activity
20242026
most citedUsing reinforcement learning to improve drone-based inference of greenhouse gas fluxes

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

physics.geo-ph2026

Evolving beyond collapse: An adaptive particle batch smoother for cryospheric data assimilation

Kristoffer Aalstad, Esteban Alonso-González, Norbert Pirk +3

We present a new adaptive particle-based data assimilation scheme for cryospheric applications that leverages promising developments in importance sampling. The proposed approach s…

physics.ao-ph2026

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

Laura Mack, Norbert Pirk

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…

physics.ao-ph2025

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

Laura Mack, Marvin Kähnert, Norbert Pirk

Land-atmosphere coupling is an important process for correctly modelling near-surface temperature profiles, but it involves various uncertainties due to subgrid-scale processes, su…

cs.LG2024

Guiding drones by information gain

Alouette van Hove, Kristoffer Aalstad, Norbert Pirk

The accurate estimation of locations and emission rates of gas sources is crucial across various domains, including environmental monitoring and greenhouse gas emission analysis. T…

cs.LG20242 cited

Using reinforcement learning to improve drone-based inference of greenhouse gas fluxes

Alouette van Hove, Kristoffer Aalstad, Norbert Pirk

Accurate mapping of greenhouse gas fluxes at the Earth's surface is essential for the validation and calibration of climate models. In this study, we present a framework for surfac…