Journal of Geographical Sciences ›› 2021, Vol. 31 ›› Issue (3): 389-402.doi: 10.1007/s11442-021-1849-5

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Application of geographically weighted regression model in the estimation of surface air temperature lapse rate

QIN Yun1(), REN Guoyu1,2,*(), HUANG Yunxin3, ZHANG Panfeng1, WEN Kangmin1   

  1. 1. Department of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
    2. Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081, China
    3. School of Resource and Environmental Science, Hubei University, Wuhan 430062, China
  • Received:2020-05-16 Accepted:2020-09-11 Online:2021-03-25 Published:2021-05-25
  • Contact: REN Guoyu E-mail:shuyunchenyun@163.com;guoyoo@cma.gov.cn
  • About author:Qin Yun (1990‒), PhD Candidate, specialized in regional climatology and climate change. E-mail: shuyunchenyun@163.com
  • Supported by:
    The National Key R&D Program(2018YFA0605603);Natural Science Foundation of China(41575003)

Abstract:

The surface air temperature lapse rate (SATLR) plays a key role in the hydrological, glacial and ecological modeling, the regional downscaling, and the reconstruction of high-resolution surface air temperature. However, how to accurately estimate the SATLR in the regions with complex terrain and climatic condition has been a great challenge for researchers. The geographically weighted regression (GWR) model was applied in this paper to estimate the SATLR in China’s mainland, and then the assessment and validation for the GWR model were made. The spatial pattern of regression residuals which was identified by Moran’s Index indicated that the GWR model was broadly reasonable for the estimation of SATLR. The small mean absolute error (MAE) in all months indicated that the GWR model had a strong predictive ability for the surface air temperature. The comparison with previous studies for the seasonal mean SATLR further evidenced the accuracy of the estimation. Therefore, the GWR method has potential application for estimating the SATLR in a large region with complex terrain and climatic condition.

Key words: temperature lapse rate, geographically weighted regression, surface air temperature, estimation, regression residual