Publikationen

Assessment of ocean bottom pressure variations in CMIP6 HighResMIP simulations

2025-10-01T00:00:00Z

Assessment of ocean bottom pressure variations in CMIP6 HighResMIP simulations Liu, Le; Schindelegger, Michael; Börger, Lara; Foth, Judith; Gou, Junyang Ocean bottom pressure (p_b) variations from highresolution climate model simulations under the CMIP6 (Coupled Model Intercomparison Project Phase 6) HighResMIP protocol are potentially useful for oceanographic and spacegeodetic research, but the overall signal content and accuracy of these p_b estimates have hitherto not been assessed. Here, we compute monthly p_b fields from five CMIP6 High-ResMIP models at 1=4° grid spacing over both historical and future time spans and compare these data, in terms of temporal variance, against observation-based p_b estimates from a 1=4° downscaled GRACE (Gravity Recovery and Climate Experiment) product and 23 bottom pressure recorders, mostly in the Pacific. The model results are qualitatively and quantitatively similar to the GRACE-based p_b variances, featuring – aside from eddy imprints – elevated amplitudes on continental shelves and in major abyssal plains of the Southern Ocean. Modeled p_b variance in these regions is ~ 10 %–80 % higher and thus overestimated compared to GRACE, whereas underestimation relative to GRACE and the bottom pressure recorders prevails in more quiescent deep-ocean regions. We also form variance ratios of detrended p_b signals over 2030–2049 under a high-emission scenario relative to 1980–1999 for three selected models and find statistically significant increases in future p_b variance by ~ 30 %–50 % across deep Arctic basins and the southern South Atlantic. The strengthening appears to be linked to projected changes in high-latitude surface winds and, in the case of the South Atlantic, intensified eddy kinetic energy. The study thus points to possibly new pathways for relating observed p_b variability from (future) satellite gravimetry missions to anthropogenic climate change.

Simultaneous selection and displacement of buildings and roads for map generalization via mixed-integer quadratic programming

2025-03-03T00:00:00Z

Simultaneous selection and displacement of buildings and roads for map generalization via mixed-integer quadratic programming Rosenberger, Leon; Shen, Yilang; Haunert, Jan-Henrik Research on map generalization has led to many algorithms for multiple elementary processes, such as object selection, aggregation, simplification, and displacement. Algorithms for different processes are usually combined to workflows or orchestrated using multi-agent systems. Here, we present a novel approach integrating object selection and displacement at an algorithmic level. We model both processes together as an optimization problem in the form of a mixed-integer quadratic program and demonstrate that it can be optimally solved using a mathematical problem solver. Moreover, we present an efficient heuristic. In experiments with roads and buildings from OpenStreetMap, our methods showed a good capability to unselect a small set of buildings whose inclusion in the output map would have caused large displacements or proximity conflicts. For a quantitative evaluation, we solved a benchmark instance once with our new model integrating selection and displacement and once with a variant of our model where the selection of objects was prescribed based on a solution found with an existing approach via simulated annealing. Comparing the two models, our integrated model yielded a solution of 33% less total cost. We conclude the article with a discussion of possible follow-up work.

Algorithms for Consistent Dynamic Labeling of Maps With a Time-Slider Interface

2025-01-08T00:00:00Z

Algorithms for Consistent Dynamic Labeling of Maps With a Time-Slider Interface Bonerath, Annika; Driemel, Anne; Haunert, Jan-Henrik; Haverkort, Herman; Langetepe, Elmar; Niedermann, Benjamin User interfaces for inspecting spatio-temporal events often allow their users to filter the events by specifying a time window with a time slider. We consider the case that filtered events are visualized on a map using textual or iconic labels. However, to ensure a clear visualization, not all filtered events are annotated with a label. We present algorithms for setting up a data structure that encodes for every possible time window the set of displayed labels. Our algorithms ensure that the displayed labels never overlap and guarantee the stability of the labeling during certain basic interactions with the time slider. Assuming that the labels have different priorities (weights), we aim to maximize the weight of the displayed labels integrated over all possible time windows. As basic interactions, we consider moving the entire time window, symmetrically scaling it, and dragging one of its endpoints. We consider two stability requirements: (1) during a basic interaction, a label should appear and disappear at most once; (2) if a label is displayed for a time window Q, then it is also displayed for all the time windows contained in Q and that contain its timestamp. We prove that finding an optimal solution is NP-hard and propose efficient constant-factor approximation algorithms for unit-square and unit-disk labels, as well as a fast greedy heuristic for arbitrarily shaped labels. In experiments on real-world data, we compare the non-exact algorithms with an exact approach through integer linear programming.

Factor graph-based ground truth trajectory estimation by fusing robotic total station and inertial measurements

2025-08-01T00:00:00Z

Factor graph-based ground truth trajectory estimation by fusing robotic total station and inertial measurements Mittelstedt, Manuel; Esser, Felix; Tombrink, Gereon; Klingbeil, Lasse; Kuhlmann, Heiner The application of mobile mapping systems (MMS) has increased continuously in the last decades in fields like infrastructure or ecosystem monitoring. Equipped with multiple laser scanners and cameras, these systems can generate high-resolution 3D point clouds of the environment in a short time. In this process, the accuracy of the trajectory of the system is of central importance as it directly affects the accuracy of the resulting point cloud. However, since the trajectory estimation depends on sensor observations that are often affected by unknown systematic errors, the actual accuracy of the trajectory remains mainly unknown. To uncover the gap in the trajectory accuracy assessment, we present a method to create ground truth trajectories for mobile mapping systems by integrating millimeter-accurate total station measurements. We mount an Inertial Measurement Unit (IMU) and two 360-degree prisms on a mobile platform, track them with two Robotic Total Stations (RTS) during motion, and fuse these prism measurements with the IMU readings using a factor graph-based trajectory estimation approach. To evaluate the quality of this ground truth trajectory, we record repeated measurements on a closed-loop rail track close to Bonn, Germany. The results show that the generated ground truth trajectory estimated with RTS and IMU data achieves a precision of around 1 mm in position and 0.05° in orientation. To show the potential of the method, we detect systematic deviations of an example MSS that uses Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) and IMU data for trajectory estimation. The results show that even under good GNSS conditions, the ground truth trajectory from our proposed approach has significantly better precision and less systematic errors than the trajectory based on RTK-GNSS and IMU data.