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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 OSTI2021

Comparing Satellite- and Ground-Based Observations of Cloud Occurrence Over High Southern Latitudes

The 2B-CLDCLASS-LIDAR R05 (2BCL5) and the raDAR/liDAR (DARDAR) satellite retrievals of cloud occurrence are compared as a function of altitude and latitude. The largest disparities are observed at low altitudes over high southern latitudes. These data sets are cross referenced to ground-based measurements from the Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE) campaign at McMurdo Station, Antarctica. Compared with AWARE observations, both 2BCL5 and DARDAR underestimate cloud occurrence below 1.5 km, with 2BCL5 and DARDAR distinguishing roughly one third of cloud occurrences observed by AWARE at 0.5 km. While DARDAR identifies greater cloud occurrences than 2BCL5 below 1.5 km, cloud occurrence values for the two data sets have similar differences relative to ground-based measurements. Therefore, the DARDAR retrievals of greater cloud occurrence at low altitudes are likely due to a larger quantity of false positives associated with radar ground clutter or attenuated lidar retrievals. DARDAR cloud occurrences match better with AWARE than 2BCL5 above 5 km. However, the likely underestimation of ground-based measurements at higher altitudes suggests DARDAR may underestimate high level cloud occurrence. Finally, both data sets indicate the presence of liquid containing clouds at temperatures within the homogeneous freezing regime, despite the fact that the ECMWF-AUX data set implemented in their processing clearly indicates temperatures below -38°C. Finally, using AWARE radiosonde (ECMWF-AUX) temperature data, we find that 2BCL5 detects 13.3% (13.8%) of mixed phase clouds below -38°C, while DARDAR detects 5.7% (6.6%) of mixed phase and 1.1% (1.3%) of liquid phase clouds below -38°C.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Earth-system-model evaluation of cloud and precipitation occurrence for supercooled and warm clouds over the Southern Ocean's Macquarie Island

Abstract. Over the remote Southern Ocean (SO), cloud feedbacks contribute substantially to Earth system model (ESM) radiative biases. The evolution of low Southern Ocean clouds (cloud-top heights < ∼ 3 km) is strongly modulated by precipitation and/or evaporation, which act as the primary sink of cloud condensate. Constraining precipitation processes in ESMs requires robust observations suitable for process-level evaluations. A year-long subset (April 2016–March 2017) of ground-based profiling instrumentation deployed during the Macquarie Island Cloud and Radiation Experiment (MICRE) field campaign (54.5∘ S, 158.9∘ E) combines a 95 GHz (W-band) Doppler cloud radar, two lidar ceilometers, and balloon-borne soundings to quantify the occurrence frequency of precipitation from the liquid-phase cloud base. Liquid-based clouds at Macquarie Island precipitate ∼ 70 % of the time, with deeper and colder clouds precipitating more frequently and at a higher intensity compared to thinner and warmer clouds. Supercooled cloud layers precipitate more readily than layers with cloud-top temperatures > 0 ∘C, regardless of the geometric thickness of the layer, and also evaporate more frequently. We further demonstrate an approach to employ these observational constraints for evaluation of a 9-year GISS-ModelE3 ESM simulation. Model output is processed through the Earth Model Column Collaboratory (EMC2) radar and lidar instrument simulator with the same instrument specifications as those deployed during MICRE, therefore accounting for instrument sensitivities and ensuring a coherent comparison. Relative to MICRE observations, the ESM produces a smaller cloud occurrence frequency, smaller precipitation occurrence frequency, and greater sub-cloud evaporation. The lower precipitation occurrence frequency by the ESM relative to MICRE contrasts with numerous studies that suggest a ubiquitous bias by ESMs to precipitate too frequently over the SO when compared with satellite-based observations, likely owing to sensitivity limitations of spaceborne instrumentation and different sampling methodologies for ground- versus space-based observations. Despite these deficiencies, the ESM reproduces the observed tendency for deeper and colder clouds to precipitate more frequently and at a higher intensity. The ESM also reproduces specific cloud regimes, including near-surface clouds that account for ∼ 25 % of liquid-based clouds during MICRE and optically thin, non-precipitating clouds that account for ∼ 27 % of clouds with bases higher than 250 m. We suggest that the demonstrated framework, which merges observations with appropriately constrained model output, is a valuable approach to evaluate processes responsible for cloud radiative feedbacks in ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

On the Estimation of In-Cloud Vertical Air Motion Using Radar Doppler Spectra

Measurements of in-cloud vertical air motion are key to quantitatively describe cloud dynamics and their role in cloud microphysics. Here, a retrieval technique for estimating the in-cloud vertical air motion using the upward edge of the radar Doppler spectrum is presented. An additional broadening correction factor that depends on the signal-to-noise ratio (SNR) is introduced. A variety of independent measurements are used to assess the performance of the new technique. The vertical air motion is unbiased with an uncertainty of 0.2 m s –1 for SNR <30. Here, the properties of in-cloud vertical air motion are investigated from 1 year of ground-based observations of warm marine boundary layer clouds. Clouds with higher LWP are characterized by stronger vertical air motions compared to those having lower LWP values.

42 ENGINEERING↗
DOE OSTI2024

How Well Does the DOE Global Storm Resolving Model Simulate Clouds and Precipitation Over the Amazon?

This study assesses a 40-day 3.25-km global simulation of the Simple Cloud-Resolving E3SM Model (SCREAMv0) using high-resolution ground-based observations from the Atmospheric Radiation Measurement (ARM) Green Ocean Amazon (GoAmazon) field campaign. SCREAMv0 reasonably captures the diurnal timing of boundary layer clouds yet underestimates the boundary layer cloud fraction and mid-level congestus. SCREAMv0 well replicates the precipitation diurnal cycle, however it exhibits biases in the precipitation cluster size distribution compared to scanning radar observations. Specifically, SCREAMv0 overproduces clusters smaller than 128 km, and does not form enough large clusters. Such biases suggest an inhibition of convective upscale growth, preventing isolated deep convective clusters from evolving into larger mesoscale systems. This model bias is partially attributed to the misrepresentation of land-atmosphere coupling. This study highlights the potential use of high-resolution ground-based observations to diagnose convective processes in global storm resolving model simulations, identify key model deficiencies, and guide future process-oriented model sensitivity tests and detailed analyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Radio Afterglow Detection and AI-driven Response (RADAR): A Federated Framework for Gravitational-wave Event Follow-up

The landmark detection of both gravitational waves (GWs) and electromagnetic (EM) radiation from the binary neutron star merger GW170817 has spurred efforts to streamline the follow-up of GW alerts in current and future observing runs of ground-based GW detectors. Within this context, the radio band of the EM spectrum presents unique challenges. Sensitive radio facilities capable of detecting the faint radio afterglow seen in GW170817, and with sufficient angular resolution, have small fields of view compared to typical GW localization areas. Additionally, theoretical models predict that the radio emission from binary neutron star mergers can evolve over weeks to years, necessitating long-term monitoring to probe the physics of the various postmerger ejecta components. These constraints, combined with limited radio observing resources, make the development of more coordinated follow-up strategies essential—especially as the next generation of GW detectors promises a dramatic increase in detection rates. Here, we present RADAR, a framework designed to address these challenges by promoting community-driven information sharing, federated data analysis, and system resilience, while integrating AI methods for both GW signal identification and radio data aggregation. We show that it is possible to preserve data rights while sharing models that can help design and/or update follow-up strategies. We demonstrate our approach through a case study of GW170817, and discuss future directions for refinement and broader application.

Gravitational waves↗
DOE OSTI2026

Multi‐Sensor Trajectory Reconstruction of the 24 April 2025 Alaska Fireball and Implications for Planetary Defense

On 24 April 2025 at 18:30:57 UTC, a bright daytime fireball over Southcentral Alaska was detected by 37 seismic stations, 16 single infrasound sensors, and four infrasound arrays, yielding 30 ballistic and multiple fragmentation arrivals. Here, the unprecedented density of seismoacoustic coverage enabled detailed reconstruction of the event using acoustic signals, with fragmentation source locations further guiding the identification of Doppler weather radar signatures of a meteorite fall. Incorporation of a radar-derived terminal point yielded a final trajectory solution, which agreed closely with an independent optical trajectory solution from video analysis. The reconstructed entry parameters from seismoacoustic analysis indicate a velocity of 25.3 km/s, an entry angle of 19°, and an energy release of ∼38 t TNT equivalent. Assuming a chondritic composition, the pre-entry object diameter was ∼0.7 m. Using orbital parameters from the optical solution, we estimate meteoroid composition as most likely an L-type ordinary chondrite. The event occurred in the sub-Arctic, where space-based optical systems face challenges in detection, demonstrating the critical role of dense ground-based seismoacoustic networks in characterizing high-latitude atmospheric entries. This uniquely well-recorded event demonstrates the capability of dense seismoacoustic networks to constrain bolide trajectories, energetics, and fragmentation, with radar and optical data providing critical confirmation and complementary perspectives. These results bridge the methodological gap between planetary-defense monitoring of natural impactors and space-traffic analyses of artificial reentries, illustrating how multi-sensor integration can deliver calibration-grade trajectories even for unpredicted events.

Fireball↗
DOE OSTI2024

minimpl (b1)

The micropulse lidar (MPL) is a ground-based, optical, remote-sensing system designed primarily to determine the altitude of clouds; however, it is also used for detection of atmospheric aerosols. The physical principle is the same as for radar. Pulses of energy are transmitted into the atmosphere; the energy scattered back to the transceiver is collected and measured as a time-resolved signal, thereby detecting clouds and aerosols in real time. From the time delay between each outgoing pulse and the backscattered signal, the distance to the scatterer is inferred. Post-processing of the lidar return characterizes the extent and properties of aerosols or other particles in a region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Cloud micro- and macrophysical properties from ground-based remote sensing during the MOSAiC drift experiment

In the framework of the Multidisciplinary drifting Observatory for the Study of Arctic Climate Polarstern expedition, the Leibniz Institute for Tropospheric Research, Leipzig, Germany, operated the shipborne OCEANET-Atmosphere facility for cloud and aerosol observations throughout the whole year. OCEANET-Atmosphere comprises, amongst others, a multiwavelength Raman lidar, a microwave radiometer, and an optical disdrometer. A cloud radar was operated aboard Polarstern by the US Atmospheric Radiation Measurement program. These measurements were processed by applying the so-called Cloudnet methodology to derive cloud properties. To gain a comprehensive view of the clouds, lidar and cloud radar capabilities for low- and high-altitude observations were combined. Cloudnet offers a variety of products with a spatiotemporal resolution of 30 s and 30 m, such as the target classification, and liquid and ice microphysical properties. Additionally, a lidar-based low-level stratus retrieval was applied for cloud detection below the lowest range gate of the cloud radar. Based on the presented dataset, e.g., studies on cloud formation processes and their radiative impact, and model evaluation studies can be conducted.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Surface-based observations of cold-air outbreak clouds during the COMBLE field campaign

Abstract. Cold-air outbreaks (CAOs) are characterized by extreme air–sea energy exchanges and low-level convective clouds over large areas in the high-latitude oceans. As such, CAOs are an important component of the Earth's climate system. The CAOs in the Marine Boundary Layer Experiment (COMBLE) deployment of the US Department of Energy Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) provided the first comprehensive view of CAOs using a suite of ground-based observations at the northern coast of Norway. Here, cloud and precipitation observations from 13 CAO cases during COMBLE are analyzed. A vertical air motion retrieval technique is applied to the Ka-band ARM Zenith-pointing Radar (KAZR) observations. The CAO cumulus clouds are characterized by strong updrafts with magnitudes between 2–8 m s−1, vertical extents of 1–3 km, and horizontal scales of 0.25–3 km. A strong relationship between our vertical air velocity retrievals and liquid water path (LWP) measurements is found. The LWP measurements exceed 1 kg m−2 in strong updraft areas, and the vertical extent of the updraft correlates well with the LWP values. The CAO cumulus clouds exhibit eddy dissipation rate values between 10−3 and 10−2 m2 s−3 in the lowest 10 km of the atmosphere, and using a radar Doppler spectra technique, evidence of secondary ice production is found during one of the cases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

All-sky aerosol direct radiative effects at the ARM SGP site

All-sky aerosol direct radiative effect (DRE) was estimated for the first time at the Atmospheric Radiation Measurement Southern Great Plains site using multi-year ground-based observations. The NASA Langley Fu-Liou radiation model was employed. Observed inputs for the radiation model include aerosol and cloud vertical extinction profile from Raman lidar; spectral aerosol optical depth, single-scattering albedo, and asymmetry factor from Aerosol Robotic Network; cloud water content profiles from radars; temperature and water vapor profiles from radiosondes; and surface shortwave spectral albedo from radiometers. A cloudy-sky radiative closure experiment was performed. The relative mean differences between modeled and observed surface downwelling shortwave total fluxes were 6% (7%) for transparent (opaque) cloudy-skies. The estimated annual mean all-sky aerosol DRE is -2.13±0.54W m -2 at the top of atmosphere (TOA) and -5.95±0.87 W m -2 at the surface, compared to -3.00±0.58 W m -2 AND -6.85±1.00 W m -2 , respectively, under clear-sky conditions. The seasonal cycle of all-sky aerosol DRE is similar to that of the clear-sky, except with secondary influences of the clouds: The cloud radiative effect is strongest (most negative) in the spring, which reduces the all-sky aerosol DRE. The relative uncertainties in all-sky aerosol DRE due to measurement errors are generally comparable to those in clear-sky conditions except for the aerosol single-scattering albedo. Finally, the TOA all-sky aerosol DRE relative uncertainty due to aerosol single-scattering albedo uncertainty is larger than that in clear-sky, leading to a larger total relative uncertainty. The measurement errors in cloud properties have small effects on the all-sky aerosol DRE.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Forecasting for ESCAPE: A Multi-Institution Hybrid Forecasting and Nowcasting Operation for Sea-Breeze Convection Supporting a Ground-Based and Airborne Field Campaign

The Experiment of Sea-Breeze Convection, Aerosols, Precipitation and Environment (ESCAPE) field project deployed two aircraft and ground-based assets in the vicinity of Houston, Texas, between 27 May and 2 July 2022, examining how meteorological conditions, dynamics, and aerosols control the initiation, early growth stage, and evolution of coastal convective clouds. To ensure that airborne- and ground-based assets were deployed appropriately, a forecasting and nowcasting team was formed. Daily forecasts guided real-time decision-making by assessing synoptic weather conditions, environmental aerosol, and a variety of atmospheric modeling data to assign a probability for meeting specific ESCAPE campaign objectives. During the research flights, a small team of forecasters provided “nowcasting” support by analyzing radar, satellite, and new model data in real time. The nowcasting team proved invaluable to the campaign operation, as sometimes changing environmental conditions affected, for example, the timing of convective initiation. In addition to the success of the forecasting and nowcasting teams, the ESCAPE campaign offered a unique “testbed” opportunity where in-person and virtual support both contributed to campaign objectives. The forecasting and nowcasting teams were each composed of new and experienced forecasters alike, where new forecasters were given invaluable experience that would otherwise be difficult to attain. Both teams received training on forecast models, map analysis, Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), and thermodynamic sounding analysis before the beginning of the campaign. In this article, the ESCAPE forecasting and nowcasting teams reflect on these experiences, providing potentially useful advice for future field campaigns requiring forecasting and nowcasting support in a hybrid virtual/in-person framework.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Remote Sensing of Electric Fields Observed Within Winter Precipitation During the 2020 Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) Field Campaign

Aircraft electric fields from NASA's Lightning Instrument Package (LIP) were coupled with other airborne and ground-based, and in situ measurements to understand electrification in winter clouds that did not produce lightning. The measurements were made during seven research flights by a NASA ER-2 during the 2020 Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Observed total electric field magnitudes were as high as 80 V m -1 and variability in the electric field was observed along the flight path of the ER-2, indicating horizontal and/or vertical inhomogeneity in the cloud's electrical structure. X-band airborne radar data indicated 20-dBZ echo tops above 5 km in regions where electrification exceeded 10 V m -1 . In these regions, 85-GHz brightness temperatures (TB) from an airborne radiometer were lower than 265 K, with the lowest T B (~210 K) associated with ice scattering collocated with the strongest electric fields. In situ microphysical measurements from the NASA P-3 aircraft on February 7 indicated that regions near strong electric field contained supercooled water, rimed ice hydrometeors, ice water p- content as high as 1 g m -3 , liquid water content as high as 0.15 g m -3 , and supersaturation as high as 3.5%. Furthermore, these observations support the role of mixed phase microphysics in the generation of electric fields in clouds. In three case studies, ground based S-band polarimetric radar observed depolarization streaks in differential reflectivity near areas where the strongest electrification was observed. This observation reinforces the utility of depolarization streaks to identify areas of electrification prior to lightning occurrence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Arctic observations and numerical simulations of surface wind effects on Multi-Angle Snowflake Camera measurements

Abstract. Ground-based measurements of frozen precipitation are heavily influenced by interactions of surface winds with gauge-shield geometry. The Multi-Angle Snowflake Camera (MASC), which photographs hydrometeors in free-fall from three different angles while simultaneously measuring their fall speed, has been used in the field at multiple midlatitude and polar locations both with and without wind shielding. Here, we present an analysis of Arctic field observations – with and without a Belfort double Alter shield – and compare the results to computational fluid dynamics (CFD) simulations of the airflow and corresponding particle trajectories around the unshielded MASC. MASC-measured fall speeds compare well with Ka-band Atmospheric Radiation Measurement (ARM) Zenith Radar (KAZR) mean Doppler velocities only when winds are light (≤5ms-1) and the MASC is shielded. MASC-measured fall speeds that do not match KAZR-measured velocities tend to fall below a threshold value that increases approximately linearly with wind speed but is generally <0.5ms-1. For those events with wind speeds ≤1.5ms-1, hydrometeors fall with an orientation angle mode of 12∘ from the horizontal plane, and large, low-density aggregates are as much as 5 times more likely to be observed. Simulations in the absence of a wind shield show a separation of flow at the upstream side of the instrument, with an upward velocity component just above the aperture, which decreases the mean particle fall speed by 55 % (74 %) for a wind speed of 5 m s−1 (10 m s−1). We conclude that accurate MASC observations of the microphysical, orientation, and fall speed characteristics of snow particles require shielding by a double wind fence and restriction of analysis to events where winds are light (≤5ms-1). Hydrometeors do not generally fall in still air, so adjustments to these properties' distributions within natural turbulence remain to be determined.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Raman Lidar (RL) Instrument Handbook

The Raman lidar at the ARM Climate Research Facility (ACRF) Southern Great Plains (SGP) Central Facility (SGPRL) is an active, ground-based laser remote sensing instrument that measures height and time resolved profiles of water vapor mixing ratio and several cloud- and aerosol-related quantities. The system is a non-commercial custom-built instrument developed by Sandia National Laboratories specifically for the ARM Program. It is fully computer automated, and will run unattended for many days following a brief (~5-minute) startup period. The self-contained system (requiring only external electrical power) is housed in a climate-controlled 8’x8’x20’ standard shipping container.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A Novel Segmentation Algorithm for the ARM User Facility All-Sky Imagers Using Machine Learning Applications

Cloud cover plays a pivotal role in modulating the Earth's energy budget through the reflection of incoming solar radiation and the trapping of outgoing longwave radiation. Ground-based all-sky imagers offer an objective assessment of cloud cover that can be used to estimate solar irradiance, classify cloud types, track cloud movement, and serve as a benchmark 10 for the evaluation of satellite and reanalysis data products. The Atmospheric Radiation Measurement (ARM) user facility has utilized all-sky imagers for more than 25 years to monitor cloud cover and augment its comprehensive suite of atmospheric measurements. Following the retirement of its Total Sky Imager (TSI), ARM recently deployed the TSI’s successor, the All Sky Imager (ASI-16 camera systems). To provide a smooth transition and continuity to the vast amount of knowledge gathered by the TSI over the years, while addressing typical deployment issues, we developed a novel pixel segmentation algorithm, 15 the ASI Sky Cover (ASISKYCOVER). ASISKYCOVER builds on the different strengths and properties of the TSI processing algorithm while integrating machine learning techniques, ensuring data validity and accuracy across diverse atmospheric conditions. It enhances cloud cover characterization with new features such as artifact detection and uncertainty quantification. ASISKYCOVER also includes cloud cover estimates for near-zenith (narrow field-of-view) and reduces susceptibility to false detections. This study introduces ASISKYCOVER, details its algorithm framework, and demonstrates its capabilities using a 20 year-long dataset from the ARM Southern Great Plains site. Comparisons with co-located TSI data and other ARM measurements, such as zenith-pointing radars and lidars, are presented, underscoring the ASISKYCOVER’s potential to improve cloud cover analyses and data evaluation efforts, as well as to be integrated into higher-level data products that synergize instrument suites to generate new and insightful information

Silber, Israel↗
DOE OSTI2023

Arctic Cloud‐Base Ice Precipitation Properties Retrieved Using Bayesian Inference

Abstract Cloud‐climate feedbacks are still the greatest source of uncertainty in current climate projections. Arctic clouds, which are predominantly stratiform and supercooled, often long‐lived, and nearly continuously precipitate ice particles, contribute roughly 10% of the uncertainty attributed to the global cloud feedback. This Arctic cloud uncertainty is driven by incomplete observational and theoretical knowledge required to estimate and explain the state and active processes occurring in those clouds. A focus on ice precipitation properties at Arctic cloud base rather than the surface deconfounds the product of cloud condensate sink processes from the influence of the atmospheric thermodynamic state below cloud base, rendering cloud‐base properties a more appealing target for inference and evaluation of model simulations. Here I describe an inverse model for the estimation of cloud base ice precipitation properties over Utqiagvik, North Slope of Alaska, using the synthesis of ground‐based radar and lidar measurements. By leveraging a Markov Chain Monte Carlo algorithm as the core of the inverse model, a wide range of particle size distributions are sampled, and different combinations of ice habit models are examined, both of which are typically fixed in other retrieval methods. Results show intriguing links between different cloud base thermodynamic and ice precipitation properties. Apparent ice number concentration enhancements at temperatures of −5 and −15°C suggest possible secondary ice production (SIP). The analysis alludes to an overestimation of SIP occurrence and intensity, especially in studies relying only on radar or lidar measurements. Finally, reflectivity‐dependent ice precipitation rate and ice water content parameterizations are presented.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

CROCUS Sodar Measurements of Lower Atmospheric Wind Profiles at Argonne Testbed for Multiscale Observational Science (ATMOS) Site

The Scintec MFAS Sodar (Multiple-Frequency Acoustic Sounder) is an autonomous, ground-based acoustic remote sensing system designed to measure vertical profiles of horizontal wind speed, wind direction, and vertical velocity in the lower atmosphere. The instrument transmits sequences of acoustic pulses and detects the Doppler-shifted sound waves backscattered by small-scale temperature and velocity fluctuations caused by atmospheric turbulence. From these Doppler shifts, the system derives three-dimensional wind vectors by combining radial velocities from multiple beam orientations.The MFAS Sodar operates with a first usable range gate beginning at approximately 30 m above ground level and a configurable vertical resolution of 10 m. Under favorable acoustic conditions, the system provides wind profiles extending up to 600 m above ground level. Measurements are processed into 15-minute averaged profiles containing wind speed, direction, vertical velocity, and diagnostic quantities such as signal-to-noise ratio and echo strength.This dataset was collected at the Argonne Testbed for Multiscale Observational Science (ATMOS) facility in Lemont, Illinois, as part of DOE's CROCUS Urban Integrated Field Laboratory (UIFL) initiative. The purpose of these observations is to characterize the vertical wind structure and boundary-layer evolution across the urban–suburban gradient of the greater Chicago region. In particular, these data are intended to improve understanding of how local meteorology, such as lake-breeze penetration, nocturnal jets, and daytime mixing, varies between the densely built urban core and the suburban periphery. The MFAS observations provide critical context for evaluating high-resolution model simulations and for integrating with complementary lidar, radar, and in-situ meteorological measurements within the broader CROCUS UIFL network.All data are archived in NetCDF (Network Common Data Form) format and include wind and diagnostic parameters. The files can be accessed and analyzed using standard software that supports NetCDF, such as Python (e.g., xarray, netCDF4), MATLAB, R (e.g., ncdf4, raster), or Panoply (NASA’s NetCDF visualization application).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Magnetic Navigation for GPS-Denied Airborne Applications

Most current flight systems are dependent on GPS for navigation. Recently, however, navigation in GPS-denied environments has become an area of intensive research. Additional navigation sensor data can be obtained from visual observations (stars or terrain), inertial measurement units, radar, measurements of the local magnetic field, or perhaps even gravity. Absolute and relative positioning via magnetic field measurements have been shown to be viable in many applications including ground navigation, low altitude aircraft flight, and spaceflight. There is greater variability in the magnetic field over shorter distances when flying at low altitude and in ground applications, leading to more accurate positioning. However, ground-based magnetic navigation is often heavily influenced by man-made structures, especially in urban environments. This is not the case for airborne magnetic navigation since the influence of buildings, roads, etc. is negligible for typical aircraft altitudes. For absolute magnetic navigation, the positioning accuracy decreases as altitude increases for a given vehicle velocity, but the observed time variability in the field can be reclaimed by traveling faster through the field. Thus, navigation accuracy becomes a balance of speed and altitude since the higher altitude can be counterbalanced by higher velocity. To understand these effects quantitatively, we explored various techniques to aid a simulated inertial measurement unit with magnetic information. Using a technique known as two-dimensional magnetic map matching, we simulated the performance of airborne magnetic navigation at fixed speed while varying the altitude, flight direction, magnetometer data collection time, reference magnetic map bias error, and type of trajectory (over land or over ocean).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗