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How Dual Polarization Technique May Improve Weather Radar on Commercial Aircraft
ISSN: 0148-7191, e-ISSN: 2688-3627
Published June 10, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
The airborne weather radar on a commercial aircraft is essential to ensure flight safety. It is able to detect severe weather, probable areas where presence of hail may be suspected, and thanks to its Doppler capability, the wind shears that may be dangerous when taking-off or landing. However, because it operates at X-band, the picture that it offers to the pilot may be seriously biased in situation of severe weather, in reason of the attenuation of the radar wave.
The adoption of the dual pol technique in this weather radar would be most beneficial for the quality of the information delivered to the pilot for the following reasons:
- 1Dual pol technique allows to operate a classification of the precipitation: distinguishing rain, melting layer, snow, hail, small ice particles.
- 2Dual pol technique allows correcting the return signal for attenuation in rain.
The paper aims reporting recent advances in the exploitation of dual pol radar data, based on the concept of normalisation of the particle size distribution (PSD) and on ZPHI® algorithm for precipitation retrieval. Their combination helps retrieving parameter N0* able to represent alone the variability of the PSD, for rain or ice particles whatever. The main interest of the combination between dual pol radar and ZPHI® software for inflight application is:
- To be able to measure ice concentration in altitude by implementing a new version of ZPHI® describing the along beam N0* evolution, in order to interpret the radar reflectivity in terms of ice particle concentration;
- To be able to calibrate the radar using self-consistency of polarimetric variables.
CitationTestud, J., Moreau, E., and Le Bouar, E., "How Dual Polarization Technique May Improve Weather Radar on Commercial Aircraft," SAE Technical Paper 2019-01-1982, 2019, https://doi.org/10.4271/2019-01-1982.
Data Sets - Support Documents
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