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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Macro-physical Properties of Shallow Cumulus from Integrated ARM Observations (Final Report)

Fair-weather shallow cumuli (ShCu) play an important role in many climate-related processes. Irregular geometry of ShCu and their strong temporal and spatial variability make it challenging to observe ShCu holistically and to represent them correctly in climate models. To improve ShCu parameterizations, information on both vertically and horizontally resolved cloud properties is required. Commonly, the vertically resolved cloud properties are provided by zenith pointing lidar-radar observations with a very narrow field of view (FOV). Thus, these “pencil-beam” properties may not be representative of a larger surrounding area. Limited number of areal-averaged cloud properties, such as fractional sky cover (FSC), are offered typically by wide-FOV observations. The main goal of our project was to integrate advantages of the narrow-FOV (vertical structure of clouds) and wide-FOV (spatial arrangement of clouds) observations for an improved characterization of single-layer ShCu observed at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for an 18-yr period (2000-2017). There are four major accomplishments of our project, First, an updated operational cloud classification for days with ShCu has been suggested and evaluated through a detailed comparison with the manually curated records. Our classification extends successfully the latest ARM cloud type Value Added Product (VAP) based on the Active Remote Sensing of Clouds (ARSCL) cloud product by incorporating both cloud fraction (CF) provided by narrow-FOV ceilometer data and FSC from wide-FOV images offered by a Total Sky Imager (TSI). Moreover, our classification allows one to identify impact of instrumentation changes at the SGP site, namely the transition to KAZRARSCL with the updated cloud radar, on the identification of periods with single-layer ShCu. Second, a new approach that resolves cloud area distributions for a given region (up to 4x4 km 2 ) has been suggested and cloud equivalent diameters (CEDs) have been estimated for the first time. These estimations have been performed over a wide range of cloud sizes (about 0.01–3.5 km) with high temporal resolution (30s) using wide-FOV TSI images and cloud base height (CBH) provided by complementary narrow-FOV lidar measurements. Our simple and computationally inexpensive approach offers a previously unavailable dataset for process studies in the convective boundary layer and evaluation of ShCu parameterizations in cloud-resolving models. Third, a long-term integrated record of ShCu macrophysical properties has been developed. The developed record represents the longest available compilation of events with ShCu and includes (i) a novel visualization of the spatial variability in cloud cover both along- and across-wind directions, (ii) updated estimates of narrow-FOV CF and wide-FOV FSC, (iii) updated narrow-FOV CBH, and (iv) complementary data, such as wind speed and direction from the 915-MHz Radar Wind Profiler (RWP) data. The developed record has been used successfully to assess conventional observational estimates of cloud cover and their sensitivity to the following two factors: (i) instrument-dependent cloud detection and data merging criteria and (ii) FOV configuration. Fourth, co-variability of the ShCu macrophysical properties and environmental parameters has been analyzed for a 3-yr period (2016-2018). Our initial analysis includes diurnal changes of FSCs obtained for clouds with small, moderate and large CEDs and several environmental parameters, such as lifted condensation level (LCL) and mixed layer height (zi). Preliminary results of our analysis suggest that the horizontal extent of ShCu is controlled substantially by the sign and magnitude of difference between these two parameters (zi-LCL): the CED tends to grow with increase of this difference (zi exceeds LCL). We have initiated relationships between the ShCu and key atmospheric parameters that control both the development and evolution of ShCu using our new data product, which combines effectively the advantages of narrow-FOV data offered by zenith pointing cloud radars and lidars and wide-FOV TSI images. While the latest instrumentation at the ARM sites may address these challenging relationships in the future, we believe that the historical ARM data at the SGP site has not yet been fully utilized. Overall, our data product can be used by researchers working on a wide range of climate-related projects. These projects may include (i) a comprehensive evaluation of outputs from the Large-Eddy Simulation (LES) and single-column models for their future improvement, (ii) the representativeness of “short-period” results obtained from the previous model and observational studies and (iii) the planning of future field campaigns with focus on improved understanding of the diurnal cycle of cumulus convection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Survival analysis of localized prostate cancer with deep learning

In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic Health Record data found in the U.S. Department of Veterans Affairs, the largest integrated healthcare system in the United States, we have developed an automated, personalized risk prediction model to support the clinical decision-making process for localized prostate cancer patients. This method combines the representative power of deep learning and the analytical interpretability of parametric regression models and can implement both time-dependent and static input data. To collect a comprehensive evaluation of model performances, we calculate time-dependent C-statistics C td over 2-, 5-, and 10-year time horizons using either a composite outcome or prostate cancer mortality as the target event. The composite outcome combines the Prostate-Specific Antigen (PSA) test, metastasis, and prostate cancer mortality. Our longitudinal model Recurrent Deep Survival Machine (RDSM) achieved C td 0.85 (0.83), 0.80 (0.83), and 0.76 (0.81), while the cross-sectional model Deep Survival Machine (DSM) attained C td 0.85 (0.82), 0.80 (0.82), and 0.76 (0.79) for the 2-, 5-, and 10-year composite (mortality) outcomes, respectively. In addition to estimating the survival probability, our method can quantify the uncertainty associated with the prediction. The uncertainty scores show a consistent correlation with the prediction accuracy. We find PSA and prostate cancer stage information are the most important indicators in risk prediction. Our work demonstrates the utility of the data-driven machine learning model in prostate cancer risk prediction, which can play a critical role in the clinical decision system.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1991

Long-term changes in the total ozone mapping spectrometer relative to world primary standard Dobson spectrometer 83

The stability of the calibration of the Nimbus 7 solar backscatter UV (SBUV) and total ozone mapping spectrometer (TOMS) instruments by comparing their ozone measurements with those made by a single, very stable Dobson instrument: the world primary standard Dobson spectrometer number 83. Measurements of ozone made with instrument 83 at Mauna Loa observatory in eight summers between 1979 and 1989 were compared with coincident TOMS ozone measurements. The comparison shows that relative to instrument 83, ozone measured by TOMS (and SBUV) was stable between 1979 and approximately 1983, had decreased by 3 percent by 1986, and had decreased by almost 7 percent by 1989. A similar time dependence is seen when data from an ensemble of 39 Dobson stations throughout the world is compared with TOMS over the period 1979-1987. The most likely reason for the relative drift is that the diffuser plate used by both SBUV and TOMS to measure solar flux has suffered an uncorrected wavelength-dependent degradation, with most of the degradation occurring after 1983. The recently released version 6 TOMS data, corrected using the internal 'pair justification' technique, show almost no drift relative to Dobson instrument 83. Accurate measurements of long-term global ozone change will require a coherent system incorporating both ground-based and satellite-based ozone measurements.

Mcpeters, Richard D.↗
NASA NTRS1984

Precession model for the nucleus of periodic Comet Kopff

The existing data base on certain characteristics of the periodic Comet Kopff, a candidate for a NASA rendezvous mission in the 1990s, is summarized. The comet was discovered in 1906. The data are used to calculate limits to the nuclear mass (0.7-1.5 x 10 to the 16th g), equatorial radius (1.26-1.64 km), and a geometric albedo of 0.08. An equatorial rotation velocity of 8.1-10.7 hr is projected. The estimates are highly dependent on the present photometric data, which in turn depend on the insolation rate, which if different than assumed could mean that all parameters would be revised upward significantly.

Sekanina, Z.↗
DOE OSTI2026

An Infrared Database of n/k Optical Constants for Calculating Aerosol Spectra for the PICARD Program

The objective of the PICARD Program is to develop fieldable sensing platforms for the rapid chemical identification of aerosol particles in plumes. Standoff detection involves interrogating the aerosol cloud from a distance using optical methods and probing the signal returned from direct backscattering from the aerosol particles or transmitted through the plume after reflection from a retroreflector or surface of opportunity (SOO). The identification of chemical species, however, in aerosols is complicated by their complex compositions and morphologies, chemical interferants, and non-uniform particle sizes. To advance standoff detection of aerosols, modelling the infrared transmittance, reflectance and scattering spectra of aerosolized liquid and solid chemical compounds is required, and then testing that model experimentally via laboratory and field experiments. To perform accurate modeling, the infrared optical constants (n/k), i.e., the complex refractive index, of the compounds of interest are required. Thus, PNNL was tasked to provide the optical constants for a set of analytes relevant to the PICARD program. This report describes the experimental techniques used to derive the optical constants of both liquid and solid compounds using established “gold-standard” protocols, how the experimental data are processed to produce the wavenumber dependent optical constant vectors, and how the data are used in the aerosol absorption spectra modeling.

complex refractive index↗
NASA NTRS1978

Radar scattering laws and wavelength dependence of the lunar surface

Data from Apollo lunar bistatic radar experiments have been processed to give probability density functions for surface slopes. These show best agreement with a Hagfors scattering law, though data having both gaussian and exponential characteristics also exist. Surface roughness estimates range from 4 deg in maria to at least 8 deg in highlands, values which are appropriate to 25 m horizontal scales and which are areal averages over tens of square kilometers. Roughness varies with wavelength, most strongly in maria.

Simpson, R. A.↗
NASA NTRS2019

Assessing Reliability of NDE Flaw Detection Using Smaller Number of Demonstration Data Points

The paper provides an engineering analysis approach for assessing reliability of NDE flaw detection using smaller number of demonstration data points. It explores dependence of probability of detection (POD), probability of false positive (POF), on contrast-to-noise ratio, and net decision threshold-to-noise ratio in a simulated data; and draws some generically applicable inferences to devise the approach. ASTM nondestructive evaluation standards provide requirements on signal-to-noise ratio and/or contrast-to-noise ratio in order to provide reliable flaw detection and limit false positive calls. POD analysis of inspection test data results in an estimated flaw size, denoted by 𝑎90/95. This flaw size has 90% POD and minimum 95% confidence. POF is also estimated in the analysis. POD demonstration requires specimens with flaws of known size. In many situations, it is very expensive to produce the large number of flaws required for the POD analysis. In some situations, only real flaws can truly represent the flaws for demonstration. Real flaws of correct size and location in part configuration specimen may be difficult to produce, if not impossible. Here, an engineering analysis approach is devised using simulation to assess reliability of NDE technique when a limited number of flaws are available for demonstration. In this simulation, a technique is considered reliable, if it provides flaw detectability size equal to or better than the theoretical 𝑎90𝑡ℎ used in simulation and also provides a POF less than or equal to a chosen value. The paper uses simulated signal response versus flaw size data to devise the approach. Linear correlation is used between the signal response data and flaw size. POD software mh1823 uses generalized linear model (GLM) in POD analysis after transforming the flaw size and signal response, if needed, using logarithm. Therefore, this approach is in agreement with the linear signal correlation used in mh1823. Using the POD analysis of data, generic conditions on contrast-to-noise ratio and net decision threshold-to-noise ratio are derived for reliable flaw detection. In order to assess technique reliability using the engineering approach, signal response-to-flaw size correlation about the flaw size of concern is needed. In addition, measurement of noise is also needed. If the technique meets the above requirements, assumption of linear signal-to-flaw size correlation and conditions on noise, then the technique can be assessed using this analysis as it fits the underlying POD model used here. The approach is conservative and is designed to provide a larger flaw size compared to the POD approach. Such NDE technique assessment approach, although, not as rigorous as POD, can be cost effective if the larger flaw size can be tolerated. Typically, this is a situation for all quality control NDE inspections. Here, an NDE technique needs to be reliable and 𝑎90/95 is not estimated, but the assessed flaw size is assumed to be larger than the unknown a90 due to conservative factors or margins. Applicability of the approach for assessing reliability of flaw detection in x-ray radiography and 2D imaging in general is also explored.

Koshti, Ajay M.↗
DOE OSTI2025

Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems

This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use the operator inference approach to model reduction that poses the problem of learning low-dimensional model terms as a regression of state space data and corresponding time derivatives by minimizing the residual of reduced system equations. Standard operator inference models perform well with accurate training data that are dense in time, but producing stable and accurate models when the state data are noisy and/or sparse in time remains a challenge. Another challenge is the lack of uncertainty estimation for the predictions from the operator inference models. Our approach addresses these challenges by incorporating Gaussian process surrogates into the operator inference framework to (1) probabilistically describe uncertainties in the state predictions and (2) procure analytical time derivative estimates with quantified uncertainties. The formulation leads to a generalized least-squares regression and, ultimately, reduced-order models that are described probabilistically with a closed-form expression for the posterior distribution of the operators. The resulting probabilistic surrogate model propagates uncertainties from the observed state data to reduced-order predictions. Furthermore, we demonstrate the method is effective for constructing low-dimensional models of two nonlinear partial differential equations representing a compressible flow and a nonlinear diffusion–reaction process, as well as for estimating the parameters of a low-dimensional system of nonlinear ordinary differential equations representing compartmental models in epidemiology.

Data-driven model reduction↗
NASA NTRS1972

Subsonic, transonic, and supersonic nozzle flow by the inverse technique.

The inverse technique is used to obtain a mathematically and physically consistent solution of the flowfield in a nozzle from the mass generation surface through the supersonic region. The inverse method employs an assumed centerline function which is of the Cauchy type in that the values and the derivatives of the function are known. Since the Cauchy boundary conditions can give rise to numerical instabilities, the governing gasdynamic equations for rotational steady flow were transformed into a form which puts the geometry into a rectangular shape, and which spaces the network of interior points more finely in regions of the greatest gradients of the dependent variables. For arbitrarily specified centerline data, the solution of the governing flow equations may not exist, and if it does it may not depend continuously on the data.

Norton, D. J.↗
NASA NTRS2006

Data Model Management for Space Information Systems

The Reference Architecture for Space Information Management (RASIM) suggests the separation of the data model from software components to promote the development of flexible information management systems. RASIM allows the data model to evolve independently from the software components and results in a robust implementation that remains viable as the domain changes. However, the development and management of data models within RASIM are difficult and time consuming tasks involving the choice of a notation, the capture of the model, its validation for consistency, and the export of the model for implementation. Current limitations to this approach include the lack of ability to capture comprehensive domain knowledge, the loss of significant modeling information during implementation, the lack of model visualization and documentation capabilities, and exports being limited to one or two schema types. The advent of the Semantic Web and its demand for sophisticated data models has addressed this situation by providing a new level of data model management in the form of ontology tools. In this paper we describe the use of a representative ontology tool to capture and manage a data model for a space information system. The resulting ontology is implementation independent. Novel on-line visualization and documentation capabilities are available automatically, and the ability to export to various schemas can be added through tool plug-ins. In addition, the ingestion of data instances into the ontology allows validation of the ontology and results in a domain knowledge base. Semantic browsers are easily configured for the knowledge base. For example the export of the knowledge base to RDF/XML and RDFS/XML and the use of open source metadata browsers provide ready-made user interfaces that support both text- and facet-based search. This paper will present the Planetary Data System (PDS) data model as a use case and describe the import of the data model into an ontology tool. We will also describe the current effort to provide interoperability with the European Space Agency (ESA)/Planetary Science Archive (PSA) which is critically dependent on a common data model.

RDF↗
NASA NTRS2018

[Micron]ADS-B Detect and Avoid Flight Tests on Phantom 4 Unmanned Aircraft System

Researchers at the National Aeronautics and Space Administration Armstrong Flight Research Center in Edwards, California and Vigilant Aerospace Systems collaborated for the flight-test demonstration of an Automatic Dependent Surveillance-Broadcast based collision avoidance technology on a small unmanned aircraft system equipped with the uAvionix Automatic Dependent Surveillance-Broadcast transponder. The purpose of the testing was to demonstrate that National Aeronautics and Space Administration / Vigilant software and algorithms, commercialized as the FlightHorizon UAS"TM", are compatible with uAvionix hardware systems and the DJI Phantom 4 small unmanned aircraft system. The testing and demonstrations were necessary for both parties to further develop and certify the technology in three key areas: flights beyond visual line of sight, collision avoidance, and autonomous operations. The National Aeronautics and Space Administration and Vigilant Aerospace Systems have developed and successfully flight-tested an Automatic Dependent Surveillance-Broadcast Detect and Avoid system on the Phantom 4 small unmanned aircraft system. The Automatic Dependent Surveillance-Broadcast Detect and Avoid system architecture is especially suited for small unmanned aircraft systems because it integrates: 1) miniaturized Automatic Dependent Surveillance-Broadcast hardware; 2) radio data-link communications; 3) software algorithms for real-time Automatic Dependent Surveillance-Broadcast data integration, conflict detection, and alerting; and 4) a synthetic vision display using a fully-integrated National Aeronautics and Space Administration geobrowser for three dimensional graphical representations for ownship and air traffic situational awareness. The flight-test objectives were to evaluate the performance of Automatic Dependent Surveillance-Broadcast Detect and Avoid collision avoidance technology as installed on two small unmanned aircraft systems. In December 2016, four flight tests were conducted at Edwards Air Force Base. Researchers in the ground control station looking at displays were able to verify the Automatic Dependent Surveillance-Broadcast target detection and collision avoidance resolutions.

avoidance↗
DOE OSTI2021

Extraction of the specific shear viscosity of quark-gluon plasma from two-particle transverse momentum correlations

The specific shear viscosity, η/s, of the quark-gluon plasma formed in ultrarelativistic heavy-ion collisions at RHIC and LHC is estimated based on the progressive longitudinal broadening of transverse momentum two-particle correlators, G 2 , reported as a function of collision centrality by the STAR and ALICE experiments. Estimates are computed as a function of collision centrality using the Gavin ansatz which relates the G 2 longitudinal broadening to the specific shear viscosity. Freeze out times required for the use of the ansatz are computed using a linear fit of freeze out times reported as a function of the cubic root of the charged particle pseudorapidity density (dN ch /dη) 1/3 . Estimates of η/s based on ALICE data exhibit little to no dependence on collision centrality at LHC energy, while estimates obtained from STAR data hint that η/s might be a function of collision centrality at top RHIC energy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1975

Lunar electromagnetic scattering. IV - Transfer functions in the long-wavelength limit

The theory for asymmetric lunar magnetic induction in the long-wavelength limit, applicable to induction in the solar wind and magnetosheath plasmas at frequencies no higher than about 0.01 Hz, is presented. The lunar response to arbitrary ambient field orientations can be synthesized from the responses to field fluctuations parallel and perpendicular to the cavity axis. Radial and tangential transfer functions for a particular lunar electrical conductivity model are shown as functions of frequency and angular distance from the cavity axis for parallel and perpendicular field directions. Transfer functions based on asymmetric theory are significantly different from those based on symmetric approximations. Conductivity profiles from inversions of a given data set are strongly dependent on the theory incorporated in the inversions, the nature of the driving field, and the assumed location of the observer. Thus lunar conductivity models from surface and orbital magnetometer data, obtained either on the dayside or the nightside of the moon, must be determined using asymmetric theory.

Schwartz, K.↗
NASA NTRS1984

The earth's thermospheric superrotation from Dynamics Explorer 2

Neutral thermospheric zonal winds measured from the Wind and Temperature Experiment on Dynamics Explorer 2 have been analyzed to provide a measure of the superrotation of the neutral thermosphere. The data were averaged in local time in 10 degree bands from the equator to 40 degrees latitude. The results show an average wind velocity on the order of 18 m/s to the East at the equator and a westward wind of the same magnitude at mid latitudes. Within the range of altitudes of the data base (200 to 700 km) no evidence of any significant altitude dependence was found. Averaging our data over the latitude bands studied indicates a net corotation of the thermosphere. This result is not in agreement with the superrotation value reported by King-Hele and Walker on the basis of satellite drag analysis.

Wharton, L. E.↗
DOE OSTI2025

Physics-guided dual implicit neural representations for source separation

Significant challenges exist in efficient data analysis of most advanced experimental and observational techniques because the collected signals often include unwanted contributions, such as background and signal distortions, that can obscure the physically relevant information of interest. To address this, we have developed a self-supervised machine-learning approach for source separation using a dual implicit neural representation framework that jointly trains two neural networks: one for approximating distortions of the physical signal of interest and the other for learning the effective background contribution. Our method learns directly from the raw data by minimizing a reconstruction-based loss function without requiring labeled data or pre-defined dictionaries. We demonstrate the effectiveness of our framework by considering a challenging case study involving large-scale simulated, as well as experimental, momentum-energy-dependent inelastic neutron scattering data in a four-dimensional parameter space, characterized by heterogeneous background contributions and unknown distortions to the target signal. The method is found to successfully separate physically meaningful signals from a complex or structured background even when the signal characteristics vary across all four dimensions of the parameter space. An analytical approach that informs the choice of the regularization parameter is presented. Our method offers a versatile framework for addressing source separation problems across diverse domains, ranging from superimposed signals in astronomical measurements to structural features in biomedical image reconstructions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

The Role of Nuclear Data Sensitivities in Prompt α-Eigenvalue Predictions of Delayed Critical Benchmarks

Alpha (α) eigenvalues, which describe the logarithmic time derivative of the neutron population in a multiplying system, are integral to time-dependent behavior and diagnostic applications. However, uncertainties in the evaluated nuclear data can significantly impact the accuracy of transport simulations for such quantities. This work explores the use of machine learning models to predict two key outputs, α-eigenvalues and keff bias, using input features derived from α-eigenvalue sensitivities to nuclear data. The criticality safety benchmark models used in this study come from the International Handbook of Evaluated Criticality Safety Benchmark Experiments. Three models, random forest, XGBoost, and NGBoost, are trained on both energy-resolved and energy-summed α sensitivities. For the α-eigenvalue bias prediction, NGBoost achieved the highest R 2 (0.9476) using energy-resolved features, while XGBoost performed best using summed sensitivities. In contrast, when predicting the keff bias, all the models showed moderate predictive capability (best R 2 ≈ 0.72), as the mapping from the static α-sensitivities to the static keff bias was less direct. SHAP (SHapley Additive exPlanations) analysis was used to interpret the model predictions. Across both prediction tasks, the features associated with neutron capture [H-1 (n, γ)], uranium scattering reactions (such as 235 U elastic/inelastic), and actinide capture/fission reactions (such as 239 Pu and 234 U) were consistently identified as the most impactful. This highlights the key role of specific nuclear reactions and energy ranges in shaping both time-dependent and steady-state criticality behavior. These results demonstrated that α-sensitivities, despite being computed for time-dependent metrics, can provide valuable insights for predicting both α-eigenvalues and the keff bias. Moreover, machine learning models offer a promising pathway for uncovering important nuclear data dependencies and guiding future data evaluation efforts.

Nuclear data↗
DOE OSTI2023

Predicted viscosity of liquid HMX up to 40 GPa

Viscous flow serves as a significant heating mechanism during the formation of hot spots, but the shear viscosity which determines this response is poorly characterized for most high explosives. Most recently, a model was proposed for the shear viscosity of liquid HMX (1,3,5,7-tetranitro-1,3,5,7-tetrazocane) that was fit to pressures reaching 5 GPa, but this work uncovered uncertainties in the viscosity at 0 GPa and remains untested at higher pressures. We use molecular dynamics (MD) simulations and the Green-Kubo formalism to predict the temperature- and pressure-dependent shear viscosity of HMX over the pressure interval 0 GPa≤P≤40 GPa. Reassessment of the viscosity at 0 GPa rules out several potential explanations for discrepancies between earlier reports; we attribute these differences to details of MD trajectory integration impacting molecular flexibility. The shear viscosity of HMX exhibits an Arrhenius temperature dependence at each pressure considered, with exponential prefactor and activation energy terms that are also strong functions of pressure. An analytic form for the viscosity is developed based on an extension of the well-known Andrade equation that simultaneously captures the temperature and pressure dependencies in the MD data up to 40 GPa. Comparison against a recently developed model for the viscosity of liquid RDX (1,3,5-trinitro-1,3,5-triazinane) shows that both materials exhibit similar functional dependencies with the viscosity of HMX being higher by roughly an order of magnitude at a given temperature-pressure state.

36 MATERIALS SCIENCE↗
NASA NTRS1982

The life cycle of thunderstorm gust fronts as viewed with Doppler radar and rawinsonde data

This paper presents the time-dependent analysis of the thunderstorm gust front with the use of Project NIMROD data. RHI cross sections of reflectivity and Doppler velocity are constructed to determine the entire vertical structure. The life cycle of the gust front is divided into four stages: (1) the formative stage; (2) the early mature stage; (3) the late mature stage; and (4) the dissipation stage. A new finding is a horizontal roll detected in the reflectivity pattern resulting from airflow that is deflected upward by the ground, while carrying some of the smaller precipitation ahead of the main echo core of the squall line. This feature is called a 'precipitation roll'. As determined from rawinsonde data, the cold air behind the gust front accounts for the observed surface pressure rise. Calculations confirm that the collision of two fluids produce a nonhydrostatic pressure at the leading edge of the outflow. The equation governing the propagation speed of a density current accurately predicts the movement of the gust front.

Wakimoto, R. M.↗