Radar Detection of Individual Raindrops

Schmidt, Jerome M.; Flatau, Piotr J.; Harasti, Paul R.; Yates, Robert D.; Delene, David J.; Gapp, Nicholas J.; Kohri, William J.; Vetter, Jerome R.; Nachamkin, Jason E.; Parent, Mark; Hoover, Joshua; Anderson, Mark J.; Green, Seth; Bennett, James E.

doi:10.1175/bams-d-18-0130.1

Cited by 6 publications

(3 citation statements)

References 42 publications

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“…1° x 1km) resolution. However, the problem related to the weak detection capability at long distances also deserve attention [1,2]. As shown in Figure . 1, the radar beam width increases with the increase of detection distance, resulting in the accuracy of radar products are susceptible to beam broadening and ground clutter.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sparse Domain Selection for Weather Radar Super-Resolution using Decision Support System

Yuan

Ihsan

2022

Preprint

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Accurate and high-resolution weather radar data reflecting detailed structure information of radar echo plays an important role in analysis and forecast of extreme weather. Typically, this is done using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated, resulting the loss of intense echo information. Focus on this limitation, a super-resolution reconstruction algorithm of weather radar data based on adaptive sparse domain selection (ASDS) is proposed in this article. First, the ASDS algorithm gets a compact dictionary by learning the pre-collected data of model weather radar echo patches. Second, the most relevant sub-dictionaries are adaptively select for each low-resolution echo patches during the spare coding using a complex decision support system. Third, two adaptive regularization terms are introduced to further improve the reconstruction effect of the edge and intense echo information of the radar echo. Experimental results show that the ASDS algorithm substantially outperforms interpolation methods for ×2 and ×4 reconstruction in terms of both visual quality and quantitative evaluation metrics.

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Section: Introductionmentioning

confidence: 99%

Adaptive Sparse Domain Selection for Weather Radar Super-Resolution using Decision Support System

Yuan

Ihsan

2022

Preprint

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show abstract

“…1 °× 1 km) resolution. However, the problem related to the weak detection capability at long distances also deserve attention [1,2]. As shown in Figure 1, the radar beam width increases with the increase of detection distance, resulting in the accuracy of radar products susceptible to beam broadening and ground clutter.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sparse Domain Selection for Weather Radar Superresolution

Yuan

Zeng

2022

Scientific Programming

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Accurate and high-resolution weather radar data reflecting detailed structure information of radar echo plays an important role in analysis and forecast of extreme weather. Typically, this is done using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated, resulting the loss of intense echo information. Focus on this limitation, a superresolution reconstruction algorithm of weather radar data based on adaptive sparse domain selection (ASDS) is proposed in this article. First, the ASDS algorithm gets a compact dictionary by learning the precollected data of model weather radar echo patches. Second, the most relevant subdictionaries are adaptively select for each low-resolution echo patches during the spare coding. Third, two adaptive regularization terms are introduced to further improve the reconstruction effect of the edge and intense echo information of the radar echo. Experimental results show that the ASDS algorithm substantially outperforms interpolation methods for ×2 and ×4 reconstruction in terms of both visual quality and quantitative evaluation metrics.

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“…The OID has been deployed on the North Dakota Citation Research Aircraft (Delene et al, 2019) during several field projects, including a 2015 field project (CAPE2015) to study Florida thunderstorms (Schmidt et al, 2019). The Citation Research Aircraft has conducted several research projects to collect cloud microphysical observations using various instrumentation configurations (Skofronick-Jackson et al, 2014;Jensen et al, 2015;Delene, 2016).…”

Section: Introductionmentioning

confidence: 99%

Technique for comparison of backscatter coefficients derived from in situ cloud probe measurements with concurrent airborne lidar

Wagner

Delene

2022

Atmos. Meas. Tech.

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Abstract. Jet engine power loss due to ice particle accumulation is a recognized aviation hazard occurring in cloud conditions difficult to forecast or visually recognize. High-altitude cirrus clouds can have ice particle concentrations high enough to be dangerous; therefore, pilots must be informed when aircraft enter such environments. One approach to determining ice particle concentration is an onboard lidar system. Concurrent lidar measurements are compared to backscatter coefficients derived from particle size distributions obtained from wing-mounted, in situ probes during four case studies consisting of sixty-second flight segments at different temperatures: +7 and +4 ∘C for water droplet analysis, and −33 and −46 ∘C for ice particle analysis. Backscatter coefficients derived from external cloud probes (ECP) are correlated (0.91) with measurements by an airborne lidar system known as the Optical Ice Detector (OID). Differences between OID and ECP backscatter coefficients range from less than 1 to over 3 standard deviations in terms of uncertainties. The backscatter coefficients are mostly in agreement for liquid clouds and are in disagreement for the −33 and −46 ∘C cases, with ECP-derived backscatter coefficients lower than the OID for three out of the four cases. Measurements over four 60 s research flight segments show that measured total water content is correlated (0.74) with the OID backscatter coefficient, which indicates that the OID is a useful instrument for determining ice particle concentrations over a broad range of environments, including at ice water contents as low as 0.02 g m−3. Additionally, concurrent measurements from cloud imaging probes and the OID provide improved knowledge of cloud conditions, which may help in understanding cloud processes.

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Radar Detection of Individual Raindrops

Cited by 6 publications

References 42 publications

Adaptive Sparse Domain Selection for Weather Radar Super-Resolution using Decision Support System

Adaptive Sparse Domain Selection for Weather Radar Super-Resolution using Decision Support System

Adaptive Sparse Domain Selection for Weather Radar Superresolution

Technique for comparison of backscatter coefficients derived from in situ cloud probe measurements with concurrent airborne lidar

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