Evaluation of Forward Operators for Polarimetric Radars Aiming for Data Assimilation

IF 2.4 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES Journal of the Meteorological Society of Japan Pub Date : 2018-05-01 DOI:10.2151/JMSJ.2018-017
T. Kawabata, H. Bauer, T. Schwitalla, V. Wulfmeyer, A. Adachi
{"title":"Evaluation of Forward Operators for Polarimetric Radars Aiming for Data Assimilation","authors":"T. Kawabata, H. Bauer, T. Schwitalla, V. Wulfmeyer, A. Adachi","doi":"10.2151/JMSJ.2018-017","DOIUrl":null,"url":null,"abstract":"In the preparation for polarimetric radar data assimilation, it is essential to examine the accuracy of forward operators based on different formulations. For this purpose, four forward operators that focus on warm rain conditions are compared both with each other and actual observations with respect to their performance for C-band dual polarimetric radars. These operators mutually consider radar beam broadening and climatological beam bending. The first operator derives polarimetric parameters assuming an exponential raindrop size distribution obtained by the models and is based on fitting functions against scattering amplitudes. The other three converters estimate the mixing ratio of rainwater from the measured polarimetric parameters. The second converter uses both the horizontal reflectivity (ZH) and the differential reflectivity (ZDR), the third uses the specific differential phase (KDP), and the fourth uses both KDP and ZDR, respectively. Comparisons with modeled measurements show that the accuracy of the third converter is superior to the other two. Another evaluation with actual observations shows that the first converter has slightly higher fractions skill scores than the other three. Considering the attenuation effect, the fitting function and the operator only with KDP are found to be the most suitable for data assimilation at C-band.","PeriodicalId":17476,"journal":{"name":"Journal of the Meteorological Society of Japan","volume":"1 1","pages":"157-174"},"PeriodicalIF":2.4000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2151/JMSJ.2018-017","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Meteorological Society of Japan","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.2151/JMSJ.2018-017","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 7

Abstract

In the preparation for polarimetric radar data assimilation, it is essential to examine the accuracy of forward operators based on different formulations. For this purpose, four forward operators that focus on warm rain conditions are compared both with each other and actual observations with respect to their performance for C-band dual polarimetric radars. These operators mutually consider radar beam broadening and climatological beam bending. The first operator derives polarimetric parameters assuming an exponential raindrop size distribution obtained by the models and is based on fitting functions against scattering amplitudes. The other three converters estimate the mixing ratio of rainwater from the measured polarimetric parameters. The second converter uses both the horizontal reflectivity (ZH) and the differential reflectivity (ZDR), the third uses the specific differential phase (KDP), and the fourth uses both KDP and ZDR, respectively. Comparisons with modeled measurements show that the accuracy of the third converter is superior to the other two. Another evaluation with actual observations shows that the first converter has slightly higher fractions skill scores than the other three. Considering the attenuation effect, the fitting function and the operator only with KDP are found to be the most suitable for data assimilation at C-band.
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
以同化为目标的极化雷达正演算子的评价
在极化雷达资料同化的准备工作中,有必要检验基于不同公式的正演算子的精度。为此,在c波段双极化雷达上,对四种关注暖雨条件的正演算子进行了相互比较和实际观测结果的比较。这些运算符相互考虑雷达波束展宽和气候波束弯曲。第一个算子基于散射振幅的拟合函数,假设模型得到的雨滴大小呈指数分布,从而推导出偏振参数。其他三个转换器根据测量的极化参数估计雨水的混合比。第二个转换器同时使用水平反射率(ZH)和差分反射率(ZDR),第三个转换器使用特定的差分相位(KDP),第四个转换器分别使用KDP和ZDR。与模型测量值的比较表明,第三种变换器的精度优于其他两种变换器。另一个实际观察的评估表明,第一个转换器的分数技能分数略高于其他三个。考虑到衰减效应,发现仅含KDP的拟合函数和算子最适合于c波段的数据同化。
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
Journal of the Meteorological Society of Japan
Journal of the Meteorological Society of Japan 地学-气象与大气科学
CiteScore
6.70
自引率
16.10%
发文量
56
审稿时长
3 months
期刊介绍: JMSJ publishes Articles and Notes and Correspondence that report novel scientific discoveries or technical developments that advance understanding in meteorology and related sciences. The journal’s broad scope includes meteorological observations, modeling, data assimilation, analyses, global and regional climate research, satellite remote sensing, chemistry and transport, and dynamic meteorology including geophysical fluid dynamics. In particular, JMSJ welcomes papers related to Asian monsoons, climate and mesoscale models, and numerical weather forecasts. Insightful and well-structured original Review Articles that describe the advances and challenges in meteorology and related sciences are also welcome.
期刊最新文献
[Comparative assessment of sensitivity and specificity of three variants of classification criteria for systemic lupus erythematosus in a cohort of Russian patients]. Livestock hauler and dairy farmer perspectives about cull dairy cattle transport and cattle transport regulations in British Columbia, Canada. A Machine Learning Approach to the Observation Operator for Satellite Radiance Data Assimilation End-to-End Deep Learning-Based Cells Detection in Microscopic Leucorrhea Images. Predictability Associated with High-Latitude Retrograde Waves in the 1979-80 Winter Season
×
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