DSAEF_LTP模式对2018年中国登陆热带气旋强降水模拟的改进

IF 1.5 4区 地球科学 Q4 METEOROLOGY & ATMOSPHERIC SCIENCES 热带气象学报 Pub Date : 2021-09-01 DOI:10.46267/J.1006-8775.2021.021
Chen Yu-xu, Jia Li, J. Zuo, Ding Chen-chen, Ren Fu-min, LI Guo-ping
{"title":"DSAEF_LTP模式对2018年中国登陆热带气旋强降水模拟的改进","authors":"Chen Yu-xu, Jia Li, J. Zuo, Ding Chen-chen, Ren Fu-min, LI Guo-ping","doi":"10.46267/J.1006-8775.2021.021","DOIUrl":null,"url":null,"abstract":"In this study, the Dynamical-Statistical-Analog Ensemble Forecast model(DSAEF_LTP model) for landfalling tropical cyclone(LTC) precipitation was employed to simulate the precipitation of 10 LTCs that occurred over China in 2018. With similarity region scheme(SRS) parameter values added and TC intensity introduced to the generalized initial value(GIV), four groups of precipitation simulation experiments were designed to verify the forecasting ability of the improved model for more TC samples. Results show that the simulation ability of the DSAEF_LTP model can be optimized regardless of whether adding SRS values only, or introducing TC intensity into GIV, while the experiment with both the two improvements shows a more prominent advantage in simulating the heavier precipitation of LTCs. Compared with four NWP models(i.e., ECMWF, GFS, GRAPES and SMS-WARMS), the overall forecasting performance of the DSAEF_LTP model achieves a better result in simulating precipitation at the thresholds over 250 mm and performs slightly better than NWP models at the thresholds over 100 mm.","PeriodicalId":17432,"journal":{"name":"热带气象学报","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"On the Improvement of the DSAEF_LTP Model to Heavy Precipitation Simulation of Landfalling Tropical Cyclones over China in 2018\",\"authors\":\"Chen Yu-xu, Jia Li, J. Zuo, Ding Chen-chen, Ren Fu-min, LI Guo-ping\",\"doi\":\"10.46267/J.1006-8775.2021.021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, the Dynamical-Statistical-Analog Ensemble Forecast model(DSAEF_LTP model) for landfalling tropical cyclone(LTC) precipitation was employed to simulate the precipitation of 10 LTCs that occurred over China in 2018. With similarity region scheme(SRS) parameter values added and TC intensity introduced to the generalized initial value(GIV), four groups of precipitation simulation experiments were designed to verify the forecasting ability of the improved model for more TC samples. Results show that the simulation ability of the DSAEF_LTP model can be optimized regardless of whether adding SRS values only, or introducing TC intensity into GIV, while the experiment with both the two improvements shows a more prominent advantage in simulating the heavier precipitation of LTCs. Compared with four NWP models(i.e., ECMWF, GFS, GRAPES and SMS-WARMS), the overall forecasting performance of the DSAEF_LTP model achieves a better result in simulating precipitation at the thresholds over 250 mm and performs slightly better than NWP models at the thresholds over 100 mm.\",\"PeriodicalId\":17432,\"journal\":{\"name\":\"热带气象学报\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2021-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"热带气象学报\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.46267/J.1006-8775.2021.021\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"热带气象学报","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.46267/J.1006-8775.2021.021","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 1

摘要

本文采用登陆热带气旋降水动态-统计-模拟集合预报模式(DSAEF_LTP模式),对2018年中国发生的10次登陆热带气旋降水进行了模拟。通过增加相似区域方案(SRS)参数值,并在广义初始值(GIV)中引入TC强度,设计了4组降水模拟实验,验证了改进模型对更多TC样本的预报能力。结果表明,无论只添加SRS值,还是在GIV中引入TC强度,DSAEF_LTP模型的模拟能力都可以得到优化,而同时进行两种改进的实验在模拟LTCs较强降水方面的优势更为突出。与四种NWP模型(即(ECMWF、GFS、GRAPES和sms - warm), DSAEF_LTP模式在250 mm以上阈值的模拟效果较好,在100 mm以上阈值的模拟效果略好于NWP模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
On the Improvement of the DSAEF_LTP Model to Heavy Precipitation Simulation of Landfalling Tropical Cyclones over China in 2018
In this study, the Dynamical-Statistical-Analog Ensemble Forecast model(DSAEF_LTP model) for landfalling tropical cyclone(LTC) precipitation was employed to simulate the precipitation of 10 LTCs that occurred over China in 2018. With similarity region scheme(SRS) parameter values added and TC intensity introduced to the generalized initial value(GIV), four groups of precipitation simulation experiments were designed to verify the forecasting ability of the improved model for more TC samples. Results show that the simulation ability of the DSAEF_LTP model can be optimized regardless of whether adding SRS values only, or introducing TC intensity into GIV, while the experiment with both the two improvements shows a more prominent advantage in simulating the heavier precipitation of LTCs. Compared with four NWP models(i.e., ECMWF, GFS, GRAPES and SMS-WARMS), the overall forecasting performance of the DSAEF_LTP model achieves a better result in simulating precipitation at the thresholds over 250 mm and performs slightly better than NWP models at the thresholds over 100 mm.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
热带气象学报
热带气象学报 METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
1.80
自引率
8.30%
发文量
2793
审稿时长
6-12 weeks
期刊介绍: Information not localized
期刊最新文献
Correcting Black Carbon Absorption Measurements with Micro-aethalometer Model 200: Insights from Comparative Analysis Improved Weather Radar Echo Extrapolation Through Wind Speed Data Fusion Using a New Spatiotemporal Neural Network Model Interannual Variation and Statistical Prediction of Summer Dry and Hot Days in South China from 1970 to 2018 Observational and Mechanistic Analysis of a Nighttime Warm-Sector Heavy Rainfall Event Within the Subtropical High over the Southeastern Coast of China Adaptive Wind Gust and Associated Gust-factor Model for the Gust-producing Weather over the Northern South China Sea
×
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