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

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.

Automated summary

This study accurately estimated the surface air temperature lapse rate (SATLR) in mainland China using the geographically weighted regression (GWR) model, and validated the model's accuracy and predictive ability. The results showed that the GWR model performed well in estimating SATLR and has potential wide application value in regions with complex terrain and climatic conditions.