Detection of meteoritic dust from a sounding rocket.
Meteoritic dust detection from sounding rocket, discussing sensor controls for real time data
Mcdonnell, J. A. M.↗
SEARCH · Search NASA
Results for “dust detection”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Micron-Sized Dust Particles Detected in the Outer Solar System by the Voyager 1 and 2 Plasma Wave Instruments
During the Voyager 1 and 2 flybys of the outer planets it has been demonstrated that the plasma wave instrument can detect small dust particles striking the spacecraft. In this paper, we examine the Voyager plasma wave data for dust impacts in the interplanetary medium at heliocentric radial distances ranging from 6 to 60 astronomical units (AU). The results show that a small but persistent level of dust impacts exists out to at least 30 to 50 AU. The average number density of these particles is about 2 x 10(exp -8)/cu m, and the average mass of the impacting particles is believed to be a few times 10(exp -11) g, which corresponds to particle diameters in the micron range. Possible sources of these particles are planets, moons, asteroids, comets, and the interstellar medium. Of these, comets appear to be the most likely source. The number densities are only weakly dependent on ecliptic latitude, which indicates that the particles probably do not originate from planets, moons, or asteroids. Comparisons with interstellar dust fluxes measured in the inner regions of the solar system by the Ulysses spacecraft indicate that the particles are not of interstellar origin.
Gurnett, D. A.↗
Micron-Sized Dust Particles Detected in the Outer Solar System by the Voyager 1 and 2 Plasma Wave Instruments
During the Voyager 1 and 2 flybys of the outer planets it has been demonstrated that the plasma wave instrument can detect small dust particles striking the spacecraft. In this paper, we examine the Voyager plasma wave data for dust impacts in the interplanetary medium at heliocentric radial distances ranging from 6 to 60 astronomical units (AU). The results show that a small but persistent level of dust impacts exists out to at least 30 to 50 AU. The average number density of these particles is about 2 x 10(exp -8)/cu m, and the average mass of the impacting particles is believed to be a few times 10(exp -11) g, which corresponds to particle diameters in the micron range. Possible sources of these particles are planets, moons, asteroids, comets, and the interstellar medium. Of these, comets appear to be the most likely source. ne number densities are only weakly dependent on ecliptic latitude, which indicates that the particles probably do not originate from planets, moons, or asteroids. Comparisons with interstellar dust fluxes measured in the inner regions of the solar system by the Ulysses spacecraft indicate that the particles are not of interstellar origin.
Gurnett, D. A.↗
The Giotto dust impact detection system
The Giotto Dust Impact Detection System (DIDSY) consists of six independent subsystems to register the impact of all particulates of significant mass incident on the probe during the post-perihelion encounter with Comet Halley. Mounted on Giotto's front dust shield, the detectors will determine the mass spectrum of the dust, with a limiting sensitivity of 10 to the minus 17th power f, increasing to the largest grain masses encountered along Giotto's trajectory throught the cometary environment with an ultimate spatial resolution of 70 km. An additional detector is located on the rear shield to monitor dust particles that penetrate the from dust shield. An ambient plasma monitor measures the impact plasma generated by dust and gass impacts on the spacecraft.
Mcdonnell, J. A. M.↗
Real-Time Detection of Dust Devils from Pressure Readings
A method for real-time detection of dust devils at a given location is based on identifying the abrupt, temporary decreases in atmospheric pressure that are characteristic of dust devils as they travel through that location. The method was conceived for use in a study of dust devils on the Martian surface, where bandwidth limitations encourage the transmission of only those blocks of data that are most likely to contain information about features of interest, such as dust devils. The method, which is a form of intelligent data compression, could readily be adapted to use for the same purpose in scientific investigation of dust devils on Earth. In this method, the readings of an atmospheric- pressure sensor are repeatedly digitized, recorded, and processed by an algorithm that looks for extreme deviations from a continually updated model of the current pressure environment. The question in formulating the algorithm is how to model current normal observations and what minimum magnitude deviation can be considered sufficiently anomalous as to indicate the presence of a dust devil. There is no single, simple answer to this question: any answer necessarily entails a compromise between false detections and misses. For the original Mars application, the answer was sought through analysis of sliding time windows of digitized pressure readings. Windows of 5-, 10-, and 15-minute durations were considered. The windows were advanced in increments of 30 seconds. Increments of other sizes can also be used, but computational cost increases as the increment decreases and analysis is performed more frequently. Pressure models were defined using a polynomial fit to the data within the windows. For example, the figure depicts pressure readings from a 10-minute window wherein the model was defined by a third-degree polynomial fit to the readings and dust devils were identified as negative deviations larger than both 3 standard deviations (from the mean) and 0.05 mbar in magnitude. An algorithm embodying the detection scheme of this example was found to yield a miss rate of just 8 percent and a false-detection rate of 57 percent when evaluated on historical pressure-sensor data collected by the Mars Pathfinder lander. Since dust devils occur infrequently over the course of a mission, prioritizing observations that contain successful detections could greatly conserve bandwidth allocated to a given mission. This technique can be used on future Mars landers and rovers, such as Mars Phoenix and the Mars Science Laboratory.
Wagstaff, Kiri↗
Autonomous Detection of Dust Devils and Clouds on Mars
Acquisition of science in space applications is shifting from teleoperated gathering to an automated on-board analysis with improvements in the use of on-board memory, CPU, bandwidth and data quality. In this paper, we describe algorithms to autonomously detect dust devils and clouds from a rover and summarize the results. These algorithms meet high hit-to-miss ratios and satisfy strict requirements of CPU, memory usage and bandwidth. The detectors have been scheduled for upload to the Mars Exploration Rovers (MER) in 2006. These are the first autonomous science processes in the rovers.
onboard science↗
Multispectral Imagery Research and Applications
The NASA Short-term Prediction Research and Transition (SPoRT) Center developed techniques to improve the quality and interpretation of multispectral imagery derived from NASA/NOAA geostationary satellites using statistical and machine learning approaches. A physically-based machine learning approach, DustTracker-AI, was developed to overcome the problem of night-time dust detection and to augment dust analysis with satellite products such as the Dust RGB. Additionally, an updated limb-correction and intercalibration methodology for short-wave, near-infrared, thermal infrared, and water vapor bands was developed for the purpose of developing a suite of high-quality RGB imagery that can be used at high viewing angles and across the constellation of geostationary sensors. This presentation will briefly highlight the techniques developed to detect dust in difficult night-time scenes and improve the quality and interpretation of multispectral imagery.
Emily Berndt↗
Summer Dust Aerosols Detected from CALIPSO Observations over the Tibetan Plateau
Summertime Tibetan dust aerosol plumes are detected from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. CALIPSO reveals that dust storms occur 4 times more frequently than previously found from Tibetan surface observations because few surface sites were available over remote northwestern Tibet. The Tibetan dust aerosol is characterized by column-averaged depolarization and color ratios around 21% and 0.83, respectively. The dust layers appear most frequently around 4-7 km above mean sea level. The depolarization ratio for about 90% of the dust particles is less than 10% at low altitudes (3-5 km), while only about 50% of the particles have a greater depolarization ratio at higher altitudes (7-10 km) suggesting a separation of larger irregular particles from smaller, near spherical ones during transport. The 4-day back trajectory analyses show that these plumes probably originate from the nearby Taklimakan desert surface and accumulate over the northern slopes of the Tibetan Plateau. These dust outbreaks can affect the radiation balance of the atmosphere of Tibet because they both absorb and reflect solar radiation.
Huang, Jianping↗
A Machine Learning Approach to Objective Identification of Dust in Satellite Imagery
Airborne dust has broad adverse effects on human activity, including aviation, human health, and agriculture. Remote sensing observations are used to detect dust and aerosols in the atmosphere using long established techniques. False color Red-Green-Blue (RGB) imagery using band differences sensitive to dust absorption (Dust RGB) is currently used operationally to assist forecasters and decision-makers in identifying dust at night, but there are still limitations, subjectivity, and nuances to image interpretation making night-time dust identification difficult even for experts. This study applies machine learning to the problem of night-time dust detection with a simple random forest (RF) model using Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) infrared imagery, band differences sensitive to dust absorption, and Dust RGB color components as inputs to the model. The RF model achieves an Area-Under-Curve (AUC) of 0.97 with a standard deviation of 0.04 for dust cases. For images with dust present, the model correctly labels 85% of dust pixels and 99.96% of no-dust pixels for all dust images in the validation data set. The addition of a single null case to the training data set drastically reduces error in labeling no-dust pixels as dust from 45% to 14.5%. Application of the machine learning model to the April 13–14, 2019 dust event demonstrates the ability of the model to identify dust during night-time hours when visual dust detection is limited by the cooling ground surface characteristics.
dust↗
Detection of dust impacts by the Voyager planetary radio astronomy experiment
The Planetary Radio Astronomy (PRA) instrument detected large numbers of dust particles during the Voyager 2 encounter with Neptune. The signatures of these impacts are analyzed in some detail. The major conclusions are described. PRA detects impacts from all over the spacecraft body, not just the PRA antennas. The signatures of individual impacts last substantially longer than was expected from complementary Plasma Wave Subsystem (PWS) data acquired by another Voyager experiment. The signatures of individual impacts demonstrate very rapid fluctuations in signal strength, so fast that the data are limited by the speed of response of the instrument. The PRA detects events at a rate consistently lower than does the Plasma Wave subsystem. Even so, the impact rate is so great near the inbound crossing of the ring plane that no reliable estimate of impact rate can be made for this period. The data are consistent with the presence of electrons accelerated by ions within an expanding plasma cloud from the point of impact. An ancillary conclusion is that the anomalous appearance of data acquired at 900 kHz appears to be due to an error in processing the PRA data prior to their delivery rather than due to overload of the PRA instrument.
Evans, David R.↗
Dust particles detected near Giacobini-Zinner by the ICE plasma wave instrument
During the ICE flyby of Giacobini-Zinner the plasma wave instrument detected numerous impulsive signals caused by dust impacts on the spacecraft. Most of the impacts occurred within 30,000 km of the comet. The impact rate averaged over the inbound and outbound legs varies approximately as 1/r-squared, as would be expected for an isotropic constant velocity radial outflow. Small differences between the inbound and outbound legs exist which may be indicative of azimuthal variations in the dust production rate. A simple model of the impact ionization and charge collection by the antenna indicates that the particles have a mass on the order of 10 to the -12 to 10 to the -10th gm, corresponding to particles with radii of a few microns.
Gurnett, D. A.↗
Convective Vortices and Dust Devils Detected and Characterized by Mars 2020
Abstract We characterize vortex and dust devils (DDs) at Jezero from pressure and winds obtained with the Mars Environmental Dynamics Analyzer (MEDA) instrument on Mars 2020 over 415 Martian days (sols) ( Ls = 6°–213°). Vortices are abundant (4.9 per sol with pressure drops >0.5 Pa correcting from gaps in coverage) and they peak at noon. At least one in every five vortices carries dust, and 75% of all vortices with Δ p > 2.0 Pa are dusty. Seasonal variability was small but DDs were abundant during a dust storm ( Ls = 152°–156°). Vortices are more frequent and intense over terrains with lower thermal inertia favoring high daytime surface‐to‐air temperature gradients. We fit measurements of winds and pressure during DD encounters to models of vortices. We obtain vortex diameters that range from 5 to 135 m with a mean of 20 m, and from the frequency of close encounters we estimate a DD activity of 2.0–3.0 DDs km −2 sol −1 . A comparison of MEDA observations with a Large Eddy Simulation of Jezero at Ls = 45° produces a similar result. Three 100‐m size DDs passed within 30 m of the rover from what we estimate that the activity of DDs with diameters >100 m is 0.1 DDs km −2 sol −1 , implying that dust lifting is dominated by the largest vortices in Jezero. At least one vortex had a central pressure drop of 9.0 Pa and internal winds of 25 ms −1 . The MEDA wind sensors were partially damaged during two DD encounters whose characteristics we elaborate in detail.
Hueso, R.↗
Combing Visible and Infrared Spectral Tests for Dust Identification
The MODIS Dark Target aerosol algorithm over Ocean (DT-O) uses spectral reflectance in the visible, near-IR and SWIR wavelengths to determine aerosol optical depth (AOD) and Angstrom Exponent (AE). Even though DT-O does have "dust-like" models to choose from, dust is not identified a priori before inversion. The "dust-like" models are not true "dust models" as they are spherical and do not have enough absorption at short wavelengths, so retrieved AOD and AE for dusty regions tends to be biased. The inference of "dust" is based on postprocessing criteria for AOD and AE by users. Dust aerosol has known spectral signatures in the near-UV (Deep blue), visible, and thermal infrared (TIR) wavelength regions. Multiple dust detection algorithms have been developed over the years with varying detection capabilities. Here, we test a few of these dust detection algorithms, to determine whether they can be useful to help inform the choices made by the DT-O algorithm. We evaluate the following methods: The multichannel imager (MCI) algorithm uses spectral threshold tests in (0.47, 0.64, 0.86, 1.38, 2.26, 3.9, 11.0, 12.0 micrometer) channels and spatial uniformity test [Zhao et al., 2010]. The NOAA dust aerosol index (DAI) uses spectral contrast in the blue channels (412nm and 440nm) [Ciren and Kundragunta, 2014]. The MCI is already included as tests within the "Wisconsin" (MOD35) Cloud mask algorithm.
aerosol optical depth↗
Mass distribution of particulates measured by Giotto's Dust Impact Detection System (DIDSY) in the close encounter period
The cumulative mass distribution of the comet Halley dust efflux in the close encounter period -300 to +300 sec was analyzed. Analysis of the cumulative mass distribution index (alpha) shows considerable short time scale variation. There is clear evidence from the flux rates of passage through at least one major dust enhancement (dust jet) just after closest approach and this is associated with a steepening of the alpha. Comparison with measurements from other sources, and with preencounter predictions, is also made.
Zarnecki, J. C.↗
Development of experimental and statistical methods for obtaining and interpreting micrometeorite data from space vehicles Semiannual status report, 1 Aug. 1965 - 31 Jan. 1966
Construction and calibration of capacitor microphones for cosmic dust detection
COSMIC DUST↗
Herschel Detects a Massive Dust Reservoir in Supernova 1987A
We report far-infrared and submillimeter observations of Supernova 1987A, the star that exploded on February 23, 1987 in the Large Magellanic Cloud, a galaxy located 160,000 light years away. The observations reveal the presence of a population of cold dust grains radiating with a temperature of approx.17-23 K at a rate of about 220 stellar luminosity. The intensity and spectral energy distribution of the emission suggests a dust mass of approx.0.4-0.7 stellar mass. The radiation must originate from the SN ejecta and requires the efficient precipitation of all refractory material into dust. Our observations imply that supernovae can produce the large dust masses detected in young galaxies at very high red shifts.
Matsuura, M.↗
Plasma-Based Detector of Outer-Space Dust Particles
A report presents a concept for an instrument to be flown in outer space, where it would detect dust particles - especially those associated with comets. The instrument would include a flat plate that would intercept the dust particles. The anticipated spacecraft/dust-particle relative speeds are so high that the impingement of a dust particle on the plate would generate a plasma cloud. Simple electric dipole sensors located equidistantly along the circumference of the plate would detect the dust particle indirectly by detecting the plasma cloud. The location of the dust hit could be estimated from the timing of the detection pulses of the different dipoles. The mass and composition of the dust particle could be estimated from the shapes and durations of the pulses from the dipoles. In comparison with other instruments for detecting hypervelocity dust particles, the proposed instrument offers advantages of robustness, large collection area, and simplicity.
Tsurutani, Bruce↗
Detecting Interplanetary Dust Particles with Radars to Study the Dynamics at the Edge of the Space
The Earth's mesosphere is the region of the atmosphere between approximately 60-120 km altitude, where the transition from hydrodynamic flow to molecular diffusion occurs. It is highly dynamic region where turbulence by wave braking is produced and energy is deposited from sources from both, below and above this altitude range. Because aircraft and nearly all balloons reach altitudes below approximately 50 km and orbital spacecrafts are well above approximately 400 km, the mesosphere has only been accessed through the use of sounding rockets or remote sensing techniques, and as a result, it is the most poorly understood part of the atmosphere. In addition, millions of Interplanetary Dust Particles (IDPs) enter the atmosphere. Within the mesosphere most of these IDPs melt or vaporize as a result of collisions with the air particles producing meteors that can be detected with radars. This provides a mean to study the dynamics of this region. In this lecture the basic principles of the utilization of meteor radars to study the dynamics of the mesosphere will be presented. A system overview of these systems will be provided as well as discuss the advantages/disadvantages of these systems, provide details of the data processing methodology and give a brief overview of the current status of the field as well as the vision for the next decade.
interplanetary dust↗