A First Evaluation of ERA5-Land Reanalysis Temperature Product Over the Chinese Qilian Mountains

Zhao, Peng; He, Zhibin

doi:10.3389/feart.2022.907730

Cited by 18 publications

(13 citation statements)

References 29 publications

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“…The correction skills are weak in winter except for Method V (Table 5, Table 8). In addition to the altitude difference, the snow cover and glaciers may also be the main reasons for the bias in winter (Zhao and He, 2022a), so the elevation correction method may not be suitable for the bias correction in winter. In addition, after comparing biases, RMSE, and r of Methods Ⅰ-Ⅳ based on Γ ERA , we concluded that the corrected results using Method Ⅲ were superior to those using Methods Ⅰ, Ⅱ, and Ⅳ.…”

Section: Analysis Of Correction Resultsmentioning

confidence: 99%

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Bias correction of ERA-Interim reanalysis temperature for the Qilian Mountains of China

Zhao

Gao

et al. 2022

Front. Environ. Sci.

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Air temperature is the primary indicator of climate change. Reanalysis temperature products are important datasets for temperature estimates over high-elevation areas with few meteorological stations. However, they contain biases in observations, so a bias correction is required to enhance the accuracy of modeling predictions. In this study, we used the temperature lapse-rate method to correct ERA-Interim reanalysis-temperature data in the Qilian Mountains of China from 1979 to 2017. These temperature lapse rates were based on observations (ΓObs) and on model internal vertical lapse rates derived from different ERA-Interim pressure levels (ΓERA). The results showed that the temperature lapse rates in warm periods were larger than those in cold periods. Both the original and corrected ERA-Interim temperature can significantly capture the warming trend exhibited by observations. In general, the temperature lapse rate method was reliable for correcting ERA-interim reanalysis-temperature data. Although ΓObs performed best in bias correction, it depends heavily on the density of ground observation stations and is not appropriate for remote areas with a low data coverage. Correction methods based on ΓERA were shown to be reliable for bias correction, and will be especially applicable to mountainous areas with few observation stations. Our results contribute to the improvement of quality of data products and enhance the accuracy of modeling of climate change effects and risks to the environment and human health.

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Section: Analysis Of Correction Resultsmentioning

confidence: 99%

“…We focused on ERA-Interim data in this study, which is the third-generation reanalysis product of the ECMWF. However, future studies can extend the methods used here to other reanalysis products such as ERA5 and ERA5-Land (Wang C. et al, 2019;Liu et al, 2021;Zhao and He., 2022a). In addition, it would be worth trying to use…”

Section: Future Researchmentioning

confidence: 99%

Bias correction of ERA-Interim reanalysis temperature for the Qilian Mountains of China

Zhao

Gao

et al. 2022

Front. Environ. Sci.

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“…The location of cell grid points ranged from 35.8 to 40°N and from 93.5 to 104°E, which covered the entire QLM region (Figure 1). According to the latitude and longitude coordinates of the 17 stations, the ERA5-Land cell closest to each station is selected to avoid spatial interpolation (Zhao and He, 2022a). ERA5-Land hourly reanalysis data is given for UTC time, with an 8-h difference from Beijing time in China; we matched the times of observation and ERA5-Land reanalysis with a timedifference conversion method (Zhao P. et al, 2020;Xu et al, 2022).…”

Section: Era5-land Temperature Data (T E )mentioning

confidence: 99%

“…In this study, observational daily temperature data including daily mean temperature (T_mean), daily maximum temperature (T_max) and daily minimum temperature (T_min) at 17 meteorological stations in the QLM were obtained from China Meteorological Data Sharing Service of National Meteorological Information Center. 3 These data can be directly used in scientific research after strict quality control by provider from five aspects: climate limit value and allowable value checking, extreme value checking, internal consistency checking between timing value, daily average, and daily extreme value, time consistency checking, and space consistency checking (Zhao C. et al, 2020;Zhao P. et al, 2020;Zhao and He, 2022a). Spatial distribution and specific information on the meteorological stations can be found in Figure 1 and Table 1, respectively.…”

Section: Observations (T O )mentioning

confidence: 99%

“…Due to the complex terrain and climatic conditions of the QLM, meteorological stations are rare, especially above 3,500 m (Lin et al, 2017;Wang L. et al, 2019;Zhou et al, 2019), necessitating the use of reanalysis data. Zhao and He (2022a) assessed ERA5-Land reanalysis monthly average temperature for the QLM and found that the observed temperature variation was captured very well. Li et al (2022) found that ERA5-Land was more suitable than China Meteorological Forcing Dataset for analyzing temperature trends in the QLM.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of ERA5-Land reanalysis datasets for extreme temperatures in the Qilian Mountains of China

Zhao

He²,

Ma³

et al. 2023

Front. Ecol. Evol.

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An increase in extreme temperature events could have a significant impact on terrestrial ecosystems. Reanalysis temperature data are an important data set for extreme temperature estimation in mountainous areas with few meteorological stations. The ability of ERA5-Land reanalysis data to capture the extreme temperature index published by the Expert Team on Climate Change Detection and Indices (ETCCDI) was evaluated by using the observational data from 17 meteorological stations in the Qilian Mountains (QLM) during 1979–2017. The results show that the ERA5-Land reanalysis temperature data can capture well for the daily maximum temperature, two warm extremes (TXx and TX90p) and one cold extreme (FD0) in the QLM. ERA5-Land’s ability to capture temperature extremes is best in summer and worst in spring and winter. In addition, ERA5-Land can capture trends in all extreme temperature indices except the daily temperature range (DTR). The main bias of ERA5-Land is due to the difference in elevation between the ground observation station and the ERA5-Land grid point. The simulation accuracy of ERA5-Land increases with the decrease of elevation difference. The results can provide a reference for the study of local extreme temperature by using reanalysis data.

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A first assessment of ERA5 and ERA5‐Land reanalysis air temperature in Portugal

Almeida,

Coelho

2023

Intl Journal of Climatology

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This study evaluates the reliability of ERA5 and ERA5‐Land reanalysis datasets in describing the mean daily air temperature of four climate domains in mainland Portugal. The reanalysis datasets were compared with ground observations from 94 meteorological stations (1980–2021). Overall, the results demonstrated a good degree of correlation between the observed and reanalysis data on both a daily and seasonal scale. Both the latitudinal distribution of the air temperature and the moderating effect of the Atlantic Ocean are well described. However, in the case of Portugal, the ERA5‐Land was shown to be considerably more effective at describing the mean daily air temperature than ERA5. The results also indicated that, in general, the reanalysis methodologies perform better when applied to air temperature simulation in flatter regions as opposed to regions with high‐altitude and complex terrain. The study further suggests that ERA5 and ERA5‐Land reanalysis should be used with caution in the case of short‐term environmental studies. In fact, relevant differences were shown to exist between the reanalyses and the observed daily mean air temperature datasets for certain specific years. Overall, considering the RMSE between the ERA5‐Land reanalysis datasets for mean daily air temperature and the observed datasets there is a 28% probability of locally having a mean RMSE <1.5°C, 52% probability of having a mean RMSE >1.5°C and <2.0°C, and 16% probability of having a RMSE >2.0°C and <3.0°C. These conclusions will hopefully contribute to improving our understanding of the uncertainty sources in relation to ERA5 and ERA5‐Land reanalysis data for different climate domains.

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A First Evaluation of ERA5-Land Reanalysis Temperature Product Over the Chinese Qilian Mountains

Cited by 18 publications

References 29 publications

Bias correction of ERA-Interim reanalysis temperature for the Qilian Mountains of China

Bias correction of ERA-Interim reanalysis temperature for the Qilian Mountains of China

Evaluation of ERA5-Land reanalysis datasets for extreme temperatures in the Qilian Mountains of China

A first assessment of ERA5 and ERA5‐Land reanalysis air temperature in Portugal

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