EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Idealised satellite data assimilation experiments with clouds and precipitation

Luca Cantarello1, Onno Bokhove1, Gordon Inverarity2, Stefano Migliorini2, and Steve Tobias1
Luca Cantarello et al.
  • 1University of Leeds, School of Mathematics, Leeds, United Kingdom of Great Britain and Northern Ireland (
  • 2Met Office, Exeter, United Kingdom of Great Britain and Northern Ireland

Operational data assimilation (DA) schemes rely significantly on satellite observations with much research aimed at their optimisation, leading to a great deal of progress. Here, we investigate the impact of the spatial-temporal variability of satellite observations for DA: is there a case for concentrating effort into the assimilation of small-scale convective features over the large-scale dynamics, or vice versa?


We conduct our study in an isentropic one-and-a-half layer model that mimics convection and precipitation, a revised and more realistic version of the idealised model based on the shallow water equations in [1,2]. Forecast-assimilation experiments are performed by means of a twin-setting configuration, in which pseudo-observations  from a high-resolution nature run are combined with lower-resolution forecasts. The DA algorithm used is the deterministic Ensemble Kalman Filter (see [3]). We focus our research on polar-orbit satellites regarding emitted microwave radiation.


We have developed a new observation operator and a representative observing system in which both ground and satellite observations can be assimilated. The convection thresholds in the model are used as a proxy for cloud formation, clouds, and precipitation. To imitate the use of weighting functions in real satellite applications, radiance values are computed as a weighted sum with contributions from both layers. In the presence of clouds and/or precipitation, we model the response of passive microwave radiation to either precipitating or non-precipitating clouds. The horizontal resolution of satellite observations can be varied to investigate the impact of scale-dependency on the analysis.


New, preliminary results from experiments including both transverse jets and rotation in a periodic domain will be reported and discussed.



[1] Kent, T., Bokhove, O., & Tobias, S. (2017). Dynamics of an idealized fluid model for investigating convective-scale data assimilation. Tellus A: Dynamic Meteorology and Oceanography, 69(1), 1369332.

[2] Kent, T. (2016). An idealised fluid model for convective-scale NWP: dynamics and data assimilation (Doctoral dissertation, PhD Thesis, University of Leeds).

[3] Sakov, P., & Oke, P. R. (2008). A deterministic formulation of the ensemble Kalman filter: an alternative to ensemble square root filters. Tellus A: Dynamic Meteorology and Oceanography, 60(2), 361-371.


How to cite: Cantarello, L., Bokhove, O., Inverarity, G., Migliorini, S., and Tobias, S.: Idealised satellite data assimilation experiments with clouds and precipitation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-332,, 2019

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