Journal of Agricultural Meteorology

Journal of Agricultural Meteorology

Evaluation of quantile mapping correction methods on AgMERRA and ERA5 precipitation and air temperature reanalysis data in Khorasan Razavi Province

Document Type : Original Article

Authors
Saeedeh Kamali 1
NOZAR GHAHREMAN 2
mohammad Bannayan 3
1 PhD student, University of tehran
2 Associate Professor, Department of Irrigation and Reclamation, University of Tehran, Karaj, Iran
3 Professor, Dept of Agro Technology Eiderdown University of Mashhad
10.22125/agmj.2025.481865.1175
Abstract
The aim of this study is to evaluate the quantile mapping methods for the bia correction of reanalysis data of AgMERRA and ERA5 daily precipitation and air temperature data. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Pearson's correlation coefficient (r) were used to assess the performance of the correction methods and corresponding Taylor diagrams were drawn for comparative assessment. Daily observed data of maximum temperature, minimum temperature, and precipitation during the period of 1980-2010 from seven synoptic stations in Khorasan-e-Razavi Province were used. In addition, the Mann-Kendall test and Sen's slope were used to determine the trend and its magnitude in the data. The results indicated that both minimum and maximum temperatures exhibited a significant increasing trend, such that the slope of the minimum temperature increase in all three data sets is higher than that of the maximum temperature data. Also, the precipitation data have a decreasing trend, but this decreasing trend is not significant at most stations. In addition, the error evaluation metrics of the two data sets, ERA5 and AgMERRA, compared to the observational data, showed that both data sets have made a good estimate of the maximum and minimum temperatures, such that the MAE and RMSE indices have low and good values. The correlation of the maximum and minimum temperature data also varies between 0.7 and 0.9, with the highest correlations related to ERA5 data. However, in the case of precipitation, the correlation values were low, especially for AgMERRA data. Among the quantile mapping correction methods, the PTF: Scale method has better efficiency than other methods in correcting the reanalysis data, as it has reduced the RMSE and MAE measures in both data sets. The Pearson correlation coefficient has increased at all stations compared to before correction.
Keywords
AgMERRA
Bias correction
ERA5
Khorasan e Razavi Province
Taylor diagram

Subjects

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