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DOE OSTI2022

Optical noise in a free-space quantum communications link from natural and nuclear disturbed environments*

Satellite communications at radio frequencies can experience a ‘blackout’ period following the atmospheric detonation of a nuclear weapon. The wavelengths used for free-space quantum communications will not incur the same ‘blackout’ effects from a nuclear detonation, but the optical systems will suffer from a phenomenon called redout. Redout occurs in an optical detector when ambient light scatters into the optical receiver, causing elevated background photon counts in the detector such that background noise overwhelms the signal. In this work, the duration of the redout effect is quantified from a nuclear disturbed environment on a ground-to-space quantum optical link. In addition, we comment on various techniques for reducing ambient and nuclear disturbed background counts in a quantum free-space optical link. For low-altitude nuclear detonations (i.e., under 50 km), the maximum interference time will be less than 1 min. Implementing a telescope, timing gate, and wavelength filter to the detector can reduce the background counts in the detector significantly. Aerosol levels and ground albedo are major contributors to background noise in a ground-to-satellite quantum channel, and ground station location should factor in both variables.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Optimizing Non-Terrestrial Hybrid RF/FSO Links With Reinforcement Learning: Navigating Through Clouds

In the pursuit of ubiquitous broadband connectivity, there has been a significant shift towards the vertical expansion of communication networks into space, particularly through the exploitation of low Earth orbit (LEO) satellite constellations, which are favored for their relatively low latency. However, this approach faces many challenges that need to be addressed, including atmospheric turbulence, high path loss, and dynamic cloud formations. High-altitude pseudo-satellites (HAPS) have emerged as promising relaying layers between LEO satellites and ground stations, enhancing coverage, latency, and direct terrestrial user connectivity. While radio frequency (RF) bands suffer from congestion and limited bandwidth, free space optical (FSO) communications offer higher data rates, but are susceptible to misalignment and weather-induced signal degradation. To address these challenges, a hybrid RF/FSO approach has been proposed to take advantage of both technologies by dynamic switching between RF and FSO based on propagation channel conditions. This paper introduces a reinforcement learning-based algorithm designed to optimize the trajectory of HAPS, maneuver around cloudy areas, and seamlessly switch between the RF and FSO communication modes to maximize the achievable capacity. The proposed approach aims to maximize system performance by intelligently adapting to environmental conditions and offering a promising solution for next-generation space communication networks.

actor-critic algorithm↗

DOE OSTI2021

Estimating Subhourly Inverter Clipping Loss From Satellite-Derived Irradiance Data

Photovoltaic system production simulations are conventionally run using hourly weather datasets. Hourly simulations are sufficiently accurate to predict the majority of long-term system behavior but cannot resolve high-frequency effects like inverter clipping caused by short-duration irradiance variability. Direct modeling of this subhourly clipping error is only possible for the few locations with high-resolution irradiance datasets. This paper describes a method of predicting the magnitude of this error using a machine learning regressor ensemble model, comprised of a random forest and an XGBoost model, and 30-minute satellite irradiance data. The method predicts a correction for each 30-minute interval with the potential to roll up into 60-minute corrections to match an hourly energy model. The model is trained and validated at locations where the error can be directly simulated from 1-minute ground data. The validation shows low bias at most ground station locations. The model is also applied to gridded satellite irradiance to produce a heatmap of the estimated clipping error across the United States. Finally, the relative importance of each predictor satellite variable is retrieved from the model and discussed.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2026

Estimating Fine-Resolution Shortwave Broadband Albedo of Croplands from Harmonized Landsat and Sentinel-2 Data

Altered surface albedo due to land-cover conversions and management is a significant driver of global climate change. Albedo can be directly measured at ground stations, and remote sensing data can be used to scale-up albedo values to regional and global levels. Some previous studies have retrieved fine-resolution (10–30 m) instantaneous albedo and coarse-resolution (500–1000 m) daily mean albedo from remote sensing data, but they all required the input of Moderate Resolution Imaging Spectroradiometer (MODIS) albedo information at 500-m resolution, and none have assembled both instantaneous and daily albedo based exclusively on fine-resolution satellite data. Here, to address this issue, we compiled 387 instantaneous and 346 daily albedo records using field net radiometer measurements from the bioenergy croplands at the W. K. Kellogg Biological Station in southwest Michigan. We then connected these albedo records with a suite of variables derived from harmonized Landsat and Sentinel-2 data through two machine learning algorithms (random forest regression and extreme gradient boosting) to retrieve clear-sky instantaneous and daily shortwave broadband albedo. The performance statistics indicate reasonable accuracy of model results [root-mean-square error (RMSE)] around or below 0.03 except for snow-covered surfaces), suggesting that the retrieval of both instantaneous and daily albedo based exclusively on fine-resolution satellite data is promising. To facilitate the use of fine-resolution albedo products at the global level, future efforts need to include more albedo records of diverse surface cover types, as well as to accurately model daily albedo for cloudy days to address the “clear-sky bias.”

Harmonized Landsat and Sentinel-2↗

DOE OSTI2021

Cloud Droplet Measurement System for the ARM Tethered Balloon System (TBS) Field Campaign Report

The Mesa Photonics' cloud droplet measurement system (CDMS) performs in situ measurement of droplet size distribution and droplet number density in clouds. These characteristics are important cloud microphysical properties that are critical input parameters for atmospheric models and are also useful for proper calibration and validation of performance of other atmospheric measurement instrumentation. This small campaign was the first project (out of two) that involved integration of the CDMS into the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s tethered balloon system (TBS) and its initial testing at the ARM Southern Great Plains (SGP) atmospheric observatory. The follow-up campaign (AFC07016) involved testing of the CDMS under relevant conditions at the third ARM Mobile Facility (AMF3) at Oliktok Point, Alaska and making in situ measurements in clouds. The goal of this small field campaign was to deploy the CDMS on the ARM TBS, demonstrate its integrity and compatibility with the TBS, and test the wireless telecommunication system in preparation for subsequent in-cloud measurements at ARM’s North Slope of Alaska (NSA) observatory at Utqiagvik (formerly Barrow). The specific technical objectives included: (1) Integration of the Mesa Photonics' CDMS into the ARM TBS, (2) testing of the wireless telecommunication system of the CDMS, (3) evaluating the data acquisition software and image processing algorithms especially under high-background-illumination conditions, and (4) testing the ruggedness of the instrument's optical alignment and other performance characteristics. All objectives have been successfully met. The mounting hardware was designed and built mostly at Sandia National Laboratories (SNL), which allowed reliable mounting of the CDMS on the TBS and co-locating the CDMS with other instruments. The field tests were performed by the principal investigator (PI) in collaboration with the TBS operational crew led by D. Dexheimer. The field testing demonstrated good compatibility of the CDMS with the TBS. During the TBS flights, the crew did not experience any operational issues with the CDMS. The instrument demonstrated reliable operational characteristics, including sufficient battery life and good thermal management. The wireless telecommunication system has been successfully tested at TBS flight altitudes of up to 1000 m. During all flights, the CDMS raw data were wirelessly transmitted to the ground station and processed in real time. The ruggedness of the instrument's optical alignment was evaluated by performing calibration of the CDMS (using Mesa Photonics' fixed-size monodisperse droplet generator) before and after the campaign. The calibration was stable within the instrument's measurement precision. In summary, the campaign demonstrated good compatibility of the Mesa Photonics' CDMS with the ARM TBS. The flight tests confirmed the mechanical integrity of the CDMS and stability of its optical layout, as well as reliability of the wireless telecommunication system. Successful completion of this campaign provided a solid basis for the next campaign (AFC07016, November 2020), that involved testing of the CDMS during in-cloud TBS flights at AMF3 at Oliktok Point.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Deep learning-based spatio-temporal estimate of greenhouse gas emissions using satellite data

Accurate estimation of greenhouse gases (GHGs) emissions is very important for developing mitigation strategies to climate change by controlling and reducing GHG emissions. This project aims to develop multiple deep learning approaches to estimate anthropogenic greenhouse gas emissions using multiple types of satellite data. NO2 concentration is chosen as an example of GHGs to evaluate the proposed approach. Two sentinel satellites (sentinel-2 and sentinel-5P) provide multiscale observations of GHGs from 10-60m resolution (sentinel-2) to ~kilometer scale resolution (sentinel-5P). Among multiple deep learning (DL) architectures evaluated, two best DL models demonstrate that key features of spatio-temporal satellite data and additional information (e.g., observation times and/or coordinates of ground stations) can be extracted using convolutional neural networks and feed forward neural networks, respectively. In particular, irregular time series data from different NO 2 observation stations limit the flexibility of long short-term memory architecture, requiring zero-padding to fill in missing data. However, deep neural operator (DNO) architecture can stack time-series data as input, providing the flexibility of input structure without zero-padding. As a result, the DNO outperformed other deep learning architectures to account for time-varying features. Overall, temporal patterns with smooth seasonal variations were predicted very well, while frequent fluctuation patterns were not predicted well. In addition, uncertainty quantification using conformal inference method is performed to account for prediction ranges. Overall, this research will lead to a new groundwork for estimating greenhouse gas concentrations using multiple satellite data to enhance our capability of tracking the cause of climate change and developing mitigation strategies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Development of a Microwave Quantum Sensing Capability

The geopolitical world is at the beginning of a quantum renaissance. Technology has reached a level of sophistication that allows the probing of a new world that operates using a different set of rules marked by quantum entanglement, superposition, and the no-cloning theorem. If we can explore this new world and its counterintuitive rules, we may be able to leverage them to build powerful tools for a wide range of applications, including enhanced encryption, computation, and sensing. Some entities have invested early and heavily in exploring this frontier: China has demonstrated quantum teleportation between a ground station and an orbiting satellite; Google recently met a computational milestone known as quantum supremacy, in which a quantum computer solves a problem that would take a classical computer an infeasible amount of time to solve. While great strides have been made, the early progress has caught many entities off guard. The scope of this project leverages an existing investment in a dilution refrigerator strategically chosen to position Pacific Northwest National Laboratory (PNNL) for a future in low-temperature microwave quantum sensing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

New Particle Formation and Growth in the Houston Atmosphere During TRACER (Final Report)

From 2020-2025, researchers from UC Irvine, UC Riverside, and Colorado State University collaborated on a Department of Energy-funded project to understand how airborne particles form and grow in urban atmospheres, conducting an intensive field campaign in Houston, Texas during summer 2022. Using advanced instruments to measure gas-phase chemicals, particle composition, and a specialized chamber to study particle growth, the team discovered that sulfur-containing compounds from industrial and power plant emissions are the dominant driver of new particle formation in Houston, with particles typically forming locally in the city and growing as air moves away in the urban plume. The research revealed an important methodological insight: measurements from fixed ground stations can be misleading when interpreting how particles actually evolve as air masses move, which has significant implications for how scientists worldwide interpret atmospheric observations. These findings improve understanding of urban air quality and help reduce uncertainties in climate models, since these particles play critical roles in cloud formation and Earth's radiation balance, while also providing detailed information about ultrafine particle composition relevant to public health. The project trained three doctoral students, developed enhanced computer models for urban particle formation, and made all data publicly available through the DOE Atmospheric Radiation Measurement data archive for use by the broader scientific community.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Observing low-altitude features in ozone concentrations in a shoreline environment via uncrewed aerial systems

Abstract. Ozone is a pollutant formed in the atmosphere by photochemical processes involving nitrogen oxides (NOx) and volatile organic compounds (VOCs) when exposed to sunlight. Tropospheric boundary layer ozone is regularly measured at ground stations and sampled infrequently through balloon, lidar, and crewed aircraft platforms, which have demonstrated characteristic patterns with altitude. Here, to better resolve vertical profiles of ozone within the atmospheric boundary layer, we developed and evaluated an uncrewed aircraft system (UAS) platform for measuring ozone and meteorological parameters of temperature, pressure, and humidity. To evaluate this approach, a UAS was flown with a portable ozone monitor and a meteorological temperature and humidity sensor to compare to tall tower measurements in northern Wisconsin. In June 2020, as a part of the WiscoDISCO20 campaign, a DJI M600 hexacopter UAS was flown with the same sensors to measure Lake Michigan shoreline ozone concentrations. This latter UAS experiment revealed a low-altitude structure in ozone concentrations in a shoreline environment showing the highest ozone at altitudes from 20–100 m a.g.l. These first such measurements of low-altitude ozone via a UAS in the Great Lakes region revealed a very shallow layer of ozone-rich air lying above the surface.

Meteorology & Atmospheric Sciences↗

DOE OSTI2020

Estimating Subhourly Inverter Clipping Loss From Satellite-Derived Irradiance Data: Preprint

Photovoltaic system production simulations are conventionally run using hourly weather datasets. Hourly simulations are sufficiently accurate to predict the majority of long-term system behavior but cannot resolve high-frequency effects like inverter clipping caused by short-duration irradiance variability. Direct modeling of this subhourly clipping error is only possible for the few locations with high-resolution irradiance datasets. This paper describes a method of predicting the magnitude of this error using a machine learning model and 30-minute satellite irradiance data. The method predicts a correction for each 30-minute interval with the potential to roll up into 60-minute corrections to match an hourly energy model. The model is trained and validated at locations where the error can be directly simulated from 1-minute ground data. The validation shows low bias at most ground station locations. The model is also applied to gridded satellite irradiance to produce a heatmap of the estimated clipping error across the United States. Finally, the relative importance of each predictor satellite variable is retrieved from the model and discussed.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2021

Beacon-based precision navigation and timing (PNT) system

Beacon-based Precision Navigation and Timing (PNT) may use a constellation of space vehicles (e.g., small, low cost satellites) coupled to a network of ground stations and a network of beacons. Such a system be provided at a cost that is approximately 100 times lower than GPS both to build and to operate. The resulting system may also provide fast acquisition, improved SNR, improved anti-jam and anti-spoofing capabilities, and six-inch scale location determination, making it applicable to both existing PNT applications and enabling new applications.

Judd, Stephen↗

DOE OSTI2022

Free-space optical communication system and methods for efficient data delivery

Communication systems and methods for high-data-rate, high-efficiency, free-space communications are described. High-speed optical modems and automatic repeat request can be employed to transmit large data files without data errors between remote devices, such as an earth-orbiting satellite and ground station. Data rates over 100 Gb/s can be achieved.

Boroson, Don M.↗

DOE OSTI2025

Simulating a Reactor with PID Temperature Control

A Simulation of a Reactor, Built Using CrowPis, which are simple and accessible programmable systems of various electronics, as ground stations and a Box containing heaters as the reactor. The goal of this project is to learn various skills and to demonstrate the feasibility of a simulated reactor, utilizing a PID control and wireless connectivity.

42 - ENGINEERING↗

DOE OSTI2022

The Strange Case of Ground-Coupled Airwaves on Seismoacoustic Stations at Local to Near-Regional Scales

Here we investigate the application of ground-coupled airwaves observed by seismoacoustic stations at local to near-regional scales to detect signals of interest and determine back-azimuth information. Ground-coupled airwaves are created from incident pressure waves traveling through the atmosphere that couple to the earth and transmit as a seismic wave with retrograde elliptical motion. Previous studies at sub-local scales (<10 km from a source of interest) found the back-azimuth to the source could be accurately determined from seismoacoustic signals recorded by acoustic and 3-component seismic sensors spatially separated on the order of 10 to 150 m. The potential back-azimuth directions are estimated from the coherent signals between the acoustic and vertical seismic data, via a propagation-induced phase shift of the seismoacoustic signal. A unique solution is then informed by the particle motion of the 3-component seismic station, which was previously found to be less accurate than the seismoacoustic-sensor method. We investigate the applicability of this technique to greater source-receiver distances, from 50-100 km and up to 400 km, which contains pressure waves with tropospheric and stratospheric ray paths, respectively. Specifically, we analyze seismoacoustic sources with ground truth from rocket motor fuel elimination events at the Utah Test and Training Range (UTTR) as well as a 2020 rocket launch in Southern California. From these sources we observe evidence that while coherent signals can be seen from both sources on multiple seismoacoustic station pairs, the determined ground-coupled airwave back-azimuths are more complicated than results at more local scales. Our findings suggest more complex factors including incidence angle, coupling location, subsurface material, and atmospheric propagation effects need to be fully investigated before the ground-coupled airwave back-azimuth determination method can be applied or assessed at these further distances.

58 GEOSCIENCES↗

DOE OSTI2021

Ensuring reliable connectivity to cellular-connected UAVs with up-tilted antennas and interference coordination

To integrate unmanned aerial vehicles (UAVs) in future large-scale deployments, a new wireless communication paradigm, namely, the cellular-connected UAV has recently attracted interest. However, the line-of-sight dominant air-to-ground channels along with the antenna pattern of the cellular ground base stations (GBSs) introduce critical interference issues in cellular-connected UAV communications. In particular, the complex antenna pattern and the ground reflection (GR) from the down-tilted antennas create both coverage holes and patchy coverage for the UAVs in the sky, which leads to unreliable connectivity from the underlying cellular network. To overcome these challenges, in this paper, we propose a new cellular architecture that employs an extra set of co-channel antennas oriented towards the sky to support UAVs on top of the existing down-tilted antennas for ground user equipment (GUE). To model the GR stemming from the down-tilted antennas, we propose a path-loss model, which takes both antenna radiation pattern and configuration into account. Next, we formulate an optimization problem to maximize the minimum signal-to-interference ratio (SIR) of the UAVs by tuning the up-tilt (UT) angles of the up-tilted antennas. Since this is an NP-hard problem, we propose a genetic algorithm (GA) based heuristic method to optimize the UT angles of these antennas. After obtaining the optimal UT angles, we integrate the 3GPP Release-10 specified enhanced inter-cell interference coordination (eICIC) to reduce the interference stemming from the down-tilted antennas. Our simulation results based on the hexagonal cell layout show that the proposed interference mitigation method can ensure higher minimum SIRs for the UAVs over baseline methods while creating minimal impact on the SIR of GUEs.

3GPP↗

DOE OSTI2020

Air quality impact of the Northern California Camp Fire of November 2018

The Northern California Camp Fire that took place in November 2018 was one of the most damaging environmental events in California history. Here, we analyze ground-based station observations of airborne particulate matter that has a diameter <2.5 µm (PM 2.5 ) across Northern California and conduct numerical simulations of the Camp Fire using the Weather Research and Forecasting model online coupled with chemistry (WRF-Chem). Simulations are evaluated against ground-based observations of PM 2.5 , black carbon, and meteorology, as well as satellite measurements, such as Tropospheric Monitoring Instrument (TROPOMI) aerosol layer height and aerosol index. The Camp Fire led to an increase in Bay Area PM 2.5 to over 50 µg m –3 for nearly 2 weeks, with localized peaks exceeding 300 µg m –3 . Using the Visible Infrared Imaging Radiometer Suite (VIIRS) high-resolution fire detection products, the simulations reproduce the magnitude and evolution of surface PM 2.5 concentrations, especially downwind of the wildfire. The overall spatial patterns of simulated aerosol plumes and their heights are comparable with the latest satellite products from TROPOMI. WRF-Chem sensitivity simulations are carried out to analyze uncertainties that arise from fire emissions, meteorological conditions, feedback of aerosol radiative effects on meteorology, and various physical parameterizations, including the planetary boundary layer model and the plume rise model. Downwind PM 2.5 concentrations are sensitive to both flaming and smoldering emissions over the fire, so the uncertainty in the satellite-derived fire emission products can directly affect the air pollution simulations downwind. Our analysis also shows the importance of land surface and boundary layer parameterization in the fire simulation, which can result in large variations in magnitude and trend of surface PM 2.5 . Inclusion of aerosol radiative feedback moderately improves PM 2.5 simulations, especially over the most polluted days. Results of this study can assist in the development of data assimilation systems as well as air quality forecasting of health exposures and economic impact studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Contribution of Electron Pressure to Ring Current and Ground Magnetic Depression Using RAM-SCB Simulations and Arase Observations During 7–8 November 2017 Magnetic Storm

Understanding the physical processes that control the dynamics of energetic particles in the inner magnetosphere is important for both space-borne and ground-based assets essential to the modern society. The storm time distribution of ring current particles in the inner magnetosphere depends strongly on their transport in the evolving electric and magnetic fields along with particle acceleration and loss. Here, we investigated the ring current particle variations using observations and simulations. We compared the ion (H + , He + , and O + ) and electron flux and plasma pressure variations from Arase observations with the self-consistent inner magnetosphere model: Ring current Atmosphere interactions Model with Self Consistent magnetic field (RAM-SCB) during the 7–8 November 2017 geomagnetic storm. We investigated the contribution of the different species (ions and electrons) to the magnetic field deformation observed at ground magnetic stations (09°–45° MLat) using RAM-SCB simulations. The results show that the ions are the major contributor with ~88% and electrons contribute ~12% to the total ring current pressure. It is also found that the electron contribution is non-negligible (~18%) to the ring current in dawn-side during the main phase of the storm. Thus, the electron contribution to the storm time ring current is important and should not be neglected.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

The AtmoSOFAR Channel: First Direct Observations of an Elevated Acoustic Duct

The Sound Fixing and Ranging (SOFAR) channel in the ocean allows for low frequency sound to travel thousands of kilometers, making it particularly useful for detecting underwater nuclear explosions. Suggestions that an elevated SOFAR-like channel should exist in the stratosphere date back over half a century and imply that sources within this region can be reliably sensed at vast distances. However, this theory has not been supported with evidence of direct observations from sound within this channel. Here we show that an infrasound sensor on a solar hot air balloon recorded the first infrasound detection of a ground truth airborne source while within this acoustic channel, which we refer to as the AtmoSOFAR channel. Our results support the existence of the AtmoSOFAR channel, demonstrate that acoustic signals can be recorded within it, and provide insight into the characteristics of recorded signals. Results also show a lack of detections on ground-based stations, highlighting the advantages of using balloon-borne infrasound sensors to detect impulsive sources at altitude.

54 ENVIRONMENTAL SCIENCES↗