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Chen, Jingyi

Publications and source records attributed to Chen, Jingyi.

32 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold-Air Outbreak over the Western North Atlantic

We report postfrontal clouds (PFC) are ubiquitous in the marine boundary layer, and their morphology is essential to estimating the radiation budget in weather and climate models. Here we examine the roles of sea surface temperature (SST) and meteorological factors in controlling the mesoscale morphology and evolution of shallow clouds associated with a cold-air outbreak that occurred on 1 March 2020 during phase I of the Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE). Our results show that the simulated PFC structure and ambient conditions by the Weather Research and Forecasting (WRF) Model are generally consistent with observations from GOES-16 and dropsonde measurements. We also examine the thermodynamical and dynamical influences in the cloud mesoscale morphology using WRF sensitivity experiments driven by two meteorological forcing datasets with different domain-mean SST and spatial gradients, which lead to dissimilar values of hydrometeor water path and cloud core fraction. The SST from ERA5 leads to weaker stability and higher inversion height than the SST from FNL does. In addition, the use of large-scale meteorological forcings from ERA5 yields a distinctive time evolution of wind direction shear in the inner domain, which favors the formation and persistence of longer cloud rolls. Both factors contribute to a change in the time evolution of domain-mean water path and cloud core fraction of cloud streets. Our study takes advantage of the simulation driven by the differences between two large-scale forcing datasets to illustrate the importance of SST and wind direction shear in the cloud street morphology in a realistic scenario.

54 ENVIRONMENTAL SCIENCES↗
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 OSTI2022

Effects of Horizontal Resolution, Domain Size, Boundary Conditions, and Surface Heterogeneity on Coarse LES of a Convective Boundary Layer

Atmospheric properties in a convective boundary layer vary over a wide range of spatial scales and are commonly studied using large-eddy simulations (LES) in various configurations. We examine how the boundary layer depth and distribution of variability across scales are affected by LES grid spacing, domain size, inhomogeneity of surface properties, and external forcing. Two different setups of the Weather Research and Forecasting (WRF) model are analyzed. A semi-idealized configuration uses a periodic domain, flat surface, prescribed homogeneous surface heat fluxes, and horizontally uniform profiles of large-scale advective tendencies. A nested LES setup employs a larger domain and realistic initial and boundary conditions, including an interactive land surface model with representative topography and vegetation and soil types. Subdomains of identical size are analyzed for all simulations. Characteristic structure sizes are quantified using the variability scales L 50 and L 95 , defined such that features smaller than that contain 50% and 95% of the total variance, respectively. Progressive increase in L 50 from vertical velocity to temperature and moisture structures is systematically reproduced in all simulation configurations. This dependence of L 50 on the considered variable complicates the development of scale-aware parameterizations for models with grid spacing in the “terra incognita”. In simulations using a larger domain with heterogeneous surface properties, the development of internal mesoscale patterns significantly affects variance distributions inside analyzed subdomains. Sizes of boundary layer structures also strongly depend on the LES grid spacing and, in case of heterogeneous surface and topography, on location of the subdomain inside a larger computational domain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Engineered FGF1 and FGF2 compositions and methods of use thereof

Engineered FGF1 and FGF2 polypeptides, polynucleotides encoding these polypeptides and DNA constructs, vectors and compositions including these engineered polypeptides are provided herein. The engineered FGF1 and FGF2 polypeptides are more stable than their wild-type counterparts and may be more effective at treating a variety of conditions that FGF1 and FGF2 are useful for treating such as wound healing.

Thallapuranam, Suresh Kumar↗
DOE OSTI2021

Large-Eddy Simulations of Marine Boundary Layer Clouds Associated with Cold-Air Outbreaks during the ACTIVATE Campaign. Part I: Case Setup and Sensitivities to Large-Scale Forcings

Large-eddy simulation (LES) is able to capture key boundary layer (BL) turbulence and cloud processes. Yet, large-scale forcing and surface turbulent fluxes of sensible and latent heat are often poorly prescribed for LESs. We derive these quantities from measurements and reanalysis obtained for two cold-air outbreak (CAO) events during Phase I of the Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE) in February–March 2020. We study the two contrasting CAO cases by performing LES and test the sensitivity of BL structure and clouds to large-scale forcings and turbulent heat fluxes. Profiles of atmospheric state and large-scale divergence and surface turbulent heat fluxes obtained from ERA5 data agree reasonably well with those derived from ACTIVATE field measurements for both cases at the sampling time and location. Therefore, we adopt the time-evolving heat fluxes, wind, and advective tendencies profiles from ERA5 data to drive the LES. We find that large-scale thermodynamic advective tendencies and wind relaxations are important for the LES to capture the evolving observed BL meteorological states characterized by the hourly ERA5 data and validated by the observations. We show that the divergence (or vertical velocity) is important in regulating the BL growth driven by surface heat fluxes in LESs. The evolution of liquid water path is largely affected by the evolution of surface heat fluxes. The liquid water path simulated in LES agrees reasonably well with the ACTIVATE measurements. This study paves the path to investigate aerosol–cloud–meteorology interactions using LES informed and evaluated by ACTIVATE field measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Bayesian Poroelastic Aquifer Characterization From InSAR Surface Deformation Data. 2. Quantifying the Uncertainty

Uncertainty quantification of groundwater (GW) aquifer parameters is critical for efficient management and sustainable extraction of GW resources. These uncertainties are introduced by the data, model, and prior information on the parameters. We develop a Bayesian inversion framework that uses Interferometric Synthetic Aperture Radar (InSAR) surface deformation data to infer the laterally heterogeneous permeability of a transient linear poroelastic model of a confined GW aquifer. The Bayesian solution of this inverse problem takes the form of a posterior probability density of the permeability. Exploring this posterior using classical Markov chain Monte Carlo (MCMC) methods is computationally prohibitive due to the large dimension of the discretized permeability field and the expense of solving the poroelastic forward problem. However, in many partial differential equation (PDE)-based Bayesian inversion problems, the data are only informative in a few directions in parameter space. For the poroelasticity problem, we prove this property theoretically for a one-dimensional problem and demonstrate it numerically for a three-dimensional aquifer model. Here we design a generalized preconditioned Crank-Nicolson (gpCN) MCMC method that exploits this intrinsic low dimensionality by using a low-rank-based Laplace approximation of the posterior as a proposal, which we build scalably. The feasibility of our approach is demonstrated through a real GW aquifer test in Nevada. The inherently two-dimensional nature of InSAR surface deformation data informs a sufficient number of modes of the permeability field to allow detection of major structures within the aquifer, significantly reducing the uncertainty in the pressure and the displacement quantities of interest.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Active layer thickness as a function of soil water content

Active layer thickness (ALT) is a critical metric for monitoring permafrost. How soil moisture influences ALT depends on two competing hypotheses: (a) increased soil moisture increases the latent heat of fusion for thaw, resulting in shallower active layers, and (b) increased soil moisture increases soil thermal conductivity, resulting in deeper active layers. To investigate their relative influence on thaw depth, we analyzed the Field Measurements of Soil Moisture and Active Layer Thickness (SMALT) in Alaska and Canada dataset, consisting of thousands of measurements of thaw depth and soil moisture collected at dozens of sites across Alaska and Canada as part of NASA’s Arctic Boreal Vulnerability Experiment (ABoVE). As bulk volumetric water content (VWC) integrated over the entire active layer increases, ALT decreases, supporting the latent heat hypothesis. However, as VWC in the top 12 cm of soil increases, ALT increases, supporting the thermal conductivity hypothesis. Regional temperature variations determine the baseline thaw depth while precipitation may influence the sensitivity of ALT to changes in VWC. Soil latent heat dominates over thermal conductivity in determining ALT, and the effect of bulk VWC on ALT appears consistent across sites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Characterization of Surface Heterogeneity-Induced Convection Using Cluster Analysis

Land-atmosphere interactions and boundary layer processes often control the formation of shallow clouds and subsequently deep convective precipitation over the Southern Great Plains. In this study, we examine the impacts of large-scale advection on the cloud populations and land-atmospheric coupling observed during the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) field campaign in 2016. In this work, we performed two Large Eddy Simulations (LES) using the Weather Research and Forecasting Model for a day in which the transition from clear-sky to shallow precipitating clouds and isolated deep convection was observed. The control simulation reproduced the overall distribution of cloud populations by using realistic soil conditions with an interactive land model. In the sensitivity simulation where large-scale advection is removed, a strong relationship between the land and boundary layer is found. To study the timing, location, and intensity of convective initiation and the relationship of clouds with land surface properties, a cluster analysis of equivalent potential temperature is performed for the simulation without large-scale advection. That analysis shows that convective clouds first form over regions with higher surface sensible heat flux. Precipitation from those convective clouds likely triggers new updrafts nearby about two hours later through the lifting associated with cold pools. Furthermore, the cluster analysis also shows that in addition to the spatial pattern of soil moisture, land use and soil texture in western Oklahoma also influence the location of convective initiation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Polydopamine Surface Coating Synergizes the Antimicrobial Activity of Silver Nanoparticles

Metal nanoparticles, especially silver nanoparticles (AgNPs), have drawn increasing attention for antimicrobial applications. Most studies have emphasized on the correlations between the antibacterial potency of AgNPs and the kinetics of metallic to ionic Ag conversion, while other antimicrobial mechanisms have been underestimated. In this work, we focused on the surface effects of polydopamine (PDA) coating on the antimicrobial activity of AgNPs. A method of fast deposition of PDA was used to synthesize the PDA-AgNPs with controllable coating thickness ranging from 3 to 25 nm. The antimicrobial activities of the PDA-AgNPs were analyzed by fluorescence-based growth curve assays on Escherichia coli. The results indicated that the PDA-AgNPs exhibited significantly higher antibacterial activities than poly(vinylpyrrolidone)-passivated AgNPs (PVP-AgNPs) and PDA themselves. It was found that the PDA coating synergized with the AgNPs to prominently enhance the potency of the PDA-AgNPs against bacteria. Additionally, the analysis of X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy elucidated that the synergistic effects could be originated from the interaction/coordination between Ag and catechol group on the PDA coating. The synergistic effects led to increased generation of reactive oxygen species and the consequent bacterial damage. Overall, these findings demonstrated the importance of the surface effects on the antimicrobial properties of AgNPs. The underlying molecular mechanisms have shined light on the future development of more potent metal nanoparticle-based antimicrobial agents.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Metal-on-Metal Growth Approach to Metal–Metal Oxide Core–Shell Nanostructures with Plasmonic Properties

Hybrid core–shell nanoparticles integrate the material properties from individual components; however, the synthesis of core–shell nanoparticles with dissimilar materials remains challenging. Herein, we applied a metal-on-metal thin film growth approach to control the conformal deposition of non-precious metal shells on the Cu-based metal cores to form core–shell structures of metal–metal oxide hybrids (M–M'O x , where M = AuCu 3 or Cu, M' = Fe, Mn, and Ni). The deposition kinetics were controlled by a temperature-regulated thermal decomposition of zerovalent transition metal complexes. It is predicted that the conformal deposition can be promoted by keeping the initial deposition temperature close to the thermal decomposition temperature of the zerovalent precursors. The mechanisms of strain reduction and interdiffusion facilitate conformal deposition over island growth in the synthesis with slow reaction kinetics. This study provides insightful guidance to metal-on-metal growth in solution at the nanoscale and thus the seed-mediated approach to hybrid core–shell structures. The optical properties of these metal–metal oxide hybrids were investigated experimentally and interpreted by theoretical simulations. Despite damping effects, the plasmonic properties of these Cu-based core-metal oxide shell structures may have the potential to enable plasmon-enhanced applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Bayesian Poroelastic Aquifer Characterization From InSAR Surface Deformation Data. Part I: Maximum A Posteriori Estimate

Characterizing the properties of groundwater aquifers is essential for predicting aquifer response and managing groundwater resources. In this work, we develop a high-dimensional scalable Bayesian inversion framework governed by a three-dimensional quasi-static linear poroelastic model to characterize lateral permeability variations in groundwater aquifers. In this work, we determine the maximum a posteriori (MAP) point of the posterior permeability distribution from centimeter-level surface deformation measurements obtained from Interferometric Synthetic Aperture Radar (InSAR). The scalability of our method to high parameter dimension is achieved through the use of adjoint-based derivatives, inexact Newton methods to determine the MAP point, and a Mat´ern class sparse prior precision operator. Together, these guarantee that the MAP point is found at a cost, measured in number of forward/adjoint poroelasticity solves, that is independent of the parameter dimension. We apply our methodology to a test case for a municipal well in Mesquite, Nevada, in which InSAR and GPS surface deformation data are available. We solve problems with up to 320,824 state variable degrees of freedom (DOFs) and 16,896 parameter DOFs. A consistent treatment of noise level is employed so that the aquifer characterization result does not depend on the pixel spacing of surface deformation data. Our results show that the use of InSAR data significantly improves characterization of lateral aquifer heterogeneity, and the InSAR-based aquifer characterization recovers complex lateral displacement trends observed by independent daily GPS measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Effects of Lateral Entrainment Mixing With Entrained Aerosols on Cloud Microphysics

The effects of entrained environment air and aerosols on cloud properties remain a critical yet understudied topic. This study first introduces a new entraining cloud parcel model considering entrained aerosols. With entrained aerosols represented by a newly introduced parameter, the impacts of entrainment rate and entrained aerosols on cloud microphysical properties are investigated. The results here show that the relationships between entrainment rate and cloud microphysical properties are highly nonlinear when the entrained aerosols vary. With the lateral entrainment mixing, a new phenomenon is revealed that the height of maximum parcel supersaturation cannot be reached when the entrainment rate is beyond a certain critical value. This new critical entrainment rate is different from the critical entrainment rate defined by Barahona and Nenes (2007, https://doi.org/10.1029/2007JD008473), beyond which clouds cannot form. This finding has important implications for developing parameterization of droplet activation, which is based primarily on the assumption of the existence of maximum supersaturation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Reconciling Contrasting Relationships Between Relative Dispersion and Volume-Mean Radius of Cloud Droplet Size Distributions

Cloud droplet spectral relative dispersion is critical to parameterizations of cloud radiative properties, warm-rain initiation, and aerosol-cloud interactions in models; however, there is no consistent relationship between relative dispersion and volume-mean radius in literature, which hinders improving relative dispersion parameterization and calls for physical explanation. Here we show, by analyzing aircraft observations of cumulus clouds during Routine AAF [Atmospheric Radiation Measurement (ARM) Aerial Facility] Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations, that the correlation between relative dispersion and volume-mean radius changes from positive to negative as volume-mean radius increases. With the new observation, we postulate that the sign of the correlation is determined by whether or not condensation (evaporation) occurs simultaneously with significant new activation (deactivation). The hypothesis is validated by simulations of both an adiabatic cloud parcel model and a parcel model accounting for entrainment-mixing. A new quantity, first bin strength, is introduced to quantify this new observation. Theoretical analysis of truncated gamma and modified gamma size distributions further supports the hypothesis and reconciles the contrasting relationships between relative dispersion and volume-mean radius, including the results in polluted fog observations. The results could shed new light on the so-called “twilight zone” between cloudy and cloud-free air, which in turn affects evaluation of aerosol-cloud interactions and retrieval of aerosol optical depth.

Lu, Chunsong↗