Meteorological Applications最新文献

{"title":"Enhancing Forecast Verification in National Meteorological and Hydrological Services","authors":"Thomas C. Pagano,&nbsp;Elizabeth E. Ebert,&nbsp;Mohammadreza Khanarmuei","doi":"10.1002/met.70051","DOIUrl":"https://doi.org/10.1002/met.70051","url":null,"abstract":"<p>Forecast verification is an essential function of National Meteorological and Hydrological Services (NMHSs), underpinning their ability to deliver accurate, reliable, and actionable weather, climate, and water-related information. As NMHSs face increasing demands for transparency, accountability, and continuous improvement, they require robust systems to assess and enhance the quality of their forecasts. This article presents a holistic forecast verification capability development framework, built from over a decade of focused effort at the Australian Bureau of Meteorology. The framework integrates best practices in governance, data management, verification metrics, and communication. It acknowledges the importance of user-centered approaches and highlights areas where verification practices can align with user needs. To support NMHSs in adopting this framework, the article introduces two practical tools: a Verification Planning Template for establishing new verification activities and systems and a Gap Analysis and Maturity Assessment (GAMA) tool for benchmarking and advancing existing practices. These tools provide structured guidance for planning, evaluating, and improving verification within a NMHS, with the ultimate goal of delivering higher quality forecasts that meet diverse stakeholder needs. The Bureau's progress in implementing this framework demonstrates significant benefits, including improved forecast quality, enhanced coordination across verification efforts, and greater trust among users. However, challenges such as data availability, system integration, and resourcing remain pervasive, both within the Bureau and globally. The tools and insights shared in this article offer a pathway for NMHSs to overcome these obstacles, enabling them to better respond to evolving user expectations and operational demands. This work highlights the value of fostering a strong verification culture, supported by collaboration and knowledge sharing across the international meteorological community. By applying the principles and tools presented here, and customizing them to their circumstances, NMHSs can advance toward resilient, evidence-based verification practices and capabilities that enhance forecast reliability and stakeholder confidence worldwide.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143938934","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":"Predicting Tropical Cyclone Extreme Rainfall in Guangxi, China: An Interpretable Machine Learning Framework Addressing Class Imbalance and Feature Optimization","authors":"Yuexing Cai,&nbsp;Cuiyin Huang,&nbsp;Fengqin Zheng,&nbsp;Guangtao Li,&nbsp;Sheng Lai,&nbsp;Liyun Zhu,&nbsp;Qiuyu Zhu","doi":"10.1002/met.70052","DOIUrl":"https://doi.org/10.1002/met.70052","url":null,"abstract":"<p>Accurate prediction of tropical cyclone-induced extreme rainfall (TCER) is of utmost importance for disaster mitigation in coastal regions. However, it remains a formidable challenge due to the intricate interactions among multi-scale meteorological factors and the inherent data imbalances. This study presented an interpretable machine learning (ML) framework aimed at predicting both the occurrence and magnitude of TCER in Guangxi (GX), China. The framework integrated three supervised learning algorithms, namely XGBoost, Random Forest, and AdaBoost, along with feature selection techniques and an explainable method. A total of 202 experiments were conducted to comprehensively evaluate the framework's performance. Genetic Algorithm (GA) optimization and Shapley additive explanations (SHAP) were utilized to identify the optimal subsets of features and accurately quantify the contributions of each variable. Results showed that the optimized XGBoost model exhibited outstanding performance, integrating 18 predictors across dynamic, thermodynamic, moisture, and precursor variables, with a Threat Score of 0.41 for the classification of TCER occurrence and a Threat Score of 0.49 for the regression of rainfall magnitude, outperforming the TIGGE ensemble data in case studies of typhoons Chaba (2022) and Doksuri (2023). SHAP analysis revealed that <i>Distance to Track</i> is the most crucial factor for TCER occurrence. It also unveiled the existence of nonlinear interactions. For instance, an increase in vertical wind shear, favorable thermal conditions, ascending motion, and subtropical high activity can substantially amplify the likelihood of TCER when coupled with low-level humidity accumulation. Moreover, time-lagged variables and time-evolution variables demonstrated their ability to capture the precursor signals of TCER events, like humidity accumulation, circulation adjustment, and typhoon intensity changes, highlighting the model's effectiveness in considering these factors. Therefore, this study showcases the great potential of ML in enhancing TCER prediction while maintaining physical interpretability. Additionally, it offers a valuable reference for addressing imbalance issues in similar research fields.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143926003","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":"A Dynamical Perspective of the Extreme Rainfall Event Over Eastern Northeast Brazil in May 2022","authors":"Shunya Koseki,&nbsp;Isabelle Vilela,&nbsp;Doris Veleda","doi":"10.1002/met.70046","DOIUrl":"https://doi.org/10.1002/met.70046","url":null,"abstract":"<p>This study investigated how the extreme rainfall event over eastern Northeast Brazil (ENEB), occurring at the end of May 2022, was induced dynamically using observational and reanalysis data. On a monthly time scale, a wet-spell condition was found over the ENEB region in May 2022, indicated by enhanced onshore-ward moisture flux and a widely spreading positive precipitation anomaly. At a shorter time scale, the ENEB region experienced continually intense rainy days from May 21st to 28th peaking on May 28th. Focusing on the most intense rainfall event on May 28th, a shallow vortex disturbance of a tropical easterly wave can be responsible for this intense event. This easterly wave is initiated over the south tropical Atlantic adjacent to the ENEB region, and we suggest that a strong zonal wind shear zone associated with a synoptic-scale high-pressure system generates the vortex as barotropic instability. Even though the vortex center did not make a landfall over the ENEB region, a part of the vortex band is elongated along the coastal line of the ENEB region and the vorticity and moisture flux convergence intensify drastically over the coastal ENEB region. Reinforced fluid deformation along the coastal line indicates the extension and intensification of the vortex band. The coastal enhancement of vorticity, convergence, and deformation can be interactive, and the sea-land contrast may cause the enhancements due to surface condition change. This study provides a new dynamical insight into the intense precipitation over the ENEB region.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143905268","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":"A Pattern-Based Machine Learning Model for Imputing Missing Records in Coastal Wind Observation Networks","authors":"Nan-Jing Wu,&nbsp;Tai-Wen Hsu,&nbsp;Ting-Chieh Lin","doi":"10.1002/met.70050","DOIUrl":"https://doi.org/10.1002/met.70050","url":null,"abstract":"<p>Promoting green energy is essential for environmental sustainability, with wind energy playing a crucial role in this effort. While the Taiwan Strait has long been developed as a prime wind farm location, the search for new sites has led the government to focus on northern Taiwan, where the Northeast Monsoon prevails during winter. Since 2022, new meteorological stations have been established to monitor wind potential in this region. However, missing wind data from these stations can undermine the accuracy of wind assessments. To address this, we develop an imputation model using the Weighted K-Nearest Neighbors (WKNN) algorithm. This study focuses on seven meteorological stations near National Taiwan Ocean University (NTOU), located along the northeastern coast of Taiwan, including six on Taiwan proper and one on a nearby offshore islet, each recording wind speed and direction hourly. Complete data points, where all stations have recorded data simultaneously, are compiled into a reference database. When data from a particular station is missing, several complete data points from the database are used to estimate the missing values through weighted averaging. Calibration, validation, and testing procedures confirm that the model reliably estimates missing data, even when only four of the seven stations are operational.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143897208","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":"Role of regional and global datasets in the simulation of intense tropical cyclones over Bay of Bengal region in a convection-permitting scale","authors":"Thatiparthi Koteshwaramma,&nbsp;Kuvar Satya Singh","doi":"10.1002/met.70044","DOIUrl":"https://doi.org/10.1002/met.70044","url":null,"abstract":"<p>The efficacy of global and regional datasets on the prediction of extremely severe cyclonic storms over the Bay of Bengal (BoB) was evaluated using the Weather Research and Forecasting (WRF) model in a double-nested domain with a 4 km finer resolution on three different datasets, namely FNL, ERA-Interim, and Indian Monsoon Data Assimilation and Analysis (IMDAA). The initial cyclonic vortex, the vertical profile of horizontal wind speed, and relative humidity from different datasets were assessed to evaluate the initial structure and validated with IMD best-fit track data. The model results highlight that simulations with FNL data predict tracks and intensities more accurately for the majority of cyclonic storms compared with the IMDAA and ERA-Interim datasets. Simulations with FNL data exhibit the least mean track errors of 70, 126, 121, and 204 km for days 1–4, respectively. Additionally, the mean wind error of five extremely severe cyclonic storms (ESCSs) using FNL data is approximately 9.3, 4.6, 7.7, and 10.9 m/s, respectively, from day 1 to day 4. It is observed that the regional reanalysis of IMDAA datasets outperformed the forecast of several parameters such as maximum surface wind speed, central sea level pressure, and rainfall for the ESCSs <i>Fani</i> and <i>Sidr</i>. The FNL dataset overpredicted the amount of 24-h accumulated rainfall compared with the ERA-Interim and IMDAA datasets, whereas the IMDAA dataset performed better with lower values of root mean square error (148 mm/day), standard deviation (124 mm/day), and higher correlation (0.68) with the TRMM dataset. Model predictions highlight that the regional dataset IMDAA performs better in predicting rainfall magnitude compared with the global dataset due to the added assimilation of numerous local observations. The regional dataset could be improved by exploring large-scale circulation features and their significant role in predicting the track, intensity, and landfall location of the tropical cyclones.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852812","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":"Impact of rainfall variability on major crops using the deficient rainfall impact parameter (DRIP): A case study over Karnataka, India","authors":"Matadadoddi Nanjundegowda Thimmegowda,&nbsp;Melekote Hanumanthaiah Manjunatha,&nbsp;Lingaraj Huggi,&nbsp;Santanu Kumar Bal,&nbsp;Malamal Alickal Sarath Chandran,&nbsp;Dadireddihalli Venkatappa Soumya,&nbsp;Rangaswamanna Jayaramaiah","doi":"10.1002/met.70032","DOIUrl":"https://doi.org/10.1002/met.70032","url":null,"abstract":"<p>Understanding the aberrant weather and farmers' behavior under those is crucial for achieving climate resilience. Among weather parameters, rainfall significantly affects crop production, from pre-sowing decisions to harvesting. However, the existing indices often overlook farmers' decision-making. To address this gap, a new deficient rainfall impact parameter (DRIP) index was utilized to evaluate rainfall variability's effects on principal rainfed crops in Karnataka, India's second-largest dryland agriculture state. Datasets from 2011 to 2022 on area, production, and productivity of major crops of Karnataka were analyzed. Notably, the state's highest DRIP score was recorded in <i>Kharif</i> sorghum during 2016 and 2019 (12.8 and 8.6), indicating an impact of deficient rainfall on its production. Similarly, a higher reduction in the area under <i>rabi</i> sorghum was observed in 2016 with higher DRIP scores (10.9). Conversely, a meager decrease in the area under rainfed rice was observed in 2018 (1.6) and 2016 (1.2) even though there was a deficit of rainfall. In contrast, maize evaded drought impact during 2015–18 with negative DRIP scores, indicating crop shifts. However, finger millet suffered moisture stress in 2016 and 2018. <i>Rabi</i> wheat showed higher DRIP scores in 2016, 2017, and 2018 (12.2, 2.2, and 19.0) due to rainfall deficits. Similarly, the positive DRIP scores for pigeonpea in 2016–2018 signified decreased cultivation due to rainfall deficits. Chickpea, mainly cultivated in <i>vertisols</i>, showed marginal impact from rainfall deficits, except in 2016 and 2021. Groundnut had positive DRIP scores in 2017–2018 (1.1 and 0.5) due to deficit rainfall and in 2020–2021 (1.7 and 0.5) due to crop replacement with onion. Castor, on the other hand, exhibited positive DRIP scores in most years, except 2019, 2020, and 2022. This study underscores the importance of understanding rainfall variability and its implications for agricultural practices, thereby contributing to informed decision-making and strategic planning to ensure regional and national food security.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857108","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":"Unconventional observations for meteorological applications","authors":"Joanne Waller,&nbsp;Tess O' Hara","doi":"10.1002/met.70034","DOIUrl":"https://doi.org/10.1002/met.70034","url":null,"abstract":"&lt;p&gt;Conventional observations, such as those from satellites, radiosondes, weather balloons, ships, aircraft, traditional surface weather stations and rain gauges are commonly used in meteorological applications. Unconventional observations are becoming an increasingly valuable source of information for meteorological applications, often providing information at much higher spatial and temporal resolution than conventional observing networks and typically at a fraction of the cost (e.g., Nipen et al., &lt;span&gt;2020&lt;/span&gt;; O'Hara et al., &lt;span&gt;2023&lt;/span&gt;; Waller, &lt;span&gt;2020&lt;/span&gt;). They are also able to provide information more representative of local situations, such as individual urban streets, where conventional observing sites are not situated (e.g., Brousse et al., &lt;span&gt;2022&lt;/span&gt;; Feichtinger et al., &lt;span&gt;2020&lt;/span&gt;). As a result, the usefulness of these observations is being investigated for a variety of different meteorological uses (Hahn et al., &lt;span&gt;2022&lt;/span&gt;; Muller et al., &lt;span&gt;2015&lt;/span&gt;). There are also coordinated efforts to improve data access, processing and application, for example, the EU OpenSense project on the opportunistic sensing of rainfall (https://opensenseaction.eu/). However, a key issue identified with unconventional observations is the need for a good understanding of their quality, and the development of appropriate quality control methods (e.g., Beele et al., &lt;span&gt;2022&lt;/span&gt;; Fenner et al., &lt;span&gt;2021&lt;/span&gt;; Napoly et al., &lt;span&gt;2018&lt;/span&gt;) to ensure their usefulness in various meteorological applications.&lt;/p&gt;&lt;p&gt;Unconventional observations for meteorological applications can be obtained in a variety of ways. Data may be obtained opportunistically with meteorological information derived from non-meteorological sensors, or via the deployment of a network of low-cost sensors (e.g., Chapman et al., &lt;span&gt;2015&lt;/span&gt;; Vetra-Carvalho et al., &lt;span&gt;2020&lt;/span&gt;). Alternatively, data can be ‘crowdsourced’ and obtained from a group of people either with or without their explicit involvement in the data collection process, for example, via private automatic weather stations or a smartphone ‘app’ or collected via citizen-science projects where information obtained from a group of people who are invited to participate in the data collection process (Hintz, Vedel, et al., &lt;span&gt;2019&lt;/span&gt;; Kirk et al., &lt;span&gt;2021&lt;/span&gt;). Such citizen science projects can be particularly valuable as they permit interaction between experts and the public, providing educational opportunities and experiential learning to aid in the appreciation of risks, for example, extreme weather impacts (Batchelder et al., &lt;span&gt;2023&lt;/span&gt;; Paul et al., &lt;span&gt;2018&lt;/span&gt;).&lt;/p&gt;&lt;p&gt;Within Numerical Weather Prediction (NWP), unconventional observations have been used to supplement conventional data for nowcasting, data assimilation, forecast post-processing and forecast verification (Hintz et al., &lt;span&gt;2019&lt;/span&gt;). For example, private weather stati","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852813","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":"Meteorological Factors and the Spread of COVID-19: A Territorial Analysis in Italy","authors":"Telesca Vito,&nbsp;Castronuovo Gianfranco,&nbsp;Favia Gianfranco,&nbsp;Marra Mariarosaria,&nbsp;Rondinone Marica,&nbsp;Ceppi Alessandro","doi":"10.1002/met.70048","DOIUrl":"https://doi.org/10.1002/met.70048","url":null,"abstract":"<p>The COVID-19 pandemic has generated significant global impacts on health and society, imposing a comprehensive analysis of its influencing factors, including weather variables. This study investigates the interaction between meteorological conditions and the spread of COVID-19 in three Italian regions: Lombardia, Emilia-Romagna, and Puglia. Effects of weather variables, such as air temperature, relative humidity, dew point, solar radiation, wind speed, and barometric pressure, are explored in the incidence of disease. Observed meteorological and health data are taken from various sources, such as the citizen-science Meteonetwork Association and the National Department of Civil Protection, respectively, and they are analyzed with statistical methods and machine learning algorithms. The study emphasizes the necessity of carefully considering key meteorological quantities as primary drivers in illness diffusion and prevention strategies, offering valuable insights to address challenges to the pandemic and ensure the safety of global communities. The results reveal a significant correlation between specific atmospheric variables and the spread of COVID-19, with dew point temperature as the most influential parameter at low air temperature values.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836339","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 bad is the rain? Applying the extreme rain multiplier globally and for climate monitoring activities","authors":"David A. Lavers,&nbsp;Gabriele Villarini,&nbsp;Hannah L. Cloke,&nbsp;Adrian Simmons,&nbsp;Nigel Roberts,&nbsp;Anna Lombardi,&nbsp;Samantha N. Burgess,&nbsp;Florian Pappenberger","doi":"10.1002/met.70031","DOIUrl":"https://doi.org/10.1002/met.70031","url":null,"abstract":"<p>A typical question posed following an extreme precipitation event is: How does this compare to past events? This question is being asked more frequently and is of importance to climate monitoring services, such as the Copernicus Climate Change Service (C3S). Currently, the statistics extensively used for this purpose are not generally understandable to the wider public, or they are not tailored towards presenting extremes. To mitigate this situation, this article uses a modified version of the Extreme Rain Multiplier (ERM), which was developed for tropical cyclones, and applies it to precipitation events globally. For daily precipitation considered herein, the ERM is calculated by dividing the daily precipitation accumulation during an event by the mean historical annual maxima of daily precipitation (RX1day), which is computed over 1991–2020. Using the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis, the calculation of the ERM is illustrated for six extreme events around the world; these included convective systems, atmospheric rivers and tropical cyclones. A maximum ERM of 4 was found during Storm Daniel, in Greece, and in Tropical Cyclone Jasper in Australia, implying that four times the mean RX1day precipitation occurred. The ERM will be useful in C3S reporting activities because it can objectively identify extreme precipitation events. Furthermore, after extracting the number of precipitation events per year at each grid point that had an ERM exceeding 1, a trend analysis was undertaken to ascertain if the frequency of extreme events had changed with time. Results showed that the most widespread increasing trends in the ERM were in the tropics, but these trends are thought to be questionable in ERA5. There were few clear trends in other regions. In conclusion, the ERM can communicate the level of extreme precipitation in a clear manner and can be used in climate monitoring activities.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822139","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":"Diagonal Scores and Neighborhood: Definitions and Application to Idealized Cases","authors":"Joël Stein","doi":"10.1002/met.70047","DOIUrl":"https://doi.org/10.1002/met.70047","url":null,"abstract":"<p>Elementary diagonal score including neighborhood is presented as a new spatial verification tool for ensemble forecasts. It allows a spatial tolerance to be taken into account in the calculation of elementary diagonal scores by considering regional quantiles calculated from cumulative density functions computed on points in a spatial neighborhood. A climatology of the observed regional quantiles is required to define these diagonal scores. As in the case of the elementary diagonal scores without neighborhood, the relationship between error penalty rates and the level of the predicted regional quantile is fixed in order to have a proper score. In addition, this penalty rate is related to the climatological frequency of the event, to ensure an equitable score. The comparison of observations and ensemble forecasts is then summarized in a contingency table for this elementary diagonal score. An integral diagonal score including neighborhood can be calculated by averaging the elementary diagonal scores including neighborhood over a relevant sample of thresholds, as for the integral diagonal score without neighborhood. The properties of these diagonal scores have been illustrated on idealized cases including realistically spatially correlated fields.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822138","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}