Meteorological Applications最新文献

{"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":"https://doi.org/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":"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":"https://doi.org/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":"https://doi.org/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}

{"title":"Surface Mesovortices Formation and Maintenance in the St. Louis Metropolitan Area","authors":"Sen Chiao,&nbsp;Robert Pasken","doi":"10.1002/met.70176","DOIUrl":"https://doi.org/10.1002/met.70176","url":null,"abstract":"<p>This study investigates the formation of reoccurring surface mesovortices within the Saint Louis Metropolitan Area. Prior research has shown the presence of surface mesovortices downwind from urban areas; however, the time evolution of the three-dimensional (3D) structure of these mesovortices is not well documented. Two instances of surface vortices associated with the urban heat island and affected by the terrain are investigated using the Weather Research Forecasting (WRF) model. The surface mesonet data over the Saint Louis Metropolitan area were assimilated with the WRF model to investigate the structure of these mesovortices. High-resolution simulations (i.e., 333-m grid spacing) were conducted when mesovortices were observed. Model results were then evaluated to determine the full 3D structure of the vortices and the formation of such surface vortices. The WRF simulations were able to recapture the observed surface structure of the vortices. The simulation results suggest that the interaction between the Saint Louis urban heat island (UHI), the Mississippi River, and topographic effects moderates the low-level wind field, creating the surface mesovortices.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/met.70176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683207","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":"Verification of Calibrated Multimodel Subseasonal Precipitation Predictions Cascaded From Global to Regional Scale Over Ceará State in Brazil","authors":"Caio A. S. Coelho,&nbsp;Francisco C. Vasconcelos Junior,&nbsp;Denis H. Cardoso,&nbsp;Eduardo S. P. R. Martins,&nbsp;Bruno S. Guimarães","doi":"10.1002/met.70179","DOIUrl":"https://doi.org/10.1002/met.70179","url":null,"abstract":"<p>This paper assesses a multimodel subseasonal prediction system developed to produce calibrated global and regional precipitation subseasonal predictions for Northeast Brazil, including Ceará State, located within this region. This system was developed using four global models, based on a linear regression procedure of retrospective predictions on past observations for calibrating the multimodel ensemble mean of these models. The procedure used 1° by 1° spatial resolution retrospective predictions initialized on Wednesdays over the common 1999–2016 period for all models. For producing predictions over Ceará, the regression procedure was applied at a finer (0.15° by 0.15°) resolution using interpolated observations. This statistical downscaling procedure allows cascading predictions from global to regionally refined scales. The assessment was performed across different timescales—weekly, fortnightly, and extended accumulation periods (30 and 44-days). Predictions expressed as accumulated precipitation anomalies and probability for the occurrence of positive anomalies were evaluated. The global assessment identified Northeast Brazil as a region with notably high subseasonal prediction performance, demonstrating reasonable quality while examining linear association (correlations exceeding 0.4) and discrimination (area under the relative operating characteristic [ROC] curve above 0.6). The benefit of using all available prediction information from the initialization day, taking advantage of initial conditions memory, to enhance longer accumulation periods (14, 30, and 44 days) performance was demonstrated by finding that predictions for these extended periods maintained performance levels comparable to those of Week 1. Longer lead predictions for Weeks 3 and 4, Fortnights 2 and 3 showed similar performance in the tropics, including Northeast Brazil, with correlations exceeding 0.4 and area under ROC curves above 0.6, with El Niño–Southern Oscillation (ENSO), Madden–Julian Oscillation (MJO), and tropical Atlantic variability suggested as potential predictability sources. Downscaled predictions over Ceará maintained similar performance levels to those obtained at coarser spatial resolutions, reassuring the adequacy of the cascading procedure used to generate regional scale predictions. The large uncertainty in the computed skill scores prevented demonstrating the benefit of calibrated multimodel predictions compared to individually calibrated model predictions. A limitation of the implemented approach is the need for high resolution historical precipitation observations to allow generating spatially refined calibrated predictions.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/met.70179","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147682963","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":"Correction to “Projections of Precipitation and Temperature Changes and Trends Using CMIP6 Global Climate Models in the Eastern Amhara, Northeastern, Ethiopia”","authors":"","doi":"10.1002/met.70175","DOIUrl":"https://doi.org/10.1002/met.70175","url":null,"abstract":"<p>Kebede, M. H., A. M. Ahmed, D. A. Birhan, G. A. Damot, and S. A. Legesse. 2026. “Projections of Precipitation and Temperature Changes and Trends Using CMIP6 Global Climate Models in the Eastern Amhara, Northeastern, Ethiopia.” <i>Meteorological Applications</i> 33, no. 1: e70145. https://doi.org/10.1002/met.70145.</p><p>In the published article, the affiliation for Mohammed Hussen Kebede is incorrect. The correct affiliation should read as “Department of Plant Sciences, College of Agriculture and Environmental Sciences, Bahir Dar University, Bahir Dar, Ethiopia”.</p><p>We apologize for this error.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/met.70175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147684247","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":"Evaluating Scenario Based Performance of DSSAT Response to Soil Depth, Initial Soil Water Content and Choice of Zea mays L. Cultivar Selection in Semi-Arid North West Province in South Africa","authors":"Christopher James Rankin,&nbsp;Trevor Lumsden,&nbsp;Shingirai S. Nangombe,&nbsp;Willem Landman,&nbsp;Asmerom Beraki,&nbsp;Mohau Mateyisi","doi":"10.1002/met.70172","DOIUrl":"https://doi.org/10.1002/met.70172","url":null,"abstract":"<p>Process-based crop models are widely used to assess crop responses to climate variability, yet their performance is highly sensitive to assumptions regarding soil properties, initial soil water content and cultivar selection, particularly in spatially heterogeneous, rainfed systems. This study evaluates the performance of the DSSAT-CERES-Maize model across the North West Province of South Africa using a fine-scale, quinary catchment-based framework. Four scenario simulations were developed to examine the influence of soil depth, pre-season soil moisture and cultivar choice on simulated maize yields. Model outputs were evaluated against district-level reported yields for the 1981–1999 period using a comprehensive multi-criteria assessment framework incorporating distributional tests, correlation analysis, weighted regression and multiple performance metrics. Results indicate that DSSAT effectively reproduces inter-annual yield variability across spatial scales, with stronger agreement at the district level than at the provincial scale. Scenario performance was highly sensitive to soil depth and initial soil water assumptions, with the scenario incorporating deeper effective rooting depth and intermediate pre-season soil moisture consistently achieving superior agreement across most evaluation criteria. Cultivar selection influenced yield variability, highlighting the importance of representative genetic parameterisation in regional applications. While simulated and reported yield medians did not differ significantly at the district scale, error magnitudes and efficiency metrics varied spatially, reflecting the dominant influence of climate variability under rainfed conditions. These findings demonstrate that spatially explicit, scenario-based evaluation enhances confidence in crop model applications and provides valuable insights for agrometeorological assessments, climate adaptation planning and decision support in semi-arid, water-limited agricultural systems.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/met.70172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147666324","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":"Fine-Tuning Lightning Nowcasting for a New Domain","authors":"Aurelienne A. S. Jorge,&nbsp;John Cintineo,&nbsp;Izabelly C. Costa,&nbsp;Leonardo B. L. Santos","doi":"10.1002/met.70174","DOIUrl":"https://doi.org/10.1002/met.70174","url":null,"abstract":"<p>Accurate lightning nowcasting is critical for mitigating weather-related risks, yet adapting existing predictive models to new spatial domains remains challenging due to computational demands and data requirements. Transfer learning offers a promising solution, but its application in weather nowcasting, particularly for tasks framed as semantic segmentation problems, is still underexplored. In this study, we employed transfer learning techniques to fine-tune the U-Net architecture of LightningCast, originally developed for the contiguous United States (CONUS) region, to predict lightning for the Brazilian domain. Given the distinct meteorological characteristics of Brazil, particularly in regions dominated by tropical systems, there is a compelling motivation to explore fine-tuning LightningCast for this new spatial domain. The methodology involved investigating the impact of fine-tuning different architectural components, comparing fine-tuned models with those trained from scratch, and analyzing the benefits of transfer learning across varying data availability scenarios. The fine-tuned model consistently outperformed the model trained from scratch, achieving superior performance even with limited data—surpassing the original model's results with just 10% of the available training data—9.3% of improvement in the Area Under the Curve for Precision and Recall (AUC-PR) and 12.8% in the Critical Success Index (CSI) at a 35% probability. Spatial analysis revealed improvements in the Critical Success Index (CSI) across most regions, with an average of 5.2%, and significant reductions in false alarms—with a mean decrease of 10.5%, addressing the original model's overestimation issue. These findings highlight the effectiveness of transfer learning in adapting a lightning nowcasting model to new domains, reducing computational demands while improving performance. The publicly available fine-tuning framework developed in this study offers a versatile tool for extending LightningCast or similar U-Net-based models to other spatial regions.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/met.70174","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147666224","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 of Spatiotemporal Patterns and Influencing Factors of Near-Surface Methane Concentration in Representative Oilfield Regions of Dongying City","authors":"Lu Fan,&nbsp;Yong Wan,&nbsp;Yuhang Liu,&nbsp;XiaoWen Li,&nbsp;YongShou Dai","doi":"10.1002/met.70169","DOIUrl":"https://doi.org/10.1002/met.70169","url":null,"abstract":"<p>Oilfield production activities are a significant source of anthropogenic methane emissions, and understanding their emission patterns is crucial for effective methane reduction. This study focuses on Dongying City. Using the XGBoost model, we constructed a high-precision, full-coverage dataset for the period from 2023 to 2025. Based on this dataset, we analyzed the spatiotemporal distribution patterns and driving factors of methane concentrations in the oilfield regions. Temporally, methane concentrations exhibited a clear seasonal trend—higher in winter, lower in summer, and relatively stable in spring and autumn. Over the long term, monthly average concentrations showed a slow upward trend. Spatially, methane concentrations displayed pronounced heterogeneity. High-concentration zones were mainly located in the northern part of Hekou District with intensive oil and gas extraction, the north-eastern part of Dongying District with concentrated industrial activity, and the coastal areas of Kenli District characterized by wetlands. Guangrao County had the lowest and most evenly distributed concentrations. On a monthly scale, December recorded the highest values, while July and August showed the lowest. The influencing factors include anthropogenic activities such as oilfield production intensity, as well as natural factors like air temperature and soil temperature and moisture.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/met.70169","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568516","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":"Diverse Cloud Regimes in the Northeast Pacific: Evaluating a Mesoscale NWP Model With Shipborne Observations","authors":"Julia M. Simonson,&nbsp;Wayne M. Angevine,&nbsp;Joseph B. Olson,&nbsp;David D. Turner","doi":"10.1002/met.70171","DOIUrl":"https://doi.org/10.1002/met.70171","url":null,"abstract":"<p>As marine clouds play an important role in Earth's radiation budget, it is important that global climate and numerical weather prediction (NWP) models accurately simulate them. These clouds are challenging to represent within models due to the incomplete understanding of the processes that control their evolution, as well as the wide range of scales (spatial and temporal) that those processes occur. Here we evaluate simulations of clouds over the northeast Pacific Ocean using the Weather Research and Forecasting (WRF) model with a quasi-operational configuration to assess the model's representation of diverse cloud regimes in order to inform future model physics development. The simulations are evaluated with shipborne observations from the Marine ARM GCSS Pacific Cross-Section Intercomparison (GPCI) Investigation of Clouds (MAGIC) campaign in 2013, where stratocumulus, transitional, and cumulus regimes were present. The stratocumulus to cumulus transition observed during Leg 15A (20–25 July 2013) is well simulated in space and time, but the stratocumulus cloud base and top are too low, a flaw partially inherited from the ERA5 initialization. Four different vertical grids are tested to determine requirements for simulation of stratocumulus. The size of simulated cumulus cloud systems depends on the model grid spacing (13 km baseline vs. 3 km) and on tuning of the Mellor-Yamada-Nakanishi-Niino eddy diffusivity-mass flux (MYNN-EDMF) boundary layer and shallow cloud scheme. The model shortfalls identified in this study have helped to distinguish multiple avenues for future analysis that will guide development of the scheme.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/met.70171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567272","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}