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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.

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NASA NTRS2017

TPSAS-NF1676L-26965-DND

A comprehensive understanding of the spatial and temporal distributions of clouds on a global scale can be best achieved when the vertical distributions and multi-layer occurrence frequencies obtained from active remote sensors are fully integrated with the horizontal distributions currently provided by passive sensors. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite lidar onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft was specially designed to acquire aerosol and cloud profiles with unprecedented high vertical resolution and accuracy. As a part of the A-Train satellite constellation, CALIPSO has been operating routinely for more than 10 years and continues to provide a wealth of cloud observations to describe the mean state and inter-annual variability. Recently a suite of level 3 (L3) cloud products has been under development by the CALIPSO lidar science working group at the NASA Langley Research Center. These products describe 3-dimensional (3D) cloud occurrence and 3D ice cloud extinction coefficients and ice water content. Future evolution of the products will add observations from the Imaging Infrared Radiometer onboard CALIPSO. Here we present a brief introduction and provide results from a product prototype. We will characterize the inter-annual vertical variability of zonal cloud occurrence and ice water content during the last 10 years. Suggestions and comments are welcome to help us design and provide better cloud climatology products using CALIOP observations for our cloud community.

Xia Cai↗
NASA NTRS2010

Orbital Debris: Quarterly News, Volume 14, Issue 2

This bulletin contains articles from the Orbital Debris Program office. This issue's articles are: "Orbital Debris Success Story --A Decade in the Making", "Old and New Satellite Breakups Identified," "Update on Three Major Debris Clouds," and "MMOD Inspection of the HST Bay 5 Multi-Layer Insulation Panel" about micrometeoroid and orbital debris (MMOD) inspection of the Hubble Space Telescope (HST) insulation panel. A project review is also included (i.e., "Small Debris Observations from the Iridium 33/Cosmos 2251 Collision.") There are also abstra cts of conference papers from the staff of the program office.

Liou, J. C.↗
DOE OSTI2024

Radar and lidar based cloud type product at the ARM ENA observatory

Following methods outlined in Remillard et al. (2012), we classify seven cloud types using radar reflectivity, best-estimated cloud base, and cloud-layer product from the Active Remotely Sensed Cloud Locations (ARSCL) product (Kollias et al. 2007). A cloud mask is created based on the detectable radar reflectivity (>-40 dBZ) combined with the best-estimated cloud base height. Each cloud object is analyzed individually as contiguous cloudy pixels, and its type is determined based on the cloud’s boundaries and duration. Focusing on marine boundary-layer clouds, low clouds are further classified into four types: shallow cumulus, broken stratocumulus (Sc) or cumulus clouds, single-layer Sc, and multi-layer Sc or Sc coupled with cumulus. The remaining three categories are middle clouds, high clouds, and deep convective clouds. For detailed definition of each cloud type, please refer to Zheng et al. (2024).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

TPSAS-NF1676L-11082-DND

Analysis of one decade of radar-lidar and Geostationary Operational Environmental Satellite (GOES) observations at the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) site reveals that there is excellent agreement in the long-term mean cloud fractions (CF) derived from the surface and GOES data, and the CF is independent of temporal resolution and spatial scales for grid boxes of size 0.5o to 2.5°. When computed over a 0.5-hr (4-hr) period, cloud frequency of occurrence (FREQ) and amount when present (AWP) derived from the point surface data agree very well with the same quantities determined from GOES for a 0.5o (2.5o) region centered on the ARM SGP site. The values of FREQ (AWP) derived from the radar-lidar observations at a given altitude increase (decrease) as the averaging period increases from 5 min to 6 hours. Similarly, CF at a given altitude increases as the vertical resolution increases from 90 to 1000 m. The profiles of CF have distinct bimodal vertical distributions with a lower peak between 1 and 2 km and a higher one between 8 and 11 km. The 10-year mean total CF, 46.9%, varies seasonally from a summer minimum of 39.8% to a maximum of 54.6% during the winter. The annual mean CF is 1-2% less than that from previous studies, ~48-49%, because fewer clouds occurred during 2005 and 2006, especially during winter. The differences in single- and multi-layered CFs between this study and an earlier analysis can be explained by the different temporal resolutions used in the two studies where single-layered CFs decrease but multilayered CFs increase from a 5-min resolution to a 1-hr resolution. The vertical distribution of nighttime GOES high-cloud tops agrees well with surface observations, but during daytime, fewer high clouds are retrieved by the GOES analysis than seen from the surface observations. The frequencies of occurrence for both daytime and nighttime GOES low-cloud tops are significantly higher than surface observations, but the cloud fractions are in good agreement.

B Xi↗
NASA NTRS1999

Mesoscale Simulations of a Florida Sea Breeze Using the PLACE Land Surface Model Coupled to a 1.5-Order Turbulence Parameterization

A sophisticated land-surface model, PLACE, the Parameterization for Land Atmospheric Convective Exchange, has been coupled to a 1.5-order turbulent kinetic energy (TKE) turbulence sub-model. Both have been incorporated into the Penn State/National Center for Atmospheric Research (PSU/NCAR) mesoscale model MM5. Such model improvements should have their greatest effect in conditions where surface contrasts dominate over dynamic processes, such as the simulation of warm-season, convective events. A validation study used the newly coupled model, MM5 TKE-PLACE, to simulate the evolution of Florida sea-breeze moist convection during the Convection and Precipitation Electrification Experiment (CaPE). Overall, eight simulations tested the sensitivity of the MM5 model to combinations of the new and default model physics, and initialization of soil moisture and temperature. The TKE-PLACE model produced more realistic surface sensible heat flux, lower biases for surface variables, more realistic rainfall, and cloud cover than the default model. Of the 8 simulations with different factors (i.e., model physics or initialization), TKE-PLACE compared very well when each simulation was ranked in terms of biases of the surface variables and rainfall, and percent and root mean square of cloud cover. A factor separation analysis showed that a successful simulation required the inclusion of a multi-layered, land surface soil vegetation model, realistic initial soil moisture, and higher order closure of the planetary boundary layer (PBL). These were needed to realistically model the effect of individual, joint, and synergistic contributions from the land surface and PBL on the CAPE sea-breeze, Lake Okeechobee lake breeze, and moist convection.

Lynn, Barry H.↗
NASA NTRS1990

Stratiform clouds and their interaction with atmospheric motion

During 1989 and 1990, the researchers saw the publication of two papers and the submission of a third for review on work supported primarily by the previous contract, NAS8-36150; the delivery of an invited talk at the SIAM Conference on Dynamical Systems in Orlando, Florida; and the start of two new projects on the radiative effects of stratocumulus on the large-scale flow. The published papers discuss aspects of stratocumulus circulations (Laufersweiler and Shirer, 1989) and the Hadley to Rossby regime transition in rotating spherical systems (Higgins and Shirer, 1990). The submitted paper (Haack and Shirer, 1990) discusses a new nonlinear model of roll circulations that are forced both dynamically and thermally. The invited paper by H. N. Shirer and R. Wells presented an objective means for determining appropriate truncation levels for low-order models of flows involving two incommensurate periods; this work has application to the Hadley to Rossby transition problem in quasi-geostrophic flows (Moroz and Holmes, 1984). The new projects involve the development of a multi-layered quasi-geostrophic channel model for study of the modulation of the large-scale flow by stratocumulus clouds that typically develop off the coasts of continents. In this model the diabatic forcing in the lowest layer will change in response to the (parameterized) development of extensive fields of stratocumulus clouds. To guide creation of this parameterization scheme, researchers are producing climatologies of stratocumulus frequency and the authors correlate these frequencies with the phasing and amplitude of the large-scale flow pattern. Researchers discuss the above topics in greater detail.

Clark, John H. E.↗
NASA NTRS2014

An Efficient, Multi-Layered Crown Delineation Algorithm for Mapping Individual Tree Structure Across Multiple Ecosystems

Deriving individual tree information from discrete return, small footprint LiDAR data may improve forest above ground biomass estimates, and provide tree-level information that is important in many ecological studies. Several crown delineation algorithms have been developed to extract individual tree information from LiDAR point clouds or rasterized canopy height models (CHM), but many of these algorithms have difficulty discriminating between overlapping crowns, and also may fail to detect understory trees. Our approach uses a watershed based delineation of a CHM, which is subsequently refined using the LiDAR point cloud. Individual tree detection was validated with stem mapped field data from the Smithsonian Environmental Research Center (SERC), Maryland, and on a plot and stand level through comparisons of stem density and basal area to delineated metrics at both SERC and a study area in the Sierra Nevada, California. For individual tree detection, the algorithm correctly identified 70% of dominant trees, 58% of co-dominant trees, 35% of intermediate trees and 21% of suppressed trees at SERC. The algorithm had difficulty distinguishing between crowns of small, dense understory trees of approximately the same height. Delineated crown volume alone explained 53% and 84% of the variability in basal area at the SERC and Sierra Nevada sites, respectively. The algorithm produced crown area distributions comparable to diameter at breast height (DBH) size class distributions observed in the field in both study sites. The algorithm detected understory crowns better in the conifer-dominated Sierra Nevada site than in the closed-canopy deciduous site in Maryland. The ability for the algorithm to reproduce both accurate tree size distributions and individual crown geometries in two dissimilar and complex forests suggests great promise for applicability to a wide range of forest systems.

LiDAR↗
NASA NTRS2024

Improved Simulations of Biomass Burning Aerosol Optical Properties and Lifetimes in the NASA GEOS Model during the ORACLES-I Campaign

In order to improve aerosol representation in the NASA Goddard Earth Observing System (GEOS) model, we evaluated simulations of the aerosol properties and transport over the southern African biomass burning source and outflow region using observations made during the first deployment of the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) field campaign in September 2016. An example case study of September 24 was analyzed in detail, during which aircraft-based in-situ and remote sensing observations showed the presence of a multi-layered smoke plume structure with significant vertical variation in single scattering albedo (SSA). Our baseline GEOS simulations were not able to represent the observed SSA variation, nor the observed organic aerosol-to-black carbon ratio (OA : BC). Analyzing the simulated smoke age suggests that the higher altitude, less absorbing smoke plume was younger (~4 days), while the lower altitude and more absorbing smoke plume was older (~7 days). We hypothesize a chemical or microphysical loss process exists to explain the change in aerosol absorption as the smoke plume ages, and we apply a simple 6-day e-folding loss rate to the model hydrophilic biomass burning OA to mimic this process. Adding this loss process required some adjustment to the model assumed scaling factors of aerosol emissions to conserve the regional aerosol loading and further improve the simulated OA : BC ratio. Accordingly, we have increased our biomass burning emissions of OA by 60 % and biomass burning BC by 15 %. We also utilized the ORACLES airborne observations to better constrain the simulation of aerosol optical properties, adjusting the assumed particle size, hygroscopic growth, and absorption. Our final GEOS model simulation with additional OA loss and updated optics showed a better performance in simulating AOD and SSA compared to independent ground and space-based retrievals for the entire month of September 2016, including the OMI Aerosol Index. In terms of radiative implications of our model adjustments, the final GEOS simulation suggested a decreased atmospheric warming of about 10 % (~2 W m -2 ) over the south-east Atlantic region and above the stratocumulus cloud decks compared to the model baseline simulations. These results improve the representation of the smoke age, transport, and optical properties in Earth system models.

aerosols↗
NASA NTRS2015

The Multi-Layer Variable Absorbers in NGC 1365 Revealed by XMM-Newton and NuSTAR

Between 2012 July and 2013 February, NuSTAR and XMM-Newton performed four long-look joint observations of the type 1.8 Seyfert, NGC 1365. We have analyzed the variable absorption seen in these observations in order to characterize the geometry of the absorbing material. Two of the observations caught NGC 1365 in an unusually low absorption state, revealing complexity in the multi-layer absorber that had previously been hidden. We find the need for three distinct zones of neutral absorption in addition to the two zones of ionized absorption and the Compton-thick torus previously seen in this source. The most prominent absorber is likely associated with broad-line region clouds with column densities of around approximately 10 (sup 23) per square centimeter and a highly clumpy nature as evidenced by an occultation event in 2013 February. We also find evidence of a patchy absorber with a variable column around approximately 10 (sup 22) per square centimeter and a line-of-sight covering fraction of 0.3-0.9, which responds directly to the intrinsic source flux, possibly due to a wind geometry. A full-covering, constant absorber with a low column density of approximately 1 by 10 (sup 22) per square centimeter is also present, though the location of this low density haze is unknown.

galaxies: active aEuro" galaxies: individual (NGC ↗
DOE OSTI2023

Cybersecurity Platform and Certification Framework Development for Extreme Fast Charging (XFC)-Integrated Charging Ecosystem (Final Project Report)

This report summarizes a pioneering effort in Electric Vehicle charging infrastructure ecosystem cybersecurity requirements, assessment methodologies, functional verification, as well as embodiment of the key technologies in the form of hardware and software tools being made available to the public. EPRI led a team of experts, as well as a stakeholder coalition encompassing all key actors in the EV charging infrastructure ecosystem that includes eXtreme Fast Charging (XFC) equipment (defined as 200kW or above). EV charging infrastructure in the United States is a patchwork of networks that have continued to grow organically and have been designed to serve the charging needs of the EV owners, who are their customers. In doing so, each network provider, as well as their connected entities such as the cloud Electric Vehicle Service Providers or EVSPs, utility back office, utility AMI networks, payment networks, as well as Original Equipment Manufacturer (EV manufacturer) telematics networks, have designed systems that may work well individually, but no single entity is responsible for the entire ecosystem to be secure in terms of data exchange. Furthermore, there is no uniformity in how each actor has implemented the cybersecurity requirements since no system-wide cybersecurity requirements existed prior to this project. The final project report describes the technical approach guided by the EV charging infrastructure cybersecurity working group, convened specifically for this project. The technical approach included definition of requirements at the ecosystem level, treated as a ‘system of systems’, and then passed down to individual systems (EVSE, EV, cloud EVSP, utility, and the payment networks), followed by developing the cybersecurity risk and vulnerability assessment methods, that were later applied to real-world cyber-physical systems at EPRI, ANL, and NREL laboratories, to validate both the process and the results. Finally, in a spotlight over the most vulnerable equipment, which is the EV charge station (AC or DC), the team developed a multi-layer cybersecurity implementation in the embedded domain embodied by the open-source Secure Network Interface Card (SNIC) demonstrating the various ways in which the infrastructure can be secured protecting against the identified attack surfaces. Finally, the entire process of EV infrastructure cybersecurity assessment was encapsulated in the Electric Vehicle Charging Cybersecurity Management (EVC2M) online GUI-based tool, that is expected to be released to the public. The report presents the objectives, the technical approach, the key results, and recommendations for future work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2015

IPRT Polarized Radiative Transfer Model Intercomparison Project-Phase A

The polarization state of electromagnetic radiation scattered by atmospheric particles such as aerosols, cloud droplets, or ice crystals contains much more information about the optical and microphysical properties than the total intensity alone. For this reason an increasing number of polarimetric observations are performed from space, from the ground and from aircraft. Polarized radiative transfer models are required to interpret and analyse these measurements and to develop retrieval algorithms exploiting polarimetric observations. In the last years a large number of new codes have been developed, mostly for specific applications. Benchmark results are available for specific cases, but not for more sophisticated scenarios including polarized surface reflection and multi-layer atmospheres. The International Polarized Radiative Transfer (IPRT) working group of the International Radiation Commission (IRC) has initiated a model intercomparison project in order to fill this gap. This paper presents the results of the first phase A of the IPRT project which includes ten test cases, from simple setups with only one layer and Rayleigh scattering to rather sophisticated setups with a cloud embedded in a standard atmosphere above an ocean surface. All scenarios in the first phase A of the intercomparison project are for a one-dimensional plane-parallel model geometry. The commonly established benchmark results are available at the IPRT website

radiative transfer↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
NASA NTRS2010

Environmental Impact Specification for Direct Space Weathering of Kuiper Belt and Oort Cloud Objects

The Direct Space Weathering Project of NASA's Outer Planets Research Program addresses specification of the plasma and energetic particle environments for irradiation and surface chemical processing of icy bodies in the outer solar system and the local interstellar medium. Knowledge of the radiation environments is being expanded by ongoing penetration of the twin Voyager spacecraft into the heliosheath boundary region of the outer heliosphere and expected emergence within the next decade into the very local interstellar medium. The Voyager measurements are being supplemented by remote sensing from Earth orbit of energetic neutral atom emission from this boundary region by NASA's Interstellar Boundary Explorer (IBEX). Although the Voyagers long ago passed the region of the Classical Kuiper Belt, the New Horizons spacecraft will encounter Pluto in 2015 and thereafter explore one or more KBOs, meanwhile providing updated measurements of the heliospheric radiation environment in this region. Modeling of ion transport within the heliosphere allows specification of time-integrated irradiation effects while the combination of Voyager and IBEX data supports projection of the in-situ measurements into interstellar space beyond the heliosheath. Transformation of model ion flux distributions into surface sputtering and volume ionization profiles provides a multi-layer perspective for space weathering impact on the affected icy bodies and may account for some aspects of color and compositional diversity. Other important related factors may include surface erosion and gardening by meteoritic impacts and surface renewal by cryovolcanism. Chemical products of space weathering may contribute to energy resources for the latter.

Cooper, John F.↗
DOE OSTI2025

Urban weather modeling using WRF: linking physical assumptions, code implementation, and observational needs

The Weather Research and Forecasting (WRF) model includes urban schemes that simulate the influence of urban surfaces on the atmosphere using parameterizations for flux, and radiative exchanges. Three core schemes – the Bulk urban parameterization, Single-Layer Urban Canopy Model (SLUCM), and Multi-Layer Urban Canopy Model (MLUCM) – represent increasing levels of complexity. Although the parameterizations within these urban schemes are described in the literature, their specific implementation remains poorly documented, thus slowing down model development efforts. This manuscript provides a roadmap to the three urban schemes in WRF version 4.5.2, presenting equations using the same symbols as in the model code, along with references to code lines, and including graphics and explanations that connect the code to its physical foundations. Our thorough review of the urban parameterizations implemented in WRF version 4.5.2 highlighted a handful of parameters that may introduce discontinuities in simulations: (i) in the SLUCM, a 1 mm h −1 rain rate threshold is employed to switch between two minimum moisture availability parameterizations, thus impacting latent heat flux calculations; (ii) in the SLUCM a threshold is used to partition shortwave radiation into direct and diffuse components; (iii) in all three urban schemes, the bulk Richardson number is employed to select the similarity function, which influences the vertical distribution of heat and momentum. We also identified a highly simplified treatment of the radiative balance on roof surfaces. The implications of these simplifications can be assessed through targeted observations across relevant conditions, including varying precipitation rates, cloud cover, and transitions between stability regimes. Furthermore, the widespread application of the Monin-Obukhov similarity theory in these urban schemes warrants model evaluation under highly stable and unstable conditions and in heterogeneous urban settings with variable land cover and building heights on scales finer than model resolution. To address these challenges, we offer guidance on observational strategies, emphasizing the need for multi-parametric measurements to capture potential compensating biases and multi-height measurements that align with the levels where quantities are diagnostic and prognosed in the model (i.e., the lowest atmospheric level of the WRF model). Finally, our inspection of the code revealed implementation bugs that have now been corrected in WRF versions 4.6.0 and 4.6.1. Sensitivity tests over the Atlanta urban area show that these corrections affect surface temperatures, underscoring the importance of performing rigorous documentation and verification of the implementation of parameterizations in model code.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

In-Situ and Remote-Sensing Data Fusion Using Machine Learning Techniques to Infer Urban and Fire Related Pollution Plumes

Airmass type characterization is key in understanding the relative contribution of various emission sources to atmospheric composition and air quality and can be useful in bottom-up model validation and emission inventories. However, classification of pollution plumes from space is often not trivial. Sub-orbital campaigns, such as SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) give us a unique opportunity to study atmospheric composition in detail, by using a vast suite of in-situ instruments for the detection of trace gases and aerosols. These measurements allow identification of spatial and temporal atmospheric composition changes due to various pollution plumes resulting from urban, biogenic and smoke emissions. Nevertheless, to transfer the knowledge gathered from such campaigns into a global spatial and temporal context, there is a need to develop workflow that can be applicable to measurements from space. In this work we rely on sub-orbital in-situ and total column remote sensing measurements of various pollution plumes taken aboard the NASA DC-8 during 2013 SEAC4RS campaign, linking them through a neural-network (NN) algorithm to allow inference of pollution plume types by input of columnar aerosol and trace-gas measurements. In particular, we use the 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) airborne measurements of wavelength dependent aerosol optical depth (AOD), particle size proxies, O3, NO2 and water vapor to classify different pollution plumes. Our method relies on assigning a-priori ground-truth labeling to the various plumes, which include urban pollution, different fire types (i.e. forest and agriculture) and fire stage (i.e. fresh and aged) using cluster analysis of aerosol and trace-gases in-situ and auxiliary (e.g. trajectory) data and the training of a NN scheme to fit the best prediction parameters using 4STAR measurements as input. We explore our misclassification rates as related to our ground-truth labels, and with multi-layered pollution plume cases. The next step in our analysis is to optimize parameter selection for a scheme that can be applied to space-borne aerosol and trace-gas observation platforms such as OMI, and future geostationary satellites such as TEMPO and GEO-CAPE.

Neural-network↗
NASA NTRSDate not supplied

Flux Comparison of Master-8 and Ordem 3.1 Modelled Space Debris Population

With ESA’s Meteoroid And Space debris Terrestrial Environment Reference (MASTER-8) model and NASA’s Orbital Debris Engineering Model(ORDEM) 3.1, the two premier orbital debris engineering models have been officially released. The two models come with significant enhancements and now represent state-of-the-art orbital debris modelling for their respective agencies. Both models provide the community with estimates of the space debris environment from low Earth orbit (LEO) up to at least geostationary altitude. The MASTER population is an event-based simulation of all known events that generate debris and objects that are part of the U.S. Space Surveillance Network (SSN) catalog, which provides coverage of objects with diameters down to approximately 10cm in LEO and 1 m in geosynchronous Earth orbit (GEO). Different models are used to simulate the artificial objects and their orbit evolution over time. These models are called “sources” since they assign an origin to each individual object and consist of fragments, solid rocket motor (SRM) remainders, sodium-potassium(NaK) droplets, paintflakes, ejecta, and multi-layer insulation (MLI) fragments. The objects from each source are characterized by having individual release mechanisms, as well as orbital distributions, material composition, size, and mass distributions. Dedicated radar and telescope observation data is used to calibrate the model for objects larger than 1 cm in LEO and larger than 10 cm in GEO. For calibrating the small-sized objects, below 1 cm, impact data from returned surfaces are analyzed. Because the >1cm object population is dominated by fragments, the fragmentation event database was updated to include new events, as well as re-evaluate past events. Special attention was drawn to re-evaluating theFengyun-1C anti-satellite test from 2007 and Cosmos-Iridium collision event from 2009 since these events shape the fragment population because of their severity. After 2009, the two largest fragmentations in terms of number of tracked debris are the Briz-M explosion in 2012 and the NOAA-16explosion in 2015. In total, there are 261 confirmed fragmentations in the database up to November 2016.The baseline population for ORDEM 3.1 is based on the U.S. SSN catalog, and observational datasets from radar, in situ, and optical sources provide a foundation from which the model populations are statistically extrapolated to smaller size regions. These regions are not well-covered by the SSN catalog yet may pose the greatest threat to operational spacecraft. The NASA Standard Satellite Breakup Model is used to generate fragments greater than 1mm from collisions and explosions, and these fragment populations are scaled using ground-based radar data. Specific major debris-producing events, including the Fengyun-1C, Iridium 33, and Cosmos2251 debris clouds, and unique populations, such as NaK droplets, were re-examined, modelled, and added to the ORDEM environment separately. Optical measurement data is used to model the GEO population down to 10 cm. The debris environment is propagated using NASA’s LEO-to-GEO Environment Debris model, and future explosions of intact objects and collisions involving objects greater than 10 cm are assessed statistically. The environment from a few millimetres down to 10 𝜇m is modelled using a special degradation model where small particles are generated from intact spacecraft and rocket bodies, then the populations are scaled to fit in situ cratering data from Space Shuttle returned surfaces. Fragments smaller than 10 cm are differentiated based on material density categories, i.e., high-, medium-, and low-density, to better characterize the potential debris risk posed to upper stages and spacecraft. This paper will discuss the MASTER and ORDEM approaches for modelling populations and compare fluxes for specific orbits, including sun-synchronous, ISS-altitude, geosynchronous transfer, and GEO. In the end, a conclusion is drawn towards the importance of having multiple fundamentally different, yet validated, models to estimate the space debris population.

Andre Horstmann↗
NASA NTRS2007

The Calipso Thermal Control Subsystem

The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) is a joint NASA-CNES mission to study the Earth s cloud and aerosol layers. The satellite is composed of a primary payload (built by Ball Aerospace) and a spacecraft platform bus (PROTEUS, built by Alcatel Alenia Space). The thermal control subsystem (TCS) for the CALIPSO satellite is a passive design utilizing radiators, multi-layer insulation (MLI) blankets, and both operational and survival surface heaters. The most temperature sensitive component within the satellite is the laser system. During thermal vacuum testing of the integrated satellite, the laser system s operational heaters were found to be inadequate in maintaining the lasers required set point. In response, a solution utilizing the laser system s survival heaters to augment the operational heaters was developed with collaboration between NASA, CNES, Ball Aerospace, and Alcatel-Alenia. The CALIPSO satellite launched from Vandenberg Air Force Base in California on April 26th, 2006. Evaluation of both the platform and payload thermal control systems show they are performing as expected and maintaining the critical elements of the satellite within acceptable limits.

Gasbarre, Joseph F.↗
NASA NTRS2007

The CALIPSO Integrated Thermal Control Subsystem

The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) is a joint NASA-CNES mission to study the Earth's cloud and aerosol layers. The satellite is composed of a primary payload (built by Ball Aerospace) and a spacecraft platform bus (PROTEUS, built by Alcatel Alenia Space). The thermal control subsystem (TCS) for the CALIPSO satellite is a passive design utilizing radiators, multi-layer insulation (MLI) blankets, and both operational and survival surface heaters. The most temperature sensitive component within the satellite is the laser system. During thermal vacuum testing of the integrated satellite, the laser system's operational heaters were found to be inadequate in maintaining the lasers required set point. In response, a solution utilizing the laser system's survival heaters to augment the operational heaters was developed with collaboration between NASA, CNES, Ball Aerospace, and Alcatel-Alenia. The CALIPSO satellite launched from Vandenberg Air Force Base in California on April 26th, 2006. Evaluation of both the platform and payload thermal control systems show they are performing as expected and maintaining the critical elements of the satellite within acceptable limits.

Gasbarre, Joseph F.↗