Journal of Atmospheric and Solar-Terrestrial Physics最新文献

{"title":"Drought and flood evolution characteristics during winter rapeseed season based on the Z-index in Hubei Province, China","authors":"Yaotian Tian,&nbsp;Enhao Zhang,&nbsp;Yongyuan Huang,&nbsp;Ming Huang,&nbsp;Haoran Shi,&nbsp;Hui Chen","doi":"10.1016/j.jastp.2025.106646","DOIUrl":"10.1016/j.jastp.2025.106646","url":null,"abstract":"<div><div>Analyzing drought and flood characteristics is of great significance to ensure production security, disaster prevention, and mitigation. Hubei Province, as a major rapeseed-producing area in China, is crucial for the nation's edible oil supply. However, there were no studies on drought and flood characteristics during rapeseed season in Hubei Province. To study the drought and flood evolution, daily meteorological data from 28 surface weather stations in Hubei Province during the rapeseed season from 1960 to 2019 was adopted to calculate the Z-index. The Mann-Kendall trend test and wavelet analysis were employed. The results indicated that precipitation at the seedling, flowering, ripening, and whole growth stages of rapeseed generally decreased by 4.79, 2.14, 2.58, and 7.38 mm (10 yr)⁻¹, respectively. Precipitation at the budding stage of rapeseed increased by 1.74 mm (10 yr)⁻¹. Based on the Mann-Kendall trend test, the mutation of precipitation at the seedling, budding, flowering, ripening, and whole growth stages of rapeseed began in 1987, 1983, 1961, 1960, and 1968, respectively. Precipitation at the seedling, ripening, and whole growth stages decreased from south to north across Hubei Province. From southeast to northwest of Hubei Province, precipitation at the budding and flowering stages decreased. A shift from drought to flood was observed at the budding stage, whereas a shift from flood to drought occurred at the other stages. Severe drought (15.0 %) and mild drought (20 %) occurred most frequently at the flowering stage. Severe flood occurred most often at the budding stage (16.7 %), moderate flood at the ripening stage (13.3 %), and mild flood at the seedling stage (13.3 %). According to the wavelet analysis, the cycles of drought and flood disasters at the seedling, budding, flowering, ripening, and whole growth stages were 28, 23, 14, 18, and 7 years, respectively. This study provides a scientific basis for predicting drought and flood disasters and guiding rapeseed production in Hubei Province.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106646"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156447","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":"Time series analysis of the impact of global warming on Türkiye","authors":"Arif Ozbek,&nbsp;Mehmet Bilgili","doi":"10.1016/j.jastp.2025.106647","DOIUrl":"10.1016/j.jastp.2025.106647","url":null,"abstract":"<div><div>According to the assessments of the Intergovernmental Panel on Climate Change (IPCC), Türkiye, located within the Mediterranean basin, is among the regions most susceptible to the adverse impacts of climate change. This heightened vulnerability is largely attributed to its geographic location, climatic characteristics, and socio-economic structure, which together amplify the risks associated with rising temperatures and increasing climate variability. In the present study, monthly mean air temperature data for Türkiye, recorded by the Turkish State Meteorological Service between 1970 and 2022 (TSMS dataset), were analyzed in combination with reanalysis-based satellite observations obtained from the ERA5 (ERA5 dataset). These historical records formed the foundation for developing temperature projections extending to the year 2050. To achieve this, two complementary time-series forecasting approaches were applied: the Long Short-Term Memory (LSTM) deep-learning model, known for its ability to capture nonlinear dependencies and long-range temporal patterns, and the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model, a classical statistical method suitable for handling seasonality and trend components in climate data. The projection results revealed that Türkiye's mean temperature anomaly relative to the 1970–1980 baseline period is expected to rise by approximately 2.52 °C when based on in-situ observational data, and by about 3.48 °C when derived from ERA5 reanalysis estimates. These findings consistently indicate a significant warming trajectory, regardless of the dataset or modeling approach applied.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106647"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156583","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":"Stacked hybridization of deep learning model with grey wolf optimization for accurate and explainable reference evapotranspiration","authors":"Truptimayee Suna ,&nbsp;Bibhuti Bhusan Sahoo ,&nbsp;Dipali Pawar ,&nbsp;Nand Lal Kushwaha ,&nbsp;Pradosh Kumar Paramaguru ,&nbsp;P.S. Brahmanand ,&nbsp;Himani Bisht","doi":"10.1016/j.jastp.2025.106655","DOIUrl":"10.1016/j.jastp.2025.106655","url":null,"abstract":"<div><div>Accurate estimation of reference evapotranspiration (ET₀) is essential for effective irrigation scheduling and water resource management, particularly in data-scarce regions such as India, which lack advanced automatic meteorological stations. The present study developed a hybrid model (DNN-GWO) and conducted an in-depth evaluation against standalone data-driven models, including Random Forest (RF), Support Vector Machine (SVM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Deep Belief Network (DBN) for forecasting monthly ET₀ in the Upper Ganga canal command region, Uttar Pradesh, India. Three input scenarios were evaluated for their correlation to ET₀ estimation. The results revealed that the DNN model showed the best performance in all three scenarios, achieving R² = 0.958, RMSE = 0.076 mm/day, NSE = 0.954, RMSLE = 0.024, MAE = 0.055, MBE = 0.012, MSRE = 0.032, and EVS = 0.987 with solar radiation (Rs), wind speed (U), maximum temperature (Tmax), minimum temperature (Tmin), and relative humidity (RH) as inputs. The developed hybrid DNN-GWO model further improved predictive accuracy, with R² = 0.992, RMSE = 0.0317 mm/day, NSE = 0.99, RMSLE = 0.023, MAE = 0.054, MBE = 0.018, and EVS = 0.992, reducing RMSE by nearly 60 % compared to the best-performing standalone DNN. SHapley Additive explanations (SHAP) analysis revealed that temperature and solar radiation were the most influential predictors of ET₀, while the model also provided stable predictions across different input scenarios, demonstrating robustness in data-limited conditions. The developed hybrid framework, by combining deep learning, swarm intelligence, and explainability, provides a robust, accurate, and interpretable solution for agricultural water management in data-constrained environments.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106655"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324924","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":"Tropical cyclones as a possible source of lower ionosphere (thermosphere) perturbation: a case study of Amphan Super Cyclone (SuCS) over Bay of Bengal","authors":"Omkar M. Patil ,&nbsp;Debarchan Kar ,&nbsp;Navin Parihar ,&nbsp;Rajesh Singh ,&nbsp;A.P. Dimri","doi":"10.1016/j.jastp.2025.106654","DOIUrl":"10.1016/j.jastp.2025.106654","url":null,"abstract":"<div><div>The study investigates Super Cyclone (SuCS) Amphan, which occurred over the Bay of Bengal (BoB) during 16–21 May 2020 as a possible source of lower ionospheric perturbations. INSAT-3D satellite observations confirmed intense convective activity through low Cloud Top Brightness Temperature (∼–80 °C) and suppressed Outgoing Longwave Radiation (below 100 W/m²). Lightning analysis indicated an increase in activity within 500 km of the cyclone center, with cloud-to-cloud (CC) lightning intensifying in the eyewall during the cyclone's peak. Over 60 % of positive CC flashes exceeded 10 kA, highlighting strong convective electrical dynamics. Temperature perturbations observed by the AIRS instrument onboard NASA's Aqua satellite appeared as concentric wave patterns at stratospheric altitudes, indicating atmospheric gravity waves (AGWs) activity in the northeast of the storm center. SABER temperature profiles further revealed enhanced wave amplitudes from May 17–20, confirming AGW propagation into the mesosphere-lower thermosphere (MLT) region. Nightglow emissions observed by the Suomi-NPP/DNB sensor provided additional evidence of concentric gravity waves at the MLT heights. This enhanced AGW activity coincided with Amphan's intensification. This multi-altitude observational analysis highlights the role of intense convection and lightning in AGW generation and their subsequent influence on upper atmospheric dynamics. The observations confirm that tropical cyclones serve as a source of lower ionospheric disturbances through AGW-driven energy and momentum deposition.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106654"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324937","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":"Long-term projections of global, northern hemisphere, and arctic sea ice concentration using statistical and deep learning approaches","authors":"Mehmet Bilgili ,&nbsp;Engin Pinar ,&nbsp;Md. Najmul Mowla ,&nbsp;Tahir Durhasan ,&nbsp;Muhammed M. Aksoy","doi":"10.1016/j.jastp.2025.106634","DOIUrl":"10.1016/j.jastp.2025.106634","url":null,"abstract":"<div><div>The accelerating decline in sea ice concentration (SIC) poses significant challenges for global climate regulation, maritime navigation, and arctic ecosystem stability. This study develops and evaluates two advanced time-series forecasting models, seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) networks, to project SIC trends through 2050 across three spatial domains: the globe, the northern hemisphere, and the arctic. Utilizing the ERA5 reanalysis dataset (1970–2024) from the European center for medium-range weather forecasts (ECMWF), the models capture seasonal cycles and complex temporal dependencies to enable robust long-term projections. Comparative analysis demonstrates that SARIMA effectively models periodic fluctuations, while LSTM excels at learning nonlinear dependencies inherent in SIC dynamics. Performance metrics, including mean absolute percentage error (MAPE), root mean square error (RMSE), and correlation coefficient (R), confirm the high accuracy of both models, with SARIMA showing superior capability in representing structured seasonal patterns. Projections indicate a persistent decline in SIC, with arctic concentrations decreasing from 55.60% in 2023 to approximately 46.84% by 2050, underscoring the pronounced effects of arctic amplification. These results provide valuable insights for climate modeling, arctic policy formulation, and the development of adaptive navigation strategies in a rapidly changing polar environment.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106634"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119565","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":"Centennial solar EUV irradiance from ionospheric currents: Varying sunspot-EUV irradiance relation and modified spot-facula ratio","authors":"K. Mursula","doi":"10.1016/j.jastp.2025.106653","DOIUrl":"10.1016/j.jastp.2025.106653","url":null,"abstract":"<div><div>Sunspots offer a uniquely long view of solar magnetic activity, and depict large variability during the last 100 years, a period known as the Modern Maximum (MM). However, if our view of solar variability was only based on the strongest magnetic fields, it would be incomplete. Therefore, other variables are needed to study the long-term evolution, e.g., of weaker magnetic fields and the different radiative emissions. Recently, the relation between sunspots and several other solar activity proxies like the F10.7 and F30 radio fluxes and the MgII index (all proxies of EUV irradiance) was found to vary during the last 70 years so that a relative sunspot dominance over EUV in the 1950s–1960s changed to EUV dominance in the 2000s (Mursula et al., 2024). Here we use data from eight long-operating observatories to calculate the yearly range of daily variation of the geomagnetic Y-component, the rY index, for the last 137 years. We show that the rY index correlates extremely well with the MgII index and the solar F30 radio flux. These three indices have no trend relative to each other over the respective intervals. On the other hand, the F10.7 flux has a significant trend with respect to the three co-varying EUV indices (MgII, F30, rY). Therefore, the rY index replaces F10.7 as the best long-term EUV proxy, and extends the MgII index by 90 years. We verify that all the four EUV proxies (rY, MgII, F30, F10.7) have an increasing trend with respect to sunspots during the last 50–70 years. This is valid both for sunspot numbers and group numbers. Extending this earlier with the rY index, we find that the relation between EUV irradiance and sunspots has a nonlinear, quadratic evolution over the MM. This implies that the Sun has more sunspots relative to EUV irradiance during the growth and maximum of the MM, while the opposite is true during its decay and minimum. We estimate that the MgII index (solar EUV irradiance) increases by 24% of its solar cycle variation with respect to the sunspot number during the last 70 years. Our results indicate a systematic difference in the evolution between sunspots (generally: photosphere) and plages (generally: chromosphere) with long-term solar activity. The implied varying spot-facula ratio has consequences to the stellar evolution of the Sun and Sun-like stars.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106653"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145359171","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":"A ground state of the geospace on November 6–7, 2009","authors":"Yongliang Zhang,&nbsp;Jesper W. Gjerloev,&nbsp;Larry J. Paxton,&nbsp;Robert Schaefer,&nbsp;Matt Friel","doi":"10.1016/j.jastp.2025.106659","DOIUrl":"10.1016/j.jastp.2025.106659","url":null,"abstract":"<div><div>We introduce a new criterion: A ground state of the geospace (the magnetosphere, ionsophere and thermosphere) where all of three regions are undisturbed by the impact of the solar wind particle, energy, and momentum inputs. It is challenging to find such days when the impact is minimized and the geospace is at its ground state (super quiet) or undisturbed condition. After a search of databases of SuperMAG SME index and DMSP SSUSI aurora data over two decades (2002–2022), we identified a few rare super quiet intervals using two criteria: (1) A very low geomagnetic activity (AE or SME &lt;50 nT, SmH &gt;0 nT), and (2) a very weak auroral activity with auroral oval above 70° magnetic latitude and N₂ Lyman-Birge-Hopfield Short (LBHS, 140–150 nm) auroral intensity below 500 R, over 48 consecutive hours or longer. Here we report one super quiet interval (November 6–7, 2009). This interval occurred during the deep solar minimum with daily F10.7 around 70. The low auroral intensity (particle precipitation) and coincident weak plasma convection indicates low ionospheric density and Joule/particle heating at high latitudes. This super quiet condition leads to an undisturbed thermosphere: without any O/N₂ depletion or nitric oxide (NO) enhancement at high latitudes. The super quiet interval was driven by a long (∼61 h), steady and weak solar wind (velocity ∼300 km/s, dynamic pressure 1–2 nPa) and a northward IMF (Interplanetary Magnetic Field) B_z (0–4 nT). These solar wind driving conditions lead to a near zero solar wind-magnetosphere coupling rate and to what we refer as a “geospace ground state”.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106659"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324939","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":"Mean-field dynamo and forecasting of solar activity","authors":"Vladimir Obridko ,&nbsp;Antonina Shibalova ,&nbsp;Dmitry Sokoloff ,&nbsp;Ilya Livshits","doi":"10.1016/j.jastp.2025.106673","DOIUrl":"10.1016/j.jastp.2025.106673","url":null,"abstract":"<div><div>According to the generally accepted theory, the field of local active areas arises from the poloidal magnetic field. The field in the polar regions, which is measured directly by magnetographs, can be a proxy for the latter. It has been shown that the mean-field dynamo is undoubtedly the main mechanism that generates solar activity and the 11-year cycle. However, the magnetic flux generated in the dynamo, although it is the genetic basis of solar activity, still does not allow us to unambiguously connect the characteristics of the magnetic field with other indices of solar activity, including the best known indicator, the number of sunspots. Magnetic fields of active regions and spots are formed from the mean magnetic flux. Its most important properties are the threshold nature and the preservation of the full flux. This is why a completely reliable long-term SSN forecast is possible after the occurrence of an equatorial wave approximately 18 months before the maximum. The processes in leptocline play a decisive role in this case.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106673"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462746","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":"Analysis of ozone pollution characteristics and meteorological factors in Yichang City, Hubei","authors":"Ting Zhou ,&nbsp;Hui Hu ,&nbsp;Pan Wang ,&nbsp;Mi Zhang ,&nbsp;Haoqi Wen ,&nbsp;Dan Liu ,&nbsp;Wei Liu","doi":"10.1016/j.jastp.2025.106668","DOIUrl":"10.1016/j.jastp.2025.106668","url":null,"abstract":"<div><div>Grasping the local ozone pollution characteristics and associated meteorological factors is of great significance for scientific support for ozone (O₃) pollution prevention. O₃ concentrations and related meteorological data from 2020 to 2022 Yichang City, Hubei were analyzed for exploring the spatiotemporal distribution of O₃ concentrations and the impact of associated meteorological factors. The results revealed that O₃ pollution in Yichang from 2020 to 2022 exhibited a clear trend of the increasing pollution severity and frequency. High incidences of ozone pollution was between May and September (mainly in summer), but high concentrations of precursors (NO₂ and TVOCs) appeared from November to January (mainly in winter), highlighting the decisive role of meteorological conditions in the ozone formation. Moreover, the diurnal variation of O₃ concentrations displayed a typical single peak distribution pattern, with hourly O₃ concentration (ρ(O₃)) peaking at 15:00–16:00, suggesting that ρ(O₃) in Yichang was primarily influenced by local emissions. In addition, the sustained high ρ(O₃) on exceedance days were related to the local topography and ρ(O₃) along the river were strongly influenced by river-land breezes. The difference of meteorological factors between O₃ exceedance and non-exceedance days indicated that temperature and relative humidity had more pronounced impacts on ρ(O₃). Specifically, 89.6 % of ρ(O₃) exceedances occurred under meteorological conditions with temperature ≥25 °C and relative humidity between 25 % and 75 %, with wind speeds consistently below 5 m/s. It was recommended that environmental management departments should deploy related emission control measures in advance when the above meteorological conditions were forecasted, while fully considering the influence of the topography and river-land breeze on pollutant transports to optimize control strategies and coordinated prevention measures.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106668"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516884","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":"Rapid determination of daily ionospheric F-layer critical frequency value using a quick-scale method based on Frequency-Time-Intensity plots","authors":"Varuliantor Dear ,&nbsp;Jiyo Harjosuwito ,&nbsp;Annis Siradj Mardiani ,&nbsp;Adi Purwono ,&nbsp;Afrizal Bahar ,&nbsp;Indah Susanti ,&nbsp;Satrio Adi Priyambada ,&nbsp;Rezy Pradipta","doi":"10.1016/j.jastp.2025.106671","DOIUrl":"10.1016/j.jastp.2025.106671","url":null,"abstract":"<div><div>The ionospheric F-layer critical frequency (foF2) is a fundamental parameter for space weather services. Conventionally, foF2 is manually scaled from ionograms using the URSI UAG-23 handbook, which, while accurate, is time-consuming and operator-dependent. This study proposes a simple Quick-Scale (QS) method to rapidly derive daily foF2 values from Frequency-Time Intensity (FTI) plots. The QS method converts FTI images to numeric data using Direct Linear Transformation (DLT) and interactive boundary tracing via a user-friendly Hypertext Markup Language (HTML) tool. The method was tested using Pameungpeuk ionosonde data for year 2020 and validated by four volunteers—two experts and two non-experts. Evaluation shows that the QS method requires approximately 3–4 min per day to extract daily foF2 values, compared to about 24 min needed for URSI UAG-23 manual scaling of 96 ionograms per day. Deviations reached ±2.5 MHz with RMSE and MBE patterns reflecting daily and seasonal ionospheric variations. Correlation coefficients exceeding 0.96 indicate strong agreement with standard foF2 data regardless of operator experience. However, minor time shifts due to image rendering and re-digitization processes were observed, suggesting potential systematic bias for high-precision applications. Overall, the QS method is feasible as a quick-look tool for operational space weather services and can be further refined with bias correction modules to enhance accuracy and temporal precision.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"277 ","pages":"Article 106671"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417545","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}