Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model

Lists

Tools

Pinnington, E., Quaife, T. ORCID: https://orcid.org/0000-0001-6896-4613 and Black, E. ORCID: https://orcid.org/0000-0003-1344-6186 (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). pp. 2575-2588. ISSN 1027-5606

Preview

Text (Open Access) - Published Version
· Available under License Creative Commons Attribution.
· Please see our End User Agreement before downloading.
4MB

Text - Accepted Version
· Restricted to Repository staff only
1MB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.5194/hess-22-2575-2018

Item Type:	Article
Refereed:	Yes
Divisions:	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > NCAS Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:	76710
Publisher:	Copernicus

Download Statistics

Downloads per month over past year

Altmetric

Deposit Details

University of Reading