Se presentan el procesamiento realizado a las imágenes infrarojas térmicas (10.20 a 11.20 & mu;) del satélite geoestacionario GOES 8. La región de estudio en el área comprendida entre: 0° a 18° S de latitud y 83° a 65 W de longitud, que corresponde a Perú. Los datos de imágenes se encuentran en formato tiff binario (8 bit). Los píxeles tienen una resolución espacial de 4.9 km y se considera que 110 km es igual a un grado en el Ecuador geográfico. Este proceso es aplicado a imágenes GOES-8 del mes de enero del año 202 y se usa una técnica físico-matemático (Convective Stratiform Technique, CST) para la obtención de lluvia convectiva y estratiforme. Se presentan los resultados, haciéndose un análisis del RMSD y BIAS encontrados.

The main objective of this paper is to estímate the monthly rainfall over Peru during EL NIÑO weak to moderate of the 2002 year. To make this GOES-8 satellite images and the convective stratiform technique are used. An analysis of the comparison between GOES-8 estimates and monthly rainfall gauges values is presented. It is observed that the monthly rain rate estímate over the Peruvian rain forest offers the best correlations. However the Peruvian mountain presents the mínimum bias. An explanation is developed to these results making an analysis of the estimates of rain rate that diverse authors carry out in other latitudes.

The goal of the present paper ís the evaluation of a physical-mathematical technique to estímate the rain rate in the Peruvian north coast during days of extreme rainfall the period considered in this work in april 2002. For this purpose infrared GOES-8 satellíte images have been used. The results show that the used technique produces a good representation of the diurna!varíability of rain; however a seríous deficiency exísts in the daily rain rate estimation. The orography conditions and rapid rain formation during an atmospheric process similar to EL NIÑO one would explaín the cause of the opposing results.

This study assesses the influence of PM10 on the optical thickness of aerosols in the central Andes of Peru, in addition to analyzing and establishing their circulation patterns from July to October 2017. This particular period is considered herein because the largest biomass burning events are usually recorded during these months. The regional meteorological-chemistry transport model WRF-Chem (version 3.7) was used with the Fire Inventory from NCAR (FINN) fire emissions dataset and aerosol optical depth (AOD) data from the Aerosol Robotic Network monitoring network (AERONET), which has a CIMEL sun photometer located at the Huancayo Observatory, the most important city in the central highlands of Peru. The simulation used a single domain with a grid size of 18 km and 32 vertical levels. Results showed an increase in PM10 concentrations with an increase in the number of fire outbreaks and...