20 Jan 2021

20 Jan 2021

Review status: this preprint is currently under review for the journal HESS.

Ensemble-based data assimilation of atmospheric boundary layer observations improves the soil moisture analysis

Tobias Sebastian Finn1,2,3, Gernot Geppert4, and Felix Ament1,5 Tobias Sebastian Finn et al.
  • 1Meteorological Institute, University of Hamburg, Hamburg, Germany
  • 2Meteorological Institute, University of Bonn, Bonn, Germany
  • 3International Max Planck Research School on Earth System Modelling, Max Planck Institute for Meteorology, Hamburg, Germany
  • 4Deutscher Wetterdienst, Offenbach, Germany
  • 5Max Planck Institute for Meteorology, Hamburg, Germany

Abstract. We revise the potential of assimilating atmospheric boundary layer observations into the soil moisture. Previous studies often stated a negative assimilation impact of boundary layer observations on the soil moisture analysis, but recent developments in physically-consistent hydrological model systems and ensemble-based data assimilation lead to an emerging potential of boundary layer observations for land surface data assimilation. To explore this potential, we perform idealized twin experiments for a seven-day period in Summer 2015 with a coupled atmosphere-land modelling platform. We use TerrSysMP for these limited-area simulations with a horizontal resolution 1.0 km in the land surface component. We assimilate sparse synthetic 2-metre-temperature observations into the land surface component and update the soil moisture with a localized Ensemble Kalman filter. We show a positive assimilation impact of these observations on the soil moisture analysis during day-time and a neutral impact during night. Furthermore, we find that hourly-filtering with a three-dimensional Ensemble Kalman filter results in smaller errors than daily-smoothing with a one-dimensional Simplified Extended Kalman filter, whereas the Ensemble Kalman filter additionally allows us to directly assimilate boundary layer observations without an intermediate optimal interpolation step. We increase the physical consistency in the analysis for the land surface and boundary by updating the atmospheric temperature together with the soil moisture, which as a consequence further reduces errors in the soil moisture analysis. Based on these results, we conclude that we can merge the decoupled data assimilation cycles for the land surface and the atmosphere into one single cycle with hourly-like update steps.

Tobias Sebastian Finn et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2020-672', Anonymous Referee #1, 26 Feb 2021 reply
  • AC1: 'Comment on hess-2020-672', Tobias Sebastian Finn, 04 Mar 2021 reply
    • RC2: 'Reply on AC1', Anonymous Referee #1, 04 Mar 2021 reply

Tobias Sebastian Finn et al.

Model code and software

tobifinn/letkf_t2m_h2osoi: Initial paper submission (Version initial) Tobias Finn

Tobias Sebastian Finn et al.


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Short summary
Through the lens of recent developments in hydrological modelling and data assimilation, we hourly update the soil moisture with ensemble data assimilation and sparse 2-metre-temperature observations in a coupled limited area model system. In idealized experiments, we improve the soil moisture analysis by coupled data assimilation across the atmosphere-land interface. We conclude that we can merge the separated assimilation cycles for the atmosphere and land surface into one single cycle.