Análise sinótica e multivariada de condições climáticas extremas na cidade de Salvador.

Abstract

Heavily populated urban areas located in the tropical region are particularly vulnerable to intense rainfall events. In the city of Salvador, capital of Bahia State, several factors contribute for the severity of the impacts caused by intense rainfall: the complex topography, the population density, the disorderly human occupation, the diversity and intensity of meteorological systems, among others. In this study the atmospheric stability conditions in the (very rainy) month of April 2009 are analyzed with the main objective of investigating the degree of relationship between stability indices and rainfall occurrence in the city. Reanalysis data, enhanced meteorological satellite imagery, daily 24-hour rainfall totals and 1200 UTC daily upper air soundings are the basis for this study. Meteorological and multivariate analysis techniques are used. There are seventeen rainy days in the month of study among which four have daily rainfall totals higher than 50 mm. One event of heavy rainfall over the entire city whose rainfall total is registered on day 19, is caused by a convective line that forms over Bahia State, on the latitude of Salvador, associated to a frontal trough with strong horizontal and vertical wind shear. Moisture content is high in the lower troposphere, before and after the event. The application of principal component analysis to nine stability indices results in a three component model which explains 91.82% of the total variance in the data. The first principal component is associated with indices related to the vertical variation of moisture and temperature, wind direction and speed, the second component to the indices related to the vertical variation of moisture, and the third component to the K index. The application of cluster analysis to the rotated factors results in four groups with distinct characteristics. Group 1 has days with rain, among which there are three intense rainfall events. Group 2 has days with opposing characteristics in terms of precipitation. Group 3 is comprised by days with very low rainfall values. Group 4 has days with low rainfall total or rainfall absence, with exception of one intense rainfall day. The K index is the best in forecasting rainfall in the month of study.