Hailstorm events in the Central Andes of Peru: Insights from historical data and radar microphysics

• Autores: Valdívia, Jairo M., Flores-Rojas, José Luis, Prado, Josep J., Guizado, David, Villalobos-Puma, Elver, Callañaupa, Stephany, Silva-Vidal, Yamina
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Servicio Nacional de Meteorología e Hidrología del Perú
• Repositorio: SENAMHI-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.senamhi.gob.pe:20.500.12542/3406
• Enlace del recurso: https://hdl.handle.net/20.500.12542/3406; https://doi.org/10.5194/amt-17-2295-2024
• Nivel de acceso: acceso abierto
• Materia: Granizo; Andes; Climatología; https://purl.org/pe-repo/ocde/ford#1.05.09; variabilidad climatica - Clima y Eventos Naturales

Hailstorms, while fascinating from a meteorological perspective, pose significant risks to communities, agriculture, and infrastructure. In regions such as the Central Andes of Peru, the characteristics and frequency of these extreme weather events remain largely uncharted. This study fills this gap by investigating the historical frequency and vertical structure of hailstorms in this region. We analyzed historical hailstorm records dating back to 1958 alongside 4 years of observations (2017-2021) from the Parsivel2 disdrometer and a cloud-profiling radar MIRA35c. Our findings indicate a trend of decreasing hail frequency (-0.5 events per decade). However, the p value of 0.07 suggests the need for further investigation, particularly in relation to environmental changes and reporting methods. The results show that hailstorms predominantly occur during the austral summer months, with peak frequency in December, and are most common during the afternoon and early evening hours. The analysis of radar variables such as reflectivity, radial velocity, spectral width, and linear depolarization ratio (LDR) reveals distinct vertical profiles for hail events. Two case studies highlight the diversity in the radar measurements of hailstorms, underscoring the complexity of accurate hail detection. This study suggests the need for refining the Parsivel2 algorithm and further understanding its classification of hydrometeors. Additionally, the limitations of conventional radar variables for hail detection are discussed, recommending the use of LDR and Doppler spectrum analysis for future research. Our findings lay the groundwork for the development of more efficient hail detection algorithms and improved understanding of hailstorms in the Central Andes of Peru.