| 1. |
- Steffan, Adrian, et al.
(författare)
-
Validation of an open source, remote web-based eye-tracking method (WebGazer) for research in early childhood
- 2024
-
Ingår i: Infancy. - 1525-0008 .- 1532-7078. ; 29:1
-
Tidskriftsartikel (refereegranskat)abstract
- Measuring eye movements remotely via the participant's webcam promises to be an attractive methodological addition to in-person eye-tracking in the lab. However, there is a lack of systematic research comparing remote web-based eye-tracking with in-lab eye-tracking in young children. We report a multi-lab study that compared these two measures in an anticipatory looking task with toddlers using WebGazer.js and jsPsych. Results of our remotely tested sample of 18-27-month-old toddlers (N=125) revealed that web-based eye-tracking successfully captured goal-based action predictions, although the proportion of the goal-directed anticipatory looking was lower compared to the in-lab sample (N=70). As expected, attrition rate was substantially higher in the web-based (42%) than the in-lab sample (10%). Excluding trials based on visual inspection of the match of time-locked gaze coordinates and the participant's webcam video overlayed on the stimuli was an important preprocessing step to reduce noise in the data. We discuss the use of this remote web-based method in comparison with other current methodological innovations. Our study demonstrates that remote web-based eye-tracking can be a useful tool for testing toddlers, facilitating recruitment of larger and more diverse samples; a caveat to consider is the larger drop-out rate.
|
|
| 2. |
- Lin, Q., et al.
(författare)
-
Performance of the WRF Model at the Convection-Permitting Scale in Simulating Snowfall and Lake-Effect Snow Over the Tibetan Plateau
- 2023
-
Ingår i: Journal of Geophysical Research-Atmospheres. - 2169-897X. ; 128:16
-
Tidskriftsartikel (refereegranskat)abstract
- This study investigated the performance of the Weather Research Forecasting (WRF) model at 4-km horizontal grid spacing in simulating precipitation, 2 m air temperature (T2), snowfall, and lake-effect snow (October 4-8, 2018) over the Tibetan Plateau (TP). Multiple simulations with different physical parameterization schemes (PPSs), including two planetary boundary layer schemes (Yonsei University and Mellor-Yamada-Janjic), no cumulus and multi-scale Kain-Fritsch, two land surface models (Noah and Noah-MP), and two microphysics schemes (Thompson and Milbrandt), were conducted and compared. Compared with gauge observations, all PPSs simulate mean daily precipitation with mean relative errors (MREs) of 27.7%-53.6%. Besides, spatial correlation coefficients (SCCs) between simulated and observed mean daily precipitation range from 0.56 to 0.71. For simulations of T2, all PPSs perform similarly well, even though the mean cold biases are up to about 3 degrees C. Meanwhile, all PPSs exhibit acceptable performance in simulating spatial distributions of snow depth, snow cover, and snowfall amount, with SCCs of 0.37-0.65 between simulations and observations. However, the WRF simulations significantly overestimate snow depth (similar to 0.4 cm mean error) and snowfall amount (MREs >372%). The Milbrandt scheme slightly outperforms the other PPSs in simulating snow-related variable magnitudes. Due to their inaccurate temperature and airflow modeling over the lake surface and its surroundings, none of the WRF simulations well reproduce the characteristics that more snow occurs over the lake and downwind area. Overall, this study provides a useful reference for future convection-permitting climate modeling of snow or other extreme events when using the WRF model in the TP and other alpine regions. Plain Language Summary Snow falls frequently in cold seasons, especially in alpine regions. When there is a lake, a very interesting snowfall phenomenon named lake-effect snow may happen, that is, more snow occurred over the lake and downwind areas. However, the lake-effect snow caused by large lakes over the Tibetan Plateau (TP) may induce snow disaster events. Thus, conducting reliable simulations of lake-effect snow events are essential for understanding the mechanism of these particular events over the TP. This study investigated the performance of the numerical model in simulating precipitation, 2 m air temperature (T2), snowfall, and lake-effect snow (October 4-8, 2018) over the TP. The results show that the simulated precipitation and T2 perform reasonably well, although wet and cold biases are observed. However, the investigated numerical model fails to reproduce the characteristics of this event well, which may be due to the inaccurate temperature and airflow modeling over the lake surface and its surroundings. Continued improvement is needed for future modeling. Hence, this study provides some suggestions for future numerical modeling of lake-effect snow or other snow events over the TP and other alpine regions.
|
|
