Abstract
The knowledge garnered in environmental science takes a crucial part in informing decision-making in various fields, including agriculture, transportation, energy, public health and safety, and more. Understanding the basic processes in each of these fields relies greatly on progress being made in conceptual, observational and technological approaches. However, existing instruments for environmental observations are often limited as a result of technical and practical constraints. Current technologies, including remote sensing systems and ground-level measuring means, may suffer from obstacles such as low spatial representativity or a lack of precision when measuring near ground-level. These constraints often limit the ability to carry out extensive meteorological observations and, as a result, the capacity to deepen the existing understanding of atmospheric phenomena and processes. Multi-system informatics and sensing technology have become increasingly distributed as they are embedded into our environment. As they become more widely deployed, these technologies create unprecedented data streams with extraordinary levels of coverage and immediacy, providing a growing opportunity to complement traditional observation techniques using the large volumes of data created. Commercial microwave links that comprise the data transfer infrastructure of cellular communication networks are an example of these types of systems. This viewpoint letter briefly reviews various works on the subject and presents aspects concerning the added value that may be obtained as a result of the integration of these new means, which are becoming available for the first time in this era, for studying and monitoring atmospheric phenomena.
摘要
环境科学所涵盖的知识为农业, 交通, 能源, 公共卫生和安全等各个领域的决策制定提供了重要的客观依据. 理解各领域的基本过程在很大程度上依赖于在概念方法, 观测方法和技术手段方面取得的进展. 然而, 现有传统的观测仪器受当前技术条件的约束和实际情况的限制. 例如, 现有的遥感系统和地面观测, 在对地表做气象观测的时候可能会受到诸如低分辨率, 或者低精度的制约. 这些往往限制了对更广泛气象观测数据获取的能力, 导致很难基于此更深入的理解自然界存在的大气现象和过程. 多系统信息学和遥感物联网技术的应用已逐渐融入, 并广泛分布于我们的生活环境. 这些技术的广泛使用创造出了前所未有的数据流, 这些数据具有数量庞大, 覆盖范围极广, 时效性极强, 以及易获取性的特点, 为补充传统观测技术提供了越来越多的机会. 例如, 构成蜂窝通信网络的数据传输基础设施的商用微波链路是这类系统的一个例子. 本文简要地回顾了有关该主题的很多相关研究工作, 介绍了由于这些新手段的整合可能带来的附加值. 这个时代首次可以将这些手段用于研究和检测大气现象.
References
Alpert, P., and Y. Rubin, 2018: First daily mapping of surface moisture from cellular network data and comparison with both observations/ECMWF product. Geophys. Res. Lett., 45(16), 8619–8628, https://doi.org/10.1029/2018GL078661.
Alpert, P., H. Messer, and N. David, 2016: Meteorology: Mobile networks aid weather monitoring. Nature, 537(7622), 617, https://doi.org/10.1038/537617e.
Berne, A., and R. Uijlenhoet, 2007: Path-averaged rainfall estimation using microwave links: Uncertainty due to spatial rainfall variability. Geophys. Res. Lett., 34(7), L07403, https://doi.org/10.1029/2007GL029409.
Chwala, C., and Coauthors, 2012: Precipitation observation using microwave backhaul links in the alpine and pre-alpine region of Southern Germany. Hydrology and Earth System Sciences, 16(8), 2647–2661, https://doi.org/10.5194/hess-16-2647-2012.
Chwala, C., and H. Kunstmann, 2019: Commercial microwave link networks for rainfall observation: Assessment of the current status and future challenges. Wiley Interdisciplinary Reviews: Water, 6(2), e1337, https://doi.org/10.1002/wat2.1337.
Chwala, C., H. Kunstmann, S. Hipp, and U. Siart, 2014: A mono-static microwave transmission experiment for line integrated precipitation and humidity remote sensing. Atmospheric Research, 144, 57–72, https://doi.org/10.1016/j.atmosres.2013.05.014.
Cox, J., and B. Plale, 2011: Improving automatic weather observations with the public Twitter stream. IU School of Informatics and Computing. https://doi.org/pdfs.semanticscholar.org/bbe1/0421b7238e4e6d4799a77bb79275994372e1.pdf
David, N., 2018: Utilizing microwave communication data for detecting fog where satellite retrievals are challenged. Natural Hazards, 94(2), 867–882, https://doi.org/10.1007/s11069-018-3428-3.
David, N., and H. O. Gao, 2016: Using cellular communication networks to detect air pollution. Environ. Sci. Technol., 50(17), 9442–9451, https://doi.org/10.1021/acs.est.6b00681.
David, N., and H. O. Gao, 2017: Atmospheric monitoring using commercial microwave networks. Proc. 15th International Conf. on Environmental Science and Technology, Rhodes, Greece, Global NEST, 1–4.
David, N., P. Alpert, and H. Messer, 2009: Novel method for water vapour monitoring using wireless communication networks measurements. Atmospheric Chemistry and Physics, 9(7), 2413–2418, https://doi.org/10.5194/acp-9-2413-2009.
David, N., P. Alpert, and H. Messer, 2011: Humidity measurements using commercial microwave links. Advanced Trends in Wireless Communications. M. Khatib, Ed., InTech, 520 pp.
David, N., P. Alpert, and H. Messer, 2013: The potential of cellular network infrastructures for sudden rainfall monitoring in dry climate regions. Atmospheric Research, 131, 13–21, https://doi.org/10.1016/j.atmosres.2013.01.004.
David, N., O. Sendik, H. Messer, and P. Alpert, 2015: Cellular network infrastructure: The future of fog monitoring? Bull. Amer. Meteor. Soc., 96(10), 1687–1698, https://doi.org/10.1175/BAMS-D-13-00292.1.
David, N., O. Harel, P. Alpert, and H. Messer, 2016: Study of attenuation due to wet antenna in microwave radio communication. Proc. 2016 IEEE International Conf. on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, IEEE, 4418–4422, https://doi.org/10.1109/ICASSP.2016.7472512.
David, N., O. Sendik, Y. Rubin, H. Messer, H. O. Gao, D. Rostkier-Edelstein, and P. Alpert, 2019: Analyzing the ability to reconstruct the moisture field using commercial microwave network data. Atmospheric Research, 219, 213–222, https://doi.org/10.1016/j.atmosres.2018.12.025.
Fabry, F., 2006: The spatial variability of moisture in the boundary layer and its effect on convection initiation: Project-long characterization. Mon. Wea. Rev., 134(1), 79–91, https://doi.org/10.1175/MWR3055.1.
Fencl, M., J. Rieckermann, P. Sýkora, D. Stránsky, and V. Bareš, 2015: Commercial microwave links instead of rain gauges: Fiction or reality? Water Science & Technology, 71(1), 31–37, https://doi.org/10.2166/wst.2014.466.
Goldshtein, O., H. Messer, and A. Zinevich, 2009: Rain rate estimation using measurements from commercial telecommunications links. IEEE Transactions on Signal Processing, 57(4), 1616–1625, https://doi.org/10.1109/TSP.2009.2012554.
Gosset, M., and Coauthors, 2016: Improving rainfall measurement in gauge poor regions thanks to mobile telecommunication networks. Bull. Amer. Meteor. Soc., 97(3), ES49–ES51, https://doi.org/10.1175/BAMS-D-15-00164.1.
Gultepe, I., and Coauthors, 2007: Fog research: A review of past achievements and future perspectives. Pure Appl. Geophys., 164, 1121–1159, https://doi.org/10.1007/s00024-007-0211-x.
Harel, O., N. David, P. Alpert, and H. Messer, 2015: The potential of microwave communication networks to detect dew- Experimental study. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(9), 4396–4404, https://doi.org/10.1109/JSTARS.2015.2465909.
Kawamura, S., and Coauthors, 2017: Water vapor estimation using digital terrestrial broadcasting waves. Radio Sci., 52(3), 367–377, https://doi.org/10.1002/2016RS006191.
Lensky, I. M., and D. Rosenfeld, 2008: Clouds-aerosols-precipitation satellite analysis tool (CAPSAT). Atmospheric Chemistry and Physics, 8(22), 6739–6753, https://doi.org/10.5194/acp-8-6739-2008.
Madaus, L. E., and C. F. Mass, 2017: Evaluating smartphone pressure observations for mesoscale analyses and forecasts. Wea. Forecasting, 32(2), 511–531, https://doi.org/10.1175/WAF-D-16-0135.1.
Mass, C. F., and L. E. Madaus, 2014: Surface pressure observations from smartphones: A potential revolution for high-resolution weather prediction? Bull. Amer. Meteor. Soc., 95(9), 1343–1349, https://doi.org/10.1175/BAMS-D-13-00188.1.
McNicholas, C., and C. F. Mass, 2018a. Smartphone pressure collection and bias correction using machine learning. J. Atmos. Oceanic Technol., 35(3), 523–540, https://doi.org/10.1175/JTECH-D-17-0096.1.
McNicholas, C., and C. F. Mass, 2018b: Impacts of assimilating smartphone pressure observations on forecast skill during two case studies in the pacific northwest. Wea. Forecasting, 33(5), 1375–1396, https://doi.org/10.1175/WAF-D-18-0085.1.
Messer, H., A. Zinevich, and P. Alpert, 2006: Environmental monitoring by wireless communication networks. Science, 312(5774), 713, https://doi.org/10.1126/science.1120034.
Michael, Y., I. M. Lensky, S. Brenner, A. Tchetchik, N. Tessler, and D. Helman, 2018: Economic assessment of fire damage to urban forest in the wildland-urban interface using planet satellites constellation images. Remote Sens., 10(9), 1479, https://doi.org/10.3390/rs10091479.
Overeem, A., J. C. R. Robinson, H. Leijnse, G. J. Steeneveld, B. K. P. Horn, and R. Uijlenhoet, 2013a: Crowdsourcing urban air temperatures from smartphone battery temperatures. Geo-phys. Res. Lett., 40(15), 4081–4085, https://doi.org/10.1002/grl.50786.
Overeem, A., H. Leijnse, and R. Uijlenhoet, 2013b: Country-wide rainfall maps from cellular communication networks. Proc. Natl. Acad. Sci., 110(8), 2741–2745, https://doi.org/10.1073/pnas.1217961110.
Pan, Z. X., H. Yu, C. Y. Miao, and C. Leung, 2017: Crowdsensing air quality with camera-enabled mobile devices. Proceedings of the. Twenty-Ninth AAAI Conference. on Innovative Applications, San Francisco, CA, AAAI, 4728–4733.
Price, C., R. Maor, and H. Shachaf, 2018: Using smartphones for monitoring atmospheric tides. Journal of Atmospheric and Solar-Terrestrial Physics, 174, 1–4, https://doi.org/10.1016/j.jastp.2018.04.015.
Rabiei, E., U. Haberlandt, M. Sester, and D. Fitzner, 2013: Rainfall estimation using moving cars as rain gauges-laboratory experiments. Hydrology and Earth System Sciences, 17(11), 4701–4712, https://doi.org/10.5194/hess-17-4701-2013.
Weckwerth, T. M., and Coauthors, 2004: An overview of the international H2O project (IHOP 2002) and some preliminary highlights. Bull. Amer. Meteor. Soc., 85, 253–278, https://doi.org/10.1175/BAMS-85-2-253.
Wong, C. J., M. Z. MatJafri, K. Abdullah, H. S. Lim, and K. L. Low, 2007: Temporal air quality monitoring using surveillance camera. Proc. 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, IEEE, 2864–2868, https://doi.org/10.1109/IGARSS.2007.4423441.
Acknowledgements
I wish to express my deepest gratitude to Professor Yoshihide SEKIMOTO and his research team for fruitful discussions and for hosting me in their laboratory as a Visiting Scientist at the Institute of Industrial Science of the University of Tokyo, Japan, during 2018-19.
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Article Highlights
• Over the past decade, multi-system informatics and IoT (Internet of Things) technologies have become increasingly distributed.
• The flow of data generated by these systems is characterized by enormous granularity, availability and coverage.
• New opportunities are being opened to utilize the newly available data for atmospheric research.
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David, N. Harnessing Crowdsourced Data and Prevalent Technologies for Atmospheric Research. Adv. Atmos. Sci. 36, 766–769 (2019). https://doi.org/10.1007/s00376-019-9022-0
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DOI: https://doi.org/10.1007/s00376-019-9022-0