Noam DAVID^,

Corresponding author: Noam DAVID,nd363@cornell.edu

1.Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
Manuscript received: 2019-02-09
Manuscript revised: 2019-02-25
Manuscript accepted: 2019-03-07
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.
Keywords: atmospheric science,
IoT (Internet of Things),
crowdsourced data,
commercial microwave links
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。
关键词:大气科学,
物联网,
众包数据,
商用微波链路

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Over the past decade, the Internet of Things (IoT) and smart devices have become increasingly common as part of the technological infrastructure that surrounds us. The flow of data generated by these systems is characterized by enormous granularity, availability and coverage. As a result, new opportunities are being opened to utilize the newly available information for various needs and, in particular, for atmospheric research. If we consider the data generated by these means, we may notice that many produce measurements with high environmental value. To name some examples——surveillance cameras that operate in the visible light spectrum are positioned in a vast number of locations. Previous works have shown that they can be used for monitoring the temporal patterns of fine atmospheric particulate matter (Wong et al., 2007). Lab experiments have indicated a direct link between the speed of movement of car wipers and rainfall intensity, meaning advanced vehicles that store these data can, in essence, be used as moving rain gauges (Rabiei et al., 2013). (Kawamura et al., 2017) revealed a novel technique for monitoring atmospheric humidity using terrestrial broadcasting waves, based on propagation delays due to water vapor. Data shared as open source from social networks have been found to be potentially effective in improving automatic weather observations. Indeed, for the most part, the initial weather observation is conducted automatically by dedicated sensors; however, some weather conditions are still better detected by the human eye. On the other hand, millions of "human observers" around the world use applications such as Twitter, which allows them to report publicly on subjects that are relevant to them, and in particular on weather phenomena (Cox and Plale, 2011). As was recently reviewed by (Price et al., 2018), in 2020 there will be more than 20 billion smartphones carried by the public worldwide. These mobile devices are equipped with sensors that can be used for environmental monitoring on a multisource basis. Recent works indicate the ability to obtain atmospheric temperature information for the urban canopy layer (Overeem et al., 2013a), to measure atmospheric pressure (Mass and Madaus, 2014; McNicholas and Mass, 2018a), or to study atmospheric tides (Price et al., 2018). Additional studies point to the potential of using any camera-enabled smart mobile device to monitor air quality (Pan et al., 2017). Given the comprehensive coverage of these new "virtual sensors" from all land locations across the whole globe, this low-cost solution introduces a wide range of possibilities that previously could not be offered through existing technologies.

A key example of utilizing existing data sources for atmospheric monitoring is the use of measurements acquired by commercial microwave links (CMLs) that comprise the infrastructure for wireless data transport between cellular base stations. This technology has essentially become a valuable weather monitoring tool over the past decade (e.g., Overeem et al., 2013b; Alpert et al., 2016; Gosset et al., 2016; Davíd and Gao, 2017; Chwala and Kunstmann, 2019). Weather phenomena and atmospheric conditions interfere with the electromagnetic waves transmitted by CMLs. Thus, these networks provide, in essence, an already-existing environmental monitoring facility. The various works done in the field indicate the ability to detect and map rainfall (e.g., Messer et al., 2006; Berne and Uijlenhoet, 2007; Chwala et al., 2012; Overeem et al., 2013b; Fencl et al., 2015), monitor fog (e.g. David et al., 2015), atmospheric moisture (David et al., 2009, David et al., 2011; Chwala et al., 2014; Alpert and Rubin, 2018) and dew (Harel et al., 2015; David et al., 2016). Recent research revealed the ability of these radio links to indirectly detect air pollution conditions (David and Gao, 2016).

Indeed, the new data available from these various means (smartphones, social networks, etc.), and particularly from CMLs, can provide observations with considerable spatial coverage and with minimal cost. However, the accuracy of each "sensor" is lower than that of a dedicated instrument. This being the case, is it possible to produce significant information compared to that derived from specialized tools? It can be assumed that these "virtual sensors" are not a substitute for conventional monitoring means, whenever those exist in the field. The correct approach, then, is to consider these newly available sources of data as complementary measures to dedicated measurements and as a substitute during the many cases in which conventional monitoring tools are unavailable. However, the data acquired by prevalent technologies, even when taken alone, often holds enormous potential. In order to demonstrate the added value which lies in IoT data and prevalent technologies, let us focus on CMLs as an example of such a system. Atmospheric moisture is more poorly characterized than wind or even precipitation, due to the difficulty in observing the humidity field. Therefore, questions such as the magnitude of small-scale variability of moisture in the boundary layer, and its effect on convection initiation, are still unanswered (Weckwerth et al., 2004). As a result, the ability to predict convective precipitation, on the storm scale, is limited. However, for significantly improving convection initiation measurements, one will need moisture measurements at meso-γ resolution with accuracies of up to 1 g m^-3 (Fabry, 2006). Notably, such a type of observations can be acquired using CMLs (David et al., 2019). High-resolution precipitation distribution maps can be generated using CMLs, and therefore the relationship between pollutant wash-off and rainfall provides an opportunity to potentially acquire important spatial information about air quality, as discussed in recent research (David and Gao, 2016). Moreover, liquid water content (LWC) constitutes a major parameter in fog research. Fog LWC changes in space, altitude, and over time, and is dependent on surface and atmospheric conditions (Gultepe et al., 2007). However, conventional sensors for acquiring LWC estimates are limited in the spatial range they can cover, and in their availability. It has been shown that CMLs are able to provide fog LWC estimates across large spatial regions where dedicated sensors are nonexistent. Indeed, the availability of various spectral channels from satellites provides the possibility to observe clouds, aerosols, the Earth surface, and in particular, fog (Lensky and Rosenfeld, 2008; Michael et al., 2018). However, CMLs have also been found to have potential advantages for detecting fog under challenging conditions where satellite retrievals are limited, e.g., when high-altitude clouds cover the fog as observed from the satellite vantage point (David, 2018). Alternatively, the ability to monitor rainfall in areas where radars suffer from clutter effects (Goldshtein et al., 2009) or are blocked by complex topography (David et al., 2013), has also been demonstrated.

The possibilities for monitoring environmental phenomena via new observational powers are many, the available information vast, and the cost minimal, since such "opportunistic sensors" are already deployed in the field. As a result, this means of monitoring the environment is becoming advantageous for atmospheric research. Notably, these newly available "virtual sensors" open the doors to the possibility of assimilating their measurements into high-resolution numerical prediction models, which could lead to improvements in the forecasting capabilities that exist today (Kawamura et al., 2017; Madaus and Mass, 2017; McNicholas and Mass, 2018b; David et al., 2019). In a practical sense, this novel approach could lay the groundwork for developing new early-warning systems against natural hazards and generating a variety of products required for a wide range of fields. Thus, the overall potential contribution to public health and safety may be invaluable.