The present study explores the cloud microphysics (MPs) impact on the simulation of two convective rainfall events (CREs) over the complex topography of Andes mountains, using the Weather Research and Forecasting- Advanced Research (WRF-ARW) model. The events occurred on December 29 2015 (CRE1) and January 7 2016 (CRE2). Six microphysical parameterizations (MPPs) (Thompson, WSM6, Morrison, Goddard, Milbrandt and Lin) were tested, which had been previously applied in complex orography areas. The one-way nesting technique was applied to four domains, with horizontal resolutions of 18, 6, and 3 km for the outer ones, in which cumulus and MP parameterizations were applied, while for the innermost domain, with a resolution of 0.75 km, only MP parameterization was used. It was integrated for 36 h with National Centers for Environmental Prediction (NCEP Final Operational Global Analysis (NFL) initial conditions at 00:00 UTC (Coordinated Universal Time). The simulations were verified using Geostationary Operational Environmental Satellites (GOES) brightness temperature, Ka band cloud radar, and surface meteorology variables observed at the Huancayo Observatory. All the MPPs detected the surface temperature signature of the CREs, but for CRE2, it was underestimated during its lifetime in its vicinity, matching well after the simulated event. For CRE1, all the schemes gave good estimations of 24 h precipitation, but for CRE2, Goddard and Milbrandt underestimated the 24 h precipitation in the inner domain. The Morrison and Lin configurations reproduced the general dynamics of the development of cloud systems for the two case studies. The vertical profiles of the hydrometeors simulated by different schemes showed significant differences. The best performance of the Morrison scheme for both case studies may be related to its ability to simulate the role of graupel in precipitation formation. The analysis of the maximum reflectivity field, cloud top distribution, and vertical structure of the simulated cloud field also shows that the Morrison parameterization reproduced the convective systems consistently with observations.

Information on the vertical structure of rain, especially near the surface is important for accurate quantitative precipitation estimation from weather and space-borne radars. In the present study, the rainfall characteristics, from a vertically pointed profile Radar in the Mantaro basin (Huancayo, Peru) are observed. In summary, diurnal variation of near-surface rainfall and bright band height, average vertical profiles of the drop size distribution (DSD), rain rate, radar reflectivity (Z_e) and liquid water content (LWC) are investigated to derive the rainfall characteristics. Diurnal variation of rain rate and bright band height show the bimodal distribution, where frequent and higher rain rate occurred during the afternoon and nighttime, and more than 70% bright band height found between 4.3–4.7 km. The average vertical profiles of Z_e show the opposite characteristics above and below the melting level (ML) and depend on the near-surface rain rate. For example, the average Z_e profiles have a negative gradient above the ML, whereas below, the ML, the gradient depends on the near-surface rain rate. The rain rate and LWC show the opposite behavior, and both consist of a positive (negative) gradient below (above) the ML. The vertical growth of DSD parameters depend on the near-surface rain rate, and a higher concentration of large-sized of droplets are observed for higher near surface rain rate, however, the dominant modes of droplets are <1 mm throughout the vertical column. However, the most significant variation in DSD growth is observed for near-surface rain rate ≥20 mm/h. These findings suggest using different retrieval techniques for near surface rain estimation than the rest of the vertical profile and high rain rate events. The improved understanding of the tropical Andes precipitation would be very important for assessing climate variability and to forecast the precipitation using the numerical models.

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.

This dataset includes data obtained at the Atmospheric Microphysics and Radiation Laboratory (LAMAR) of the Huancayo Observatory (12.04° S, 75.32° W, 3313 m ASL). Two Parsivel2 and two tipping bucket rain gauges are used in this dataset which are operating together since 2018. Data is given in NetCDF format, including two types of files, one NetCDF for precipitation totals and another which contains Parsivel2 data. This data set was collected in the complex topography conditions of the tropical Andes, and its potential use is to study the microphysics of orographic rainfall, atmospheric models and rainfall estimation algorithms.

A vertically pointing Ka-band radar (Metek MIRA-35C) installed at the Instituto Geofísico del Perú, Atmospheric Microphysics and Radiation Laboratory (LAMAR) Huancayo Observatory, which is located at an elevation of 3.3 km MSL in the Andes Mountains of Peru, is used to investigate the effects of terrain on satellite-based precipitation measurement in the Andes. We compare the vertical structure of precipitation observed by the MIRA-35C with Ka-band radar measurements from the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) mission core satellite using an approach based on Taylor’s hypothesis of frozen turbulence that attempts to reduce the impact of spatiotemporal offsets between these two radar measurements. From 3 April 2014 to 20 May 2018, the DPR measured precipitation near LAMAR during 15 of its 157 coincident overpasses. There were six simultaneous observations with MIRA-35C. We found that the average of the DPR’s lowest clutter-free bin is 1.62 km AGL, but the presence of precipitation worsens the situation, causing a 0.4-km-deeper algorithm-detected blind zone for the DPR at the Huancayo Observatory. In the study area, the depth of the clutter layer observed with DPR often extends above the melting layer but can be highly variable, extending even as high as 5 km AGL. These results suggest that DPR estimates of stratiform precipitation over the Andes Mountains are likely underestimated because of the terrain effects on the satellite measurements and problems in its blind zone detection algorithms, highlighting the difficulty in estimating precipitation in mountainous terrain from spaceborne radar.

The research presents the inter-comparison of atmospheric variables measured by 9 automatic weather stations. This set of data was compared with the measurements of other weather stations in order to standardize the values that must be adjusted when taken to different areas. The data of a set of a total of 9 GMX500, which measures conventional meteorological variables, and 10 WS100 sensors, which measures precipitation parameters. The automatic stations were set up at the Huancayo Observatory (Geophysical Institute of Peru) for a period of 5 months. The data set of GMX500 were evaluated comparing with the average of the 9 sensors and the WS100 was compared with an optical disdrometer Parsivel${}^2$. The temperature, pressure, relative humidity, wind speed, rainfall rate, and drop size distribution were evaluated. A pair of GMX500 sensors presented high data dispersion; it was found found that the errors came from a bad configuration; once this problem was solved, good agreement was archived, with low RMSE and high correlation. It was found that the WS100 sensors overestimate the precipitation with a percentage bias close to 100% and the differences increase with the greater intensity of rain. The drop size distribution retrieved by WS100 have unrealistic behavior with higher concentrations in diameters of 1 mm and 5 mm, in addition to a flattened curve.