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

Abstract

Abstract. Spatially representative estimates of surface energy exchange from field measurements are required for improving and validating Earth system models and 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 well-calibrated posterior uncertainty 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 for reducing the posterior spread. Sampling strategies prioritizing space–time exploration without temporal averaging, instead of hovering at fixed locations while averaging, 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.

Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation

“…The ensemble Kalman-based DA approach pursued herein could also be adopted as a proposal distribution in particle-based DA (e.g. Pirk et al, 2022), such that our approach may also be relevant to particle-based snow DA frameworks (e.g. Cluzet et al, 2021).…”

“…The iterative ensemble Kalman methods used herein could nonetheless readily be extended to filtering (Sakov and Oke, 2008;Emerick and Reynolds, 2012). Although the iterations incur an additional computational cost, they allow for likelihood tempering (Stordal and Elsheikh, 2015) that leads to improved performance compared to non-iterative methods when the model mapping from parameters to observations is non-linear Emerick and Reynolds (2013); Alonso-González et al (2022a); Pirk et al (2022); Evensen et al (2022). The snow DA problem addressed herein falls under this non-linear category.…”

Inverse problem for retrieving greenhouse gas fluxes at the non-uniform underlying surface from measurements of their concentrations at several levels

“…, then the inverse problem of ( ) y x q n , retrieval can be formulated as follows: we have to find a function ( ) (9), where ( ) y x q n , is used as input data.…”

“…Considering the potential limitations of different in situ methods for regional flux estimation, remote sensing in this case can be a very useful tool for flux estimation over non-uniform land surfaces. Over the past decades, numerous inverse modelling techniques have been applied to derive GHG fluxes, especially CO 2 , from remote sensing data [9][10][11][12][13][14]. However, the novel techniques and models for GHG assessment using remote sensing information are still very much needed.…”

Disaggregating the carbon exchange of degrading permafrost peatlands using Bayesian deep learning

“…In the interim, the BNN proposed herein serves as an uncertainty-and sparsity-aware data-driven approach that can help guide future method developments. For example, this flux disaggregation approach can be used to validate emerging drone data assimilation-based flux estimation methods (Pirk et al, 2022), guide land surface model developments (Aas et al, 2019), and incorporate uncertainty in flux gap filling approaches (Pirk et al, 2023).…”