Evaluation of the Change in Some Meteorological Variables Measured with the Automatic Station at the Yabu Meteorological Station, Cuba

Abstract

Ricardo Oses Rodríguez and Nancy Ruiz Cabrera

For this work, the Regressive Objective Regression (ROR) methodology was used to model the three meteorological variables: extreme temperatures, maximum and minimum temperatures, and maximum rainfall within 24 hours. For this purpose, a step variable was designed, which takes a value of zero before the change, that is, before 2023, month 8, day 31, and takes a value of 1 after this date, which corresponds to the new automatic station. It can be seen that the model depends on temperatures and rainfall regressed over 11 years.Specifically, the new station represents a 1.39°C drop in minimum temperature. The trend is positive but very small and highly significant at 100%. For maximum temperature, the trend is positive but very small and highly significant at 100%. Similarly, for minimum temperature, the new station represents a 1.48°C drop. It is further confirmed that with the automatic station, both maximum and minimum temperatures are below what was measured at the previous station used by meteorologists. For maximum rainfall over 24 hours, the trend is positive but very small and highly significant. The new meteorological station reports a 2.9 mm decrease in rainfall. In the short term, the errors are smaller, and the impact of using the automatic station results in small and non-significant parameters. Therefore, it can be concluded that the average values from the Yabu station can be used, combined with the new data, at least in the short term.

PDF