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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

STS-64 Space Shuttle mission report

The STS-64 Space Shuttle Program Mission Report summarizes the Payload activities as well as the Orbiter, External Tank (ET), Solid Rocket Booster (SRB), Redesigned Solid Rocket Motor (RSRM), and the Space Shuttle main engine (SSME) systems performance during the sixty-fourth flight of the Space Shuttle Program and the nineteenth flight of the Orbiter vehicle Discovery (OV-103). In addition to the Orbiter, the flight vehicle consisted of an ET that was designated ET-66; three SSMEs that were designated as serial numbers 2031, 2109, and 2029 in positions 1, 2, and 3, respectively; and two SRB's that were designated Bl-068. The RSRM's that were installed in each SRB were designated as 360L041 A for the left SRB, and 360L041 B for the right SRB. The primary objective of this flight was to successfully perform the planned operations of the Lidar In-Space Technology Experiment (LITE), and to deploy the Shuttle Pointed Autonomous Research Tool for Astronomy (SPARTAN) -201 payload. The secondary objectives were to perform the planned activities of the Robot Operated Materials Processing System (ROMPS), the Shuttle Amateur Radio Experiment - 2 (SAREX-2), the Solid Surface Combustion Experiment (SSCE), the Biological Research in Canisters (BRIC) experiment, the Radiation Monitoring Equipment-3 (RME-3) payload, the Military Application of Ship Tracks (MAST) experiment, and the Air Force Maui Optical Site Calibration Test (AMOS) payload.

Fricke, Robert W., Jr.↗
NASA NTRS2020

Clouds and Convective Self-Aggregation in a Multi-Model Ensemble of Radiative-Convective Equilibrium Simulations

The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective56equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climatecsensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique amongst intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than un-aggregated simulations. Plain Language Summary This study investigates tropical clouds and climate using results from more than 30 different numerical models set up in a simplified framework. The dataset of model simulations is unique in that it includes a wide range of model types configured in a consistent manner. We address some of the biggest open questions in climate science, including how cloud properties change with warming and the role that the tendency of clouds to form clusters plays in determining the average climate and how climate changes. While there are large differences in how the different models simulate average temperature, humidity, and cloudiness, in a majority of models, the amount of high clouds decreases as climate warms. Nearly all models simulate a tendency for clouds to cluster together. There is agreement that when the clouds are clustered, the atmosphere is drier with fewer clouds overall. We don’t find a conclusive result for how cloud clustering changes as the climate warms.

Allison A. Wing↗