Meteorological Applications最新文献

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148093739","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":"Modelling the Black Carbon Variability and Dynamics in Almaty, Kazakhstan, During Winter and Summer Seasons","authors":"Madina Tursumbayeva,&nbsp;Giancarlo Ciarelli,&nbsp;Ludovico Di Antonio,&nbsp;Manuel Bettineschi,&nbsp;Daulet Kissebayev,&nbsp;Nassiba Baimatova","doi":"10.1002/met.70183","DOIUrl":"10.1002/met.70183","url":null,"abstract":"<p>Large cities with poor air quality can affect surrounding areas through pollutant transport and advection, especially in remote regions with complex mountain environments. One such example is the city of Almaty (Kazakhstan), which frequently experiences severe air pollution episodes and is located at the foothills of the Ile Alatau (part of the northern Tien Shan mountains). In this work, the impact of locally emitted black carbon (BC) from Almaty on the surrounding mountain areas was investigated. The WRF-CHIMERE regional chemical transport model (CTM) was employed with three nested domains up to 1 km resolution for January and July 2023. Simulation results indicated that during winter, BC concentrations remained trapped over the Almaty basin, at the lower elevations north of the city, and along the main valleys. In contrast, in summer, despite lower anthropogenic emissions arising from the city, BC was found to reach the mountain tops more effectively, likely due to increased vertical mixing and enhanced mountain-valley circulation. Additionally, model runs with and without online exchange between meteorology and chemistry were conducted to quantify the effect of BC concentrations on the radiative fluxes. The direct radiative effect of BC at the bottom of the atmosphere (BC DRE_BOA) due to Almaty emissions was estimated to be −1.20 (January) and −0.41 (July) W m⁻², while within the atmosphere (BC DRE_ATM) was +1.33 (January) and +0.45 (July) W m⁻². Analysis of the potential temperature gradients in both months indicated, on average, no significant effect of BC concentrations on vertical atmospheric mixing, due to the presence of strong temperature inversions over the region. This research provides new insights into BC dynamics in the Central Asian region and highlights the need to extend the analysis to transitional periods (spring, autumn) when the temperature inversions are weaker or absent.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70183","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148039946","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148096484","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":"Machine Learning-Based Air Temperature Prediction for Homa Bay County in Kenya: Implications for Weather-Sensitive Sector Resilience","authors":"Hellen Omiti,&nbsp;Kennedy Senagi,&nbsp;Elias Muhatia,&nbsp;Betsy Muriithi,&nbsp;John Olukuru","doi":"10.1002/met.70188","DOIUrl":"10.1002/met.70188","url":null,"abstract":"<p>This paper explores the challenges in temperature prediction, particularly in Homa Bay County, Kenya, a region with complex climatic conditions. Although traditional weather prediction models, such as numerical weather prediction, have limitations in regions with diverse topography, machine learning (ML), a data-driven modeling approach, offers promising solutions for localized forecasting. Two-year (2019–2021) meteorological data from Homa Bay County in Kenya were used to develop linear regression, decision tree, artificial neural network, random forest, and extreme gradient boosting ML models. The validation and test datasets were used to check the performance of the models. A systematic hyperparameter tuning was applied to improve predictive performance and generalization. A comparative evaluation of the five ML models was done. Random forest recorded the best performance with an <i>R</i>², mean squared error, and mean bias error of approximately 0.91, 1.33, and 0.0031, respectively. The high influence of relative humidity and solar irradiance underscores the importance of atmospheric moisture and radiative processes in regional climate modeling. This study shows the potential of machine learning techniques in localized temperature prediction. However, the study is geographically and temporally bounded, which may limit the wider applicability of the results across regions with distinct climatic dynamics and environmental conditions. The findings in this study can support climate-informed agricultural and water resource planning.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70188","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148039909","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148096483","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":"Hybrid Deep Learning-Based Networks for Deterministic and Probabilistic Wind Nowcasting Within a Wake Vortex Warning System for Vertiport Operations","authors":"Sooseok Lee,&nbsp;Frank Holzäpfel,&nbsp;Anton Stephan,&nbsp;Dieter Moormann","doi":"10.1002/met.70173","DOIUrl":"10.1002/met.70173","url":null,"abstract":"<p>Accurate wind prediction is essential for forecasting the transport and decay of wake vortices that might cause an adverse impact to light aircraft operations. This paper proposes an innovative framework for deterministic and probabilistic wind nowcasting, designed to be integrated into a Wake Vortex Warning System for Vertiports (WVWS-V) near major airports. The study presents and tests different deterministic nowcasting models. The superior approach employs a hybrid concept combining complex Continuous Wavelet Transform (CWT), two-dimensional Convolutional Neural Networks (2D-CNN), Long Short-Term Memory (LSTM) networks, and Light Gradient Boosting Machine (LGBM), enabling it to capture both the general patterns and abrupt fluctuations of wind occurring intermittently and repeatedly. The proposed hybrid model achieved a Mean Absolute Error (MAE) of 0.75 m/s for wind speed and 9.1° for wind direction over a 20-min prediction horizon, outperforming all other evaluated models including the statistical persistence prediction method serving as baseline. The probabilistic nowcasting establishing 95% confidence intervals of wind speed and direction utilizes a multivariate-Mixture Density Network (MDN) with weighted Mahalanobis distance. The MDN-based model provides a narrower probability range than the statistical persistence baseline model, with average improvements by 0.91 m/s for wind speed and 12.05° for wind direction. The comparison of the statistical persistence baseline and other machine learning architectures demonstrates that the proposed models achieve superior performance, enabling the WVWS-V to ensure safe and reliable light aircraft operations at vertiports.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/met.70173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683594","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148073124","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148071112","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":"An Ensemble Sensitivity Analysis for Optimizing Physical Schemes in Summer Heavy Rainfall Predictions Over DPR Korea","authors":"Kum-Ryong Jo,&nbsp;Song-In Pak,&nbsp;Hyok-Chol Kim,&nbsp;Chang-Bok Rim,&nbsp;Song-Hak Nam,&nbsp;Won-Sok Hong","doi":"10.1002/met.70181","DOIUrl":"10.1002/met.70181","url":null,"abstract":"<p>Extreme summer rainfall events pose a major hydrological hazard in the Democratic People's Republic of Korea (DPRK), where majority of annual heavy precipitation occurs during the July–August monsoon season. Accurate prediction of these events is essential for flood early warning and disaster risk reduction, yet remains challenging due to complex terrain and uncertainties in model physics. This study addresses a critical gap by systematically evaluating the sensitivity of the Weather Research and Forecasting (WRF) model to combinations of physical parameterization schemes for simulating 15 major summer heavy rainfall events between 2011 and 2022. Using a dense observational network of 130 rain gauges and a multi-event ensemble approach, 16 physics configurations were tested—spanning four microphysics, two cumulus, and two planetary boundary layer schemes—and their performance was compared against operational Global Forecast System (GFS) forecasts. The Lin microphysics, Kain–Fritsch cumulus, and YSU planetary boundary layer combination (Lin–KF–YSU) consistently outperformed all others, achieving the highest spatial correlation (0.68), lowest root-mean-square error (10.2 mm), and best threat score (0.38)—a statistically significant improvement over GFS. While all simulations showed some underestimation of peak intensities above 400 mm/day, likely due to unresolved microphysical and terrain effects, the optimal configuration captured event timing, spatial structure, and rainfall totals more reliably across diverse synoptic conditions. These results demonstrate that regionally tuned, convection-permitting WRF simulations offer substantial added value over global models for extreme rainfall prediction in complex terrain, while maintaining or improving performance for standard meteorological variables. For operational forecasting in monsoon-affected regions like the DPRK, adopting such optimized configurations can meaningfully reduce false alarms and missed events within the context of extreme rainfall warning systems—enhancing public safety and resilience. This work underscores the importance of localized model validation and the potential for high-resolution numerical weather prediction to support effective climate adaptation in vulnerable areas.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/met.70181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683660","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":"Transport Pathways of Ozone in Beijing–Tianjin–Hebei Based on Causal Networks","authors":"Zhen Song,&nbsp;Na Ying,&nbsp;Zhigang Xue,&nbsp;Weiling Xiang,&nbsp;Hongli Song,&nbsp;Jingxu Wang,&nbsp;Xiangzhe Zhu","doi":"10.1002/met.70154","DOIUrl":"10.1002/met.70154","url":null,"abstract":"<p>In recent years, the Beijing–Tianjin–Hebei (BTH) region has experienced severe ozone (O₃) pollution, which constrains further air quality improvement. Identifying O₃ transport pathways is essential for regional joint prevention and control. This study constructed O₃ transport networks for multi-year, summer, and winter periods using complex network methods, enabling assessment of both direction and intensity at the station level. Reliability was confirmed through backward trajectory cluster analysis, potential source contribution analysis, and a nested air quality prediction model. Results show that O₃ transport in BTH is mainly driven by distinct regional patterns. The network exhibited a graph density of 0.62, an average path length of 1.384, and an average clustering coefficient of 0.627, indicating highly interconnected transport, especially in central and southern areas. Southern stations predominantly showed O₃ output, while northern ones were dominated by input: Beijing acted as an input city, whereas Handan and Xingtai were output (exporting) cities. High-value input–output stations were also identified in Shijiazhuang and Tianjin, where both in- and out-weighted degrees exceeded 25. Seasonally, regional transport capacity was stronger in winter than in summer. Dense linkages were observed among southern cities. In summer, O₃ in Shijiazhuang and Baoding was mainly influenced by Handan and Xingtai, while in winter, Hengshui and Cangzhou were primarily connected to surrounding cities. Verification analysis showed transport contributions of 58.64% in summer and 53.85% in winter, confirming interregional transport as a major source of O₃ pollution. Shijiazhuang was most affected by Handan and Xingtai in summer (43.72%) and by Hengshui and Cangzhou in winter (26.96%), along with long-distance inputs from the northwest. These findings align with the identified transport pathways. Additionally, potential O₃ sources were more widely distributed in winter than in summer.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/met.70154","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683199","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}