Earth and Space Science最新文献

Expendable Bathythermographs (XBTs) are oceanographic instruments that fall through the ocean's water column and measure ocean temperature with depth. In many instances, however, XBTs continue to record temperature after they impact the seabed. Here we show evidence that XBTs produce unique temperature responses when they impact the seabed that depend directly on seabed physical properties. Specifically, standard-use XBTs (e.g., T-4s and T-5s), when deployed above a mud-rich seabed, require significant time (tens of minutes) to equilibrate to steady-state seafloor temperatures after seabed impact. In contrast, XBTs deployed above sand-rich sediments equilibrate to seabed temperatures rapidly (<5 min) after seafloor impact. One explanation for this difference in temperature response is that XBTs deployed above mud-rich sediment penetrate into low permeability marine muds that jacket the XBT, where diffusive heat flow dominates. Both observations and numerical modeling results support the hypothesis that XBTs impacting muddy seafloors exhibit slow, diffusion-dominated heat flow, while XBTs impacting harder, sand-rich seabed sites exhibit rapid seafloor temperature equilibration, consistent with advection-driven heat flow and little if any XBT seabed penetration. Given that >644k XBT measurements exist publicly (via the National Oceanographic and Atmospheric Administration website), and >74,000 XBTs record temperatures post seabed impact, we suggest that XBT data represents a large, low-cost, and currently untapped data set for characterizing seabed physical properties globally.

Current understanding of the ammonia distribution in Jupiter's atmosphere is provided by observations from major ground-based facilities and spacecraft, and analyzed with sophisticated retrieval models that recover high fidelity information, but are limited in spatial and temporal coverage. Here we show that the ammonia abundance in Jupiter's upper troposphere, which tracks the overturning atmospheric circulation, can be simply, but reliably determined from continuum-divided ammonia and methane absorption-band images made with a moderate-sized Schmidt-Cassegrain telescope (SCT). In 2020–2021, Jupiter was imaged in the 647-nm ammonia absorption band and adjacent continuum bands with a 0.28-m SCT, demonstrating that the spatially resolved ammonia optical depth could be determined with such a telescope. In 2022–2023, a 619 nm methane-band filter was added to provide a constant reference against which to correct the ammonia abundances (column-averaged mole fraction) for cloud opacity variations. These 0.28-m SCT results are compared with observations from: (a) the MUSE instrument on ESO's Very Large Telescope (b) the TEXES mid-infrared spectrometer used on NASA's InfraRed Telescope Facility; and (c) the Gemini telescopes, and are shown to provide reliable maps of ammonia abundance. Meridional and longitudinal features are examined, including the Equatorial Zone (EZ) ammonia enhancement, the North Equatorial Belt depletion, depletion above the Great Red Spot, and longitudinal enhancements in the northern EZ. This work demonstrates meaningful ammonia monitoring can be achieved with small telescopes that can complement spacecraft and major ground-based facility observations.

Accurate topographic data acquired at appropriate spatio-temporal resolution is often the cornerstone of geomorphic research. Recent decades have seen advances in our ability to generate highly accurate topographic data, primarily through the application of remote sensing techniques. Structure from Motion-Multi View Stereo (SfM-MVS) and lidar have revolutionised the spatial resolution of surveys across large spatial extents. Technological developments have led to commercialisation of small form factor (SFF) 3D lidar sensors that are suited to deployment on both mobile (e.g., uncrewed aerial systems), and in fixed semi-permanent installations. Whilst the former has been adopted, the potential for the latter to generate data suitable for geomorphic investigations has yet to be assessed. We address this gap here in the context of a 3-month deployment where channel change is assessed in an adjusting fluvial system. We find that SFF 3D lidar sensors generate change detection products comparable to those generated using a conventional lidar system. Areas of no geomorphic change are characterised as such (mean 3D change of 0.014 m compared with 0.0014 m for the Riegl VZ-4000), with differences in median change in eroding sections of between 0.02 and 0.04 m. We illustrate that this data enables: (a) accurate characterisation of river channel adjustments through extraction of bank long-profiles; (b) the assessment of bank retreat patterns which help elucidate failure mechanics; and (c) the extraction of water surface elevations. The deployment of this technology will enable a better understanding of processes across a variety of geomorphic systems, as data can be captured in 4D with near real-time processing.

The discrepancy in the observed global mean sea level budget increased significantly since 2016, but the budget discrepancy over basin-scales is unclear. In this contribution, we investigate the sea level budget discrepancies in major basins with observations from satellite altimetry, satellite gravimetry, and Argo floats. During 2016–2020, we find substantial discrepancy of 5.72 ± 0.98 mm/yr over the North Atlantic Ocean, and the basin scale discrepancies are smaller elsewhere. Our analysis suggests that three factors, including the wet tropospheric correction (WTC) effect, deep ocean warming signal, and the contemporary ocean bottom deformation (OBD), together reduce the discrepancy by only 1 mm/yr for the North Atlantic Ocean. We decompose sea level observations into the spherical harmonic domain and observe increased discrepancy in low-degree variations of C₁₀ and C₂₁ since 2016. These two coefficients result in a contrasting signal between the North and South Atlantic Ocean and contribute to the large discrepancy over the North Atlantic Ocean. We further demonstrate that the C₁₀ and C₂₁ discrepancies are independent of the three factors. However, we find regional salinity biases in the Argo data that reduce the discrepancy for the North Atlantic Ocean. Our findings add to the debate about recent sea level budget and imply that further analysis of the Argo North Atlantic data set may be useful.

The provision of accurate wet tropospheric corrections (WTC), accounting for the delay of the radar pulses caused mostly by the atmospheric water vapor in the altimeter-range observations, is pivotal for the full exploitation of altimeter-derived surface heights. The WTC is best retrieved by measurements from Microwave Radiometers (MWR) on board the same altimeter mission. However, these instruments fail to provide valid WTC over land and ice and under rainy conditions. The GNSS-derived Path Delay Plus (GPD+) algorithm has been designed to provide WTC over these surfaces where the onboard MWR WTC is invalid. This study focuses on the estimation of enhanced GPD+ WTC for the Copernicus Sentinel-3A and Sentinel-3B satellites, for the latest Baseline Collection 005.02 (BC005.2), spanning the period since the beginning of the missions until March 2023. GPD+ corrections are being provided operationally since 2022 and have been adopted as the default WTC in the calculation of the sea level anomaly (SLA). Compared to previous versions, the BC005.2 GPD+ WTC features improved data combination procedures, possesses a larger percentage of points estimated from observations, a better intermission alignment and reduced systematic differences among ascending and descending passes. Overall, GPD+ WTC are consistent, calibrated corrections, valid over all points present in the Non Time Critical marine product, allowing to recover, on average, about 17% of the altimeter observations with valid SLA, which otherwise, most of them would be rejected. Impacts of these WTC are most significant over coastal and inland water regions, at high latitudes and during rain events.

Methane, with a global warming potential roughly 86 times greater than carbon dioxide over a 20-year timeframe, plays a crucial role in global warming. Remote sensing retrieval is a pivotal methodology for identifying methane emission sources, with accuracy influenced largely by surface and atmospheric properties, including aerosols. In this study, we propose an Aerosol-Calibrated Matched Filter (ACMF) algorithm to improve the traditional Matched Filter (MF) method. Our new approach incorporates an aerosol scattering correction factor to reduce the aerosol-induced bias on methane retrievals. Validating our algorithm through simulated spectra, we demonstrate that considering the aerosol scattering effect significantly reduces retrieval errors compared to MF methods by an average of approximately 90%. We apply our newly developed algorithm to hyperspectral data obtained from the Airborne Visible/Infrared Imaging Spectrometer—Next Generation in the Los Angeles Basin and focus on 11 plumes identified through case studies. Our results reveal that ACMF estimates of emission rates and inversion uncertainties exhibit an average reduction of approximately 4% compared to corresponding MF results, with deviation increasing with aerosol optical depth (AOD).

A data-driven model for predicting the surface temperature using neural networks was proposed to alleviate the computational burden of numerical weather prediction (NWP). Our model, named TPTNet uses only 2 m temperature measured at the weather stations of the South Korean Peninsula as input to predict the local temperature at finite forecast hours. The turbulent fluctuation component of the temperature was extracted from the station measurements by separating the climatology component accounting for the yearly and daily variations. The effect of station altitude was then compensated by introducing a potential temperature. The resulting turbulent potential temperature (TPT) data at irregularly distributed stations were used as input for predicting the TPT at forecast hours through three trained networks based on convolutional neural network, Swin Transformer, and a graph neural network. By comparing the prediction performance of our network with that of persistence and NWP, we found that our model can make predictions comparable to NWP for up to 12 hr.

Spectral features of olivine across the intermediate infrared region (IMIR, 4–8 μm) shift systematically with iron-magnesium content, enabling determination of olivine composition. Previous IMIR studies have used laboratory data with signal-to-noise ratios (SNRs) and spectral resolutions potentially greater than those of data derived from planetary missions. Here we employ a feature fitting algorithm to quantitatively assess the influence of data quality on olivine detection and compositional interpretation from IMIR data of 29 spectra of pure olivine of synthetic, terrestrial, lunar, and Martian origins, as well as 5 spectra of lunar pyroclastic beads measured as bulk samples. First, we demonstrate the effectiveness of the feature fitting algorithm in the interpretation of IMIR olivine spectra, predicting olivine composition with an average error of 6.4 mol% forsterite across all test spectra using laboratory-quality data. We then extend this analysis to degraded test spectra with reduced SNRs and sampling rates and find a range of data qualities required to predict olivine composition within ±11 Mg# (molar Mg/[Mg + Fe] × 100) for the test spectra explored here. Spectra for the sample most relevant to lunar exploration, an Apollo 74002 drive tube consisting of microcrystalline olivine and glass-rich pyroclastics, required SNRs ≥ 200 for sampling rates ≤25 nm to predict composition within ±11 Mg# of the sample's true composition. Derived limits on SNRs and sampling rates will serve as valuable inputs for the development of IMIR spectrometers, enabling comprehensive knowledge of olivine composition on the lunar surface.

We revisit the nature of the ocean bottom pressure (p_b) seasonal cycle by leveraging the mounting GRACE-based p_b record and its assimilation in the ocean state estimates produced by the project for Estimating the Circulation and Climate of the Ocean (ECCO). We focus on the mean seasonal cycle from both data and ECCO estimates, examining their similarities and differences and exploring the underlying causes. Despite substantial year-to-year variability, the 21-year period studied (2002–2022) provides a relatively robust estimate of the mean seasonal cycle. Results indicate that the p_b annual harmonic tends to dominate but the semi-annual harmonic can also be important (e.g., subpolar North Pacific, Bellingshausen Basin). Amplitudes and short-scale phase variability are enhanced near coasts and continental shelves, emphasizing the importance of bottom topography in shaping the seasonal cycle in p_b. Comparisons of GRACE and ECCO estimates indicate good qualitative agreement, but considerable quantitative differences remain in many areas. The GRACE amplitudes tend to be higher than those of ECCO typically by 10%–50%, and by more than 50% in extensive regions, particularly around continental boundaries. Phase differences of more than 1 (0.5) months for the annual (semiannual) harmonics are also apparent. Larger differences near coastal regions can be related to enhanced GRACE data uncertainties and also to the absence of gravitational attraction and loading effects in ECCO. Improvements in both data and model-based estimates are still needed to narrow present uncertainties in p_b estimates.

High-precision observation of significant wave height (SWH) is crucial for marine research. The Surface Waves Investigation and Monitoring (SWIM) aboard the China France Oceanography Satellite (CFOSAT) provides the ocean wave spectrum that allows for the calculation of the off-nadir SWH parameters, but there exists a certain bias with the in-situ SWH values. To improve the accuracy of the SWH calculation bias from the off-nadir 6°, 8°, 10° wave spectra and the whole combined spectrum, this paper establishes a spatio-temporal hybrid model that combines convolutional neural network (CNN) and long short-term memory network (LSTM). Additionally, to further correct bias exhibited under high sea state, we introduce a bias correction module based on deep neural network (DNN) to adjust the SWIM off-nadir SWH greater than 4 m. The experimental results demonstrate a significant enhancement in the accuracy of corrected SWIM off-nadir SWH, and the best calibration result is 10° with 0.267 m root mean square error (RMSE), and 0.979 correlation coefficient (R) compared with the ERA5 value. We conducted a comprehensive study and analysis on the performance of the proposed model under different wave heights, extreme sea states, and wind and swell regions. Meanwhile, the buoy and altimeters are leveraged to render further evaluation the RMSE of the corrected SWH is less than 0.5 m.