Analysis of Extreme Meteorological Events in the Central Andes of Peru Using a Set of Specialized Instruments

Abstract

A set of instruments to measure several physical, microphysical, and radiative properties of the atmosphere and clouds are essential to identify, understand and, subsequently, forecast and prevent the effects of extreme meteorological events, such as severe rainfall, hailstorms, frost events and high pollution events, that can occur with some regularity in the central Andes of Peru. However, like many other Latin American countries, Peru lacks an adequate network of meteorological stations to identify and analyze extreme meteorological events. To partially remedy this deficiency, the Geophysical Institute of Peru has installed a set of specialized sensors (LAMAR) on the Huancayo observatory (12.04° S, 75.32° W, 3350 m ASL), located in the Mantaro river basin, which is a part of the central Andes of Peru, especially in agricultural areas. LAMAR consists of a set of sensors that are used to measure the main atmosphere and soil variables located in a 30-meter-high tower. It also has a set of high-quality radiation sensors (BSRN station) that helps measure the components of short-wave (SW) (global, diffuse, direct and reflected) and long-wave (LW) (emitted and incident) irradiance mounted in a 6-meter-high tower. Moreover, to analyze the microphysics properties of clouds and rainfall, LAMAR includes a set of profiler radars: A Ka-band cloud profiler (MIRA-35c), a UHF wind profiler (CLAIRE), and a VHF wind profiler (BLTR), along with two disdrometers (PARSIVEL2) and two rain gauges pluviometers. The present study performs a detailed dynamic and energetic analysis of two extreme rainfall events, two intense frost events, and three high-pollution events occurring on the Huancayo observatory between 2018 and 2019. The results show that the rainfall events are similar to the 1965–2019 climatological 90th percentile of the daily accumulated rainfall. The results also highlighted the patterns of reflectivity in function of height for both events, which is measured by highlighting the presence of convective and stratiform rainfall types for both events. The first intense rainfall event was associated with strong easterly circulations at high levels of the atmosphere, and the second one was associated with the presence of strong westerly circulations and the absence of BH-NL system around the central Andes. The first frost event was mainly associated with continuous clear sky conditions in the few previous days, corresponding to a radiative frost event. The second one was mainly associated with the intrusion of cold surges from extra-tropical South America. For both events, the energy budget components were strong-lower in comparison to the mean monthly values during early morning hours. Finally, for the high pollution events, the study identified that the main source of aerosols were the forest fires that took place in Peru with certain contributions from the fires in the northern area of Bolivia.

Calibration of X-Band Radar for Extreme Events in a Spatially Complex Precipitation Region in North Peru: Machine Learning vs. Empirical Approach

“…This leads to a rather low availability of rainfall data for some parts of the domain, because there is simply not enough precipitation during the course of the year. On the other hand, the full spectrum of daily rainfall is quite extreme in the region with several events exceeding 100 mm per day and reflectivity values up to 55 dBZ, which are significantly higher than the values reported from an intensely monitored study site in the central Peru Andes [17].…”

Assessment of Parameters of the Generalized Extreme Value Distribution in Rainfall of the Peruvian North

“…An extreme event is related to the tails of the probability distribution of the observed values (Guillén-Oviedo et al, 2020), and can be applied to any variable that involves an evaluation of the maximum values within a data set (Flores-Rojas et al, 2021). In this context, the current trend of its use has commonly been extended to climatic variables, whose records can come from conventional climatic, pluviometric or pluviographic stations and even from satellite data (Diriba & Debusho, 2021).…”