Earth and Space Science最新文献

Ocean color models, facilitated by satellite-based remote sensing technologies, have transformed our ability to monitor chlorophyll-a concentrations at the ocean's surface. These models decode changes in the spectral properties of reflected sunlight to map phytoplankton biomass across extensive spatial and temporal scales. Such capabilities are crucial for understanding marine primary productivity, carbon cycling, and ecosystem reactions to environmental change. Additionally, improvements in atmospheric correction and in-water algorithms over the past decades have reduced the uncertainties associated with chlorophyll-a retrieval, rendering satellite-derived ocean color data more suitable for fisheries management, climate research, and coastal water quality monitoring. However, the most commonly used ocean color products (e.g., Sentinel data sets) do not provide uncertainties, which poses challenges in their application for analyzing events like algal blooms and for risk assessment. Moreover, these models are typically evaluated using deterministic error metrics that may not always capture the subtleties in model performance. This paper presents a probabilistic alternative to traditional ocean color models, including the maximum band and color index models, utilizing Bayesian regression and underpinned by the largest database of validated bio-optical match-ups. We further propose a method for comparing ocean color models using information criteria. When conventional errors are indistinguishable, information criteria (Bayesian and Akaike information criteria) provide a principled, complexity-aware preference among ocean color models for the data sets analyzed, with the OC3 generally favored when considering likelihood-based approaches, suggesting that additional parameters might not be warranted.

On 27 July and 25 October 2022, an Mw 7.0 earthquake and an Mw 6.4 earthquake struck Abra, northwest of Luzon, Philippines, respectively, with a time interval of less than 3 months and a distance of about 16 km. In this research, interferometric synthetic aperture radar (InSAR) was utilized to retrieve the co-seismic surface deformation field for each earthquake. Furthermore, leveraging the co-seismic deformation data derived from both InSAR and Global Navigation Satellite System (GNSS) measurements, the source parameters of both events were determined based on dislocation models in an elastic half-space. The results indicated that the July earthquake occurred on an east-dipping and left-lateral strike-slip fault with a small thrust component, and the maximum slip reached 1.5 m. The October event occurred on an NNE-dipping fault with a strike angle of 82° and a rake angle of 88°, signifying that this earthquake was a thrust event. The maximum fault slip of the October event was approximately 0.6 m, occurring at the depth of roughly 9.9 km. Neither the July nor the October events ruptured to the surface, but there was a clear triggering relationship between them, with the July earthquake likely promoting the rupture of the October fault, as suggested by the static Coulomb failure stress changes. The Vigan-Aggao Fault, Abra River Fault, and Tubao Fault exhibit increased seismic hazards and warrant special attention in future studies.

The Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument provides continuous, high-resolution observations of atmospheric pollutants over North America from geostationary orbit. This study introduces an on-orbit spectral calibration algorithm implemented in the TEMPO Version 3 Level 0–1 processor, covering both operational irradiance and radiance wavelength calibrations and offline slit function retrievals. Irradiance wavelength calibration accuracy was evaluated, with the TSIS-1 hybrid solar reference spectrum chosen due to its low fitting residuals. Accordingly, first- and second-order Chebyshev-polynomial fittings are applied to UV and VIS, respectively, to derive the wavelength grid. Earth-view radiance wavelength calibration updates the wavelength grid based on the latest solar irradiance calibration result by fitting a wavelength shift. To optimize efficiency and accuracy, a narrow spectral window of 100 channels (320–340 nm for UV and 630–650 nm for VIS) was selected, with wavelength shift uncertainties of 0.002 nm (UV) and 0.006 nm (VIS). Radiance calibration results shows that the wavelength shifts of inhomogeneous pixels vary relatively significantly. We perform a 22-month trend analysis of the TEMPO solar irradiance spectral performance. Compared to first light, the wavelength shift gradually increases, reaching 0.08–0.09 nm in July 2024, and then remains stable. The offline slit function parameters, retrieved from several narrow spectral windows using a super-Gaussian function, show minor variations during the 22-month on-obit operation and did not deviate significantly from prelaunch. This study supports the long-term L1b data processing for TEMPO and provides an instrument spectral calibration framework applicable for future geostationary orbit spectrometers.

The Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument is the first spaceborne hyperspectral spectrometer that measures backscattered sunlight over North America in a geostationary orbit. The two charge-coupled device (CCD) detectors of TEMPO, with spectral coverages of 293–494 and 538–741 nm and resolutions of 0.53–0.63 nm, are capable of identifying absorption features of key trace gases, including ozone, nitrogen dioxide, formaldehyde, and others. Using a step-and-stare scanning mechanism, the TEMPO instrument measures backscattered Earth radiance spectra hourly during the middle of the day (nominal scans) and every 40 min in the early morning and late afternoon (optimized scans) to effectively cover the sunlit portions of the continent, with scans during twilight when permitted by instrument safety constraints. Solar irradiance measurements are also conducted nominally with a weekly frequency. This article describes the Version 3 algorithm for TEMPO Level 1b data processing, which provides radiometrically and spectrally calibrated solar irradiance and Earth radiance spectra as the primary outputs. The processing includes the conversion of digitized Level-0 signals to radiance or irradiance, image navigation and registration for Earth spectra, and spectral calibration. This article also discusses calibration key data derived from pre-launch instrument characterization and in-flight calibration results.

Saturn's moon Enceladus harbors a global subsurface ocean beneath its icy crust. Understanding the structure and composition of this ocean and ice is critical to assessing its potential habitability. Modern electromagnetic (EM) sounding techniques, which measure a celestial body's induced response to external electromagnetic fields, offer a powerful tool for probing internal structures. These techniques are well-established for Earth and the Moon, modeled for Europa, and here evaluated for Enceladus. By modeling higher frequency range (1 mHz−1 kHz), which sound to shallower depths than lower frequencies, this study shows that induction can provide a constraint on ice composition. The induced response also gives insight into other ice-shell properties, including potential water layers, as well as different stratified ocean conditions. The findings of this study highlight the potential for future missions to use EM sounding to constrain properties of the ice-shell, including composition, as well as identifying potential ocean stratification.

The size of sea ice floes in the marginal ice zone (MIZ) is a key factor influencing ice coverage, albedo, wave propagation, and ocean–atmosphere energy exchanges. Floe size can be observed by processing visual-range imagery from ships, aircraft, or satellites. However, autonomously capturing floe boundaries remains challenging, particularly due to sea ice heterogeneity, which impairs boundary definition and reduces image clarity. This study evaluates the accuracy of sea ice floe segmentation using the gradient vector flow (GVF) active contour method, the deep learning-based Segment Anything Model (SAM), and a hybrid approach combining GVF and SAM. Methods are evaluated on a representative subset of a large data set of close-range, high-resolution imagery collected from cameras aboard an icebreaker during an Antarctic winter expedition. Spanning a wide range of ice conditions and image clarity in the MIZ, the subset provides a rigorous segmentation test bed. Performance is assessed in terms of floe detection accuracy, size distribution, and ice concentration, with results compared against a manually segmented benchmark. Results indicate SAM, in prompt-driven mode, offers the best balance between accuracy and computational efficiency. Its strong performance in estimating sea ice concentration and detecting floes, while maintaining close agreement with benchmark floe size distributions, makes it suitable for real-time applications and scalable analyses of large imagery data sets. Compared with SAM, the combined SAM-GVF method provides more accurate floe boundary delineation, although at much higher computational cost, and is therefore better suited for analyses requiring precise floe shapes.

The transport medium, mode, energy, and distance are recorded in the grain-size and grain-shape distributions in a sedimentary deposit. While grain-size analysis has long been used in sedimentology, grain-shape analysis is increasingly recognized as a valuable tool for reconstructing sedimentary processes and palaeoenvironments. Using Dynamic Image Analysis, this study focuses on endmember modeling of combined grain-size-shape distributions as an additional and robust sedimentological tool. To refine the technique, the topmost 10 m of a sedimentary section from the Mangshan Loess Plateau, China, was analyzed. Endmember modeling of the size-shape distributions revealed three sediment populations indicating different transport modes: sandy silt via short-term modified saltation (decreasing convexity with increasing grain-size), coarse silt via short-term suspension (decreasing aspect ratio and Cox circularity with grain-size), and coarse silt via long-term suspension (relatively low decreasing aspect ratio with grain-size, relatively high Cox circularity and convexity). A strong negative correlation was found between the finest endmember and a loess microcodium oxygen isotope record (precipitation proxy) from a nearby site, indicating that analyzing shape of the particles may help distinguish between dry and wet deposition. The nature of shape sorting seems to change with grain-size, transport mode and transport distance. For silt-sized sediments, shape sorting mainly occurs during deposition and is dominated by overall shape of the particles, whereas for the sand-sized sediments predominant shape sorting occurs already during entrainment based on grain regularity. These findings highlight the significance of integrating grain-shape with grain-size analyses to better resolve sediment transport processes.

The radar surface echo can be separated into coherent and incoherent components by statistical approaches, and the coherent component can be described by a backscattering model related to the RMS height. According to backscattering models for fractal surfaces, the coherent power in decibels decreases with RMS height on a scale independent of the wavelength at a rate depending on the Hurst exponent and the roughness scale. We extract the coherent power in four research areas by fitting the amplitude distribution of the Martian surface echoes recorded by the SHARAD radar, and compare the coherent power with the RMS height derived from pulse width of the MOLA laser altimeter. Scatter plots of squared MOLA-derived RMS height-coherent power are drawn to estimate the rates of coherent power fall-off by linear fitting, and the fitting power fall-off rates are compared to the Hurst exponents derived from digital terrain models in those areas. The fitting rates decrease with the Hurst exponent, similar to the theoretical rates. However, the fitting rates decrease with the Hurst exponent more sharply than the theoretical prediction. We explain the mismatch with a linear assumption between different roughness parameters, which helps to estimate the Hurst exponent, and a significant discrepancy between the wavelength and the roughness scale might influence the estimation results due to the scaling dependence of the Hurst exponent. This paper offers an opportunity to learn about the Hurst exponent at a tens-of-meter scale.

Geomagnetic storms, intense disturbances in the Earth's magnetosphere, pose risks to both technology and human activity in space. In this study, we analyzed geomagnetic field measurements from the Dusheti Observatory in Georgia during the intense geomagnetic storms of March 3, March 24, and 11 May 2024. Using cross-correlation, wavelet coherence, and detrended fluctuation analysis, we investigated the relationship between the $��B_z�$ component of interplanetary magnetic field, dynamic pressure, plasma $��{\beta }_p�$ in the upstream solar wind, and the H-component of the geomagnetic field. Our results reveal significant correlations with $��B_z�$ and $��P�$, characterized by distinct time lags of the order of 200 min, compatible with timescales observed in the literature. Wavelet coherence on both shorter and longer temporal scales revealed complex, multiscale characteristics of solar wind-magnetosphere coupling dynamics. Plasma $��{\beta }_p�$ showed an increase in coherence when a time shift is introduced, with maximal coherence for a shift of 12.5 hr, which may be related to the structure of the impinging coronal mass ejection and to the state of the magnetosphere. Detrended Fluctuation Analysis highlights regime changes in the Hurst exponent, indicating an increase in self-organization prior to storms. These findings emphasize the importance of localized studies in understanding the impacts of geomagnetic storms in Georgia.

Revealing the characteristics of moisture transport in heavy rainfall events in eastern China under the Northeast China Cold Vortex (NCCV) background holds important scientific and practical value for improving precipitation prediction accuracy. This study uses reanalysis data, surface observations, and a Lagrangian trajectory model to compare the characteristics and moisture transport mechanisms of NCCV and non-NCCV (NNCCV) rainstorms in Northeast China (NEC), North China (NC), and the Yangtze-Huaihe River basin (YHR). The results show that: (a) The high-value area of NCCV rainstorm intensity in NEC is located along the southern coast; in NC, rainstorms are in the southeast, with NNCCV rainstorms being more intense. In YHR, NCCV rainstorms are distributed along the Yangtze River, while NNCCV rainstorms are concentrated in the central-north and southeastern coast. NCCV rainstorm frequency peaks in July in NEC and NC, and in June in YHR. The NCCV significantly changes the rainstorm distribution in YHR in June and August. All three regions show an upward trend in NCCV rainstorm frequency, with the fastest increase in YHR. (b) For NCCV rainstorms in NEC, the southwest pathway moisture contributes the most (40%), while NNCCV rainstorms are dominated by the southeast pathway (48%). In NC, both NCCV and NNCCV rainstorms are primarily influenced by the southwest pathway moisture (45% and 42%, respectively). In YHR, the southeast pathway moisture accounts for 62% of NCCV rainstorms, compared to 46% for NNCCV rainstorms. (c) In NEC, the NCCV easily triggers strong local cyclonic convergence, with more moisture from the eastern coast than usual. In NC, the NCCV introduces dry and cold air during rainstorms, reducing moisture but enhancing dynamic uplift through north-south wind convergence. In YHR, the southward shift of the NCCV interacts with the western Pacific subtropical high, leading to collisions between dry easterly currents and warm moist southwesterly jets, triggering convective instability and intensified precipitation.