Cloud Thermodynamic Phase Distributions Observed By Airborne High Spectral Resolution Lidar
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Ewan Crosbie↗
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Results for “thermodynamic phase”
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Thermodynamics of the adiabatic expansion of a mixture of two phases.
Thermodynamics of adiabatic expansion of mixture of two phases capable of interchanging heat and matter across phase boundary
Ahrens, T. J.↗
Classifying thermodynamic cloud phase using machine learning models
Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.
ARM Aerial Facility↗
Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX
Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.
54 ENVIRONMENTAL SCIENCES↗
ARM Thermodynamic Cloud Phase (THERMOCLDPHASE) Value-Added Product Report
The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s Thermodynamic Cloud Phase (THERMOCLDPHASE) Value-Added Product (VAP) provides vertically resolved thermodynamic cloud phase classifications (Zhang and Levin 2024). This VAP applies the multi-sensor methodology introduced by Shupe (2007) to identify cloud phase at the pixel level as liquid, drizzle, liquid + drizzle, rain, ice, snow, or mixed-phase. In addition, the VAP determines the overall cloud layer phase—classified as liquid, mixed-phase, or ice—based on the fraction of ice-containing pixels within the entire layer.
54 ENVIRONMENTAL SCIENCES↗
Thermodynamics and phase separation of dense fully-ionized hydrogen-helium fluid mixtures
The free energy of a hydrogen-helium fluid mixture is evaluated for the temperatures and densities appropriate to the deep interior of a giant planet such as Jupiter. The electrons are assumed to be fully pressure-ionized and degenerate. In this regime, an appropriate first approximation to the ionic distribution functions can be found by assuming hard sphere interactions. Corrections to this approximation are incorporated by means of the perturbation theory of Anderson and Chandler. Approximations for the three-body interactions and the nonlinear response of the electron gas to the ions are included. It is predicted that a hydrogen-helium mixture, containing 10% by number of helium ions, separates into hydrogen-rich and helium-rich phases below about 8000 K, at the pressures relevant to Jupiter (4-40 Megabars). It is also predicted that the alloy occupies less volume per ion than the separated phases. The equations of state and other thermodynamic derivatives are tabulated. Implications of these results are discussed.
Stevenson, D. J.↗
Bromine Incorporation Affects Phase Transformations and Thermal Stability of Lead Halide Perovskites
Mixed-cation and mixed-halide lead halide perovskites show great potential for their application in photovoltaics. Many of the high-performance compositions are made of cesium, formamidinium, lead, iodine, and bromine. However, incorporating bromine in iodine-rich compositions and its effects on the thermal stability of the perovskite structure has not been thoroughly studied. In this work, we study how replacing iodine with bromine in the state-of-the-art Cs 0.17 FA 0.83 PbI 3 perovskite composition leads to different dynamics in the phase transformations as a function of temperature. Through a combination of structural characterization, cathodoluminescence mapping, X-ray photoelectron spectroscopy, and first-principles calculations, we reveal that the incorporation of bromine reduces the thermodynamic phase stability of the films and shifts the products of phase transformations. Our results suggest that bromine-driven vacancy formation during high temperature exposure leads to irreversible transformations into PbI 2 , whereas materials with only iodine go through transformations into hexagonal polytypes, such as the 4H-FAPbI 3 phase. This work sheds light on the structural impacts of adding bromine on thermodynamic phase stability and provides new insights into the importance of understanding the complexity of phase transformations and secondary phases in mixed-cation and mixed-halide systems.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Estimating Glaciation Temperature of Deep Convective Clouds with Remote Sensing Data
Major uncertainties exist for observing and modeling ice content inside deep convective clouds (DCC). One of the difficulties has been the lack of characterization of vertical profiles of cloud hydrometeor phase. Here we propose a technique to estimate the DCC glaciation temperature using passive remote sensing data. It is based on a conceptual model of vertical hydrometeor size profiles inside DCCs. Estimates from the technique agree well with our general understanding of the problem. Furthermore, the link between vertical profiles of cloud particle size and hydrometeor thermodynamic phase is confirmed by a 3-13 cloud retrieval technique. The technique is applied to aircraft measurements of cloud side reflectance and the result was compared favorably with an independent retrieval of thermodynamic phase based on different refractive indices at 2.13 micron and 2.25 micron. Possible applications of the technique are discussed.
Yuan, Tianle↗
Comparison of the MODIS Collection 5 Multilayer Cloud Detection Product with CALIPSO
CALIPSO, launched in June 2006, provides global active remote sensing measurements of clouds and aerosols that can be used for validation of a variety of passive imager retrievals derived from instruments flying on the Aqua spacecraft and other A-Train platforms. The most recent processing effort for the MODIS Atmosphere Team, referred to as the Collection 5 scream, includes a research-level multilayer cloud detection algorithm that uses both thermodynamic phase information derived from a combination of solar and thermal emission bands to discriminate layers of different phases, as well as true layer separation discrimination using a moderately absorbing water vapor band. The multilayer detection algorithm is designed to provide a means of assessing the applicability of 1D cloud models used in the MODIS cloud optical and microphysical product retrieval, which are generated at a 1 km resolution. Using pixel-level collocations of MODIS Aqua, CALIOP, we investigate the global performance of multilayer cloud detection algorithms (and thermodynamic phase).
Platnick, Steven↗
Partition between supercooled liquid droplets and ice crystals in mixed-phase clouds based on airborne in situ observations
Abstract. The onset of ice nucleation in mixed-phase clouds determines the lifetime and microphysical properties of ice clouds. In this work, we develop a novel method that differentiates between various phases of mixed-phase clouds, such as clouds dominated by pure liquid or pure ice segments, compared with those having ice crystals surrounded by supercooled liquid water droplets or vice versa. Using this method, we examine the relationship between the macrophysical and microphysical properties of Southern Ocean mixed-phase clouds at −40 to 0 °C (e.g. stratiform and cumuliform clouds) based on the in situ aircraft-based observations during the US National Science Foundation Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) flight campaign. The results show that the exchange between supercooled liquid water and ice crystals from a macrophysical perspective, represented by the increasing spatial ratio of regions containing ice crystals relative to the total in-cloud region (defined as ice spatial ratio), is positively correlated with the phase exchange from a microphysical perspective, represented by the increasing ice water content (IWC), decreasing liquid water content (LWC), increasing ice mass fraction, and increasing ice particle number fraction (IPNF). The mass exchange between liquid and ice becomes more significant during phase 3 when pure ice cloud regions (ICRs) start to appear. Occurrence frequencies of cloud thermodynamic phases show a significant phase change from liquid to ice at a similar temperature (i.e. −17.5 °C) among three types of definitions of mixed-phase clouds based on ice spatial ratio, ice mass fraction, or IPNF. Aerosol indirect effects are quantified for different phases using number concentrations of aerosols greater than 100 or 500 nm (N>100 and N>500, respectively). N>500 shows stronger positive correlations with ice spatial ratios compared with N>100. This result indicates that larger aerosols potentially contain ice-nucleating particles (INPs), which facilitate the formation of ice crystals in mixed-phase clouds. The impact of N>500 is also more significant in phase 2 when ice crystals just start to appear in the mixed phase compared with phase 3 when pure ICRs have formed, possibly due to the competing aerosol indirect effects on primary and secondary ice production in phase 3. The thermodynamic and dynamic conditions are quantified for each phase. The results show stronger in-cloud turbulence and higher updraughts in phases 2 and 3 when liquid and ice coexist compared with pure liquid or ice (phases 1 and 4, respectively). The highest updraughts and turbulence are seen in phase 3 when supercooled liquid droplets are surrounded by ice crystals. These results indicate both updraughts and turbulence support the maintenance of supercooled liquid water amongst ice crystals. Overall, these results illustrate the varying effects of aerosols, thermodynamics, and dynamics through various stages of mixed-phase cloud evolution based on this new method that categorizes cloud phases.
54 ENVIRONMENTAL SCIENCES↗
A Jacobian-free pseudo-arclength continuation method for phase transitions in inhomogeneous thermodynamic systems
Developing phase diagrams for inhomogeneous systems in thermodynamics is difficult, in part, due to the large phase space and the possibility of unstable and metastable solutions arising from first-order phase transitions. Pseudo-arclength continuation (PAC) is a method that allows one to trace out stable and unstable solutions of nonlinear systems. Typically, PAC utilizes the Jacobian in order to implement Newton (or quasi-Newton) steps. In this work, we present a Jacobian-free PAC method that is amenable to the usual workflows in inhomogeneous thermodynamics. We demonstrate our method in systems that have first-order phase transitions, including a novel example of polyelectrolyte complex coacervation in confinement, where multiple surface phase transitions occur and can overlap with one another.
Chemistry↗
Kinetic Understanding of Field-Induced Phase Transition from Tetragonal to Ferroelectric Orthorhombic Phase in Ferroelectric CeO 2 –HfO 2 –ZrO 2 Films
The ferroelectric properties and structural phase transition behaviors of fluorite-type CeO 2 −HfO 2 −ZrO 2 films were investigated. The epitaxial films on indium tin oxide (ITO) (111)/yttria-stabilized zirconia (YSZ) (111) substrates were grown through pulsed laser deposition at room temperature and subsequently heat-treated at 1000 °C under a N 2 gas flow. The crystalline phases and Curie temperatures of the films were investigated by X-ray diffraction. An increase in the Ce or Zr content in the films led to a higher crystallographic symmetry, such as orthorhombic or tetragonal. In addition, electrical characterization revealed that the orthorhombic films and some of the tetragonal films displayed ferroelectricity. This was due to the field-induced phase transition from the tetragonal to ferroelectric orthorhombic phase in the films, where the Curie temperatures were relatively low. The tetragonal metastable phase was kinetically frozen and could not change into the stable orthorhombic phase at such a low temperature. The critical electric field where the field-induced phase transition occurred was below 0.8 MV/cm, which was sufficiently small compared to the coercive field. These results evidence the kinetic driving force that causes a field-induced phase transition from the paraelectric tetragonal phase to the ferroelectric orthorhombic phase in HfO 2 -based ferroelectrics. They also enhance our understanding of the thermodynamic phase stabilities of HfO 2 -based material polymorphs.
36 MATERIALS SCIENCE↗
CAP: A Computer Code for Generating Tabular Thermodynamic Functions from NASA Lewis Coefficients
For several decades the NASA Glenn Research Center has been providing a file of thermodynamic data for use in several computer programs. These data are in the form of least-squares coefficients that have been calculated from tabular thermodynamic data by means of the NASA Properties and Coefficients (PAC) program. The source thermodynamic data are obtained from the literature or from standard compilations. Most gas-phase thermodynamic functions are calculated by the authors from molecular constant data using ideal gas partition functions. The Coefficients and Properties (CAP) program described in this report permits the generation of tabulated thermodynamic functions from the NASA least-squares coefficients. CAP provides considerable flexibility in the output format, the number of temperatures to be tabulated, and the energy units of the calculated properties. This report provides a detailed description of input preparation, examples of input and output for several species, and a listing of all species in the current NASA Glenn thermodynamic data file.
Zehe, Michael J.↗
CAP: A Computer Code for Generating Tabular Thermodynamic Functions from NASA Lewis Coefficients
For several decades the NASA Glenn Research Center has been providing a file of thermodynamic data for use in several computer programs. These data are in the form of least-squares coefficients that have been calculated from tabular thermodynamic data by means of the NASA Properties and Coefficients (PAC) program. The source thermodynamic data are obtained from the literature or from standard compilations. Most gas-phase thermodynamic functions are calculated by the authors from molecular constant data using ideal gas partition functions. The Coefficients and Properties (CAP) program described in this report permits the generation of tabulated thermodynamic functions from the NASA least-squares coefficients. CAP provides considerable flexibility in the output format, the number of temperatures to be tabulated, and the energy units of the calculated properties. This report provides a detailed description of input preparation, examples of input and output for several species, and a listing of all species in the current NASA Glenn thermodynamic data file.
Zehe, Michael J.↗
An Automatic Phase-Change Detection Technique for Colloidal Hard Sphere Suspensions
Colloidal suspensions of monodisperse spheres are used as physical models of thermodynamic phase transitions and as precursors to photonic band gap materials. However, current image analysis techniques are not able to distinguish between densely packed phases within conventional microscope images, which are mainly characterized by degrees of randomness or order with similar grayscale value properties. Current techniques for identifying the phase boundaries involve manually identifying the phase transitions, which is very tedious and time consuming. We have developed an intelligent machine vision technique that automatically identifies colloidal phase boundaries. The algorithm utilizes intelligent image processing techniques that accurately identify and track phase changes vertically or horizontally for a sequence of colloidal hard sphere suspension images. This technique is readily adaptable to any imaging application where regions of interest are distinguished from the background by differing patterns of motion over time.
McDowell, Mark↗
Machine Learning the COSMO Model for Predicting Thermodynamics of Electrolyte Mixtures
Bottom-up design of electrolyte mixtures for battery systems requires predicting macro thermodynamic properties from molecular constituents. For instance, molten salt electrolyte batteries require conditions far above room temperature to operate. Therefore, discovering mixtures with increasingly lower eutectic melting points is desirable. A model that can approximate chemical activity is a valuable tool to search through the vast compositional design space. Machine learning can predict properties of materials such as vibrational free energies, electronic energy gaps, and thermal conductivities. Moreover, they can learn physical models such as interatomic potentials. The COSMO-SAC model uses theory and empirical parameterization to predict liquid-vapor and liquid-solid properties using first-principles calculations. However, obtaining activity coefficients required for parameterizing the COSMO-SAC model is costly and limited to a select chemical space. In this work, we explored if machine learning methods could improve the COSMO-SAC model and bridge density functional theory calculations to liquid phase thermodynamic properties. Our data-driven approach uses existing databases for sigma-profiles of organic solvents and reconciles their methodological differences via ensemble averaging. First, an optimal machine learning model is constructed for each dataset. Our machine learning algorithms use the sigma-profile as an input feature to predict binary mixtures' activity coefficients using multi-output regression. Each dataset uses different choices of functionals, methods, and basis sets. Therefore, our ensemble model attempts to predict corrected activity coefficients given the combination of all the model outputs. The activity coefficients used for training are generated using the COSMO-SAC model. This approach enables the extraction of meaningful information from the existing datasets to improve the COSMO-SAC model for obtaining thermodynamic properties of electrolyte mixtures. With the liquid phase activities, we can identify electrolyte mixtures that meet desired phase equilibria conditions.
Thermodynamics↗
Pressure-coupled vaporization and combustion responses of liquid-fuel droplets in high-pressure environments
The dynamic responses of liquid-fuel droplet vaporization and combustion to ambient pressure oscillations are examined. The analysis is based on the complete sets of conservation equations for both gas and liquid phases, and accommodates detailed treatments of finite-rate chemical kinetics and variable properties. With a full account of thermodynamic phase equilibrium at the droplet surface, the model enables a systematic examination of the effects of ambient flow conditions on the droplet behavior. The responses of hydrocarbon fuel droplets in both sub- and super-critical environments are investigated. Results indicate that the droplet gasification and burning mechanisms depend greatly on the ambient pressure. In particular, a rapid enlargement of the vaporization and combustion responses occurs when the droplet surface reaches its critical point, mainly due to the strong variations of latent heat of vaporization and thermophysical properties at the critical state.
Yang, Vigor↗