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DOE OSTI2025

Subject-specific modeling framework for particle deposition using computational fluid dynamics

Quantifying particle deposition and dose in the respiratory tract requires a physiologically realistic representation and reproducible computational workflows. However, existing modeling frameworks, such as the International Commission on Radiological Protection (ICRP) compartmental models and the Multiple Path Particle Dosimetry (MPPD) tool, lack detailed deposition profiles and subject-specific capabilities. The combination of advances in computer vision algorithms applied to the respiratory tract and Computational Fluid and Particle Dynamics (CFPD) allows high-fidelity simulations of particle behavior in anatomically accurate geometries derived from individual CT scans. The segmentation, preprocessing, and file preparation task for a CFPD simulation was often time-consuming, and no prior studies to-date have yet presented a fully automated framework. This work presents a fully automated workflow to obtain individualized particle deposition profiles in the human respiratory tract. The pipeline starts with segmenting upper and lower airway geometries using morphological and deep learning-based methods, generating three-dimensional (3D) models from CT imaging data. Next, a series of algorithms are presented to quality check and prepare the 3D geometry for a CFD or CFPD simulation. The preprocessing step includes correcting geometric artifacts, enforcing a physically consistent mesh, and automatically identifying and capping multiple outlets, which is required for CFD/CFPD simulations. These processed models are then input into open-source (OpenFOAM) or commercial (StarCCM+) CFD solvers, where flow and transient particle transport equations — including turbulence and particle–wall interactions are solved under realistic breathing conditions. Finally, the resulting particle deposition profiles can be integrated with Monte Carlo radiation transport codes and state-of-the-art computational phantoms to assess organ-specific absorbed doses in scenarios of radioactive aerosol inhalation. The presented work streamlines respiratory tract segmentation, preprocessing for CFD/CFPD simulations, and integration with dose assessment workflows, reducing manual intervention and improving access to high-fidelity, subject-specific modeling. The high precision in predicted particle deposition and dose distributions can improve personalized treatment strategies in respiratory medicine and refine dose estimates for radiation protection.

AI↗

DOE OSTI2022

Characterizing warm atmospheric boundary layer over land by combining Raman and Doppler lidar measurements

PBL plays a critical role in the atmosphere by transferring heat, moisture, and momentum. The warm PBL has a distinct diurnal cycle including daytime convective mixing layer (ML) and nighttime residual layer developments. Thus, for PBL characterization and process study, simultaneous determinations of PBL height (PBLH) and ML height (MLH) are necessary. Here, new approaches are developed to provide reliable PBLH and MLH to characterize warm PBL evolution. The approaches use Raman lidar (RL) water vapor mixing ratio (WVMR) and Doppler lidar (DL) vertical velocity measurements at the Southern Great Plains (SGP) atmospheric observatory, which was established by the Atmospheric Radiation Measurement (ARM) user facility. Compared with widely used lidar aerosol measurements for PBLH, WVMR is a better trace for PBL vertical mixing. For PBLH, the approach classifies PBL water vapor structures into a few general patterns, then uses a slope method and dynamic threshold method to determine PBLH. For MLH, wavelet analysis is used to re-construct 2-D variance from DL vertical wind velocity measurements according to the turbulence eddy size to minimize the impacts of gravity wave and eddy size on variance calculations; then, a dynamic threshold method is used to determine MLH. Remotely-sensed PBLHs and MLHs are compared with radiosonde measurements based on the Richardson number method. Good agreements between them confirm that the proposed new algorithms are reliable for PBLH and MLH characterization. The algorithms are applied to warm seasons’ RL and ML measurements at the SGP site for five years to study warm season PBL structure and processes. The weekly composited diurnal evolutions of PBLHs and MLHs in warm climate were provided to illustrate diurnal and seasonal PBL evolutions. This reliable PBLH and MLH dataset will be valuable for PBL process study, model evolution, and PBL parameterization improvement.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Towards operational atmospheric correction of airborne hyperspectral imaging spectroscopy: Algorithm evaluation, key parameter analysis, and machine learning emulators

Atmospheric correction of airborne hyperspectral imaging spectroscopy (AHIS) to obtain high-quality surface reflectance is the prerequisite for remote sensing applications. Over the last decades, different atmospheric correction methods have been developed based on radiative transfer models (RTMs), however, the relative performances of different algorithms are unclear. Automated operational atmospheric correction methods to process large-volume AHIS data in a high-accurate and high-throughput manner are still lacking. Therefore, this study proposed an operational atmospheric correction pipeline for deriving surface reflectance from AHIS data. To ensure the accuracy and efficiency of the pipeline, we focused on three specific aspects: (1) selecting a suitable RTM for the development of atmospheric lookup tables (LUTs) by comparing the commercial MODerate resolution atmospheric TRANsmission (MODTRAN) and open-sourced Library for Radiative TRANsfer (LibRadTRAN) models, where the widely-used software, Atmospheric/Topographic Correction for Airborne Imagery (ATCOR), was used as benchmarks; (2) identifying key atmospheric correction parameters and determining suitable sources for parameter retrievals including AHIS, Moderate Resolution Imaging Spectroradiometer (MODIS), and AErosol RObotic NETwork (AERONET); and (3) testing the performance of using machine learning emulators to speed up the RTM-based atmospheric correction. Results indicate that (1) atmospheric correction based on MODTRAN LUTs can produce surface reflectance accurately with mean absolute errors < 0.05 and cosine similarities > 0.98 compared to field measurements, which is comparable to the software ATCOR and slightly outperforms the LibRadTRAN LUTs; (2) sobol global sensitivity analysis demonstrates that in the atmospheric correction, visibility and water vapor are two key parameters that can be accurately derived from AHIS in contrast to MODIS or AERONET data; and (3) Random Forest emulators can produce accurate estimations of surface reflectance with mean absolute errors < 0.03 and cosine similarities > 0.98 for higher processing efficiency and determine a suitable set of wavelengths for retrieving atmospheric visibility and water vapor. In conclusion, the proposed atmospheric correction pipeline also improved the four-stream radiative transfer theory for airborne applications by considering adjacent effects from airborne surrounding pixels and can also be applied for atmospheric correction of hyperspectral data from spaceborne missions.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Diffraction data from aerosolized Coliphage PR772 virus particles imaged with the Linac Coherent Light Source

Single Particle Imaging (SPI) with intense coherent X-ray pulses from X-ray free-electron lasers (XFELs) has the potential to produce molecular structures without the need for crystallization or freezing. Here we present a dataset of 285,944 diffraction patterns from aerosolized Coliphage PR772 virus particles injected into the femtosecond X-ray pulses of the Linac Coherent Light Source (LCLS). Additional exposures with background information are also deposited. The diffraction data were collected at the Atomic, Molecular and Optical Science Instrument (AMO) of the LCLS in 4 experimental beam times during a period of four years. The photon energy was either 1.2 or 1.7 keV and the pulse energy was between 2 and 4 mJ in a focal spot of about 1.3 μm x 1.7 μm full width at half maximum (FWHM). The X-ray laser pulses captured the particles in random orientations. The data offer insight into aerosolised virus particles in the gas phase, contain information relevant to improving experimental parameters, and provide a basis for developing algorithms for image analysis and reconstruction.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Bayesian Cloud Property Retrievals from ARM Active and Passive Measurements

The optimum use of the continuous measurements of thermodynamics, radiation, aerosols, clouds and precipitation from the DOE Atmospheric Radiation Measurement (ARM) program is key to achieve the DOE Atmospheric System Research (ASR)’s objectives. One of the key mission requirements is to retrieve cloud and precipitation properties, as well as vertical motion parameters, along the vertical cross- section defined by the profiling active sensors. Such retrievals are challenging to perform continuously in the entire spectrum of cloud and precipitation conditions due to the large natural microphysical and dynamical variability, the often-limited information content in the measurements, and the lack of proper characterization of measurement quality and uncertainty. Today, the acquisition of new remote and in-situ sensors by the ARM program creates opportunities to address the microphysical retrieval problem by exploiting new, more robust retrieval techniques and integrating various scattered advancements in both sensor techniques and retrieval algorithms. During this project, we constructed a robust Bayesian Markov chain Monte Carlo (MCMC) cloud property retrieval algorithm that includes a state of the art radar forward model. Our MCMC-based retrieval produces both the best estimate of height-resolved cloud and precipitation properties in the radar profile, as well as an estimate of the in-cloud vertical motion and turbulence. In addition, the MCMC algorithm automatically produces robust and flexible estimates of retrieval uncertainty. We tested the algorithm on several synthetic cloud profiles obtained from large eddy simulation (LES) models with bin-resolved microphysics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

CACTI Radar b1 Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility deployed a large number of instruments to a region nearby the Sierra de Córdobas mountains in Argentina as part of the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign (1). During this campaign, four radars were installed at a site outside of Villa Yacanto as shown in Figure 1. As part of a post-campaign effort to improve the usability of these data, a significant activity was undertaken towards the calibration, correction, and improvement of the data quality of these radar datastreams. This process in ARM nomenclature is referred to as generating a “b1” datastream. While these “b1” standards may imply different corrections or standards for various ARM instruments, for radars it refers to a datastream that has been calibrated (and cross-calibrated), including a serious effort to deliver the highest-quality (well-characterized) data possible. This report details (i) the status/quality of the original “a1” (raw) data, (ii) the corrections and calibrations that are applied to generate these b1 datastreams, (iii) the details of the applied algorithms and how radar offset/calibration numbers were determined for the eventual corrections, and (iv) the new and flexible plug-in-based processing system designed during CACTI for radar b1 activities (current, future) that interfaces with ARM’s Data Integrator (ADI) and high-performance computing (HPC) system.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Shallow- and deep-convection characteristics in the greater Houston, Texas, area using cell tracking methodology

The convective lifecycle, from initiation to maturity and dissipation, is driven by a combination of kinematic, thermodynamic, microphysical, and radiative processes that are strongly coupled and variable in time and space. Weather radars have been traditionally used to provide various convective-cloud characteristics. Here, we analyzed climatological convective-cell radar characteristics to obtain and assess the diurnal cycles of three convective-cell types – shallow, modest deep, and vigorous deep convective cells – that formed in the greater Houston area, using the National Weather Service radar from Houston, Texas, and a multi-cell identification and tracking algorithm. The examined dataset spans 4 years (2018–2021) and covers the warm-season months (June to September) in those years. The analysis showed clear diurnal cycles in cell initiation (CI) consistent with the sea breeze circulation and showed diurnal and normalized lifetime relationships in cell evolution parameters (e.g., maximum reflectivity, echo-top height, Geostationary Operational Environmental Satellite-16 (GOES-16) channel 13 brightness temperature, and the height of maximum reflectivity). The cell evolution is well represented by relationships between (1) the height and value of the maximum radar reflectivity, (2) the minimum GOES-16 channel 13 brightness temperature and the maximum vertically integrated liquid, (3) the maximum reflectivity and columnar-average reflectivity, and (4) the echo-top ascent rate and cell lifetime. The relationships presented herein help to identify the cell lifecycle stages such as early shallow convection, vigorous vertical development, anvil development, and convective core dissipation. GOES-16 Aerosol Optical Depth values are also used as a proxy for cell initiation aerosol concentrations to investigate any potential relationships between initiation location and aerosol concentration. Overall, no significant relationships between initiation location and aerosol concentration were found for the three cell types investigated, but there are some minor differences in the pre-CI aerosol optical depth for vigorous deep convective cells.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Image masks of global ship tracks for NASA MODIS data products

Ship tracks, long thin artificial cloud features formed from the pollutants in ship exhaust, are satellite-observable examples of aerosol-cloud interactions (ACI) that can lead to increased cloud albedo and thus increased solar reflectivity, phenomena of interest in solar radiation management. In addition to ship tracks being of interest to meteorologists and policy makers, their observed cloud perturbations provide benchmark evidence of ACI that remain poorly captured by climate models. To broadly analyze the effects of ship tracks, high-resolution satellite imagery data highlighting their presence are required. To support this, we provide a hand labelled dataset to serve as a benchmark for a variety of subsequent analyses. Established from a previous dataset that identified ship track presence using NASA’s MODIS Aqua satellite imager, our first-of-its-kind dataset is comprised of image masks: capturing full ship track regions, including their contours, emission points and dispersive patterns. In total, 300 images, or around 2,500 masked ship tracks, observed under varying conditions are provided, and may facilitate training of machine learning algorithms to automate extraction.

Atmospheric dynamics↗

DOE OSTI2025

Marine Boundary Layer Cloud Boundaries and Phase Estimation Using Airborne Radar and In Situ Measurements During the SOCRATES Campaign over Southern Ocean

The Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) was an aircraft-based campaign (15 January–26 February 2018) that deployed in situ probes and remote sensors to investigate low-level clouds over the Southern Ocean (SO). A novel methodology was developed to identify cloud boundaries and classify cloud phases in single-layer, low-level marine boundary layer (MBL) clouds below 3 km using the HIAPER Cloud Radar (HCR) and in situ measurements. The cloud base and top heights derived from HCR reflectivity, Doppler velocity, and spectrum width measurements agreed well with corresponding lidar-based and in situ estimates of cloud boundaries, with mean differences below 100 m. A liquid water content–reflectivity (LWC-Z) relationship, LWC = 0.70Z0.29, was derived to retrieve the LWC and liquid water path (LWP) from HCR profiles. The cloud phase was classified using HCR measurements, temperature, and LWP, yielding 40.6% liquid, 18.3% mixed-phase, and 5.1% ice samples, along with drizzle (29.1%), rain (3.2%), and snow (3.7%) for drizzling cloud cases. The classification algorithm demonstrates good consistency with established methods. This study provides a framework for the boundary and phase detection of MBL clouds, offering insights into SO cloud microphysics and supporting future efforts in satellite retrievals and climate model evaluation.

MBL clouds over Southern Ocean↗

DOE OSTI2022

Retrieval of the sea spray aerosol mode from submicron particle size distributions and supermicron scattering during LASIC

Abstract. Improved quantification of sea spray aerosol concentration and size is important for determining aerosol effects on clouds and the climate, though attempts to accurately capture the size distribution of the sea spray mode remain limited by the availability of supermicron size distributions. In this work, we introduce a new approach to retrieving lognormal mode fit parameters for a sea spray aerosol mode by combining submicron size distributions with supermicron scattering measurements using a Mie inversion. Submicron size distributions were measured by an ultra-high-sensitivity aerosol spectrometer (UHSAS), and supermicron scattering was taken as the difference between <10 µm and <1 µm three-wavelength integrating nephelometer measurements (NEPH). This UHSAS-NEPH method was applied during background marine periods of the Department of Energy Atmospheric Radiation Measurement Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign on Ascension Island (November 2016–May 2017), when the contribution of sea spray aerosol was expected to represent a large fraction of the aerosol mass and total scattering. Lognormal sea spray modal parameters were retrieved from comparisons between nephelometer measurements and a lookup table of Mie theory-simulated scattering coefficients for low-error solutions that minimized the 0.4–1 µm residual in the UHSAS size distribution. We evaluated the UHSAS-NEPH method with a set of clean marine measurements in the North Atlantic that included supermicron size and chemical measurements, showing that measured supermicron size distributions are needed to constrain the sea spray number concentration but that mass concentration was reasonably characterized using supermicron scattering. For LASIC, the UHSAS-NEPH method retrieved sea spray mode properties for approximately 88 % of the background marine times when the scattering variability and total particle concentration were low (<± 5 Mm−1 and <400 cm−3, respectively), with mass mean diameter ranging from 0.6 to 1.9 µm (1.47 ± 0.17 µm), modal width ranging from 1.1 to 3.97 (2.4±0.3), and mass concentration ranging from 0.18 to 23.0 µg m−3 (8.37. ± 4.1 µg m−3). The measured nephelometer scattering at three wavelengths was found to constrain the mode width marginally at the largest particle sizes in the absence of additional size and chemical measurements for defining parameters for the Mie solutions. Comparing UHSAS-NEPH retrievals to those of a fitting algorithm applied only to the submicron UHSAS number size distribution showed that correlations between retrieved mass concentration and the available mass-based sea spray tracers (coarse scattering, wind speed, and chloride) are low when supermicron measurements are not considered. This work demonstrates the added value of supermicron scattering measurements for retrieving reasonable sea spray mass concentrations, providing the best-available observationally constrained estimate of the sea spray mode properties when supermicron size distribution measurements are not available.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data

Abstract. Tikhonov regularization is a tool for reducing noise amplification during data inversion. This work introduces RegularizationTools.jl, a general-purpose software package for applying Tikhonov regularization to data. The package implements well-established numerical algorithms and is suitable for systems of up to ∼ 1000 equations. Included is an abstraction to systematically categorize specific inversion configurations and their associated hyperparameters. A generic interface translates arbitrary linear forward models defined by a computer function into the corresponding design matrix. This obviates the need to explicitly write out and discretize the Fredholm integral equation, thus facilitating fast prototyping of new regularization schemes associated with measurement techniques. Example applications include the inversion involving data from scanning mobility particle sizers (SMPSs) and humidified tandem differential mobility analyzers (HTDMAs). Inversion of SMPS size distributions reported in this work builds upon the freely available software DifferentialMobilityAnalyzers.jl. The speed of inversion is improved by a factor of ∼ 200, now requiring between 2 and 5 ms per SMPS scan when using 120 size bins. Previously reported occasional failure to converge to a valid solution is reduced by switching from the L-curve method to generalized cross-validation as the metric to search for the optimal regularization parameter. Higher-order inversions resulting in smooth, denoised reconstructions of size distributions are now included in DifferentialMobilityAnalyzers.jl. This work also demonstrates that an SMPS-style matrix-based inversion can be applied to find the growth factor frequency distribution from raw HTDMA data while also accounting for multiply charged particles. The outcome of the aerosol-related inversion methods is showcased by inverting multi-week SMPS and HTDMA datasets from ground-based observations, including SMPS data obtained at Bodega Marine Laboratory during the CalWater 2/ACAPEX campaign and co-located SMPS and HTDMA data collected at the US Department of Energy observatory located at the Southern Great Plains site in Oklahoma, USA. Results show that the proposed approaches are suitable for unsupervised, nonparametric inversion of large-scale datasets as well as inversion in real time during data acquisition on low-cost reduced-instruction-set architectures used in single-board computers. The included software implementation of Tikhonov regularization is freely available, general, and domain-independent and thus can be applied to many other inverse problems arising in atmospheric measurement techniques and beyond.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Virtual tower measurements during the American WAKE ExperimeNt (AWAKEN)

Dual-Doppler lidar measurements were made during the American WAKE ExperimeNt to provide height-resolved measurements of wind speed and direction at multiple locations immediately south of the leading row turbines in the King Plains wind farm in Oklahoma. These so-called virtual tower measurements were performed to characterize the inflow into the wind farm and to assess possible upwind blockage effects due to the collective action of the wind farm. The campaign was conducted from 12 November 2022 to 17 October 2023, during which time 14 unique virtual tower locations were sampled with heights ranging from 240 to 490 m AGL. The wind retrieval algorithm provided estimates of the horizontal winds and their uncertainties with a vertical resolution of about 10 m, while also accounting for the tilt of the lidar platform. The virtual tower results are compared to collocated lidar wind profiling data at the A1 site, which was located roughly 2.4 rotor diameters south of the nearest turbine. The wind speed difference between the wind profiler and the virtual tower was found to be quite sensitive to atmospheric stability and wind direction below 250 m AGL. The largest differences were observed for inflow under stable conditions, where the profiler wind speeds were observed to be about 22% lower than the virtual tower near hub height. These results suggest that there are persistent horizontal gradients in the flow upwind of the wind farm which result in biased estimates using standard ground-based lidar wind profiling methods.

17 WIND ENERGY↗