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 was under review for the journal GMD. A final paper is not foreseen.

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

Thomas Kent, Luca Cantarello, Gordon Inverarity, Steven Tobias, and Onno Bokhove

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.

This preprint has been withdrawn.

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Thomas Kent, Luca Cantarello, Gordon Inverarity, Steven Tobias, and Onno Bokhove

Interactive discussion

Status: closed

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
    • AC1: 'Reply on EC1', Luca Cantarello, 23 May 2023
  • RC1: 'Comment on gmd-2022-269', Anonymous Referee #1, 08 Dec 2022
    • AC2: 'Reply on RC1', Luca Cantarello, 23 May 2023
  • RC2: 'Comment on gmd-2022-269', Anonymous Referee #2, 10 Jan 2023
    • AC3: 'Reply on RC2', Luca Cantarello, 23 May 2023

Interactive discussion

Status: closed

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
    • AC1: 'Reply on EC1', Luca Cantarello, 23 May 2023
  • RC1: 'Comment on gmd-2022-269', Anonymous Referee #1, 08 Dec 2022
    • AC2: 'Reply on RC1', Luca Cantarello, 23 May 2023
  • RC2: 'Comment on gmd-2022-269', Anonymous Referee #2, 10 Jan 2023
    • AC3: 'Reply on RC2', Luca Cantarello, 23 May 2023
Thomas Kent, Luca Cantarello, Gordon Inverarity, Steven Tobias, and Onno Bokhove

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, Luca Cantarello, Gordon Inverarity, Steven Tobias, and Onno Bokhove

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This preprint has been withdrawn.

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.