Earth and Space Science最新文献

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.

The observation errors in precipitation gauges contribute to diminished precision in precipitation data sets. To reduce the impact of these errors, the World Meteorological Organization Solid Precipitation Intercomparison Experiments recommended the Double Fence Intercomparison Reference as a reference standard for precipitation measurements. This study proposed a new rain, snow, and mixed precipitation adjustment method for national standard precipitation gauges, using DFIR-measured precipitation as the standard values. This method was used to adjust for systematic errors, including wind-induced errors, wetting loss, and trace precipitation, in precipitation data collected by 785 stations in China from 1961 to 2020. After bias adjustment, the annual precipitation increased by 6.1–177.9 mm (with an average of 52.7 mm), accounting for 3.3%–52.1% (8.9%) of the total precipitation. The average annual error-adjustment amounts for wind-induced error, wetting loss, and trace precipitation were 21.9 (3.6% of total precipitation), 26.6 (4.7%), and 4.2 mm (1.3%), respectively. The adjustment percentage in winter was higher than that in summer, with the high-adjusted-percentage regions predominantly located in areas with drought, high proportion of snowfall, and strong wind speeds. Additionally, the annual average error-adjustment amounts for rain, snow, and mixed precipitation respectively accounted for 5.2%, 38.2%, and 17.1% of their corresponding total amounts, indicating the significance of bias adjustment, particularly for snow and mixed precipitation, in the northern and Qinghai-Tibet Plateau regions. Therefore, bias adjustment is necessary to enhance the accuracy of the precipitation data set in China.

PRISMA is a hyperspectral satellite mission launched by the Italian Space Agency (ASI) in April 2019. The mission is designed to collect data at global scale for a variety of applications, including those related to the cryosphere. This study presents an evaluation of PRISMA Level 1 (L1) and Level 2 (L2D) products for different snow conditions. To the aim, PRISMA data were collected at three sites: two in the Western European Alps (Torgnon and Plateau Rosa) and one in East Antarctica (Nansen Ice Shelf). PRISMA data were acquired contemporary to both field measurements and Sentinel-2 data. Simulated Top of the Atmosphere (TOA) radiance data were then compared to L1 PRISMA and Sentinel-2 TOA radiance. Bottom Of Atmosphere (BOA) reflectance from PRISMA L2D and Sentinel-2 L2A data were then evaluated by direct comparison with field data. Both TOA radiance and BOA reflectance PRISMA products were generally in good agreement with field data, showing a Mean Absolute Difference (MAD) lower than 5%. L1 PRISMA TOA radiance products resulted in higher MAD for the site of Torgnon, which features the highest topographic complexity within the investigated areas. In Plateau Rosa we obtained the best comparison between PRISMA L2D reflectance data and in situ measurements, with MAD values lower than 5% for the 400–900 nm range. The Nansen Ice Shelf instead resulted in MAD values <10% between PRISMA L2D and field data, while Sentinel-2 BOA reflectance showed higher values than other data sources.

Reconstructing fine-grained, detailed spatial structures from time-evolving coarse-scale geophysical fields has been a long-standing challenge. Current deep learning approaches addressing this issue generally require massive fine-scale fields as supervision, which is often unavailable due to limitations in existing observational systems and the scarcity of widespread high-precision sensors. Here, we present AdaptDeep, a self-supervised framework for refined reconstruction of geophysical fields via domain adaptation from the coarse-scale source domain to the fine-scale target domain. This method incorporates two pretext tasks, cropped field reconstruction and temporal augmentation-assisted contrastive learning, to leverage spatial and temporal correlations in the target domain. A global propagation structure is proposed in the feature extraction network to leverage bidirectional information for enhanced long-range dependencies and robustness against estimation errors. In experiments, AdaptDeep correctly identifies local, fine structures and significantly recovers 81.2% detailed information in sea surface temperature fields.

Lunar seismic data are essential for understanding the Moon's internal structure and geological history. After five decades, the Apollo data set remains the only available one and continues to offer significant value for current and future lunar seismic data analyses. Recent advances in artificial intelligence for seismology have identified seismic signals that were previously unrecognized. In our study, we utilized deep learning for unsupervised clustering of lunar seismograms, leading to the discovery of a new type of long-period lunar seismic signal that existed every lunar night from 1969 to 1976. We then conducted a thorough analysis covering the timing, frequency, polarization, and temporal distribution characteristics of this signal to study its properties, occurrence, and probable origins. This signal has a physical cause instead of artificial, such as voltage changes, according to its amplitudes during peaked and flat modes, as well as the digital converter status. Based on its relation to the lunar temperature and documents on Apollo instruments, we conclude that this signal is likely induced by the cyclic heater, with several unresolved questions that might challenge our hypothesis. Excluding interference from this newly identified signal is crucial when analyzing lunar seismic data, particularly in detecting lunar free oscillations. Our research introduced a new method for discovering new types of planetary seismic signals and helped advance our understanding of Apollo seismic data. Furthermore, the discovery of this signal holds valuable implications for the design of future planetary seismometers to avoid encountering similar issues.