A Primer on Phased Array Radar Technology for the Atmospheric Sciences

Palmer, Robert D.; Bodine, David J.; Kollias, Pavlos; Schvartzman, David; Zrnić, Dúsan S.; Kirstetter, P. E.; Zhang, Guifu; Yu, Tian-You; Kumjian, Matthew R.; Cheong, Boon Leng; Collis, Scott; Frasier, Stephen J.; Fulton, Caleb; Hondl, Kurt; Kurdzo, James M.; Ushio, Tomoo; Rowe, Angela K.; Salazar-Cerreño, Jorge L.; Torres, Sebastián; Weber, M.; Yeary, Mark

doi:10.1175/bams-d-21-0172.1

Cited by 33 publications

(15 citation statements)

References 117 publications

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“…The general value of such augmentation was evident to essentially the entire weather radar community given the impact of networks of groundbased weather radars such as NEXRAD and that of visible and near infrared (VIS/NIR) geostationary imagery. In fact, even in the context of ground-based weather radar observations, numerous advancements have been pursued to obtain faster acquisition of 3-D volumes of weather radar data (see e.g., [6,7]). The relevant timescales in this general endeavor span from what could be achieved by TRMM in its tropical orbit (that is, the very occasional ability to revisit a storm within 1-2 hours, but in general having significantly larger time spans between consecutive observations) to rapid-scan digitally beamformed phased arrays on the ground (which can image entire 3-D volumes in less than 1 minute, but are limited in their coverage to small portions of the globe, generally in developed countries).…”

Section: Introductionmentioning

confidence: 99%

Investigation of convective updrafts (INCUS): status after phase A

Tanelli,

Haddad,

van den Heever

2023

CubeSats, SmallSats, and Hosted Payloads for Remote Sensing VII

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RainCube (dar a Sat) and (Temporal Experiment for Storms and Tropical Systems - Demonstration) demonstrated in 2018 that deployment of active and passive microwave sensors to monitor storms and precipitation from space is possible on platforms as small as 6U CubeSats. Despite their implementation as high-risk technology demonstrations, with very low budgets compared to their predecessors, they both survived more than two years in orbit (well beyond their commitments). These demonstrations opened the gates to satisfy several long-standing unmet needs by the scientific and operational weather and climate communities. Among them is the need to observe the evolution of the vertical structure of convective storms in the Tropics at the temporal scales relevant to convective processes (i.e., tens of seconds to few minutes) in order to advance our understanding of convective processes and the environmental conditions behind them via modeling and analysis. The (Investigation of Convective Updrafts) mission concept aims at addressing this need by deploying 3 small satellites each carrying an augmented version of the RainCube radar. One of the 3 small satellites also includes a millimeter wave radiometer inherited from TEMPEST-D. In this presentation we present the status of the INCUS project at the end of Phase A.

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Section: Introductionmentioning

confidence: 99%

Investigation of convective updrafts (INCUS): status after phase A

Tanelli,

Haddad,

van den Heever

2023

CubeSats, SmallSats, and Hosted Payloads for Remote Sensing VII

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“…To methodically advance observation‐based understanding of fundamental convective cloud processes, new observational approaches are needed. Emerging new technologies such as rapid scanning or phased‐array radars can sample the rapid transient nature of convection (Bluestein et al., 2010; Kollias, Palmer, et al., 2022; Palmer et al., 2022; Tanamachi & Heinselman, 2016, etc. ), but robust and detailed measurements of the vertical evolution of convection have not been largely explored.…”

Section: Introductionmentioning

confidence: 99%

Time Resolved Reflectivity Measurements of Convective Clouds

Dolan,

Kollias,

van den Heever

et al. 2023

Geophysical Research Letters

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National Aeronautics and Space Administration's Investigations of Convective Updrafts (INCUS) mission aims to document convective mass flux through changes in the radar reflectivity (ΔZ) in convective cores captured by a constellation of three Ka‐band radars sampling the same convective cells over intervals of 30, 90, and 120 s. Here, high spatiotemporal resolution observations of convective cores from surface‐based radars that use agile sampling techniques are used to evaluate aspects of the INCUS measurement approach using real observations. Analysis of several convective cells confirms that large coherent ΔZ structure with measurable signal (>5 dB) can occur in less than 30 s and are correlated with underlying convective motions. The analysis indicates that the INCUS mission radar footprint and along track sampling are adequate to capture most of the desirable ΔZ signals. This unique demonstration of reflectivity time‐lapse provides the framework for estimating convective mass flux independent from Doppler techniques with future radar observations.

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“…Nowadays, the development of phased array radars (PARs) is crucial for fast and accurate estimation of weather phenomena (Schvartzman et al, 2022;Palmer et al, 2022). PARs has the capability of electronic scanning as well as adaptive digital beamforming (DBF) to rapidly collect radar measurements with high time resolution and precision (Isom et al, 2013;Zrnic et al, 2007;Pazmany and Bluestein, 2011;Vivekanandan et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network for fast adaptive beamforming of phased array weather radar

Sallam

2023

COMPEL

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Purpose The purpose of this paper is to present a deep-learning-based beamforming method for phased array weather radars, especially whose antenna arrays are equipped with large number of elements, for fast and accurate detection of weather observations. Design/methodology/approach The beamforming weights are computed by a convolutional neural network (CNN), which is trained with input–output pairs obtained from the Wiener solution. Findings To validate the robustness of the CNN-based beamformer, it is compared with the traditional beamforming methods, namely, Fourier (FR) beamforming and Capon beamforming. Moreover, the CNN is compared with a radial basis function neural network (RBFNN) which is a shallow type of neural network. It is shown that the CNN method has an excellent performance in radar signal simulations compared to the other methods. In addition to simulations, the robustness of the CNN beamformer is further validated by using real weather data collected by the phased array radar at Osaka University (PAR@OU) and compared to, besides the FR and RBFNN methods, the minimum mean square error beamforming method. It is shown that the CNN has the ability to rapidly and accurately detect the reflectivity of the PAR@OU with even less clutter level in comparison to the other methods. Originality/value Motivated by the inherit advantages of the CNN, this paper proposes the development of a CNN-based approach to the beamforming of PAR using both simulated and real data. In this paper, the CNN is trained on the optimum weights of Wiener solution. In simulations, it is applied on a large 32 × 32 planar phased array antenna. Moreover, it is operated on real data collected by the PAR@OU.

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A Primer on Phased Array Radar Technology for the Atmospheric Sciences

Cited by 33 publications

References 117 publications

Investigation of convective updrafts (INCUS): status after phase A

Investigation of convective updrafts (INCUS): status after phase A

Time Resolved Reflectivity Measurements of Convective Clouds

Convolutional neural network for fast adaptive beamforming of phased array weather radar

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