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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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At least 235 records · Page 13

Mesosphere and Lower Thermosphere Temperatures Simulated by WACCM‐X With NAVGEM‐HA Meteorological Analyses and Compared to SABER Observations

Realistic modeling of the dynamics and variability in the upper mesosphere and lower thermosphere (UMLT) is critical to understand the coupling between different layers of the whole atmosphere system. Here we present simulations of the UMLT temperatures at ∼100 km altitude for one year during 2014 by the Whole Atmosphere Community Climate Model with thermosphere-ionosphere extension (WACCM-X) constrained below ∼90 km using meteorological analysis products of the high-altitude version of Navy Global Environmental Model (NAVGEM-HA). The model results are sampled at the same times and latitudes and longitudes as the satellite observations from Thermosphere Ionosphere and Mesosphere Electric Dynamics/Sounding of the Atmosphere using Broadband Emission Radiometry (TIMED/SABER). Comparisons show that the observed and modeled daily zonal mean temperatures are correlated (r ∼0.5–0.7) at most latitudes between ±50°. Both the observations and simulations show an annual variation at mid-latitudes in two hemispheres with the temperature maximum in summer and the minimum in winter, and at lower latitudes the semiannual variation becomes stronger having the temperature maximums at equinoxes and the minimums during solstices. However, the temperatures observed are on average ∼5–10 K (3%–5%) smaller than the model and the observations show a larger variability. Moreover, migrating tidal amplitudes are mostly overestimated by the model. Though differences are noticed, the WACCM-X simulations with NAVGEM-HA meteorological analyses are overall consistent with the SABER observations. These results support that whole atmosphere models informed by high altitude observations would help to simulate the UMLT variability and the atmosphere and ionosphere coupling.

Guiping Liu↗

NASA Power: Global Solar Insolation, Meteorological Parameter Data, and Web Services to Support Sustainable Building Design and Operations

The buildings industry is currently striving to adopt green solutions to make infrastructure more energy-efficient in order to meet the 2050 net-zero climate goals. This planning requires reliable environmental datasets that are crucial in designing, building, and maintaining our world’s-built environment, as well as other energy-related processes and investments. This webinar for the National Institute of Building Sciences provides an overview of NASA’s Prediction Of Worldwide Energy Resources (POWER) Project that informs decision-making and development for sustainable building design and operations by enabling public open discovery, efficient access, and convenient distribution of NASA’s Earth Observations and global atmospheric model datasets. POWER’s datastore is comprised of solar radiation and surface meteorology parameters, spanning nearly 40 years of hourly data, that are easily accessible via several access methods and tools to support three focus areas: 1) renewable energy deployment and management, 2) sustainable infrastructure, and 3) agroclimatology applications. POWER and NASA Earth Science both plan future data parameters, updated tools, and improved observations that could directly support U.S. and international sustainable development goals, climate strategies, and building information modeling. To this end, solar data from several NASA projects and meteorological data from NASA assimilation models have already been reformatted and disseminated to the public via a user-friendly web GIS-enabled based data portal through the POWER Project. POWER data is analysis-ready and accessible through an Application Programming Interface (API), ArcGIS Image Services, and the project’s Data Access Viewer enhanced (DAVe), an interactive online tool. The POWER DAVe also features data consistent with ASHRAE Climate Design Conditions and has developed web image services showing Building Climate Zones and their variability. Through those tools, the data can be downloaded into multiple formats that support the infrastructure community, including CSV and Energy Plus Weather (EPW). POWER’s entire data product catalog is available through Amazon Web Services (AWS) Open Data Registry (ODR) via a free and publicly accessible Simple Storage Service (S3). This webinar provides a full overview of the NASA POWER Project's data and services developed in collaboration with the sustainable infrastructure community. Examples of how the renewable energy and building communities have utilized POWER data products to make decisions and a preview of future data product expansion, including climate projections, and web services will also be provided. Additionally, use case stories from our broad community of users will be presented.

Paul W. Stackhouse↗

Characterizing Spatiotemporal Uncertainty in Interpolated Meteorological Data

Interpolated meteorological data invariably contain errors. These errors have structure in time and space, particularly autocorrelation, which can cause the effects of errors to compound when model outputs are aggregated temporally or spatially. One way to account for this uncertainty is with a probabilistic model from which samples can be drawn that are coherent with respect to underlying spatial and temporal covariance structure. This work describes a probabilistic method for spatial interpolation of point-wise meteorological time series. Observational data from weather stations are generally sparse in space and dense in time (but sometimes missing). The method works by projecting time series onto orthogonal basis vectors and spatially interpolating each resulting component independently. Under suitable assumptions, and data transformations to better satisfy those assumptions, Gaussian process regression provides a complete description of the joint predictive distribution over a Gaussian random field. Spatiotemporally coherent realizations are generated as the sum of conditional (spatial) simulations of each orthogonal (temporal) component. Data-derived and generic orthogonal bases are considered. In addition to spatial interpolation, imputation of missing observational data is examined. The method is applied using near-surface air temperature over the Western United States and validated by comparing theoretical versus actual coverage of predictive distributions and analyzing the degree to which spatial and temporal covariance structure is reproduced. Computational considerations, relating to conditional simulation of random fields, are also addressed.

Conor T Doherty↗

Meteorology from space.

Rockets and spacecraft as tools for meteorological observations in upper atmosphere, discussing temperature variations, wind pressure patterns and demarcated zone

UPPER ATMOSPHERE↗

Flight Investigation of the Performance of a Two-stage Solid-propellant Nike-deacon (DAN) Meteorological Sounding Rocket

A flight investigation of two Nike-Deacon (DAN) two-stage solid-propellant rocket vehicles indicated satisfactory performance may be expected from the DAN meteorological sounding rocket. Peak altitudes of 356,000 and 350,000 feet, respectively, were recorded for the two flight tests when both vehicles were launched from sea level at an elevation angle of 75 degrees. Performance calculations based on flight-test results show that altitudes between 358,000 feet and 487,000 feet may be attained with payloads varying between 60 pounds and 10 pounds.

MISSILES, SPECIFIC TYPES↗

METEOROLOGICAL SATELLITES

This paper presents a status report on the progress of the Meteorological Satellite Program. In this review, I shall try to emphasize primarily the accomplishments since May 1961 when a report on the program was given to the First National Conference on Peaceful Uses of Space in Tulsa, Oklahoma. It is now one year later, and it is fitting that we stop and take stock of what we have been doing and where we are heading.

Meteorological satellite↗