气候变化领域集成服务门户(CCPortal): Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model

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DOI	10.5194/hess-22-2575-2018
	Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model
	Pinnington E.; Quaife T.; Black E.
发表日期	2018
ISSN	1027-5606
起始页码	2575
结束页码	2588
卷号	22 期号:4
英文摘要	We show that satellite-derived estimates of shallow soil moisture can be used to calibrate a land surface model at the regional scale in Ghana, using data assimilation techniques. The modified calibration significantly improves model estimation of soil moisture. Specifically, we find an 18% reduction in unbiased root-mean-squared differences in the north of Ghana and a 21% reduction in the south of Ghana for a 5-year hindcast after assimilating a single year of soil moisture observations to update model parameters. The use of an improved remotely sensed rainfall dataset contributes to 6% of this reduction in deviation for northern Ghana and 10% for southern Ghana. Improved rainfall data have the greatest impact on model estimates during the seasonal wetting-up of soil, with the assimilation of remotely sensed soil moisture having greatest impact during drying-down. In the north of Ghana we are able to recover improved estimates of soil texture after data assimilation. However, we are unable to do so for the south. The significant reduction in unbiased root-mean-squared difference we find after assimilating a single year of observations bodes well for the production of improved land surface model soil moisture estimates over sub-Saharan Africa. © 2018 Author(s).
语种	英语
scopus关键词	Data reduction; Rain; Remote sensing; Soil surveys; Surface measurement; Wetting; Data assimilation; Data assimilation systems; Data assimilation techniques; Land surface modeling; Remotely sensed soil moisture; Root mean squared; Soil moisture predictions; Sub-saharan africa; Soil moisture; calibration; data assimilation; hindcasting; land surface; precipitation (climatology); prediction; remote sensing; satellite altimetry; soil moisture; soil texture; Ghana
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/160042
作者单位	Pinnington, E., Department of Meteorology, University of Reading, Reading, United Kingdom, National Centre for Earth Observation, University of Reading, Reading, United Kingdom; Quaife, T., Department of Meteorology, University of Reading, Reading, United Kingdom, National Centre for Earth Observation, University of Reading, Reading, United Kingdom; Black, E., Department of Meteorology, University of Reading, Reading, United Kingdom, National Centre for Atmospheric Science, University of Reading, Reading, United Kingdom
推荐引用方式 GB/T 7714	Pinnington E.,Quaife T.,Black E.. Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model[J],2018,22(4).
APA	Pinnington E.,Quaife T.,&Black E..(2018).Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model.Hydrology and Earth System Sciences,22(4).
MLA	Pinnington E.,et al."Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model".Hydrology and Earth System Sciences 22.4(2018).