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

Pinnington, Ewan; Quaife, Tristan; Black, Emily

doi:https://doi.org/10.5194/hess-22-2575-2018

Articles | Volume 22, issue 4

https://doi.org/10.5194/hess-22-2575-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-22-2575-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 4

Research article

|

26 Apr 2018

Research article |

| 26 Apr 2018

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

Ewan Pinnington, Tristan Quaife, and Emily Black

Data sets

JULES data assimilation Ghana E. M. Pinnington https://github.com/Ewan82/JULES_DA_Ghana

Short summary

This paper combines satellite observations of precipitation and soil moisture to understand what key information they offer to improve land surface model estimates of soil moisture over Ghana. When both observations are combined with the chosen land surface model we reduce the unbiased root-mean-squared error in a 5-year model hindcast by 27 %; this bodes well for the production of improved soil moisture estimates over sub-Saharan Africa where subsistence farming remains prevalent.