Meteorological Applications最新文献

{"title":"Could Climate Projections Be Verifiable?","authors":"Angeline G. Pendergrass","doi":"10.1002/met.70200","DOIUrl":"https://doi.org/10.1002/met.70200","url":null,"abstract":"<p>Verification for weather prediction is highly developed and has guided, motivated, and documented improvements in recent decades. In comparison, verification is far less developed for climate projections. Verification is an important part of the scientific process for testing and identifying ways to improve our understanding, and for establishing credibility and trust. In contrast to weather forecasts, climate projections are not initialized with an estimate of the state of the atmosphere or climate system as an initial condition; instead, the goal is to solve a boundary condition problem of estimating the climate state given a set of greenhouse gas and aerosol emissions and other forcings that are external to the model components. Because the primary use of climate projections is around future changes, which are often cast as anomalies relative to a baseline state, verification should include or even focus on the responses of the climate to external forcing, rather than just evaluation of the baseline climatology. Verification of climate projections is a challenge because (1) the relevant lead times are long, resulting in very small sample sizes, and (2) climate projections are conditional on the forcing pathway, which determines the boundary conditions, so they depend on additional information about the future external to the projection itself. Some existing activities contain elements of verification, such as the evaluation of climatological characteristics and the comparison of simulations of the past historical forcing with observed trends. Three concepts from weather forecast verification that present opportunities for advancing verification of climate projections are discussed: representativeness, forecast skill, and system design for verifiability.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70200","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167113","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":"Snow Accumulation and Melt Estimation Incorporating Temporal Variations in Snow Density","authors":"Yafen Zhang,&nbsp;Yulong Zhu,&nbsp;Fanchao Kong","doi":"10.1002/met.70193","DOIUrl":"https://doi.org/10.1002/met.70193","url":null,"abstract":"<p>In cold regions across the globe, snowpack dynamics play a fundamental role in regulating hydrological regimes, influencing water resource availability, and triggering natural hazards such as floods, landslides, and debris flows. As climate warming accelerates, particularly in high-latitude and high-altitude areas, the need for accurate snow accumulation and snowmelt estimation has become increasingly critical for both environmental management and disaster risk reduction. To overcome the limitations of traditional constant-density assumptions, this study presents an improved snow accumulation and snowmelt estimation approach incorporating temporal snow density variability and compaction effects. Subsequently, a comparative assessment of six widely used snow density estimation models is conducted based on the observed snow depth and density measurements. Results demonstrate that snow density estimation requires partitioning the entire snow cover duration into two distinct phases, that is, snowpack season and snowmelt season, and accurate snow density estimation must separately account for compaction effects induced by snow depth and duration of snow cover during the snowpack season and snowmelt season. That is, during the snowpack season, the increases in snow density are mainly caused by the gravitational compaction of overlying fresh snow layers, while during the snowmelt season, the snow density enhancement is predominantly governed by the duration of snow cover. Afterward, based on the proposed snow accumulation and snowmelt estimation approach, the impacts of temperature rise induced by climate change on snowpack dynamics were systematically investigated. Results indicate that the rising temperatures contribute to a reduction in snow depth during the snowpack season, primarily in the initial unstable phase of snowpack formation. Conversely, during the snowmelt season, temperature increases exert a more pronounced influence on snowmelt dynamics. Analysis of meteorological data from Nakayama Pass, Hokkaido, Japan, indicates that for every 1.0°C increase in temperature, the duration of snow cover at this location will be reduced by approximately 14.5 days, which may have implications for understanding snowpack responses to climate warming in other snow-dominated regions. The findings of this study provide critical assessment benchmarks and modeling support for evaluating snowmelt-induced disasters, including but not limited to floods, landslides, and debris flows.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70193","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174681","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":"Digital Twin-Based Uncertain Weather Condition Monitoring for Enhanced Crop Yield Prediction","authors":"A. Alphonse John Kenneth,&nbsp;A. Stella,&nbsp;T. C. Jermin Jeaunita,&nbsp;S. Sajithra Varun,&nbsp;Haiter Lenin Allasi,&nbsp;Mary Vasanthi Soosaimariyan","doi":"10.1002/met.70198","DOIUrl":"https://doi.org/10.1002/met.70198","url":null,"abstract":"<p>Crop yield is impacted by uncertain weather conditions, including irregular rainfall, changing temperatures, and humidity variations. Existing crop yield prediction models fail to capture the dynamic nature of weather and mostly depend on limited data. Accurate modeling of weather variability is crucial for improving the reliability of crop yield prediction and climate-aware agricultural decision-making. Digital twin (DT) technology provides a solution by creating a virtual farm that replicates the real-world farm conditions. This study develops a DT-based crop yield prediction framework, CropTwin, to predict crop yield under uncertain weather conditions without relying on localized sensor networks. Meteorological data from the NASA POWER Data Access Viewer (DAV) were collected to predict crop yields in rice-growing fields of the study location. We used a crop growth model to predict yields based on this original weather data. To validate how uncertain weather conditions affect yield, we created multiple scenarios with slightly varying weather conditions using a Monte Carlo simulation. We evaluated the CropTwin on all these scenarios to obtain a range of possible yields. The crop yield value is determined using various metrics, and the generalizability across different types of crops. Experimental results show that the proposed model accurately predicts the crop yields with a value of 5.90 t/ha under real-time NASA data conditions and 5.62 t/ha under simulated uncertain weather conditions. Furthermore, the framework demonstrates good generalization performance across multiple crops and regions. This shows the efficient crop yield performance of the proposed DT-based crop yield prediction framework.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70198","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148049972","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":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148095134","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 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148095132","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":"Spatial Analysis of Tropical Cyclone Precipitation Through Landfall: A Multi-Basin Perspective","authors":"Samrin Sumaiya Sauda,&nbsp;Stephanie E. Zick","doi":"10.1002/met.70191","DOIUrl":"https://doi.org/10.1002/met.70191","url":null,"abstract":"<p>Tropical cyclone precipitation (TCP) impacts are difficult to predict due to rapid structural changes during landfall. This study applies a shape metric methodology to analyze TCP evolution across the North Indian (NI), Western Pacific (WP), and North Atlantic (NA) basins using the Integrated Multisatellite Retrievals for GPM (IMERG) and IBTrACS datasets from 2014 to 2024. TCP characteristics are evaluated at three stages: pre-landfall, landfall, and post-landfall, using six spatial metrics (area, closure, solidity, fragmentation, dispersion, and elongation) and three rain rate thresholds (2, 5, and 10 mm hr.⁻¹). The Kruskal-Wallis test is used to identify significant changes in precipitation structures across the landfall stages. Results show that TCP becomes progressively less compact and more dispersed after landfall, with closure decreasing and dispersion increasing, indicating a more exposed TC center. Basin-specific differences were observed: the WP consistently exhibits the largest precipitation area and fragmentation and lowest solidity, while the NA shows the highest solidity and lowest fragmentation, and the NI displays the greatest elongation, potentially associated with monsoonal influences. Maximum precipitation rates decline significantly after landfall across all basins, with the steepest decrease in NI. There are also distinct directional pattern difference among basins. This study demonstrates how shape metrics can effectively capture basin-specific changes in TCP structure during landfall. The findings can improve understanding of rainfall evolution and support enhanced flood risk mitigation in coastal regions.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148049955","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":"The Role of Moisture Transport Deficits in Modulating Drought and Fire Patterns in the Upper Paraguay River Basin, Brazil","authors":"Liz Barreto Coelho Belém,&nbsp;Raquel Nieto,&nbsp;Leonardo F. Peres,&nbsp;Nilson Oliveira da Silva,&nbsp;Milica Stojanovic,&nbsp;Marta Vázquez,&nbsp;Ronaldo Albuquerque,&nbsp;Allan Arantes Pereira,&nbsp;Carlos C. DaCamara,&nbsp;Luis Gimeno,&nbsp;Ana M. B. Nunes,&nbsp;Letícia Couto Garcia,&nbsp;Geraldo Alves Damasceno Junior,&nbsp;Fabio de Oliveira Roque,&nbsp;Renata Libonati","doi":"10.1002/met.70195","DOIUrl":"https://doi.org/10.1002/met.70195","url":null,"abstract":"<p>In recent decades, the Upper Paraguay River Basin (UPRB) has experienced increasingly frequent and intense wildfires, often linked to severe droughts. While these events reflect broader hydrological imbalances driven by changes in precipitation and atmospheric moisture support, the role of moisture sources in modulating drought–fire dynamics remains unclear. This study addresses this gap by identifying key moisture sources for the UPRB and analyzing their shifts during extreme drought years coinciding with high fire. We assessed the drought–fire relationship through correlations between the Standardized Precipitation-Evapotranspiration Index (SPEI) and burned area (1986–2018), selecting the top-3 extreme drought years using the SPEI-based rarity index (|R|), and using the Lagrangian FLEXPART model outputs to track changes in moisture contributions. Results reveal a strong SPEI-burned area correlation, 2007, 2010, and 1999 being the top-3 extreme drought years aligning with peak wildfire activity. These years exhibited notable shifts in moisture contributions, particularly with persistent negative anomalies during the dry-season, reinforcing the occurrence of extremes. The most severe droughts were preceded by contrasting wet-season moisture conditions and consistently followed by strong dry-season deficits, particularly from key continental and ocean sources, contributing to drought intensification. While drought severity modulates wildfire risk, fire occurrence and extent also reflect the influence of multiple interacting factors. The findings highlight the importance of moisture source dynamics in shaping drought–fire interactions.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70195","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148049971","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":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148095133","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":"Rainfall Variability and Agroecological Resilience in Ghana: Evidence From Spatial and Volatility Models","authors":"Fred Fosu Agyarko,&nbsp;Stephen Yeboah,&nbsp;Patricia Amankwaa-Yeboah,&nbsp;Michael Gameli Dziwornu,&nbsp;Stephen Bekoe","doi":"10.1002/met.70194","DOIUrl":"https://doi.org/10.1002/met.70194","url":null,"abstract":"<p>The study focuses on rainfall variability within Ghana's agroecological zones, employing varied techniques including spatial autocorrelation, GARCH family volatility models, vector autoregression, impulse response functions, and extreme rainfall analysis. The mean result of Moran's <i>I</i> (−0.317) indicates negative spatial autocorrelation. This means that extremes in rainfall are highly localised because only 1.04% of considered periods showed significant clustering. Volatility modelling reveals zones as separate regimes: highly persistent shocks in the Sudan and Guinea Savanna zones (<i>β</i>₁ ≈0.97), asymmetric drought sensitivity in the Deciduous Forest under the E-GARCH (<i>α</i>₁ ≈ −0.206), and event-driven volatility in the Transitional Zone. Weak correlations between the savannas and the forest zones are found within the VAR analysis, which suggests correlations lagged in time within defined zones. The forest zone leads rainfall over the Guinea and Sudan Savannas 2–3 days later. IRF results indicate systematic moisture-carrying flows operating at 1–2-day intervals, with sustained northward cross-border zonal responses lasting over 20 days. Criteria for extreme rainfall show a strong north–south gradient, with the transitional zone containing the most intense events (95th percentile = 25.3 mm; 99th = 53.5 mm) and the deciduous forest enduring persistent droughts. The results of this study exemplify the fragmentation of rainfall regimes and their ecological volatility with a specific focus on the negative impacts of unadopted volatility. This study serves as a clarion call for zonal-specific measures to safeguard agriculture, water, climate adaptability and resilience.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70194","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148049706","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":"2026-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148090003","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}