Preprints
https://doi.org/10.5194/egusphere-2022-589
https://doi.org/10.5194/egusphere-2022-589
26 Jul 2022
 | 26 Jul 2022

Monitoring Urban Heat Island Intensity with Ground-based GNSS Observations and Space-based Radio Occultation and Radiosonde Historical Data

Pengfei Xia, Wei Peng, Shirong Ye, Min Guo, and Fangxin Hu

Abstract. Since Urban Heat Islands (UHI) not only negatively impact human health but consume more energy when cooling buildings, accurate monitoring of its impact is critical. In this study, we propose a ground based GNSS technique to fuse GNSS Radio Occultation (RO) and radiosonde products to monitor the UHI intensity, which described as follows: First, the first and second grid tops are defined using the historical RO and radiosonde products. Then, the wet refractivity between the first and second grid tops is fitted to the higher-order spherical harmonic function based on the RO and radiosonde products, and they are used as the inputs of GNSS tomography, which can reduce the number of unknowns voxels of tomography while increasing the effective number of satellite rays, and improving the accuracy of tomography results. Next, according to the relationships among wet refractivity, temperature, and water vapor partial, as well as the function relationships among temperature, wet pressure, and height in adjacent vertical layers, the temperature and water vapor partial pressure can be obtained using the best search method according to the tomography-derived wet refractivity. Finally, the UHI intensity is monitored by the temperature difference between the urban regions and the surrounding rural regions. The radio occultation and radiosonde products of the Hong Kong region from 2010 to 2019, and the observed GNSS network data of the Hong Kong region for the year of 2020 are employed to evaluate the UHI intensity algorithm. The validation of the algorithm is done by comparing the UHI intensity estimated from the algorithm with the temperature data obtained from weather stations. The result shows that the proposed algorithm can achieve an accuracy of 1.2 K at a 95 % confidence level.

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Pengfei Xia, Wei Peng, Shirong Ye, Min Guo, and Fangxin Hu

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2022-589', Zhounan Dong, 05 Aug 2022
    • AC1: 'Reply on CC1', Pengfei Xia, 19 Aug 2022
  • CC2: 'Comment on egusphere-2022-589', xianjie Li, 05 Aug 2022
    • AC2: 'Reply on CC2', Pengfei Xia, 19 Aug 2022
  • RC1: 'Comment on egusphere-2022-589', Anonymous Referee #1, 19 Aug 2022
  • RC2: 'Comment on egusphere-2022-589', Anonymous Referee #2, 20 Aug 2022
  • CEC1: 'Comment on egusphere-2022-589', Juan Antonio Añel, 23 Aug 2022
    • AC6: 'Reply on CEC1', Pengfei Xia, 19 Sep 2022
      • CEC2: 'Reply on AC6', Juan Antonio Añel, 20 Sep 2022
        • AC7: 'Reply on CEC2', Pengfei Xia, 20 Sep 2022
  • CC3: 'Comment on egusphere-2022-589', qimin he, 09 Sep 2022
    • AC5: 'Reply on CC3', Pengfei Xia, 16 Sep 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2022-589', Zhounan Dong, 05 Aug 2022
    • AC1: 'Reply on CC1', Pengfei Xia, 19 Aug 2022
  • CC2: 'Comment on egusphere-2022-589', xianjie Li, 05 Aug 2022
    • AC2: 'Reply on CC2', Pengfei Xia, 19 Aug 2022
  • RC1: 'Comment on egusphere-2022-589', Anonymous Referee #1, 19 Aug 2022
  • RC2: 'Comment on egusphere-2022-589', Anonymous Referee #2, 20 Aug 2022
  • CEC1: 'Comment on egusphere-2022-589', Juan Antonio Añel, 23 Aug 2022
    • AC6: 'Reply on CEC1', Pengfei Xia, 19 Sep 2022
      • CEC2: 'Reply on AC6', Juan Antonio Añel, 20 Sep 2022
        • AC7: 'Reply on CEC2', Pengfei Xia, 20 Sep 2022
  • CC3: 'Comment on egusphere-2022-589', qimin he, 09 Sep 2022
    • AC5: 'Reply on CC3', Pengfei Xia, 16 Sep 2022
Pengfei Xia, Wei Peng, Shirong Ye, Min Guo, and Fangxin Hu
Pengfei Xia, Wei Peng, Shirong Ye, Min Guo, and Fangxin Hu

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Short summary
We first present a novel method of monitoring the UHI intensity using GNSS data. We overcomes two major challenges in the algorithm development. The first challenge is the determination of the GNSS tomographic top grid height, and the second challenge is the estimation of temperature from wet refractivity. The result shows that the proposed algorithm can achieve an accuracy of 1.2 K at a 95 % confidence level.