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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

NASA Tech Briefs, January 2012

Contents of this issue are: (1) Energy-Based Tetrahedron Sensor for High-Temperature, High-Pressure Environments (2) Handheld Universal Diagnostic Sensor (3) Large-Area Vacuum Ultraviolet Sensors (4) Fiber Bragg Grating Sensor System for Monitoring Smart Composite Aerospace Structures (5) Health-Enabled Smart Sensor Fusion Technology (6) Extended-Range Passive RFID and Sensor Tags (7) Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks (8) Self-Healing, Inflatable, Rigidizable Shelter (9) Improvements in Cold-Plate Fabrication (10) Technique for Radiometer and Antenna Array Calibration - TRAAC (11) Real-Time Cognitive Computing Architecture for Data Fusion in a Dynamic Environment (12) Programmable Digital Controller (13) Use of CCSDS Packets Over SpaceWire to Control Hardware (14) Key Decision Record Creation and Approval Module (15) Enhanced Graphics for Extended Scale Range (16) Debris Examination Using Ballistic and Radar Integrated Software (17) Data Distribution System (DDS) and Solar Dynamic Observatory Ground Station (SDOGS) (18) Integration Manager (19) Eclipse-Free-Time Assessment Tool for IRIS (20) Automated and Manual Rocket Crater Measurement Software (21) MATLAB Stability and Control Toolbox Trim and Static Stability Module (22) Patched Conic Trajectory Code (23) Ring Image Analyzer (24) SureTrak Probability of Impact Display (25) Implementation of a Non-Metallic Barrier in an Electric Motor (26) Multi-Mission Radioisotope Thermoelectric Generator Heat Exchangers for the Mars Science Laboratory Rover (27) Uniform Dust Distributor for Testing Radiative Emittance of Dust-Coated Surfaces (28) MicroProbe Small Unmanned Aerial System (29) Highly Stable and Active Catalyst for Sabatier Reactions (30) Better Proton-Conducting Polymers for Fuel-Cell Membranes (31) CCD Camera Lens Interface for Real-Time Theodolite Alignment (32) Peregrine 100-km Sounding Rocket Project (33) SOFIA Closed- and Open-Door Aerodynamic Analyses (34) Sonic Thermometer for High-Altitude Balloons (35) Near-Infrared Photon-Counting Camera for High-Sensitivity Observations (36) Integrated Optics Achromatic Nuller for Stellar Interferometry (37) High-Speed Digital Interferometry (38) Ultra-Miniature Lidar Scanner for Launch Range Data Collection (39) Shape and Color Features for Object Recognition Search (40) Explanation Capabilities for Behavior-Based Robot Control (41) A DNA-Inspired Encryption Methodology for Secure, Mobile Ad Hoc Networks (42) Quality Control Method for a Micro-Nano-Channel Microfabricated Device (43) Corner-Cube Retroreflector Instrument for Advanced Lunar Laser Ranging (44) Electrospray Collection of Lunar Dust (45) Fabrication of a Kilopixel Array of Superconducting Microcalorimeters with Microstripline Wiring Spacecraft Attitude Tracking and Maneuver Using Combined Magnetic Actuators (46) Coherent Detector for Near-Angle Scattering and Polarization Characterization of Telescope Mirror Coatings

Source record↗
NASA NTRS1993

Quantitative EEG patterns of differential in-flight workload

Four test pilots were instrumented for in-flight EEG recordings using a custom portable recording system. Each flew six, two minute tracking tasks in the Calspan NT-33 experimental trainer at Edwards AFB. With the canopy blacked out, pilots used a HUD display to chase a simulated aircraft through a random flight course. Three configurations of flight controls altered the flight characteristics to achieve low, moderate, and high workload, as determined by normative Cooper-Harper ratings. The test protocol was administered by a command pilot in the back seat. Corresponding EEG and tracking data were compared off-line. Tracking performance was measured as deviation from the target aircraft and combined with control difficulty to achieve an estimate of 'cognitive workload'. Trended patterns of parietal EEG activity at 8-12 Hz were sorted according to this classification. In all cases, high workload produced a significantly greater suppression of 8-12 Hz activity than low workload. Further, a clear differentiation of EEG trend patterns was obtained in 80 percent of the cases. High workload produced a sustained suppression of 8-12 Hz activity, while moderate workload resulted in an initial suppression followed by a gradual increment. Low workload was associated with a modulated pattern lacking any periods of marked or sustained suppression. These findings suggest that quantitative analysis of appropriate EEG measures may provide an objective and reliable in-flight index of cognitive effort that could facilitate workload assessment.

Sterman, M. B.↗
NASA NTRS2008

Processing LiDAR Data to Predict Natural Hazards

ELF-Base and ELF-Hazards (wherein 'ELF' signifies 'Extract LiDAR Features' and 'LiDAR' signifies 'light detection and ranging') are developmental software modules for processing remote-sensing LiDAR data to identify past natural hazards (principally, landslides) and predict future ones. ELF-Base processes raw LiDAR data, including LiDAR intensity data that are often ignored in other software, to create digital terrain models (DTMs) and digital feature models (DFMs) with sub-meter accuracy. ELF-Hazards fuses raw LiDAR data, data from multispectral and hyperspectral optical images, and DTMs and DFMs generated by ELF-Base to generate hazard risk maps. Advanced algorithms in these software modules include line-enhancement and edge-detection algorithms, surface-characterization algorithms, and algorithms that implement innovative data-fusion techniques. The line-extraction and edge-detection algorithms enable users to locate such features as faults and landslide headwall scarps. Also implemented in this software are improved methodologies for identification and mapping of past landslide events by use of (1) accurate, ELF-derived surface characterizations and (2) three LiDAR/optical-data-fusion techniques: post-classification data fusion, maximum-likelihood estimation modeling, and hierarchical within-class discrimination. This software is expected to enable faster, more accurate forecasting of natural hazards than has previously been possible.

Fairweather, Ian↗
NASA NTRS1983

Accreting degenerate dwarfs in close binary systems

Advances in the study of cataclysmic variables made during the past few years are reviewed. The classification of cataclysmic binaries and their dynamic properties are summarized. The hard and soft X-ray emission from these objects is discussed, and two alternative accretion geometries for producing this radiation from deep in the potential well of the degenerate dwarf are considered. The ultraviolet and optical spectrum is addressed, including disk emission and contributions from the companion star. Magnetic fields in cataclysmic variables are discussed, and the temporal behavior of these stars is addressed, including periodic modulations associated with orbital motion and rotation as well as flickering and pulsation reflecting the mass transfer process and the dynamics of matter near the surface of the accreting star. The outburst process is considered, including classical novae, recurrent novae, and dwarf novae.

Cordova, F. A.↗
NASA NTRS2001

Microscale Particulate Classifiers (MiPAC) Being Developed

The NASA Glenn Research Center is developing microscale sensors to characterize atmospheric-borne particulates. The devices are fabricated using MEMS (microelectromechanical systems) technologies. These technologies are derived from those originally developed in support of the semiconductor processing industry. The resulting microsensors can characterize a wide range of particles and are, therefore, suitable to a broad range of applications. This project is supported under a collaborative program called the Glennan Microsystems Initiative. The initiative comprises members of NASA Glenn Research Center, various university affiliates from the State of Ohio, and a number of participating industrial partners. Funding is jointly provided by NASA, the State of Ohio, and industrial members. The work described here is a collaborative arrangement between researchers at Glenn, the University of Minnesota, The National Institute of Standards and Technology (NIST), and the Cleveland State University. Actual device fabrication is conducted at Glenn and at the laboratories of Case Western Reserve University. Case Western is also located in Cleveland, Ohio, and is a participating member of the initiative. The principal investigator for this project is Paul S. Greenberg of Glenn. Two basic types of devices are being developed, and target different ranges of particle sizes. The first class of devices, which is used to measure nanoparticles (i.e., particles in the range of 0.002 to 1 mm), is based on the technique of Electrical Mobility Classification. This technique also affords the valuable ability of measuring the electrical charge state of the particles. Such information is important in the understanding of agglomeration mechanisms and is useful in the development of methods for particle repulsion. The second type of device being developed, which utilizes optical scattering, is suitable for particles larger than 1 mm. This technique also provides information on particle shape and composition. Applications for these sensors include fundamental planetary climatology, monitoring and filtration in spacecraft, human habitation modules and related systems, characterization of particulate emissions from propulsion and power systems, and as early warning sensors for both space-based and ter-restrial fire detection. These devices are also suitable for characterizing biological compounds such as allergens, infectious agents, and biotoxic agents.

Greenberg, Paul S.↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗
NASA NTRSDate not supplied

The Ejectable Data Recorder: A Lean, Risk-Informed Approach for Hardware Development

NASA is developing the Orion spacecraft to transport crew from the Earth to the Moon as part of the Artemis series of missions. To provide a crew escape capability from pre-launch through ascent, the Orion vehicle is equipped with a Launch Abort System (LAS), built by Lockheed Martin, which pulls the capsule away from the launch vehicle in the event of an abort scenario. The Ascent Abort 2 (AA-2) test flight occurred on July 2, 2019,and tested a production version of the LAS to ensure that it can operate as intended, and to collect a large data set from hundreds of sensors on the vehicle to support Orion flight certification. In the original AA-2 architecture, a single-string set of communications antennas on the LAS would downlink all of the in-flight test data to ground stations. However, that communications architecture was predicted to have data dropouts during abort and jettison of the LAS, and would not support data transmission at all after LAS jettison. As a result, a comprehensive trade study was completed, yielding the addition of antennas on the crew module (CM), a buffer/rebroadcast capability for key portions of the flight, and an ejectable data recorder (EDR) subsystem. This EDR subsystem would serve as a backup to the radio frequency (RF) communications system, and would be non-flight critical, providing a unique capability that enabled management to take a different approach with the hardware and software development. The Crew Module and Separation Ring were developed as “Class 1”Flight Hardware, albeit with some tailoring approaches to enable efficiencies. The Class 1 designation requires full rigor for flight hardware and software, documenting everything that happens to a piece of hardware from procurement through disposal, requiring a full spectrum of acceptance tests, and the highest rigor of quality assurance processes. At the other end of the spectrum, Class 3hardware is controlled, but not intended for flight, and leaves the level of rigor up to the project manager. This classification is often used for research and development projects. Similarly,Class-1E has been recently defined at NASA for ISS payloads and technology development projects that are not flight critical and do not need the full rigor of Class 1 to be successful. The EDR subsystem was challenged at commencement to adopt a skunkworks and agile-like approach to hardware development, allowing for a different risk posture than the rest of the AA-2 hardware. After initially pursuing Class 1 processes, the EDR subsystem design evolved to incorporating numerous commercial components, leading to re-designation as a Class-1E subsystem. The resulting EDR subsystem was fully successful in meeting all flight system requirements, and achieved 100% retrieval of flight test data. This paper will discuss the risk posture of the EDR subsystem and the subsequent tailoring that was enacted as part of its Class-1E status.

EDR↗
NASA NTRS2004

Experimental Test-Bed for Intelligent Passive Array Research

This document describes the test-bed designed for the investigation of passive direction finding, recognition, and classification of speech and sound sources using sensor arrays. The test-bed forms the experimental basis of the Intelligent Small-Scale Spatial Direction Finder (ISS-SDF) project, aimed at furthering digital signal processing and intelligent sensor capabilities of sensor array technology in applications such as rocket engine diagnostics, sensor health prognostics, and structural anomaly detection. This form of intelligent sensor technology has potential for significant impact on NASA exploration, earth science and propulsion test capabilities. The test-bed consists of microphone arrays, power and signal distribution modules, web-based data acquisition, wireless Ethernet, modeling, simulation and visualization software tools. The Acoustic Sensor Array Modeler I (ASAM I) is used for studying steering capabilities of acoustic arrays and testing DSP techniques. Spatial sound distribution visualization is modeled using the Acoustic Sphere Analysis and Visualization (ASAV-I) tool.

Solano, Wanda M.↗
NASA NTRS1993

An investigative study of multispectral data compression for remotely-sensed images using vector quantization and difference-mapped shift-coding

A study is conducted to investigate the effects and advantages of data compression techniques on multispectral imagery data acquired by NASA's airborne scanners at the Stennis Space Center. The first technique used was vector quantization. The vector is defined in the multispectral imagery context as an array of pixels from the same location from each channel. The error obtained in substituting the reconstructed images for the original set is compared for different compression ratios. Also, the eigenvalues of the covariance matrix obtained from the reconstructed data set are compared with the eigenvalues of the original set. The effects of varying the size of the vector codebook on the quality of the compression and on subsequent classification are also presented. The output data from the Vector Quantization algorithm was further compressed by a lossless technique called Difference-mapped Shift-extended Huffman coding. The overall compression for 7 channels of data acquired by the Calibrated Airborne Multispectral Scanner (CAMS), with an RMS error of 15.8 pixels was 195:1 (0.41 bpp) and with an RMS error of 3.6 pixels was 18:1 (.447 bpp). The algorithms were implemented in software and interfaced with the help of dedicated image processing boards to an 80386 PC compatible computer. Modules were developed for the task of image compression and image analysis. Also, supporting software to perform image processing for visual display and interpretation of the compressed/classified images was developed.

Jaggi, S.↗
NASA NTRS1980

Optical identification of 4U1907 + 09 using the HEAO-1 scanning modulation collimator position

The paper reports an optical identification of 4U1907 + 09 with a m(v) = 16.4 stellar object in the location determined by the scanning modulation collimator experiment on the first High Energy Astronomy Observatory (HEAO-1). The identification is based on the presence of very strong and broad H-alpha emission. The optical data constrain the distance to be 2-13 kpc, and this gives a range of uncertainty to the typical 2-10 keV luminosity of (1 x 10 to the 35th to 3 x 10 to the 36th) erg/s. The X-ray spectrum and variability is reported, and consideration is given to the hypothesis that the object is an OB supergiant, although its faintness in the blue makes precise spectral classification impossible. It is suggested that this system is an example of a luminous, massive primary emitting a stellar wind which is accreted on the compact object.

Schwartz, D. A.↗
NASA NTRS1991

Three Hierarchies in Skeletal Muscle Fibre Classification Allotype, Isotype and Phenotype

Immunocytochemical analyses using specific anti-myosin antibodies of mammalian muscle fibers during regeneration, development, and after denervation have revealed two distinct myogenic components determining fiber phenotype. The jaw-closing muscles of the cat contain superfast fibers which express a unique myosin not found in limb muscles. When superfast muscle is transplanted into a limb muscle bed, regenerating myotubes synthesize superfast myosin independent of innervation. Reinnervation by the nerve to a fast muscle leads to the expression of superfast and not fast myosin, while reinnervation by the nerve to a slow muscle leads to the expression of a slow myosin. When limb muscle is transplanted into the jaw muscle bed, only limb myosins are synthesized. Thus jaw and limb muscles belong to distinct allotypes, each with a unique range of phenotype options, the expressions of which may be modulated by the nerve. Primary and secondary myotubes in developing jaw and limb muscles are observed to belong to different categories characterized by different patterns of myosin gene expression. By taking into consideration the pattern of myosins synthesized and the changes in fiber size after denervation, 3 types of primary (fast, slow, and intermediate) fibers can be distinguished in rat fast limb muscles. All primaries synthesize slow myosin soon after their formation, but this is withdrawn in fast and intermediate primaries at different times. After neonatal denervation, slow and intermediate primaries express slow primaries hypertrophy with other fibers atrophy. In the mature rat, the number of slow fibers in the EDL is less than the number of slow primaries. Upon denervation, hypertrophic slow fibers matching the number and topographic distribution of slow primaries appear, suggesting that a subpopulation of the slow primaries acquire the fast phenotype during adult life, but reveal their original identity as slow primaries in response to denervation by hypertrophying and synthesizing slow myosin. It is proposed that within each muscle allotype, the various isotypes of primary and secondary fibers are myogenically determined, and are derived from different lineage of myoblasts.

Hoh, Joseph F. Y.↗
NASA NTRS2018

A NICER Look at the Aql X-1 Hard State

We report on a spectral-timing analysis of the neutron star low-mass X-ray binary(LMXB)AqlX-1 with the Neutron Star Interior Composition Explorer (NICER) on the International Space Station (ISS). AqlX-1 wasobserved with NICER during a dim outburst in 2017 July, collecting approximately 50 ks of good exposure. The spectral and timing properties of the source correspond to that of a (hard) extreme island state in the atoll classification. We find that the fractional amplitude of the low-frequency (<0.3Hz) band-limited noise shows adramatic turnover as a function of energy: it peaks at 0.5keV with nearly 25% rms, drops to 12% rms at 2keV,and rises to 15% rms at 10keV. Through the analysis of covariance spectra, we demonstrate that band-limited noise exists in both the soft thermal emission and the power-law emission. Additionally, we measure hard timelags, indicating the thermal emission at 0.5keV leads the power-law emission at 10 keV on a timescale of 100ms at 0.3Hz to10ms at 3Hz. Our results demonstrate that the thermal emission in the hard state is intrinsically variable, and is driving the modulation of the higher energy power-law. Interpreting the thermal spectrum as disk emission, we find that our results are consistent with the disk propagation model proposed for accretion onto black holes.

Bult, Peter↗
NASA NTRS1985

New identifications of bright X-ray sources with the HEAO-1 Scanning Modulation Collimator

Based on data obtained with the HEAO-1 Scanning Modulation Collimator (MC) experiment, candidate Be star systems and BL Lac objects are reported. Identification of the V = 6.6 star HD91188 as a Be star is confirmed, and X-ray luminosities ranging from 9 x 10 to the 32nd to 1.4 x 10 to the 34th ergs/s are derived. A B2 spectral type with a reddening E(B - V) = 0.42 is deduced for a V = 9.87 star which appears in an MC location diamond for the source 1H0550 + 286. A V = 16.5 ultraviolet excess object in the location 2A1058 - 226 = 3A1057 - 224 = 4U1057 - 21 = H1100 - 230 gave a 20 cm flux of 83 mJy, and a V = 16.2 ultraviolet excess object at 3A1422 + 425 = 1H143 + 423 was found to have a 20 cm flux of 33 mJy, suggesting their classification as BL Lac objects.

Schwartz, D. A.↗
NASA NTRS2009

Quantifying Parameter Sensitivity, Interaction and Transferability in Hydrologically Enhanced Versions of Noah-LSM over Transition Zones

We use sensitivity analysis to identify the parameters that are most responsible for shaping land surface model (LSM) simulations and to understand the complex interactions in three versions of the Noah LSM: the standard version (STD), a version enhanced with a simple groundwater module (GW), and version augmented by a dynamic phenology module (DV). We use warm season, high-frequency, near-surface states and turbulent fluxes collected over nine sites in the US Southern Great Plains. We quantify changes in the pattern of sensitive parameters, the amount and nature of the interaction between parameters, and the covariance structure of the distribution of behavioral parameter sets. Using Sobol s total and first-order sensitivity indexes, we show that very few parameters directly control the variance of the model output. Significant parameter interaction occurs so that not only the optimal parameter values differ between models, but the relationships between parameters change. GW decreases parameter interaction and appears to improve model realism, especially at wetter sites. DV increases parameter interaction and decreases identifiability, implying it is overparameterized and/or underconstrained. A case study at a wet site shows GW has two functional modes: one that mimics STD and a second in which GW improves model function by decoupling direct evaporation and baseflow. Unsupervised classification of the posterior distributions of behavioral parameter sets cannot group similar sites based solely on soil or vegetation type, helping to explain why transferability between sites and models is not straightforward. This evidence suggests a priori assignment of parameters should also consider climatic differences.

Rosero, Enrique↗
NASA NTRS1992

Issues in knowledge representation to support maintainability: A case study in scientific data preparation

Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g. spectral densities or running averages). A software system called PIPE provides intelligent assistance to users developing scientific data preparation plans using a programming language called Master Plumber. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developer's attention upon those processing steps and data elements that were used in computing the faulty output values. Finally, the dependency model of a plan can be used to perform plan optimization and runtime estimation. These capabilities allow scientists to spend less time developing data preparation procedures and more time on scientific analysis tasks. Because the scientific data processing modules (called fittings) evolve to match scientists' needs, issues regarding maintainability are of prime importance in PIPE. This paper describes the PIPE system and describes how issues in maintainability affected the knowledge representation used in PIPE to capture knowledge about the behavior of fittings.

Chien, Steve↗
NASA NTRSDate not supplied

Improved Characterization of PSC Processes Derived from a Third-Generation CALIOP and MLS Detection and Composition Classification Algorithm

The new 3-year CloudSat and CALIPSO Science Team project described in this poster will use a unique combination of data from the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) instrument on CALIPSO and the Microwave Limb Sounder (MLS) on Aura, in conjunction with supporting meteorological information and detailed modeling studies, to advance our understanding of polar stratospheric cloud (PSC) processes and their role in ozone depletion. We will develop a third-generation (Gen3) PSC detection and composition algorithm that incorporates a new, more robust two-dimensional, multi-channel CALIOP feature detection scheme (2D-McDA). We will also devise and implement an improved two-dimensional PSC composition classification scheme that utilizes multiple parameters (e.g., CALIOP 532-nm parallel and perpendicular scattering ratios, CALIOP 1064-nm total scattering ratio, MLS HNO3 and H2O, ambient temperature, and temperature histories) in a Bayesian approach to determine the most likely PSC composition and help constrain solid PSC particle number density and size/shape. The combined CALIOP/MLS analyses will allow us to study in detail the full life cycle of PSCs and their resulting impact on gas-phase HNO3 and H2O, which should lead to improved parameterizations of PSC microphysics in global CCMs where detailed particle information is not available. The Gen3 CALIOP PSC algorithm will be a natural stepping-stone toward the analysis of data collected during future spaceborne lidar missions, such as NASA’s Atmosphere Observation System (AtmOS) mission currently scheduled for launch late in this decade. We will also investigate possible trends in PSC occurrence and composition over the entire CALIOP data record and through further comparisons with the Stratospheric Aerosol Measurement (SAM) II solar occultation PSC record from 1979-1989. Finally, we will validate the mountain-wave parameterization and PSC schemes used in the UM-UKCA (Unified Model coupled to the United Kingdom Chemistry and Aerosol module) chemistry-climate model through detailed comparisons with earlier CALIOP PSC data products and those developed under this proposal.

CALIPSO↗
NASA NTRSDate not supplied

Improved Characterization of PSC Processes Derived from a Third-Generation CALIOP and MLS Detection and Composition Classification Algorithm

The new 3-year CloudSat and CALIPSO Science Team project described in this poster will use a unique combination of data from the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) instrument on CALIPSO and the Microwave Limb Sounder (MLS) on Aura, in conjunction with supporting meteorological information and detailed modeling studies, to advance our understanding of polar stratospheric cloud (PSC) processes and their role in ozone depletion. We will develop a third-generation (Gen3) PSC detection and composition algorithm that incorporates a new, more robust two-dimensional, multi-channel CALIOP feature detection scheme (2D-McDA). We will also devise and implement an improved two-dimensional PSC composition classification scheme that utilizes multiple parameters (e.g., CALIOP 532-nm parallel and perpendicular scattering ratios, CALIOP 1064-nm total scattering ratio, MLS HNO3 and H2O, ambient temperature, and temperature histories) in a Bayesian approach to determine the most likely PSC composition and help constrain solid PSC particle number density and size/shape. The combined CALIOP/MLS analyses will allow us to study in detail the full life cycle of PSCs and their resulting impact on gas-phase HNO3 and H2O, which should lead to improved parameterizations of PSC microphysics in global chemistry-climate models (CCMs) where detailed particle information is not available. The Gen3 CALIOP PSC algorithm will be a natural stepping-stone toward the analysis of data collected during future spaceborne lidar missions, such as NASA’s Atmosphere Observation System (AOS) mission currently scheduled for launch late in this decade. We will also investigate possible trends in PSC occurrence and composition over the entire CALIOP data record and through further comparisons with the Stratospheric Aerosol Measurement (SAM) II solar occultation PSC record from 1979-1989. Finally, we will validate the mountain-wave parameterization and PSC schemes used in the UM-UKCA (Unified Model coupled to the United Kingdom Chemistry and Aerosol module) CCM through detailed comparisons with earlier CALIOP PSC data products and those developed under this proposal.

CALIPSO↗