气候变化领域集成服务门户(CCPortal): Assimilation of SMAP and ASCAT soil moisture retrievals into the JULES land surface model using the Local Ensemble Transform Kalman Filter

CCPortal

DOI	10.1016/j.rse.2020.112222
	Assimilation of SMAP and ASCAT soil moisture retrievals into the JULES land surface model using the Local Ensemble Transform Kalman Filter
	Seo E.; Lee M.-I.; Reichle R.H.
发表日期	2021
ISSN	00344257
卷号	253
英文摘要	A land data assimilation system is developed to merge satellite soil moisture retrievals into the Joint U.K. Land Environment Simulator (JULES) land surface model (LSM) using the Local Ensemble Transform Kalman Filter (LETKF). The system assimilates microwave soil moisture retrievals from the Soil Moisture Active Passive (SMAP) radiometer and the Advanced Scatterometer (ASCAT) after bias correction based on cumulative distribution function fitting. The soil moisture assimilation estimates are evaluated with ground-based soil moisture measurements over the continental U.S. for five consecutive warm seasons (May–September of 2015–2019). The result shows that both SMAP and ASCAT retrievals improve the accuracy of soil moisture estimates. Especially, the SMAP single-sensor assimilation experiment shows the best performance with the increase of temporal anomaly correlation by ΔR ~ 0.05 for surface soil moisture and ΔR ~ 0.03 for root-zone soil moisture compared with the LSM simulation without satellite data assimilation. SMAP assimilation is more skillful than ASCAT assimilation primarily because of the greater skill of the assimilated SMAP retrievals compared to the ASCAT retrievals. The skill improvement also depends significantly on the region; the higher skill improvement in the western U.S. compared to the eastern U.S. is explained by the Kalman gain in the two experiments. Additionally, the regional skill differences in the single-sensor assimilation experiments are attributed to the number of assimilated observations. Finally, the soil moisture assimilation estimates provide more realistic land surface information than model-only simulations for the 2015 and the 2016 western U.S. droughts, suggesting the advantage of using satellite soil moisture retrievals in the current drought monitoring system. © 2020 The Author(s)
英文关键词	ASCAT; JULES LSM; LETKF; SMAP; Soil moisture assimilation
语种	英语
scopus关键词	Distribution functions; Drought; Kalman filters; Meteorological instruments; Satellites; Soil moisture; Surface measurement; Cumulative distribution function; Land data assimilation systems; Root zone soil moistures; Satellite data assimilation; Satellite soil moisture; Soil moisture active passive (SMAP); Soil moisture measurement; Soil moisture retrievals; Soil surveys; ASCAT; data assimilation; drought stress; error correction; Kalman filter; radiometric survey; satellite altimetry; satellite data; soil moisture
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179015
作者单位	School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea; Global Modeling and Assimilation Office, NASA Goddard Spaceflight Center, Greenbelt, MD, United States
推荐引用方式 GB/T 7714	Seo E.,Lee M.-I.,Reichle R.H.. Assimilation of SMAP and ASCAT soil moisture retrievals into the JULES land surface model using the Local Ensemble Transform Kalman Filter[J],2021,253.
APA	Seo E.,Lee M.-I.,&Reichle R.H..(2021).Assimilation of SMAP and ASCAT soil moisture retrievals into the JULES land surface model using the Local Ensemble Transform Kalman Filter.Remote Sensing of Environment,253.
MLA	Seo E.,et al."Assimilation of SMAP and ASCAT soil moisture retrievals into the JULES land surface model using the Local Ensemble Transform Kalman Filter".Remote Sensing of Environment 253(2021).