Mesospheric wind estimation with the Jicamarca MST radar using spectral mainlobe identification

• Autores: Lee, Kiwook, Kudeki, Erhan, Reyes, Pablo M., Lehmacher, Gerald A., Milla, Marco
• Formato: artículo
• Fecha de Publicación: 2019
• Institución: Instituto Geofísico del Perú
• Repositorio: IGP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.igp.gob.pe:20.500.12816/4911
• Enlace del recurso: http://hdl.handle.net/20.500.12816/4911; https://doi.org/10.1029/2019RS006892
• Nivel de acceso: acceso abierto
• Materia: Radar; Mesoespheric winds; Mesosphere; Stratosphere; Troposphere; http://purl.org/pe-repo/ocde/ford#1.05.01

MST (mesosphere, stratosphere, troposphere) radar observations at Jicamarca use four antenna beams, one vertical, others tilted to the east, west, and south, to detect the scattered pulse returns from mesospheric heights (∼55–85 km). Doppler shifts of scattered returns, estimated by fitting the observed signal spectra by generalized Gaussian‐shaped models, are used to estimate mesospheric wind vectors. At some heights two spectral peaks are seen in which case a dual‐peaked model is fitted the spectrum. Dual peaks are more common for returns from the east and west tilted beams with stronger sidelobes. When sidelobe‐caused peaks are dominant and are mistaken for mainlobe peaks, wind errors occur since the estimation algorithm uses the pointing angle of the mainbeam. To avoid such errors we implemented a clustering‐based machine learning procedure to identify and use only the mainbeam components of dual peaked spectra. Wind estimates made before and after the procedure will be presented to assess the improvements achieved by this new method to be used routinely in Jicamarca mesospheric wind measurements and applied to past MST data.