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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Machine‐Learning‐Assisted Stochastic Cloud Population Model as a Parameterization of Cumulus Convection

Abstract A machine‐learning‐assisted stochastic cloud population model is coupled with the Advanced Research Weather Research and Forecasting (WRF) model to represent fluctuations in the cloud‐base mass flux associated with the life cycles and interactions among cumulus convection cells. In this cloud population model, the size distribution and the associated cloud‐base mass flux of the convective cells are related to their previous state and to the change in the total convective area via a transition function. The convective area tendency in turn is assumed to depend on the cloud‐base mass flux that is resolved by the host WRF model. The transition function is represented by a single hidden‐layer neural network trained by the evolution of convective cell size distributions in a 1‐km grid‐spacing WRF simulation run over the Australian Monsoon region. At every grid point of the host model, the cloud population model predicts the cell size and cloud‐base mass flux distributions from which a random sample of cells is fed to an entraining parcel model that calculates precipitation as well as the associated liquid water potential temperature and total moisture tendencies. These tendencies are averaged over the cells and provided to the host model. Several regional simulations are performed over tropical and midlatitude domains to test this as a potential approach to scale‐aware parameterization. It is shown that such an approach could be a new promising path to simulating realistic precipitation statistics and propagation of precipitation associated with the Madden‐Julian Oscillation while maintaining realistic depictions of the diurnal cycle over both land and ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Adiabatic Evolution of Cloud Droplet Spectral Width: A New Look at an Old Problem

Abstract Spectral width of the cloud droplet spectrum is important for radiative properties and drizzle/rain development in warm ice‐free clouds. We use an adiabatic rising parcel model to study activation and diffusional growth of cloud droplets, focusing on the spectral width evolution, and contrasting clean and polluted environments. A comprehensive droplet growth equation is used that includes kinetic, solute, and surface tension effects. We show that those effects have an appreciable impact on the spectral width evolution above the cloud base. Without those effects, the droplet area standard deviation should not change once activation is completed. In contrast, simulation results show that the area standard deviation does increase with height, especially for weak updrafts and polluted environments. Implications of those results for cloud modeling, especially applying conventional bin microphysics, are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Accelerating the Lagrangian Particle Tracking in Hydrologic Modeling to Continental-Scale

Unprecedented climate change and anthropogenic activities have induced increasing ecohydrological problems, which have motivated the development of large-scale modeling for solutions. Water age/quality is as important as water quantity for understanding the water cycle. However, current scientific progress in tracking water parcels at large-scale with high spatiotemporal resolutions is far behind that in water balance/quantity owing to the lack of powerful tools. EcoSLIM is a particle tracking model that works with the hydrologic model ParFlow-CLM, which couples surface-subsurface hydrology with land surface processes. Here, we demonstrate a parallel framework to accelerate EcoSLIM to continental-scale on a distributed, multi-GPU platform with CUDA-Aware MPI. In tests from catchment-, to regional-, and then to continental-scale using 25-million to 1.6-billion particles, EcoSLIM shows significant speedup and excellent parallel performance. The parallel framework is portable to atmospheric and oceanic particle tracking models, where parallelization is inadequate and a standard parallel framework is absent. Parallelized EcoSLIM is a promising tool to accelerate our understanding of the terrestrial water cycle and the upscaling of subsurface hydrology to Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Role of Atmospheric Rivers on Groundwater: Lessons Learned From an Extreme Wet Year

Abstract In the coastal regions of the western United States, atmospheric rivers (ARs) are associated with the largest precipitation generating storms and contribute up to half of annual precipitation, but the impact of ARs on the integrated hydrologic cycle, specifically on groundwater storage and hydrodynamics, is largely unknown. To better explore the hydrologic behavior of AR versus non‐AR event precipitation, we present a novel combination of two water tracking methods (one in the atmosphere and one in the subsurface) to explicitly track the full lifecycle of water parcels generated by ARs. Simulations of northern California's Cosumnes River watershed during the record wet 2017 water year are performed via the coupling of a high‐resolution regional climate model and a land surface‐groundwater model accounting for lateral groundwater flow. Despite ARs contributing more precipitation than non‐AR storms, we find less AR water is preferentially stored in aquifers by year end. Fractionally, ARs result in 300% less snow derived groundwater‐recharged compared to non‐AR precipitation. Rain‐on‐snow (RoS) plays an important role in AR‐driven discharge, where over 50% of total discharge from ARs snow is from RoS events. Finally, despite record‐breaking annual precipitation, simulated groundwater depletion occurs by year end due to estimates of groundwater pumping activities. The results from these simulations serve as a partial analogue of future hydrologic conditions where ARs are expected to intensify and provide a greater fraction of annual precipitation due to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Ice Supersaturation Variability in Cirrus Clouds: Role of Vertical Wind Speeds and Deposition Coefficients

Abstract Aircraft measurements reveal ice supersaturation statistics in cirrus (ISSs) with broad maxima around ice saturation and pronounced variance. In this study, processes shaping ISSs in midlatitude and tropical upper tropospheric conditions are systematically investigated. Water vapor deposition and sublimation of size‐resolved ice crystal populations are simulated in an air parcel framework. Mesoscale temperature fluctuations (MTFs) due to gravity waves force the temporal evolution of supersaturation. Various levels of background wave forcing and cirrus thickness are distinguished in stochastic ensemble simulations. Kinetic limitations to ice mass growth are brought about by supersaturation‐dependent deposition coefficients that represent efficient and inefficient growth modes as a function of ice crystal size. The simulations identify a wide range of deposition coefficients in cirrus, but most values stay above 0.01 such that kinetic limitations to water uptake remain moderate. Supersaturation quenching times are long, typically 0.5–2 hr. The wave forcing thus causes a remarkably large variability in ISSs and cirrus microphysical properties except in the thickest cirrus, producing ensemble‐mean ISSs in line with in‐situ measurements. ISS variance is controlled by MTFs and increases with decreasing cirrus integral radii. In comparison, the impact of ice crystal growth rates on ISSs is small. These results contribute to efforts directed at identifying and solving issues associated with ice‐supersaturated areas and non‐equilibrium cirrus physics in global models.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

Incorporating an Interactive Fire Plume-Rise Model in the DOE's Energy Exascale Earth System Model Version 1 (E3SMv1) and Examining Aerosol Radiative Effect

The vertical distribution of biomass burning aerosol (BBA) is important in regulating their impacts on weather and climate. The plume-rise process affects the injection height of BBA and interacts with the air parcel lifting and cloud processes. However, these processes are not represented in most global climate models. In this study, we replaced the fixed vertical profiles of monthly BBA emissions in the Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1) with an interactive fire plume-rise model. The vertical distribution of BBA emissions was calculated as a function of ambient thermodynamic conditions from the host E3SMv1, with distributions of fire sizes and sensible heat fluxes derived from the observations. The maximum fire radiative power (FRP) technique was used to determine the fire size. Scaling-FRP technique is used to calculate the wildfire heat release. Daily BBA emission, superimposed with a fire diurnal cycle retrieved from the satellite observation, was included in model simulations. The model shows improved agreement with satellite retrievals and in situ measurement during the National Oceanic and Atmospheric Administration Wildfire Experiment for Cloud chemistry, Aerosol absorption, and Nitrogen campaign. The model-observation comparison demonstrates the importance of the plume-rise model and fire diurnal cycle assumption in determining the BBA fields. We also find that E3SMv1 with new features produces a larger carbonaceous aerosol burden, leading to 0.13 W m –2 warming at the top of atmosphere compared to the default E3SMv1. This highlights the importance of accurately representing the BBA injection height and indicates a no-linear nature in the BBA-induced radiative effect.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Hyper‐Local Temperature Prediction Using Detailed Urban Climate Informatics

The accurate modeling of urban microclimate is a challenging task given the high surface heterogeneity of urban land cover and the vertical structure of street morphology. Recent years have witnessed significant efforts in numerical modeling and data collection of the urban environment. Nonetheless, it is difficult for the physical‐based models to fully utilize the high‐resolution data under the constraints of computing resources. The advancement in machine learning (ML) techniques offers the computational strength to handle the massive volume of data. In this study, we proposed a modeling framework that uses ML approach to estimate point‐scale street‐level air temperature from the urban‐resolving meso‐scale climate model and a suite of hyper‐resolution urban geospatial data sets, including three‐dimensional urban morphology, parcel‐level land use inventory, and weather observations from a sensor network. We implemented this approach in the City of Chicago as a case study to demonstrate the capability of the framework. The proposed approach vastly improves the resolution of temperature predictions in cities, which will help the city with walkability, drivability, and heat‐related behavioral studies. Moreover, we tested the model's reliability on out‐of‐sample locations to investigate the modeling uncertainties and the application potentials to the other areas. This study aims to gain insights into next‐gen urban climate modeling and guide the observation efforts in cities to build the strength for the holistic understanding of urban microclimate dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantifying the Turbulent Entrainment‐Mixing Processes Based on Z ‐LWC Relationships of Cloud Droplets

Abstract Turbulent entrainment‐mixing processes profoundly influence the relationship between radar reflectivity factor and liquid water content ( Z ‐LWC) of cloud droplets. However, quantification of the entrainment‐mixing mechanisms based on the Z ‐LWC relationship is still lacking. To address this gap, 12,218 entrainment‐mixing cases are simulated using the Explicit Mixing Parcel Model. We examine the variations of the parameters in the power‐law relationship Z = a LWC b , and the relationship between parameter b and homogeneous mixing degree ( ψ ), a measure quantifying entrainment‐mixing processes. The results indicate that parameter b distributes within the range of 1–2, with a positive correlation between parameter b and ψ . The b ‐ ψ relationship is fitted, which connects the Z ‐LWC relationship for various entrainment‐mixing types. The results suggest the potential for employing a remote sensing approach to investigate the entrainment‐mixing mechanisms of non‐precipitating small cumulus/stratocumulus clouds, thereby overcoming the limitations of traditional observational studies that rely solely on aircraft observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluation of a New Approach for Entrainment and Detrainment Rate Estimation

Entrainment and detrainment rates (ε and δ) constitute the most critical free parameters in mass flux schemes commonly employed for cumulus parameterizations. Recently, Zhu et al. (2021) introduced a new approach that utilizes aircraft observations to simultaneously estimate ε and δ for cumulus clouds, overcoming the limitation of other observation-based approaches that solely yield ε without offering insights into δ. This study aims to comprehensively evaluate the reliability of this new approach. First, evaluation using an Explicit Mixing Parcel Model demonstrates the capability of the new approach to back-calculate predetermined ε and δ based on the physical properties before and after the entrainment mixing. Second, evaluation using large-eddy simulations illustrates that the new approach yields consistent ε and δ profiles compared to the traditional approach. Sensitivity tests indicate a weak sensitivity of the estimated δ with the new approach to the entrained air source. A decrease in the proportion of cloudy air in the assumed detrained air leads to a reduction in the estimated δ, while ε remains unaffected. Finally, the most appropriate assumptions for entrained and detrained air are discussed. Estimating ε for cumulus parameterizations involves acquiring ambient air more than 500 m away from the cloud edge as entrained air. Due to implicit mean field approximations in the traditional approach, determining the optimal assumption for detrained air properties proves challenging. Finally, this study confirms the reliability of the new approach in estimating ε and δ, providing confidence in its application to extensive observational data and advancement in parameterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Updraft Width Modulates Ambient Atmospheric Controls on Convective Cloud Depth

Abstract The depth of convective clouds affects vertical transport of atmospheric constituents, influencing downstream weather and climate. Atmospheric controls on the maximum depth reached by moist convection are investigated with radar‐tracked convective cells tagged with sounding‐derived atmospheric parameters from a field campaign in central Argentina. Regression analyses show that narrow (<12‐km diameter) and wide (>16‐km diameter) cell depths respond to disparate factors, where cell areas are defined using composite reflectivity signatures. Undiluted lifted parcel indices including convective available potential energy (CAPE) and level of neutral buoyancy (LNB) are top predictors of wide cell maximum depth while mid‐tropospheric relative humidity is the top predictor of narrow cell maximum depth. Because narrow cells are more numerous than wide cells, the overall outcome of the full cell population does not strongly correlate with CAPE and LNB conditions. Tracked cells and atmospheric conditions in a simulation with 3‐km grid spacing covering the field campaign produce similar results to those observed. Narrow cells that are relatively deep have a cooler and moister mid‐troposphere with weaker free tropospheric subsidence, while relatively deep wide cells have much warmer and moister lower tropospheric conditions. These atmospheric differences are present 1 hr before cell initiation at both a fixed observing site and variable cell initiation locations. Simulated narrow cell maximum equivalent potential temperature decreases with height at a rate similar to the ambient vertical gradient, causing these cells to fall short of their LNB and supporting the view that entrainment‐driven dilution is a dominant control on their depth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Using Machine Learning to Predict Cloud Turbulent Entrainment–Mixing Processes

Different turbulent entrainment–mixing mechanisms between clouds and environment are essential to cloud–related processes; however, accurate representation of entrainment–mixing in weather/climate models still poses a challenge. This study exploits the use of machine learning (ML) to address this challenge. Four ML (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting, Random Forest, and Support Vector Regression) are examined and compared. It is found that LGB performs best, and thus is selected to understand the impact of entrainment–mixing on microphysics using simulation data from Explicit Mixing Parcel Model. Compared with traditional parameterizations, the trained LGB provides more accurate microphysical properties (number concentration and cloud droplet spectral dispersion). The partial dependences of predicted microphysics on features exhibit a strong alignment with physical mechanisms and expectations, as determined by the interpreting method, thus overcoming the limitations of the “black box” scheme. The underlying mechanisms are that the smaller number concentration and larger spectral dispersion correspond to more inhomogeneous entrainment–mixing. Specifically, number concentration after entrainment–mixing is positively correlated with adiabatic number concentration and liquid water content affected by entrainment–mixing, and inversely correlated with adiabatic volume mean radius. Spectral dispersion after entrainment–mixing is negatively correlated with liquid water content affected by entrainment–mixing, turbulent dissipation rate and relative humidity of entrained air. Sensitivity analysis further suggests that number concentration is mainly determined by cloud microphysical properties whereas spectral dispersion is influenced by both cloud microphysical properties and environmental variables. The results indicate that the LGB scheme has the potential to enhance the representation of entrainment–mixing in weather/climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

The Role of Land‐Atmosphere Feedbacks in Midlatitude Wintertime Surface Temperature Variability

Accurately representing synoptic near-surface temperature variability is crucial to predict weather extremes, yet models remain biased. Existing studies primarily attribute wintertime midlatitude near-surface temperature variability to tropospheric large-scale advection, assuming minimal land influence. However, nudging the model's circulation toward observations yields little improvement in wintertime temperature variance over Northern Hemisphere land, suggesting that land-atmosphere interactions also warrant attention. We introduce a new scaling framework for temperature variance that incorporates local land-atmosphere feedbacks. Comparing our framework to the mixing length approach—which links temperature variance to the meridional temperature gradient and air parcel displacement (mixing length)—shows that land-atmosphere feedbacks are inherently embedded in the mixing length, a connection previously overlooked. Roles of land–atmosphere feedbacks are evaluated via model experiments with perturbed circulation, land, or both. We find that longwave radiative damping dominates temperature variance responses over meridional temperature gradient when both land and circulation are perturbed.

atmosheric science↗
DOE OSTI2026

Marine Aerosol to Refinery Emissions: Transport and Evolution of CCN in the Houston Metropolitan Area and Their Impact on Cloud Formation

The Experiment of Sea Breeze Convection, Aerosols, Precipitation and Environment (ESCAPE) campaign aimed to untangle the impacts of contrasting aerosol sources on cloud microphysical properties within deep convective cells using airborne observations with the National Research Council Canada (NRC) Convair CV‐580 research aircraft. Horizontal and vertical gradients of aerosol number size distributions, total aerosol concentrations and cloud condensation nuclei (CCN) spectra were measured in the lower troposphere to quantify the impact of different aerosol sources on aerosol‐cloud interactions. This study focuses on a research flight dedicated to characterizing the aerosol and CCN properties in the Houston Metropolitan region and identifies five main categories of aerosols based on characteristics of aerosol number size distributions, their CCN properties and meteorological conditions. These categories encompassed more than two orders of magnitude differences in aerosol and CCN concentrations, yet their hygroscopic properties remained similar. Aerosol number size distributions and effective hygroscopicity parameters are used to generate continuous CCN spectra to represent the major aerosol types. The different CCN spectra are then incorporated into a 1‐D aerosol‐cloud parcel model using a large range of updrafts selected in the range of those observed during the ESCAPE campaign to assess the impact of the major aerosol sources in Houston on deep convective cloud microphysical properties.

aerosol-cloud interactions↗
DOE OSTI2026

A Model Intercomparison Study to Investigate Mixing Characteristics in Non‐Precipitating Stratocumulus Clouds

Recent aircraft observations of marine stratocumulus clouds consistently showed that cloud microphysical relationships vary with altitude, indicating inhomogeneous mixing characteristics near cloud top and homogeneous mixing characteristics in mid-levels of clouds. Here, we conduct model intercomparison of an idealized, non-precipitating stratocumulus cloud to evaluate model consistency and examine whether simulations can reproduce the observed mixing characteristics. The results show that eleven large-eddy simulations with various dynamics and microphysics schemes show good agreement on the thermodynamical, microphysical, and dynamical properties of the stratocumulus-topped boundary layer in a steady state. The inter-model spread in steady-state liquid water path is significantly reduced compared to previous model intercomparison studies. This improvement might be due to better models and more consistent initial conditions than those used decades ago. In addition, most simulations, including a low-dimensional simulation, capture inhomogeneous mixing characteristics near the cloud top and homogeneous mixing characteristics inside the cloud. Moreover, simulations using Lagrangian microphysics schemes agree better with the observed mixing characteristics compared with those using the bin microphysics schemes. Since most simulations do not fully resolve the entrainment process, the apparent mixing characteristics arise from the variations in the resolved cloud properties. Our results support the vertical circulation mixing hypothesis, which suggests that homogeneous mixing characteristics in mid-levels of clouds are due to the vertical circulation of entrainment-affected and diluted parcels from the cloud top moved to lower levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Social inequalities in climate change-attributed impacts of Hurricane Harvey

Climate change is already increasing the severity of extreme weather events such as with rainfall during hurricanes. But little research to date investigates if, and to what extent, there are social inequalities in climate change-attributed extreme weather event impacts. Here, we use climate change attribution science paired with hydrological flood models to estimate climate change-attributed flood depths and damages during Hurricane Harvey in Harris County, Texas. Using detailed land-parcel and census tract socio-economic data, we then describe the socio-spatial characteristics associated with these climate change-induced impacts. We show that 30 to 50% of the flooded properties would not have flooded without climate change. Climate change-attributed impacts were particularly felt in Latina/x/o neighborhoods, and especially so in Latina/x/o neighborhoods that were low-income and among those located outside of FEMA’s 100-year floodplain. Our focus is thus on climate justice challenges that not only concern future climate change-induced risks, but are already affecting vulnerable populations disproportionately now.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Rapid vertical exchange at fronts in the Northern Gulf of Mexico

Over the Texas-Louisiana Shelf in the Northern Gulf of Mexico, the eutrophic, fresh Mississippi/Atchafalaya river plume isolates saltier waters below, supporting the formation of bottom hypoxia in summer. The plume also generates strong density fronts, features of the circulation that are known pathways for the exchange of water between the ocean surface and the deep. Using high-resolution ocean observations and numerical simulations, we demonstrate how the summer land-sea breeze generates rapid vertical exchange at the plume fronts. We show that the interaction between the land-sea breeze and the fronts leads to convergence/divergence in the surface mixed layer, which further facilitates a slantwise circulation that subducts surface water along isopycnals into the interior and upwells bottom waters to the surface. This process causes significant vertical displacements of water parcels and creates a ventilation pathway for the bottom water in the northern Gulf. The ventilation of bottom water can bypass the stratification barrier associated with the Mississippi/Atchafalaya river plume and might impact the dynamics of the region’s dead zone.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Aircraft observations in a tropical supercluster over the equatorial Indian Ocean during MISO-BOB field campaign

The Monsoon Intra-Seasonal Oscillations in the Bay of Bengal (MISO-BOB) field campaign was conducted in the Indian Ocean during the 2018 and 2019 summer monsoon seasons. WC-130J aircraft of the 53rd Weather Reconnaissance Squadron of the US Air Force participated in the campaign in June 2018. The dropsonde observations across a tropical supercluster showed zonal wind variations in association with the structure of the convectively coupled Kelvin wave (CCKW). Within the supercluster, easterlies (westerlies) were observed in the upper (lower) troposphere; this transformation occurred just below the 0° C level. The cold pool had an easterly component throughout, and it was coldest (by 2.5° C) at the center of the supercluster, deepest (~ 1000 m) at its rear/western end, and shallowest (~ 300 m) at the front/eastern end. The level of free convection (LFC) at the front end was at 897 m altitude. At the eastern flank of the supercluster, zonal convergence in the lower troposphere occurred between 500-1500 m levels above the surface between the westerlies within the supercluster and opposing ambient easterlies. Thus, the uplifting of conditionally unstable air parcels above LFC to the east of the supercluster was likely to occur due to this convergence rather than the cold pool influence. Conversely, the western flank of the supercluster had low-level zonal divergence. These observations support the notion of ‘self-similarity’ among the mesoscale convective systems and large-scale waves.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Upper troposphere dust belt formation processes vary seasonally and spatially in the Northern Hemisphere

Dust aerosols impact global energy balance substantially by acting as efficient ice nuclei to alter cold cloud properties. However, the estimate of dust indirect effect remains uncertain due to simulating dust distributions poorly and lacking reliable dust observations, especially in the upper-troposphere. Here, we characterize and understand upper-troposphere dust sources and transport with an improved dust dataset derived from A-train satellite lidar and radar measurements and an air parcel trajectory model. The distinct upper-troposphere dust belt over the northern hemisphere has seasonally varying base and top heights of 3.65 ± 2.84 and 8.35 ± 1.50 km above mean sea level and its column loading is strongest during spring (March-April-May). The out-of-phase annual cycles of mid-level dust concentration and westerly wind over source regions control the seasonal upper-tropospheric dust loading variations. African deserts contribute the most (46.3%) to the upper-troposphere dust belt in spring and the synoptic trough is the leading (49%) dust lifting mechanism.

54 ENVIRONMENTAL SCIENCES↗