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NASA NTRSDate not supplied

Generation of Merged Radar-Lidar Data Products during the IMPACTS 2020 Field Campaign

A novel, composite lidar and radar data products was created using lidar and radar data products generated from the NASA ER-2 aircraft during the NASA IMPACTS 2020 field campaign. This product is intended to supplement the raw data sources, tease out additional aspects of the mechanisms underpinning wintertime cyclones, and enhance our understanding of microphysical properties in sensor overlap regions. Initial combined products focused on generating normalized fields of ER-2 based radar and lidar data to provide a more comprehensive visualization of storm structure (precipitation bands, cloud tops, melting levels, etc.) Ongoing work has focused on developing a combined radar-lidar reflectivity product and deriving hydrometeor particle properties (type, orientation, etc.) using ER-2-based depolarization data. To validate these hydrometeor properties, we leverage coincident overpasses of the high-altitude ER-2 aircraft with the suite of cloud particle probes on the in-situ P-3 aircraft during coincident flight overpasses. This work aims to provide vital information to help IMPACTS achieve its mission goal of improving microphysical properties retrievals from airborne and spaceborne platforms for these high-impact winter storm events.

Stephen D Nicholls↗
NASA NTRSDate not supplied

Observations of Small Debris from the Cosmos 1408 Anti-Satellite Test using the HUSIR and Goldstone Radars

On 15 November 2021, Russia conducted a direct-ascent, anti-satellite (ASAT) test against its Cosmos 1408 satellite, which had been in orbit since 1982. The test produced at least 1500 fragments trackable by the U.S. Space Surveillance Network (SSN). This is a significant event because the resulting cloud has the potential to endanger the International Space Station and other satellites in low Earth orbit (LEO).For almost 30 years, the NASA Orbital Debris Program Office (ODPO) has been using the Haystack Ultrawideband Imaging Radar (HUSIR), located in Tyngsborough, Massachusetts and operated by the Massachusetts Institute of Technology’s Lincoln Laboratory, to perform statistical measurements of debris in LEO too small to be tracked by the SSN, nominally down to 5.5 mm at 1000 km altitude. The ODPO also utilizes the Goldstone Orbital Debris Radar (Goldstone), located near Barstow, California and operated by the NASA Jet Propulsion Laboratory, to characterize the small debris environment in LEO down to approximately 3 mm at 1000 km altitude. To characterize the small debris component of the Cosmos 1408 ASAT test, a series of observation campaigns were conducted with the HUSIR and Goldstone radars. This paper discusses the observation planning, including beam overlap analysis required for the Goldstone’s bistatic operation, using a model of the debris cloud produced using the NASA Standard Satellite Breakup Model. A description of the radars and the data processing techniques used to analyze the data are also discussed. Finally, results of the measurement campaigns including cumulative flux versus size, altitude, and inclination are presented.

James Murray↗
NASA NTRS2022

DataSet: Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated

Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this e that it is feasible to classify sensor collected trajectories using a classifier trained flight controller data.

Chester V Dolph↗
NASA NTRSDate not supplied

Observations of Small Debris from the Cosmos 1408 Anti-Satellite Test using the HUSIR and Goldstone Radars

On 15 November 2021, Russia conducted a direct-ascent anti-satellite (ASAT) test against its Cosmos 1408 satellite, which had been in orbit since 1982. The test produced at least 1500 fragments trackable by the U.S. Space Surveillance Network (SSN). The test is a significant event because the resulting cloud has the potential to endanger the International Space Station and other satellites in low Earth orbit (LEO). For almost 30 years, the NASA Orbital Debris Program Office (ODPO) has used the Haystack Ultrawideband Satellite Imaging Radar (HUSIR), operated by the Massachusetts Institute of Technology’s Lincoln Laboratory to perform statistical measurements of debris in LEO too small to be tracked by the SSN, nominally down to 5.5 mm at 1000 km altitude. The ODPO also utilizes the Goldstone Orbital Debris Radar (Goldstone), operated by NASA’s Jet Propulsion Laboratory, to characterize the small debris environment in LEO down to approximately 3 mm at 1000 km altitude. To characterize the small debris component of the Cosmos 1408 ASAT test, a series of observation campaigns were conducted with the HUSIR and Goldstone radars. This paper discusses the observation planning, including beam overlap analysis needed for Goldstone bistatic operation, using a model of the debris cloud produced with the NASA Standard Satellite Breakup Model. A description of the radars and the data processing techniques used to analyze the data are also discussed. Finally, results of the measurement campaigns including cumulative count rate versus size and surface area flux versus altitude and inclination are presented

James Murray↗
NASA NTRSDate not supplied

An Overview of Ground-Based Radar and Optical Measurements Utilized By the Nasa Orbital Debris Program Office

For over 30 years, the NASA Orbital Debris Program Office (ODPO) has led the characterization of orbital debris (OD) too small to be tracked by the U.S. Space Surveillance Network (SSN), yet which may pose the greatest threat to human spaceflight and robotic missions. Measurements from specialized sensors, including ground-based radars and telescopes capable of detecting smaller objects, provide the foundation for developing statistical models to describe the current state and future evolution of the OD environment from low Earth orbit (LEO) to geosynchronous Earth orbit (GEO). Since 1990, the ODPO has partnered with the U.S. Department of Defense and the Massachusetts Institute of Technology Lincoln Laboratory (MIT/LL) to collect data using the Haystack Ultrawideband Satellite Imaging Radar (HUSIR) – formerly Haystack – to characterize OD in LEO with a sensitivity of approximately 5 mm at 1000 km altitude. In addition, since 1993, the Goldstone Orbital Debris Radar, operated by NASA’s Jet Propulsion Laboratory, has provided data on OD as small as approximately 2-3 mm for altitudes below 1000 km, some of the most sensitive ground-based measurements achievable at these altitudes. Recently, collaborations with the 18 th Space Control Squadron of the U.S. Space Force have also provided the ODPO with special datasets from the Space Fence to extend coverage below the historical SSN limit of 10 cm and to characterize individual breakup events in LEO. For GEO altitudes, the Eugene Stansbery Meter Class Autonomous Telescope (ES-MCAT), a joint NASA-Air Force Research Laboratory project that reached full operational capability in 2021, collects data on debris smaller than 1 m and provides coverage of debris in historically under-sampled high-altitude orbital regimes. This paper summarizes the radar and optical sensors utilized by the ODPO, their unique capabilities, and recent datasets and applications for statistical sampling of the dynamic OD environment.

Alyssa Manis↗
NASA NTRSDate not supplied

Enhancing Radiative Transfer Models for Optimized Assimilation of Microwave and Radar Observations

Radiative transfer models are extensively used for the assimilation of satellite observations into NWP models as well as retrieving geophysical products from satellite measurements. The Community Radiative Transfer Model (CRTM) is a community model developed by NOAA JCSDA and widely used for different purposes requiring RT calculations. CRTM requires bulk optical properties of hydrometeors in the form of lookup tables in order to perform all-sky RT calculations. However, the current cloud scattering lookup tables in CRTM assume spherical shapes for all frozen hydrometeors, whereas actual clouds contain frozen particles with diverse shapes. The first part of this talk presents the implementation and validation of a comprehensive Discrete Dipole Approximation (DDA) cloud scattering database into CRTM, specifically targeting microwave frequencies. The DDA technique proves effective in simulating the optical properties of non-spherical hydrometeors in the microwave region. The original DDA database assumes total random orientation in calculating single scattering properties. To generate the required mass scattering parameters for CRTM, the single scattering properties and water content dependent particle size distributions are used. The evaluation of results involved a collocated dataset comprising short-term forecasts from the Integrated Forecast System of the European Center for Medium-Range Weather Forecasts and satellite microwave data. The findings demonstrate that the DDA lookup tables significantly reduce discrepancies between simulated and observed values when compared to the Mie tables. Passive instruments lack the ability to provide vertically resolved measurements of clouds and precipitation, which can be obtained by active radar instruments. However, incorporating these active measurements into data assimilation systems presents challenges due to the absence of fast forward radiative transfer models and difficulties in error modeling. The second part of the talk focuses on the development, evaluation, and sensitivity analysis of a forward radar model integrated into CRTM. The forward radar model utilizes scattering properties obtained from hydrometeor lookup tables generated using the discrete dipole approximation. By utilizing CRTM instrument-specific coefficients, the model can calculate both reflectivity and attenuated reflectivity for any given radar instrument and zenith angles. Evaluation using CloudSat measurements demonstrates a strong agreement between simulations and observations when the input profiles of hydrometeors align with the measured reflectivity profiles.

Isaac Moradi↗
NASA NTRSDate not supplied

An Overview of Ground-Based Radar and Optical Measurements Utilized By the Nasa Orbital Debris Program Office

For over 30 years, the NASA Orbital Debris Program Office (ODPO) has led the characterization of orbital debris (OD) too small to be tracked by the U.S. Space Surveillance Network (SSN), yet which may pose the greatest threat to human spaceflight and robotic missions. Measurements from specialized sensors, including ground-based radars and telescopes capable of detecting smaller objects, provide the foundation for developing statistical models to describe the current state and future evolution of the OD environment from low Earth orbit (LEO) to geosynchronous Earth orbit (GEO). Since 1990, the ODPO has partnered with the U.S. Department of Defense and the Massachusetts Institute of Technology Lincoln Laboratory (MIT/LL) to collect data using the Haystack Ultrawideband Satellite Imaging Radar (HUSIR) – formerly Haystack – to characterize OD in LEO with a sensitivity of approximately 5 mm at 1000 km altitude. In addition, since 1993, the Goldstone Orbital Debris Radar, operated by NASA’s Jet Propulsion Laboratory, has provided data on OD as small as approximately 2-3 mm for altitudes below 1000 km, some of the most sensitive ground-based measurements achievable at these altitudes. Recently, collaborations with the 18 th Space Control Squadron of the U.S. Space Force have also provided the ODPO with special datasets from the Space Fence to extend coverage below the historical SSN limit of 10 cm and to characterize individual breakup events in LEO. For GEO altitudes, the Eugene Stansbery Meter Class Autonomous Telescope (ES-MCAT), a joint NASA-Air Force Research Laboratory project that reached full operational capability in 2021, collects data on debris smaller than 1 m and provides coverage of debris in historically under-sampled high-altitude orbital regimes. This paper summarizes the radar and optical sensors utilized by the ODPO, their unique capabilities, and recent datasets and applications for statistical sampling of the dynamic OD environment.

Alyssa Manis↗
NASA NTRSDate not supplied

Optimizing Assimilation of Microwave and Radar Observations in the NWP Models

Radiative transfer models are extensively used for the assimilation of satellite observations into NWP models as well as retrieving geophysical products from satellite measurements. The Community Radiative Transfer Model (CRTM) is a community model developed by NOAA JCSDA and widely used for different purposes requiring RT calculations. CRTM requires bulk optical properties of hydrometeors in the form of lookup tables in order to perform all-sky RT calculations. However, the current cloud scattering lookup tables in CRTM assume spherical shapes for all frozen hydrometeors, whereas actual clouds contain frozen particles with diverse shapes. The first part of this talk presents the implementation and validation of a comprehensive Discrete Dipole Approximation (DDA) cloud scattering database into CRTM, specifically targeting microwave frequencies. The DDA technique proves effective in simulating the optical properties of non-spherical hydrometeors in the microwave region. The original DDA database assumes total random orientation in calculating single scattering properties. To generate the required mass scattering parameters for CRTM, the single scattering properties and water content dependent particle size distributions are used. The evaluation of results involved a collocated dataset comprising short-term forecasts from the Integrated Forecast System of the European Center for Medium-Range Weather Forecasts and satellite microwave data. The findings demonstrate that the DDA lookup tables significantly reduce discrepancies between simulated and observed values when compared to the Mie tables. Passive instruments lack the ability to provide vertically resolved measurements of clouds and precipitation, which can be obtained by active radar instruments. However, incorporating these active measurements into data assimilation systems presents challenges due to the absence of fast forward radiative transfer models and difficulties in error modeling. The second part of the talk focuses on the development, evaluation, and sensitivity analysis of a forward radar model integrated into CRTM. The forward radar model utilizes scattering properties obtained from hydrometeor lookup tables generated using the discrete dipole approximation. By utilizing CRTM instrument-specific coefficients, the model can calculate both reflectivity and attenuated reflectivity for any given radar instrument and zenith angles. Evaluation using CloudSat measurements demonstrates a strong agreement between simulations and observations when the input profiles of hydrometeors align with the measured reflectivity profiles.

Isaac Moradi↗
NASA NTRSDate not supplied

Exploring Environmental and Aerosol Impacts on Maritime Tropical Convection using Airborne Radiometer, Radar, Lidar, and Dropsondes

The field deployment phase of NASA’s Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) took place around the Philippines during August–October 2019, with the primary goal of jointly investigating aerosols and tropical meteorology at the micro-β to meso-β scales. A suite of instruments was deployed on NASA’s P-3B Orion aircraft to accomplish this mission, including: the Advanced Microwave Precipitation Radiometer (AMPR), Airborne Precipitation and cloud Radar 3rd Generation (APR-3), High Spectral Resolution Lidar 2 (HSRL2), and Advanced Vertical Atmospheric Profiling System (AVAPS) dropsondes. To examine potential aerosol influences on maritime tropical convection, P-3 flight segments throughout CAMP2Ex were binned into similar environmental groups using “low,” “medium,” and “high” values of nine AVAPS-derived parameters with known physical connections to convective frequency and/or intensity. Aerosol concentrations in each flight segment were evaluated using three HSRL2 variables at 355 and 532 nm: aerosol backscatter, aerosol extinction, and aerosol optical thickness. A set of radiometer- and radar-derived variables directly related to convective frequency and/or intensity was used to characterize convection, which included: AMPR-derived integrated cloud liquid water; polarization-corrected temperatures at 10.7, 19.35, 37.1, and 85.5 GHz; peak equivalent radar reflectivity factor (ZH); peak height of 30-dBZ ZH; and the number of radar data columns with composite ZH > 30 dBZ. The ZH analyses were performed using both Ku- and Ka-band APR-3 data. For each flight segment, correlation coefficients were calculated between each convective parameter and aerosol concentrations within the “low,” “medium,” and “high” groups for each environmental variable. The environmental stratification thresholds were then varied in a series of sensitivity tests. Several noteworthy correlations were observed between the convective and aerosol parameters within the environmental groups, especially when stratifying the environments based on their 850–500-hPa temperature lapse rate, 700–500-hPa temperature lapse rate, and K-Index. The convective parameters were often correlated most strongly with 355-nm extinction, 532-nm extinction, and 532-nm backscatter, while the presence of precipitation-sized liquid and ice hydrometeors contributed to some unexpected negative correlations. In general, as environmental conditions became more favorable for convection, a stronger correlation was observed between the convective parameters and aerosol concentrations. However, a “Goldilocks” zone of medium aerosol concentration was correlated most strongly with the convective parameters in many cases. These results stress the importance of considering environmental and aerosol conditions together when evaluating their impacts on convection. This presentation will provide a detailed discussion of these results and their implications, a summary of the limitations associated with such an analysis, and suggestions for future work.

Corey G. Amiot↗
NASA NTRSDate not supplied

Assimilation of Spaceborne Microwave and Radar Observations

Radiative transfer models are extensively used for the assimilation of satellite observations into NWP models as well as retrieving geophysical products from satellite measurements. The Community Radiative Transfer Model (CRTM) is a community model developed by NOAA JCSDA and widely used for different purposes requiring RT calculations. CRTM requires bulk optical properties of hydrometeors in the form of lookup tables in order to perform all-sky RT calculations. However, the current cloud scattering lookup tables in CRTM assume spherical shapes for all frozen hydrometeors, whereas actual clouds contain frozen particles with diverse shapes. The first part of this talk presents the implementation and validation of a comprehensive Discrete Dipole Approximation (DDA) cloud scattering database into CRTM, specifically targeting microwave frequencies. The DDA technique proves effective in simulating the optical properties of non-spherical hydrometeors in the microwave region. The original DDA database assumes total random orientation in calculating single scattering properties. To generate the required mass scattering parameters for CRTM, the single scattering properties and water content dependent particle size distributions are used. The evaluation of results involved a collocated dataset comprising short-term forecasts from the Integrated Forecast System of the European Center for Medium-Range Weather Forecasts and satellite microwave data. The findings demonstrate that the DDA lookup tables significantly reduce discrepancies between simulated and observed values when compared to the Mie tables. Passive instruments lack the ability to provide vertically resolved measurements of clouds and precipitation, which can be obtained by active radar instruments. However, incorporating these active measurements into data assimilation systems presents challenges due to the absence of fast forward radiative transfer models and difficulties in error modeling. The second part of the talk focuses on the development, evaluation, and sensitivity analysis of a forward radar model integrated into CRTM. The forward radar model utilizes scattering properties obtained from hydrometeor lookup tables generated using the discrete dipole approximation. By utilizing CRTM instrument-specific coefficients, the model can calculate both reflectivity and attenuated reflectivity for any given radar instrument and zenith angles. Evaluation using CloudSat measurements demonstrates a strong agreement between simulations and observations when the input profiles of hydrometeors align with the measured reflectivity profiles.

Isaac Moradi↗
NASA NTRS2024

A Review of Twenty-Six Years of U.S. Weather Radar Detection of Meteorite Falls

Weather radars have proven to be a valuable tool for detection, rapid recovery, and analysis of meteorite falls and fall statistics. The U.S. operates a nationwide weather radar network (NEXRAD) which will be discussed here, but it is important to acknowledge that weather radars operate worldwide and efforts are underway to utilize this rich resource. By the author’s count, since the NEXRAD system came online in the late 1990s it has detected 34 recovered meteorite falls and another 33 unrecovered probable falls. Weather radars are powerful tools because they directly detect meteorites falling after the end of their luminous flight, during the "dark flight" period. This allows rapid recovery through detection and modeling of fall locations down to as little as tens of meters. They are also the only tool that analyzes meteorites that survive their parent fireball, before many or all of them are lost to terrain. This allows direct measurement of meteorite mass, particle size distribution, and with additional work should allow calculation of pre-atmospheric mass and a measure of whole-body strength.

M D Fries↗
NASA NTRSDate not supplied

Assimilation of Active MW and Radar Observations in the NWP Models

Passive instruments lack the ability to provide vertically resolved measurements of clouds and precipitation, which can be obtained by active radar instruments. However, incorporating these active measurements into data assimilation systems presents challenges due to the absence of fast forward radiative transfer models and difficulties in error modeling. This talk focuses on the development, evaluation, and sensitivity analysis of a forward radar model integrated into CRTM. The forward radar model utilizes scattering properties obtained from hydrometeor lookup tables generated using the discrete dipole approximation. By utilizing CRTM instrument-specific coefficients, the model can calculate both reflectivity and attenuated reflectivity for any given radar instrument and zenith angles. Evaluation using CloudSat measurements demonstrates a strong agreement between simulations and observations when the input profiles of hydrometeors align with the measured reflectivity profiles.

Isaac Moradi↗
NASA NTRS2023

High-Resolution Snowstorm Measurements and Retrievals Using Cross-Platform Multi-Frequency and Polarimetric Radars

Many studies have provided microphysical retrievals using radars of different frequencies, platforms, and methodologies. However, little is known about the consistency of retrievals derived from different radar platforms (i.e., airborne or spaceborne vs. groundbased) and their methodologies. This study is the first to directly compare snow mean volume diameter (Dm) retrievals from both nadir-pointing airborne multi-frequency radars and ground-based polarimetric range-height indicator (RHI) radar scans along a coincident airborne flight track. Compared to typical airborne/spaceborne/ground validation studies which at best provide O(1 km) resolution, these two platforms together provide directly collocated, high-resolution O(10m) retrievals. Dm retrieval values were over all both largest and smallest when using two separate dual-wavelength methods from aircraft measurements. A Triple-frequency analysis suggests the possibility that snow aggregates were generally composed of needles. The results shown here can be used as a benchmark for comparing retrieval methodologies.

Edwin L. Dunnavan↗
NASA NTRS2024

Synergistic Retrievals of Ice in High Clouds From Elastic Backscatter Lidar, Ku-band Radar and Submillimeter Wave Radiometer Observations

In this study, we investigate the synergy of elastic backscatter lidar, Ku-band radar, and sub-millimeter-wave radiometer measurements in the retrieval of ice from satellite observations. The synergy is analyzed through the generation of a large dataset of IceWater Content (IWC) profiles and simulated lidar, radar and radiometer observations. The characteristics of the instruments e.g. frequencies, sensitivities, etc. are set based on the expected characteristics of instruments of the Atmosphere Observing System (AOS) mission. A hold-out validation methodology is used to assess the accuracy of the IWC profiles retrieved from various combinations of observations from the three instruments. Specifically, the IWC and associated observations are randomly divided into two datasets, one for training and the other for evaluation. The training dataset is used to train the retrieval algorithm, while the evaluation dataset is used to assess the retrieval performance. The dataset of IWC profiles is derived from CloudSat reflectivity and CALIOP lidar observations. The retrieval of the ice water content IWC profiles from the computed observations is achieved in two steps. In the first step, a class, out of 18 potential classes characterized by different vertical distribution of IWC, is estimated from the observations. The 18 classes are predetermined based on the k-Means clustering algorithm. In the second step, the IWC profile is estimated using an Ensemble Kalman Smoother (EKS) algorithm that uses the estimated class as a priori information. The results of the study show that the synergy of lidar, radar, and radiometer observations is significant in the retrieval of the IWC profiles. Nevertheless, it should be mentioned that this synergy was found under idealized conditions, and additional work might be required to materialize it in practice. The inclusion of the lidar backscatter observations in the retrieval process has a larger impact on the retrieval performance than the inclusion of the radar observations. As ice clouds have a significant impact on atmospheric radiative processes, this work is relevant to ongoing efforts to reduce uncertainties in climate analyses and projections.

Mircea Grecu↗
NASA NTRSDate not supplied

Sensing Small Uncrewed Aerial Vehicles with Distributed Radars for Advanced Air Mobility Surveillance

In the context of Advanced and Urban Air Mobility major attention is being reserved to the development of sensing strategies for small Uncrewed Aerial Vehicles (sUAVs) to enable their safe operations in and around urban areas. Such strategies should rely on non-cooperative distributed sensors to strengthen the surveillance solution towards the unreliability of Global Navigation Satellite System (GNSS) positioning information, which is typical for low-altitude-flying platforms in urban regions, and increase the monitored airspace volume. To this aim, this paper proposes a fusion solution for a network of distributed ground-based radars, which can be exploited to not only increase the coverage over large airspace volumes but also improve the overall detectability and traceability of sUAVs by leveraging on multiple views over the same area. The solution exploits a centralized fusion scheme in which measurements collected by each radar are shared with a Fusion Center where Kalman Filtering is exploited to build a unique, fused track. Tests conducted on experimental data collected using two sUAVs as flying targets and three distributed radars showed that the proposed solution can produce an increase in coverage from about 20 % (single radar configuration) to about 80 % of the targets’ flight path, as well as a finer accuracy yielding meter and meter-per-second root mean square error values on position and velocity components.

Federica Vitiello↗
NASA NTRS1984

The radar cross section of dielectric disks

A solution is presented for the backscatter (monostatic) radar cross section of dielectric disks of arbitrary shape, thickness, and dielectric constant. The result is obtained by employing a Kirchhoff-type approximation to obtain the fields inside the disk. The internal fields induce polarization and conduction currents from which the scattered fields and the radar cross section can be computed. The solution for the radar cross section obtained in this manner will be shown to agree with known results in the special cases of normal incidence, thin disks, and perfect conductivity. It will also be shown that the solution can be written as a product of the reflection coefficient of an identically oriented slab times the physical optics solution for the backscatter cross section of a perfectly conducting disk of the same shape. This result follows directly from the Kirchhoff-type approximation without additional assumptions.

Radar cross section↗