Displacement Error Research Articles

Improved detection limits of J-coupled neurometabolites in the human brain at 7T with a J-refocused sLASER sequence.

In a standard spin echo, the time evolution due to homonuclear couplings is not reversed, leading to echo time (TE)-dependent modulation of the signal amplitude and signal loss in the case of overlapping multiplet resonances. This has an adverse effect on quantification of several important metabolites such as glutamate and glutamine. Here, we propose a J-refocused variant of the sLASER sequence (J-sLASER) to improve quantification of J-coupled metabolites at ultrahigh field (UHF). The use of the sLASER sequence is particularly advantageous at UHF as it minimizes chemical shift displacement error and results in relatively homogenous refocusing. We simulated the MRS signal from brain metabolites over a broad range of TE values with sLASER and J-sLASER, and showed that the signal of J-coupled metabolites was increased with J-sLASER with TE values up to ~80 ms. We further simulated "brain-like" spectra with both sequences at the shortest TE available on our scanner. We showed that, despite the slightly longer TE, the J-sLASER sequence results in significantly lower Cramer-Rao lower bounds (CRLBs) for J-coupled metabolites compared with those obtained with sLASER. Following phantom validation, we acquired spectra from two brain regions in 10 healthy volunteers (age 38 ± 15 years) using both sequences. We showed that using J-sLASER results in a decrease of CRLBs for J-coupled metabolites. In particular, we measured a robust ~38% decrease in the mean CRLB (glutamine) in parietal white matter and posterior cingulate cortex (PCC). We further showed, in 10 additional healthy volunteers (age 34 ± 15 years), that metabolite quantification following two separate acquisitions with J-sLASER in the PCC was repeatable. The improvement in quantification of glutamine may in turn improve the independent quantification of glutamate, the main excitatory neurotransmitter in the brain, and will simultaneously help to track possible modulations of glutamine, which is a key player in the glutamatergic cycle in astrocytes.

A New Prediction Method of Displacement Errors Caused by Low Stiffness for Industrial Robot.

This paper presents a new method, a fast prediction method based on the Cartesian stiffness model and equivalent spring stiffness (FPM-CSES), to calculate displacement errors of deformation caused by low stiffness for industrial robot. First, the Cartesian stiffness model based on the Jacobian matrix was established for a robot, and then the displacement error model of deformations caused by external force was established based on Cartesian stiffness. Second, the transmission system of the robot’s joint was analyzed, and an equivalent method for joint stiffness was presented based on a series spring system. Meanwhile, the stiffness of the key components including the servo motor, harmonic reducer, and timing belt was deduced in detail. Finally, a compared simulation and a measurement experiment were conducted on a 6-joint series robot. It was found that the FPM-CSES could calculate any configuration among the robot’s workspace. Compared with the finite element analysis (FEA) method, the presented method is feasible and more efficient. The experimental results showed that the prediction accuracy of the FPM-CSES is rather high, with an average rate of more than 83.72%. Hence, the prediction method presented in this study is simple, fast, and reliable, and could be used to predict and analyze the displacement errors caused by the cutting force, and provide the basis for trajectory planning and error compensation, enhancing the robot’s machining performance.

Testing the Feature Alignment Technique (FAT) in an Ensemble-Based Data Assimilation and Forecast System with Multiple-Storm Scenarios

Abstract Storm displacement errors can arise from a number of potential sources of error within a data assimilation (DA) and forecast system. Conversely, storm displacement errors can cause issues for storm-scale, ensemble-based systems using an ensemble Kalman filter (EnKF), such as NSSL’s Warn-on-Forecast System (WoFS). A previous study developed a fully grid-based feature alignment technique (FAT) to mitigate these phase errors and their impacts. However, that study developed and tested the FAT for single-storm cases. This study advances that work by implementing an object-based merging and matching technique into the FAT and tests the updated FAT in more complex scenarios of multiple storms. Ensemble-based experiments are conducted with and without the FAT for each of the scenarios. The experiments’ analyses and forecasts of storm-related fields are then evaluated using subjective and objective methods. Results from these idealized multiple-storm experiments continue to reveal the potential benefits of correcting storm displacement errors. For example, running the FAT even once can mitigate the “spinup” period experienced by the no-FAT experiments. The new results also show that running the FAT prior to every DA cycling step generally leads to more skillful forecasts at the smaller scales, especially in earlier-initialized forecasts. However, repeatedly running the FAT prior to every DA step can eventually lead to deterioration in analyses and forecasts. Potential solutions to this problem include using longer cycling intervals and running the FAT prior to DA less often. Additional ways to improve the FAT along with other results are presented and discussed. Significance Statement The purpose of this work is to explore the impact of correcting storm displacements on analyses and forecasts of storms using an ensemble-based data assimilation and forecast system in an idealized framework. Storm displacement errors are a common problem in current operational and experimental storm-scale forecast systems, so understanding their impact on these systems and providing a method to help mitigate them is important. Results from this study indicate that correcting storm displacement errors with the feature alignment technique can greatly improve analyses and forecasts in multiple-storm scenarios. Future work will focus on exploring the impact of correcting storm displacement errors in a real-data, storm-scale data assimilation and forecast system.

Research on hydraulic motor control system based on fuzzy neural network combing sliding mode control and time delay estimation

The precise motion control of a hydraulic motor system has some problems due to uncertain disturbance, complex nonlinear dynamics. Traditional methods are difficult to obtain the desired control performance. In this paper, a new fuzzy neural network (FNN) combined with terminal sling mode control (TSMC) and time delay estimation (TDE) is proposed. FNN is used to adjust the parameter of TSMC to reduce the time for the system to reach the equilibrium point and chatting. To increase the accuracy of the system, TDE is used to compensate the error caused by uncertain disturbance. This controller was simulated in Amesim and Simulink, and the results showed that the control scheme proposed in this paper has the smallest angular displacement error, angular velocity error and variance than other control schemes, such as PID and sliding mode control (SMC). Furthermore, the designed controller was implemented on a drill pipe automatic handling manipulator, and its control performance was verified.

Feedforward Control of Piezoelectric Ceramic Actuators Based on PEA-RNN.

Multilayer perceptron (MLP) has been demonstrated to implement feedforward control of the piezoelectric actuator (PEA). To further improve the control accuracy of the neural network, reduce the training time, and explore the possibility of online model updating, a novel recurrent neural network named PEA-RNN is established in this paper. PEA-RNN is a three-input, one-output neural network, including one gated recurrent unit (GRU) layer, seven linear layers, and one residual connection in the linear layers. The experimental results show that the displacement linearity error of piezoelectric ceramics reaches 8.96 m in the open-loop condition. After using PEA-RNN compensation, the maximum displacement error of piezoelectric ceramics is reduced to 0.465 m at the operating frequency of 10 Hz, which proves that PEA-RNN can accurately compensate piezoelectric ceramics’ dynamic hysteresis nonlinearity. At the same time, the training epochs of PEA-RNN are only 5% of the MLP, and fewer training epochs provide the possibility to realize online updates of the model in the future.

Multi-level context-driven interaction modeling for human future trajectory prediction

Human trajectory prediction is a challenging task with important applications such as intelligent surveillance and autonomous driving. We recognize that pedestrians in close and distant neighborhoods have different impacts on the person’s decision of future movements. Local scene context and global scene layout also affect the movement decision differently. Existing methods have not adequately addressed these interactions between humans and the multi-level contexts occurring at different spatial and temporal scales. To this end, we propose a multi-level context-driven interaction modeling (MCDIM) method for human future trajectory learning and prediction. Specifically, we construct a multilayer graph attention network (GAT) to model the hierarchical human–human interactions. An extra set of long short-term memory networks is designed to capture the correlations of these human–human interactions at different temporal scales. To model the human–scene interactions, we explicitly extract and encode the global scene layout features and local context features in the neighborhood of the person at each time step and capture the spatial–temporal information of the interactions between human and the local scene contexts. The human–human and human–scene interactions are incorporated into the multi-level GAT-based network for accurate prediction of future trajectories. We have evaluated the method on benchmark datasets: the walking pedestrians dataset provided by ETH Zurich (ETH) and the crowd data provided by the University of Cyprus. The results demonstrate that our MCDIM method outperforms existing methods, being able to generate more accurate and plausible trajectories for pedestrians. The average performance gain is 2 and 3 percentage points in terms of the average displacement error and final displacement error, respectively.

A flood predictability study for Hurricane Harvey with the CREST-iMAP model using high-resolution quantitative precipitation forecasts and U-Net deep learning precipitation nowcasts

A flood is one of the most hazardous natural disasters, and it commonly causes fatalities and socioeconomic damages. The advances of modeling techniques and observation data in flood prediction have found success in field operations. This paper presents a comprehensive flood prediction of Hurricane Harvey in 1-hour lead-time that is not limited to 1D streamflow forecast but also 2D flood extent and 3D inundation depth. It uses high-resolution quantitative precipitation forecasts (QPFs, from operational Rapid Refresh-RAP, and High Resolution Rapid Refresh-HRRR models) and deep learning nowcasts (AI nowcasts). The results show that the QPFs have a well-known displacement issue and the AI nowcast cannot predict the precipitation intensity, and an attempt to combine the two methods (AI hybrid) failed to improve the overall accuracy. However, the 2D flood extent predictions with the HRRR and AI hybrid forcings can provide information indicating the future flooded area with about 50% accuracy (hit rate) and stream flow prediction showed that the HRRR QPF can provide relatively accurate flood peak prediction (-8%). In contrast, the AI nowcast reveals minimal displacement errors but underpredicts precipitation intensity. The deep learning method also indicates that the binary tests with low threshold, which are commonly employed in the deep learning field, neglect the importance of precipitation intensity errors for extreme event studies.

A Hybrid 3D-2D Image Registration Framework for Pedicle Screw Trajectory Registration between Intraoperative X-ray Image and Preoperative CT Image.

Pedicle screw insertion is considered a complex surgery among Orthopaedics surgeons. Exclusively to prevent postoperative complications associated with pedicle screw insertion, various types of image intensity registration-based navigation systems have been developed. These systems are computation-intensive, have a small capture range and have local maxima issues. On the other hand, deep learning-based techniques lack registration generalizability and have data dependency. To overcome these limitations, a patient-specific hybrid 3D-2D registration principled framework was designed to map a pedicle screw trajectory between intraoperative X-ray image and preoperative CT image. An anatomical landmark-based 3D-2D Iterative Control Point (ICP) registration was performed to register a pedicular marker pose between the X-ray images and axial preoperative CT images. The registration framework was clinically validated by generating projection images possessing an optimal match with intraoperative X-ray images at the corresponding control point registration. The effectiveness of the registered trajectory was evaluated in terms of displacement and directional errors after reprojecting its position on 2D radiographic planes. The mean Euclidean distances for the Head and Tail end of the reprojected trajectory from the actual trajectory in the AP and lateral planes were shown to be 0.6–0.8 mm and 0.5–1.6 mm, respectively. Similarly, the corresponding mean directional errors were found to be and . The mean trajectory length difference between the actual and registered trajectory was shown to be 2.67 mm. The approximate time required in the intraoperative environment to axially map the marker position for a single vertebra was found to be 3 min. Utilizing the markerless registration techniques, the designed framework functions like a screw navigation tool, and assures the quality of surgery being performed by limiting the need of postoperative CT.

Free and forced vibrations of composite cylindrical–cylindrical​ shells with partial bolt loosening connections: Theoretical and experimental investigation

In the present study, the free and forced vibrations of composite cylindrical–cylindrical shells with partial bolt loosening connections are investigated both experimentally and theoretically. Initially, a theoretical model is proposed based on the assumption of primary and secondary connection springs with reduced stiffnesses related to different loosening positions of connection bolts. The equation of motion is derived for predicting the fundamental frequencies, mode shapes, and vibration displacement responses on the basis of the Rayleigh–Ritz method, the Jacobian polynomial approach, the modal superposition technique, etc. To ensure the calculation accuracy of the present model, a convergence study is conducted to determine the truncation number. Also, to give a solid validation of the model, some detailed tests, such as the pulse, sweeping-frequency, and resonant excitations, are conducted on a combined shell specimen, which is obtained by assembling two different composite cylindrical shells via twelve M8 bolts, with a torque wrench being adopted to control the torques in the connection bolts to achieve the desired partial bolt loosening effect. By comparing the calculated and tested results, it can be found that the present model is reliable to predict the structural dynamic parameters because the calculation errors of natural frequencies and resonant displacements are less than 7.4 and 11.9 %, respectively. Finally, the impact of critical bolt loosening parameters on vibration characteristics of the combined shells is evaluated, with some important conclusions being highlighted to illustrate the reduction mechanism of dynamic properties as the partial bolt loosening degree and number increase or the loosening pattern becomes more complex.

GA-STT: Human Trajectory Prediction With Group Aware Spatial-Temporal Transformer

Human trajectory prediction is a crucial yet challenging problem, which is of fundamental importance to robotics and autonomous driving vehicles. The core challenge lies in effectively modeling the socially aware spatial interaction and complex temporal dependencies among crowds. However, previous methods usually model spatial and temporal information separately and only use individual features, while ignoring group-level motion characteristics. We propose a novel trajectory prediction framework termed GA-STT, a group aware spatial-temporal transformer network to address these issues. Specifically, we first get the individual representations supervised by group-based annotations. Then, we model the complex spatial-temporal interactions with spatial and temporal transformers separately and fuse spatial-temporal embedding through the cross-attention mechanism. Results on the publicly available ETH/UCY datasets show that our model outperforms the state-of-the-art method by 19.4% in average displacement error(ADE) and 16.9% in final displacement error(FDE) and successfully predicts complex spatial-temporal interactions.

Hydrometeorological analysis of a flash flood event in an ungauged Mediterranean watershed under an operational forecasting and monitoring context

AbstractThe current study presents the first attempt to investigate the November 2019 catastrophic flash flood in Olympiada (North Greece) under an operational forecasting and monitoring context, based on the mesoscale weather and research forecasting (WRF) model and the integrated multi‐satellite retrievals for global precipitation measurement (GPM‐IMERG) algorithm. When evaluated against ground‐based rainfall measurements, WRF showed an adequate predictive capability concerning the severity of the observed rainfall, even though the model's performance suffered from phase and spatial displacement errors. The GPM‐IMERG algorithm yielded the best performance in capturing the timing of the observed excessive rainfall. Concerning the hydrological outcome using the Hydrologic Engineering Center‐Hydrologic Modelling System (HEC‐HMS), a strong indication of the forthcoming flash flood could have been provided at least 2 days in advance based on the WRF‐based HEC‐HMS‐simulated flood peak (139.3 m3 s−1), as it was close to the drainage capacity of a constructed bridge in the plain stream bed, and to the 100‐year return period flood discharge.

Multi-Agent Trajectory Prediction With Heterogeneous Edge-Enhanced Graph Attention Network

Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential for safe and efficient operation of connected automated vehicles under complex driving situations. Two main challenges for this task are to handle the varying number of heterogeneous target agents and jointly consider multiple factors that would affect their future motions. This is because different kinds of agents have different motion patterns, and their behaviors are jointly affected by their individual dynamics, their interactions with surrounding agents, as well as the traffic infrastructures. A trajectory prediction method handling these challenges will benefit the downstream decision-making and planning modules of autonomous vehicles. To meet these challenges, we propose a three-channel framework together with a novel Heterogeneous Edge-enhanced graph ATtention network (HEAT). Our framework is able to deal with the heterogeneity of the target agents and traffic participants involved. Specifically, agents’ dynamics are extracted from their historical states using type-specific encoders. The inter-agent interactions are represented with a directed edge-featured heterogeneous graph and processed by the designed HEAT network to extract interaction features. Besides, the map features are shared across all agents by introducing a selective gate-mechanism. And finally, the trajectories of multiple agents are predicted simultaneously. Validations using both urban and highway driving datasets show that the proposed model can realize simultaneous trajectory predictions for multiple agents under complex traffic situations, and achieve state-of-the-art performance with respect to prediction accuracy. The achieved final displacement error (FDE@3sec) is 0.66 meter under urban driving, demonstrating the feasibility and effectiveness of the proposed approach.

Complementary Attention Gated Network for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is crucial in many practical applications due to the diversity of pedestrian movements, such as social interactions and individual motion behaviors. With similar observable trajectories and social environments, different pedestrians may make completely different future decisions. However, most existing methods only focus on the frequent modal of the trajectory and thus are difficult to generalize to the peculiar scenario, which leads to the decline of the multimodal fitting ability when facing similar scenarios. In this paper, we propose a complementary attention gated network (CAGN) for pedestrian trajectory prediction, in which a dual-path architecture including normal and inverse attention is proposed to capture both frequent and peculiar modals in spatial and temporal patterns, respectively. Specifically, a complementary block is proposed to guide normal and inverse attention, which are then be summed with learnable weights to get attention features by a gated network. Finally, multiple trajectory distributions are estimated based on the fused spatio-temporal attention features due to the multimodality of future trajectory. Experimental results on benchmark datasets, i.e., the ETH, and the UCY, demonstrate that our method outperforms state-of-the-art methods by 13.8% in Average Displacement Error (ADE) and 10.4% in Final Displacement Error (FDE). Code will be available at https://github.com/jinghaiD/CAGN

Linear Displacement and Straightness Measurement by Fabry-Perot Interferometer Integrated with an Optoelectronic Module

This research develops a three degrees of freedom (DOF) measurement system by integrating Fabry-Perot interferometer and photoelectronic inspection module to determine linear displacement, horizontal and vertical straightness geometric error parameters simultaneously. The interferometer and the photoelectronic inspection module in a three DOF measurement system share the same light source, and the two structures are used to measure linear displacement and straightness errors. The experimental results are utilized to calculate the relevant error parameters according to ISO standards and numerical analysis. They show that after the machine error compensation, the positioning deviation of the system is reduced from 55 μm to 19 μm, corresponding to the reduction of 65%. The accuracy is promoted from 65 μm to 31 μm, about the improvement of 52%. The horizontal and vertical straightness errors of the machine are 4.30 μm and 5.71 μm respectively.

Tensile and flexural failure behaviors of bolted T-joint consisting of T300/BMP316 composite laminates and TC4 alloy

T-joints are increasingly applied as the connection between vane and case in aero-engines. However, their mechanical behavior and damage mechanisms are unclear, restricting a broader range of applications. In this paper, a bolted T-joint was designed, which was composed of a T300/BMP316 laminated case and TC4 vane, followed by tensile and flexural tests to investigate the mechanical behavior. Subsequently, a progressive damage model based on continuum damage mechanics was employed to predict the bearing capacity and damage evolution of the joints. A comparison between simulation results and the tests shows that the maximum error in displacement is 6.6%, and the maximum error in load is 2.9%. The damage modes are similar, which verifies the reliability of the simulations. The results indicate that the structure reaches the tensile capacity when fiber damage extends to the whole width of the case. And the fiber damage mainly occurs on the loading side and dominates the strength of the T-joint under flexural loading. Massive matrix damage and delamination occur in the laminated case under both loading modes, which reduces the stiffness of the T-joints. Moreover, it is observed that plastic deformation extends on the flange of TC4 vane along the fillet under flexural loading, which aggravates the decrease of structural stiffness. This work is to provide references for the engineering design of the connection structure between aero-engine vanes and the case.

Phosphorus MRS of healthy human spleen.

Phosphorus (31P‐) MRS in vivo enables detection and quantification of important phosphorus‐containing metabolites in biological tissues. 31P‐MRS of the normal spleen is challenging due to the relatively small volume and the larger distance between the spleen and surface coil. However, reference spectra of the healthy spleen are invaluable in studies of splenic malignancies and benign causes of splenomegaly, as well as in the study of its physiology. The purpose of this work was to investigate the feasibility of localized 31P‐MRS of healthy spleen in situ in a clinically acceptable measurement time using a clinical 3 T MR scanner. In this work, 31P spectra of five healthy volunteers were measured using single‐voxel image‐selected in vivo spectroscopy (ISIS). The measurement sequence was augmented by broadband proton decoupling and nuclear Overhauser effect enhancement. It is demonstrated that localized 31P‐MRS of the spleen in situ using single‐voxel ISIS is feasible on a clinical 3 T scanner in a clinically acceptable acquisition time. However, results have to be corrected for the transmitter excitation profile, and chemical shift displacement errors need to be taken into consideration during placement of the volume of interest. Results presented here could be used as a reference in future studies of splenomegaly caused by haematological malignancies.

Multi-probe linear fitting and time of arrival linear correction method to analyze blade vibration based on blade tip timing without once-per-revolution

Blade vibration monitoring can ensure the safe operation of aero-engine rotor blades. Among the methods of blade vibration monitoring, Blade Tip Timing (BTT) method has attracted more and more attention because of its advantages of non-contact measurement. However, it is difficult to install the Once-Per-Revolution (OPR) probe in the confined space of aero-engine, and the failure and instability of the OPR signal will reduce the reliability of the blade vibration analysis results, which directly affects the accuracy of the blade vibration parameters identification. The Multi-Probe linear fitting and Time of Arrival (ToA) Linear Correction method based on the BTT (MP-LC-BTT) without OPR is proposed to reduce the errors of single probe linear fitting method for blade vibration displacement analysis. The proposed method can also correct the calculation error of blade vibration displacement due to the nonlinear change of rotation speed, which can improve the analysis accuracy of the blade vibration displacement. A new blade vibration model conforming to the actual vibration characteristics is established, and the effectiveness of the proposed method is verified by numerical simulation. Finally, the reliability and accuracy of the MP-LC-BTT method have been verified by the experiments which include two high-speed blade test-benches and an industrial axial fan. This method can be used in the actual aero-engine monitoring instead of the BTT method with OPR.

Predictive skill comparative assessment of WRF 4DVar and 3DVar data assimilation: An Indian Ocean tropical cyclone case study

Tropical cyclones (TCs) predictions are crucial and of significant scientific and socio-economic interest. To improve the TC's simulation, this study aimed to use the Weather Research and Forecasting (WRF) model to assimilate satellite radiances and surface and upper-air conventional observations cyclically at the intervals of 6 h. The predictive skill of WRF with 4D variational (4DVar) and 3D variational (3DVar) data assimilation (DA) systems are investigated to examine the added value of 4DVar considering four cyclones namely Vardah, Titli, Bulbul, and Yaas over the North Indian Ocean (NIO). Each analysis was subjected to a short-range free forecast, and the estimates obtained from the 4DVar DA showed more realistic TC characteristics in terms of structure, intensity, rainfall, and track. The simulation of mean sea-level pressure and maximum sustained wind has demonstrated significant improvements in the 4DVar run for all the cases. Furthermore, the vector displacement error in cyclone Vardah was reduced by 54%–57% in the 4DVar simulation, whereas this improvement was found to be 9%–18% in the case of Titli. More or less similar results were found for Bulbul and Yaas also. Simulation of rainfall also showed marked improvement from the 4DVar scheme. Thus, the performance of the WRF model integrated with the 4DVar DA method enhanced the simulations of TC intensity, tracks, and structures over the NIO.

A Preliminary Examination of Warm Season Precipitation Displacement Errors in the Upper Midwest in the HRRRE and HREF Ensembles

Abstract Increased operational use of convection-allowing models and ensembles offers substantial improvements for some aspects of convective weather forecasting; however, errors in quantitative precipitation forecasts (QPFs) from these models, especially those related to incorrect placement of heavy rainfall systems, limit their usefulness as an input into hydrological models. To improve understanding of QPF location errors, this study quantifies the displacement errors for the centroids of both 0–18-h accumulated rainfall and rainfall in the first hour after initiation of precipitation systems in both the High-Resolution Rapid Refresh Ensemble (HRRRE) and the High-Resolution Ensemble Forecast (HREF) for 30 events in the 2018 warm season. Ensemble member QPFs are compared to quantitative precipitation estimates (QPEs) obtained from the North Central River Forecast Center (NCRFC). HRRRE is found to have less spread in centroid locations than HREF, and both HRRRE and HREF 0–18-h QPF accumulations have less spread than the 1-h QPF accumulation when the precipitation event initiates. Furthermore, QPF centroids are most often displaced to the west in HRRRE for both 0–18-h QPF accumulation and the 1-h QPF accumulation when the precipitation event initiates. The 0–18-h QPF accumulation displacement errors can be reduced when adjustments are made to the forecasted position based upon displacement errors present in the first hour of precipitation, but only when the adjustments are a function of the intercardinal quadrant in which the initial hour QPF centroid was displaced.

Measuring Displacement Errors with Complex Wavelets

Abstract When highly resolved precipitation forecasts are verified against observations, displacement errors tend to overshadow all other aspects of forecast quality. The appropriate treatment and explicit measurement of such errors remains a challenging task. This study explores a new verification technique that uses the phase of complex wavelet coefficients to quantify spatially varying displacements. Idealized and realistic test cases from the MesoVICT project demonstrate that our approach yields helpful results in a variety of situations where popular alternatives may struggle. Potential benefits of very high spatial resolutions can be identified even when the observational dataset is coarsely resolved itself. The new score can furthermore be applied not only to precipitation but also variables such as wind speed and potential temperature, thereby overcoming a limitation of many established location scores. Significance Statement One important requirement for a useful weather forecast is its ability to predict the placement of weather events such as cold fronts, low pressure systems, or groups of thunderstorms. Errors in the predicted location are not easy to quantify: some established quality measures combine location and other error sources in one score, others are only applicable if the data contain well-defined and easily identifiable objects. Here we introduce an alternative location score that avoids such assumptions and is thus widely applicable. As an additional benefit, we can separate displacement errors into different spatial scales and localize them on a weather map.