Prediction of Daily Maximum Air Temperature for Transitional Seasons by Statistical Methods in Baghdad
Keywords:maximum air temperature forecast, climate change, correlation coefficient, multiple linear regression formula, bias
Predicting the maximum temperature is of great importance because it is related to various aspects of life, starting from people’s lives and their comfort, passing through the medical, industrial, agricultural and commercial fields, as well as concerning global warming and what can result from it. Thus, the historical observations of maximum and minimum air temperature, wind speed and relative humidity were analyzed in this work. In Baghdad, the climatic variables were recorded on clear sky days dawn at 0300 GMT for the period between (2005-2020). Using weather station's variables multiple linear regression equation, their correlation coefficients were calculated to predict the daily maximum air temperature for any day during the transitional seasons (autumn, spring). By analyzing the results, a comparison was made between the expected and recorded maximum air temperature to improve the equation. The bias was tracked by analyzing the number of relative frequencies of the occurrence of these errors. (0.2 ) for the autumn season and (0.15 ) for the spring season was added to the multiple linear regression equation as a correction value.