Evaluation of ERA-Interim Air Temperature Data over the Qilian Mountains of China

Abstract

In this study, 2 m air temperature data from 24 meteorological stations in the Qilian Mountains (QLM) are examined to evaluate ERA-Interim reanalysis temperature data derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the period of 1979-2017. ERA-Interim generally captures the monthly, seasonal, and annual variation very well. High daily correlations ranging from 0.956 to 0.996 indicate that ERA-Interim captures the daily temperature observations very well. However, an average root-mean-square error (RMSE) of ±2.7°C of all stations reveals that ERA-Interim should not be directly applied at individual sites. e biases are mainly attributed to the altitude differences between ERA-Interim grid points and stations. e positive trend (0.457°C/decade) is significant over the Qilian Mountains based on the 1979-2017 observations. ERA-Interim captures the warming trend very well with an increase rate of 0.384°C/decade. e observations and ERA-Interim both show the largest positive trends in summer with the values of 0.552°C/decade and 0.481°C/decade, respectively. We conclude that in general ERA-Interim captures the trend very well for observed 2 m air temperatures and ERA-Interim is generally reliable for climate change research over the Qilian Mountains.

How Well Does the ERA5 Reanalysis Capture the Extreme Climate Events Over China? Part II: Extreme Temperature

“…Gao et al (2016b) concluded that the altitude difference between meteorological stations and ERA-20CM led to the temperature bias. Zhao et al (2020) found the bias increases with the elevation difference between ERA5-Interim and observation temperature data. Figure 9 demonstrates the distribution of elevation gaps between stations and ERA5 data (ERA5 original grid point height minus Obs elevation) to further explore the reasons for the deviation of ERA5 temperature data.…”

“…In summary, the results demonstrated that ERA5 captured the intensity of extreme temperature events in spring, summer, and autumn with higher reliability than in winter. The possible reason may be due to the air temperature being more changeable and complex in winter (Zhao et al, 2020). Additionally, the low simulation accuracy of snow cover and snow depth resulted in large uncertainty of temperature modeling in winter (Kanamitsu et al, 2002;Ma et al, 2008).…”

“…Additionally, the low simulation accuracy of snow cover and snow depth resulted in large uncertainty of temperature modeling in winter (Kanamitsu et al, 2002;Ma et al, 2008). Previous studies have also found that the accuracy of reanalysis to capture air temperature is better in summer, whereas worse in winter (Zhao et al, 2008;Zhao et al, 2020;Yu et al, 2021). Furthermore, there is a large discrepancy of the climate and topography characteristics in various subregions of China.…”

Factors contributing to spatial–temporal variations of observed oxygen concentration over the Qinghai-Tibetan Plateau

“…What caused such a mismatch can be owed to how well the two datasets represented the local conditions. The 500 hPa air temperature, which was derived from ERA-Interim reanalysis temperature data (the European Centre for Medium-Range Weather Forecasts) and has a spatial resolution of 0.25° × 0.25° (roughly 30 km × 30 km), may introduce some errors when applied directly to the sample sites due to its relatively coarse spatial and temporal resolutions 20 . The real-time measured air temperature used in this study should be much more reliable, and the controlled investigations in Qilian Mountains during winter and summer and the observations at Fangshan Station, Beijing further confirmed this positive correlation.…”

Performance evaluation of CFSR, MERRA-2 and TRMM3B42 data sets in simulating river discharge of data-scarce tropical catchments: a case study of Manafwa, Uganda

“…The usage of multiyear global gridded representations of weather known as reanalysis datasets has become widely accepted for hydrological and climatological modelling for catchments (Nkiaka et al 2017;Bahati et al 2021;Byakatonda et al 2021). National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (Tarana & Slobodan 2010), Climate Forecasting System Reanalysis (CFSR) (Daniel et al 2014), European Center for Medium-Range Weather Forecasts (ECMWF) ERA-Interim (Peng et al 2020) and Modern-Era Retrospective Analysis for Research and Applications (MERRA) (Gelaro et al 2017) are some of the widely used datasets. Reanalysis datasets have been used as input for hydrological modelling in many research studies (Kigobe et al 2011;Kisembe et al 2018;Jingrui et al 2020).…”