Spatio-temporal sampling of the coherence function for step height measurements

2018 ◽  
Author(s):  
Aleksandar Simic ◽  
Andreas Hyra ◽  
Claas Falldorf ◽  
Ralf B. Bergmann
Keyword(s):  
Coherence Function ◽  
Step Height ◽  
Temporal Sampling ◽  
Spatio Temporal

6 TEMPORAL AND SPATIO-TEMPORAL SAMPLING PROBLEMS

2006 ◽  
Vol 6 (1) ◽  
pp. 65-76 ◽  
Author(s):  
F. W. Whicker ◽  
K. Bunzl ◽  
P. Dixon ◽  
E. M. Scott ◽  
S. C. Sheppard ◽  
...  
Keyword(s):  
Temporal Sampling ◽  
Spatio Temporal ◽  
Sampling Problems

scholarly journals Cross-validation of IASI/MetOp derived tropospheric δD with TES and ground-based FTIR observations

2014 ◽  
Vol 7 (11) ◽  
pp. 11087-11135
Author(s):  
J.-L. Lacour ◽  
L. Clarisse ◽  
J. Worden ◽  
M. Schneider ◽  
S. Barthlott ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Atmospheric Composition ◽  
Free Troposphere ◽  
Temporal Sampling ◽  
Atmospheric Sounding ◽  
Spatio Temporal ◽  
Isotopic Variations ◽  
Level Of Agreement ◽  
Good Agreement

Abstract. The Infrared Atmospheric Sounding Interferometer (IASI) flying on-board MetOpA and MetOpB is able to capture fine isotopic variations of the HDO to H2O ratio (δD) in the troposphere. Such observations at the high spatio temporal resolution of the sounder are of great interest to improve our understanding of the mechanisms controlling humidity in the troposphere. In this study we aim to empirically assess the validity of our error estimation previously evaluated theoretically. To achieve this, we compare IASI δD retrieved profiles with other available profiles of δD, from the TES infrared sounder onboard AURA and from three ground-based FTIR stations produced within the MUSICA project: the NDACC (Network for the Detection of Atmospheric Composition Change) sites Kiruna and Izana, and the TCCON site Karlsruhe, which in addition to near-infrared TCCON spectra also records mid-infrared spectra. We describe the achievable level of agreement between the different retrievals and show that these theoretical errors are in good agreement with empirical differences. The comparisons are made at different locations from tropical to Arctic latitudes, above sea and above land. Generally IASI and TES are similarly sensitive to δD in the free troposphere which allows to compare their measurements directly. At tropical latitudes where IASI's sensitivity is lower than that of TES, we show that the agreement improves when taking into account the sensitivity of IASI in the TES retrieval. For the comparison IASI-FTIR only direct comparisons are performed because of similar sensitivities. We identify a quasi negligible bias in the free troposphere (−3‰) between IASI retrieved δD with the TES one, which are bias corrected, but an important with the ground-based FTIR reaching −47‰. We also suggest that model-satellite observations comparisons could be optimized with IASI thanks to its high spatial and temporal sampling.

scholarly journals Eye Movement and Pupil Measures: A Review

2022 ◽  
Vol 3 ◽  
Author(s):  
Bhanuka Mahanama ◽  
Yasith Jayawardana ◽  
Sundararaman Rengarajan ◽  
Gavindya Jayawardena ◽  
Leanne Chukoskie ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Eye Movements ◽  
Eye Movement ◽  
Motor Coordination ◽  
Complex Interaction ◽  
Prior Work ◽  
Research Directions ◽  
Temporal Sampling ◽  
Prior Literature ◽  
Spatio Temporal

Our subjective visual experiences involve complex interaction between our eyes, our brain, and the surrounding world. It gives us the sense of sight, color, stereopsis, distance, pattern recognition, motor coordination, and more. The increasing ubiquity of gaze-aware technology brings with it the ability to track gaze and pupil measures with varying degrees of fidelity. With this in mind, a review that considers the various gaze measures becomes increasingly relevant, especially considering our ability to make sense of these signals given different spatio-temporal sampling capacities. In this paper, we selectively review prior work on eye movements and pupil measures. We first describe the main oculomotor events studied in the literature, and their characteristics exploited by different measures. Next, we review various eye movement and pupil measures from prior literature. Finally, we discuss our observations based on applications of these measures, the benefits and practical challenges involving these measures, and our recommendations on future eye-tracking research directions.

Spatio-Temporal Sampling of Complex Visual Cues and Behavioral Typology

1972 ◽  
Vol 80 (2) ◽  
pp. 283-288
Author(s):  
Thomas E. Webb ◽  
James M. Anker
Keyword(s):  
Visual Cues ◽  
Temporal Sampling ◽  
Spatio Temporal

scholarly journals Temporal and spatial limitations in global surveillance for bat filoviruses and henipaviruses

2019 ◽  
Vol 15 (12) ◽  
pp. 20190423 ◽  
Author(s):  
Daniel J. Becker ◽  
Daniel E. Crowley ◽  
Alex D. Washburne ◽  
Raina K. Plowright
Keyword(s):  
Meta Analysis ◽  
Virus Detection ◽  
Spatial And Temporal Variation ◽  
Viral Detection ◽  
Reporting Practices ◽  
Temporal Sampling ◽  
Bat Virus ◽  
Temporal And Spatial ◽  
Spatio Temporal ◽  
Reservoir Hosts

Sampling reservoir hosts over time and space is critical to detect epizootics, predict spillover and design interventions. However, because sampling is logistically difficult and expensive, researchers rarely perform spatio-temporal sampling of many reservoir hosts. Bats are reservoirs of many virulent zoonotic pathogens such as filoviruses and henipaviruses, yet the highly mobile nature of these animals has limited optimal sampling of bat populations. To quantify the frequency of temporal sampling and to characterize the geographical scope of bat virus research, we here collated data on filovirus and henipavirus prevalence and seroprevalence in wild bats. We used a phylogenetically controlled meta-analysis to next assess temporal and spatial variation in bat virus detection estimates. Our analysis shows that only one in four bat virus studies report data longitudinally, that sampling efforts cluster geographically (e.g. filovirus data are available across much of Africa and Asia but are absent from Latin America and Oceania), and that sampling designs and reporting practices may affect some viral detection estimates (e.g. filovirus seroprevalence). Within the limited number of longitudinal bat virus studies, we observed high heterogeneity in viral detection estimates that in turn reflected both spatial and temporal variation. This suggests that spatio-temporal sampling designs are important to understand how zoonotic viruses are maintained and spread within and across wild bat populations, which in turn could help predict and preempt risks of zoonotic viral spillover.

scholarly journals On the spatio-temporal representativeness of observations

2017 ◽  
Vol 17 (16) ◽  
pp. 9761-9780 ◽  
Author(s):  
Nick Schutgens ◽  
Svetlana Tsyro ◽  
Edward Gryspeerdt ◽  
Daisuke Goto ◽  
Natalie Weigum ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Measurement Errors ◽  
Remote Sensing Data ◽  
Temporal Sampling ◽  
Representation Error ◽  
Temporal Averaging ◽  
Carbon Mass ◽  
Spatio Temporal

Abstract. The discontinuous spatio-temporal sampling of observations has an impact when using them to construct climatologies or evaluate models. Here we provide estimates of this so-called representation error for a range of timescales and length scales (semi-annually down to sub-daily, 300 to 50 km) and show that even after substantial averaging of data significant representation errors may remain, larger than typical measurement errors. Our study considers a variety of observations: ground-site or in situ remote sensing (PM2. 5, black carbon mass or number concentrations), satellite remote sensing with imagers or lidar (extinction). We show that observational coverage (a measure of how dense the spatio-temporal sampling of the observations is) is not an effective metric to limit representation errors. Different strategies to construct monthly gridded satellite L3 data are assessed and temporal averaging of spatially aggregated observations (super-observations) is found to be the best, although it still allows for significant representation errors. However, temporal collocation of data (possible when observations are compared to model data or other observations), combined with temporal averaging, can be very effective at reducing representation errors. We also show that ground-based and wide-swath imager satellite remote sensing data give rise to similar representation errors, although their observational sampling is different. Finally, emission sources and orography can lead to representation errors that are very hard to reduce, even with substantial temporal averaging.

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