气候变化领域集成服务门户(CCPortal): 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

CCPortal

DOI	10.1016/j.jag.2021.102295
	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
	Zhang, Hongbo; Immerzeel, W. W.; Zhang, Fan; de Kok, Remco J.; Gorrie, Sally J.; Ye, Ming
通讯作者	Zhang, F (通讯作者)，Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Tibetan Environm Changes & Land Surface P, Beijing 100101, Peoples R China.
发表日期	2021
ISSN	1569-8432
EISSN	1872-826X
卷号	97
英文摘要	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 that Tair observations 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 the Tair estimation, 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 mean Tair from eight reanalysis and assimilation datasets to obtain Tair at the 1-km resolution using the MODIS-estimated TLRs. The Gradient Boosting (GB) model is selected for integrating the eight downscaled Tair and 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 downscaled Tair from JRA-55 is the best among the eight downscaled datasets followed by ERA-Interim, MERRA-2, CFSR and others. Finally, the GB-integrated Tair further outperforms the downscaled JRA-55 Tair with the mean root-mean-squared-deviation (RMSD) of 1.7 degrees C versus 2.0 degrees C, especially in high-elevation stations with mean RMSD of 1.9 degrees C versus 2.7 degrees 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.
关键词	SURFACE TEMPERATURE ERA-INTERIM DAILY MAXIMUM INTERPOLATION ELEVATION PATTERNS NORTHERN CHINA PRECIPITATION RESOLUTION
英文关键词	MODIS land surface temperature; Tibetan Plateau; Temperature lapse rate; Reanalysis data; Spatial downscaling
语种	英语
WOS研究方向	Remote Sensing
WOS类目	Remote Sensing
WOS记录号	WOS:000616288400004
来源期刊	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/255049
作者单位	[Zhang, Hongbo] China Agr Univ, Coll Water Resources & Civil Engn, Beijing, Peoples R China; [Zhang, Hongbo; Zhang, Fan] Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Tibetan Environm Changes & Land Surface P, Beijing 100101, Peoples R China; [Zhang, Hongbo] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, State Key Lab Cryospher Sci, Lanzhou, Peoples R China; [Zhang, Hongbo] Nanjing Hydraul Res Inst, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing, Peoples R China; [Zhang, Hongbo; Immerzeel, W. W.; de Kok, Remco J.] Univ Utrecht, Dept Phys Geog, POB 80115, Utrecht, Netherlands; [Zhang, Fan] CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing, Peoples R China; [Zhang, Fan] Univ Chinese Acad Sci, Beijing, Peoples R China; [Gorrie, Sally J.; Ye, Ming] Florida State Univ, Dept Earth Ocean & Atmospher Sci, Tallahassee, FL 32306 USA
推荐引用方式 GB/T 7714	Zhang, Hongbo,Immerzeel, W. W.,Zhang, Fan,et al. 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[J]. 中国科学院西北生态环境资源研究院,2021,97.
APA	Zhang, Hongbo,Immerzeel, W. W.,Zhang, Fan,de Kok, Remco J.,Gorrie, Sally J.,&Ye, Ming.(2021).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.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,97.
MLA	Zhang, Hongbo,et al."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".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 97(2021).