Preprints
https://doi.org/10.5194/amt-2024-117
https://doi.org/10.5194/amt-2024-117
22 Aug 2024
 | 22 Aug 2024
Status: this preprint is currently under review for the journal AMT.

Mitigating Radome Induced Bias in X-Band Weather Radar Polarimetric moments using Adaptive DFT Algorithm

Thiruvengadam Padmanabhan, Guillaume Lesage, Ambinintsoa Volatiana Ramanamahefa, and Joël Van Baelen

Abstract. In recent years, the application of compact and cost-effective deployable X-band polarimetric radars has gained in popularity, particularly in regions with complex terrain. The deployable radars generally use a radome constructed by joining multiple panels using metallic threads to facilitate easy transportation. As a part of the ESPOIRS project, Laboratoire de l’Atmosphère et des Cyclones has acquired an X-band meteorological radar with four panel radome configuration. In this study, we investigated the effect of the radome on the measured polarimetric variables, particularly differential reflectivity and differential phase. Our observations reveal that the metallic threads connecting the radome panels introduce power loss at vertical polarization, leading to a positive bias in the differential reflectivity values. To address the spatial variability bias observed in differential reflectivity and differential phase, we have developed a novel algorithm based on the Discrete Fourier Transform. The algorithm's performance was tested during an intense heavy rainfall event caused by the Batsirai cyclone on Reunion Island. The comparative and joint histogram analysis demonstrates the algorithm's effectiveness in correcting the spatial bias in the polarimetric variables.

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Thiruvengadam Padmanabhan, Guillaume Lesage, Ambinintsoa Volatiana Ramanamahefa, and Joël Van Baelen

Status: open (until 27 Sep 2024)

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Thiruvengadam Padmanabhan, Guillaume Lesage, Ambinintsoa Volatiana Ramanamahefa, and Joël Van Baelen
Thiruvengadam Padmanabhan, Guillaume Lesage, Ambinintsoa Volatiana Ramanamahefa, and Joël Van Baelen

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Short summary
This study explores how the joints in a weather radar's protective cover affect its measurements. We developed a new method to correct these errors, improving the accuracy of the radar's data. Our method was tested during an intense cyclone on Reunion Island, demonstrating significant improvements in data accuracy. This research is crucial for enhancing weather predictions and understanding, particularly in challenging terrains.