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Abstract Tracking technologies have widely expanded our understanding of bird migration routes, destinations and underlying strategies. However, determining the entire trajectory of small birds equipped with lightweight geolocators remains a challenge.We develop a highly optimized hidden Markov model (HMM) for reconstructing bird trajectories. The observation model is defined by pressure and, optionally, light measurements, while the movement model incorporates wind data to constrain consecutive positions based on realistic airspeeds. To reduce the computational costs associated with a large state space, we prune the HMM states and transitions based on flight and observation constraints to efficiently model the entire trajectory.The approach presented is based on a mathematically exact procedure and is fast to compute. We demonstrate how to compute (1) the most likely trajectory, (2) the marginal probability map of each stationary period, (3) simulated trajectories and (4) the wind conditions (wind support/drift) encountered by the bird during each migratory flight.We construct a version of an HMM optimized for reconstructing a bird's migration trajectory based on lightweight geolocator data. To render this approach easily accessible to researchers, we designed a dedicated R packageGeoPressureR(https://raphaelnussbaumer.com/GeoPressureR/). 

Realistically rough stochastic realizations of subglacial bed topography are crucial for improving our understanding of basal processes and quantifying uncertainty in sea level rise projections with respect to topographic uncertainty. This can be achieved with sequential Gaussian simulation (SGS), which is used to generate multiple nonunique realizations of geological phenomena that sample the uncertainty space. However, SGS is very CPU intensive, with a computational complexity of O(NkNk3), where NN is the number of grid cells to simulate, and kk is the number of neighboring points used for conditioning. This complexity makes SGS prohibitively time-consuming to implement at ice sheet scales or fine resolutions. To reduce the time cost, we implement and test a multiprocess version of SGS using Python’s multiprocessing module. By parallelizing the calculation of the weight parameters used in SGS, we achieve a speedup of 9.5 running on 16 processors for an NN of 128,097. This speedup—as well as the speedup from using multiple processors—increases with NN. This speed improvement makes SGS viable for large-scale topography mapping and ensemble ice sheet modeling. Additionally, we have made our code repository and user tutorials publicly available (GitHub and Zenodo) so that others can use our multiprocess implementation of SGS on different datasets.