Creating 1-km long-term (1980–2014) daily average air temperatures over the Tibetan Plateau by integrating eight types of reanalysis and land data assimilation products downscaled with MODIS-estimated temperature lapse rates based on machine learning
Creating 1-km long-term (1980–2014) daily average air temperatures over the Tibetan Plateau by integrating eight types of reanalysis and land data assimilation products downscaled with MODIS-estimated temperature lapse rates based on machine learning
复制标题
通过整合八种类型的再分析和陆地数据同化产品，通过基于机器学习的 MODIS 估计温度递减率缩小规模，创建青藏高原 1 公里长期（1980 年至 2014 年）日平均气温
DOI：
10.1016/j.jag.2021.102295
复制
发表时间：
2021
影响因子：
7.5
通讯作者：
Ming Ye
中科院分区：
文献类型：
--
作者：
Hongbo Zhang;W.W. Immerzeel;Fan Zhang;Remco J. de Kok;Sally J. Gorrie;Ming Ye
Air temperature (Tair) is critical to modeling environmental processes (e.g. snow/glacier melting) in high-elevation areas of the Tibetan Plateau (TP). To resolve the issue thatTairobservations are scarce in the TP western part and at high elevation, many studies have estimated daily air temperatures by using MODIS land surface temperature (LST) and various reanalysis datasets. These estimates are however inadequate for supporting high-resolution long-term hydrological simulations or climate analysis due to the high cloud cover, short time span or low spatial resolution. To improve theTairestimation, this study develops a novel machine-learning based method that uses the Gradient Boosting model to efficiently integrate observations from high-elevation stations with eight widely used air temperature reanalysis and assimilation datasets (i.e., NNRP-2, 20CRV2c, JRA-55, ERA-Interim, MERRA-2, CFSR, ERA5 and GLDAS2) downscaled with remote sensing-based temperature lapse rates (TLR). This method is used to generate a new dataset of daily air temperature with the 1-km resolution for the period of 1980–2014. To overcome the problem that TLR derived from limited stations may be unreliable, a new TLR estimation method is developed to first estimate spatially continuous monthly TLRs from MODIS LST and then downscale daily meanTairfrom eight reanalysis and assimilation datasets to obtainTairat the 1-km resolution using the MODIS-estimated TLRs. The Gradient Boosting (GB) model is selected for integrating the eight downscaledTairand five other auxiliary variables. The models are trained and validated using observations from 100 common stations (i.e. China Meteorology Administration stations) and 13 independent high-elevation stations (4 on glaciers). The results show that the proposed TLR estimation method can efficiently reduce exceptional TLRs in the meantime keeping acceptable downscaling accuracy. The downscaledTairfrom JRA-55 is the best among the eight downscaled datasets followed by ERA-Interim, MERRA-2, CFSR and others. Finally, the GB-integratedTairfurther outperforms the downscaled JRA-55Tairwith the mean root-mean-squared-deviation (RMSD) of 1.7 °C versus 2.0 °C, especially in high-elevation stations with mean RMSD of 1.9 °C versus 2.7 °C. Both the MODIS-estimated TLR and the high-elevation training observations are demonstrated to significantly improve the air temperature estimation accuracy of the GB model in high-elevation stations. This study also provides a framework for integrating multiple reanalysis and assimilation temperature data with elevation correction in mountainous regions that is not restricted to the TP.
