arXiv subjects

Laura Mack

Publications and source records attributed to Laura Mack.

3 recordsLinked to original sources

arXiv2026

Reddy: An open-source toolbox for analyzing eddy-covariance measurements in heterogeneous environments

Land-atmosphere exchange is mediated by turbulent fluxes that can be quantified using eddy-covariance (EC) measurements. EC has been widely used to measure ecosystem-scale vertical exchange between atmosphere and vegetation, and to test and refine atmospheric turbulence theories with the aim to improve the representation of turbulent fluxes in numerical models. Traditionally, research has focused on idealized, homogeneous and flat surfaces, but recent work increasingly targets turbulent exchange in complex, heterogeneous environments under non-ideal conditions, where challenges include advective fluxes, mesoscale circulations between contrasting surface types, and non-stationary nighttime turbulence. Here, we introduce the open-source R package Reddy, which combines multiple EC analysis methods into a single modular tool. Reddy enables users to tailor post-processing choices to site-specific conditions, supports station management and facilitates detailed scientific analyses. The package is accompanied by extensive documentation and a suite of Jupyter notebooks that provide hands-on introductions to EC data processing. We demonstrate Reddy using measurements from three Norwegian sites: (1) a morning transition following a strongly stably stratified night at an alpine tundra valley, (2) spectral and ogive analysis before and after an ice-cover transition at a boreal lake, and (3) fitting flux-variance relations at a permafrost-affected palsa peatland. Reddy extends existing EC software and helps moving towards a more holistic turbulence data analysis framework for heterogeneous, real-world environments.

physics.ao-ph↗

arXiv2025

Probabilistic modelling of atmosphere-surface coupling with a copula Bayesian network

Land-atmosphere coupling is an important process for correctly modelling near-surface temperature profiles, but it involves various uncertainties due to subgrid-scale processes, such as turbulent fluxes or unresolved surface heterogeneities, suggesting a probabilistic modelling approach. We develop a copula Bayesian network (CBN) to interpolate temperature profiles, acting as alternative to T2m-diagnostics used in numerical weather prediction (NWP) systems. The new CBN results in (1) a reduction of the warm bias inherent to NWP predictions of wintertime stable boundary layers allowing cold temperature extremes to be better represented, and (2) consideration of uncertainty associated with subgrid-scale spatial variability. The use of CBNs combines the advantages of uncertainty propagation inherent to Bayesian networks with the ability to model complex dependence structures between random variables through copulas. By combining insights from copula modelling and information entropy, criteria for the applicability of CBNs in the further development of parameterizations in NWP models are derived.

physics.ao-ph↗

arXiv2022

Identifying atmospheric fronts based on diabatic processes using the dynamic state index (DSI)

Atmospheric fronts are associated with precipitation and strong diabatic processes. Therefore, detecting fronts objectively from reanalyses is a prerequisite for the long-term study of their weather impacts. For this purpose, several algorithms exist, e.g., based on the thermic front parameter (TFP) or the F diagnostic that combines relative vorticity and horizontal temperature gradient. It is shown that both methods have problems to identify weak warm fronts since they are characterized by low baroclinicity. To avoid this inaccuracy, a new algorithm is developed that considers fronts as deviation from an adiabatic and steady state. These deviations can be accurately measured using the dynamic state index (DSI). The DSI shows a coherent dipole structure along fronts and is strongly correlated with precipitation sums. Using the DSI, a new front detection algorithm is developed (called DSI method), which allows to clearly identify the global storm track regions. The properties of the identified fronts depend on the applied front detection method, whereby fronts identified with the DSI method have particularly high specific humidity. Using a simple estimate for front speed, it is shown that also the front speed depends on the front detection method and that fronts identified using the DSI method have a higher front speed than fronts identified with the TFP method. This can be attributed to the dipole structure of the DSI and thus demonstrates the potential of the DSI to inherently indicate the movement speed and direction in atmospheric flows.

physics.ao-ph↗