عنوان مقاله [English]
Daily minimum air temperature data are greatly needed in climatic studies of first-fall and last-spring frosts, frost periods, evaluation and improvement of crop production potentials, and eventually their effects upon food security. Despite the fact that climate stations, set up at high elevations play important roles in accurate estimate of temperature parameter gradients, and on their mappings, the number of such established stations in Iran is limited, causing many gaps to be served in their data time series. Hence, reconstruction of temperature data for elevated stations is considered to be essential, especially for studies requiring long-term homogeneous data items. This study was aimed at making a comparison of the different methods of readjustment of the daily minimum temperature data (obtained from highly elevated stations) and to determine the most suitable method for readjusting and lengthening of their record periods. To follow the purpose, a number of 12 stations at elevations exceeding 1900 m were selected. A number of 500 randomly sampled (minimum daily temperature) data were taken and reconstructed through 31 classic methods, and as well, through a new proposed approach, based on Cumulative Distribution Function (CDF) of minimum temperature data. Accuracies of these methods were tested using RMSE within 90 and 95 % of confidence interval of errors. Results revealed that Principle Component Analysis, proposed method based on CDF, and Artificial Neural Network stood in priority for reconstruction of daily minimum temperature data, with 95% of confidence intervals, reconstructed error of ±2.0, ±2.2 and ± 3.1 °c, respectively. This study led to completion of daily minimum temperature data series of highly elevated stations for the period of 1965-2010. This can be employed in climate change studies and as well in first-fall vs. last-spring frost risks, and reform of farming calendar depending upon climate change.