Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Assessment of Arctic snow stratigraphy and water equivalent using a portable Frequency Modulated Continuous Wave RADAR
Abstract There is significant spatial variability in snow cover characteristics across the Arctic. Current physical or empirical approaches to simulate or measure snow state variables suffer from poor spatial and/or temporal resolutions. Our current understanding of the spatio-temporal variability in Arctic snow cover leads to uncertainties in existing snow property retrievals from space or in models, thus leading to a poor representation of the snow cover in various climate models and reanalysis products. In this paper, we developed a method to derive total snow depth from a ground-based radar as well as distinguishing the two main layers generally observed in Arctic snowpacks: Depth Hoar and Wind Slab. This algorithm was developed for a 24 GHz Frequency Modulated Continuous Wave radar. The novelty of our approach resides in the fact that no validation snow pits are required if previous data on snow conditions (snow depth and density) are known. The impact of the underlying ecotypes on the radar-derived snow stratigraphy was also investigated. The RMSE of the snow depth ranges from 3.5 to 25 cm, but mostly varies between 10 and 15 cm (< 25%). The RMSE is based on snow pit data that has a corresponding radar measurement.
Highlights Depth Hoar and Wind Slab thicknesses in the Arctic are retrieved with an error of 15% using FMCW radar. Retrieval algorithm works using an averaged empirical value for density and refractive index. Snow water equivalent can be retrieved with an estimated error of <30%, with a dependence on ecotype. Radar is portable, lightweight and can be mounted on remotely piloted aircraft systems.
Assessment of Arctic snow stratigraphy and water equivalent using a portable Frequency Modulated Continuous Wave RADAR
Abstract There is significant spatial variability in snow cover characteristics across the Arctic. Current physical or empirical approaches to simulate or measure snow state variables suffer from poor spatial and/or temporal resolutions. Our current understanding of the spatio-temporal variability in Arctic snow cover leads to uncertainties in existing snow property retrievals from space or in models, thus leading to a poor representation of the snow cover in various climate models and reanalysis products. In this paper, we developed a method to derive total snow depth from a ground-based radar as well as distinguishing the two main layers generally observed in Arctic snowpacks: Depth Hoar and Wind Slab. This algorithm was developed for a 24 GHz Frequency Modulated Continuous Wave radar. The novelty of our approach resides in the fact that no validation snow pits are required if previous data on snow conditions (snow depth and density) are known. The impact of the underlying ecotypes on the radar-derived snow stratigraphy was also investigated. The RMSE of the snow depth ranges from 3.5 to 25 cm, but mostly varies between 10 and 15 cm (< 25%). The RMSE is based on snow pit data that has a corresponding radar measurement.
Highlights Depth Hoar and Wind Slab thicknesses in the Arctic are retrieved with an error of 15% using FMCW radar. Retrieval algorithm works using an averaged empirical value for density and refractive index. Snow water equivalent can be retrieved with an estimated error of <30%, with a dependence on ecotype. Radar is portable, lightweight and can be mounted on remotely piloted aircraft systems.
Assessment of Arctic snow stratigraphy and water equivalent using a portable Frequency Modulated Continuous Wave RADAR
Kramer, Daniel (Autor:in) / Langlois, Alexandre (Autor:in) / Royer, Alain (Autor:in) / Madore, Jean-Benoît (Autor:in) / King, Joshua (Autor:in) / McLennan, Donald (Autor:in) / Boisvert-Vigneault, Érika (Autor:in)
15.09.2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Arctic snow , FMCW , Radar , Ecotype , Snow pit , Snow depth retrieval
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