Earth and Space Science最新文献

High-precision Earth Rotation Parameter (ERP) products often experience delays that range from several days to weeks. The use of precise forecasting models can effectively compensate for the impact of such delays. Based on a systematic analysis of the forecasting capabilities of the traditional harmonic least squares fitting and autoregressive (AR) combined model, this study proposes a hybrid prediction model incorporating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), an AR model, and a Transformer-Long Short-Term Memory network for accurate polar motion (PM) time series prediction, termed M-CAL. Within this hybrid framework, we initially classify the decomposed signal components, using multiscale entropy obtained from CEEMDAN, into three categories: pseudo-white noise, low-frequency signals (reflecting long-term trends and seasonal variations), and high-frequency signals (indicating non-stationary fluctuations). These components are then forecasted, respectively, using white noise simulation, AR modeling, and deep learning approaches. Finally, the prediction results are generated through superposition. To evaluate the long-term effectiveness of the hybrid model, 80 experiments were conducted, each involving 30-day PM forecasts, which were then compared with the IERS Bulletin A products. The validation results indicate that, over the 30-day forecast horizon covering ultra-short- and short-term intervals, the X- and Y-components of PM were improved by approximately 53% and 61%, respectively, with maximum improvements reaching 90%. We therefore recommend the application of this model for practical implementation in ERP forecasting to further enhance prediction accuracy and reliability.

The Joint Total Solar Irradiance Monitor (JTSIM) onboard the Fengyun-3E meteorological satellite has been launched successfully on 4th of July 2021. It aims at measuring the Total Solar Irradiance (TSI) from the Low Earth Orbit. The instruments on the Fengyun-3E/JTSIM include the Digital Absolute Radiometer (DARA) from the Physikalisch Meteorologisches Observatorium, Davos and World Radiation Center (PMOD/WRC) and the Solar Irradiance Absolute Radiometer (SIAR) from the Changchun Institute of Optics, Fine Mechanics and Physics Chinese Academy of Sciences (CIOMP/CAS). The first light measurements and TSI value determined from DARA and SIAR are compared with other active missions (SOHO-VIRGO,TSIS-1).

As an important grassland ecological function area on the Tibetan Plateau, the risk of grassland fires in Qinghai Province has gradually increased due to climate warming and human activities. To quantitatively assess changes in grassland fire susceptibility under future climate scenarios, this study used historical fire data and CMIP6 model data, combined with multiple regression and the MaxEnt model, to simulate the distribution and trend of NDVI and fire susceptibility. Results showed that NDVI decreased under the low emission scenario (SSP119), and NDVI of grassland with medium and low coverage increased under medium and high emission scenarios (SSP245 and SSP585), while that of high coverage grassland decreased slightly. Fire susceptibility was higher in the east and south, and lower in the Qaidam Basin and northwest, with wind speed, distance from settlements, NDVI, slope, and human footprint as main driving factors. Mann-Kendall and Theil-Sen slope analyses showed that future fire susceptibility areas under medium- and high-emission scenarios increased significantly and fluctuated, concentrating in the periphery of the Qaidam Basin and Southern Qinghai Plateau. Risk varied significantly among grasslands of different coverage. The study reveals the impact of global emission pathways on regional fire risk, emphasizing the need to strengthen adaptation, mitigation, and optimize grassland fire prevention to safeguard ecological security of the Qinghai-Tibetan Plateau.

Climate variability affects multiple processes on Earth, with significant system effects driving hydrometeorological, glaciological, atmospheric, and geophysical variability. Research into these fields is driven by acquisition and processing of voluminous amount of data at multiple spatial and temporal scales. Intersection of data and tools to work around this complexity, to extract a consistent and useful picture of the effects of climate change in the Earth System, requires handling of big data sets and their processing tools. This effort is generating novel approaches to the analysis of big data sets and new perspective on the predictive power of the tools used. For this reason, in March 2023, AGU launched the Special Collection Analyzing Big Data for Understanding Climate Variability, Natural Phenomena, and Rapid Environmental Changes, inviting contributions to showcase the latest advances and the role of machine learning and deep learning in climate data analysis. In this introduction, we outline the key findings and insights presented in 16 articles published in the special collection, and we highlight the emerging trends within this field of research. The following journals participated in the special collection: Journal of Geophysical Research: Solid Earth, Journal of Geophysical Research: Atmospheres, Geophysical Research Letters, and Earth and Space Science.

In southeastern Korea, various synoptic conditions are responsible for heavy rainfall events (HREs) exceeding 30 $��\text{mm}��h^{�-�1�}�$ during the Changma, that is, the Korean summer monsoon season. We objectively classify such synoptic patterns of HREs using a self-organizing map with sea level pressure and 850-hPa geopotential height fields as input variables. A total of eight synoptic clusters (SCs) are identified. The first three clusters (SC1$��-�$3), which explain about 18% of HREs, are influenced by tropical cyclones. Excluding minor cluster SC4, SC5$��-�$8 are dominated by midlatitude weather systems with a southeast-high or northwest-low pattern. When examining HREs not influenced by tropical cyclones, SC5 and SC8 show contrasting synoptic patterns. SC5 is influenced by an eastward-propagating surface low, whereas SC8 is associated with an expansion of the Western North Pacific Subtropical High (WNPSH). In quasi-geostrophic motion, SC5 exhibits the strongest upward motion, primarily driven by dynamic forcing and diabatic heating. SC8 shows the weakest ascent. Observations show that SC5 is accompanied by widespread nighttime HREs propagating eastward alongside the synoptic system. In contrast, SC8 is accompanied by a localized daytime HRE that propagates northeastward, governed by background southwesterlies along the WNPSH periphery. SC6$��-�$7 share the synoptic characteristics of SC5 and SC8 but also have distinct characteristics. For example, SC7, which produces intense localized daytime rainfall, is characterized by warm surface and strong upslope winds, indicating terrain-induced effect with surface heating as a major driver. These results suggest that HREs in southeastern Korea are organized by multiscale processes under various background conditions.

The subduction interface geometry is particularly important for estimating interplate coupling and surface geodetic motion, which has significant implications for seismic hazard mapping. Several published Cascadia subduction interface geometries derived from different seismic data sets vary significantly from one another. However, results from deformation models that use the different interface geometries are rarely systematically compared. Here, we assess the impact of subduction interface geometry on surface motion predictions, slip inversion results, and interface coupling estimates from four published Cascadia subduction interface geometries. We isolate the effect of the interface geometry on the predicted surface motion by applying uniform unit slip or Gaussian slip patterns to each interface geometry and calculate the predicted displacements at locations of GNSS stations. The forward model-predicted horizontal displacements can differ by >20% and show azimuthal differences up to 10°; such differences correlate spatially to geometric differences amongst the interface realizations. Inversions of surface displacements estimated using a Gaussian distribution of slip, mimicking an earthquake, recover the applied slip distribution with differing spatial patterns and residuals of up to 38% of the maximum applied slip. Block models that use the four interface realizations produce coupling estimates on the interface with regions of significant coupling (>50%) that differ noticeably in down-dip extent and lateral continuity. The results we present suggest that models utilizing interface geometry as an input, such as earthquake and tsunami models, should consider comparing models with differing interface geometries to critically evaluate model uncertainty stemming from this fundamental input.

Accurate hurricane intensity prediction remains a critical challenge in numerical weather prediction (NWP). This study implements and evaluates a newly developed Storm-Following Three-Dimensional Incremental Analysis Update (3DIAU) methodology for high-resolution regional hurricane models with storm-following nest capabilities. Built upon the feature-relative 4DIAU approach proposed by Lu and Wang (2021), https://doi.org/10.1175/MWR-D-21-0068.1, the method gradually introduces Data Assimilation (DA) increments relative to the storm's position, reducing spin-up imbalances and improving intensity predictions. Retrospective experiments were conducted over three Atlantic hurricane seasons (2021–2023) using the 2024 operational Hurricane Analysis and Forecast System (HAFS) version 2.0A configuration. Sensitivity experiments suggest that increment weighting should depend on storm strength. The storm-strength-dependent configuration yields an average improvement of 3% in intensity prediction skill, with modest gains in long-term track predictions. A case study further demonstrates that gradual, storm-relative adjustments mitigate disruptions caused by intermittent DA and enhance forecast performance. The Storm-Following 3DIAU will be incorporated into the 2025 operational HAFS V2.1 upgrade.

On the evening of 18 August 2022, a sequence of five gigantic jets (GJs) were recorded within 7 min over an isolated coastal thunderstorm approximately 170 km to the southwest of Luoding County, Guangdong Province of China. The thunderstorm exhibited a higher −10°C isotherm altitude (500 m above average), a significant CAPE value (∼2,158 J/kg), and a combination of weak low-level wind shear (<3 m/s) and strong upper-level shear (∼14.5 m/s), composing favorable conditions for the GJ outbreak. GJs occurred during the developing-to-mature stage of the thunderstorm, characterized by the emergence of overshooting tops and cloud-top brightness temperatures below −70°C. Lightning activity was dominated by intracloud (IC) flashes, with IC frequency peaking at 47 events per 5-min interval around 20:10 UTC. High peak-current (>−30 kA) negative cloud-to-ground (−CG) flashes, which were active prior to 19:50 UTC, disappeared approximately 10–15 min before the GJ outbreak began. Additionally, all five GJs were likely preceded by narrow bipolar events, which are typically regarded as the onset signatures of GJ parent lightning, consistent with previous observations.

Spectral structure of gravity waves (GWs) attracted much attention because it is related to wave dissipation mechanism and parameterization effect. Although many works have studied the spectral variation of GWs and attributed this change to factors such as height, season and latitude, impact of observational resolution on wave spectrum was rarely reported. By averaging and downsampling 5-m resolution radiosonde temperature, we construct two data sets with lower resolutions to investigate the effect of data resolution on vertical wavenumber spectrum of GWs. The constructed average and downsample data sets correspond to two measurement techniques in actual observations. The results indicate that a lower resolution causes a shallower slope of power spectrum density (PSD) due to its filter effect on high wavenumber spectrum close to the saturation spectrum. Nevertheless, there are some differences in spectral dependence on resolution between the two data. The shallowing slope is further enhanced in the downsample data due to significant increase of PSD amplitudes near maximum wavenumbers by superposition of subgrid scale perturbations, in contrast, slightly counteracted in the average data owing to weak attenuation of PSD amplitudes near maximum wavenumbers by smoothing. Hence, variation of observed wave spectrum may stem from resolution as well as data sampling technique, rather than only geophysical causes.

Planetary analog simulations are a powerful exercise for understanding the utility of deployable instruments, their operational protocols, and the visualization of data products during ExtraVehicular Activities (EVAs). This paper presents results of a field campaign by the NASA Solar System Exploration Research Virtual Institute (SSERVI) Remote, In Situ and Synchrotron Studies for Science and Exploration-2 (RISE2) team to Kilbourne Hole, New Mexico in March/April 2023 to test the utility of a portable thermal infrared (TIR) hyperspectral imager (HSI) during four EVA simulations. The HSI provides emitted radiance spectra from 7 to 14 μm to map spectral variations likely caused by composition and physical properties, which allows HSI data products to aid in sample selection and site documentation. Four pairs of analog astronauts performed a mock EVA at three stations with field deployable instruments including an HSI. The HSI was found to be a useful tool for performing reconnaissance observations, field site documentation, and sample selection for visibly indistinct materials. From these analog simulations we prioritize two recommendations for use of HSI's in crewed missions. First, HSI-derived data products should be tailored for the specific science objectives and/or sampling objectives of the mission to expedite interpretation and decision-making. Second, the HSI instrument would ideally have a wide field-of-view/panoramic capability to reduce crew time selecting sites to image. Additionally, pre-EVA reconnaissance from a remotely operated rover could be conducted with an HSI to collect data prior to disturbance and again post human activity.