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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Atmospheric oxidation in the presence of clouds during the Deep Convective Clouds and Chemistry (DC3) study

Deep convective clouds are critically important to the distribution of atmospheric constituents throughout the troposphere but are difficult environments to study. The Deep Convective Clouds and Chemistry (DC3) study in 2012 provided the environment, platforms, and instrumentation to test oxidation chemistry around deep convective clouds and their impacts downwind. Measurements on the NASA DC-8 aircraft included those of the radicals hydroxyl (OH) and hydroperoxyl (HO 2 ), OH reactivity, and more than 100 other chemical species and atmospheric properties. OH, HO 2 , and OH reactivity were compared to photochemical models, some with and some without simplified heterogeneous chemistry, to test the understanding of atmospheric oxidation as encoded in the model. In general, the agreement between the observed and modeled OH, HO 2 , and OH reactivity was within the combined uncertainties for the model without heterogeneous chemistry and the model including heterogeneous chemistry with small OH and HO 2 uptake consistent with laboratory studies. This agreement is generally independent of the altitude, ozone photolysis rate, nitric oxide and ozone abundances, modeled OH reactivity, and aerosol and ice surface area. For a sunrise to midday flight downwind of a nighttime mesoscale convective system, the observed ozone increase is consistent with the calculated ozone production rate. Even with some observed-to-modeled discrepancies, these results provide evidence that a current measurement-constrained photochemical model can simulate observed atmospheric oxidation processes to within combined uncertainties, even around convective clouds. For this DC3 study, reduction in the combined uncertainties would be needed to confidently unmask errors or omissions in the model chemical mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Supersaturation, buoyancy, and deep convection dynamics

Motivated by recent discussions concerning differences of convective dynamics in polluted and pristine environments, the so-called convective invigoration in particular, this paper provides an analysis of factors affecting convective updraft buoyancy, such as the in-cloud supersaturation, condensate and precipitation loading, and entrainment. We use the deep convective period from simulations of daytime convection development over land discussed in our previous publications. An entraining parcel framework is used in the theoretical analysis. We show that for the specific case considered here, finite (positive) supersaturation noticeably reduces pseudo-adiabatic parcel buoyancy and cumulative convective available potential energy (cCAPE) in the lower troposphere. This comes from keeping a small fraction of the water vapor in a supersaturated state and thus reducing the latent heating. Such a lower-tropospheric impact is comparable to the effects of condensate loading and entrainment in the idealized parcel framework. For the entire tropospheric depth, loading and entrainment have a much more significant impact on the total CAPE. For the cloud model results, we compare ensemble simulations applying either a bulk microphysics scheme with saturation adjustment or a more comprehensive double-moment scheme with supersaturation prediction. We compare deep convective updraft velocities, buoyancies, and supersaturations from all ensembles. In agreement with the parcel analysis, the saturation-adjustment scheme provides noticeably stronger updrafts in the lower troposphere. For the simulations predicting supersaturation, there are small differences between pristine and polluted conditions below the freezing level that are difficult to explain by standard analysis of the in-cloud buoyancy components. By applying the piggybacking technique, we show that the lower-tropospheric buoyancy differences between pristine and polluted simulations come from a combination of temperature (i.e., latent heating) and condensate loading differences that work together to make polluted buoyancies and updraft velocities slightly larger when compared to their pristine analogues. Overall, the effects are rather small and contradict previous claims of a significant invigoration of deep convection in polluted environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Shallow- and deep-convection characteristics in the greater Houston, Texas, area using cell tracking methodology

The convective lifecycle, from initiation to maturity and dissipation, is driven by a combination of kinematic, thermodynamic, microphysical, and radiative processes that are strongly coupled and variable in time and space. Weather radars have been traditionally used to provide various convective-cloud characteristics. Here, we analyzed climatological convective-cell radar characteristics to obtain and assess the diurnal cycles of three convective-cell types – shallow, modest deep, and vigorous deep convective cells – that formed in the greater Houston area, using the National Weather Service radar from Houston, Texas, and a multi-cell identification and tracking algorithm. The examined dataset spans 4 years (2018–2021) and covers the warm-season months (June to September) in those years. The analysis showed clear diurnal cycles in cell initiation (CI) consistent with the sea breeze circulation and showed diurnal and normalized lifetime relationships in cell evolution parameters (e.g., maximum reflectivity, echo-top height, Geostationary Operational Environmental Satellite-16 (GOES-16) channel 13 brightness temperature, and the height of maximum reflectivity). The cell evolution is well represented by relationships between (1) the height and value of the maximum radar reflectivity, (2) the minimum GOES-16 channel 13 brightness temperature and the maximum vertically integrated liquid, (3) the maximum reflectivity and columnar-average reflectivity, and (4) the echo-top ascent rate and cell lifetime. The relationships presented herein help to identify the cell lifecycle stages such as early shallow convection, vigorous vertical development, anvil development, and convective core dissipation. GOES-16 Aerosol Optical Depth values are also used as a proxy for cell initiation aerosol concentrations to investigate any potential relationships between initiation location and aerosol concentration. Overall, no significant relationships between initiation location and aerosol concentration were found for the three cell types investigated, but there are some minor differences in the pre-CI aerosol optical depth for vigorous deep convective cells.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology

Abstract. Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between the biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different timescales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of vegetation phenology represented in land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and high-temporal-resolution canopy greenness observations through digital repeat photography by the PhenoCam network. We compare a multiple linear regression model, a no-memory-effect LSTM model and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates across 50 sites and 317 site years during 2009–2018, covering deciduous broadleaf forests, evergreen needleleaf forests and grasslands. Results show that the deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than the no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957 and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands). We also find that the full-memory-effect LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the full-memory-effect LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that (1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and (2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach.

Geology↗
DOE OSTI2023

Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado

Abstract. Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model calibration by directly estimating parameters from observations. However, the increasing computational demand for running the state-of-the-art hydrological model limits sufficient ensemble runs for its calibration. In this work, we present a novel knowledge-informed deep learning method that can efficiently conduct the calibration using a few hundred realizations. The method involves two steps. First, we determine decisive model parameters from a complete parameter set based on the mutual information (MI) between model responses and each parameter computed by a limited number of realizations (∼50). Second, we perform more ensemble runs (e.g., several hundred) to generate the training sets for the inverse mapping, which selects informative model responses for estimating each parameter using MI-based parameter sensitivity. We applied this new DL-based method to calibrate a process-based integrated hydrological model, the Advanced Terrestrial Simulator (ATS), at Coal Creek Watershed, CO. The calibration is performed against observed stream discharge (Q) and remotely sensed evapotranspiration (ET) from the water year 2017 to 2019. Preliminary MI analysis on 50 realizations resulted in a down-selection of 7 out of 14 ATS model parameters. Then, we performed a complete MI analysis on 396 realizations and constructed the inverse mapping from informative responses to each of the selected parameters using a deep neural network. Compared with calibration using observations covering all time steps, the new inverse mapping improves parameter estimations, thus enhancing the performance of ATS forward model runs. The Nash–Sutcliffe efficiency (NSE) of streamflow predictions increases from 0.53 to 0.8 when calibrating against Q alone. Using ET observations, on the other hand, does not show much improvement on the performance of ATS modeling mainly due to both the uncertainty of the remotely sensed product and the insufficient coverage of the model ET ensemble in capturing the observation. By using observed Q only, we further performed a multiyear analysis and show that Q is best simulated (NSE > 0.8) by including in the calibration the dry-year flow dynamics that show more sensitivity to subsurface characteristics than the other wet years. Moreover, when continuing the forward runs till the end of 2021, the calibrated models show similar simulation performances during this evaluation period as the calibration period, demonstrating the ability of the estimated parameters in capturing climate sensitivity. Our success highlights the importance of leveraging data-driven knowledge in DL-assisted hydrological model calibration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

The Newberry Deep Drilling Project (NDDP) workshop

The important scientific questions that will form the basis of a full proposal to drill a deep well to the ductile–brittle transition zone (T>400 °C) at Newberry Volcano, central Oregon state, USA, were discussed during an International Continental Drilling Program (ICDP) sponsored workshop held at the Oregon State University-Cascades campus in Bend, Oregon, from 10 to 13 September 2017. Newberry Volcano is one of the largest geothermal heat reservoirs in the USA and has been extensively studied for the last 40 years. The Newberry Deep Drilling Project (NDDP) will be located at an idle geothermal exploration well, NWG 46-16, drilled in 2008, 3500 m deep and 340–374 °C at bottom, which will be deepened another 1000 to 1300 m to reach 500 °C. The workshop concluded by setting ambitious goals for the NDDP: (1) test the enhanced geothermal system (EGS) above the critical point of water, (2) collect samples of rocks within the brittle–ductile transition, (3) investigate volcanic hazards, (4) study magmatic geomechanics, (5) calibrate geophysical imaging techniques, and (6) test technology for drilling, well completion, and geophysical monitoring in a very high-temperature environment. Based on these recommendations, a full drilling proposal was submitted in January 2018 to the ICDP for deepening an existing well. The next steps will be to continue building a team with project, technology, and investment partners to make the NDDP a reality.

58 GEOSCIENCES↗
DOE OSTI2026

Legacy Survey of Space and Time Data Preview 2: deep_coadd dataset type

We present Rubin Data Preview 2 (DP2), the second data preview from the NDF-DOE Vera C. Rubin Observatory. Data Preview 2 (DP2) comprises coadds, detection catalogs, and ancillary data products; and when fully released will also include single-epoch images and difference images. DP2 is derived from observations acquired by the LSST Science Camera (LSSTCam) on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile, primarily during the on-sky commissioning campaign between 2025-04-16 and 2025-09-21, supplemented by observations taken between 2025-10-25 and 2026-01-06 that overlap the commissioning footprint. The DP2 footprint comprises the Science Validation wide-area survey, five Deep Drilling Fields, and a number of targeted small-field regions, including Trifid and Lagoon, Prawn, M49, and New Horizons, all observed as part of the Rubin First Look campaign. Each field was imaged in up to six broad photometric bands, ugrizy, and coadded to produce deep imaging covering an estimated 3,000 deg2. The addition of single-visit-only areas expands the total DP2 footprint to an estimated 15,000 deg2, with coverage in at least one filter. The median per-visit PSF FWHM across the wide-area survey ranges from 1.17 arcsec in the z band to 1.26 arcsec in g and r bands. The deepest field, reaches estimated coadded 5σ depths of u=26 mag, g=26.8 mag, r=26.3 mag, i=26.1 mag, z=25.3 mag, y=23.9 mag. Based on a roughly five-month primary observing baseline and covering only part of the eventual LSST footprint, DP2's area, depth, and multiband coverage nonetheless support a broad range of early science investigations ahead of LSST Data Release This dataset is a subset of the full data release consisting of the deep_coadd dataset type. These are the combination of multiple processed, calibrated, and background- subtracted images, for a patch of sky, for each of the six filters. This release contains 925,460 datasets of this type.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1971

Microbial survival in deep space environment.

Review of the knowledge available on the extent to which microorganisms (mainly microbial spores, vegetative cells, and fungi) are capable of surviving the environment of deep space, based on recent simulation experiments of deep space. A description of the experimental procedures used is followed by a discussion of deep space ecology, the behavior of microorganisms in ultrahigh vacuum, and factors influencing microbial survival. It is concluded that, so far, simulation experiments have proved far less lethal to microorganisms than to other forms of life. There are, however, wide gaps in the knowledge available, and no accurate predictions can as yet be made on the degree of lethality that might be incurred by a microbial population on a given mission. Therefore, sterilization of spacecraft surfaces is deemed necessary if induced panspermia (i.e., interplanetary life propagation) is to be avoided.

Silverman, G. J.↗
NASA NTRS1973

The deep space network, volume 13

The objectives, functions, and organization of the Deep Space Network are summarized. The deep space instrumentation facility, the ground communications facility, and the network control system are described. Other areas reported include: Helios Mission support, DSN support of the Mariner Mars 1971 extended mission, Mariner Venus/Mercury 1973 mission support, Viking mission support, radio science, tracking and ground-based navigation, network control and data processing, and deep space stations.

Source record↗
NASA NTRS1974

Deep space network support of the manned space flight network for Apollo, volume 3

The Deep Space Network (DSN) activities in support of Project Apollo during the period of 1971 and 1972 are reported. Beginning with the Apollo 14 mission and concluding with the Apollo 17 mission, the narrative includes, (1) a mission description, (2) the NASA support requirements placed on the DSN, and, (3) a comprehensive account of the support activities provided by each committed DSN deep space communication station. Associated equipment and activities of the three elements of the DSN (the Deep Space Instrumentation Facility (DSIF), the Space Flight Operations Facility (SFOF), and the Ground Communications Facility (GCF)) used in meeting the radio-metric and telemetry demands of the missions are documented.

Hartley, R. B.↗
NASA NTRS1975

Implementation of the 64-meter-diameter Antennas at the Deep Space Stations in Australia and Spain

The management and construction aspects of the Overseas 64-m Antenna Project in which two 64-m antennas were constructed at the Tidbinbilla Deep Space Communications Complex in Australia, and at the Madrid Deep Space Communications Complex in Spain are described. With the completion of these antennas the Deep Space Network is equipped with three 64-m antennas spaced around the world to maintain continuous coverage of spacecraft operations. These antennas provide approximately a 7-db gain over the capabilities of the existing 26-m antenna nets. The report outlines the project organization and management, resource utilization, fabrication, quality assurance, and construction methods by which the project was successfully completed. Major problems and their solutions are described as well as recommendations for future projects.

Bartos, K. P.↗
NASA NTRS1977

Simply actuated closure for a pressure vessel - Design for use to trap deep-sea animals

A pressure vessel is described that can be closed by a single translational motion within 1 sec. The vessel is a key component of a trap for small marine animals and operates automatically on the sea floor. As the vessel descends to the sea floor, it is subjected both internally and externally to the high pressures of the deep sea. The mechanism for closing the pressure vessel on the sea floor is activated by the timed release of the ballast which was used to sink the trap. As it rises to the sea surface, the internal pressure of the vessel remains near the value present on the sea floor. The pressure vessel has been used in simulated ocean deployments and in the deep ocean (9500 m) with a 75%-85% retention of the deep-sea pressure. Nearly 100% retention of pressure can be achieved by using an accumulator filled with a gas.

Yayanos, A. A.↗
NASA NTRS1980

Concentrator-enhanced photovoltaic arrays for deep space applications

The useful operational range of photovoltaic solar arrays has been limited to 1.5 AU (Mars orbit) due primarily to solar intensity constraints. Preliminary results indicate that the use of concentrator mirrors in conjunction with solar arrays can extend the practical operating range of photovoltaic space power sources to at least 9.5 AU (Saturn orbit). Various aspects of concentrator-enhanced photovoltaic arrays such as size, structure, thermal characteristics, intensity uniformity, pointing accuracy requirements, deployment methods, power performance and system mass are presented. Based on this information, concentrator-enhanced photovoltaic arrays are superior, with respect to power-to-mass and cost, to existing power sources used for deep space missions. The sensitivity of concentrator-enhanced solar arrays to particulate radiation was also examined for representative deep space missions. Results are presented which show that a radiation-degraded, deep space, concentrator-enhanced solar array's performance exceeds that of existing RTG power sources.

Rockey, D. E.↗
NASA NTRS1980

Deep-level defects and recombination parameters in proton irradiated AlGaAs-GaAs solar cells

The deep-level defects produced in high efficiency AlGaAs-GaAs solar cells by proton energies of 50, 100, 200, and 290 keV are investigated. Proton fluences were from 10 to the 10th to 10 to the 13th p/sq cm. The results show that the diode ideality factor varies between 1.9 and 2.1, indicating that the dominant current component is due to the recombination of electron-hole pairs via deep-level traps in the junction space charge region of these diodes. The recombination current increases with increasing proton fluence and proton energy. The reduction of open circuit voltage, short circuit current, and conversion efficiency in the irradiated cells relates directly to the density of the deep-level defects induced by proton irradiation.

Li, S. S.↗
NASA NTRS1982

New CCIR papers on telecommunications for deep space research

Three draft reports submitted by JPL to the International Radio Consultative Committee (CCIR) for inclusion in their 1982 edition of reports and recommendations are presented. Potential interference between deep space telecommunications and the fixed satellite and broadcasting satellite services in harmonically related bands, protection criteria and sharing considerations relating to deep space research, and preferred frequencies and bandwidths for deep space research are considered.

Degroot, N. F.↗
NASA NTRS1982

Deep impurity trapping concepts for power semiconductor devices

High voltage semiconductor switches using deep impurity doped silicon now appear feasible for high voltage (1-100 kV), high power (10 Kw) switching and protection functions for future space power applications. Recent discoveries have demonstrated several practical ways of gating deep impurity doped silicon devices in planar configurations and of electrically controlling their characteristics, leading to a vast array of possible circuit applications. A new family of semiconductor switching devices and transducers are possible based on this technology. New deep impurity devices could be simpler than conventional p-n junction devices and yet use the same basic materials and processing techniques. In addition, multiple functions may be possible on a single device as well as increased ratings.

Sundberg, G. R.↗
NASA NTRS1982

NASA Deep Space Network Operations Scheduling

The functioning of the Deep Space Network Operations Scheduling, Jet Propulsion Laboratory, CA is reviewed. The primary objectives of the Operations Scheduling are: to schedule the worldwide global allocation of ground communications, tracking facilities, and equipment; and to provide deep space telecommunications for command, tracking, telemetry, and control in support of flight mission operations and tests. Elements of the earth set are Deep Space Stations (DSS) which provide the telecommunications link between the earth and spacecraft; NASA Communications Network; Network Data Processing Area; Network Operations Control Area which provides operational direction to the DSS; Mission Control and Computing systems; and Mission Support areas which provide flight control of the spacecraft. Elements of the space set include mission priorities and requirements which determine the spacecraft queue for allocating network resources. Scheduling is discussed in terms of long-range (3 years), mid-range (8 weeks), and short-range (2 weeks).

Enari, D. M.↗
NASA NTRS1984

Minor elements in relict olivine grains of deep-sea spheres: Match with Mg-rich olivines from C2 meteorites

The bulk composition and relict minerals of meteoroid ablation spheres from deep sea sediments can be related to the parental material, and bulk compositions and elemental ratios favor a CI/CM affinity for most spheres. Although largely melted, some deep sea spheres (DSS) have retained rare grains apparently unmodified chemically by ablation heating or seawater alteration. Minor elements in relict olivines for comparison with compositions of olivines in known meteorites were analyzed. All relict olivines are very Mg rich. No terrestrial olivines match the chemical features which reinforces other evidence for an extraterrestrial origin. There is no match with achondritic olivines. Mg rich olivines occur in all types of carbonaceous meteorites, but the minor elements of most DSS olivines do not match with those for Allende (C3) olivines, and fit poorly with those of Murchison (C2) olivines. There is a good fit for Fe and Cr with those of the olivines in the unusual Belgica 7904 (C2) meteorite (3). It seems likely that the relict olivines of at least many deep sea spheres are chemically related to olivines in at least one C2 meteorite.

Smith, J. V.↗