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Multiscale Assessments of Three Reanalysis Temperature Data Systems over China

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  • Xiaolong Huang

    (Sichuan Meteorological Observation and Data Centre, Chengdu 610072, China
    Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072, China)

  • Shuai Han

    (National Meteorological Information Center, China Meteorological Administration, Beijing 100081, China)

  • Chunxiang Shi

    (National Meteorological Information Center, China Meteorological Administration, Beijing 100081, China)

Abstract

Temperature is one of the most important meteorological variables for global climate change and human sustainable development. It plays an important role in agroclimatic regionalization and crop production. To date, temperature data have come from a wide range of sources. A detailed understanding of the reliability and applicability of these data will help us to better carry out research in crop modelling, agricultural ecology and irrigation. In this study, temperature reanalysis products produced by the China Meteorological Administration Land Data Assimilation System (CLDAS), the U.S. Global Land Data Assimilation System (GLDAS) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version5 (ERA5)-Land are verified against hourly observations collected from 2265 national automatic weather stations (NAWS) in China for the period 2017–2019. The above three reanalysis systems are advanced and widely used multi-source data fusion and re-analysis systems at present. The station observations have gone through data Quality Control (QC) and are taken as “true values” in the present study. The three reanalysis temperature datasets were spatial interpolated using the bi-linear interpolation method to station locations at each time. By calculating the statistical metrics, the accuracy of the gridded datasets can be evaluated. The conclusions are as follows. (1) Based on the evaluation of temporal variability and spatial distribution as well as correlation and bias analysis, all the three reanalysis products are reasonable in China. (2) Statistically, the CLDAS product has the highest accuracy with the root mean square error (RMSE) of 0.83 °C. The RMSEs of the other two reanalysis datasets produced by ERA5-Land and GLDAS are 2.72 °C and 2.91 °C, respectively. This result indicates that the CLDAS performs better than ERA5-Land and GLDAS, while ERA5-Land performs better than GLDAS. (3) The accuracy of the data decreases with increasing elevation, which is common for all of the three products. This implies that more caution is needed when using the three reanalysis temperature data in mountainous regions with complex terrain. The major conclusion of this study is that the CLDAS product demonstrates a relatively high reliability, which is of great significance for the study of climate change and forcing crop models.

Suggested Citation

  • Xiaolong Huang & Shuai Han & Chunxiang Shi, 2021. "Multiscale Assessments of Three Reanalysis Temperature Data Systems over China," Agriculture, MDPI, vol. 11(12), pages 1-20, December.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:12:p:1292-:d:706019
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    References listed on IDEAS

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    1. Chen, Shuai & Gong, Binlei, 2021. "Response and adaptation of agriculture to climate change: Evidence from China," Journal of Development Economics, Elsevier, vol. 148(C).
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    1. Long Qian & Lifeng Wu & Xiaogang Liu & Yaokui Cui & Yongwen Wang, 2022. "Comparison of CLDAS and Machine Learning Models for Reference Evapotranspiration Estimation under Limited Meteorological Data," Sustainability, MDPI, vol. 14(21), pages 1-24, November.
    2. Yanqin Xu & Shuai Han & Chunxiang Shi & Rui Tao & Jiaojiao Zhang & Yu Zhang & Zheng Wang, 2023. "Comparative Analysis of Three Near-Surface Air Temperature Reanalysis Datasets in Inner Mongolia Region," Sustainability, MDPI, vol. 15(17), pages 1-21, August.

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    Keywords

    temperature; evaluation; CLDAS; GLDAS; ERA5-Land;
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