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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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4 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Similarity theory of the buoyantly interactive planetary boundary layer with entrainment

A similarity model is developed for the vertical profiles of turbulent flow variables in an entraining turbulent boundary layer of arbitrary buoyant stability. In the general formulation the vertical profiles, internal rotation of the velocity vector, discontinuities or jumps at a capping inversion and bulk aerodynamic coefficients of the boundary layer are given by solutions to a system of ordinary differential equations in the similarity variable. To close the system, a formulation for buoyantly interactive eddy diffusivity in the boundary layer is introduced which recovers Monin-Obukhov similarity near the surface and incorporates a hypothesis accounting for the observed variation of mixing length throughout the boundary layer. The model is tested in simplified versions which depend only on roughness, surface buoyancy, and Coriolis effects by comparison with planetary-boundary-layer wind- and temperature-profile observations, measurements of flat-plate boundary layers in a thermally stratified wind tunnel and observations of profiles of terms in the turbulent kinetic-energy budget of convective planetary boundary layers. On balance, the simplified model reproduced the trend of these various observations and experiments reasonably well, suggesting that the full similarity formulation be pursued further.

Hoffert, M. I.↗
NASA NTRS1986

Comments on 'Frontogenesis in a moist semigeostrophic model'

The development of narrow updrafts or jetlike features in the vertical motion field (VMF) over the leading edge of a surface frontal zone is examined on the basis of model simulations, summarizing and clarifying the results presented by Keyser and Anthes (1982) and responding to critical remarks by Mak and Bannon (1984). Typical velocity and potential-temperature cross sections are shown, and it is concluded that the inclusion of generally parameterized planetary-boundary-layer (PBL) physics in the model has a significant effect on the VMF, suggesting that frictional processes alone (without latent heating) can explain the formation of jetlike frontal updrafts. In a reply by Mak and Bannon it is argued that the increased strength of the VMF in models including PBL physics is not significant, whereas other models show that the VMF can be significantly strengthened and narrowed by condensational heating alone.

Keyser, D.↗
NASA NTRS1990

The scatterometer - Data and applications

The techniques used to extract oceanographic and meteorological parameters from satellite radar scatterometer data are described and demonstrated, primarily on the basis of Seasat data. The principles of backscatter measurement and the general relationships between wind and wave fields are summarized, and the construction of geophysical data records from scatterometer swaths is explained. Particular attention is given to surface-layer dynamics and stresses; planetary-boundary-layer dynamics; averaging methods; the estimation of heat, moisture, and CO2 flux coefficients; the determination of pressure fields; storm monitoring; synoptic-scale analysis; climate analysis; and ocean dynamics. The capabilities and potential applications of the planned ERS-1, NSCAT, and SCANSCAT satellite scatterometers are briefly indicated.

Brown, R. A.↗
DOE OSTI2024

A physics-based ensemble machine-learning approach to identifying a relationship between lightning indices and binary lightning hazard

To convert lightning indices generated by numerical weather prediction experiments into binary lightning hazard, a machine-learning tool was developed. This tool, consisting of parallel multilayer perceptron classifiers, was trained on an ensemble of planetary boundary layer schemes and microphysics parameterizations that generated four different lightning indices over 1 week. In a subsequent week, the multi-physics ensemble was applied and the machine-learning tool was used to evaluate the accuracy. Unintuitively, the machine-learning tool performed better on the testing dataset than the training dataset. Much of the error may be attributed to mischaracterizing the convection. The combination of the machine learning model and simulations could not differentiate between cloud-to-cloud lightning and cloud-to-ground lightning, despite being trained on cloud-to-ground lightning. It was found that the simulation most representative of the local operational model was the most accurate simulation tested.

54 ENVIRONMENTAL SCIENCES↗