17 Aug 2022
17 Aug 2022
Status: this preprint is currently under review for the journal AMT.

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

Norbert Pirk1, Kristoffer Aalstad1, Sebastian Westermann1, Astrid Vatne1, Alouette van Hove1, Lena Merete Tallaksen1, Massimo Cassiani2, and Gabriel Katul3 Norbert Pirk et al.
  • 1Department of Geosciences, University of Oslo, Sem Sælands vei 1, 0371 Oslo, Norway
  • 2NILU - Norwegian Institute for Air Research, Instituttveien 18, 2007 Kjeller, Norway
  • 3Department of Civil and Environmental Engineering, Duke University, 121 Hudson Hall, Durham, NC, 27708, USA

Abstract. Spatially representative estimates of surface energy exchange from field measurements are required for improving and validating Earth system models as well as satellite remote sensing algorithms. The scarcity of flux measurements can limit understanding of ecohydrological responses to climate warming, especially in remote regions with limited infrastructure. Direct field measurements often apply the eddy covariance method on stationary towers, but recently drone-based measurements of temperature, humidity, and wind speed have been suggested as a viable alternative to quantify the turbulent fluxes of sensible (H) and latent heat (LE). A data assimilation framework to infer uncertainty-aware surface flux estimates from sparse and noisy drone-based observations is developed and tested using a turbulence-resolving large eddy simulation (LES) as a forward model to connect surface fluxes to drone observations. The proposed framework explicitly represents the sequential collection of drone data, accounts for sensor noise, includes uncertainty in boundary and initial conditions, and jointly estimates the posterior distribution of a multivariate parameter space. Assuming typical flight times and observational errors of light-weight, multi-rotor drone systems, we first evaluate the information gain and performance of different ensemble-based data assimilation schemes in experiments with synthetically generated observations. It is shown that an iterative ensemble smoother outperforms both the non-iterative ensemble smoother and the particle batch smoother in the given problem, yielding low bias and variance posterior distributions with continuous ranked probability scores of 12 W m-2 for both H and LE with standard deviations of 37 W m-2 (H) and 46 W m-2 (LE) for a 12 min vertical step profile by a single drone. Increasing flight times, using observations from multiple drones, and further narrowing the prior distributions of the initial conditions, are viable to reducing the posterior spread. Sampling strategies prioritizing space-time exploration instead of temporal averaging at fixed locations enhance the non-linearities in the forward model and can lead to biased flux results with ensemble-based assimilation schemes. In a set of 18 real-world field experiments at two wetland sites in Norway, drone data assimilation estimates agree with independent eddy covariance estimates, with root-mean-square error values of 37 W m-2 (H), 52 W m-2 (LE), and 58 W m-2 (H + LE), and correlation coefficients of 0.90 (H), 0.40 (LE), and 0.83 (H + LE). While this comparison uses the simplifying assumptions of flux homogeneity, stationarity, and flat terrain, it is emphasized that the drone data assimilation framework is not confined to these assumptions and can thus readily be extended to more complex cases and other scalar fluxes, such as for trace gases in future studies.

Norbert Pirk et al.

Status: open (until 05 Oct 2022)

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  • RC1: 'Comment on amt-2022-219', Anonymous Referee #1, 13 Sep 2022 reply

Norbert Pirk et al.

Norbert Pirk et al.


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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 could in future be applied to estimate energy and gas exchange in heteorogeneous landscapes more representatively than conventional methods.