arXiv subjects

Kristoffer Aalstad

Publications and source records attributed to Kristoffer Aalstad.

5 recordsLinked to original sources

arXiv2026

Quantity, quality, and timing: Guiding glacier data assimilation strategies in the high Arctic

Accurate simulation of glacier surface mass balance is essential for predicting sea level rise and freshwater resources, but it is constrained by uncertainties in meteorological forcing and model parameters. Here, we deploy glacier data assimilation strategies to assess the value of observations for improving surface mass balance simulation, focusing on observation quantity, quality, and timing. We perform synthetic twin experiments on Kongsvegen glacier, Svalbard, using a Particle Batch Smoother with 1000 ensemble members. Synthetic observations of albedo, snow depth, and surface temperature are assimilated at two quality levels, under two climatic scenarios, and over 12 years. Assimilation benefit is measured as the percentage improvement in the continuous ranked probability score of the posterior glacier surface mass balance relative to the prior. A single optimally timed high quality observation yields mean improvements of up to 80\%. Larger numbers of low quality observations partially compensate for lower improvement. In the accumulation zone, however, additional snow depth observations degrade performance through particle degeneracy. Optimal timing is governed by the seasonal transitions of the truth trajectory rather than by prior ensemble spread alone. The optimal windows shift by up to six weeks between early and late melting years. Joint assimilation adds value through temporal diversity rather than observational diversity, while independently timed observations outperform same day combinations. The asynchronously optimally timed combined assimilation of three variables sustains improvements of 85 to 97\% across all years in the ablation zone. These findings provide guidelines for adaptive observation scheduling in glacier monitoring and reanalysis.

physics.data-an↗

arXiv2026

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

We present a new adaptive particle-based data assimilation scheme for cryospheric applications that leverages promising developments in importance sampling. The proposed approach seeks to combine some of the advantages of two widely used classes of schemes: particle methods and iterative ensemble Kalman methods. Specifically, it extends the PBS that is commonly used in cryospheric data assimilation, with the AMIS algorithm. This adaptive formulation transforms the PBS into an iterative scheme with improved resilience against ensemble collapse and the ability to implement early-stopping strategies. As such, computational cost is automatically adapted to the complexity of the problem at hand, even down to the grid-cell and water year level in distributed multiyear simulations. In homage to the schemes that it builds on, we coin this new algorithm the Adaptive Particle Batch Smoother (AdaPBS) and we test it across a range of scenarios. First, we conducted an intercomparison of some of the most commonly used cryospheric data assimilation algorithms using MCMC simulation as a costly gold-standard benchmark in a simplified temperature index model assimilating snow depth observations. We further evaluated AdaPBS by assimilating snow depth observations from the ESMSnowMIP project at 6 different sites spanning 3 continents, using an ensemble of simulations generated with the more complex FSM2. Our results demonstrate that AdaPBS is a robust and reliable tool, outperforming or at least matching the performance of other commonly used algorithms and successfully handling complex cases with dense observational datasets. All experiments were carried out using the open-source MuSA toolbox, which now includes AdaPBS and MCMC among the growing list of available cryospheric data assimilation methods.

physics.geo-ph↗

arXiv2025

Glacier data assimilation on an Arctic glacier: Learning from large ensemble twin experiments

Glacier modeling is crucial for quantifying the evolution of cryospheric processes. At the same time, uncertainties hamper process understanding and predictive accuracy. Here, we suggest improving glacier mass balance simulations for the Kongsvegen glacier in Svalbard through the application of Bayesian data assimilation techniques in a set of large ensemble twin experiments. Noisy synthetic observations of albedo and snow depth, generated using the multilayer CryoGrid community model with a full energy balance, are assimilated using two ensemble-based data assimilation schemes: the particle batch smoother and the ensemble smoother. A comprehensive evaluation exercise demonstrates that the joint assimilation of albedo and snow depth improves the simulation skill by up to 86% relative to the prior in specific glacier regions. The particle batch smoother excels in representing albedo dynamics, while the ensemble smoother is particularly effective for snow depth under low snowfall conditions. By combining the strengths of both observations, the joint assimilation achieves improved mass balance simulations across different glacier zones using either assimilation scheme. This work underscores the potential of ensemble-based data assimilation methods for refining glacier models by offering a robust framework to enhance predictive accuracy and reduce uncertainties in cryospheric simulations. Further advances in glacier data assimilation will be critical to better understanding the fate and role of Arctic glaciers in a changing climate.

physics.geo-ph↗

arXiv2024

Guiding drones by information gain

The accurate estimation of locations and emission rates of gas sources is crucial across various domains, including environmental monitoring and greenhouse gas emission analysis. This study investigates two drone sampling strategies for inferring source term parameters of gas plumes from atmospheric measurements. Both strategies are guided by the goal of maximizing information gain attained from observations at sequential locations. Our research compares the myopic approach of infotaxis to a far-sighted navigation strategy trained through deep reinforcement learning. We demonstrate the superior performance of deep reinforcement learning over infotaxis in environments with non-isotropic gas plumes.

cs.LG↗

arXiv2024

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

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 surface flux estimation with drones. Our approach uses data assimilation (DA) to infer fluxes from drone-based observations, and reinforcement learning (RL) to optimize the drone's sampling strategy. Herein, we demonstrate that a RL-trained drone can quantify a CO2 hotspot more accurately than a drone sampling along a predefined flight path that traverses the emission plume. We find that information-based reward functions can match the performance of an error-based reward function that quantifies the difference between the estimated surface flux and the true value. Reward functions based on information gain and information entropy can motivate actions that increase the drone's confidence in its updated belief, without requiring knowledge of the true surface flux. These findings provide valuable insights for further development of the framework for the mapping of more complex surface flux fields.

cs.LG↗