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NASA NTRS2009

Next-Generation Spaceborne Cloud Profiling Radars

One of the instruments recommended for deployment on the Aerosol/Cloud/Echosystems (ACE) mission is a new advanced Cloud Profiling Radar (ACE-CPR). The atmospheric sciences community has initiated the effort to define the scientific requirements for this instrument. Initial studies focusing on system configuration, performance and feasibility start from the successful experience of the Cloud Profiling Radar on CloudSat Mission (CS-CPR), the first 94-GHz nadir-looking spaceborne radar which has been acquiring global time series of vertical cloud structure since June 2, 2006. In this paper we address the significance of CloudSat's accomplishments in regards to the design and development of radars for future cloud profiling missions such as EarthCARE and ACE.

CloudSat↗

NASA NTRS2014

Evidence for Crater Ejecta on Venus Tessera Terrain from Earth-Based Radar Images

We combine Earth-based radar maps of Venus from the 1988 and 2012 inferior conjunctions, which had similar viewing geometries. Processing of both datasets with better image focusing and co-registration techniques, and summing over multiple looks, yields maps with 1-2 km spatial resolution and improved signal to noise ratio, especially in the weaker same-sense circular (SC) polarization. The SC maps are unique to Earth-based observations, and offer a different view of surface properties from orbital mapping using same-sense linear (HH or VV) polarization. Highland or tessera terrains on Venus, which may retain a record of crustal differentiation and processes occurring prior to the loss of water, are of great interest for future spacecraft landings. The Earth-based radar images reveal multiple examples of tessera mantling by impact ''parabolas'' or ''haloes'', and can extend mapping of locally thick material from Magellan data by revealing thinner deposits over much larger areas. Of particular interest is an ejecta deposit from Stuart crater that we infer to mantle much of eastern Alpha Regio. Some radar-dark tessera occurrences may indicate sediments that are trapped for longer periods than in the plains. We suggest that such radar information is important for interpretation of orbital infrared data and selection of future tessera landing sites.

surface↗

NASA NTRS2015

Planetary Radar

This chapter describes the principles of planetary radar, and the primary scientific discoveries that have been made using this technique. The chapter starts by describing the different types of radar systems and how they are used to acquire images and accurate topography of planetary surfaces and probe their subsurface structure. It then explains how these products can be used to understand the properties of the target being investigated. Several examples of discoveries made with planetary radar are then summarized, covering solar system objects from Mercury to Saturn. Finally, opportunities for future discoveries in planetary radar are outlined and discussed.

Radar↗

NASA NTRS2016

Next Generation P-Band Planetary Synthetic Aperture Radar

The Space Exploration Synthetic Aperture Radar (SESAR) is an advanced P-band beamforming radar instrument concept to enable a new class of observations suitable to meet Decadal Survey science goals for planetary exploration. The radar operates at full polarimetry and fine (meter scale) resolution, and achieves beam agility through programmable waveform generation and digital beamforming. The radar architecture employs a novel low power, lightweight design approach to meet stringent planetary instrument requirements. This instrument concept has the potential to provide unprecedented surface and near- subsurface measurements applicable to multiple DecadalSurvey Science Goals.

Wide band↗

NASA NTRS2017

Next Generation P-Band Planetary Synthetic Aperture Radar

P-band↗

NASA NTRS2019

Summary of the High Ice Water Content (HIWC) RADAR Flight Campaigns

NASA and the FAA (Federal Aviation Administration) conducted two flight campaigns to quantify onboard weather radar measurements with in-situ measurements of high concentrations of ice crystals found in deep convective storms. The ultimate goal of this research was to improve the understanding and develop onboard weather radar processing to detect regions of high ice water content ahead of an aircraft and enable tactical avoidance of the potentially hazardous conditions. Both High Ice Water Content (HIWC) RADAR campaigns utilized the NASA DC-8 Airborne Science Laboratory which was equipped with a Honeywell RDR-4000 weather radar and icing instruments to characterize the ice crystal clouds. The purpose of this paper is to summarize how these campaigns were conducted and highlight key results.

Airborne Radar↗

NASA NTRS2019

Summary of the High Ice Water Content (HIWC) RADAR Flight Campaigns

NASA and the FAA conducted two flight campaigns to quantify onboard weather radar measurements with in-situ measurements of high concentrations of ice crystals found in deep convective storms. The ultimate goal of this research was to improve the understanding and develop onboard weather radar processing to detect regions of high ice water content ahead of an aircraft and enable tactical avoidance of the potentially hazardous conditions. Both High Ice Water Content (HIWC) RADAR campaigns utilized the NASA DC-8 Airborne Science Laboratory which was equipped with a Honeywell RDR-4000 weather radar and icing instruments to characterize the ice crystals clouds. The purpose of this paper is to summarize how these campaigns were conducted and highlight key results.

Aircraft icing↗

NASA NTRS2020

Summary of the High Ice Water Content (HIWC) RADAR Flight Campaigns

NASA and the FAA conducted two flight campaigns to quantify onboard weather radar measurements with in-situ measurements of high concentrations of ice crystals found in deep convective storms. The ultimate goal of this research was to improve the understanding and develop onboard weather radar processing to detect regions of high ice water content ahead of an aircraft and enable tactical avoidance of the potentially hazardous conditions. Both High Ice Water Content (HIWC) RADAR campaigns utilized the NASA DC-8 Airborne Science Laboratory which was equipped with a Honeywell RDR-4000 weather radar and icing instruments to characterize the ice crystal clouds. The purpose of this paper is to summarize how these campaigns were conducted and highlight key results.

Airborne Radar↗

NASA NTRS1995

Application of an Interferometric Phase Unwrapping Technique to Dealiasing of Weather Radar Velocity Fields

It is pointed out that dealiasing of Doppler weather radar velocity measurements is identical to the problem of phase unwrapping encountered in radar and optical interferometry. A technique developed for phase unwrapping in interferometric synthetic aperture radar is described and successfully applied to weather radar velocity dealiasing.

Atmosphere↗

NASA NTRS1995

(abstract) SIR-C/X-SAR: A Multifaceted Radar

The Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar (SAR-C/X-SAR) was a joint US/German/Italian project, with flights aboard the shuttle Endeavor in April and October 1994. SIR-C/X-SAR is the first spaceborne multifrequency, multipolarization radar. The SIR-C and the X-SAR were designed to operate in conjunction with each other, collecting data over common sites. Scientists around the world are using the data in conjunction with ground measurements to conduct experiments relating to Earth's ecology, geology, hydrology, and oceanography. In addition, the data are the most comprehensive set yet available for engineering characterization of spaceborne SAR capabilities relative to various radar phenomenology and methodology. Preliminary results show changes between missions in vegetation, ice, snow, flooding, and volcano activity.

imaging↗

NASA NTRS2022

The Earth Model Column Collaboratory (EMC2) v1.1: An Open-Source Ground-Based Lidar and Radar Instrument Simulator and Subcolumn Generator for Large-Scale Models

Climate models are essential for our comprehensive understanding of Earth's atmosphere and can provide critical insights on future changes decades ahead. Because of these critical roles, today's climate models are continuously being developed and evaluated using constraining observations and measurements obtained by satellites, airborne, and ground-based instruments. Instrument simulators can provide a bridge between the measured or retrieved quantities and their sampling in models and field observations while considering instrument sensitivity limitations. Here we present the Earth Model Column Collaboratory (EMC2), an open-source ground-based lidar and radar instrument simulator and subcolumn generator, specifically designed for large-scale models, in particular climate models, but also applicable to high-resolution model output. EMC2 provides a flexible framework enabling direct comparison of model output with ground-based observations, including generation of subcolumns that may statistically represent finer model spatial resolutions. In addition, EMC2 emulates ground-based (and air- or space-borne) measurements while remaining faithful to large-scale models' physical assumptions implemented in their cloud or radiation schemes. The simulator uses either single particle or bulk particle size distribution lookup tables, depending on the selected scheme approach, to perform the forward calculations. To facilitate model evaluation, EMC2 also includes three hydrometeor classification methods, namely, radar- and sounding-based cloud and precipitation detection and classification, lidar-based phase classification, and a Cloud Feedback Model Intercomparison Project Observational Simulator Package (COSP) lidar simulator emulator. The software is written in Python, is easy to use, and can be straightforwardly customized for different models, radars, and lidars. Following the description of the logic, functionality, features, and software structure of EMC2, we present a case study of highly supercooled mixed-phase cloud based on measurements from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE). We compare observations with the application of EMC2 to outputs from four configurations of the NASA Goddard Institute for Space Studies (GISS) climate model (ModelE3) in single-column model (SCM) mode and from a large-eddy simulation (LES) model. We show that two of the four ModelE3 configurations can form and maintain highly supercooled precipitating cloud for several hours, consistent with observations and LES. While our focus is on one of these ModelE3 configurations, which performed slightly better in this case study, both of these configurations and the LES results post-processed with EMC2 generally provide reasonable agreement with observed lidar and radar variables. As briefly demonstrated here, EMC2 can provide a lightweight and flexible framework for comparing the results of both large-scale and high-resolution models directly with observations, with relatively little overhead and multiple options for achieving consistency with model microphysical or radiation scheme physics.

Earth Model Column Collaboratory↗

NASA NTRS2022

Highly Supercooled Riming and Unusual Triple-Frequency Radar Signatures Over McMurdo Station, Antarctica

Riming of ice crystals by supercooled water droplets is an efficient ice growth process, but its basic properties are still poorly known. While it has been shown to contribute significantly to surface precipitation at mid-latitudes, little is known about its occurrence at high latitudes. In Antarctica, two competing effects can influence the occurrence of riming: (i) the scarcity of supercooled liquid water clouds due to the extremely low tropospheric temperatures and (ii) the low aerosol concentration, which may lead to the formation of fewer and larger supercooled drops potentially resulting in an enhanced riming efficiency. In this work, by exploiting the deployment of an unprecedented number of multiwavelength remote sensing systems (including triple-frequency radar measurements) in West Antarctica, during the Atmospheric Radiation Measurements West Antarctic Radiation Experiment (AWARE) field campaign, we evaluate the riming incidence at McMurdo Station and find that riming occurs at much lower temperatures when compared to previous results in the mid-latitudes. This suggests the possible occurrence of a common atmospheric state over Antarctica that includes a rather stable atmosphere inhibiting turbulent mixing, and a high riming efficiency driven by large cloud droplets. We then focus on a peculiar case study featuring a persistent layer with a particularly pronounced riming signature in triple-frequency radar data but only a relatively modest amount of supercooled liquid water. In-depth analysis of the radar observations suggests that such signatures can only be explained by the combined effects of moderately rimed aggregates or similarly shaped florid polycrystals and a narrow particle size distribution (PSD). Simulations of this case study performed with a one-dimensional bin model indicate that similar triple-frequency radar observations can be reproduced when narrow PSDs are simulated. Such narrow PSDs can in turn be explained by two key factors: (i) the presence of a shallow homogeneous droplet or humidified aerosol freezing layer aloft seeding an underlying supercooled liquid layer, and (ii) the absence of turbulent mixing throughout a stable polar atmosphere that sustains narrow PSDs, as hydrometeors grow from the nucleation region aloft to ice particles of several millimeters in size, by vapor deposition and then riming.

Riming↗

NASA NTRS2023

Developing a Radar Signal Simulator for the Community Radiative Transfer Model

Active radar instruments provide vertically resolved clouds and precipitation measurements that cannot be provided by the passive instruments. These active measurements are not conventionally assimilated into the data assimilation systems because of the lack of fast forward radiative transfer models and also difficulties in the error modelling of the measurements. This paper describes the development, evaluation, and sensitivity analysis for a forward radar model implemented in the Community Radiative Transfer Model (CRTM). The scattering properties required by the forward model are provided by the hydrometeor lookup tables that were generated using the discrete dipole approximation. The model is able to calculate both the reflectivity and the attenuated reflectivity for any given radar instrument at any given zenith angles as long as CRTM instrument specific coefficients are available. The evaluation using CloudSat measurements shows a very good agreement between the simulations and measurements as long as the input profiles of hydrometeors are consistent with the measured reflectivity profiles. Major sources contributing to the differences between the measured and simulated reflectivities are input hydrometeor profiles, scattering lookup tables, lack of melting layer in the forward model, CRTM scattering solvers, and attenuation calculations. In addition to the forward model, both Tangent Linear and Adjoint of the model are also implemented and tested within CRTM. These components may be required by some data assimilation systems for the assimilation of radar measurements.

radar↗

NASA NTRSDate not supplied

Space Exploration Synthetic Aperture Radar - Lunar Investigations Targeted Experiment (SESAR-LITE)

The SESAR-LITE (Space Exploration Synthetic Aperture Radar - Lunar Investigations Targeted Experiment) instrument is a compact P-band (70 cm wavelength) polarimetric synthetic aperture radar under development at the NASA Goddard Space Flight Center to measure the surface and subsurface of the Moon at full polarimetry and at meter-scale resolution. The radar will use a compact deployable antenna, distributed RF electronics, and multi-channel digital processing system to enable a set of focused mission goals for small payload opportunities. The instrument development is leveraging proven technology advancements recently developed and demonstrated at NASA Goddard Space Flight center for SESAR (Space Exploration Synthetic Aperture Radar), a flagship version of the instrument that was tailored for larger orbital missions. The development of SESAR-LITE addresses accommodation flexibility on multiple launch vehicle families that require small packages while providing unprecedented surface and subsurface imaging of the Moon called for NASA’s Artemis program.

Synthetic Aperture Radar↗

NASA NTRSDate not supplied

Space Exploration Synthetic Aperture Radar - Lunar Investigations Targeted Experiment (Sesar-Lite)

The SESAR-LITE (Space Exploration Synthetic Aperture Radar - Lunar Investigations Targeted Experiment) instrument is a compact P-band (70 cm wavelength) polarimetric synthetic aperture radar under development at the NASA Goddard Space Flight Center (GSFC) to measure the surface and upper subsurface of the Moon at full polarimetry and at meter-scale resolution. The radar will use a compact deployable antenna, distributed RF electronics, and multi-channel digital processing system to enable a set of focused mission goals for small payload opportunities. The instrument development leverages proven technology advancements recently developed and demonstrated at NASA GSFC for SESAR (Space Exploration Synthetic Aperture Radar), a flagship version of the instrument that was tailored for larger orbital missions. The development of SESAR-LITE addresses accommodation flexibility on multiple launch vehicle families that require small packages while providing unprecedented surface and subsurface imaging of the Moon as required by NASA’s Artemis program.

Synthetic Aperture Radar↗

DOE OSTI2017

Python ARM Radar Toolkit (Py-ART) Roadmap

Radar software is key for producing scientific insight from radar data. There are several platforms for interacting with radar data; the open-source variants are well documented in Heistermann et al. 2014. The Python ARM Radar Toolkit (Py-ART; Helmus and Collis, 2016) is one of these. This document is a guiding roadmap for development for Py-ART. It sets priorities for U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility-funded work and it advises the community which contributions are desired and will receive the most assistance from ARM-funded software engineers.

Python ARM Radar Toolkit, linear programming, valu↗

DOE OSTI2025

The Sensor Dilemma in Intelligent Transportation Systems: Evaluating Radar, Lidar and Camera: Preprint

Intelligent transportation systems (ITS) are at the forefront in advancing the way we interact with and perceive the transportation network. This revolution is fueled by the significant advancement in sensor perception technologies such as radar, lidar, and video imaging, which are the most popular modalities for ITS. Real-time perception data from these sensors allow intelligent infrastructure-side decision-making to improve the energy, efficiency, and safety at traffic intersections. As traffic departments across the United States transition from traditional loop detectors and emulators and embrace newer technologies, they are often left with a dilemma in choosing a sensor technology for infrastructure-based perception that is reliable, inexpensive, and easy to set up and that has robust performance in varying weather conditions. However, choosing a sensor that checks all these boxes is not straightforward, as every sensor type has unique benefits and drawbacks. Radar is excellent at detecting long-range vehicles and weather resistance but lacks high resolution. Lidar is expensive and weather-sensitive, while cameras provide rich visual data at a low cost but are constrained by lighting and visibility. This study examines radar, lidar, and camera sensor capabilities to ascertain whether any of these qualifies as the "best" sensor for ITS perception. While no single sensor can meet all the demands of ITS, a hybrid approach combining multiple sensor modalities like radar, lidar, and cameras offers the most robust solution for enhancing the safety and efficiency of ITS. Through this evaluation, we hope to draw attention to the necessity of the National Renewable Energy Laboratory's infrastructure perception and control framework, which presents a multisensor track data fusion engine to assimilate multiple data streams in order to provide robust and reliable perception.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Spatiotemporal Automatic Calibration of Infrastructure Lidar, Radar, and Camera with a Global Navigation Satellite System

Robust and accurate perception is important for modern intelligent transportation systems (ITS), which use sensors of various modalities for data fusion to create a digital twin of an intersection. Sensor calibration is an important process that creates a unified coordinate frame for the sensor output data so that it can be used for data fusion. Classical approaches for sensor calibration are time-consuming, require an overlapping field of view for feature matching, and are not feasible for ITS application as they cause disruptions in the flow of traffic. In this paper, we present a spatiotemporal automatic calibration approach to calibrate multiple infrastructure lidar, radar, and cameras installed at a traffic intersection. The approach uses global navigation satellite system (GNSS) positioning information shared by connected vehicles, and when the vehicle is detected by the sensor, we match the sensor detections with the GNSS coordinates. The proposed algorithm is evaluated with a real-world dataset utilizing detections from two radars, cameras, and lidars with a test vehicle instrumented with a post-processing kinematic (PPK)-corrected GNSS driving past the sensors installed at a four-way traffic intersection. The experimental results show that the proposed automatic calibration approach can achieve the transformation with a root mean squared error of less than 0.5 for radar and lidar and less than 2 for camera detections. The ability to rapidly calibrate sensors not only benefits initial installations, but can also be used for system health monitoring, while utilizing available connected vehicle data to test the real-time sensor fidelity and operational status.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗