Advanced tsunami detection and forecasting by radar on unconventional airborne observing platforms

Abstract

Sustaining an accurate, timely, and global tsunami forecast system remains a challenge for scientific communities. To this end, various viable geophysical monitoring devices have been deployed. However, it is difficult to implement new observation networks in other regions and maintaining the existing systems is costly. This study proposes a new and complementary approach to monitoring the tsunami using existing moving platforms. The proposed system consists of a radar altimeter, Global Navigation Satellite Systems receiver, and an adequate communication link on airborne platforms such as commercial airplanes, drones, or dedicated high-speed aircraft, and a data assimilation module with a deterministic model. We demonstrated, through twin-data experiment, the feasibility of the proposed system in forecasting tsunami at the Nankai Trough of Japan. Our results demonstrated the potential of an airborne tsunami observation as a viable future technology through proxy observations and rigorous numerical experiments. The wide coverage of the tsunamigenic regions without a new observation network is an advantage while various regulatory constraints need to be overcome. This study offered a novel perspective on the developments in tsunami detection and forecasting technology. Such multi-purpose observation using existing platforms provides a promising and practical solution in establishing sustainable observational networks.

Applying a Deep Learning Algorithm to Tsunami Inundation Database of Megathrust Earthquakes

“…However, the efficacy of the method relies on the spatial distribution of the observation points, which should not be a problem in our study area owing to the newly deployed seafloor observation network for earthquakes and tsunami along the Japan Trench (S‐net) (Kanazawa, 2013). Alternatively, for other regions, prospective tsunami observing systems utilizing commercial vessels (Mulia et al, 2017) and airplanes (Mulia et al, 2020) as observational platforms are also suitable for the data assimilation method. The combined modeling strategy could serve as a powerful tool for forecasting future tsunamis.…”

Review on Recent Progress in Near-Field Tsunami Forecasting Using Offshore Tsunami Measurements: Source Inversion and Data Assimilation

“…Similar to the tsunami source inversion (Sect. 2), the acquisition of various types of data has also contributed to improving the forecasting accuracy of tsunami data assimilation, including that from OBPGs, GPS buoys, and ship height positioning data of GNSS (Hossen et al, 2021;Mulia et al, 2017Mulia et al, , 2020a. Mulia et al (2017) conducted a synthetic experiment of tsunamis in the Nankai Trough, assimilating offshore tsunami data recorded in various observational systems.…”

Data Assimilation for Tsunami Forecast With Ship‐Borne GNSS Data in the Cascadia Subduction Zone

“…(2015) first showed the possibility of the DA method for tsunami forecasting and in numerical experiments successfully applied it to a retrospective forecast of the 2011 Tohoku Earthquake tsunami. Subsequently, numerical experiments with the DA method have been applied to tsunami events with NOAA Deep‐Ocean Assessment and Reporting of Tsunamis (DART) sea‐floor pressure data, Cascadia Initiative sea‐floor pressure gauge data, ship‐borne Global Navigation Satellite System (GNSS) data, and airborne radar observations (e.g., Gusman et al., 2016; Maeda et al., 2015; Mulia et al., 2017, 2020; Sheehan et al., 2019; Wang et al., 2017). The DA method does not require precomputed Green's functions (GFs), though some studies have shown that precomputed GFs can reduce computation time for fixed observation sites (Wang et al., 2017).…”