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An approach for improving the NRLMSISE-00 model using a radiosonde at Golmud of the Tibetan Plateau

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Abstract

It is believed that the Tibetan Plateau plays an important role in shaping the global atmospheric circulation and climate change. Due to the lack of observations in its surrounding area, previous studies mostly relied on numerical and empirical models to characterize the atmosphere over the Tibetan Plateau. For example, NRLMSISE-00 is a neutral atmospheric empirical model that has been widely used. In this study, NRLMSISE-00 model temperature data were compared with radiosonde temperature profiles obtained over Golmud, which is located in the northeast part of the Tibetan Plateau. Certain degree of deviations has been observed, especially in the warm season (here we defined from May 16 to October 15), with the temperature difference up to 19 K. A three-layer feed-forward neural network (NN) has been built to improve the NRLMSISE-00 model. Radiosonde temperature profiles from year 2013 to 2014 were used as training database for this NN. The radiosonde temperatures in year 2015 were then used to evaluate this method. Our results showed that the deviations between the NRLMSISE-00 model and the in situ radiosonde data have been significantly reduced. This study demonstrates the feasibility of NN for improving the atmospheric model accuracy using radiosonde data as training samples.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC) (Grant no. 41127901). The authors would like to acknowledge the Golmud Meteorological Bureau for providing radiosonde data.

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Authors and Affiliations

  1. Near-Space Environment Laboratory, National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China

    Yaru Dai & Xiong Hu

  2. Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China

    Yaru Dai, Weilin Pan, Zhixuan Bai, Chao Ban & Hengheng Zhang

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yaru Dai & Weilin Pan

  4. School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Hengheng Zhang

  5. Key Laboratory for Cloud Physics, Chinese Academy of Meteorological Sciences, Beijing, 100081, China

    Yunfei Che

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  1. Yaru Dai
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  2. Weilin Pan
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  3. Xiong Hu
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  4. Zhixuan Bai
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  6. Hengheng Zhang
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  7. Yunfei Che
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Correspondence to Weilin Pan.

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Dai, Y., Pan, W., Hu, X. et al. An approach for improving the NRLMSISE-00 model using a radiosonde at Golmud of the Tibetan Plateau. Meteorol Atmos Phys 132, 451–459 (2020). https://doi.org/10.1007/s00703-019-00700-w

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