Earth and Space Science最新文献

An automated air quality forecasting system (AI-Air) was developed to optimize and improve air quality forecasting for different typical cities, combined with the China Meteorological Administration Unified Atmospheric Chemistry Environmental Model (CUACE), and used in a typical inland city of Zhengzhou and a coastal city of Haikou in China. The performance evaluation results show that for the PM_2.5 forecasts, the correlation coefficient (R) is increased by 0.07–0.13, and the mean error (ME) and root mean square error (RMSE) is decreased by 3.2–3.5 and 3.8–4.7 μg/m³. Similarly, for the O₃ forecasts, the R value is improved by 0.09–0.44, and ME and RMSE values are reduced by 7.1–22.8 and 9.0–25.9 μg/m³, respectively. Case analyses of operational forecasting also indicate that the AI-Air system can significantly improve the forecasting performance of pollutant concentrations and effectively correct underestimation, or overestimation phenomena compared to the CUACE model. Additionally, explanatory analyses were performed to assess the key meteorological factors affecting air quality in cities with different topographic and climatic conditions. The AI-Air system highlights the potential of AI techniques to improve forecast accuracy and efficiency, and with promising applications in the field of air quality forecasting.

In response to the 2017 Decadal Survey, NASA conducted a five-year study on the Surface Deformation and Change (SDC) designated observable to study potential mission concepts. As part of the SDC mission study, the Commercial Synthetic Aperture Radar (ComSAR) subgroup was tasked with evaluating the current landscape of the SAR and interferometric SAR (InSAR) industry to assess whether NASA could leverage commercial smallsat products to meet the needs of the SDC science mission. The assessment found that although the commercial SAR industry is growing rapidly, off-the-shelf products can currently only make a small—albeit distinct—contribution to SDC mission goals. This gap is due to different design goals between current commercial systems (which prioritize targeted high-resolution, non-interferometric observations at short wavelengths with a daily or faster revisit) and a future SDC architecture (which focuses on broad, moderate-resolution, and interferometric observations at long wavelengths). Even by 2030, planned commercial constellations are expected to only cover $��\sim �$65% of the area needed to match NISAR coverage. Still, high-resolution and rapid-repeat capabilities can augment scientific findings from a future SDC mission, as demonstrated by recent contributions from commercial data to applied sciences, cryosphere, and volcanology. Future innovations on smallsat constellation concepts could further contribute to SDC science and applications. Although current constellation designs are not fully able to satisfy desired SDC science capabilities, initial positive feedback to a request for information indicates a potential future path for a customized SDC commercial architecture; more studies will be needed to determine the feasibility of these approaches.

Because of the remote nature of permafrost, it is difficult to collect data over large geographic regions using ground surveys. Remote sensing enables us to study permafrost at high resolution and over large areas. The Arctic-Boreal Vulnerability Experiment's Permafrost Dynamics Observatory (PDO) contains data about permafrost subsidence, active layer thickness (ALT), soil water content, and water table depth, derived from airborne radar measurements at 66 image swaths in 2017. With nearly 58,000,000 pixels available for analysis, this data set enables new discoveries and can corroborate findings from previous studies across the Arctic-Boreal region. We analyze the distributions of these variables and use a space-for-time substitution to enable interpretation of the effects of climate trends. Higher soil volumetric water content (VWC) is associated with lower ALT and subsidence, suggesting that Arctic soil may become drier as the climate warms. Soil VWC is bimodal, with saturated soil occurring more commonly in burned areas, while unburned areas are more commonly unsaturated. All permafrost variables show statistically significant differences from one land cover type to another; in particular, cropland has thicker active layers and developed land has lower seasonal subsidence than most other land cover types, potentially related to disturbance and permafrost thaw. While vegetation browning is not strongly associated with any of the measured permafrost variables, more greening is associated with less subsidence and ALT and with higher bulk soil VWC.

We characterized the bi-directional spectro-polarimetry of olivine sands of varying grain size distributions for a comprehensive set of measurement and illumination angles over a wavelength range of 350–2,500 nm. Our laboratory instrumentation included a hyperspectral goniometer, a broadband linear polarizer, and a tungsten-halogen illumination source. Three distinct grain size distributions of olivine sand samples were used in our experiments. As a function of azimuth, we measured a significant degree of anisotropic scattering, that depends directly on polarization angles, resulting in a distribution that cannot be accurately described solely using phase angle. For media of uniform or similar composition, we observed robust separability of grain size distributions using spectro-polarimetry. We compared Hapke's polarimetric model for semi-infinite granular media with a new empirical polarimetric model that we developed. This empirical model more accurately replicates the scattering of unpolarized incident light as a function of all view azimuth, view zenith, and polarization angles for all incident zenith angles. Parameters of our empirical polarimetric model that determine the magnitude of polarization correlate linearly with the inverse diffuse reflectances of the olivine sand samples, exhibiting phenomenology that is most likely due to the Umov effect. Because of the linearity of the correlations, our results show that polarimetry can be used to retrieve medium parameters, such as grain size distributions. We provide our data online and freely available in a Zenodo/GitHub repository.

Watershed sediment yield commonly increases after wildfire, often causing negative impacts to downstream infrastructure and water resources. Post-fire erosion is important to understand and quantify because it is increasingly placing water supplies, habitat, communities, and infrastructure at risk as fire regimes intensify in a warming climate. However, measurements of post-fire sediment mobilization are lacking from many regions. We measured sediment yield from a forested, heavily managed 25.4-km² watershed in the western Sierra Nevada, California, over 2 years following the 2021 Caldor Fire, by repeat mapping of a reservoir where sediment accumulated from terrain with moderate to high soil burn severity. Sediment yield was less than the geochronology-derived long-term average in the first year post-fire (conservatively estimated at 21.8–28.0 t/km²), low enough to be difficult to measure with uncrewed airborne system (UAS) and bathymetric sonar survey methods that are most effective at detecting larger sedimentary signals. In the second year post-fire the sediment delivery was 1,560–2,010 t/km², an order of magnitude above long-term values, attributable to greater precipitation and intensive salvage logging. Hillslope erosion simulated by the Water Erosion Prediction Project (WEPP) model overestimated the measured amount by a factor of 90 in the first year and in the second year by a factor (1.9) that aligned with previously determined model performance in northern California. We encourage additional field studies, and validation of erosion models where feasible, to further expand the range of conditions informing post-fire hazard assessments and management decisions.

The journal Earth and Space Science (ESS) was founded in 2014 to offer the scientific community a new platform for the dissemination of key new data, observations, methods, instruments, and models, presented within the context of their application. Thus, the aim of the journal was (and is) to highlight the complexity and importance of experimental design, methodology, data acquisition and processing, intertwined with data interpretation. Such approach is consistent with the mission of most AGU journals, but the distinctive element for ESS is its focus on the concept of the useful impact of publication, progressively replacing that on conventional publication metrics. In this context, the journal has been, since its inception, the preferred home for studies stemming from both global and local geoscience research. This special collection contains 16 papers published in ESS, selected by the Editorial Board to highlight the aims, scope and path of evolution and growth of the journal since it inaugural issue, in 2014.

To improve the radar data assimilation scheme for the high-resolution Tropical Regional Atmospheric Model System (TRAMS) model, this study investigates the sensitivity of simulating a warm-sector rainfall event in southern China to different radar reflectivity retrieval methods and incremental updating strategies. The findings indicate that the ice cloud retrieval (ICR) method yields more reasonable cloud hydrometeors. However, the impact of different retrieval methods is minimal without corresponding adjustments to the dynamic field. Further assimilation of the wind field effectively reduced the overestimated south winds and successfully simulated the observed low-level convergence in northern Guangdong, significantly improving precipitation forecasts. Both incremental analysis update (IAU) and Nudging methods were able to adjust the forecast to better match the observations, with IAU performing slightly better. These findings are beneficial for further improving the forecast accuracy of precipitation intensity. Extending the IAU relaxation time from 4 to 10 min has almost no impact on the actual forecasting. However, prioritizing the adjustment of the wind field through time-dependent IAU weighting factors, the impact of cloud particle adjustments on the dynamical field can be avoided (e.g., the drag caused by the sinking of cloud particles may offset the upward motion induced by dynamical convergence adjustments). This allows for more realistic low-level wind convergence and precipitation forecasts to be obtained. Overall, the ICR method for retrieving cloud hydrometeors, combined with the IAU method using time-dependent distribution weighting factors appears to be a more suitable option for the radar data assimilation scheme in TRAMS model.

This study reviews the progress made in modeling precipitations in four generations of reanalyses from the European Center for Medium-Range Weather Forecasts, using traditional metrics and a new set of regional metrics. Regional metrics at oceanic basin scales and large land catchment areas over the continents allow for a more comprehensive analysis of the performance of the reanalyses. This leads to the conclusion that significant progress has been made in the past several decades in both the atmospheric model and the assimilation system at the ECMWF, leading to more realistic precipitation. The most recent ERA5 reanalysis outperforms ERA-Interim and its predecessors by all metrics considered. ERA5 is then used to force a modern ocean general circulation model, and the results show an improvement in terms of the freshwater budget, particularly after the year 2000. However, uncertainties remain about the magnitude and trends of the modeled evaporation.

The correlation and physical interconnection between space weather indices and cosmic ray flux has been well-established with extensive literature on the topic. Our investigation is centered on the relationships among the solar radio flux, geomagnetic field activity, and cosmic ray flux, as observed by the Neutron Monitor at the Lomnický štít Observatory in Slovakia. We processed the raw neutron monitor data, generating the first publicly accessible data set spanning 42 years. The curated continuous data are available in.csv format in hourly resolution from December 1981 to July 2023 and in minute resolution from January 2001 to July 2023 (Institute of Experimental Physics SAS, 2024, https://doi.org/10.5281/zenodo.10790915). Validation of this processed data was accomplished by identifying distinctive events within the data set. As part of the selection of events for case studies, we report the discovery of TGE-s visible in the data. Applying the Pearson method for statistical analysis, we quantified the linear correlation of the data sets. Additionally, a prediction power score was computed to reveal potential non-linear relationships. Our findings demonstrate a significant anti-correlation between cosmic ray and solar radio flux with a correlation coefficient of −0.74, coupled with a positive correlation concerning geomagnetic field strength. We also found that the neutron monitor measurements correlate better with a delay of 7–21 hr applied to the geomagnetic field strength data. The correlation between these data sets is further improved when inspecting periods of extreme solar events only. Lastly, the computed prediction power score of 0.22 for neutron flux in the context of geomagnetic field strength presents exciting possibilities for developing real-time geomagnetic storm prediction models based on cosmic ray measurements.

A rational three-dimensional (3D) geological model with complex characteristics generated on a small amount of data is a crucial data infrastructure for scientific research and many applications. However, reconstructing structures with multi-Z values on a single point caused by folding or overthrusting is still one of the bottlenecks in 3D geological modeling. Combined with the multi-point statistics (MPS) method and fully connected neural networks (FCNs), this study presented a hybrid framework for 3D geological modeling. The loss functions of FCN and the conventional MPS method jointly form the kernel function of the proposed method, which is constrained by stratigraphic sequence and stratum thickness. The input and output parameters of the FCN are the coordinates and corresponding elevations of geological contacts, respectively. To solve the kernel function, the initial model, in which geological surfaces are generated by the FCNs, is generated using a sequential process. An iterative MPS process with an Expectation Maximization-like (EM-like) algorithm is carried out to illuminate the artifacts in the initial model. Ten orthogonal cross-sections are extracted from the overthrust model created by SEG/EAGE as the modeling data source. The results illustrated that the geometry and spatial relationships of strata and faults are retained well with the geological constraints. The comparison of virtual boreholes from the results and the real model shows that the accuracy of the geological object reaches 75%. The presented method provides a new idea for simulating 3D structures with multi-Z values, which overcomes the limitations of the conventional MPS-based 3D modeling method.