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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Global Cloud-Resolving Models

Global cloud-resolving models (GCRMs) are a new category of atmospheric global models designed to solve different flavors of the nonhydrostatic equations through the use of kilometer-scale global meshes. GCRMs make it possible to explicitly simulate deep convection, thereby avoiding the need for cumulus parameterization and allowing for clouds to be “resolved” by microphysical models responding to grid-scale forcing. GCRMs require high-resolution discretization over the globe, for which a variety of mesh structures have been proposed and employed. The first GCRM was constructed 15 years ago, and in recent years, other groups have also begun adopting this approach, enabling the first intercomparison studies of such models. Because conventional general circulation models (GCMs) suffer from large biases associated with cumulus parameterization, GCRMs are attractive tools for researchers studying global weather and climate. In this review, GCRMs are described, with some emphasis on their historical development and the associated literature documenting their use. The advantages of GCRMs are presented, and currently existing GCRMs are listed and described. Future prospects for GCRMs are also presented in the final section.

Satoh, Masaki↗
NASA NTRS1993

Investigation of the effects of the macrophysical and microphysical properties of cirrus clouds on the retrieval of optical properties: Results for FIRE 2

Due to the prevalence and persistence of cirrus cloudiness across the globe, cirrus clouds are believed to have an important effect on the climate. Stephens et al., (1990) among others have shown that the important factor determining how cirrus clouds modulate the climate is the balance between the albedo and emittance effect of the cloud systems. This factor was shown to depend in part upon the effective sizes of the cirrus cloud particles. Since effective sizes of cirrus cloud microphysical distributions are used as a basis of parameterizations in climate models, it is crucial that the relationships between effective sizes and radiative properties be clearly established. In this preliminary study, the retrieval of cirrus cloud effective sizes are examined using a two dimensional radiative transfer model for a cirrus cloud case sampled during FIRE Cirrus 11. The purpose of this paper is to present preliminary results from the SHSG model demonstrating the sensitivity of the bispectral relationships of reflected radiances and thus the retrieval of effective sizes to phase function and dimensionality.

Stackhouse, Paul W., Jr.↗
NASA NTRS1993

Case study of a low-reflectivity pulsating microburst - Numerical simulation of the Denver, 8 July 1989, storm

Results from a 3D simulation of a very strong microburst, detected by the Low-Level Windshear Alert System, are presented. The simulation was performed using the Terminal Area Simulation System (TASS) model (Proctor, 1987) which is a 3D nonhydrostatic cloud model that includes parameterizations for both liquid and ice-phase microphysics. Emphasis is placed om the pulsating characteristic and the very-low radar reflectivity of the event. The simulation revealed that the dissipating cloud induces a type-1 pulsating microburst event, with at least three distinct microburst pulses over a period of 20 min.

Proctor, Fred H.↗
DOE OSTI2025

A Fortran–Python interface for integrating machine learning parameterization into earth system models

Abstract. Parameterizations in earth system models (ESMs) are subject to biases and uncertainties arising from subjective empirical assumptions and incomplete understanding of the underlying physical processes. Recently, the growing representational capability of machine learning (ML) in solving complex problems has spawned immense interests in climate science applications. Specifically, ML-based parameterizations have been developed to represent convection, radiation, and microphysics processes in ESMs by learning from observations or high-resolution simulations, which have the potential to improve the accuracies and alleviate the uncertainties. Previous works have developed some surrogate models for these processes using ML. These surrogate models need to be coupled with the dynamical core of ESMs to investigate the effectiveness and their performance in a coupled system. In this study, we present a novel Fortran–Python interface designed to seamlessly integrate ML parameterizations into ESMs. This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and scikit-learn. We demonstrate the interface's modularity and reusability through two cases: an ML trigger function for convection parameterization and an ML wildfire model. We conduct a comprehensive evaluation of memory usage and computational overhead resulting from the integration of Python codes into the Fortran ESMs. By leveraging this flexible interface, ML parameterizations can be effectively developed, tested, and integrated into ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Fortran-Python Interface for Integrating Machine Learning Parameterization into Earth System Models

Parameterizations in Earth System Models (ESMs) are subject to biases and uncertainties arising from subjective empirical assumptions and incomplete understanding of the underlying physical processes. Recently, the growing representational capability of machine learning (ML) in solving complex problems has spawned immense interests in climate science applications. Specifically, ML-based parameterizations have been developed to represent convection, radiation and microphysics processes in ESMs by learning from observations or high-resolution simulations, which have the potential to improve the accuracies and alleviate the uncertainties. Previous works have developed some surrogate models for these processes using ML. These surrogate models need to be coupled with the dynamical core of ESMs to investigate the effectiveness and their performance in a coupled system. In this study, we present a novel Fortran-Python interface designed to seamlessly integrate ML parameterizations into ESMs. This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and Scikit-learn. We demonstrate the interface's modularity and reusability through two cases: a ML trigger function for convection parameterization and a ML wildfire model. We conduct a comprehensive evaluation of memory usage and computational overhead resulting from the integration of Python codes into the Fortran ESMs. By leveraging this flexible interface, ML parameterizations can be effectively developed, tested, and integrated into ESMs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Stochastic Convection Parameterizations

computational fluid dynamics, radiation, clouds, turbulence, convection, gravity waves, surface interaction, radiation interaction, cloud and aerosol microphysics, complexity (vegetation, biogeochemistry, radiation versus turbulence/convection stochastic approach, non-linearities, Monte Carlo, high resolutions, large-Eddy Simulations, cloud structure, plumes, saturation in tropics, forecasting, parameterizations, stochastic, radiation-clod interaction, hurricane forecasts

atmospheric models↗
DOE OSTI2024

Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model

Abstract Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another important process for the total cloud ice budget, which also remains largely unconstrained. In this study, we examine the impact of PIP, SIP, and ice aggregation on Arctic clouds, using the Norwegian Earth System Model, version 2 (NorESM2). Simulations with both prognostic and diagnostic PIP show that heterogeneous freezing alone cannot reproduce the observed cloud ice content. The implementation of missing SIP mechanisms (collisional breakup, drop shattering, and sublimation breakup) in NorESM2 improves the modeled ice properties, while improvements in liquid content occur only in simulations with prognostic PIP. However, results are sensitive to the description of collisional breakup. This mechanism, which dominates SIP in the examined conditions, is very sensitive to the treatment of the sublimation correction factor, a poorly constrained parameter that is included in the utilized parameterization. Finally, variations in ice aggregation treatment can also significantly impact cloud properties, mainly through their impact on collisional breakup efficiency. Overall, enhancement in ice production through the addition of SIP mechanisms and the reduction in ice aggregation (in line with radar observations of shallow Arctic clouds) result in enhanced cloud cover and decreased TOA radiation biases, compared to satellite measurements, especially during the cold months. Significance Statement Arctic clouds remain a large source of uncertainty in projections of the future climate due to the poor representation of the microphysical processes that govern their life cycle. Ice formation is among the least understood processes. While it is widely recognized that better constraints on primary ice production (PIP) are needed to improve existing parameterizations, we show that secondary ice production (SIP) and ice aggregation can have also a significant impact on ice number concentrations. Constraining ice formation through the addition of missing SIP mechanisms and reducing ice aggregation can improve the representation of the cloud macrophysical properties and enhance total cloud cover in the Arctic region, which in turn contributes to decreased TOA radiation biases in the cold months.

Meteorology & Atmospheric Sciences↗
DOE OSTI2025

Dependence of Convective Cloud Microphysical Properties on Environmental Conditions during the TRACER and ESCAPE Field Campaigns: A Synergistic Approach of Observations, Machine Learning and Parcel Models

The sensitivity of convective clouds to aerosols and their interactions with environment, combined with limited observational constraints in parameterizations, introduces significant uncertainties in atmospheric models. Here, this study investigates the dependence of convective cloud microphysical properties on environmental conditions using a synergistic approach that combines unique observations from the TRACER and ESCAPE field campaigns, machine learning techniques, and parcel model simulations with a super-droplet microphysics scheme. A random forest algorithm identifies in-situ vertical velocity (w), temperature (T), and surface fine-mode aerosol mass concentration as the three most important environmental conditions influencing cloud properties including liquid water content (LWC), number concentration for particles with D max < 50 μm (N c ,<50), 50 μm ≤ D max ≤ 3000 μm (N c,50–3000 ), and droplet effective diameter (D e ). Results show that LWC, N c,<50 , and N c,50–3000 significantly increase with w in updrafts. Across w bins, as T decreases, LWC, D e , and N c,50–3000 increase, while N c,<50 decreases, which are closely linked to the distance above cloud bases. Warmer cloud bases yield higher LWC, greater N c,50–3000 , and smaller N c,<50 , while polluted environments produce greater N c,<50 . Parcel model simulations successfully replicate these observed dependencies. The simulation results indicate that warmer cloud bases enhance condensation generating larger droplets, and differences in droplet sizes are then amplified through collision-coalescence, resulting in a greater N c,50–3000 . Polluted conditions result in a greater N c,<50 primarily due to enhanced cloud condensation nuclei activation despite increased collision-coalescence rates compared to pristine conditions. This study provides observed quantitative patterns characterizing cloud microphysical properties as a function of key environmental parameters, offering valuable constraints for improving physics parameterizations and numerical models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Optimal parameterizations for observational constraints on thawing dark energy

Time-varying dark energy is often modeled in observational analyses through generic parameterizations of its equation of state w(z), which typically use two free parameters {w 0 , w a } to span a broad range of behaviors as a function of redshift. However, this broad range of behaviors can only approximately capture the dynamics of any given microphysical theory of dark energy. A complementary approach is to use targeted parameterizations designed to model specific classes of dynamical dark energy with greater precision. Focusing on the class of thawing dark energy, we quantify and compare the precision with which nineteen generic and targeted parameterizations can capture the dynamics of physically motivated thawing quintessence theories. We find that a targeted parameterization derived from a Padé expansion of w is the most reliable of these, producing accurate reconstructions of w(z), the expansion history H(z), and cosmological parameters such as H 0 and Ω m for a broad range of microphysical theories.

dark energy experiments↗
DOE OSTI2025

Immersion Freezing in Particle-Based Aerosol-Cloud Microphysics: A Probabilistic Perspective on Singular and Time-Dependent Models

Cloud droplets containing immersed ice-nucleating particles (INPs) may freeze at temperatures above the homogeneous freezing threshold temperature in a process referred to as immersion freezing. In modeling studies, immersion freezing is often described using either so-called “singular” or “time-dependent” parameterizations. Here, we compare both approaches and discuss them in the context of probabilistic particle-based (super-droplet) cloud microphysics modeling. First, using a box model, we contrast how both parameterizations respond to idealized ambient cooling rate profiles and quantify the impact of the polydispersity of the immersed surface spectrum on the frozen fraction evolution. Presented simulations highlight that the singular approach, constituting a time-integrated form of a more general time-dependent approach, is only accurate under a limited range of ambient cooling rates. The time-dependent approach is free from this limitation. Second, using a prescribed-flow two-dimensional cloud model, we illustrate the macroscopic differences in the evolution in time of ice particle concentrations in simulations with flow regimes relevant to ambient cloud conditions. The flow-coupled aerosol-budget-resolving simulations highlight the benefits and challenges of modeling cloud condensation nuclei activation and immersion freezing on insoluble ice nuclei with super-particle methods. The challenges stem, on the one hand, from heterogeneous ice nucleation being contingent on the presence of relatively sparse immersed INPs, and on the other hand, from the need to represent a vast population of particles with relatively few so-called super particles (each representing a multiplicity of real particles). We discuss the critical role of the sampling strategy for particle attributes, including the INP size, the freezing temperature (for singular scheme) and the multiplicity.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1987

The terminal area simulation system. Volume 1: Theoretical formulation

A three-dimensional numerical cloud model was developed for the general purpose of studying convective phenomena. The model utilizes a time splitting integration procedure in the numerical solution of the compressible nonhydrostatic primitive equations. Turbulence closure is achieved by a conventional first-order diagnostic approximation. Open lateral boundaries are incorporated which minimize wave reflection and which do not induce domain-wide mass trends. Microphysical processes are governed by prognostic equations for potential temperature water vapor, cloud droplets, ice crystals, rain, snow, and hail. Microphysical interactions are computed by numerous Orville-type parameterizations. A diagnostic surface boundary layer is parameterized assuming Monin-Obukhov similarity theory. The governing equation set is approximated on a staggered three-dimensional grid with quadratic-conservative central space differencing. Time differencing is approximated by the second-order Adams-Bashforth method. The vertical grid spacing may be either linear or stretched. The model domain may translate along with a convective cell, even at variable speeds.

Proctor, F. H.↗
NASA NTRS2006

Cloud Radiative Forcing at the ARM Climate Research Facility: Technique, Validation, and Comparison to Satellite-derived Diagnostic Quantities - Part 1

It has been hypothesized that continuous ground-based remote sensing measurements from active and passive remote sensors combined with regular soundings of the atmospheric thermodynamic structure can be combined to describe the effects of clouds on the clear sky radiation fluxes. We critically test that hypothesis in this paper and a companion paper (Part II). Using data collected at the Southern Great Plains (SGP) Atmospheric Radiation Measurement (ARM) site sponsored by the U.S. Department of Energy, we explore an analysis methodology that results in the characterization of the physical state of the atmospheric profile at time resolutions of five minutes and vertical resolutions of 90 m. The description includes thermodynamics and water vapor profile information derived by merging radiosonde soundings with ground-based data, and continues through specification of the cloud layer occurrence and microphysical and radiative properties derived from retrieval algorithms and parameterizations. The description of the atmospheric physical state includes a calculation of the infrared and clear and cloudy sky solar flux profiles. Validation of the methodology is provided by comparing the calculated fluxes with top of atmosphere (TOA) and surface flux measurements and by comparing the total column optical depths to independently derived estimates. We find over a 1-year period of comparison in overcast uniform skies, that the calculations are strongly correlated to measurements with biases in the flux quantities at the surface and TOA of less than 10% and median fractional errors ranging from 20% to as low as 2%. In the optical depth comparison for uniform overcast skies during the year 2000 where the optical depth varies over 3 orders of magnitude we find a mean positive bias of 46% with a median bias of less than 10% and a 0.89 correlation coefficient. The slope of the linear regression line for the optical depth comparison is 0.86 with a normal deviation of 20% about this line. In addition to a case study where we examine the cloud radiative effects at the TOA, surface and atmosphere by a middle latitude synoptic-scale cyclone, we examine the cloud top pressure and optical depth retrievals of ISCCP and LBTM over a period of 1 year. Using overcast period from the year 2000, we find that the satellite algorithms tend to bias cloud tops into the middle troposphere and underestimate optical depth in high optical depth events (greater than 100) by as much as a factor of 2.

Mace, Gerald G.↗
NASA NTRS2015

Organic Aerosol Volatility Parameterizations and Their Impact on Atmospheric Composition and Climate

Despite their importance and ubiquity in the atmosphere, organic aerosols are still very poorly parameterized in global models. This can be explained by two reasons: first, a very large number of unconstrained parameters are involved in accurate parameterizations, and second, a detailed description of semi-volatile organics is computationally very expensive. Even organic aerosol properties that are known to play a major role in the atmosphere, namely volatility and aging, are poorly resolved in global models, if at all. Studies with different models and different parameterizations have not been conclusive on whether the additional complexity improves model simulations, but the added diversity of the different host models used adds an unnecessary degree of variability in the evaluation of results that obscures solid conclusions. Aerosol microphysics do not significantly alter the mean OA vertical profile or comparison with surface measurements. This might not be the case for semi-volatile OA with microphysics.

Aerosols↗
NASA NTRS2005

The Impact of Surface Albedo on the Retrievals of Low-Level Stratus Cloud Properties: An Updated Parameterization

An updated version of Dong et al. (1998, hereafter D98) parameterization is developed from a total of 40 hours of data with a broad range of surface albedos (0.1-0.8) during the 2000-2002 winter seasons at the DOE ARM SGP site. The updated parameterization includes the impact of surface albedo on the retrievals of stratus cloud microphysical and radiative properties, and has a significant improvement over D98 when surface albedo is high. Comparing the retrievals, the cloud-droplet effective radii (r(sub e)) calculated from the updated parameterization have a higher correlation coefficient (0.733) and lower Root-Mean-Square (RMS) error (1.74 m or 17.4%) than those (0.602, 4.0 m or 40%) from the D98. The cloud albedos also have a much higher correlation coefficient (0.983) and lower RMS (3%) than those 0.465, 26%) from the D98. The upper limit of surface albedo is 0.3 in applying the D98.

Dong, Xiquan↗
NASA NTRS1988

Theory on the uncertainty in cloud microphysical processes and climate

The interactive role of clouds and precipitation processes in a climate model is investigated. The basic model structure and parameterizations for condensation, evaporation, and precipitation are outlined. Results simulated from the cloud-climate model are discussed. The potential link between microphysical cloud processes and climate is theorized. The need to develop a climatology of the mean particle radius for clouds at the global scale, in order to understand the role of clouds in climate is stressed.

Liou, Kuo-Nan↗
NASA NTRS2012

Sensitivity of Cirrus and Mixed-phase Clouds to the Ice Nuclei Spectra in McRAS-AC: Single Column Model Simulations

The salient features of mixed-phase and ice clouds in a GCM cloud scheme are examined using the ice formation parameterizations of Liu and Penner (LP) and Barahona and Nenes (BN). The performance of LP and BN ice nucleation parameterizations were assessed in the GEOS-5 AGCM using the McRAS-AC cloud microphysics framework in single column mode. Four dimensional assimilated data from the intensive observation period of ARM TWP-ICE campaign was used to drive the fluxes and lateral forcing. Simulation experiments where established to test the impact of each parameterization in the resulting cloud fields. Three commonly used IN spectra were utilized in the BN parameterization to described the availability of IN for heterogeneous ice nucleation. The results show large similarities in the cirrus cloud regime between all the schemes tested, in which ice crystal concentrations were within a factor of 10 regardless of the parameterization used. In mixed-phase clouds there are some persistent differences in cloud particle number concentration and size, as well as in cloud fraction, ice water mixing ratio, and ice water path. Contact freezing in the simulated mixed-phase clouds contributed to transfer liquid to ice efficiently, so that on average, the clouds were fully glaciated at T approximately 260K, irrespective of the ice nucleation parameterization used. Comparison of simulated ice water path to available satellite derived observations were also performed, finding that all the schemes tested with the BN parameterization predicted 20 average values of IWP within plus or minus 15% of the observations.

Betancourt, R. Morales↗
NASA NTRS2023

Electrification Within Wintertime Stratiform Regions Sampled During the 2020/2022 NASA IMPACTS Field Campaign

Two nor'easter events—sampled during the NASA Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign—were examined to characterize the microphysics in relation to the underlying electrification processes within wintertime stratiform regions. A theoretical model was developed to determine whether accretion or diffusion growth regimes were preferential during periods of greatest electrification. Model simulation with electrification parameterization was used to provide supplemental context to the physical processes of in-cloud microphysics and electrification. The strongest electric fields (i.e., ∼80 V m −1 at 20 km) during the 2020 NASA IMPACTS deployment was associated with large non-rimed ice crystals colliding with each other. During the 29–30 January 2022 science flight, the NASA P-3 microphysical probe data demonstrated that non-inductive charging was possible off the coastline of Cape Cod, Massachusetts. Later in the science flight, when the NASA P-3 and ER-2 were coordinating with each other, measured electric fields consistently were less than 8 V m −1 and electrification was subdued owing to reduced concentrations of graupel and large ice hydrometeors. Altogether, the in-situ observations provide evidence for the non-riming collisional charging mechanism and demonstrates that graupel and supercooled liquid water may not be necessary for weak electrification within wintertime stratiform regions. Model output from simulation of both events suggested that the main synoptic snowbands were associated with elevated hydrometeor snow charge density and electric fields.

snowfall↗
NASA NTRS2014

The Impacts of Microphysics and Planetary Boundary Layer Physics on Model Simulations of U. S. Deep South Summer Convection

Inspection of output from various configurations of high-resolution, explicit convection forecast models such as the Weather Research and Forecasting (WRF) model indicates significant sensitivity to the choices of model physics parameterizations employed. Some of the largest apparent sensitivities are related to the specifications of the cloud microphysics and planetary boundary layer physics packages. In addition, these sensitivities appear to be especially pronounced for the weakly-sheared, multicell modes of deep convection characteristic of the Deep South of the United States during the boreal summer. Possible ocean-land sensitivities also argue for further examination of the impacts of using unique ocean-land surface initialization datasets provided by the NASA Short-term Prediction Research and Transition (SPoRT Center to select NOAA/NWS weather forecast offices. To obtain better quantitative understanding of these sensitivities and also to determine the utility of the ocean-land initialization data, we have executed matrices of regional WRF forecasts for selected convective events near Mobile, AL (MOB), and Houston, TX (HGX). The matrices consist of identically initialized WRF 24-h forecasts using any of eight microphysics choices and any of three planetary boundary layer choices. The resulting 24 simulations performed for each event within either the MOB or HGX regions are then compared to identify the sensitivities of various convective storm metrics to the physics choices. Particular emphasis is placed on sensitivities of precipitation timing, intensity, and coverage, as well as amount and coverage of lightning activity diagnosed from storm kinematics and graupel in the mixed phase layer. The results confirm impressions gleaned from study of the behavior of variously configured WRF runs contained in the ensembles produced each spring at the Center for the Analysis and Prediction of Storms, but with the benefit of more straightforward control of the physics package choices. The design of the experiments thus allows for more direct interpretation of the sensitivities to each possible physics combination. The results should assist forecasters in their efforts to anticipate and correct for possible biases in simulated WRF convection patterns, and help the modeling community refine their model parameterizations.

McCaul, E. W., Jr.↗