IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v11y2021i12p1292-d706019.html
   My bibliography  Save this article

Multiscale Assessments of Three Reanalysis Temperature Data Systems over China

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/11/12/1292/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/11/12/1292/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Le Sun & Congmou Zhu & Shaofeng Yuan & Lixia Yang & Shan He & Wuyan Li, 2022. "Exploring the Impact of Digital Inclusive Finance on Agricultural Carbon Emission Performance in China," IJERPH, MDPI, vol. 19(17), pages 1-18, September.
    2. Yan Chen & Xiaohong Chen & Hongshan Ai & Xiaoqing Tan, 2022. "Temperature and Migration Intention: Evidence from the Unified National Graduate Entrance Examination in China," IJERPH, MDPI, vol. 19(16), pages 1-23, August.
    3. Xin Xuan & Bing Liu & Fan Zhang, 2021. "Climate Change and Adaptive Management: Case Study in Agriculture, Forestry and Pastoral Areas," Land, MDPI, vol. 10(8), pages 1-17, August.
    4. Xiaoguang Chen & Madhu Khanna & Lu Yang, 2022. "The impacts of temperature on Chinese food processing firms," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 66(2), pages 256-279, April.
    5. Jiao, Xiyu & Pretis, Felix & Schwarz, Moritz, 2024. "Testing for coefficient distortion due to outliers with an application to the economic impacts of climate change," Journal of Econometrics, Elsevier, vol. 239(1).
    6. Zhang, Qizheng & Qian, Zesen & Wang, Shuo & Yuan, Lingran & Gong, Binlei, 2022. "Productivity drain or productivity gain? The effect of new technology adoption in the oilfield market," Energy Economics, Elsevier, vol. 108(C).
    7. Zesen Qian & Lingran Yuan & Shuo Wang & Qizheng Zhang & Binlei Gong, 2021. "Epidemics, Convergence, and Common Prosperity: Evidence from China," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 29(6), pages 117-138, November.
    8. repec:ags:aaea22:335522 is not listed on IDEAS
    9. Lingran Yuan & Shurui Zhang & Shuo Wang & Zesen Qian & Binlei Gong, 2021. "World agricultural convergence," Journal of Productivity Analysis, Springer, vol. 55(2), pages 135-153, April.
    10. Ding, Yugang & Xu, Jiangmin, 2023. "Global vulnerability of agricultural commodities to climate risk: Evidence from satellite data," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 669-687.
    11. Wang, Teng & Yi, Fujin & Liu, Huilin & Wu, Ximing & Zhong, Funing, 2021. "Can Agricultural Mechanization Have a Mitigation Effect on China's Yield Variability?," 2021 Conference, August 17-31, 2021, Virtual 315098, International Association of Agricultural Economists.
    12. Hai Li & Hui Liu, 2023. "Climate Change, Farm Irrigation Facilities, and Agriculture Total Factor Productivity: Evidence from China," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    13. Xun Su & Minpeng Chen, 2022. "Econometric Approaches That Consider Farmers’ Adaptation in Estimating the Impacts of Climate Change on Agriculture: A Review," Sustainability, MDPI, vol. 14(21), pages 1-23, October.
    14. François Bareille & Raja Chakir, 2024. "Structural identification of weather impacts on crop yields: Disentangling agronomic from adaptation effects," American Journal of Agricultural Economics, John Wiley & Sons, vol. 106(3), pages 989-1019, May.
    15. Ning Zhang & Jun Fu & Zhi Chen & Xuegeng Chen & Luquan Ren, 2021. "Optimization of the Process Parameters of an Air-Screen Cleaning System for Frozen Corn Based on the Response Surface Method," Agriculture, MDPI, vol. 11(8), pages 1-18, August.
    16. Guo, Kun & Li, Yichong & Zhang, Yunhan & Ji, Qiang & Zhao, Wanli, 2023. "How are climate risk shocks connected to agricultural markets?," Journal of Commodity Markets, Elsevier, vol. 32(C).
    17. Feriga, Moustafa & Lozano Gracia, Nancy & Serneels, Pieter, 2024. "The Impact of Climate Change on Work Lessons for Developing Countries," IZA Discussion Papers 16914, Institute of Labor Economics (IZA).
    18. Jiancai Pi & Yuhan Luo, 2024. "Climate change and wage inequality," Journal of Economics, Springer, vol. 143(3), pages 279-303, December.
    19. Dang, Hai-Anh H & Hallegatte, Stephane & Trinh, Trong-Anh, 2023. "Does Global Warming Worsen Poverty and Inequality? An Updated Review," IZA Discussion Papers 16570, Institute of Labor Economics (IZA).
    20. Binlei Gong & Robin C. Sickles, 2021. "Resource allocation in multi-divisional multi-product firms," Journal of Productivity Analysis, Springer, vol. 55(2), pages 47-70, April.
    21. Liu, Ziheng & Lu, Qinan, 2023. "Ozone stress and crop harvesting failure: Evidence from US food production," Food Policy, Elsevier, vol. 121(C).

    More about this item

    Keywords

    temperature; evaluation; CLDAS; GLDAS; ERA5-Land;
    All these keywords.

    JEL classification:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:11:y:2021:i:12:p:1292-:d:706019. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.