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Transducer utilizes absorptive properties of water to determine variations in depth without disturbing liquid. Instrument is inexpensive, simple, and small and thus can be used in lieu of direct graduated scale readout or capacitive, ultrasonic, resistive or inducive sensors when these are impractical because of complexity or cost.
The Mars Science Laboratory will enter the Martian atmosphere in 2012 with multiple char depth sensors and in-depth thermocouples in its heatshield. The aerothermal environment experienced by MSL may be computationally recreated using the data from the sensors and a material response program, such as the Fully Implicit Ablation and Thermal (FIAT) response program, through the matching of the char depth and thermocouple predictions of the material response program to the sensor data. A tool, CHanging Inputs from the Environment of FIAT (CHIEF), was developed to iteratively change different environmental conditions such that FIAT predictions match within certain criteria applied to an external data set. The computational environment is changed by iterating on the enthalpy, pressure, or heat transfer coefficient at certain times in the trajectory. CHIEF was initially compared against arc-jet test data from the development of the MSL heatshield and then against simulated sensor data derived from design trajectories for MSL. CHIEF was able to match char depth and in-depth thermocouple temperatures within the bounds placed upon it for these cases. Further refinement of CHIEF to compare multiple time points and assign convergence criteria may improve accuracy.
The novel contribution of this instrument is to take the first depth images on the moon. A commercial Kinect camera from Microsoft has been ruggedized for a CLPS mission to the Lunar South Pole. The 12-megapixel color camera is combined with a 1-megapixel color time-of-flight (ToF) depth sensor. The solid-state depth sensor/LIDAR provides greater resolution, a wider field of view, pixel binning, and reduced power consumption. The high-resolution data collected from the mission can be used to construct a near-field virtual environment of the lunar surface for scientific applications. The depth camera can provide a 360-degree view of the target area by combining ToF data with RGB imagery and rover turning. The unit was evaluated for space flight compliant materials and parts at NASA Ames. The microphone array, RF shield, front face, and outer body parts were removed to reduce mass. Plastic parts were replaced with vacuum-compatible materials; manufactured cables were added to properly interface with the host rover, built by Lunar Outpost. The environmental testing was performed on the Kinect with Random Vibration/Sine Testing (Fig 1a), per GEVS (NASA’s General Environmental Verification Standard for spaceflight launch survival). Thermal Vacuum testing cycled the instrument between expected hot (+ 85 C) and cold survival temperatures, as well as +50 C and -25 C operational temps (Fig 1b). The instrument functioned nominally at the conclusion of vibration and thermal cycling tests. The Azure Kinect is manifested on the Nova-C lander which flies on the Intuitive Machines mission IM-2, landing at the lunar south pole for a mission duration of 14 days (one lunar daylight cycle). The unit will be integrated to the MAPP (Mobile Autonomous Prospecting Platform) rover at Lunar Outpost. The Azure Kinect with ToF feature will improve the resolution of lunar geology data near the south pole and enable ground-based VR experience of details of the lunar surface (Fig 1c).
The Mars 2020 aeroshell's thermal protection system was nearly identical to the Mars Science Laboratory system that successfully completed its mission for the Curiosity rover's landing in 2012. It was predicted that, like Mars Science Laboratory, the Mars 2020 heatshield would experience boundary layer transition and that the thermal protection system would provide sufficient material thickness margins against the aerothermal environments, even after adding radiative heating that was not included for Mars Science Laboratory. The Mars 2020 flight instrumentation suite included heatshield sensors similar to Mars Science Laboratory and new backshell measurements. A full set of surface pressure and in-depth temperature data were collected during atmospheric entry. This paper provides an initial analysis of the flight data and focuses on the reconstructed total surface heat flux inferred from the measured temperatures. Turbulent boundary layer conditions again were observed on the Mars 2020 heatshield, and the temperatures at all eleven heatshield and six backshell thermocouple in-depth sensor locations were well within system capabilities due to favorable entry conditions and conservative uncertainties. New computational fluid dynamics results on the reconstructed entry trajectory are compared to the measured surface pressures and reconstructed total heat fluxes. The predicted heatshield surface pressures at six locations match the data qualitatively and quantitatively well, as expected. Smooth-wall laminar heating predictions prior to boundary layer transition fall above the reconstructed heating on the heatshield. After the observed boundary layer transition time, total heat flux based on algebraic turbulence model calculations generally match the heat flux trends relative to one another. The convective heat flux was predicted to constitute the majority of the total heating. On the backshell, smooth-wall laminar total heat flux predictions generally exceed the reconstructed total heating at six locations. At each of these locations, it is estimated that radiative heating provided almost all of the total heating. The Mars 2020 as-flown aerothermal environments were well below the design levels for all measurement locations due to conservative design assumptions and a stressing design trajectory.
The Mars 2020 aeroshell's thermal protection system was nearly identical to the Mars Science Laboratory system that successfully completed its mission for the Curiosity rover's landing in 2012. It was predicted that, like Mars Science Laboratory, the Mars 2020 heatshield would experience boundary layer transition and that the thermal protection system would provide sufficient material thickness margins against the aerothermal environments, even after adding radiative heating that was not included for Mars Science Laboratory. The Mars 2020 flight instrumentation suite included heatshield sensors similar to Mars Science Laboratory and new backshell measurements. A full set of surface pressure and in-depth temperature data were collected during atmospheric entry. This paper provides an initial analysis of the flight data and focuses on the reconstructed total surface heat flux inferred from the measured temperatures. Turbulent boundary layer conditions again were observed on the Mars 2020 heatshield, and the temperatures at all eleven heatshield and six backshell thermocouple in-depth sensor locations were well within system capabilities due to favorable entry conditions and conservative uncertainties. New computational fluid dynamics results on the reconstructed entry trajectory are compared to the measured surface pressures and reconstructed total heat fluxes. The predicted heatshield surface pressures at six locations match the data qualitatively and quantitatively well, as expected. Smooth-wall laminar heating predictions prior to boundary layer transition fall above the reconstructed heating on the heatshield. After the observed boundary layer transition time, total heat flux based on algebraic turbulence model calculations generally match the heat flux trends relative to one another. The convective heat flux was predicted to constitute the majority of the total heating. On the backshell, smooth-wall laminar total heat flux predictions generally exceed the reconstructed total heating at six locations. At each of these locations, it is estimated that radiative heating provided almost all of the total heating. The Mars 2020 as-flown aerothermal environments were well below the design levels for all measurement locations due to conservative design assumptions and a stressing design trajectory.
Two applications of optimal stochastic filters to navigation systems are described. The first is an air navigation system consisting of an inertial device (INS) and a Loran, plus an altimeter. The second is an application to a system of submarine navigation consisting of an inertial device (SINS) and an Omega plus a depth sensor.
Two sources of H1/3 estimates were available at Platform harvest; a NOAA buoy and CU depth sensor highrate data.
This project will develop an efficient smoothing software for incremental estimation of the relative poses and velocities between multiple, small spacecraft in a formation, and a small, long range depth sensor based on coded aperture imaging that is capable of identifying other spacecraft in the formation. The smoothing algorithm will obtain the maximum a posteriori estimate of the relative poses between the spacecraft by using all available sensor information in the spacecraft formation.This algorithm will be portable between different satellite platforms that possess different sensor suites and computational capabilities, and will be adaptable in the case that one or more satellites in the formation become inoperable. It will obtain a solution that will approach an exact solution, as opposed to one with linearization approximation that is typical of filtering algorithms. Thus, the algorithms developed and demonstrated as part of this program will enhance the applicability of small spacecraft to multi-platform operations, such as precisely aligned constellations and fractionated satellite systems.
We report here on a survey of a lava tube cave by a rover that is instrumented for astrobiology missions. The NASA Ames testbed rover, CaveR, was deployed in Valentine Cave in Lava Beds National Monument (N. CA, USA) during August of 2018. The rover carried an instrument package consisting of Near Infrared and Visible Spectrometer System (NIRVSS) a point spectrometer operating in 1590-3400 nm range, sensitive to H2O and -OH bearing minerals, pyroxenes, and carbonates (Roush, et al 2018); the bore sighted Drill Operations Camera (DOC), a monochrome imager illuminated by LEDs at 410, 540, 640, 740, 905 and 940 nm; a Realsense ™ depth sensor system for 3D model generation; and a high resolution DSLR stereo camera. The payload was mounted on a tiltable instrument platform attached to the left side of the rover. The rover was driven manually in the cave by field operators, following instructions from a remote science operations team, and simulating a mission concept with science-guided autonomy. A simulated mission took place for 3 days with a team of 3 scientists selecting targets and interpreting data from the payload. To begin the mission, the rover drove along one wall of the cave imaging continuously with the Realsense in 20 m cave segments, three total. At the start of each day, the images from a 20m segment and a panorama stitched from them were provided to the science team to examine. The science team used these data to prioritize specific points along the cave wall for the collection of NIRVSS, DOC, and DSLR data. The objective of the data collection was to identify and study putative biological and mineralogical features in the cave. The data were delivered in xGDS, a customized mapping, planning, and data base management software developed at NASA Ames (Lee, et al 2013). Once the targets for further observations were selected, a plan for collecting the observations (positions in the cave and pointing for each requested observation) was constructed using xGDS and delivered to a rover team to execute the science data collection plan. Acquired data were delivered back to the science team for analysis. Preliminary results from the experiment illustrate the utility of the system (rover plus payload) to study the cave geology and mineralogy and its potential for identifying biomineral features.
Aerosol Optical Depth (AOD) is a crucial parameter for understanding atmospheric aerosol distribution and their impact on climate and air quality. With the growing number of Earth observation satellites, there is an abundance of AOD products derived from various sensors onboard both geostationary and low-orbit satellites. The availability of multiple datasets provides an opportunity to harness the strengths of each sensor and create comprehensive and accurate AOD datasets for climate and air quality studies at different temporal and spatial scales. Our NASA aerosol MEaSURES project has made significant strides in recent years by undertaking the ambitious task of developing an open-source package tailored for fusing AOD products from different sources. The package is based on OOP (Object-Oriented Programming) design and is implemented in Python modules. Generic interfaces enable easy inclusion of large and heterogeneous data. The package may be utilized to produce harmonized AOD datasets with enhanced spatial and temporal coverage. The latest version of the package is able to process and integrate the dark-target AOD data from six different sensors: AHI Himawari-8, ABI GOES-West, ABI GOES-East, MODIS AQUA, MODIS TERRA, and VIIRS SNPP. Rigorous validation and intercomparison studies have been performed to assess the accuracy and reliability of the fused AOD product against ground-based measurements and reference datasets. The open-source nature of the developed package ensures transparency, reproducibility, and community engagement. The research community and stakeholders can access, contribute to, and further improve the fusion methodology, making it adaptable to other studies, or expanding it to include new satellite data as they become available. In this poster presentation, we will introduce the accomplishments and challenges faced during the development of the open-source package for AOD data fusion, and demonstrate the advantages of combining AOD products from the six aforementioned satellite sensors. The presentation aims to foster discussions, collaborations, and future directions in integrating Earth observation and remote sensing data, which may contribute to a better understanding of atmospheric aerosols and their impacts on our environment.
No abstract available
Coarse dust aerosols absorb terrestrial radiation leading to significant longwave heating/cooling rates. Modules accounting for aerosol impacts on radiation have been implemented into CRTM framework, but operational centers continue to assume aerosol-free conditions when assimilating infrared radiances into NWP models. This assumption can introduce significant biases in analysis fields (temp, moisture, etc.), which can reduce forecast skill.
No abstract available
Optical depth data gathered by the stratospheric aerosol measurement (SAM II) satellite during the 1979-80 winter season are analyzed to study mean atmospheric motions. The spacecraft photometer yielded extinction rates over the Northern Hemisphere in the 8-30 km altitude interval. Filtering was performed to remove the effects of high clouds and polar stratospheric clouds. Free horizontal mixing was prevalent below 14 km, as was a systematic difference across the polar jet stream above that altitude. The aerosol declined in altitude as the winter progressed. The polar vortex is concluded to have a base at the 14 km altitude and an outer boundary which coincides with the jet stream axis. The model accords with atmospheric tracer measurements made during the open-air nuclear testing programs in the 1950s.
Aerosol reanalysis datasets are model-based, observationally constrained, continuous 3D aerosol fields with a relatively high temporal frequency that can be used to assess aerosol variations and trends, climate effects, and impacts on socioeconomic sectors, such as health. Here we compare and assess the recently published MONARCH (Multiscale Online Non-hydrostatic AtmospheRe CHemistry) high-resolution regional desert dust reanalysis over northern Africa, the Middle East, and Europe (NAMEE) with a combination of ground-based observations and space-based dust retrievals and products. In particular, we compare the total and coarse dust optical depth (DOD) from the new reanalysis with DOD products derived from MODIS (MODerate resolution Imaging Spectroradiometer), MISR (Multi-angle Imaging SpectroRadiometer), and IASI (Infrared Atmospheric Sounding Interferometer) spaceborne instruments. Despite the larger uncertainties, satellite-based datasets provide a better geographical coverage than ground-based observations, and the use of different retrievals and products allows at least partially overcoming some single-product weaknesses in the comparison. Nevertheless, limitations and uncertainties due to the type of sensor, its operating principle, its sensitivity, its temporal and spatial resolution, and the methodology for retrieving or further deriving dust products are factors that bias the reanalysis assessment. We, therefore, also use ground-based DOD observations provided by 238 stations of the AERONET (AErosol RObotic NETwork) located within the NAMEE region as a reference evaluation dataset. In particular, prior to the reanalysis assessment, the satellite datasets were evaluated against AERONET, showing moderate underestimations in the vicinities of dust sources and downwind regions, whereas small or significant overestimations, depending on the dataset, can be found in the remote regions. Taking these results into consideration, the MONARCH reanalysis assessment shows that total and coarse-DOD simulations are consistent with satellite- and ground-based data, qualitatively capturing the major dust sources in the area in addition to the dust transport patterns. Moreover, the MONARCH reanalysis reproduces the seasonal dust cycle, identifying the increased dust activity that occurred in the NAMEE region during spring and summer. The quantitative comparison between the MONARCH reanalysis DOD and satellite multi-sensor products shows that the reanalysis tends to slightly overestimate the desert dust that is emitted from the source regions and underestimate the transported dust over the outflow regions, implying that the model's removal of dust particles from the atmosphere, through deposition processes, is too effective. More specifically, small positive biases are found over the Sahara desert (0.04) and negative biases over the Atlantic Ocean and the Arabian Sea (−0.04), which constitute the main pathways of the long-range dust transport. Considering the DOD values recorded on average there, such discrepancies can be considered low, as the low relative bias in the Sahara desert (< 50 %) and over the adjacent maritime regions (< 100 %) certifies. Similarly, over areas with intense dust activity, the linear correlation coefficient between the MONARCH reanalysis simulations and the ensemble of the satellite products is significantly high for both total and coarse DOD, reaching 0.8 over the Middle East, the Atlantic Ocean, and the Arabian Sea and exceeding it over the African continent. Moreover, the low relative biases and high correlations are associated with regions for which large numbers of observations are available, thus allowing for robust reanalysis assessment.
Absorbing aerosols produced from biomass burning and dust outbreaks are often found to overlay lower level cloud decks and pose greater potentials of exerting positive radiative effects (warming) whose magnitude directly depends on the aerosol loading above cloud, optical properties of clouds and aerosols, and cloud fraction. Recent development of a 'color ratio' (CR) algorithm applied to observations made by the Aura/OMI and Aqua/MODIS constitutes a major breakthrough and has provided unprecedented maps of above-cloud aerosol optical depth (ACAOD). The CR technique employs reflectance measurements at TOA in two channels (354 and 388 nm for OMI; 470 and 860 nm for MODIS) to retrieve ACAOD in near-UV and visible regions and aerosol-corrected cloud optical depth, simultaneously. An inter-satellite comparison of ACAOD retrieved from NASA's A-train sensors reveals a good level of agreement between the passive sensors over the homogeneous cloud fields. Direct measurements of ACA such as carried out by the NASA Ames Airborne Tracking Sunphotometer (AATS) and Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) can be of immense help in validating ACA retrievals. We validate the ACA optical depth retrieved using the CR method applied to the MODIS cloudy-sky reflectance against the airborne AATS and 4STAR measurements. A thorough search of the historic AATS-4STAR database collected during different field campaigns revealed five events where biomass burning, dust, and wildfire-emitted aerosols were found to overlay lower level cloud decks observed during SAFARI-2000, ACE-ASIA 2001, and SEAC4RS- 2013, respectively. The co-located satellite-airborne measurements revealed a good agreement (RMSE less than 0.1 for AOD at 500 nm) with most matchups falling within the estimated uncertainties in the MODIS retrievals. An extensive validation of satellite-based ACA retrievals requires equivalent field measurements particularly over the regions where ACA are often observed from satellites, i.e., south-eastern Atlantic Ocean, tropical Atlantic Ocean, northern Arabian Sea, South-East and North-East Asia.
Sensors measure surface erosion rate of ablating material in reentry vehicle heat shield. Each sensor, which is placed at precise depths in the heat shield is activated when the ablator surface erodes to the location of a sensing point. Sensor depth and activation time determine ablator surface erosion rate.
Sensing the 3D environment of a moving robot is essential for collision avoidance. Most 3D sensors produce dense depth maps, which are subject to imperfections due to various environmental factors. Temporal fusion of depth maps is crucial to overcome those. Temporal fusion is traditionally done in 3D space with voxel data structures, but it can be approached by temporal fusion in image space, with potential benefits in reduced memory and computational cost for applications like reactive collision avoidance for micro air vehicles. In this paper, we present an efficient Gaussian Mixture Models based depth map fusion approach, introducing an online update scheme for dense representations. The environment is modeled from an ego-centric point of view, where each pixel is represented by a mixture of Gaussian inverse-depth models. Consecutive frames are related to each other by transformations obtained from visual odometry. This approach achieves better accuracy than alternative image space depth map fusion techniques at lower computational cost.