Abstract
In regional studies, reanalysis datasets can extend precipitation time series with insufficient observations. In the present study, the ERA5 precipitation dataset was compared to observational datasets from meteorological stations in nine different precipitation zones of Iran (0.125° × 0.125° grid box) for the period 2000–2018, and measurement criteria and skill detection criteria were applied to analyze the datasets. The results of the daily analysis revealed that the correlation between ERA5 and observed precipitation were larger than 0.5 at 90% of stations. Also, The daily standard relative bias indicated that precipitation was overestimated in zone 6. As detection criteria, the frequency bias index (FBI) and proportion correct (PC) showed that the ERA5 data could capture daily precipitation events. Correlation confidence comparisons between the ERA5 and observational time series at daily, monthly, and seasonal scales revealed that the correlation confidence was higher at monthly and seasonal scales. The standard relative bias results at monthly and seasonal scales followed the daily relative bias results, and most of the ERA5 underestimations during the summer belonged to zone 1 in the coastal area of the Caspian Sea with convective precipitation. In addition, some complex mountainous regions were associated with overestimated precipitation, especially in northwest Iran (zone 6) in different time scales.
Highlights
A reliable precipitation dataset with a sufficient spatiotemporal distribution is essential for different water management and agricultural sectors
Other studies have evaluated the application of ERA5 data in various fields [3,21,22] Considering the critical role of daily precipitation reanalysis products in climate change and hydro-climate studies [23,24], this study evaluates the performance of ERA5 precipitation data based on observational dataset for nine precipitation zones in Iran at daily, monthly, and seasonal scales from 2000 to 2018
The daily ERA5 precipitation data were retrieved by a methodology that is downloadable from the below address for the period of 2000–2018, and monthly and seasonal precipitation values were calculated from daily data
Summary
A reliable precipitation dataset with a sufficient spatiotemporal distribution is essential for different water management and agricultural sectors. A lack of sufficient precipitation data with a proper distribution in catchments that are extended on highlands or in arid–semi-arid areas result in a poor climate representation and inadequate surface precipitation measurements [1,2,3]. Observed precipitation data from weather stations typically provide the required data for hydro-climate studies [4]. Due to the precipitation role in various studies, gridded precipitation data have been applied as an accurate proxy for observed data in different parts of the world [5,6]. Precipitation reanalysis datasets are ordinarily developed by incorporating an extensive range of ground-based observations and numerical weather prediction products, with or without using a data assimilation technique [7,8]
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