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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Lunar accelerometer network gravitational observatory (LANGO)

With ground-based interferometers detecting hundreds of gravitational-wave (GW) events, GW astronomy has continued to blossom. U.S. and European scientists are developing plans to construct third-generation ground-based interferometers. ESA has proceeded through the mission formulation phase of space-based interferometer in a lower-frequency band, 10 –4 –0.1 Hz. Despite all these exciting developments, there is still a missing frequency band, 0.1–10 Hz. This mid-frequency band is rich with interesting astrophysical events. Coalescence and merger of intermediate-mass black holes (IMBHs) will occur in this frequency band. Coalescing stellar-mass BHs will pass through this frequency band days before they reach the frequency band of LIGO and Virgo. Detection of such signals would enable a mid-frequency detector to issue an advance notice to the high-frequency GW detectors, as well as to optical, x-ray and γ-ray telescopes. We propose Lunar Accelerometer Network Gravitational Observatory (LANGO) to detect GWs in this frequency band. In the first phase (LANGO 1), we propose to deploy four ambient-temperature (250 K) accelerometers in the tetrahedral or in a square configuration on the hemisphere facing the Earth. After successful operation at 250 K, LANGO would be upgraded to a cryogenic (4 K) version with over two orders of magnitude increased sensitivity (LANGO 2), with coherent rejection of seismic noise implemented. LANGO is a full-tensor detector, capable of determining the source direction and wave polarization. Each test mass (TM) is suspended as a pendulum with resonance frequency ∼ 0.01 Hz, thus is only weakly coupled to the lunar surface horizontally. LANGO is designed to detect the relative motion of globally separated, nearly free, TMs by using the Moon as a large quiet platform. LANGO 1 and 2 accelerometers aim at sensitivities ⩽ 10 –11 m s –2 Hz –1/2 and ⩽ 10 –13 m s –2 Hz –1/2 in the horizontal axes over the frequency band of 1 mHz–10 Hz, which yield GW sensitivities 1.1 x 10 -21 Hz -1/2 and 3.8 x 10 -24 Hz -1/2 at 1 Hz, respectively. The LANGO accelerometers will be 10 3 –10 5 times more sensitive than Apollo seismometers. With such sensitivity, LANGO will also make great contribution to the advancement of lunar geophysics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Reducing Native Ant Abundance Decreases Predation Rates in Midwestern Grasslands

Diverse and robust predator communities are important for effective prey suppression in natural and managed communities. Ants are ubiquitous components of terrestrial systems but their contributions to natural prey suppression is relatively understudied in temperate regions. Growing evidence suggests that ants can play a significant role in the removal of insect prey within grasslands, but their impact is difficult to separate from that of nonant predators. To test how ants may contribute to prey suppression in grasslands, we used poison baits (with physical exclosures) to selectively reduce the ant population in common garden settings, then tracked ant and nonant ground predator abundance and diversity, and removal of sentinel egg prey for 7 wk. We found that poison baits reduced ant abundance without a significant negative impact on abundance of nonant ground predators, and that a reduction in ant abundance decreased the proportion of sentinel prey eggs removed. Even a modest decrease (~20%) in abundance of several ant species, including the numerically dominant Lasius neoniger Emery (Hymenoptera: Formicidae), significantly reduced sentinel prey removal rates. Our results suggest that ants disproportionately contribute to ground-based predation of arthropod prey in grasslands. Changes in the amount of grasslands on the landscape and its management may have important implications for ant prevalence and natural prey suppression services in agricultural landscapes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Acoustic waves from the 2023 April 20 SpaceX Starship rocket explosion travelling in the elevated ‘AtmoSOFAR’ channel

SUMMARY The ability to detect low-frequency sounds from distant energetic events depends on the temperature and wind structure of the atmosphere. Thus, from time to time surface-based acoustic detectors may not be able to capture sounds arriving from certain directions. However, the temperature minimum at the tropopause may create an acoustic duct called the ‘AtmoSOFAR’ channel that could transmit acoustic waves laterally—but perhaps not to the ground. If true, then elevated sensors such as those borne aloft by balloons would record the signatures even in regions where ground-based sensors cannot. This has been difficult to prove because high altitude acoustic sources are rare and balloon deployments are sporadic. This work describes the detection and characterization of powerful acoustic waves generated during the launch and terminal explosion of the SpaceX Starship rocket on 2023 April 20 using a pair of microbarometers on a stratospheric balloon. The signals travelled through the AtmoSOFAR channel, carrying information about the size and nature of their source. This channel also appears to leak some acoustic energy to the ground, in agreement with previous studies. The acoustic yield of the Starship terminal explosion was on the order of $10^3$ tons trinitrotoluene equivalent, which agrees with the estimated fuel load of the vehicle to about a factor of 2, but is two orders of magnitude larger than optical estimates. These results support an earlier study that claimed lateral transmission of sound from a smaller rocket through the AtmoSOFAR channel. The transmission of source information through the AtmoSOFAR channel motivates its use for monitoring other natural and anthropogenic events using balloon-borne sensors. This may become increasingly important as more and more private and government entities conduct spacecraft launches and reentries. It may also provide a means of monitoring clear air turbulence and other sound-generating atmospheric phenomena at a distance.

58 GEOSCIENCES↗
DOE OSTI2021

A Novel Machine Learning Algorithm for Planetary Boundary Layer Height Estimation Using AERI Measurement Data

Accurately determining the height of the planetary boundary layer (PBL) is important since it can affect the climate, weather, and air quality. Ground-based infrared hyperspectral remote sensing is an effective way to obtain this parameter. Compared with radiosonde measurements, its temporal resolution is much higher. In this study, a method to retrieve the PBL height (PBLH) from the ground-based infrared hyperspectral radiance data is proposed based on machine learning. In this method, the channels that are sensitive to temperature and humidity profiles are selected as the feature vectors, and the PBLHs derived from radiosonde are taken as the true values. The support vector machine (SVM) is applied to train and test the data set, and the parameters are optimized in the process. The data set collected at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) from 2012 to 2015 is analyzed. The instruments used in this letter include Atmospheric Emitted Radiance Interferometer (AERI), Vaisala CL31 ceilometer, and radiosonde. It shows that the root mean square error (RMSE) between the PBLHs calculated by the proposed method using AERI data and those from radiosonde data can be within 370 m, and the square correlation coefficient (SCC) is greater than 0.7. Compared with the PBLHs derived from the ceilometer, it can be found that the new method is more stable and less affected by clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis

Accurate cloud type identification and coverage analysis are crucial in understanding the Earth’s radiative budget. Traditional computer vision methods rely on low-level visual features of clouds for estimating cloud coverage or sky conditions. Several handcrafted approaches have been proposed; however, scope for improvement still exists. Newer deep neural networks (DNNs) have demonstrated superior performance for cloud segmentation and categorization. These methods, however, need expert engineering intervention in the preprocessing steps—in the traditional methods—or human assistance in assigning cloud or clear sky labels to a pixel for training DNNs. Such human mediation imposes considerable time and labor costs. We present the application of a new self-supervised learning approach to autonomously extract relevant features from sky images captured by ground-based cameras, for the classification and segmentation of clouds. We evaluate a joint embedding architecture that uses self-knowledge distillation plus regularization. We use two datasets to demonstrate the network’s ability to classify and segment sky images—one with ~85,000 images collected from our ground-based camera and another with 400 labeled images from the WSISEG database. We find that this approach can discriminate full-sky images based on cloud coverage, diurnal variation, and cloud base height. Additionally, it semantically segments the cloud areas without labels. The approach shows competitive performance in all tested tasks, suggesting a new alternative for cloud characterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

A New Criterion to Improve Operational Drizzle Detection with Ground-Based Remote Sensing

Light shallow precipitation in the form of drizzle is one of the mechanisms for liquid water removal, affecting cloud lifetime and boundary layer dynamics and thermodynamics. The early formation of drizzle drops is of particular interest for quantifying aerosol–cloud–precipitation interactions. In models, drizzle initiation is represented by the autoconversion, that is, the conversion of liquid water from a cloud liquid water category (where particle sedimentation is ignored) into a precipitating liquid water category. Various autoconversion parameterizations have been proposed in recent years, but their evaluation is challenging due to the lack of proper observations of drizzle development in the cloud. This work presents a new algorithm for Classification of Drizzle Stages (CLADS). CLADS is based on the skewness of the Ka-band radar Doppler spectrum. Skewness is sensitive to the drizzle growth in the cloud: the observed Gaussian Doppler spectrum has skewness zero when only cloud droplets are present without any significant fall velocity. Defining downward velocities positive, skewness turns positive when embryonic drizzle forms and becomes negative when drizzle starts to dominate the spectrum. CLADS identifies spatially coherent structures of positive, zero, and negative skewness in space and time corresponding to drizzle seeding, drizzle growth/nondrizzle, and drizzle mature, respectively. We test CLADS on case studies from the Jülich Observatory for Cloud Evolution Core Facility (JOYCE-CF) and the Barbados Cloud Observatory (BCO) to quantitatively estimate the benefits of CLADS compared to the standard Cloudnet target categorization algorithm. We suggest that CLADS can provide additional observational constraints for understanding the processes related to drizzle formation better.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Observed Surface Disturbances Associated with the DPRK’s 3 September 2017 Underground Nuclear Test

Satellite-based Synthetic Aperture Radar (SAR) data of the Punggye-ri nuclear test site, along with pre- and post-test optical commercial satellite imagery and a short official video (which was apparently recorded during the 3 September 2017 test), provide additional insights on the widespread surface disturbances around the peak of Mt. Mantap. These were generated by North Korea’s sixth and by far largest nuclear test (estimated to be roughly a quarter of a megaton; Zerbo, 2017). Although a number of visible landslides that are associated with this test had been previously reported, these additional data reveal more information about the widespread nature of the detected larger-scale movements that have been reported by other researchers, in addition to some smaller scale instances of localized slumping or compression within the top 200 m of the mountain; these took place in an area that was observed to contain stratified volcanic deposits not otherwise described to date. A closer look at two previously described zones of localized slippage, subsidence, or slumping (located within those identified volcanic layer deposits on the western side of Mt. Mantap) revealed useful information about the landscape of the area. Several healthy trees have been knocked down in one zone; in the other, a rock prominence had collapsed, and a 60-m-long crack or crevice appeared to have opened. It remains unclear whether these surface disturbances were associated with what had been allegedly reported by the news media as a “cave-in that was externally observable.” However, given the likely 800 m depth of the test in the lower hard-rock geology, we have been led to conclude that the notable observed localized surface disturbances cannot be interpreted as a “collapse crater” that was a result of a “cave-in” above any nuclear test created cavity and/or associated tunneling. Although the broadscale nature of the SAR and visually detected movements inhibits more precise geolocation of the test within the mountain, the radar imagery did show that the various surface ground movements were generated in opposing directions. The movements were directed away from the center on either side of the mountain; as such, they provided an empirical basis to confirm the previous seismological estimates that the detonation had occurred somewhere under the peak of Mt. Mantap. Moreover, a North Korean-sourced short video that was acquired with a ground-based camera focused directly on the peak shows a large dust cloud rising up from the mountain. In conclusion, this provides additional evidence that is consistent with that conclusion.

58 GEOSCIENCES↗
DOE OSTI2017

Low-Noise Amplifiers and Superconducting Flex Circuits for Frequency Domain Multiplexed Readout of Detector Arrays

The next-generation ground-based cosmic microwave background (CMB) experimental program aims to provide definitive measurements of the early universe using telescopes configured with large focal plane arrays requiring ~100,000 detectors and associated readout amplifiers and interconnecting components that are currently unavailable commercially. We describe research to demonstrate the feasibility of developing low-noise cryogenic amplifiers based on series SQUID arrays that are suitable for frequency domain multiplexed readout of large numbers of CMB detectors, and the feasibility of developing robust superconducting flex cables that will simplify cryogenic systems integration for large CMB instruments. Two iterations of prototype series SQUID array amplifier designs were completed. The designs were optimized using modeling and simulation tools to meet the requirements for CMB detector readouts. Two fabrication runs were completed, and a large number of devices from each design iteration were tested and characterized at 4 K. We also completed the development of processes for the fabrication of prototype superconducting flex cables on 4" wafers. Samples were tested for strength and robustness, and to determine the critical current at 4 K as a function of trace width. The new generation of series SQUID array amplifier designs exhibit excellent performance characteristics – very smooth output characteristics that simplify tuning and operation, low input inductance, low current noise referred to the input, and low power dissipation that for the first time will enable these devices to be operated at the focal plane array stage of CMB instruments. The processes developed in Phase I to produce superconducting flex cables were used to complete the fabrication of a full wafer with several test and sample flex circuits. The 6-micron thick flex material is very strong with a breaking strength of several pounds for a 20-mm wide strip. Critical current density was reasonable and scaled with trace width as expected.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Technical Report on Waveform Fit Metrics for Global Models

The new WAVEFORMS Initiative in the Ground-based Nuclear Detonation Detection (GNDD) program includes an increased emphasis on the development of Earth models and methods to predict entire seismic and acoustic waveforms more accurately. In general, this increased emphasis is predicated on the need to better characterize seismic events and provide improved model-based discrimination between event types including earthquakes and explosions. More specifically, while current moment tensor inversion methods tend to work well for larger events (M>~4) using tuned 1-D Earth models, the development of state-of-the-art 3-D models and methods is required for the prediction of shorter period waves over large areas for discrimination of smaller events. There is no standard metric for model-based waveform prediction accuracy used in the waveform modeling/inversion community. However, there are several popular waveform misfit definitions; and minimizing the corresponding objective functions is the goal of waveform inversion. Some example misfit definitions employed for adjoint waveform tomography include measures of simple travel time differences (e.g. Tape et al., 2010), cross-correlation travel time differences (e.g. Luo and Schuster, 1991), multi-taper frequency dependent methods (e.g. Lei et al., 2020), time-frequency phase misfit functions (e.g. Fichtner 2010; Rodgers et al., 2022), normalized cross-correlation methods (e.g. Tao et al., 2018), and others. In some cases, these misfit definitions also involve complicated weighting schemes and summations over multiple frequency bands making it difficult to duplicate the misfit measurement with alternative models and datasets. Although each of the misfit definitions mentioned above are useful for developing waveform models, the actual misfit values are not usually meaningful outside of a given project, model, and/or dataset. Therefore, it is difficult to understand and communicate model performance for predicting waveforms and comparing to other models and/or new model iterations with a different dataset. Therefore, there is a need for a generalized method for evaluating overall model performance that is independent from the specific misfit chosen to develop the waveform models that is also intuitive and meaningful. In this report, we describe a new metric we refer to as ‘Percent of Correlated Signal’. The following sections describe and demonstrate the metric with a case study event and a more rigorous test using a random selection of globally distributed events. While the focus here is on global tomography models, the metric is meant to applicable to regional ‘wiggle-for-wiggle’ waveform models/studies as well.

58 GEOSCIENCES↗
DOE OSTI2020

Image Collection Simulation Using High-Resolution Atmospheric Modeling

A new method is described for simulating the passive remote sensing image collection of ground targets that includes effects from atmospheric physics and dynamics at fine spatial and temporal scales. The innovation in this research is the process of combining a high-resolution weather model with image collection simulation to attempt to account for heterogeneous and high-resolution atmospheric effects on image products. The atmosphere was modeled on a 3D voxel grid by a Large-Eddy Simulation (LES) driven by forcing data constrained by local ground-based and air-based observations. The spatial scale of the atmospheric model (10–100 m) came closer than conventional weather forecast scales (10–100 km) to approaching the scale of typical commercial multispectral imagery (2 m). This approach was demonstrated through a ground truth experiment conducted at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. In this experiment, calibrated targets (colored spectral tarps) were placed on the ground, and the scene was imaged with WorldView-3 multispectral imagery at a resolution enabling the tarps to be visible in at least 9–12 image pixels. The image collection was simulated with Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, using the 3D atmosphere from the LES model to generate a high-resolution cloud mask. The high-resolution atmospheric model-predicted cloud coverage was usually within 23% of the measured cloud cover. The simulated image products were comparable to the WorldView-3 satellite imagery in terms of the variations of cloud distributions and spectral properties of the ground targets in clear-sky regions, suggesting the potential utility of the proposed modeling framework in improving simulation capabilities, as well as testing and improving the operation of image collection processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

OGLE-2016-BLG-0168 Binary Microlensing Event: Prediction and Confirmation of the Microlens Parallax Effect from Space-based Observations

The microlens parallax is a crucial observable for conclusively identifying the nature of lens systems in microlensing events containing or composed of faint (even dark) astronomical objects such as planets, neutron stars, brown dwarfs, and black holes. With the commencement of a new era of microlensing in collaboration with space-based observations, the microlens parallax can be routinely measured. In addition, space-based observations can provide opportunities to verify the microlens parallax measured from ground-only observations and to find a unique solution to the lensing light-curve analysis. Furthermore, since most space-based observations cannot cover the full light curves of lensing events, it is also necessary to verify the reliability of the information extracted from fragmentary space-based light curves. We conduct a test based on the microlensing event OGLE-2016-BLG-0168, created by a binary lens system consisting of almost equal mass M-dwarf stars, to demonstrate that it is possible to verify the microlens parallax and to resolve degeneracies using the space-based light curve even though the observations are fragmentary. Since space-based observatories will frequently produce fragmentary light curves due to their short observing windows, the methodology of this test will be useful for next-generation microlensing experiments that combine space-based and ground-based collaboration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

New Measurements of the Lyα Forest Continuum and Effective Optical Depth with LyCAN and DESI Y1 Data

Abstract We present the Ly α Continuum Analysis Network (LyCAN), a convolutional neural network that predicts the unabsorbed quasar continuum within the rest-frame wavelength range of 1040–1600 Å based on the red side of the Ly α emission line (1216–1600 Å). We developed synthetic spectra based on a Gaussian mixture model representation of nonnegative matrix factorization (NMF) coefficients. These coefficients were derived from high-resolution, low-redshift ( z < 0.2) Hubble Space Telescope/Cosmic Origins Spectrograph (COS) quasar spectra. We supplemented this COS-based synthetic sample with an equal number of DESI Year 5 mock spectra. LyCAN performs extremely well on testing sets, achieving a median error in the forest region of 1.5% on the DESI mock sample, 2.0% on the COS-based synthetic sample, and 4.1% on the original COS spectra. LyCAN outperforms principal component analysis (PCA) and NMF-based prediction methods using the same training set by 40% or more. We predict the intrinsic continua of 83,635 DESI Year 1 spectra in the redshift range of 2.1 ≤ z ≤ 4.2 and perform an absolute measurement of the evolution of the effective optical depth. This is the largest sample employed to measure the optical depth evolution to date. We fit a power law of the form τ ( z ) = τ 0 ( 1 + z ) γ to our measurements and find τ 0 = (2.46 ± 0.14) × 10 −3 and γ = 3.62 ± 0.04. Our results show particular agreement with high-resolution, ground-based observations around z = 2, indicating that LyCAN is able to predict the quasar continuum in the forest region with only spectral information outside the forest.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Reanalysis of NOAA H 2 observations: implications for the H 2 budget

Abstract. Hydrogen (H2) is a promising low-carbon alternative to fossil fuels for many applications. However, significant gaps in our understanding of the atmospheric H2 budget limit our ability to predict the impacts of greater H2 usage. Here we use NOAA H2 dry air mole fraction observations from air samples collected from ground-based and ship platforms during 2010–2019 to evaluate the representation of H2 in the NOAA GFDL-AM4.1 atmospheric chemistry-climate model. We find that the base model configuration captures the observed interhemispheric gradient well but underestimates the surface concentration of H2 by about 10 ppb. Additionally, the model fails to reproduce the 1–2 ppb yr−1 mean increase in surface H2 observed at background stations. We show that the cause is most likely an underestimation of current anthropogenic emissions, including potential leakages from H2-producing facilities. We also show that changes in soil moisture, soil temperature, and snow cover have most likely caused an increase in the magnitude of the soil sink, the most important removal mechanism for atmospheric H2, especially in the Northern Hemisphere. However, there remains uncertainty due to fundamental gaps in our understanding of H2 soil removal, such as the minimum moisture required for H2 soil uptake, for which we performed extensive sensitivity analyses. Finally, we show that the observed meridional gradient of the H2 mixing ratio and its seasonality can provide important constraints to test and refine parameterizations of the H2 soil sink.

Paulot, Fabien (ORCID:0000000175344922)↗
DOE OSTI2021

MicroPulse DIAL (MPD) – a diode-laser-based lidar architecture for quantitative atmospheric profiling

Abstract. Continuous water vapor and temperature profiles are critically needed for improved understanding of the lower atmosphere and potential advances in weather forecasting skill. Ground-based, national-scale profiling networks are part of a suite of instruments to provide such observations; however, the technological method must be cost-effective and quantitative. We have been developing an active remote sensing technology based on a diode-laser-based lidar technology to address this observational need. Narrowband, high-spectral-fidelity diode lasers enable accurate and calibration-free measurements requiring a minimal set of assumptions based on direct absorption (Beer–Lambert law) and a ratio of two signals. These well-proven quantitative methods are known as differential absorption lidar (DIAL) and high-spectral-resolution lidar (HSRL). This diode-laser-based architecture, characterized by less powerful laser transmitters than those historically used for atmospheric studies, can be made eye-safe and robust. Nevertheless, it also requires solar background suppression techniques such as narrow-field-of-view receivers with an ultra-narrow bandpass to observe individual photons backscattered from the atmosphere. We discuss this diode-laser-based lidar architecture's latest generation and analyze how it addresses a national-scale profiling network's need to provide continuous thermodynamic observations. The work presented focuses on general architecture changes that pertain to both the water vapor and the temperature profiling capabilities of the MicroPulse DIAL (MPD). However, the specific subcomponent testing and instrument validation presented are for the water vapor measurements only. A fiber-coupled seed laser transmitter optimization is performed and shown to meet all of the requirements for the DIAL technique. Further improvements – such as a fiber-coupled near-range receiver, the ability to perform quality control via automatic receiver scanning, advanced multi-channel scalar capabilities, and advanced processing techniques – are discussed. These new developments increase narrowband DIAL technology readiness and are shown to allow higher-quality water vapor measurements closer to the surface via preliminary intercomparisons within the MPD network itself and with radiosondes.

Spuler, Scott M.↗
DOE OSTI2024

Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models

Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in situ surface platforms, and numerical weather prediction models. Among the employed instruments are microwave radiometers (MWRs), infrared spectrometers (IRSs), radio acoustic sounding systems (RASSs), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud-base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWR and/or IRS instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the above-mentioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (a.g.l.) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Novel estimation of aerosol processes with particle size distribution measurements: a case study with the TOMAS algorithm v1.0.0

Atmospheric aerosol microphysical processes are a significant source of uncertainty in predicting climate change. Specifically, aerosol nucleation, emissions, and growth rates, which are simulated in chemical transport models to predict the particle size distribution, are not understood well. However, long-term size distribution measurements made at several ground-based sites across Europe implicitly contain information about the processes that created those size distributions. This work aims to extract that information by developing and applying an inverse technique to constrain aerosol emissions as well as nucleation and growth rates based on hourly size distribution measurements. We developed an inverse method based upon process control theory into an online estimation technique to scale aerosol nucleation, emissions, and growth so that the model–measurement bias in three measured aerosol properties exponentially decays. The properties, which are calculated from the measured and predicted size distributions, used to constrain aerosol nucleation, emission, and growth rates are the number of particles with a diameter between 3 and 6 nm, the number with a diameter greater than 10 nm, and the total dry volume of aerosol ( N 3–6 , N 10 , V dry ), respectively. In this paper, we focus on developing and applying the estimation methodology in a zero-dimensional “box” model as a proof of concept before applying it to a three-dimensional simulation in subsequent work. The methodology is first tested on a dataset of synthetic and perfect measurements that span diverse environments in which the true particle emissions, growth, and nucleation rates are known. The inverse technique accurately estimates the aerosol microphysical process rates with an average and maximum error of 2 % and 13 %, respectively. Next, we investigate the effect that measurement noise has on the estimated rates. The method is robust to typical instrument noise in the aerosol properties as there is a negligible increase in the bias of the estimated process rates. Finally, the methodology is applied to long-term datasets of in situ size distribution measurements in western Europe from May 2006 through June 2007. At Melpitz, Germany, and Hyytiälä, Finland, the average diurnal profiles of estimated 3 nm particle formation rates are reasonable, having peaks near noon local time with average peak values of 1 and 0.15 cm –3 s –1 , respectively. The normalized absolute error in estimated N 3–6 , N 10 , and V dry at three European measurement sites is less than 15 %, showing that the estimation framework developed here has potential to decrease model–measurement bias while constraining uncertain aerosol microphysical processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

EPIC Calibration/Validation Experiment Field Campaign Report

A field exercise involving several different kinds of Unmanned Aerial Systems (UAS) and supporting instrumentation systems provided by DOE/ARM and NOAA/NSSL was conducted at the ARM SGP site in Lamont, Oklahoma on 29-30 October 2016. This campaign was part of a larger National Oceanic and Atmospheric Administration (NOAA) UAS Program Office program awarded to the National Severe Storms Laboratory (NSSL). named Environmental Profiling and Initiation of Convection (EPIC). The EPIC Field Campaign (Test and Calibration/Validation) proposed to ARM was a test or “dry-run” for a follow-up campaign to be requested for spring/summer 2017. The EPIC project addresses NOAA’s objective to “evaluate options for UAS profiling of the lower atmosphere with applications for severe weather.” The project goal is to demonstrate that fixed-wing and rotary-wing small UAS have the combined potential to provide a unique observing system capable of providing detailed profiles of temperature, moisture, and winds within the atmospheric boundary layer (ABL) to help determine the potential for severe weather development. Specific project objectives are: 1) to develop small UAS capable of acquiring needed wind and thermodynamic profiles and transects of the ABL using one fixed-wing UAS operating in tandem with two different fixed rotary-wing UAS pairs; 2) adapt and test miniaturized, high-precision, and fast-response atmospheric sensors with high accuracy in strong winds characteristic of the pre-convective ABL in Oklahoma; 3) conduct targeted short-duration experiments at the ARM Southern Great Plains site in northern Oklahoma concurrently with a second site to be chosen in “real-time” from the Oklahoma Mesonet in coordination with the (National Weather Service (NWS)-Norman Forecast Office; and 4) gain valuable experience in pursuit of NOAA’s goals for determining the value of airborne, mobile observing systems for monitoring rapidly evolving high-impact severe weather conditions not observed with current operational systems. OU operated three UAS at the Lamont SGP site – the OU CopterSonde, the OU Iris, and the Meteomatics Meteodrone – under a blanket Federal Aviation Administration (FAA) Certificate of Authorization (COA) allowing flights up to 400 feet above ground level (AGL). The mission for the rotary-wing UAS involved four aircraft, three from the ARM Lamont site and one from an Oklahoma Mesonet site located at Medford, involving a vertical ascent to an altitude of 400 ft (130 m) AGL at ~ 30 minute intervals for ~ 5 hours duration on each of the two experiment days. This operation was conducted in close coordination with NSSL-launched rawinsonde balloons at the two sites, and operation of a fixed-wing UAS from the University of Colorado called the TTwistor that flew mission flight legs between the Lamont site and Medford. The NSSL operation at Medford (outside of ARM) also involved use of their Collaborative Lower Atmosphere Mobile Profiling System (CLAMPS) ground-based remote-sensing system for measuring atmospheric profiles of temperature, moisture, and winds with atmospheric emitted radiance interferometer (AERI), microwave radiometer, and Doppler wind lidar systems. The ARM Facility supported the project by providing access to their instrumented tower data at Lamont (at three levels), as well as AERI and Doppler wind lidar data obtained from systems quite similar to those used by CLAMPS. These non-UAS data from both ARM and NSSL provided the observations used to validate the experimental UAS observations.

atmospheric boundary layer↗
DOE OSTI2019

W87-1 Modification Program (Maintaining the Stockpile)

NNSA resumed work on a W78 warhead replacement, designated as the W87-1, in fiscal year (FY) 2019 following the 2018 Nuclear Posture Review. By replacing the legacy W78, the W87-1 will maintain continuity for the ground-based leg of the U.S. nuclear deterrent. The warhead is named the W87-1 to reflect that it has a similar primary design to the W87-0 and will fly in a similar reentry vehicle. The W87-1 is based on a previously-tested nuclear component and will include an insensitive high explosive primary that had been designed and tested with advanced safety features. The warhead will be fielded by FY 2030 on the U.S. Air Force (USAF)’s Ground Based Strategic Deterrent. The W87-1 will not provide new military capabilities, yet it will provide enhanced safety and security compared to the legacy W78. The W87-1 will be certified without the need for additional underground nuclear explosive testing.

Source record↗