Detection of the melting level with polarimetric weather radar
<p>Accurate estimation of the melting level (ML) is essential in radar rainfall estimation to mitigate the bright band enhancement, classify hydrometeors, correct for rain attenuation and calibrate radar measurements. This paper presents a novel and robust ML-detection algorithm based on eithe...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2021-04-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/14/2873/2021/amt-14-2873-2021.pdf |
| Summary: | <p>Accurate estimation of the melting level (ML) is essential in radar rainfall estimation to mitigate the bright band enhancement, classify hydrometeors, correct for rain attenuation and calibrate radar measurements. This paper presents a novel and robust ML-detection algorithm based on either vertical profiles (VPs) or quasi-vertical profiles (QVPs) built from operational polarimetric weather radar scans. The algorithm depends only on data collected by the radar itself, and it is based on the combination of several polarimetric radar measurements to generate an enhanced profile with strong gradients related to the melting layer. The algorithm is applied to 1 year of rainfall events that occurred over southeast England, and the results were validated using radiosonde data. After evaluating all possible combinations of polarimetric radar measurements, the algorithm achieves the best ML detection when combining VPs of <span class="inline-formula"><i>Z</i><sub>H</sub></span>, <span class="inline-formula"><i>ρ</i><sub>HV</sub></span> and the gradient of the velocity (<span class="inline-formula">grad<i>V</i></span>), whereas, for QVPs, combining profiles of <span class="inline-formula"><i>Z</i><sub>H</sub></span>, <span class="inline-formula"><i>ρ</i><sub>HV</sub></span> and <span class="inline-formula"><i>Z</i><sub>DR</sub></span> produces the best results, regardless of the type of rain event. The root mean square error in the ML detection compared to radiosonde data is <span class="inline-formula">∼200</span> m when using VPs and <span class="inline-formula">∼250</span> m when using QVPs.</p> |
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| ISSN: | 1867-1381 1867-8548 |