Preprints
https://doi.org/10.5194/gmd-2022-269
https://doi.org/10.5194/gmd-2022-269
Submitted as: model experiment description paper
29 Nov 2022
Submitted as: model experiment description paper | 29 Nov 2022
Status: this preprint is currently under review for the journal GMD.

Experiments with the modified Rotating Shallow Water model (modRSW, v.1.0): assessing the relevance for convective-scale data assimilation research

Thomas Kent1,2,a,, Luca Cantarello1,2,b,, Gordon Inverarity3, Steven Tobias1,2, and Onno Bokhove1,2 Thomas Kent et al.
  • 1School of Mathematics, University of Leeds, UK
  • 2Leeds Institute for Fluid Dynamics, University of Leeds, UK
  • 3Met Office, Exeter, UK
  • acurrently at: Norlys Energy Trading A/S, Aalborg, Denmark
  • bcurrently at: European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany
  • These authors contributed equally to this work.

Abstract. Following the development of the modified rotating shallow water model (modRSW), which includes simplified dynamics of convection and precipitation, we present the results of a series of idealised forecast-assimilation experiments to demonstrate its relevance for data assimilation research at convective scales, focusing on its ability to imitate an operational Numerical Weather Prediction system. We address in a rigorous manner how to ascertain whether an idealised model is relevant for data assimilation research by comparing our modRSW model configuration – based on a twin-setting approach in which synthetic observations are assimilated hourly into a Deterministic Ensemble Kalman filter – with the most common properties of an operational system. We demonstrate forecast-assimilation experiments that produce values of error growth rates (6–9 hours) and observational influence on the analyses (around 30 %) comparable to those found in operational systems. In addition, we provide a description of the approach we took to reach a well-tuned configuration by comparing the ensemble spread with the Root Mean Square Error of the ensemble mean, and examining the Continuous Ranked Probability Score. We also provide subjective assessments of forecasts at different lead times.

Thomas Kent et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • EC1: 'Comment on gmd-2022-269', Yuefei Zeng, 30 Nov 2022
  • RC1: 'Comment on gmd-2022-269', Anonymous Referee #1, 08 Dec 2022
  • RC2: 'Comment on gmd-2022-269', Anonymous Referee #2, 10 Jan 2023

Thomas Kent et al.

Data sets

GMD paper data T. Kent, L. Cantarello, G. Inverarity https://doi.org/10.5281/zenodo.7244173

Model code and software

modRSW_DEnKF T. Kent, L. Cantarello, G. Inverarity https://doi.org/10.5281/zenodo.7241058

Thomas Kent et al.

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Short summary
Data assimilation combines recent model forecasts and observations to estimate current atmospheric conditions for use as initial conditions for numerical weather prediction. We analyse the results of a series of data assimilation experiments using a simplified and inexpensive mathematical model of the atmosphere. We closely compare key properties of the models used by weather centres with our idealised setup, proving that it can help support operational data assimilation research.