Meteorological Applications最新文献

{"title":"An Observational Study of the Inn Valley Exit Jet in Southern Germany","authors":"Katrin Sedlmeier,&nbsp;Meinolf Kossmann,&nbsp;Ivan Paunovic,&nbsp;Oliver Nitsche,&nbsp;Eileen Päschke,&nbsp;Ronny Leinweber,&nbsp;Astrid Eichhorn-Müller,&nbsp;Gudrun Mühlbacher","doi":"10.1002/met.70247","DOIUrl":"https://doi.org/10.1002/met.70247","url":null,"abstract":"<p>The Inn Valley exit jet (VEJ) is a frequently occurring nocturnal wind phenomenon in the exit region of the Inn Valley, north of a valley constriction before the valley broadens into the foreland. Using upper-air and surface measurements conducted during the pre-campaign of the TEAMx programme in June–August 2022, we found new insights on the structure of the VEJ, especially its turbulence properties, as well as on the effects of the jet at the surface level. During the TEAMx pre-campaign, VEJs occurred in 47 of the 93 nights considered and showed similar characteristics in all jet nights. Wind speeds during nocturnal VEJ events are strongest between 04:00 and 06:00 UTC, with medians between 10 and 11 m/s and maxima of up to 19 m/s at 100–200 m AGL. Below and above the jet height, shear-induced turbulence is found. The jets clearly influence the wind field, temperature and turbulence at the surface level, with a clear rise/drop in wind speeds and TKE during the onset/decay of the jet.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70247","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942942","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":"Compound Cold Events, Impacts and Weather Patterns: A Case Study of the Scottish Rail Network","authors":"Kanzis L. Mattu,&nbsp;Christopher J. White,&nbsp;Hannah Bloomfield,&nbsp;Joanne Robbins","doi":"10.1002/met.70242","DOIUrl":"https://doi.org/10.1002/met.70242","url":null,"abstract":"<p>Winter weather events disrupt critical national infrastructure, with rail networks particularly vulnerable to blocked tracks, mechanical faults, and frozen infrastructure leading to service delays and cancellations. The impacts of these cold weather events can be amplified by the compounding effect of another meteorological variable, particularly whether heavy precipitation is present or absent, with subsequent impacts dependent on the nature of the cold event. For example, a ‘compounding’ cold-wet event could incur heavy snowfall, whereas a cold-dry event could result in extreme low temperatures and icy conditions. Despite their prevalence, such compound cold extremes remain understudied compared to compound heat extremes. In this study, we analyse 10,500 Scottish rail incidents (2006–2023, October to March) using Network Rail impact data, the set of Met Office 30 weather patterns (MO30), and HadUK-Grid observations to link incidents with compound cold events. We identify high-impact days based on two classifications: (1) days exceeding the 99th percentile of aggregated daily incidents; (2) days exceeding the 99th percentile of daily customer minutes lost. The majority of high-impact days were snow-related, occurring under cold-dry or cold-wet conditions with the definition depending on whether snow was falling or lying. These findings highlight the importance of considering co-occurring cold hazards, as real-world impacts may diverge from traditional hazard-based detection. This provides useful insight for compound cold early warning systems.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70242","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942945","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":"Machine Learning-Based Postprocessing Ensemble of AI and NWP Models for Short-Term Air Temperature Forecasting Over Beijing","authors":"Peng He,&nbsp;Chenxi Jin,&nbsp;Yan Dong,&nbsp;Chen Qi,&nbsp;Ting Lei","doi":"10.1002/met.70238","DOIUrl":"https://doi.org/10.1002/met.70238","url":null,"abstract":"<p>Accurate short-term air temperature forecasts are critical for societal and economic activities, particularly for energy management and urban planning. This study evaluates 6–72 h air temperature forecasts over Beijing from January 2022 to December 2023 using both numerical weather prediction (NWP) and AI-based models. The AI models, GraphCast and Pangu, were initialized with the ECMWF Integrated Forecasting System (IFS) and NOAA Global Forecast System (GFS), resulting in GraphCast-IFS, Pangu-IFS, GraphCast-GFS, and Pangu-GFS. Forecast skill exhibits pronounced seasonal and diurnal variability, with AI models consistently outperforming their driving NWP models, particularly when initialized with IFS. Among them, Pangu-IFS exhibits superior predictive skill. To further enhance forecast performance, outputs from AI and NWP models are postprocessed using a multimodel ensemble of three machine learning (ML) algorithms: multiple linear regression (MLR), gradient boosting regression tree (GBRT), and extreme gradient boosting (XGBoost). The XGBoost ensemble achieves the largest improvements, reducing the root-mean-square error (RMSE) by 24.1% and increasing the temperature forecast accuracy (TFA) by 22.0% (<i>p</i> &lt; 0.01) relative to the simple ensemble mean (SEM), with particularly pronounced enhancements at stations in the mountainous regions of western and northern Beijing. Error decomposition indicates that these improvements primarily arise from reductions in systematic bias and phase errors, with bias correction being the dominant factor. These results indicate that integrating AI, NWP, and ML-based postprocessing can improve short-term temperature forecasts, particularly over complex terrain, and may benefit operational and energy-related applications.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942707","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":"Hydroclimatic Changes in the Marsyangdi River Basin, Central Himalaya Under CMIP6 Scenarios: Implications for the Water–Energy–Food–Ecosystem Nexus","authors":"Diwakar Adhikari,&nbsp;Madhav Upadhaya,&nbsp;Nitesh Khadka,&nbsp;Deep Narayan Shah,&nbsp;Lal B. Thapa,&nbsp;Ram Kailash Prasad Yadav,&nbsp;Ramesh Raj Pant","doi":"10.1002/met.70240","DOIUrl":"https://doi.org/10.1002/met.70240","url":null,"abstract":"<p>Climate change is fundamentally altering hydroclimatic regimes across the Himalayas, with cascading impacts on water, energy, food, and ecosystems (WEFE). We applied the SWAT+ hydrological model in the Marsyangdi River Basin (MRB) of central Nepal, using bias-corrected CMIP6 projections under SSP2-4.5 and SSP5-8.5 scenarios to study hydroclimatic regimes. We employed an envelope approach spanning warm-wet, warm-dry, cold-wet, and cold-dry states to capture plausible futures for mid-century (2041–2070) and end-century (2071–2100). By end-century, warming reaches +3.0°C (SSP2-4.5) and +5.0°C (SSP5-8.5), with precipitation increasing by 13% and 27%, respectively, driven by monsoon intensification. Annual discharge rises by &gt; 30% and &gt; 50%, yet snowfall declines by ~20% and ~35%, eroding the cryospheric buffer that sustains dry season flows. Pre-monsoon discharge decreases under both scenarios, exacerbating early-season water scarcity. Climate extremes intensify with extremely wet-day contributions increasing up to 85% under SSP2-4.5 and more than tripling under SSP5-8.5, daily rainfall intensity strengthens by 45%–55%, and consecutive dry days lengthen by 30%–87%, signaling concurrent flood and drought risks. Sectoral consequences are compounding with dry-season hydropower deficits reaching 44% under drier futures, while wheat irrigation demand nearly doubles by end-century under SSP5-8.5; paddy remains stable and maize rainfed. Simultaneously, declining dry-season flows and intensified runoff increase environmental-flow stress impacting aquatic connectivity, and elevate sediment-related risks in Annapurna Conservation Area headwaters. Our findings reveal a pronounced seasonal mismatch between water availability and cross-sectoral demand in the MRB, underscoring the urgent need for coordinated, climate-state-informed WEFE planning to enhance resilience under future conditions.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70240","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942637","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":"Extreme Rainfall Estimation Methods: A Review","authors":"Duncan Faulkner,&nbsp;Jonathan Tawn,&nbsp;Christine McKenna","doi":"10.1002/met.70233","DOIUrl":"https://doi.org/10.1002/met.70233","url":null,"abstract":"<p>Information on extreme rainfall is needed for flood risk management and design and assessment of infrastructure. We review techniques used for estimating the characteristics of extreme rainfall events, covering both research and its application in practice. The review is motivated by a desire to update rainfall estimation methods for the UK, but we include examples from many other parts of the world. We cover estimation of rainfall depth-duration-frequency relationships, including regionalisation, development of extreme value models, and their incorporation of seasonality, climatic non-stationarity and the areal extent of rainfall. Other aspects covered include methods for characterising the temporal and spatial variation of rainfall depths for design rainfall events. For each aspect we identify the main categories of method seen in the literature and then review their advantages, disadvantages and prevalence in practice. We highlight opportunities for incorporating meteorological understanding in the development and application of statistical models of extreme rainfall. We find increasing use of spatial statistics, machine learning and climate model outputs to complement traditional rainfall frequency analysis techniques. The design rainfall event concept, while the focus of this paper, is not necessarily capable of answering some more demanding questions about societal resilience. These may require information on the meteorological context of rainfall: its larger-scale spatial and longer-term temporal structure.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70233","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942653","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":"Joint Postprocessing of Air and Dew Point Temperature Ensemble Forecasts With Machine Learning","authors":"Enric Casellas,&nbsp;Josep-Ramon Miró,&nbsp;Maria Cortès,&nbsp;Vicent Altava-Ortiz,&nbsp;Jordi Moré","doi":"10.1002/met.70237","DOIUrl":"https://doi.org/10.1002/met.70237","url":null,"abstract":"&lt;p&gt;Estimation of forecast uncertainty is essential for operational meteorology, especially in the context of global warming, where the frequency and intensity of extreme events such as heat waves are increasing. Ensemble prediction systems are widely used to represent forecast uncertainty, but their raw output typically requires statistical postprocessing to improve reliability and correct systematic errors. This study focuses on the joint probabilistic postprocessing of air temperature (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;mi&gt;a&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {T}_{mathrm{a}} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;) and dew point temperature (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;mi&gt;d&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {T}_{mathrm{d}} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;), two closely related variables that are key inputs for the estimation of humidity-related variables and thermal comfort indices. Two postprocessing frameworks are compared. The first is a two-step approach based on univariate calibration using Distributional Regression Networks (DRN), combined with Ensemble Copula Coupling (ECC) to reconstruct the dependence structure between the two variables. The second is a one-step multivariate approach in which marginal distributions and their dependence structure are modelled simultaneously. Within this framework, two alternative parametrisation are compared: a bivariate DRN, which estimates all parameters through a neural network, and a classical bivariate Ensemble Model Output Statistics (EMOS) model, in which the same parameters are estimated as affine functions of the raw ensemble. The methods are evaluated using a 10-member multi-model ensemble and observations from an automatic weather station network. Results indicate that the bivariate DRN approach improves performance in terms of both univariate and multivariate verification metrics. Compared with the DRN-Univariate + ECC approach, the bivariate DRN achieves improved multivariate skill while maintaining similar marginal performance, highlighting the benefit of explicitly modelling inter-variable dependence rather than reconstructing it from the raw ensemble. Compared with Bivariate EMOS, which adopts the same one-step bivariate Gaussian formulation, the bivariate DRN consistently achieves better univariate and multivariate skill, suggesting that the flexibility of the neural network implementation provides additional predictive skill. Rank histograms, however, show similar calibration performance for both approaches. Overall, the results suggest that a bivariate DRN approach is a promising option fo","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942499","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":"Uncertainty of SYNOP Cloud Classifications by Human Observers","authors":"Markus Rosenberger,&nbsp;Manfred Dorninger,&nbsp;Martin Weissmann","doi":"10.1002/met.70239","DOIUrl":"https://doi.org/10.1002/met.70239","url":null,"abstract":"<p>Human cloud observations are by design to some extent subjective since they are based on the observer's visual perception of the sky and features therein. In this work, we assess the uncertainty of operational SYNOP cloud observations in three different settings: (i) seven observers at the same station but at different times, (ii) observations conducted at the same time but at different stations about 20 km apart, and (iii) five volunteer meteorologists classifying clouds from the same set of ground-based camera images. Investigations indicate the presence of substantial systematic differences for different observers and also at different stations, which cannot be explained purely by meteorological circumstances. When evaluated on a complex cloud classification scheme, where for each instance up to three out of 30 cloud types have to be chosen, inter-observer agreement on image-based classifications reached MCC scores up to 0.33. However, combining observations from different participants to a single report and using a classification scheme of reduced complexity strongly increases MCC scores up to 0.67. Results suggest that human cloud observations have to be used with caution if they are used for example, to evaluate new automatized cloud classification methods.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70239","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942166","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":"Comparative Assessment of Post-Processing Approaches for GEFSv12 Summer Maximum Temperature and Heat Extreme Forecasts Over CONUS","authors":"M. M. Nageswararao,&nbsp;Vijay Tallapragada","doi":"10.1002/met.70232","DOIUrl":"https://doi.org/10.1002/met.70232","url":null,"abstract":"<p>The increasing frequency of summer time heat extremes across the continental United States highlights the need for reliable forecasts of daily maximum temperature, especially at longer lead times. This study evaluates three post-processing approaches: the Detrended Quantile Mapping (QM), Artificial Neural Networks (ANN), and a sequential Hybrid ANN–QM technique, to improve sub-seasonal summer maximum temperature (T_max) and heat-extreme forecasts from the NOAA NCEP Global Ensemble Forecast System version 12 (GEFSv12) over the continental United States (CONUS) during 2000–2019. Forecasts were verified against CPC observations using deterministic, categorical, and probabilistic metrics across multiple lead times. Raw GEFSv12 forecasts reproduced large-scale T_max patterns but exhibited significant regional biases and declining skill at extended lead times. While QM effectively corrected systematic biases, it provided limited improvements in forecast–observation association. Conversely, ANN significantly enhanced forecast skill by reducing RMSE and better preserving interannual variability. The Hybrid approach synthesized the strengths of both, leveraging ANN for nonlinear error correction and QM for distributional calibration, consistently yielding the most balanced performance and highest skill for heat-extreme prediction. These findings underscore the efficacy of combining machine learning and statistical post-processing to advance sub-seasonal temperature forecasting.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70232","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943552","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":"Rare Tropical Cyclones: Tropical Cyclone Hidaya, A High-Impact Low-Latitude Event","authors":"Kwesi Twentwewa Quagraine,&nbsp;Loretta Pearl Poku","doi":"10.1002/met.70235","DOIUrl":"https://doi.org/10.1002/met.70235","url":null,"abstract":"<p>Tropical cyclones pose significant hazards to coastal communities through destructive winds, intense rainfall, storm surge, and flooding. In May 2024, tropical cyclone Hidaya made landfall along the Tanzanian coast at an unusually low latitude (within ±7.5° of the equator), where tropical cyclone development and persistence are generally suppressed by weak planetary vorticity. This rare event provides a unique opportunity to investigate the atmospheric and oceanic processes that enabled cyclogenesis and maintenance in a marginal dynamical environment. Using atmospheric reanalysis, oceanic analyses, and storm-centered diagnostics, we examine the environmental conditions governing Hidaya's evolution. Our results show that a weakened and southeastward-displaced South Indian Ocean High modified the large-scale steering flow, directing the cyclone westward and equatorward toward Tanzania. Simultaneously, anomalously warm sea surface temperatures and elevated tropical cyclone heat potential supplied sufficient upper-ocean energy to sustain organized deep convection. Storm-centered analyses reveal persistent low-level cyclonic rotation, boundary-layer convergence, abundant mid-tropospheric moisture, sustained ascent, and relatively weak environmental wind shear that collectively maintained the cyclone despite limited Coriolis forcing. Structural diagnostics further indicate that enhanced diabatic heating promoted vertical coupling of the vortex through cloud–infrared radiation feedback, while the subsequent depletion of upper-ocean heat content over shallow coastal waters contributed to storm weakening prior to dissipation. Historical analyses confirm that tropical cyclone landfalls of this nature remain exceptionally rare along the Tanzanian coast. By integrating large-scale circulation, storm-scale dynamics, and upper-ocean energetics, this study provides a physically consistent framework for understanding how rare near-equatorial tropical cyclones can develop and persist. These findings improve understanding of tropical cyclone behavior in marginal environments and have important implications for forecasting, hazard assessment, and disaster preparedness across coastal East Africa.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70235","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943677","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":"How Good Is Your Forecast? The Importance of User-Centred, Decision-Relevant Evaluation of Forecasts and Climate Services","authors":"Rebecca Emerton,&nbsp;Schalk Jan van Andel,&nbsp;Katherine Egan,&nbsp;Calum Baugh,&nbsp;Deborah Dotta Correa,&nbsp;Emie Klein Holkenborg,&nbsp;Daniele Castellana,&nbsp;Marc van den Homberg,&nbsp;Yiheng Du,&nbsp;Ilias Pechlivanidis,&nbsp;Sumiran Rastogi,&nbsp;Micha Werner","doi":"10.1002/met.70234","DOIUrl":"https://doi.org/10.1002/met.70234","url":null,"abstract":"<p>Forecasts and climate services, spanning weather and climate timescales, play a vital role in supporting short- and long-term decision-making across a wide range of climate-sensitive sectors. Understanding their skill is crucial to ensuring that the information provided is relevant, trustworthy and usable in real-world contexts. Traditional evaluation and verification methods typically focus on statistical accuracy, which remains essential for model development and improvement. However, these metrics alone may not capture the aspects of forecast performance that matter most to users and often fail to reflect how well forecasts could support practical decision-making. This paper highlights the importance of user-driven, decision-relevant evaluation: an approach that centres on the needs, experiences and decision-making contexts of forecast and climate service users. We present a flexible framework for user-driven forecast evaluation. Drawing on case studies from diverse regions and hazard types—including cold waves in Lesotho, precipitation variability in Spain and drought in Lesotho, the Netherlands, Italy and Georgia—we demonstrate how user engagement can tailor evaluation to decision-making contexts and local knowledge. The framework combines event-based contingency table approaches, using user-defined thresholds that are linked to the decision they make, to derive transparent metrics such as hits, misses and false alarms, alongside scores such as probability of detection, false alarm ratio and threat score. Traditional verification metrics (such as mean absolute error or the continuous ranked probability score) are applied as complementary diagnostics, while co-evaluation of forecast performance for historical events is used to further support dialogue, accessibility and trust. The case studies show the value of combining long-term performance metrics with context-rich narratives and emphasise the importance of data visualisation and communication in shaping forecast usability. We conclude with recommendations for integrating user-centred evaluation into operational workflows to ensure climate services are both scientifically robust and decision-relevant.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148856714","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}