气候变化领域集成服务门户(CCPortal): Integrating remote sensing data with WRF model for improved 2-m temperature and humidity simulations in China

CCPortal

DOI	10.1016/j.dynatmoce.2019.101127
	Integrating remote sensing data with WRF model for improved 2-m temperature and humidity simulations in China
	Yan D.; Liu T.; Dong W.; Liao X.; Luo S.; Wu K.; Zhu X.; Zheng Z.; Wen X.
发表日期	2020
ISSN	03770265
卷号	89
英文摘要	The default green vegetation fraction (GVF) in the Weather Research and Forecasting (WRF) Model version 3.7.1 was derived between 1985 and 1990 from the 1990s Normalized Difference Vegetation Index (NDVI) achieved from the NOAA Advanced Very High Resolution Radiometer (AVHRR), and its representation is deteriorating when used to simulate recent weather and climate events. In this study, we applied in WRF v3.7.1 the updated GVF estimated by the real-time NDVI of the Moderate Resolution Imaging Spectroradiometer (MODIS) data to provide a better representation of the prescribed surface GVF condition. A one-year simulation was carried out in China, and the simulated 2-m air temperature and specific humidity were compared between the WRF model control experiment that employs the default GVF data (WRF-CTL), the WRF simulations with updated GVF (WRF-MODIS), and the observations from 824 weather stations in China. Results are significantly improved for both the 2-m air temperature and the specific humidity by WRF-MODIS, which has effectively reproduced the observed pattern and increased the correlation coefficient between the model simulations and observations. The RMSE and bias of specific humidity are also reduced in WRF-MODIS. In general, the real-time MODIS-NDVI based GVF reflected the realistic increase of vegetation cover in China when comparing to the WRF default GVF, and also provided a more accurate seasonal variation for the simulated year of 2009. As a result, the WRF-MODIS simulation significantly improves its representation in the simulated 2-m air temperature and specific humidity, both in spatial distributions and seasonal variations, due to the GVF's great contribution in modulating the coupled land-atmosphere interactions. © 2019 The Authors
英文关键词	MODIS-NDVI; Specific humidity; Temperature; WRF model
语种	英语
scopus关键词	Advanced very high resolution radiometers (AVHRR); Atmospheric temperature; Climate models; Humidity control; Remote sensing; Temperature; Vegetation; Land atmosphere interaction; Moderate resolution imaging spectroradiometer datum; Modis ndvi; Normalized difference vegetation index; Specific humidity; Temperature and humidities; Weather research and forecasting models; WRF Model; Weather forecasting; accuracy assessment; AVHRR; climate modeling; computer simulation; humidity; MODIS; NDVI; real time; remote sensing; temperature profile; weather forecasting; China
来源期刊	Dynamics of Atmospheres and Oceans
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178332
作者单位	State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China; Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu, 610225, China; School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai, 519082, China; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Cold and Arid Regions Environment and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, 730000, China; Department of Land, Air, and Water Resources, University of California, Davis, CA, United States; Shenyang Institute of Atmospheric Environment, China Meteorological Administration, Shenyang, 110116, China
推荐引用方式 GB/T 7714	Yan D.,Liu T.,Dong W.,et al. Integrating remote sensing data with WRF model for improved 2-m temperature and humidity simulations in China[J],2020,89.
APA	Yan D..,Liu T..,Dong W..,Liao X..,Luo S..,...&Wen X..(2020).Integrating remote sensing data with WRF model for improved 2-m temperature and humidity simulations in China.Dynamics of Atmospheres and Oceans,89.
MLA	Yan D.,et al."Integrating remote sensing data with WRF model for improved 2-m temperature and humidity simulations in China".Dynamics of Atmospheres and Oceans 89(2020).