Bibliographic citations
Medina, M., (2020). Determinación de patrones de vientos horizontales en la tropósfera y su relación con la precipitación en el altiplano peruano boliviano [Universidad Nacional Agraria La Molina]. https://hdl.handle.net/20.500.12996/4347
Medina, M., Determinación de patrones de vientos horizontales en la tropósfera y su relación con la precipitación en el altiplano peruano boliviano []. PE: Universidad Nacional Agraria La Molina; 2020. https://hdl.handle.net/20.500.12996/4347
@mastersthesis{sunedu/3286553,
title = "Determinación de patrones de vientos horizontales en la tropósfera y su relación con la precipitación en el altiplano peruano boliviano",
author = "Medina Burga, Melissa de Jesús",
publisher = "Universidad Nacional Agraria La Molina",
year = "2020"
}
This study aims to identify the circulation patterns (CPs) that pertain to the seasons of the year that reflect, the recurrent characteristics of the intra-seasonal atmospheric variability in the Peruvian-Bolivian highlands and a portion of the Bolivian Amazon. To classify the patterns, daily wind data was used at 200 hPa of ERA - Interim reanalysis for the period from 1979 to 2016 and each pattern was associated with the on-site precipitation of 173 weather stations located in the area of study (15º S - 25º S, 80º W - 60º W) and at different altitudes from 156 m to 4 460 m. Interpolated precipitation data of the PISCO – SENAMHI product was also used from 1981 to 2015 that uses weather station data and satellite data information of the CHIRPS product on a monthly basis with a resolution of 5 km and OLR (Outgoing Longwave Radiation) data obtained from the NCAR / NOAA website with a resolution of 2.5° x 2.5° during the same period. The analysis was performed in all four seasons of the year, in summer (DEF) 6 atmospheric circulation patterns were determined, in autumn (MAM) 7 atmospheric circulation patterns, in winter (JJA) 7 atmospheric circulation patterns and in spring (SON) 8 atmospheric circulation patterns. Hierarchical ascending classification and SOM neural networks were used for the grouping. The patterns identified show well defined time cycles and patterns related to extreme events such as floods and droughts. The outcomes show consistencies when representing the data of PISCO, the on-site stations of SENAMHI-Bolivia and OLR, which represent new perspectives on the extreme hydrological events that lie behind atmospheric circulation in certain circulation patterns
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