| 摘 要： |
The warming amplification of the Tibetan Plateau (TP) has exerted great impacts on the environment in and around the region. It is necessary to thoroughly investigate the temperature changes over the TP. However, the commonly used station observations, satellite products and reanalysis data for relevant studies suffer from deficiencies of sparse spatial distribution, limited temporal coverage and large uncertainties, respectively. This leaves the current understanding of temperature change on the TP still inadequate. Therefore, we propose a multi-source data fusion method to integrate the advantages of different data. Combining the Moderate-resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products and station observations, this new method first obtains short-term satellite -derived surface air temperature (SAT) estimates over grids. The European Center for Medium-range Weather Forecasts ReAnalysis 5 land portion (ERA5-Land) temperature with long time range is then clustered and used as independent variables for the Bayesian Ridge Regression (BRR) model. Based on this BRR model, the temporal span of the estimated gridded satellite SAT is extended, and a fused 1-km monthly mean SAT dataset is generated over the TP from 1961 to 2020. The results indicate that the fused data generated by our proposed method has good accuracy with overall RMSE and MBE of 1.33 ? and 1.03 ?, respectively. Despite the temporal and spatial heterogeneity, the performance of the fused SATs is acceptable across seasons and geographical locations. The dataset also shows a great potential for detecting accurate long-term temperature changes across the TP. This fused SAT data owns the advantages from multiple data sources with high accuracy, good spatial continuity, fine spatial resolution and wide temporal coverage, which confirms that our fusion method can provide a favorable opportunity to explore the warming over the TP. |
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