Results for “Variational data augmentation” — Page 5

NASA NTRS2006

The Use of Dynamic Visual Acuity as a Functional Test of Gaze Stabilization Following Space Flight

After prolonged exposure to a given gravitational environment the transition to another is accompanied by adaptations in the sensorimotor subsystems, including the vestibular system. Variation in the adaptation time course of these subsystems, and the functional redundancies that exist between them make it difficult to accurately assess the functional capacity and physical limitations of astro/cosmonauts using tests on individual subsystems. While isolated tests of subsystem performance may be the only means to address where interventions are required, direct measures of performance may be more suitable for assessing the operational consequences of incomplete adaptation to changes in the gravitational environment. A test of dynamic visual acuity (DVA) is currently being used in the JSC Neurosciences Laboratory as part of a series of measures to assess the efficacy of a countermeasure to mitigate postflight locomotor dysfunction. In the current protocol, subjects visual acuity is determined using Landolt ring optotypes presented sequentially on a computer display. Visual acuity assessments are made both while standing and while walking at 1.8 m/s on a motorized treadmill. The use of a psychophysical threshold detection algorithm reduces the required number of optotype presentations and the results can be presented immediately after the test. The difference between the walking and standing acuity measures provides a metric of the change in the subject s ability to maintain gaze fixation on the visual target while walking. This functional consequence is observable regardless of the underlying subsystem most responsible for the change. Data from 15 cosmo/astronauts have been collected following long-duration (approx. 6 months) stays in space using a visual target viewing distance of 4.0 meters. An investigation of the group mean shows a change in DVA soon after the flight that asymptotes back to baseline approximately one week following their return to earth. The performance of some subjects nicely parallels the stereotypical recovery curve observed in the group mean data. Others show dramatic changes in DVA from one test day to another. These changes may be indicative of a re-adaptation process that is not characterized by a steady improvement with the passage of time, but is instead a dynamic search for appropriate coordinative strategy to achieve the desired gaze stabilization goal. Ground-based data have been collected in our lab using DVA with one of the goals being to improve the DVA test itself. In one of these studies, the DVA test was repeated using a visual target viewing distance of 0.5 meters. While walking, the relative contributions of the otoliths and semi-circular canals that are required to stabilize gaze are affected by visual target viewing distance. It may be possible to exploit this using the current treadmill DVA test to differentially assess changes in these vestibular subsystems. The postflight DVA evaluations currently used have been augmented to include the near target version of the test. Preliminary results from these assessments, as well as the results from the ground-based tests will also be reported. DVA provides a direct measure of a subject's ability to see clearly in the presence of self-motion. The use of the current tests for providing a functionally relevant metric is evident. However, it is possible to expand the scope of DVA testing to include scenarios other than walking. A facility for measuring DVA in the presence of passive movements is being created. Using a mechanized platform to provide the perturbation, it should be possible to simulate aircraft and automobile vibration profiles. Used in conjunction with the far and near visual displays this facility should be able to assess a subject s ability to clearly see distant objects as well as those that appear on the dashboard or instrument control panel during functionally relevant situations.

Peters, B. T.↗

NASA NTRSDate not supplied

Understanding and Utilizing PBL Height Data from Multiple Observing Systems in the GEOS System

The accuracy of PBL height simulation is a key issue in many applications including forecasting near surface meteorology and air quality, however, it is a very challenging problem due to the lack of not only comprehensive, global Planetary Boundary Layer (PBL) observations but also a strategy and infrastructure to utilize PBL height data from a variety of sensors. Following the designation of PBL as an incubation class observable in the 2017 Decadal Survey, the PBL Incubation Study Team Report [14] made clear that “a future global PBL observing system requires modeling and data assimilation as essential components.” There is an urgent need for global modeling development in order to utilize Program of Record (POR) observations, assess their impacts, and identify gaps to be filled by future PBL missions. Our overall objective is to develop PBL data assimilation capabilities in the NASA Global Earth Observing System (GEOS), focusing on PBL height from multiple observing systems, to support the assessment and use of future PBL observations. The NASA GEOS system is composed of the GEOS global atmospheric general circulation model (AGCM) and the atmospheric data assimilation system (ADAS). The PBL parameterizations include the “Lock” K-profile scheme driven by surface and cloud-top buoyancy fluxes ([4]), and the “Louis” local scheme for stable conditions based on the Richardson number ([5]). Above the mixed layer defined by the Lock surface plume, shallow cumulus convection is represented by the mass flux scheme of [9]. Additional parameterizations are summarized in [1]. The ADAS employs the hybrid 4D Ensemble- Variational (EnVar) configuration ([15]), with the ensemble providing flow-dependent background error covariance information. The resultant analysis increments are fed back to the forecast model through the 4D incremental analysis update (IAU) approach ([11]). In this study, PBL height data are being or have been generated from radiosondes, GNSS RO, satellite (CATS, CALIPSO and ICESat-2) and ground-based (MPLNET) lidars, and wind profiler. Investigations have been conducted to specify quality marks for PBL height retrievals for the data assimilation purpose. These PBL height data have different strengths and weaknesses ([2], [3], [6], [7], [8], [10]), and the satellite PBL height data provide better global coverage and complement in-situ PBL height data. Radiosondes offer high accuracy and in situ measurement of temperature and humidity profiles, but with poor spatio-temporal sampling. The in-situ observing systems like MPLNET and wind profiler provide long history of PBL height records at each station. The GNSS RO based PBL height is retrieved based on the sharp gradients in refractivity profile that represent the fine vertical structure of temperature and moisture changes above the PBL. However, not all RO refractivity profiles reach the surface depending on location and regime, and RO refractivity retrievals can be negatively biased below 2km. The PBL height data from satellite lidars provide high resolution along track PBL height retrievals, but over land they are affected by previous day convective PBL aerosol and strongly associated with mixing layer and retrievals cannot be made below thick, attenuating clouds. A successful assimilation of PBL height data requires a thorough understanding of the observing method and the retrieval algorithm for each observing system in order to use the PBL height data from multiple observing systems properly. Due to the sensitivity of PBL height data to the observing method and choice of algorithm, it is important to use a model definition appropriate for each observation type to compute differences between PBL height data and model PBL height (OmFs). The GEOS model currently includes two PBL height definitions suitable for direct comparison with observed PBL height, and additional definitions are being added in this study. Evaluation of different model PBL height definitions is underway. Meanwhile, efforts have been made in the GEOS data assimilation system to develop PBL height data assimilation capability. PBL height data can be assimilated using two different approaches. The traditional approach is to construct an observation operator and its tangent linear and adjoint, which link control variables to PBL height data from each observing system. This observation operator can be very complicated, e.g., the lidar-based PBL height observation operator includes the backscatter lidar forward observation operator, the algorithm to derive PBL height from attenuated total backscatter, interpolation, and calculations handling the mismatch between observed and model scales. The other approach is to augment PBL height to the control variable vector, and it is adopted in this study. The latter approach was also used in previous studies, e.g., the assimilation of PBL height data from radiosonde and aircraft in the Real Time Mesoscale Analysis (RTMA) system for a dispersion modelling study ([13]); the PBL height assimilation study using lidar PBL height data at Greensburg, Kansas for a field campaign ([12]). The PBL height assimilation from multiple observing systems in this study allows us to take advantage of the diverse PBL height data that provide much better global coverage collectively under different meteorological conditions and with different temporal and spatial scales. As all the PBL heights are tightly coupled with the PBL thermodynamic variables, the strong correlations, which are provided by the 4D ensemble forecast, enable PBL height data from various sources to interact and combine coherently and provide additional information for PBL temperature and moisture fields. The results of comparisons among PBL height data from different sources and the evaluation of the model PBL height definitions with the PBL height data will be presented, and the PBL height data synergy strategies and preliminary results will also be discussed at the conference.

Y. Zhu↗