Traditional and Novel Methods of Rainfall Observation and Measurement: A Review

Author:

Wang Xing1ORCID,Shi Shuaiyi2ORCID,Zhu Litao3,Nie Yunfeng4,Lai Guojun5

Affiliation:

1. a School of Computer Engineering, Nanjing Institute of Technology, Nanjing, China

2. b State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

3. c College of Information Engineering, Nanjing University of Finance and Economics, Nanjing, Jiangsu, China

4. d School of Information Engineering, Nanchang Hangkong University, Nanchang, China

5. e Department of Computer Science, Blekinge Institute of Technology, Karlskrona, Sweden

Abstract

Abstract Because of its high spatial and temporal variability, rainfall remains one of the most challenging meteorological variables to measure accurately. Obtaining high-quality rainfall products is essential for flood monitoring, disaster warning, and weather forecasting systems, but this is not always possible on the basis of current rainfall observation networks. Innovative alternatives draw inspiration from “citizen science” and “crowdsourcing,” allowing for opportunistic sensing of rainfall from existing measurements at a low cost, which has become a popular topic and is beginning to play an important role in developing rainfall observation systems. This paper reviews the current state of new rainfall observation approaches and explores their opportunities to complement more traditional ways of rainfall data collection in a hydrological context. Furthermore, the challenges of each new approach are discussed. Although these new options show great potential in enhancing the current rainfall network, they still face problems in terms of their accuracy, real-time accessibility, and limited applicability when individually employed. In contrast, the fusion of new measurements with traditional observation networks is feasible and will be effective for regional rainfall monitoring. This study also serves as an important reference in developing monitoring techniques for other environmental factors. Significance Statement New rainfall observation techniques provide a meaningful supplement to current rainfall networks in terms of spatiotemporal resolution and accuracy. In this paper, we present a comprehensive overview of the innovations in rainfall observation and their popularity in different regions around the world. Then, the application value and future opportunities that new techniques bring to hydrological research are analyzed. It is anticipated that this paper will be of value to researchers with an interest in improving the quality of rainfall data, thus paving the way to accelerate these studies, as well as the application and implementation of their findings, to the next stage. Furthermore, we expect to prompt a rethink on utilizing and exploiting these new rainfall products to enhance our understanding and optimization of current rainfall sensing systems.

Funder

National Natural Science Foundation of China

the State Scholarship Fund from the China Scholarship Council

Publisher

American Meteorological Society

Subject

Atmospheric Science

Reference224 articles.

1. Comparison of different fittings of drop spectra for rainfall retrievals;Adirosi, E.,2015

2. Alfonso, L., J. C. Chacón, and G. Peña-Castellanos, 2015: Allowing citizens to effortlessly become rainfall sensors. Proc. 36th IAHR World Congress, The Hague, Netherlands, IAHR, 1–6.

3. Toward the camera rain gauge;Allamano, P.,2015

4. A convective/stratiform precipitation classification algorithm for volume scanning weather radar observations;Anagnostou, E. N.,2004

5. Andersson, J., P. Berg, J. Hansryd, A. Jacobsson, J. Olsson, and J. Wallin, 2017: Microwave links improve operational rainfall monitoring in Gothenburg, Sweden. 15th Int. Conf. on Environmental Science and Technology, Rhodes, Greece, Global Network of Environmental Science and Technology, 1–4, https://cest2017.gnest.org/sites/default/files/presentation_file_list/cest2017_00249_oral_paper.pdf.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Estimating rainfall intensity based on surveillance audio and deep-learning;Environmental Science and Ecotechnology;2024-11

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3