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The TVS on Doppler radar indicates a midtropospheric vortex of Rossby number Ro 1000 that develops in a supercell storm shortly before a tornado descends. This paper shows how the PIO model, which was first described at the 1991 AGU Spring Meeting, could produce a TVS.
We are developing a new method for the growth of small diamond crystals at very high temperatures and pressures directly from a carbon melt. A prototype "Supercell" has been developed for this purpose. This system is capable of high rate crystal growth in relatively large working volumes. The resulting high quality diamond crystals will be incorporated into a triphasic diamond/titanium carbide/titanium composite tool, with an oriented diamond crystal at its tip. High pressure is needed to prevent degradation of diamond at high temperature, and to ensure the formation of a crack & composite structure. After grinding and polishing, the composite material will be joined to a steel holder, thus forming a diamond-tipped tool for turning and smoothing of a mirror surface. A properly oriented single-crystal diamond cuts and smoothes much better than a conventional polycrystalline diamond crystal. This is because the hardness depends on crystallographic orientation-the difference corresponds to 60-100 GPa on the Knoop scale. Our goal is to achieve surface roughness of about 1 nm, which will be accomplished by precision cutting and smoothing. The hardness of the functionally-graded diamond/titanium carbide/titanium composite tool varies from 100 GPa at its tip to 15 GPa at its base. Previous work has shown that the mass of machined material using an oriented-diamond tool is much larger than that for a standard diamond-metal composite tool.
The July 21,1998 thunderstonn observed during the European Lightning Nitrogen Oxides Project (EULINOX) project was simulated using the three-dimensional Goddard Cumulus Ensemble (GCE) model. The simulation successfully reproduced a number of observed storm features including the splitting of the original cell into a southern cell which developed supercell characteristics, and a northern cell which became multicellular. Output from the GCE simulation was used to drive an offline cloud-scale chemical transport model which calculates tracer transport and includes a parameterization of lightning NO(x) production which uses observed flash rates as input. Estimates of lightning NO(x) production were deduced by assuming various values of production per intracloud and production per cloud-to-ground flash and comparing the results with in-cloud aircraft observations. The assumption that both types of flashes produce 360 moles of NO per flash on average compared most favorably with column mass and probability distribution functions calculated from observations. This assumed production per flash corresponds to a global annual lightning NOx source of 7 Tg N per yr. Chemical reactions were included in the model to evaluate the impact of lightning NO(x), on ozone. During the storm, the inclusion of lightning NOx in the model results in a small loss of ozone (on average less than 4 ppbv) at all model levels. Simulations of the chemical environment in the 24 hours following the storm show on average a small increase in the net production of ozone at most levels resulting from lightning NO(x), maximizing at approximately 5 ppbv per day at 5.5 km. Between 8 and 10.5 km, lightning NO(x) causes decreased net ozone production.
Numerous case studies and recent modeling studies have found that various metrics of updraft intensity appear to be reasonably well correlated to lightning production in thunderstorms, particularly severe thunderstorms. Indeed, the relationship between updraft and lightning flash rate is hypothesized to be the physical connection between a lightning "jump" signature and manifestations of severe weather such as tornadic activity. This study further examines this connection using a combination of dual Doppler wind retrievals made with the UAH ARMOR dual polarimetric and KHTX WSR 88D Doppler radar pair, together with northern Alabama Lightning Mapping Array (LMA) data. The dual Doppler data were used to construct three dimensional wind fields and the retrieved vertical velocity fields were subsequently compared to collocated total lightning flash rates observed by the LMA. Particular attention was paid to the timing of updraft pulses relative to changes in the flash rate, with the goal of assessing impacts on warning decision lead time. Results from the analysis of severe and non severe thunderstorms in Northern Alabama will be presented including the EF 4 tornado producing supercell on 6 February 2008.
Previous studies have demonstrated that rapid increases in total lightning activity (intracloud + cloud-to-ground) are often observed tens of minutes in advance of the occurrence of severe weather at the ground. These rapid increases in lightning activity have been termed "lightning jumps." Herein, we document a positive correlation between lightning jumps and the manifestation of severe weather in thunderstorms occurring across the Tennessee Valley and Washington D.C. A total of 107 thunderstorms were examined in this study, with 69 of the 107 thunderstorms falling into the category of non-severe, and 38 into the category of severe. From the dataset of 69 isolated non-severe thunderstorms, an average peak 1 minute flash rate of 10 flashes/min was determined. A variety of severe thunderstorm types were examined for this study including an MCS, MCV, tornadic outer rainbands of tropical remnants, supercells, and pulse severe thunderstorms. Of the 107 thunderstorms, 85 thunderstorms (47 non-severe, 38 severe) from the Tennessee Valley and Washington D.C tested 6 lightning jump algorithm configurations (Gatlin, Gatlin 45, 2(sigma), 3(sigma), Threshold 10, and Threshold 8). Performance metrics for each algorithm were then calculated, yielding encouraging results from the limited sample of 85 thunderstorms. The 2(sigma) lightning jump algorithm had a high probability of detection (POD; 87%), a modest false alarm rate (FAR; 33%), and a solid Heidke Skill Score (HSS; 0.75). A second and more simplistic lightning jump algorithm named the Threshold 8 lightning jump algorithm also shows promise, with a POD of 81% and a FAR of 41%. Average lead times to severe weather occurrence for these two algorithms were 23 minutes and 20 minutes, respectively. The overall goal of this study is to advance the development of an operationally-applicable jump algorithm that can be used with either total lightning observations made from the ground, or in the near future from space using the GOES-R Geostationary Lightning Mapper.
The upgrade of the National Weather Service (NWS) network of S ]band dual-polarization radars is currently underway, and the incorporation of polarimetric information into the real ]time forecasting process will enhance the forecaster fs ability to assess thunderstorms and their near ]storm environments. Recent research has suggested that the combination of polarimetric variables differential reflectivity (ZDR) and specific differential phase (KDP) can be useful in the assessment of low level wind shear within a thunderstorm. In an environment with strong low ]level veering of the wind, ZDR values will be largest along the right inflow edge of the thunderstorm near a large gradient in horizontal reflectivity (indicative of large raindrops falling with a relative lack of smaller drops), and take the shape of an arc. Meanwhile, KDP values, which are proportional to liquid water content and indicative of a large number of smaller drops, are maximized deeper into the forward flank precipitation shield than the ZDR arc as the smaller drops are being advected further from the updraft core by the low level winds than the larger raindrops. Using findings from previous work, three severe weather events that occurred in North Alabama were examined in order to assess the utility of these signatures in determining the potential for tornadic activity. The first case is from October 26, 2010, where a large number of storms indicated tornadic potential from a standard reflectivity and velocity analysis but very few storms actually produced tornadoes. The second event is from February 28, 2011, where tornadic storms were present early on in the event, but as the day progressed, the tornado threat transitioned to a high wind threat. The third case is from April 27, 2011, where multiple rounds of tornadic storms ransacked the Tennessee Valley. This event provides a dataset including multiple modes of tornadic development, including QLCS and supercell structures. The overarching goal of examining these three events is to compare dual ]polarization features from this larger dataset to previous work and to determine if these signatures can be a useful indication of the potential for tornadic activity associated with the amount of low ]level wind shear in the near ]storm environment.
The Terminal Area Simulation System (TASS) is a three-dimensional, time-dependent, large eddy simulation model that has been developed for studies of wake vortex and weather hazards to aviation, along with other atmospheric turbulence, and cloud-scale weather phenomenology. This document describes the source code for TASS version 10.0 and provides users with needed documentation to run the model. The source code is programed in Fortran language and is formulated to take advantage of vector and efficient multi-processor scaling for execution on massively-parallel supercomputer clusters. The code contains different initialization modules allowing the study of aircraft wake vortex interaction with the atmosphere and ground, atmospheric turbulence, atmospheric boundary layers, precipitating convective clouds, hail storms, gust fronts, microburst windshear, supercell and mesoscale convective systems, tornadic storms, and ring vortices. The model is able to operate in either two- or three-dimensions with equations numerically formulated on a Cartesian grid. The primary output from the TASS is time-dependent domain fields generated by the prognostic equations and diagnosed variables. This document will enable a user to understand the general logic of TASS, and will show how to configure and initialize the model domain. Also described are the formats of the input and output files, as well as the parameters that control the input and output.
The Deep Convective Clouds and Chemistry (DC3) experiment seeks to examine the relationship between deep convection and the production of nitrogen oxides (NO (sub x)) via lightning (LNO (sub x)). A critical step in estimating LNO (sub x) production in a cloud-resolving model (CRM) without explicit lightning is to estimate the flash rate from available model parameters that are statistically and physically correlated. As such, the objective of this study is to develop, improve and evaluate lightning flash rate parameterizations in a variety of meteorological environments and storm types using radar and lightning mapping array (LMA) observations taken over Northern Alabama from 2005-2012, including during DC3. UAH's Advanced Radar for Meteorological and Operational Research (ARMOR) and the Weather Surveillance Radar - 1988 Doppler (WSR 88D) located at Hytop (KHTX) comprises the dual-Doppler and polarimetric radar network, which has been in operation since 2004. The northern Alabama LMA (NA LMA) in conjunction with Vaisala's National Lightning Detection Network (NLDN) allow for a detailed depiction of total lightning during this period. This study will integrate ARMOR-KHTX dual Doppler/polarimetric radar and NA LMA lightning observations from past and ongoing studies, including the more recent DC3 results, over northern Alabama to form a large data set of 15-20 case days and over 20 individual storms, including both ordinary multicell and supercell convection. Several flash rate parameterizations will be developed and tested, including those based on 1) graupel/small hail volume; 2) graupel/small hail mass, and 3) convective updraft volume. Sensitivity of the flash rate parameterizations to storm intensity, storm morphology and environmental conditions will be explored.
The focus of this analysis is on lightning-generated nitrogen oxides (LNOx) and their distribution for two thunderstorms observed during the Deep Convective Clouds and Chemistry (DC3) field campaign in May-June 2012. The Weather Research and Forecasting Chemistry (WRF-Chem) model is used to perform cloud-resolved simulations for the May 29-30 Oklahoma severe convection, which contained one supercell, and the June 6-7 Colorado squall line. Aircraft and ground-based observations (e.g., trace gases, lightning and radar) collected during DC3 are used in comparisons against the model-simulated lightning flashes generated by the flash rate parameterization schemes (FRPSs) incorporated into the model, as well as the model-simulated LNOx predicted in the anvil outflow. Newly generated FRPSs based on DC3 radar observations and Lightning Mapping Array data are implemented in the model, along with previously developed schemes from the literature. The results of these analyses will also be compared between storms to investigate which FRPSs were most appropriate for the two types of convection and to examine the variation in the LNOx production. The simulated LNOx results from WRF-Chem will also be compared against other previously studied mid-latitude thunderstorms.
During November 2018 through April 2019, an 11-station NASA lightning mapping array (LMA) was installed in the Cordoba region of Argentina, in support of GOES-16 Geostationary Lightning Mapper (GLM) calibration and validation, as well as the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. This region of Argentina is well known for frequent, intense thunderstorms and severe weather. The LMA was monitored remotely via the Internet throughout its deployment, but due to bandwidth limitations no real-time data were available. Custom GOES-16 imagery provided by NASA SPoRT assisted with monitoring of thunderstorm cases. Occasional site visits were done to obtain data disks, perform routine maintenance, and troubleshoot problems. During the deployment the network captured lightning in a variety of storm modes, including ordinary and severe multicells, supercells, and mesoscale convective systems. Many examples of normal-polarity thunderstorms, as well as a few examples of anomalously charged thunderstorms, were observed. Long (100+ km) horizontally stratified lightning flashes, as well as lightning in overshooting tops, also were frequently observed. Supporting research radar observations were available through January 2019, with operational radar coverage available after that time. Some cases featured supporting ABI meso scanning. This presentation will report on the LMA deployment in context with the RELAMPAGO field campaign, show results from some representative case studies, and will provide initial comparisons to GLM observations.
The above anvil cirrus plume (AACP) is a weather phenomenon that signifies an intense tropopause penetrating updraft which can inject cirrus clouds several kilometers into the stratosphere. Storms that have such intense updrafts are often supercells which generate severe weather such as tornadoes, high winds, and hail. In addition, AACPs moisten the stratosphere and influence the Earth's radiative balance. Though an AACP can be identified by the human eye, no automated AACP detection methods currently exist. Lack of detection inhibits understanding of where and how often AACPs occur, and how these storms influence stratospheric air composition. Previous work involved synthesis of multiple remote sensing and severe weather report/warning data sources to identify AACPs in Geostationary Operational Environmental Satellite system (GOES) satellite imagery and better understand their weather impacts (Bedka et al. (Wea. Forecasting, 2018)). This current study demonstrates an automated AACP identification method based on the application of a deep learning segmentation model known as a U-net. This study documents the development of a U-net model capable of identifying emergent AACPs using only satellite infrared (IR) and visible reflected sunlight imagery. The performance of a U-net is quantitatively benchmarked with human AACP identifications and qualitatively assessed through animations of detections generated from GOES-16 1-minute temporal resolution imagery.
Researchers at NASA Langley Research Center have been developing an automated pattern recognition algorithm to identify overshooting convective cloud tops (OTs) in support of the GOES-R satellite program. This algorithm identify regions of overshooting at the individual 1-4 km geostationary satellite pixel scale using visible (during daytime only) and infrared channel imagery and numerical weather analysis data. The algorithm has been developed based upon analysis of 0.25-1 km spatial resolution Aqua MODIS imagery, using a database of over 2000 manually identified OT features throughout the world in storms with varying intensity and morphology. The OT database includes storms ranging from small, warm topped cells in Alaska and Mongolia, tornadic supercells over the U.S. Central Plains and Europe, large tropical mesoscale convective systems, and overshooting in the eyewalls and spiral bands of category 5 tropical cyclones. This database is available for use by the research community. The algorithm is designed to operate on data from any current and historical satellite imager, allowing for development of a highly accurate global OT detection climatology that extends back into the 1990's at up to a 15-30 min temporal resolution throughout the diurnal cycle. As members of the McIDAS Users Group, NASA LaRC has immediate access to the full global archive of geostationary imager data which would allows rapid development of OT climatologies and short-term databases. This type of capability has never been available within the weather and climate research community. Regional geostationary OT databases have been already developed over CONUS during SEAC4RS, for 18-years over the Eastern U.S., and for 5-10 years over Australia, Europe, Southeast Asia, and East Africa among many other regions. Some of these datasets are being used by climate researchers and private industry to examine UTLS-penetrating storm spatial distributions and their temporal variability, in addition to weather hazards associated with these storms at unprecedented spatial detail. This presentation will describe the OT pattern recognition algorithm and highlight recent product applications.
Environmental barrier coatings (EBCs) are a necessary enabling technology for the transition from superalloys to silicon carbide (SiC) ceramic matrix composites (CMCs) in gas turbine engines for increased efficiency and decreased fuel costs. SiC-based CMCs are prone to oxidation-based degradation in the engine hot section, and rare-earth (RE) silicates are promising candidates for EBCs due to their close thermal expansion match to the composite substrate and oxidation resistance. However, the design of EBCs is hindered by the large chemical space of candidate materials and the difficulty in obtaining material properties for engineering optimization. This is especially difficult as research continues into mixed-cation or “high-entropy” RE silicates. First-principles computational methods such as density functional theory (DFT) are highly effective at calculating material properties to guide coating design but are limited by their computational cost. Atomistic simulations have the potential to both accelerate property calculations and expand the properties able to be calculated due to their lower computational compared to DFT. However, they require interatomic potentials (IAPs) specific to the material system of interest, and, to our knowledge, there are no suitable IAPs for RE silicates. Machine learning (ML) is a promising technique to accelerate material property predictions indirectly by generating IAPs for atomistic simulations or via direct prediction. In this work, we present two ML approaches to accelerate the calculation of RE silicate properties relevant to EBC design: 1) a ML-derived interatomic potential (IAP) for atomistic simulations of yttrium disilicate (Y2Si2O7) from DFT training data, and 2) a neural network (NN) model to directly predict thermochemical properties of RE silicates and oxides directly from easily obtainable unit cell parameters. Classical MD simulations using the IAP yield lattice properties and bond lengths in good agreement with both DFT and experimental results from x-ray diffraction. Thermodynamic properties calculated using the finite-displacement phonon method and quasi-harmonic approximation were orders of magnitude faster than DFT with good agreement to the DFT results. The IAP was also used to calculate properties such as coefficient of thermal expansion (CTE) that require large simulation supercells and are therefore difficult with DFT. The IAP correctly predicted the anisotropic nature of the CTE in three different phases of Y2Si2O7. The NN model predicts constant pressure heat capacity, Cp, orders of magnitude faster than DFT calculations, which can enable its use as a surrogate model for multiscale simulations. The two methods presented in this work demonstrate the utility of ML for accelerating the prediction of RE silicate properties, which can in turn accelerate EBC design and optimization.
Severe weather events (e.g., hurricanes, tornadoes) are responsible for most weather-related infrastructure and building damages. The destruction caused by these events can cross several states, making damage estimates difficult, especially in heavily vegetated or rural areas. Drone or optical imagery is often relied upon in these cases but is limited by solar and atmospheric conditions. Synthetic Aperture Radar (SAR) is an active sensor allowing for day and night collections in all weather conditions. This research highlights the benefits and limitations of using SAR to detect tornado tracks and associated damages while also assessing the strengths and weaknesses of two SAR sensors with varying wavelengths and spatial resolutions from the March 2020 Tornado Outbreak. The outbreak occurred overnight on March 2-3, when several supercell thunderstorms tracked across multiple states producing numerous tornadoes (EF-0 through EF-4) and large hail. Most of the damage occurred in central Tennessee, resulting in 25 fatalities, hundreds of injuries, and over a billion dollars worth of damage. Publicly available C-band (~6 cm) imagery from the European Space Agency’s Sentinel-1 satellite and commercial X-band (~3 cm) imagery from Airbus’s TerraSAR-X satellite were used to generate amplitude and coherence products. The closest post-event collections were used for the amplitude products, and the closest pre- and post-collections were used to create the coherence pairs. If damage tracks were identified in any of the SAR products, the length (miles) and max width (yards) were recorded and compared to the National Weather Service Storm Data official records. SAR successfully detected five rated EF-1 or higher out of ten recorded tornadoes. However, none of the four EF-0 tornadoes were identified. X- and C-band SAR sensors are heavily impacted by dense vegetation, underestimating the extent of damage. Despite these limitations, SAR products can be beneficial when looking at severe weather impacts, especially during the winter and early spring when the coherence products are less influenced by vegetation. The upcoming L-band (~24 cm) NISAR mission will provide more accurate estimates of damage, with the longer wavelength, expanding the applications of SAR to assist in damage assessment caused by severe thunderstorms.
A method for designing new materials for superlubricity comprises developing, on a computational system, a computational supercell comprising x unit cells of a base material, each unit cell comprising y atoms of the base material. The computational system replaces randomly chosen z atoms of the base material with an impurity atom of an impurity material to form a candidate material. The computational system determines volumetric strain of the candidate material. In response to the volumetric strain exceeding a predetermined threshold, the computational system determines that the candidate material has superlubricity. The computational system displays the candidate material to a user if the candidate material has superlubricity.
All-nitride semiconductor/superconductor heterojunctions utilizing cubic niobium nitride (NbN) are a promising approach to superconducting quantum circuits for next-generation quantum-information systems. However, there are fundamental open questions on the atomic structure of NbN. In calculations with several levels of density functional theory (DFT), we find that the cubic rocksalt structure NbN (Fm-3m, 225) is energetically very unstable against the ground state hexagonal NbN in a tungsten carbide (P-6m2, 187) type lattice. To better understand the appearance of a cubic phase in numerous experiments, we perform a DFT study on possible NbN structures, determining the energy ordering between different polymorphs from databases and structure prediction. We perform supercell calculations of disordered NbN, finding that the rocksalt structure is dynamically unstable and relaxes to a lower energy monoclinic phase (C2/m, 12), which retains an approximate average cubic symmetry. However, the associated energy gain is not substantial enough for a plausible explanation for cubic NbN. We further investigate the role of external factors such as in-plane strain during epitaxial growth as well as the presence of off-stoichiometry and impurity doping on the energy ordering.
Sensing of stress under high elevated environmental conditions with high resolution has critical importance for range of applications including earth’s subsurface scanning and geological CO2 storage monitoring. Using first principles density functional theory (DFT) approach combined with the theoretical modelling of low energy Hamiltonian, here we investigate a novel approach to detect unprecedented level of pressure by taking advantage of solid-state electron’s spin of Nitrogen vacancy (NV) centers in nanodiamond. We computationally explore the effect of strain on the defect band edges and band gaps by varying the lattice parameters of diamond supercell hosting a single NV center. A low energy Hamiltonian is developed that includes effect of stress on the energy level of ±1 spin manifold at the ground state. By quantifying the energy level shift and split, we predict the pressure sensing of up to 0.3 MPa/√Hz. We show superiority of the quantum sensing over traditional optical sensing techniques by discussing our results from DFT and theoretical modelling for the frequency shift per unit pressure. The proposed quantum stress sensing could be useful to measure earth’s subsurface vibrations. Our results open avenues for development of sensing technology with a high sensitivity and resolution under extreme pressure conditions that potentially has a wider applicability than existing pressure sensing technologies.
Dominated by photovoltaics and wind, current renewable energy sources generate mostly electricity, but 80% of the global final energy consumption occurs in form of fuels. Therefore, direct solar fuel generation would be a major breakthrough for the energy transition. Solar thermochemical hydrogen (STCH) is one of the very few potential routes towards scalable renewable fuels, but currently suffers from lack of an oxide working material that could optimally perform energy conversion within the thermodynamic boundary conditions. Theory and computation can contribute in two distinct ways, through materials search and discovery, but also by providing detailed mechanistic models for specific systems so to advance our understanding of possible design strategies. To enable high-throughput materials screening, we developed a defect graph neural network (dGNN) machine learning approach,[1] which accelerates the prediction of defect formation energies by replacing the tedious density functional theory (DFT) supercell calculations for all possible defect sites. This approach enables high-throughput database screening of oxides, which was integrated with thermodynamic modeling to extract the reduction entropies as additional selection criterion for STCH. Once potential candidate materials are identified, detailed models can guide materials design by predicting performance characteristics. One challenge is to quantitatively predict thermochemical equilibria at high concentrations when the redox active defects start to interact with each other, thereby impeding the formation of additional defects. Introducing a model for the free energy of defect interaction, parametrized on the basis of DFT data, we simulated the complete STCH redox cycle for (Sr,Ce)MnO3 alloys, achieving near-quantitative agreement with experimental data.[2] The analysis of these simulations reveals how defect interactions diminish the reduction entropy and H2 yield, suggesting to include these interactions in design considerations. Finally, we revisit the popular van't Hoff method for analyzing reduction enthalpies and entropies. This method is not ideal, as it involves a temperature-dependent convolution of gas-phase and solid-state entropies, causing uncertainties in the same order of magnitude as the physical quantities of interest. To avoid this problem, we suggest a simple alternative approach which can be applied to experimental and simulated data alike.