Water vapor estimation using digital terrestrial broadcasting waves

Abstract

A method of estimating water vapor (propagation delay due to water vapor) using digital terrestrial broadcasting waves is proposed. Our target is to improve the accuracy of numerical weather forecast for severe weather phenomena such as localized heavy rainstorms in urban areas through data assimilation. In this method, we estimate water vapor near a ground surface from the propagation delay of digital terrestrial broadcasting waves. A real‐time delay measurement system with a software‐defined radio technique is developed and tested. The data obtained using digital terrestrial broadcasting waves show good agreement with those obtained by ground‐based meteorological observation. The main features of this observation are, no need for transmitters (receiving only), applicable wherever digital terrestrial broadcasting is available and its high time resolution. This study shows a possibility to estimate water vapor using digital terrestrial broadcasting waves. In the future, we will investigate the impact of these data toward numerical weather forecast through data assimilation. Developing a system that monitors water vapor near the ground surface with time and space resolutions of 30 s and several kilometers would improve the accuracy of the numerical weather forecast of localized severe weather phenomena.

10th European Conference on Radar in Meteorology and Hydrology (ERAD 2018) : 1-6 July 2018, Ede-Wageningen, The Netherlands

“…The authors have been developing a method of water vapor measurements near the surface with propagation delay of digital terrestrial broadcasting wave due to water vapor (Kawamura et al, 2017). The basic idea of using propagation delay is the same as that of retrieving PWV using GNSS.…”

“…Results show that the analysed algorithms are strongly dependent on the radar site and must be necessarily customized to each radar location, because the ground and sea clutter signatures are strongly dependent on the height above and proximity to the sea, surrounding orography etc. Our previous research presented that excellent sidelobe reduction capability in phased array digital beam forming is realized by minimum mean square error (MMSE) approach (Yoshikawa et al, 2013). This work applied the MMSE approach to Doppler spectral processing that is governed by the same equation as phased array digital beam forming.…”

“…Recent progress of information and communication technologies has been enabling us to realize a rapid scanning radar system. In 2012, Toshiba Corporation and Osaka University succeeded in developing a new type of Phased Array Radar (PAR) system (Yoshikawa et al 2013, Ushio et al 2015) under a grant of National Institute of Communication and Information Technology (NICT), and installed in Suita Campus, Osaka University. This PAR system can scan the whole sky within 30 seconds up to 60 km in radius over 100 elevation angles with digital beam forming technique, and the initial observation results demonstrate the unique capability of the new PAR system.…”

Applicability of the ultra-high radio frequency band (UHF) for hydrometeor detection by phase difference method

“…The phase changes are estimated by the phase-locked loop (PLL) or by some another phase estimation technique. Kawamura et al (2017) found that the propagation delay of digital terrestrial broadcasting waves can be related to the content of water vapour in the respective volume of the atmosphere. They measured the time delay of the reflected signal between the receiver and the reflector in the form of the phase delay which is obtained as a phase in the maximum of the side lobe magnitude of the complex time-lag (power delay) profile.…”

Harnessing Crowdsourced Data and Prevalent Technologies for Atmospheric Research

“…Hence, advanced vehicles that store these data can, in essence, be used as moving rain gauges (Rabiei et al, 2013). Kawamura et al (2017) have 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.…”