Ensemble-based monthly to seasonal precipitation forecasting for Iran using a regional weather model

IF 3.5 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES International Journal of Climatology Pub Date : 2024-08-19 DOI:10.1002/joc.8586
Mohammad Saeed Najafi, Vahid Shokri Kuchak
{"title":"Ensemble-based monthly to seasonal precipitation forecasting for Iran using a regional weather model","authors":"Mohammad Saeed Najafi,&nbsp;Vahid Shokri Kuchak","doi":"10.1002/joc.8586","DOIUrl":null,"url":null,"abstract":"<p>Monthly and seasonal precipitation forecasts can potentially assist disaster risk reduction and water resource management. The aim of this study is to assess the skill of an ensemble framework for monthly and seasonal precipitation forecasts over Iran by focusing on system design and model performance evaluation. The ensemble framework presented in this paper is based on a one-way double-nested model that uses Weather Research and Forecasting (WRF) modelling system to downscale the second version of the NCEP Climate Forecast System (CFSv2). The performance is evaluated for October–April period at 1-, 2- and 3-month lead time. Multiple initial conditions, model parameters and physics are used to construct ensemble members. Using quantile mapping (QM) method, the outputs of the model are bias corrected. This methodology is applied for two periods: (i) climatology from 2000 to 2019 to evaluate the model's ability to precipitation forecast on a monthly and seasonal time scale; (ii) the forecast for 2020 to evaluate the model's performance operationally. The model evaluation is performed using the continuous (e.g., RMSE, r, MBE, NSE) and categorical (e.g., POD, FAR, PC, Heidke skill score) assessment metrics. We conclude that model outputs were improved by the QM bias correction method. According to results, the proposed ensemble framework can accurately predict amount of monthly and seasonal precipitation in Iran with an accuracy of 58 to 45% for lead-1 to 3. For all three lead times, the averaged NSE, CC, MBE, and RMSE were 0.4, 0.56, −15.5, and 41.6, indicating that the framework has reasonable performance. Our results suggest that precipitation forecast accuracy varies with lead time, so the accuracy for lead-1 is higher than lead-2 and lead-3. Additionally, the model's accuracy differs in various regions of the country and decreases in the spring. Using the approach for an operational case, it was found that the spatial features of precipitation predicted by the framework were close to those observed.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 12","pages":"4366-4387"},"PeriodicalIF":3.5000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Climatology","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/joc.8586","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Monthly and seasonal precipitation forecasts can potentially assist disaster risk reduction and water resource management. The aim of this study is to assess the skill of an ensemble framework for monthly and seasonal precipitation forecasts over Iran by focusing on system design and model performance evaluation. The ensemble framework presented in this paper is based on a one-way double-nested model that uses Weather Research and Forecasting (WRF) modelling system to downscale the second version of the NCEP Climate Forecast System (CFSv2). The performance is evaluated for October–April period at 1-, 2- and 3-month lead time. Multiple initial conditions, model parameters and physics are used to construct ensemble members. Using quantile mapping (QM) method, the outputs of the model are bias corrected. This methodology is applied for two periods: (i) climatology from 2000 to 2019 to evaluate the model's ability to precipitation forecast on a monthly and seasonal time scale; (ii) the forecast for 2020 to evaluate the model's performance operationally. The model evaluation is performed using the continuous (e.g., RMSE, r, MBE, NSE) and categorical (e.g., POD, FAR, PC, Heidke skill score) assessment metrics. We conclude that model outputs were improved by the QM bias correction method. According to results, the proposed ensemble framework can accurately predict amount of monthly and seasonal precipitation in Iran with an accuracy of 58 to 45% for lead-1 to 3. For all three lead times, the averaged NSE, CC, MBE, and RMSE were 0.4, 0.56, −15.5, and 41.6, indicating that the framework has reasonable performance. Our results suggest that precipitation forecast accuracy varies with lead time, so the accuracy for lead-1 is higher than lead-2 and lead-3. Additionally, the model's accuracy differs in various regions of the country and decreases in the spring. Using the approach for an operational case, it was found that the spatial features of precipitation predicted by the framework were close to those observed.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用区域天气模式对伊朗的月至季节降水量进行集合预报
月降水量和季节降水量预报可能有助于减少灾害风险和水资源管理。本研究旨在通过系统设计和模型性能评估,评估伊朗月度和季节降水预报集合框架的技能。本文介绍的集合框架基于一个单向双嵌套模型,该模型使用天气研究与预报(WRF)建模系统,对第二版 NCEP 气候预报系统(CFSv2)进行降尺度处理。在 1 个月、2 个月和 3 个月的准备时间内,对 10 月至 4 月期间的性能进行了评估。使用多种初始条件、模式参数和物理参数构建集合成员。使用量子映射(QM)方法对模型输出进行偏差校正。该方法适用于两个时期:(i) 2000 年至 2019 年的气候学,以评估模型在月度和季节时间尺度上的降水预报能力;(ii) 2020 年的预报,以评估模型的运行性能。模型评估采用连续(如 RMSE、r、MBE、NSE)和分类(如 POD、FAR、PC、Heidke 技能评分)评估指标。我们的结论是,QM 偏差校正方法改善了模型输出。结果表明,所提出的集合框架可以准确预测伊朗的月降水量和季节降水量,在第 1 至第 3 个提前期的准确率为 58% 至 45%。 在所有三个提前期,平均 NSE、CC、MBE 和 RMSE 分别为 0.4、0.56、-15.5 和 41.6,表明该框架具有合理的性能。我们的结果表明,降水预报精度随提前期而变化,因此提前期-1 的精度高于提前期-2 和提前期-3。此外,该模型在全国不同地区的准确度也不尽相同,在春季准确度会有所下降。将该方法用于实际案例时发现,该框架预测的降水空间特征与观测到的降水空间特征接近。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
International Journal of Climatology
International Journal of Climatology 地学-气象与大气科学
CiteScore
7.50
自引率
7.70%
发文量
417
审稿时长
4 months
期刊介绍: The International Journal of Climatology aims to span the well established but rapidly growing field of climatology, through the publication of research papers, short communications, major reviews of progress and reviews of new books and reports in the area of climate science. The Journal’s main role is to stimulate and report research in climatology, from the expansive fields of the atmospheric, biophysical, engineering and social sciences. Coverage includes: Climate system science; Local to global scale climate observations and modelling; Seasonal to interannual climate prediction; Climatic variability and climate change; Synoptic, dynamic and urban climatology, hydroclimatology, human bioclimatology, ecoclimatology, dendroclimatology, palaeoclimatology, marine climatology and atmosphere-ocean interactions; Application of climatological knowledge to environmental assessment and management and economic production; Climate and society interactions
期刊最新文献
Issue Information Issue Information Time Series Clustering of Sea Surface Temperature in the Mediterranean and Black Sea Marine System An Elevated Influence of the Low-Latitude Drivers on the East Asian Winter Monsoon After Around 1990 Improvement in the Low Temperature Prediction Skill During Cold Winters Over the Mid–High Latitudes of Eurasia in CFSv2
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1