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3D Cloud Tomography and Droplet Size Retrieval from Multi-Angle Polarimetric Imaging of Scattered Sunlight from Above
Tomography aims to recover a three-dimensional (3D) density map of a medium or an object. In medical imaging,it is extensively used for diagnostics via X-ray computed tomography (CT). We define and derive a tomographyof cloud droplet distributions via passive remote sensing. We use multi-view polarimetric images to fit a 3Dpolarized radiative transfer (RT) forward model. Our motivation is 3D volumetric probing of vertically-developedconvectively-driven clouds that are ill-served by current methods in operational passive remote sensing. Currenttechniques are indeed based on strictly 1D RT modeling and applied to a single cloudy pixel, where cloud geometrydefaults to that of a plane-parallel slab. Incident unpolarized sunlight, once scattered by cloud droplets, changesits polarization state according to droplet size. Therefore, polarimetric measurements in the rainbow and gloryangular regions can be used to infer the droplet size distribution. This work defines and derives a framework for afull 3D tomography of cloud droplets for both their mass concentration in space and their distribution across arange of sizes. This gridded 3D retrieval of key microphysical properties is made tractable by our novel approachthat involves a restructuring and partial linearization of an open-source polarized 3D RT code to accommodate aspecial two-step iterative optimization technique. Physically-realistic synthetic clouds are used to demonstrate themethodology with rigorous uncertainty quantification, while a real-world cloud imaged by AirMSPI is processedto illustrate the new remote sensing capability
Application of Radon Transform to Multi-Angle Measurements Made by the Research Scanning Polarimeter: A New Approach to Cloud Tomography. Part I: Theory and Tests on Simulated Data.
The Research Scanning Polarimeter (RSP) is an airborne along-track scanner measuring the polarized and total reflectances in 9 spectral channels. The RSP was a prototype for the Aerosol Polarimetry Sensor (APS) launched on-board the NASA Glory satellite. Currently the retrieval algorithms developed for the RSP are being adopted for the measurements of the space-borne polarimeters on the upcoming NASA’s Plankton, Aerosol, Cloud Ocean Ecosystem (PACE)satellite mission. The RSP’s uniquely high angular resolution coupled with the high frequency of measurements allows for characterization of liquid water cloud droplet sizes using the polarized rainbow structure. It also provides geometric constraints on the cumulus cloud’s 2D cross section yielding the cloud’s geometric shape estimates. In this study we further build on the latter technique to develop a new tomographic approach to retrieval of cloud internal structure from remote sensing measurements. While tomography in the strict definition is a technique based on active measurements yielding a tomogram (directional optical thickness as a function of angle and offset of the view ray), we developed a “semi-tomographic” approach in which tomogram of the cloud is estimated from passive observations instead of being measured directly. This tomogram is then converted into 2D spatial distribution of the extinction coefficient using inverse Radon transform (filtered back projection) which is the standard tomographic procedure used e.g., in medical CT scans. This algorithm is computationally inexpensive compared to techniques relying on highly-multi-dimensional least-square fitting; it does not require iterative 3D RT simulations. The resulting extinction distribution is defined up to an unknown constant factor, so we discuss the ways to calibrate it using additional independent measurements. In the next step we use the profile of the droplet size distribution parameters from the cloud’s side (derived by fitting the polarized rainbows) to convert the 2D extinction distribution into that of the droplet number concentration. We illustrate and validate the proposed technique using 3D-RT-simulatedRSP observations of a LES-generated Cu cloud. Quantitative comparisons between the retrieved and the original optical and microphysical parameters are presented.
CoSMIR Along-Track Scan for Cloud Tomography
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NASA Glenn Propulsion Systems Lab: 2012 Inaugural Ice Crystal Cloud Calibration Procedure and Results
The inaugural calibration of the ice crystal and supercooled liquid water clouds generated in NASA Glenn's engine altitude test facility, the Propulsion Systems Lab (PSL) is reported herein. This calibration was in support of the inaugural engine ice crystal validation test. During the Fall of 2012 calibration effort, cloud uniformity was documented via an icing grid, laser sheet and cloud tomography. Water content was measured via multi-wire and robust probes, and particle sizes were measured with a Cloud Droplet Probe and Cloud Imaging Probe. The environmental conditions ranged from 5,000 to 35,000 ft, Mach 0.15 to 0.55, temperature from +50 to -35 F and relative humidities from less than 1 percent to 75 percent in the plenum.
NASA Icing Research Update 2024
This presentation summarizes NASA’s current icing research portfolio for an invited talk on August 1, 2024, at the AIAA Aviation Forum and Exposition. The presentation overviews NASA facilities, computational tools, and latest research. This research is categorized as application focused research, atmospheric characterization, and fundamental research / capability improvement. The application focused research includes (1) the Transonic Truss Brace Wing, (2) High Lift Common Research Model, (3) Advanced Air Mobility, and (4) Efficient Quiet Integrated Propulsor. The atmospheric characterization work includes High Ice Water Content Flight Research, while the fundamental research / capability improvement area includes (1) the Simulated Inter-compressor Duct Research Model, (2) Characterization of Low Ice Adhesion Materials, (3) Collection Efficiency Measurements, (4) Impedance Based Ice/ Water Sensor, (5) Tomography for Cloud Uniformity in the Icing Research Tunnel, and (6) Supercooled Large Drop Research. Finally, the presentation summarizes collaboration and partnership opportunities with NASA.
3D Cloud Masking Across A Broad Swath Using Multi-Angle Polarimetry and Deep Learning
Understanding the 3-dimensional structure of clouds is of crucial importance to modeling our changing climate. Active sensors, such as radar and lidar, provide accurate vertical cloud profiles, but are mostly restricted to along-track sampling. Passive sensors can capture a wide swath, but struggle to see beneath cloud tops. In essence, both types of products are restricted to two dimensions: as a cross-section in the active case, and an image in the passive case. However, multi-angle sensor configurations contain implicit information about 3D structure, due to parallax and atmospheric path differences. Extracting that implicit information can be challenging, requiring computationally expensive radiative transfer techniques. Machine learning, as an alternative, may be able to capture some of the complexity of a full 3D radiative transfer solution with significantly less computational expense. In this work, we make three contributions towards understanding 3D cloud structure from multi-angle polarimetry. First, we introduce a large-scale, publicly available dataset that fuses existing cloud products into a format more amenable to machine learning. This dataset treats multi-angle polarimetry as an input, and radar-based vertical cloud profiles as an output. Second, we describe and evaluate strong baseline machine learning models based that predict these profiles from the passive imagery. Notably, these models are trained only on center-swath labels, but can predict cloud profiles over the entire passive imagery swath. Third, we leverage the information-theoretic nature of machine learning to draw conclusions about the relative utility of various sensor configurations, including spectral channels, viewing angles, and polarimetry. These findings have implications for Earth-observing missions such as NASA’s Plankton, Aerosol, Cloud-ocean Ecosystem (PACE) and Atmosphere Observing System (AOS) missions, as well as in informing future applications of computer vision to atmospheric remote sensing.
Advanced Optical Diagnostics for Ice Crystal Cloud Measurements in the NASA Glenn Propulsion Systems Laboratory
A light extinction tomography technique has been developed to monitor ice water clouds upstream of a direct connected engine in the Propulsion Systems Laboratory (PSL) at NASA Glenn Research Center (GRC). The system consists of 60 laser diodes with sheet generating optics and 120 detectors mounted around a 36-inch diameter ring. The sources are pulsed sequentially while the detectors acquire line-of-sight extinction data for each laser pulse. Using computed tomography algorithms, the extinction data are analyzed to produce a plot of the relative water content in the measurement plane. To target the low-spatial-frequency nature of ice water clouds, unique tomography algorithms were developed using filtered back-projection methods and direct inversion methods that use Gaussian basis functions. With the availability of a priori knowledge of the mean droplet size and the total water content at some point in the measurement plane, the tomography system can provide near real-time in-situ quantitative full-field total water content data at a measurement plane approximately 5 feet upstream of the engine inlet. Results from ice crystal clouds in the PSL are presented. In addition to the optical tomography technique, laser sheet imaging has also been applied in the PSL to provide planar ice cloud uniformity and relative water content data during facility calibration before the tomography system was available and also as validation data for the tomography system. A comparison between the laser sheet system and light extinction tomography resulting data are also presented. Very good agreement of imaged intensity and water content is demonstrated for both techniques. Also, comparative studies between the two techniques show excellent agreement in calculation of bulk total water content averaged over the center of the pipe.
Cloud Impacts on Photochemistry: Building a Climatology of Photolysis Rates from the Atmospheric Tomography Mission
Measurements from actinic flux spectroradiometers on board the NASA DC-8 during the Atmospheric Tomography (ATom) mission provide an extensive set of statistics on how clouds alter photolysis rates (J values) throughout the remote Pacific and Atlantic Ocean basins. J values control tropospheric ozone and methane abundances, and thus clouds have been included for more than three decades in tropospheric chemistry modeling. ATom made four profiling circumnavigations of the troposphere capturing each of the seasons during 2016–2018. This work examines J values from the Pacific Ocean flights of the first deployment, but publishes the complete Atom-1 data set (29 July to 23 August 2016). We compare the observed J values (every 3 s along flight track) with those calculated by nine global chemistry– climate/transport models (globally gridded, hourly, for a mid-August day). To compare these disparate data sets, we build a commensurate statistical picture of the impact of clouds on J values using the ratio of J -cloudy (standard, sometimes cloudy conditions) to J -clear (artificially cleared of clouds). The range of modeled cloud effects is inconsistently large but they fall into two distinct classes: (1) models with large cloud effects showing mostly enhanced J values aloft and or diminished at the surface and (2) models with small effects having nearly clear-sky J values much of the time. The ATom-1 measurements generally favor large cloud effects but are not precise or robust enough to point out the best cloud-modeling approach. The models here have resolutions of 50–200 km and thus reduce the occurrence of clear sky when averaging over grid cells. In situ measurements also average scattered sunlight over a mixed cloud field, but only out to scales of tens of kilometers. A primary uncertainty remains in the role of clouds in chemistry, in particular, how models average over cloud fields, and how such averages can simulate measurements.
3D MC. I. X-Ray Tomography Begins to Unravel the 3D Structure of a Molecular Cloud in our Galaxy’s Center
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Update on the NASA Glenn Propulsion Systems Lab Icing and Ice Crystal Cloud Characterization (2017)
NASA Glenn's Propulsion Systems Lab, an altitude engine test facility, generates icing clouds with a spray system. While the spray system is used mostly to create ice crystal clouds (Appendix D/P), the 2017 cloud characterization effort added the requirement to produce exactly supercooled liquid clouds in Appendix C and Appendix O. Success was demonstrated to supercool the largest drops at the warmest conditions, but not freeze out the smallest drops at the coldest conditions. This paper documents primarily the total water content characterization methodology and results from an Iso-Kinetic Probe in ice crystals and Multi-Wire sensor in supercooled liquid, along with the cloud uniformity provided by light extinction tomography. Particle size distribution results from High Speed Imaging probes and a Phase Doppler Interferometer are discussed. Also, a new numerical model for tracking the thermodynamics of the air-drop interactions in PSL from the plenum toward the cloud characterization plane are noted. Both of these latter topic are more fully documented in companion papers.
The Demonstration of a Light Extinction Tomography System at the NASA Glenn Research Center's Icing Research Tunnel
A prototype light extinction tomography system has been developed for acquiring real-time in-situ icing cloud uniformity and density measurements in the NASA Glenn Research Center's Icing Research Tunnel (IRT). These measurements are currently obtained through periodic manual calibrations of the IRT. These calibrations are time consuming and assume that cloud uniformity and density does not greatly vary between the periodic calibrations. It is envisioned that the new light extinction tomography system will provide the means to make these measurements in-situ in real-time and minimize the need for these manual calibrations. This new system uses the principle of light extinction tomography to measure the spray density and distribution in the test section. The prototype system was installed and successfully demonstrated in the Icing Research Tunnel in early 2018. Data sets were acquired for several standard spray and simulated fault conditions to assess system capability and sensitivity. This paper will describe the prototype light extinction system, the theory behind it, and the results of the demonstration test that was conducted in the IRT.
The Demonstration of a Light Extinction Tomography System at the NASA Glenn Research Center's Icing Research Tunnel
A prototype light extinction tomography system has been developed for acquiring real-time in-situ icing cloud uniformity and density measurements in the NASA Glenn Research Center's Icing Research Tunnel (IRT). These measurements are currently obtained through periodic manual calibrations of the IRT. These calibrations are time consuming and assume that cloud uniformity and density does not greatly vary between the periodic calibrations. It is envisioned that the new light extinction tomography system will provide the means to make these measurements in-situ in real-time and minimize the need for these manual calibrations. This new system uses the principle of light extinction tomography to measure the spray density and distribution in the test section. The prototype system was installed and successfully demonstrated in the Icing Research Tunnel in early 2018. Data sets were acquired for several standard spray and simulated fault conditions to assess system capability and sensitivity. This paper will describe the prototype light extinction system, the theory behind it, and the results of the demonstration test that was conducted in the IRT.