pure and applied geophysics最新文献

{"title":"On the Possibility of Ionospheric Memory Over the Low-Latitude Mexican Region","authors":"M. A. Sergeeva,&nbsp;I. D. Orrala-Legorreta,&nbsp;E. Rodriguez-Orozco,&nbsp;J. H. Castro-Chacon,&nbsp;O. A. Maltseva,&nbsp;J. A. Gonzalez-Esparza,&nbsp;P. Corona-Romero,&nbsp;V. J. Gatica-Acevedo,&nbsp;M. Pazos,&nbsp;M. E. Gonzalez-Alvarez,&nbsp;R. Bonifaz,&nbsp;A. Melgarejo-Morales,&nbsp;E. Cabral-Cano,&nbsp;F. Amezcua,&nbsp;N. A. Barboza-Tello,&nbsp;C. I. Castellanos-Velazco,&nbsp;E. Andrade-Mascote,&nbsp;P. Villanueva","doi":"10.1007/s00024-026-04061-5","DOIUrl":"10.1007/s00024-026-04061-5","url":null,"abstract":"<div><p>Features of possible regional ionospheric memory manifested themselves at low-latitudes under solar eclipse conditions.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 8","pages":"4179 - 4185"},"PeriodicalIF":2.1,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00024-026-04061-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Appraisal of Soil Hydraulic Properties and Models Derived Using Pedotransfer Functions Over the Gadanki Region, India","authors":"Jiteshwar Dadich,&nbsp;Amit P. Kesarkar,&nbsp;Jyoti Bhate,&nbsp;Anantharaman Chandrasekar","doi":"10.1007/s00024-026-04067-z","DOIUrl":"10.1007/s00024-026-04067-z","url":null,"abstract":"<div><p>Soil hydraulic conductivity and water retention model parameters are crucial in Land Surface Models (LSMs). This study evaluates soil hydraulic parameters derived from 23 widely used pedotransfer functions (PTFs) and their influence on simulated soil hydraulic behavior in soils of the Gadanki region (13.4593° N, 79.1684° E), Andhra Pradesh, India, using soil property inputs obtained from the global SoilGrids database. Volumetric water content was measured at different depths at this site during an isolated thunderstorm at 0500 h IST on October 18, 2018 (23:30 UTC October 17, 2018), followed by a two-week dry spell, to determine soil suction and unsaturated hydraulic conductivity. The results showed good agreement in this region. The Brooks-Corey model consistently predicts higher hydraulic conductivity and faster changes in soil suction than the Van Genuchten -Mualem model across multiple depths. These findings highlight that model choice substantially affects soil water flow simulations and should be carefully considered in light of soil characteristics and study objectives.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 9","pages":"4607 - 4625"},"PeriodicalIF":2.1,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Full Waveform Inversion of DAS Crosswell Data to Monitor HT-ATES: A Synthetic Feasibility Study","authors":"Clara E. Fraile,&nbsp;Emmanuel Gaucher,&nbsp;Christophe Barnes","doi":"10.1007/s00024-026-04051-7","DOIUrl":"10.1007/s00024-026-04051-7","url":null,"abstract":"<div><p>Monitoring underground reservoirs is essential to ensure their efficient and sustainable operation. This study investigates the detectability and imaging, using seismic methods, of modifications in elastic properties caused by cyclic injection and production of geothermal fluid in high-temperature aquifer thermal energy storage (HT-ATES) systems. In such systems, the induced variations in elastic properties are expected to be very small, requiring high-sensitivity methods for reliable detection. The work is conceived as a feasibility study in a setting where field data are not yet available. Synthetic modelling is used to evaluate the effectiveness and sensitivity of Full Waveform Inversion (FWI) to reservoir variations. FWI is selected for its ability to exploit the full recorded wavefield, which is critical for resolving subtle elastic changes in thin, low-contrast reservoirs. The DeepStor HT-ATES research project in Karlsruhe, Germany, where layers thinner than 10 m at 1.3 km depth are targeted, serves as a case study. Two elastic models were developed: one unperturbed and one incorporating anomalies expected after five years of cyclic operations. Due to the thickness and depth of the targets, surface seismic methods were excluded in favour of cross-well configurations using Distributed Acoustic Sensing (DAS). Active sources are adopted to ensure controlled and repeatable illumination required for time-lapse monitoring. Results indicate that geometries with receivers intersecting the reservoirs significantly improve imaging and parameter estimation. This study evaluates the applicability of established FWI approaches in thin, stratified geothermal systems and highlights the importance of survey design for successful HT-ATES monitoring.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 8","pages":"4085 - 4111"},"PeriodicalIF":2.1,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00024-026-04051-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectral Characterization of Coastal Tsunami Response in Suruga Bay, Japan, Using Stochastic Simulation Ensembles for Nankai Trough Earthquakes","authors":"Takuya Miyashita,&nbsp;Tung-Cheng Ho,&nbsp;Nobuhito Mori,&nbsp;Tomoya Shimura","doi":"10.1007/s00024-026-04060-6","DOIUrl":"10.1007/s00024-026-04060-6","url":null,"abstract":"<div><p>Coastal tsunami impacts are strongly influenced by local bathymetric features, which can induce resonance and prolong wave activity after the initial arrival. While previous studies have characterized resonant behavior using observations or deterministic simulations, the extent to which such frequency-dependent responses can be systematically related to offshore tsunami characteristics remains unclear. In this study, we investigate coastal tsunami response in Suruga Bay, Japan, using a large ensemble of numerical simulations based on stochastic earthquake source models for the Nankai Trough. For each scenario, we analyze the spectral characteristics of simulated wavefields at offshore and coastal locations and estimate frequency-dependent response functions associated with bathymetric features. These analyses reveal consistent amplification patterns at specific period ranges that are associated with local topography. Building on this, we examine statistical relationships between offshore spectral characteristics and the amplitude of later-arriving coastal waves. The results indicate that certain frequency bands show moderate but consistent correlations with the time-averaged amplitude of post-arrival oscillations, suggesting that offshore spectral information may provide useful indicators of prolonged coastal response under similar source conditions. The objective of this study is not to fully resolve the physical coherence of tsunami sources or nonlinear resonance processes, but rather to provide a statistical framework for linking offshore wave characteristics with site-specific coastal responses. The proposed approach offers a complementary perspective for interpreting tsunami-induced oscillations and may contribute to improving the assessment of prolonged tsunami effects in coastal regions with complex bathymetry.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 8","pages":"3869 - 3891"},"PeriodicalIF":2.1,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00024-026-04060-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Storm Surge Forecast and Its Validation for Severe Cyclonic Storm ‘Dana’","authors":"Siva Srinivas Kolukula,&nbsp;P. L. N. Murty,&nbsp;J. Padmanabham,&nbsp;V. Chandrasekhar,&nbsp;Monica Sharma,&nbsp;M. Mohapatra,&nbsp;T. M. Balakrishnan Nair","doi":"10.1007/s00024-026-04064-2","DOIUrl":"10.1007/s00024-026-04064-2","url":null,"abstract":"<div><p>The coastal regions of India are highly vulnerable to storm surge flooding during tropical cyclones. On 22 October 2024, at 05:30 IST, a severe cyclonic storm named Dana formed in the Bay of Bengal. It made landfall between 01:30 and 02:30 IST on 25 October 2024, with wind gusts reaching 120 km/h at Bhitarkanika and Dhamra ports in Odisha. Cyclone Dana caused heavy rainfall, strong winds, and inundation in low-lying areas of Kendrapara, Bhadrak, and Balasore districts, resulting in significant damage and disruption across Odisha and West Bengal. Approximately 500,000 people were evacuated from coastal Odisha by the National Disaster Response Force. The current study evaluates the performance of the Storm Surge Early Warning Centre at the Indian National Centre for Ocean Information Services during Cyclone Dana. The ADCIRC hydrodynamic model was forced with Holland parametric wind fields derived from 24-h and 48-h India Meteorological Department forecast tracks and compared against the best track. Atmospheric forcing was validated using Sentinel-1 Synthetic Aperture Radar winds and in-situ observations from the Ocean Moored Buoy Network for the Northern Indian Ocean. Simulated surge heights were validated against tide gauge records from Dhamra and Paradip, while inundation extents were compared with Sentinel-1 flood maps. Results indicate that the 24-h forecast accurately reproduced wind fields, surge heights, and inundation extents, whereas the 48-h forecast underestimated the intensity and spatial coverage. Although best-track simulations provide the most accurate results, real-time 24–48-h forecasts remain valuable for early warning and risk reduction. Future improvements will focus on incorporating river discharge, rainfall inputs, and land-use information to enhance forecast reliability.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 8","pages":"4411 - 4430"},"PeriodicalIF":2.1,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SPEI-Based Drought Regionalization and Cluster-Specific Machine Learning Forecasting in the U.S. Black Belt","authors":"Negin Nahidi,&nbsp;Wesley C. Zech,&nbsp;Mohammad Ismaeil Kamali,&nbsp;Rouzbeh Nazari","doi":"10.1007/s00024-026-04045-5","DOIUrl":"10.1007/s00024-026-04045-5","url":null,"abstract":"<div><p>This study evaluates the historical spatial variability of drought in the Black Belt region of the southeastern U.S. by classifying the study area into drought-variation clusters and assesses the performance of three machine learning models for short-term drought forecasting. The Black Belt is a distinct agricultural subregion of the southeastern U.S., characterized by fertile Blackland soils, strong dependence on precipitation-based farming, limited irrigation infrastructure, uneven climatic monitoring coverage, socioeconomic challenges, and a history of repeated severe drought events that have caused substantial agricultural and water-supply impacts…Monthly gridded Standardized Precipitation Evapotranspiration Index (SPEI) [~ 5 km (3.1 miles) spatial resolution] was computed from precipitation and temperature data from the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) nClimGrid-Daily dataset, based on Global Historical Climatology Network-Daily (GHCN-Daily) observations, and used as the primary drought indicator for the 1990–2025 study period. SPEI includes precipitation and temperature-driven evapotranspiration, which strongly influences drought onset in warm subtropical regions such as the Black Belt. The 6-month accumulation period (<span>({text{S}text{P}text{E}text{I}}_{6})</span>) is suitable for capturing agricultural drought by representing soil moisture conditions related to crop stress. Since the Black Belt has sparse and unevenly distributed weather stations, high-resolution gridded NOAA data provides spatially continuous, quality-controlled coverage (NOAA NCEI, n.d.). The Silhouette method was used to select influential <span>({text{S}text{P}text{E}text{I}}_{6})</span> statistical parameters and validate three clustering algorithms (i.e., K-means, Hierarchical, and Expectation–Maximization) to create drought clusters from <span>({text{S}text{P}text{E}text{I}}_{6})</span> data. K-means performed best, identifying three distinct drought-characteristic clusters. Three machine-learning models, Support Vector Regression (SVR), the M5P model tree (a decision-tree algorithm that fits linear regressions at its terminal nodes), and a Hybrid ensemble model combining SVR and M5P outputs, were trained for each cluster for 1-, 2-, and 3-month forecasting horizons using lagged <span>({text{S}text{P}text{E}text{I}}_{6})</span> time series. Model performance declined for longer horizons and for the extreme-like drought characteristics, while the Hybrid model achieved the best results. These findings highlight the effectiveness of combining drought clustering with region-specific machine learning models for improved short-term drought forecasting in heterogeneous environments such as the U.S. Black Belt.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 8","pages":"4361 - 4396"},"PeriodicalIF":2.1,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00024-026-04045-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impacts of Rainfall and Temperature Variabilities on Coffee Habitat Altitudinal Ranges in Southern Ethiopia","authors":"Birhane Gebrehiwot Tesfamariam,&nbsp;Alemu Dessa Derebe,&nbsp;Wondifraw Nigussie","doi":"10.1007/s00024-026-04055-3","DOIUrl":"10.1007/s00024-026-04055-3","url":null,"abstract":"<div><p>In response to the impacts of climate change on rainfed agriculture, this study evaluates interannual variability in dekadal rainfall and monthly temperatures during the tropical spring season (March, April, and May/MAM) to understand their effect on coffee habitat altitudinal ranges in southern Ethiopia. The Climate Hazards Center InfraRed Temperature with Stations (CHIRTS) monthly air temperatures (1983–20,216) and the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS-v3) dekadal rainfall (1981–2025) were used as the primary data sources for this study. CHIRTS minimum (T_min) and maximum temperature (T_max) were extracted from seven representative sites for trend analysis. Then, the modified Mann–Kendall test (MMK) for temperature trends, coefficient of variation (CV) for rainfall variability, and the standardized precipitation index (SPI) for spatiotemporal drought assessment were applied. Furthermore, a key informant interview (KII) and field observation were employed to collect qualitative data. The study revealed a general upward trend in CHIRTS T_min and T_max during the spring season (MAM) across all sites (stations), although statistical significance varied among sites. Regardless of elevation differences, more than 85% of the upward trends in T_max and T_min in April and May were statistically significant (α &lt; 0.05), while only T_min underwent significant trends in March across the sites. Specifically, the significant rising trends in T_max and T_min were 0.033–0.051 °C/year and 0.041–0.075 °C/year, equivalent to 1.12–1.73 °C and 1.39–2.55 °C, respectively, over three decades during the study period (1983–2016). Moreover, the study revealed high CVs for dekadal rainfall (31–96%) and recurrent drought events, with more frequent droughts in March and more severe ones in April. The implications of the pronounced fluctuations in spring rainfall and rising temperatures on coffee production cannot be overlooked. Consequently, as we verified through field observations and key informant interviews, producers in the lower altitudinal belts have begun replacing coffee with other crops. In contrast, coffee cover has recently expanded into new, higher-elevation areas in response to promising productivity. Therefore, given the rising temperatures and recurrent meteorological droughts observed during the study period, as well as the ongoing replacement of coffee with other crops, climate adaptation efforts should focus on developing stress-tolerant coffee varieties and expanding shade tree cover to prevent the potential retreat of coffee habitats at lower altitudes.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 9","pages":"4833 - 4855"},"PeriodicalIF":2.1,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of Deep Learning with the Discrete Element Method for Porosity Prediction in Gravel-Bed Rivers","authors":"Phong Nguyen Thanh,&nbsp;Duong Tran Anh,&nbsp;Ahad Hasan Tanim,&nbsp;Bang Tran,&nbsp;Quoc Bao Pham,&nbsp;Son T. Mai,&nbsp;Hung Phan Duy,&nbsp;Van Hieu Bui","doi":"10.1007/s00024-026-04069-x","DOIUrl":"10.1007/s00024-026-04069-x","url":null,"abstract":"<div><p>Porosity is a key parameter governing the hydraulic, geomorphic, and ecological functions of gravel-bed rivers. Although the discrete element method (DEM) can reproduce particle-scale packing structures with high accuracy, its computational cost limits its application for rapid porosity assessment. To address this limitation, this study proposes an integrated framework that combines DEM simulations with deep learning (DL) models, including a two-dimensional convolutional neural network (2D-CNN) and a long short-term memory (LSTM) network. DEM-generated grayscale images and grain-size distributions (GSDs) were used to train the 2D-CNN and LSTM models, respectively, under clogging and penetration scenarios. The results show that the 2D-CNN achieved the highest predictive accuracy, with R values of 0.9712 and 0.9509 for the penetration and clogging cases, respectively, outperforming both the LSTM model and the empirical Yu model. Although the LSTM model also provided satisfactory predictions, its accuracy was generally lower than that of the 2D-CNN. These findings demonstrate that integrating DEM-generated datasets with DL provides an efficient surrogate framework for rapid porosity prediction over the range of GSDs considered in this study, thereby reducing the need for computationally intensive DEM simulations.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 9","pages":"4585 - 4606"},"PeriodicalIF":2.1,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gap Filling and Anomaly Separation Using Inverse Principal Components Analysis","authors":"Qingmou Li,&nbsp;Xiaojun Liu","doi":"10.1007/s00024-026-04053-5","DOIUrl":"10.1007/s00024-026-04053-5","url":null,"abstract":"<div><p>Gap filling in geophysical time series is particularly challenging when observed data is non-stationarity and has sharp temporal transitions. Many existing approaches rely on global functional representations or stationarity assumptions, which can compromise the preservation and identification of physically meaningful local features. In this study, we introduce EigenSig, an eigenspace-based framework that reformulates gap filling as a coordinate-recovery problem in a reduced eigenspace. The method partitions a time series into epochs, embeds them in a vector space, and constructs an eigenspace using principal component analysis, where dominant eigenvectors capture within-epoch variability and eigenspace coordinates represent large-scale coherence across epochs. Missing epochs are addressed by estimating and analyzing these coordinates from neighboring epochs and projecting them back into the original data space. Unlike least-squares harmonic estimation (LSHE), which uses global Fourier series representation, EigenSig uses a fully data-driven orthogonal basis as the base of eigenspace and designed to preserve signal shape without stationary assumption. We also incorporate the widely used LSHE method to estimate coordinates of unknown epochs in eigenspace to enhance its robustness and uncertainty quantification. EigenSig is further extended for anomaly separation using a leave-one-out strategy, in which each epoch is reconstructed from all remaining data in eigenspace, and anomalies are quantified as the residual between the observed and reconstructed epochs. Applications to synthetic data gaps and geomagnetic time series demonstrate that EigenSig effectively reconstructs data gaps, separates localized anomalies while maintaining global structure, and has calculation efficiency, highlighting its methodological novelty and practical relevance for geophysical signal analysis.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 8","pages":"3977 - 4001"},"PeriodicalIF":2.1,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00024-026-04053-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis and Simulation of Anisotropic Thin-Layered Structures in the Atmosphere","authors":"S. P. Kshevetskii,&nbsp;S. N. Kulichkov,&nbsp;Yu. A. Kurdyaeva,&nbsp;N. M. Gavrilov","doi":"10.1007/s00024-026-04065-1","DOIUrl":"10.1007/s00024-026-04065-1","url":null,"abstract":"<div><p>Anisotropic thin-layered structures in the atmosphere are investigated both theoretically and numerically. These formations may be interpreted as internal gravity waves (IGWs) with vertical lengths significantly smaller than the horizontal wavelengths and vertical stratification scales. Approximate analytical expressions describing the spatial configuration of such waves are derived. A novel method is proposed for using these fine-scale structures for initialization of a high-resolution nonlinear numerical atmospheric model in the form of effective wave sources. These sources generate anisotropic turbulence over a finite time interval and are deactivated when the target structures are established. Consequently, the resulting fine-scale formations are fully governed by nonlinear hydrodynamic equations. Examples of simulations of various thin-layered structures are presented. The results provide a robust framework for conducting numerical studies of fine-scale layers caused by IGW propagation in the atmosphere.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 9","pages":"4707 - 4728"},"PeriodicalIF":2.1,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}