SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This research code base includes functions to compute statistics of image distributions in (spatial DFT) frequency space, train deep learning image classifiers on these distributions with variable depth and weight decay, and finally measure the sensitivity of the trained models to perturbations along (spatial DFT) frequency components.
Publication For a thorough description of the methods, see the peer-reviewed paper: Jones, A.D., Rastogi, D., Vahmani, P. et al. Continental United States climate projections based on thermodynamic modification of historical weather. Sci Data 10, 664 (2023). https://doi.org/10.1038/s41597-023-02485-5 Overview The IM3 / HyperFACETS climate simulations provide 40-year historical (1980-2019) as well as four 80-year future simulations (2020-2099) over the U.S. The future simulations are split into near (2020-2059) and far future (2060-2099) segments. The future scenarios span a range of plausible changes in future climate (both Global Circulation Model (GCM) and Representative Concentration Pathways/Shared Socioeconomic Pathway (RCP/SSP) dimensions). The simulations provide climate variables with high spatiotemporal resolution (25 hourly variables and 207 3-hourly variables at 12 km2). The datasets are generated using dynamical downscaling with the WRF (Weather Research and Forecasting) model (version 4.2.1) and therefore preserve physical consistency across variables. WRF is a state-of-the-art, fully compressible, non-hydrostatic, mesoscale numerical weather prediction model. WRF is coupled with an urban canopy model (UCM), which resolves urban surfaces. The future scenarios were developed using a thermodynamic global warming approach where past events are replayed under a range of future warming conditions. These scenarios therefore provide a perspective on potential increases in extreme event intensity, geographic scope, and duration, with previously non-extreme conditions potentially crossing new thresholds to be considered extreme by today's standards. This approach is not intended to estimate future changes in extreme event frequency that might result from changes in large-scale atmospheric dynamics. This dataset has NOT been bias corrected. A bias corrected version of selected variables is under development and will be released here when available. Scenarios Files Data for each scenario is provided in weekly NetCDF files. 25 variables are available at hourly resolution, and 207 variables are available at three-hourly resolution. Spatial resolution is 12km and spans the conterminous United States (CONUS), including some areas of Canada and Mexico, resulting in a grid of 424 by 299 cells. The spatial projection is a Lambert Conformal Conic with the following proj-string: "+proj=lcc +lat_0=40.0000076293945 +lon_0=-97 +lat_1=30 +lat_2=45 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs". The available scenarios and simulation periods are listed below: historical | 1980 - 2019 rcp45cooler | 2020 - 2059 rcp45cooler | 2060 - 2099 rcp45hotter | 2020 - 2059 rcp45hotter | 2060 - 2099 rcp85cooler | 2020 - 2059 rcp85cooler | 2060 - 2099 rcp85hotter | 2020 - 2059 rcp85hotter | 2060 - 2099 * The first year (1979, 2019, and 2059) of data within each scenario represents a model warmup period and should not be used. These are located in the `spinup_files` directory. Historical year 2020 is considered an extra year of data beyond the simulation period and can be found in the `additional_files` directory. For information on specific variables and a more in-depth discussion of methodology, please refer to the data landing page at https://tgw-data.msdlive.org. Delta Warming Files The global and CONUS warming deltas for each scenario are provided in degrees Celsius annually and monthly. Restart Files Yearly restart files are provided for each scenario which can be used to restart the WRF model at a particular point in time. Spinup Files The first year of data within each simulation period represents a model warmup period and should not be used. The files are provided here for the sake of reproducibility. Additional Files Additional years of data are provided as an extension of the historic simulation.
Contemporary fire dynamics is one of the most complex and least understood land surface phenomena. Global fire controls related to climate, vegetation, and anthropogenic activity are usually intertwined, and difficult to disentangle in a quantitative way. Here, we leveraged an ensemble of five machine learning (ML) models and multiple satellite-based observations to conduct global fire modeling for three fire metrics (burned area, fire number, and fire size), and quantified driving mechanisms underlying annual fire changes in a spatially resolved manner for the period 2003–2019. Ensemble learning is a meta-approach that combines multiple ML predictions to improve accuracy, robustness, and generalization performance. We found that the optimized ensemble ML well reproduced annual dynamics of global burned area (R 2 = 0.90, P < 0.001), total fire numbers (R 2 = 0.86, P < 0.001), and averaged fire size (R 2 = 0.70, P < 0.001). Additionally, the ensemble ML captured key spatial patterns of multi-year mean magnitudes, annual variabilities, anomalies, and trends for different fire metrics. Our ML-based fire attributions further highlighted the dominant role of enhanced anthropogenic activity in reducing global burned area (–1.9 Mha/yr, P < 0.01), followed by climate control (–1.3 Mha/yr, P < 0.01) and insignificant positive vegetation control (0.4 Mha/yr, P = 0.60). Spatially, climate dominated a much larger burned area (53.7%) than human (23.4%) or vegetation control (22.9%); however, the counteracting effects from regional wetting and drying trends weakened the net climate impacts on global burned area. The fire number and fire size exhibited similar spatial control patterns with burned area; globally, however, fire number tended to be more affected by climate while fire size more influenced by human activities. Overall, our study confirmed the feasibility and efficiency of ensemble ML in global fire modeling and subsequent control attributions, providing a better understanding of contemporary fire regimes and contributing to robust fire projections in a changing environment.
The magnitude, spectral composition, and variability of the Arctic sea ice surface albedo are key to understanding and numerically simulating Earth’s shortwave energy budget. Spectral and broadband albedos of Arctic sea ice were spatially and temporally sampled by on-ice observers along individual survey lines throughout the sunlit season (April–September, 2020) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The seasonal evolution of albedo for the MOSAiC year was constructed from spatially averaged broadband albedo values for each line. Specific locations were identified as representative of individual ice surface types, including accumulated dry snow, melting snow, bare and melting ice, melting and refreezing ponded ice, and sediment-laden ice. The area-averaged seasonal progression of total albedo recorded during MOSAiC showed remarkable similarity to that recorded 22 years prior on multiyear sea ice during the Surface Heat Budget of the Arctic Ocean (SHEBA) expedition. In accord with these and other previous field efforts, the spectral albedo of relatively thick, snow-free, melting sea ice shows invariance across location, decade, and ice type. In particular, the albedo of snow-free, melting seasonal ice was indistinguishable from that of snow-free, melting second-year ice, suggesting that the highly scattering surface layer that forms on sea ice during the summer is robust and stabilizing. In contrast, the albedo of ponded ice was observed to be highly variable at visible wavelengths. Notable temporal changes in albedo were documented during melt and freeze onset, formation and deepening of melt ponds, and during melt evolution of sediment-laden ice. While model simulations show considerable agreement with the observed seasonal albedo progression, disparities suggest the need to improve how the albedo of both ponded ice and thin, melting ice are simulated.
Frontal boundaries have been shown to cause large changes in CO 2 mole-fractions, but clouds and the complex vertical structure of fronts make these gradients difficult to observe. It remains unclear how the column average CO 2 dry air mole-fraction (XCO 2 ) changes spatially across fronts, and how well airborne lidar observations, data assimilation systems, and numerical models without assimilation capture XCO 2 frontal contrasts (ΔXCO 2 , i.e., warm minus cold sector average of XCO 2 ). We demonstrated the potential of airborne Multifunctional Fiber Laser Lidar (MFLL) measurements in heterogeneous weather conditions (i.e., frontal environment) to investigate the ΔXCO 2 during four seasonal field campaigns of the Atmospheric Carbon and Transport-America (ACT-America) mission. Most frontal cases in summer (winter) reveal higher (lower) XCO 2 in the warm (cold) sector than in the cold (warm) sector. During the transitional seasons (spring and fall), no clear signal in ΔXCO 2 was observed. Intercomparison among the MFLL, assimilated fields from NASA's Global Modeling and Assimilation Office (GMAO), and simulations from the Weather Research and Forecasting-—Chemistry (WRF-Chem) showed that (a) all products had a similar sign of ΔXCO 2 though with different levels of agreement in ΔXCO 2 magnitudes among seasons; (b) ΔXCO 2 in summer decreases with altitude; and (c) significant challenges remain in observing and simulating XCO 2 frontal contrasts. A linear regression analyses between ΔXCO 2 for MFLL versus GMAO, and MFLL versus WRF-Chem for summer-2016 cases yielded a correlation coefficient of 0.95 and 0.88, respectively. The reported ΔXCO 2 variability among four seasons provide guidance to the spatial structures of XCO 2 transport errors in models and satellite measurements of XCO 2 in synoptically-active weather systems.
Changes in the amplitude of decadal climate variability over the twentieth century have been noted, with most evidence derived from tropical Pacific sea surface temperature records. However, the length, spatial coverage, and stability of most instrumental records are insufficient to robustly identify such nonstationarity, or resolve its global spatial structure. Here, it is found that the long-term, stable, observing platform provided by tide gauges reveals a dramatic increase in the amplitude and spatial coherence of decadal (11–14-yr period) coastal sea level (ζ) variability between 1960 and 2000. During this epoch, western North American ζ was approximately out of phase with ζ in Sydney, Australia, and led northeastern U.S. ζ by approximately 1–2 years. The amplitude and timing of changes in decadal ζ variability in these regions are consistent with changes in atmospheric variability. Specifically, central equatorial Pacific wind stress and Labrador Sea heat flux are highly coherent and exhibit contemporaneous, order-of-magnitude increases in decadal power. These statistical relationships have a mechanistic underpinning: Along the western North American coastline, equatorial winds are known to drive rapidly propagating ζ signals along equatorial and coastal waveguides, while a 1–2-yr lag between Labrador Sea heat fluxes and northeastern United States ζ is consistent with a remotely forced, buoyancy-driven, mechanism. Tide gauges thus provide strong independent support for an increase in interbasin coherence on decadal time scales over the second half of the twentieth century, with implications for both the interpretation and prediction of climate and sea level variability.
ABSTRACT We analyse 1602 microlensing events found in the VISTA Variables in the Via Lactea (VVV) near-infrared (NIR) survey data. We obtain spatially resolved, efficiency-corrected time-scale distributions across the Galactic bulge (|ℓ| < 10°, |b| < 5°), using a Bayesian hierarchical model. Spatially resolved peaks and means of the time-scale distributions, along with their marginal distributions in strips of longitude and latitude, are in agreement at a 1σ level with predictions based on the Besançon model of the Galaxy. We find that the event time-scales in the central bulge fields (|ℓ| < 5°) are on average shorter than the non-central (|ℓ| > 5°) fields, with the average peak of the lognormal time-scale distribution at 23.6 ± 1.9 d for the central fields and 29.0 ± 3.0 d for the non-central fields. Our ability to probe the structure of the bulge with this sample of NIR microlensing events is limited by the VVV survey’s sparse cadence and relatively small number of detected microlensing events compared to dedicated optical surveys. Looking forward to future surveys, we investigate the capability of the Roman telescope to detect spatially resolved asymmetries in the time-scale distributions. We propose two pairs of Roman fields, centred on (ℓ = ±9, 5°, b = −0.125°) and (ℓ = −5°, b = ±1.375°) as good targets to measure the asymmetry in longitude and latitude, respectively.
Abstract. Climate model output emulation has long been attempted to support impact research, mainly to fill in gaps in the scenario space. Given the computational cost of running coupled earth system models (ESMs), which are usually the domain of supercomputers and require on the order of days to weeks to complete a century-long simulation, only a handful of different scenarios are usually chosen to externally force ESM simulations. An effective emulator, able to run on standard computers in times of the order of minutes rather than days could therefore be used to derive climate information under scenarios that were not run by ESMs. Lately, the necessity of accounting for internal variability has also made the availability of initial-condition ensembles, under a specific scenario, important, further increasing the computational demand. At least so far, emulators have been limited to simplified ESM-like output, either seasonal, annual, or decadal averages of basic quantities, like temperature and precipitation, often emulated independently of one another. With this work, we propose a more comprehensive solution to ESM output emulation. Our emulator, STITCHES, uses existing archives of earth system models' (ESMs) scenario experiments to construct ESM-like output under new scenarios or enrich existing initial-condition ensembles, which is what other emulators also aim to do. Importantly, however, STITCHES' output has the same characteristics of the ESM output it sets out to emulate: multivariate, spatially resolved, and high frequency, representing both the forced component and the internal variability around it. STITCHES extends the idea of time sampling – according to which climate outcomes are stratified by the global warming level at which they manifest themselves, irrespective of the scenario and time at which they occur – to the construction of a continuous history of ESM-like output over the whole 21st century, consistent with a 21st-century trajectory of global surface air temperature (GSAT) derived from the scenario that has been chosen as the target of the emulation. STITCHES does so by first splitting the target GSAT trajectory into decade-long windows, then matching each window in turn to a decade-long window within an existing model simulation from the available scenario runs according to its proximity to the target in absolute size of the temperature anomaly and its rate of change. A look-up table is therefore created of a sequence of existing experiment–time-window combinations that, when stitched together, create a GSAT trajectory “similar” to the target. Importantly, we can then stitch together much more than GSAT from these windows, i.e., any output that the ESM has saved for these existing experiment–time-window combinations, at any frequency and spatial scale available in its archive. We show that the stitching does not introduce artifacts in the great majority of cases (we look at temperature and precipitation at monthly frequency and on the native grid of the ESM and at an index of ENSO activity, the Southern Oscillation Index). This is true even if the criteria for the identification of the decades to be stitched together are chosen to work for a smoothed time series of annual GSAT, a result we expect given the larger amount of noise affecting most other variables at finer spatial scales and higher frequencies, which therefore are more “forgiving” of the stitching. We successfully test the method's performance over many ESMs and scenarios. Only a few exceptions surface, but these less-than-optimal outcomes are always associated with a scarcity of the archived simulations from which we can gather the decade-long windows that form the building blocks of the emulated time series. In the great majority of cases, STITCHES' performance is satisfactory according to metrics that reward consistency in trends, interannual and inter-ensemble variance, and autocorrelation structure of the time series stitched together. The method therefore can be used to create ESM-like output according to new scenarios, on the basis of a trajectory of GSAT produced according to that scenario, which could be easily obtained by a simple climate model. It can also be used to increase the size of existing initial-condition ensembles. There are aspects of our emulator that will immediately disqualify it for specific applications, like when climate information is needed whose characteristics result from accumulated quantities over windows of times longer than those used as pieces by STITCHES, droughts longer than a decade for example. But for many applications, we argue that a stitched product can satisfy the climate information needs of impact researchers. STITCHES cannot emulate ESM output from scenarios that result in GSAT trajectories outside of the envelope available in the archive, nor can it emulate trajectories with shapes different from existing ones (overshoots with negative derivative, for example). Therefore, the size and characteristics of the available archives of ESM output are the principal limitations for STITCHES' deployment. Thus, we argue for the possibility of designing scenario experiments within, for example, the next phase of the Coupled Model Intercomparison Project according to new principles, relieved of the need to produce a number of similar trajectories that vary only in radiative forcing strength but more strategically covering the space of temperature anomalies and rates of change.
Because of unique characteristics such as clean and cost-competitive electricity as well as fast-ramping and storage abilities, the power industry continues to evolve its operation strategies for cascading hydroelectric (CHE) systems for providing enhanced values to the grid, especially under the deeper renewable resource integration. However, existing operation practices of CHEs predate the integration of renewables, which could prohibit the effective utilization of their inherent flexibilities in delivering maximum financial benefits and providing valuable grid services to the power system and electricity market operations. Indeed, modeling and optimizing these resource-limited while flexible CHE assets with uncertainties and imperfect information across multiple spatial-temporal dimensions present significant challenges. To facilitate CHE facility operators in effectively coordinating water usage and hydropower plant operations across multiple timescales, this project aims to fill the existing gaps by developing a suite of accurate water inflow (WI) forecast models as well as enhanced CHE modeling and optimization approaches with proper consideration of their unique characteristics, which would help explore their multidimensional spatial-temporal operational flexibility potentials. The developed approaches could better align reservoir operation strategies with variability and uncertainty of future water availability. They can also promote more effective utilization of multidimensional spatial-temporal hydropower operational flexibility potentials by designing long-term evacuation plans of reservoirs and short-term operation of CHEs, along with their coordination with other types of renewables. The project leverages various resources to facilitate the research and development activities, including actual characteristics data of CHE systems and a library of current and future cases of Portland General Electric (PGE). These realistic data enable the project team to study how to maximize the value of CHEs under current and future portfolios and evaluate opportunities to improve operation practices.
The most revealing indicator for oxidative processes or state of degraded plastics is usually carbonyl formation, a key step in materials degradation as part of the carbon cycle for man-made materials. Hence, the identification and quantification of carbonyl species with infrared spectroscopy have been the method of choice for generations, thanks to their strong absorbance and being an essential intermediate in carbon oxidation pathways. Despite their importance, precise identification and quantification can be challenging and rigorous fully traceable data are surprisingly rare in the existing literature. An overview of the complexity of carbonyl quantification is presented by the screening of reference compounds in solution with transmission and polymer films with ATR IR spectroscopy, and systematic data analyses. Significant variances in existing data and their past use have been recognized. Guidance is offered how better measurements and data reporting could be accomplished. Experimental variances depend on the combination of uncertainty in exact carbonyl species, extinction coefficient, contributions from neighboring convoluting peaks, matrix interaction phenomena and instrumental variations in primary IR spectral acquisition (refractive index and penetration depth for ATR measurements). In addition, diverging sources for relevant extinction coefficients may exist, based on original spectral acquisition. For common polymer degradation challenges, a relative comparison of carbonyl yields for a material is easily accessible, but quantification for other purposes, such as degradation rates and spatially dependent interpretation, requires thorough experimental validation. All variables featured in this overview demonstrate the significant error margins in carbonyl quantification, with exact carbonyl species and extinction coefficients already being major contributors on their own.
Presented here is a transcription of the lecture notes from Professor Allan N. Kaufman’s graduate statistical mechanics course Physics 212A and 212B at the University of California Berkeley from the 1972–1973 academic year. 212A addressed equilibrium statistical mechanics with topics: fundamentals (micro-canonical and sub-canonical ensembles, adiabatic law and action conservation, fluctuations, pressure, and virial theorem), classical fluids and other systems (equation of state, deviations from ideality, virial coefficients and van der Waals potential, canonical ensemble and partition function, quasistatic evolution, grand-canonical ensemble and partition function, chemical potential, simple model of a phase transition, quantum virial expansion, numerical simulation of equations of state, and phase transition), chemical equilibrium (systems with multiple species and chemical reactions, law of mass action, Saha equation, chemical equilibrium including ionization and excited states), and long-range interactions (including Coulomb, dipole, and gravitational interactions, Debye–Hückel theory, and shielding). 212B addressed nonequilibrium statistical mechanics with topics: fundamentals (definitions: realizations, moments, characteristic function, and discrete variables), Brownian motion (Langevin equation, fluctuation–dissipation theorem, spatial diffusion, Boltzmann’s H-theorem), Liouville and Klimontovich equations, Landau equation (derivation, elaboration, and H-theorem, and irreversibility), Markov processes and Fokker–Planck equation (derivations of the Fokker–Planck equation and a master equation), linear response and transport theory (linear Boltzmann equation, linear response theory of Kubo and Mori, relation of entropy production to electrical conductivity, transport relations and coefficients, normal mode solutions of the transport equations, sketch of a generalized Langevin equation method for transport theory), and an introduction to nonequilibrium quantum statistical mechanics.
Abstract Nowcasting is a term originating from economics, finance, and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper, we describe a simple two‐parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain‐hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable time series can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5° × 5° in latitude‐longitude centered on Los Angeles, a region which we used in previous papers to build similar time series using more involved methods (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020EA001097 ; Rundle, Donnellan et al., 2021, https://doi.org/10.1029/2021EA001757 ; Rundle, Stein et al., 2021, https://doi.org/10.1088/1361-6633/abf893 ). Here we show that not only does the state variable time series have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward, and rigorous way.
Global warming is expected to enhance drought extremes in the United States throughout the twenty-first century. Projecting these changes can be complex in regions with large variability in atmospheric and soil moisture on small spatial scales. Vapor Pressure Deficit (VPD) is a valuable measure of evaporative demand as moisture moves from the surface into the atmosphere and a dynamic measure of drought. Here, VPD is used to identify short-term drought with the Standardized VPD Drought Index (SVDI); and used to characterize future extreme droughts using grid dependent stationary and non-stationary generalized extreme value (GEV) models, and a random sampling technique is developed to quantify multimodel uncertainties. The GEV analysis was performed with projections using the Weather Research and Forecasting model, downscaled from three Global Climate Models based on the Representative Concentration Pathway 8.5 for present, mid-century and late-century. Results show the VPD based index (SVDI) accurately identifies the timing and magnitude short-term droughts, and extreme VPD is increasing across the United States and by the end of the twenty-first century. The number of days VPD is above 9 kPa increases by 10 days along California’s coastline, 30–40 days in the northwest and Midwest, and 100 days in California’s Central Valley.
Achieving 100% carbon-free or renewable power systems can be facilitated by the deployment of energy storage technologies at all timescales, including short-duration, long-duration, and seasonal scales; however, most current literature focuses on cost assessments of energy storage for a given timescale or type of technology. In this work, we use an optimization framework with high spatial and temporal resolution to simultaneously assess the variable renewable power deployment and the optimal storage portfolio for seven independent system operators in the United States. Results indicate that achieving high (75–90%) and ultrahigh (>90%) energy mixes requires combining several flexibility options, including renewable curtailment, short-duration, long-duration, and seasonal storage. For instance, carbon-free and renewable energy mix targets of up to 80% are achieved with economic curtailment and a combination of short- and long-duration energy storage for the performance and cost assumptions used. After that, there is a point between 80% and 95% where seasonal storage becomes cost-competitive, depending on the specific power system. Moreover, our results indicate that storage-to-storage operation—one storage device used to charge another storage device—and the decoupling of charging and discharging storage power capacity are cost-effective options for the integration of high and ultrahigh shares of carbon-free or renewable power sources. Additionally, the results from this study show that an 85% carbon-free or renewable energy mix can be achieved at a cost of avoided CO 2 emissions of US$66.0 per tonne or less, regardless of the power system.
An X-pinch load driven by an intense current pulse (>100 kA in ~100 ns) can result in the formation of a small radius, runaway compressional micro-pinch. A micro-pinch is characterized by a hot (>1 keV), current-driven (>100 kA), high-density plasma column (near solid density) with a small neck diameter (1–10 µm), a short axial extent (<1 mm), and a short duration (≲1 ns). With material pressures often well into the multi-Mbar regime, a micro-pinch plasma often radiates an intense, sub-ns burst of sub-keV to multi-keV x rays. A low-density coronal plasma immediately surrounding the dense plasma neck could potentially shunt current away from the neck and thus reduce the magnetic drive pressure applied to the neck. Here, to study the current distribution in the coronal plasma, a Faraday rotation imaging diagnostic (1064 nm) capable of producing simultaneous high-magnification polarimetric and interferometric images has been developed for the MAIZE facility at the University of Michigan. Designed with a variable magnification (1–10×), this diagnostic achieves a spatial resolution of ~35 µm, which is useful for resolving the ~100-μm-scale coronal plasma immediately surrounding the dense core. This system has now been used on a reduced-output MAIZE (100–200 kA, 150 ns) to assess the radial distribution of drive current immediately surrounding the dense micro-pinch neck. The total current enclosed was found to increase as a function of radius, r, from a value of ≈ 50 ± 25 kA at r ≈ 140 µm (at the edge of the dense neck) to a maximal value of ≈150 ± 75 kA for r ≥ 225 µm. This corresponds to a peak magnetic drive pressure of ≈75 ± 50 kbar at r ≈ 225 µm. The limitations of these measurements are discussed in the paper.
Atmospheric moisture tracking models are used to identify and quantify sources and sinks of water in the atmospheric branch of the hydrologic cycle. These models are primarily used to investigate the origin of moisture resulting in precipitation for particular regions around the globe. Moisture tracking models vary widely in their level of complexity, depending on the number of physical processes represented. Complex models are comprehensive in their physical representation, but computationally much more expensive than simple models, which only focus on specific physical processes and use simplifying assumptions. We present the mathematical derivation of the new two-layer dynamical recycling model (2L-DRM), a simple analytical moisture tracking model that relaxes the vertically-integrated formulation of the original one-layer DRM (Dominguez et al., 2006). By comparing the simple DRM to a very complex moisture tracking model that uses water vapor tracers embedded within the Weather Research and Forecast regional climate model (WRF-WVT) for the North American Monsoon region, we pinpoint the absence of vertical wind shear as the main deficiency in the simple DRM. When comparing both simple models (DRM and 2L-DRM) to the WRF-WVT (which we treat as ‘truth’), the 2L-DRM better captures the spatial extent, the net amount and the temporal variability of precipitation that originates from oceanic and local terrestrial sources. The 2L-DRM is well suited to study the large-scale climatological sources of moisture, and for these applications, performs on-par with the much more complex and computationally demanding WRF-WVT model.
This dataset comprises direct numerical simulations (DNS) of decaying stably-stratified turbulence influenced by a linear background density gradient, initialized using an array of Taylor-Green vortices, as described in [Riley & de Bruyn Kops (2003)](https://doi.org/10.1063/1.1578077). The initial Prandtl, Froude, and Reynolds numbers are (Pr, Fr, Re) = (1, 4, 3200). A total of 15,000 snapshots are recorded at uniform time intervals, each with a spatial resolution of 512x512x256 grid points. Four flow variables are associated with each snapshot: the three velocity components (u,v,w) and the perturbed density field (rho) away from the background gradient. All fields are stored in binary format (32-bit little-endian), each with a size of 255 MB, yielding a total dataset size of 15.3 TB. Further details are referenced in the attached README file, and a current list of publications and associated analysis tools are provided at https://stratified-turbulence.github.io/web/.
This dataset comprises direct numerical simulations (DNS) of decaying stably-stratified turbulence influenced by a linear background density gradient, initialized using an array of Taylor-Green vortices, extending the Pr=1 simulations performed in [Riley and de Bruyn Kops (2003)](https://doi.org/10.1063/1.1578077). The initial Prandtl, Froude, and Reynolds numbers are (Pr, Fr, Re) = (50, 4, 3200). A total of 1,680 snapshots are recorded at uniform time intervals, each with a spatial resolution of 3584x3584x1792 grid points. Four flow variables are associated with each snapshot: the three velocity components (u,v,w) and the perturbed density field (rho) away from the background gradient. All fields are stored in binary format (32-bit little-endian), each with a size of 85.8 GB, yielding a total dataset size of 577 TB. Further details are referenced in the attached README file, and a current list of publications and associated analysis tools are provided at https://stratified-turbulence.github.io/web/.