Articles | Volume 15, issue 24
https://doi.org/10.5194/amt-15-7293-2022
https://doi.org/10.5194/amt-15-7293-2022
Research article
 | 
20 Dec 2022
Research article |  | 20 Dec 2022

Inferring surface energy fluxes using drone data assimilation in large eddy simulations

Norbert Pirk, Kristoffer Aalstad, Sebastian Westermann, Astrid Vatne, Alouette van Hove, Lena Merete Tallaksen, Massimo Cassiani, and Gabriel Katul

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Cited articles

Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, L.: Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites, The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018, 2018. a, b, c, d, e
Aalstad, K., Westermann, S., and Bertino, L.: Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography, Remote Sens. Environ., 239, 111618, https://doi.org/10.1016/j.rse.2019.111618, 2020. a
Alonso-González, E., Gutmann, E., Aalstad, K., Fayad, A., Bouchet, M., and Gascoin, S.: Snowpack dynamics in the Lebanese mountains from quasi-dynamically downscaled ERA5 reanalysis updated by assimilating remotely sensed fractional snow-covered area, Hydrol. Earth Syst. Sci., 25, 4455–4471, https://doi.org/10.5194/hess-25-4455-2021, 2021. a, b
Alonso-González, E., Aalstad, K., Baba, M. W., Revuelto, J., López-Moreno, J. I., Fiddes, J., Essery, R., and Gascoin, S.: MuSA: The Multiscale Snow Data Assimilation System (v1.0), Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2022-137, in review, 2022. a, b
Ardeshiri, H., Cassiani, M., Park, S. Y., Stohl, A., Pisso, I., and Dinger, A. S.: On the Convergence and Capability of the Large-Eddy Simulation of Concentration Fluctuations in Passive Plumes for a Neutral Boundary Layer at Infinite Reynolds Number, Bound.-Lay. Meteorol., 176, 291–327, https://doi.org/10.1007/s10546-020-00537-6, 2020. a
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Short summary
In this study, we show how sparse and noisy drone measurements can be combined with an ensemble of turbulence-resolving wind simulations to estimate uncertainty-aware surface energy exchange. We demonstrate the feasibility of this drone data assimilation framework in a series of synthetic and real-world experiments. This new framework can, in future, be applied to estimate energy and gas exchange in heterogeneous landscapes more representatively than conventional methods.
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