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On 9 September 2019, rain-bands of category-1 Hurricane Dorian passed over a ground instrumentation site in Delmarva peninsula, USA. Drop shapes derived from 2D Video Disdrometer measurements at this site were used to compute the S-band radar cross sections (RCS) for horizontal and vertical polarizations for each drop with equi-volume diameter > 2 mm. These are combined with RCS for the smaller drops assuming equilibrium shapes. Radar reflectivity (Z H ) and differential reflectivity (Z DR ) are calculated for each of the 3 minutes throughout the event which lasted for more than 8 hours. These are compared with simultaneous observations from an S-band polarimetric radar 38 km away. The comparisons highlight the impact of large amplitude drop oscillations on Z DR .

The Integrated Multi-satellitE Retrievals for GPM (IMERG) is a global gridded precipitation product that unifies measurements from a network of satellites in the GPM constellation. Multi-temporal evaluation of IMERG products for a specific region is essential to the algorithm improvement and product applications. This study evaluates the Version-06B IMERG Early, Late and Final Runs using ground-based Korean Quantitative Precipitation Estimation (QPE).

The NASA dual-frequency, dual-polarization, Doppler radar (D3R) was deployed in the International Collaborative Experiment during the PyeongChang 2018 Olympics and Paralympic winter games (ICE-POP 2018). The radar captured many rain and interesting snowfall events during the deployment. In this work, the snowfall events are studied in detail to interpret the microphysics from a radar’s perspective. Interesting snow studies such as detecting the presence of pristine oriented ice crystals in the mixed phase snow clouds will be discussed in detail.

The international collaborative experiment held during the PyeongChang 2018 Olympics and Paralympic winter games (ICE-POP 2018) is a large field campaign in which science instruments from across the world were brought together. This campaign aims to give a better understating of the dependence of precipitation on terrain features and improve the meteorological models for such complex terrain features such as in South Korea. The NASA dual-frequency, dual-polarization, Doppler radar (D3R) which operates in Ku and Ka frequency bands is a key instrument in this campaign for measurements of snow. This paper gives a overview of the deployment and performance of the D3R during the ICE-POP campaign and sample observations will be presented.

The NASA dual-frequency, dual-polarization, Doppler radar (D3R) [1] was conceived and developed to support ground validation (GV) operations of the Global Precipitation Measurement (GPM) mission [2]. The D3R operates in the same frequencies bands, Ku- and Ka-band, as GPM's dual-frequency precipitation radar enabling direct comparisons of microphysical observations of precipitation. To support the GPM GV mission, D3R substantively participated in four field campaigns in North America with diverse geographic features covering both winter and summer conditions.

The NASA dual-frequency, dual-polarization, Doppler radar (D3R) [1] was conceived and developed to support ground validation (GV) operations of the Global Precipitation Measurement (GPM) mission [2]. The D3R operates in the same frequencies bands, Ku- and Ka-band, as GPM's dual-frequency precipitation radar enabling direct comparisons of microphysical observations of precipitation. The D3R radar is shown in Figure 1. To support the GPM GV mission, D3R substantively participated in four field campaigns in North America with diverse geographic features covering both winter and summer conditions.

The NASA D3R was successfully deployed and operated throughout the NASA OLYMPEx field campaign. A differential phase based attenuation correction technique has been implemented for D3R observations. Hydrometeor classification has been demonstrated for five distinct classes using Ku-band observations of both convection and stratiform rain. The stratiform rain hydrometeor classification is compared against LDR observations and shows good agreement in identification of mixed-phase hydrometeors in the melting layer.

In order to investigate how well observations from precipitation-monitoring satellites match up to the best estimate of the true precipitation measured at ground level and how to use the collected precipitation data to evaluate models that describe and predict the hydrology, the Integrated Precipitation and Hydrology Experiment (IPHEx) was conducted in the southern Appalachian Mountains in the eastern United States from May 1 to June 15, 2014. The NASA dual-frequency dual-polarization Doppler radar (D3R), co-located with NASA NPOL radar, was deployed as part of the IPHEx field campaign to characterize precipitation properties at Ku- and Ka-band frequencies. This paper presents the deployment and performance of D3R during the IPHEx field experiment. Sample observations will be presented, with particular attention paid to cross-comparison between D3R and NPOL.

The Iowa Flood Studies (IFloodS) field experiment was conducted to better understand the strengths and limitations of Global Precipitation Measurement (GPM) mission satellite products in the context of hydrologic applications. The NASA dual-frequency dual-polarization Doppler radar (D3R), designed as part of the GPM ground validation program, participated in the IFloodS field campaign to characterize precipitation properties at Ku- and Ka-band frequencies. This paper presents the deployment of the D3R and summarizes the D3R observations during the IFloodS field campaign. The quality of the D3R measurements is evaluated by comparing with the NASA NPOL S-band radar observations. In addition, the capability for rainfall estimation using the D3R is also described and validated using ground gauge measurements.

This paper presents the first published systematic study of face recognition performance as a function of light level using intensified near infrared imagery. This technology is the most prevalent in both civilian and military night vision equipment, and provides enough intensification for human operators to perform standard tasks under extremely low-light conditions. We describe a comprehensive data collection effort undertaken by the authors to image subjects under carefully controlled illumination and quantify the performance of standard face recognition algorithms on visible and intensified imagery as a function of light level. Performance comparisons for automatic face recognition are reported using the standardized implementations from the CSU Face Identification Evaluation System. The results contained in this paper should constitute the initial step for analysis and deployment of face recognition systems designed to work in low-light level condi

Research has shown that naturally occurring light outdoors and underwater is partially linearly polarized. The polarized components can be combined to form an image that describes the polarization of the light in the scene. This image is known as the degree of linear polarization (DOLP) image or partial polarization image. These naturally occurring polarization signatures can provide a diver or an unmanned underwater vehicle (UUV) with more information to detect, classify, and identify threats such as obstacles and/or mines in the shallow water environment. The SHallow water Real-time IMaging Polarimeter (SHRIMP), recently developed under sponsorship of Dr. Tom Swean at the Office of Naval Research (Code 321OE), can measure underwater partial polarization imagery. This sensor is a passive, three-channel device that simultaneously measures the three components of the Stokes vector needed to determine the partial linear polarization of the scene. The testing of this sensor has been completed and the data has been analyzed. This paper presents performance results from the field-testing and quantifies the gain provided by the partial polarization signature of targets in the Very Shallow Water (VSW) and Surf Zone (SZ) regions.

The method of using probability density functions (PDF) of rain rates (R) for validation of rain rate estimates from different sensors at different scales is investigated. Distributions of rain volume by R as derived from the TRMM spaceborne radar are compared with those based on collocated data from ground-based radar observations. The uncertainties in the ground-based radar PDF are reduced upon adjustment to the gauge-based R distribution. This paper discusses how to better represent the actual R distribution at the scale of a radar pixel using ground observations. Utilizing super dense gauge networks will further reduce the uncertainties, and will permit better evaluation of instantaneous rainfall products based on ground and space observations.

As part of the NASA tropical rainfall measuring mission ground validation (TRMM-GV) program, a field campaign was conducted in Florida Keys during August-September 2002. The purpose of the field campaign was to study the characteristics of rainfall over the Florida Keys, utilizing Key West WSR-88D, NASA's s-band polarimetric radar (NPOL), and a network of rain gauges and disdrometers. This study focuses on the performance of the rain gauges and disdrometers and its impact on the radar rainfall algorithms.

A robust and accurate polarization phase-based technique for material classification is presented. The novelty of this technique is three-fold In (i) its theoretical development, (ii) its application, and, (iii) its experimental implementation. The concept of phase of polarization of a light wave is introduced to computer vision for discrimination between materials according to their intrinsic electrical conductivity, such as distinguishing conducting metals, and poorly-conducting dielectrics. Previous work has used intensity, color and polarization component ratios. This new method is based on the physical principle that metals retard orthogonal components of light upon reflection while dielectrics do not. This method has significant complementary advantages with respect to existing techniques, is computationally efficient, and can be easily implemented with existing imaging technology. Experiments for real circuit board inspection, non-conductive and conductive glass, and, outdoor object recognition have been performed to demonstrate its accuracy and potential capabilities.

Physiological measurements like branch angles, branch lengths, branch diameters and branch cross-sectional area of the 3-D pulmonary tree structures are clinically essential in evaluating the function of normal and diseased lung and during the breathing process. In order to facilitate these measurements and study relative structural changes, the 3-D lung tree volumes are reduced to a 3-D Euclidean straight line central axis tree. The central axis tree captures the branch topology and geometric features of the tree volume. Since matching 3-D tree volumes is complex, as they change in branch topology and geometry, the authors accomplish it by designing an efficient algorithm that matches their corresponding central axis trees. The algorithm takes two binary central axis trees T/sub 1/=(V/sub 1/,E/sub 1/,W/sub 1/) and T/sub 2/=(V/sub 2/,E/sub 2/,W/sub 2/), where W/sub 1/ and W/sub 2/ are set of tuples containing geometric attributes corresponding to the nodes in T/sub 1/ and T/sub 2/, as inputs and returns the one-to-one matching function f of nodes in T/sub 1/ to T/sub 2/ that preserves the tree topology and closely matches the geometric attributes of these trees, i.e. branch points, branch lengths, and branch angles between mapped nodes of T/sub 1/ and T/sub 2/. Since the topology match alone could result in many choices of the mapping function f, the authors prune these choices by incorporating constraints on the geometric attributes of nodes in T/sub 1/ and T/sub 2/. The authors design a linear time algorithm that matches the branch topology and geometric features of T/sub 1/ and T/sub 2/. The authors' algorithm produced accurate matchings on various airway data sets of a dog lung obtained from Computed Tomography under simulated breathing conditions. T/sub 1/ and T/sub 2/ are obtained by running a two-pass central axis algorithm on the tree volumes.