents a generalizable method for the detection of anomalous data points in spacecraft observations, enabling rapid data prioritization without substantial computational overhead or the need for additional infrastructure on the ground. Specifically, Principal Components Analysis and One-Class Support Vector Machines are used to generate an alternative representation of the data and provide an indication, for each point, of the data's potential for scientific utility. The technique's performance and generalizability is demonstrated through application to intervals of observations, including magnetic field data and plasma moments, from the CASSIOPE e-POP/Swarm-Echo and MMS missions.

Accurate topographic data are essential for quantitative structural analysis, both in natural settings and in the laboratory. The selection of modeling materials (with appropriate rheological properties) is known to be fundamental for the success of scaled physical analog experiments. However, the optical properties of analog materials and their impact on the reliability and precision of high-resolution topographic reconstructions have not (to our knowledge) previously been assessed. Here we evaluate the effects of material composition, color, and grain size on Structure-from-Motion (SfM) photogrammetry reconstruction efficacy for deformed and undeformed model configurations in the laboratory. Image collections for photogrammetry are acquired from multiple camera positions with a handheld digital camera, and reconstructions are registered using ground control points in a local coordinate system. Static experiments show that low reflectivity granular materials (e.g., silica sand, volcanic ash, pumice, and Al₂O₃) yield relatively reliable photogrammetry data for a wide range of grain sizes (44–2,400 μm) but larger grain sizes (≥250 μm) provide more robust results. Reflective materials (e.g., glass beads, wet clay) yield less reliable point-clouds but the addition of low-reflectivity granular materials (e.g., Al₂O₃ grains) on the surface of wet clay improves reconstruction results, with higher grain densities typically yielding lower point-cloud residuals. SfM-photogrammetry reconstruction of deformed clay analog models tends to improve at higher extension magnitudes because of fault and associated texture development on model surfaces. We anticipate that our results will help practitioners to improve the precision and reliability of photogrammetric data acquired in the analog modeling laboratory.

This paper describes a catalog of simultaneous observations of the Earth's magnetosheath by ESA's Cluster and NASA's MMS missions. The catalog is built from a visual inspection of summary plots provided by the two missions complemented by an analysis of high-resolution magnetic field data. The catalog includes 117 events when Cluster 4 and MMS 4 crossed simultaneously the magnetosheath between January–April, 2017–2021. The dynamical and turbulent features of the magnetosheath are strongly influenced by θ_Bn, the angle between the interplanetary magnetic field (IMF) and the shock normal direction. To facilitate such investigations, we also determine the bow shock geometry for each event based on two different approaches: (a) a minimum variance analysis of in-situ magnetic field measurements, and (b) a geometrical approach which considers a bow shock model parameterized by OMNI data. A description of spacecraft trajectory during each event is also provided. Additional data describe the relative distances between Cluster 4 and MMS 4, a classification of each event as either quasi-parallel or quasi-perpendicular, and the distribution of events per magnetospheric flank. The time intervals for the Cluster - MMS conjunctions are included in the catalog, as well as all associated figures and tables discussed in this paper are made available through an independent online data repository, and can be freely downloaded and used by any interested researcher.

After impounding the Baihetan Water Reservoir in a historically seismically active area, numerous earthquakes occurred, but it is uncertain if they are connected to the impoundment. Based on the dense seismic network, this study used local earthquake tomography to construct high-resolution V_P (P-wave velocity) and V_P/V_S models (P- to S-wave velocity ratio) and update earthquake locations after the impoundment of Baihetan reservoir. Our study revealed that the reservoir water spreads from the dam site to the Qiaojia Basin and the Heishuihe branch, and pore pressure diffusion along faults induces many earthquakes. Reservoir water migrates through hidden faults and fractures beneath Hulukou, spreading to both sides and saturating some rocks below 7 km beneath the dam site-Lianhuatang section, causing multiple magnitude 3.5+ earthquakes. This study reveals that reservoir water migration drives earthquakes in the reservoir area, offering new insights into seismogenesis following the Baihetan reservoir's impoundment, potentially applicable to understanding reservoir-induced earthquakes in other reservoirs.

We evaluate a decadal ozone profile record derived from the Suomi National Polar-orbiting Partnership (SNPP) Ozone Mapping and Profiler Suite (OMPS) Limb Profiler (LP) satellite instrument. In 2023, the OMPS LP data were re-processed with the new version 2.6 retrieval algorithm that combines measurements from the ultraviolet (UV) and visible (VIS) parts of the spectra. It employs the second order Tikhonov regularization to retrieve a single vertical ozone profile between 12.5 km (or cloud tops) and 57.5 km with a vertical resolution of about 1.9–2.5 km between 20 and 55 km. The algorithm uses radiances measured at six UV ozone-sensitive wavelengths (295, 302, 306, 312, 317, and 322 nm) paired with 353 nm, and one VIS wavelength at 606 nm combined with 510 and 675 nm to form a triplet. Each wavelength pair or triplet is used over a limited range of tangent altitudes where the sensitivity to ozone change is strongest. A new aerosol correction scheme was implemented based on a gamma-function particle size distribution. In addition, numerous calibration changes that affected ozone retrievals were applied to measured LP radiances, including updates in altitude registration, radiometric calibration, stray light, and spectral registration. The key version 2.6 enhancement is the improved stability of the LP ozone record confirmed by the reduction in relative drifts between LP ozone and correlative measurements, linked previously to a drift in the version 2.5 LP altitude registration.

Grain sizes of Martian sand dunes are critical sedimentological data on sand provenance and transport pathways. Thermal inertia values are used to characterize the grain sizes of dune sand. Most early characterizations involved single dune fields. Recent work based on global data sets has provided more wide-spread dune sand locations, though these data sets include the non-sandy interdune areas. To provide a more accurate grain size characterization, we leverage a global thermal inertia data set, a global dune database and a global imaging mosaic to develop a freeware-based methodology for deriving grain sizes. This methodology involves delineation of sand-only areas within dune fields and collection of thermal inertia values from those areas. We consider a unimodal histogram of values with a mode <∼350 thermal inertia units (J m⁻² K⁻¹ s^−1/2) to imply an effective exclusion of non-sand surfaces. Application of this methodology to dune fields for which thermal inertia values have been previous derived shows our results fall within the envelope of those values. We apply our methodology to tropical dune fields on Mars for which Dust Cover Index data imply dust-free surfaces. Conversion of these thermal inertia values to sand grain sizes yields a range of sand classifications of fine sand to granules. Comparison of sand size classifications with geographic location shows grain size ranges that are distinctive by location, consistent with local sourcing. This work points toward geographically diverse sand formation mechanisms yielding diverse grain sizes, while providing a freeware-based and thus widely accessible method for expanding the derivation of these critical data.

Earthquake early warning (EEW) is of great significance in mitigating seismic disasters. Traditional EEW algorithms, which are knowledge-driven approaches, rely on seismologists' analysis. The limited intensity measures were extracted by seismologists from P-wave signals. And there is considerable uncertainty for predicting epicentral distance, magnitude, peak ground acceleration (PGA), and peak ground velocity (PGV). Currently, data-driven deep learning methods with the strong learning abilities do not consider knowledge information from seismologists in EEW; thus, there is unexplored potential in enhancing the performance of deep learning models for EEW. Here, we construct the Data-knowledge driven Hybrid deep Learning network (DHLnet) for EEW using the waveform input, knowledge embedding, convolutional neural network and graph convolutional network, aiming to integrate knowledge information from knowledge-driven methods and the strong learning ability of data-driven deep learning methods, that is, improving the performance of EEW. For the same test data set, compared with knowledge-driven methods and data-driven deep learning models, we demonstrate that DHLnet enhances the timeliness and robustness in predicting the epicentral distance, magnitude, PGA, and PGV during 10 s time window following the arrival of P-wave. Furthermore, to validate the generalization and robustness of the DHLnet in EEW, we applied the trained DHLnet to an independent data set, within first few seconds after an earthquake occurs, DHLnet can provide robust magnitude estimation, epicentral distance estimation and high alarm accuracy. The potential of the proposed network is to enhance the performance of EEW systems and provides new insights into the exploration of deep learning methods for EEW domain.

Atmospheric state analysis is a difficult scientific problem but essential for atmospheric sciences. Data assimilation can generate accurate analyses by integrating information on the atmospheric state using probability density functions (PDFs), where the Gaussian approximation is typically used and PDFs are described by error covariance matrices (ECMs). However, ECMs have been estimated empirically, and dependency of the ECMs on meteorological conditions (flow) is only partially represented. These limit atmospheric state analysis accuracy. This is especially problematic for water substance-sensitive microwave radiances (WS-MWRs) because of their strong flow dependence. We objectively estimated ECMs of all data including flow-dependence of the ECMs of WS-MWRs. Since the ECM of each data is a component of one ECM representing one joint PDF as a whole, it is theoretically better to objectively estimate ECMs of all data, not just a particular data. For WS-MWRs, we categorized flow into four using water substance amount and estimating an ECM for each category. Numerical experiments using the new ECMs on an operational global numerical weather prediction system show the followings. The new error standard deviations are generally smaller than those of empirical. Standard deviations and interchannel correlations of observation errors of WS-MWRs increase with water substance amount. The effects of WS-MWRs on analysis were approximately doubled. The analysis fields differ systematically such as increase of low-level clouds over cold oceans. The forecast accuracy improved with 95% statistical significance up to 9%. Both the flow dependence of correlation and variance of WS-MWRs contributed to the improvement of forecast accuracy.