Treatment Missing Data of Daily and Monthly Air Temperature in Iraq by Using Mean Method - Yassen, Ali Hamid; Abdul Kareem , Asraa Khtan (Iraqi Journal of Science, 2023)
Frequent data in weather records is essential for forecasting, numerical model development, and research, but data recording interruptions may occur for various reasons. So, this study aims to find a way to treat these missing data and know their accuracy by comparing them with the original data values. The mean method was used to treat daily and monthly missing temperature data. The results show that treating the monthly temperature data for the stations (Baghdad, Hilla, Basra, Nasiriya, and Samawa) in Iraq for all periods (1980-2020), the percentage for matching between the original and the treating values did not exceed (80%). So, the period was divided into four periods. It was noted that most of the congruence values increased, reached in summer (70%-100%), and decreased somewhat in winter. While the daily treatment using the mean method for the stations Baghdad and Basra (2010-2020), it turns out that most of the congruence values in the summer ranged (70%-100%), but in winter, the congruence is often decreased. Therefore, this method gives high accuracy when treating monthly and daily temperatures in summer and less in winter.