Estimativas horárias de temperatura do ar em Passo Fundo - RS

Abstract

The meteorological elements are decisive parameters determinants in agriculture, being the air temperature, one of the main elements closely linked to agricultural activities. The meteorological elements tend to follow a pattern of daily temporal variation and these in turn are measured by meteorological stations that can be conventional or automatic. Weather stations differ in the form of collection (distinct reading times), storage and availability of data. The conventional weather stations perform daily readings at three time schedules standardized at (00AM, 12AM and 18AM UTC), providing less accurate description of the daily time variation of the meteorological elements, once that they oscillate continuously, and it can result in reading and registry errors. And on many places in the country, the availability of data from automatic meteorological stations that provide the meteorological data 24 hours per day is not yet available. Therefore, the aim of this work is check the behavior of the daily curve of air temperature variation in Passo Fundo, from analytical and semi-analytical equations and get models that explain the variation. The data were obtained from the Meteorological Station of Passo Fundo who was transformed into a bank of hourly data estimated from temperature. Were adjusted analytical models of temperature in different times of the day, i.e., in the known points of the curve of temporal variation relative to agreed schedules and it was made simple interpolation and adaptations for sinusoidal equations of these values. For each schedule, an estimate of the meteorological elements was given by the model that presented the lowest value of the mean deviation. Therefore, the models describes well the temporal variation of the analyzed meteorological elements (air temperature), starting from their punctual values obtained in the readings of the agreed times, being the sinusoidal function that submitted on 11 of 21 schedules the smallest mean square root of the error (RMSE) and lower average deviation. In the dawn schedules and after the twilight moments, when there were the more accentuated changes in the air temperature, the simple interpolation of the values performed the better function compared to the sinusoidal function.