Remote sensing and in-flow particle tracking in avalanches: A sensor fusion of AvaNodes, mGeodar Radar with open-source avalanche simulations

Davos

2022

1 S.

Sonderdruck

40003728

A non-invasive in-flow measurement device that tracks particle motion within snow avalanches is currently being developed. These so-called AvaNodes reveal the internal dynamic processes that determine the flow mobility of avalanches and run-out behavior as well as the influence of snow and particle properties, such as temperature or density. Our current understanding of flow dynamics on a particle level is mostly based on laboratory experiments and computer simulations. Thus, the tracking of particle motions and the respective flow trajectories within field scale avalanches is believed to increase the understanding on processes like segregation, phase separation and eventually flow regimes evolution and transitions. Here, we combine the different measurement approaches of radar remote sensing and in-flow sensor nodes with numeric simulations of the avalanche by comparing tracked particle trajectories with their simulated counterparts. For that, we identify the optimal model parameters by fitting to the mGeodar radar data that offers the possibility to evaluate numerical simulation tools with respect to the temporal evolution of the flow variables. These radar data are typically displayed in a range-time diagram along the antenna line of sight and the distance to the approaching avalanche front is extracted for each time step. Transforming the computed avalanche front into the radar coordinate system yields a synthetic range-time radargram that facilitates a direct comparison. From a probabilistic ensemble of simulations covering multiple dimensions of the parameter space, the best-fitted numerical simulation is identified for a detailed comparison with the real-world data from the AvaNodes.