Articles | Volume 13, issue 8
https://doi.org/10.5194/gmd-13-3789-2020
https://doi.org/10.5194/gmd-13-3789-2020
Model description paper
 | 
27 Aug 2020
Model description paper |  | 27 Aug 2020

The ABC-DA system (v1.4): a variational data assimilation system for convective-scale assimilation research with a study of the impact of a balance constraint

Ross Noel Bannister

Related authors

Inverse modelling for surface methane flux estimation with 4DVar: impact of a computationally efficient representation of a non-diagonal B-matrix in INVICAT v4
Ross Noel Bannister and Chris Wilson
EGUsphere, https://doi.org/10.5194/egusphere-2024-655,https://doi.org/10.5194/egusphere-2024-655, 2024
Short summary
Investigating ecosystem connections in the shelf sea environment using complex networks
Ieuan Higgs, Jozef Skákala, Ross Bannister, Alberto Carrassi, and Stefano Ciavatta
Biogeosciences, 21, 731–746, https://doi.org/10.5194/bg-21-731-2024,https://doi.org/10.5194/bg-21-731-2024, 2024
Short summary
The Hydro-ABC model (Version 2.0): a simplified convective-scale model with moist dynamics
Jiangshan Zhu and Ross Noel Bannister
Geosci. Model Dev., 16, 6067–6085, https://doi.org/10.5194/gmd-16-6067-2023,https://doi.org/10.5194/gmd-16-6067-2023, 2023
Short summary
Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
Geosci. Model Dev., 16, 4233–4247, https://doi.org/10.5194/gmd-16-4233-2023,https://doi.org/10.5194/gmd-16-4233-2023, 2023
Short summary
The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system
Nicholas Williams, Nicholas Byrne, Daniel Feltham, Peter Jan Van Leeuwen, Ross Bannister, David Schroeder, Andrew Ridout, and Lars Nerger
The Cryosphere, 17, 2509–2532, https://doi.org/10.5194/tc-17-2509-2023,https://doi.org/10.5194/tc-17-2509-2023, 2023
Short summary

Related subject area

Atmospheric sciences
Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Geosci. Model Dev., 17, 6571–6587, https://doi.org/10.5194/gmd-17-6571-2024,https://doi.org/10.5194/gmd-17-6571-2024, 2024
Short summary
Analysis of model error in forecast errors of extended atmospheric Lorenz 05 systems and the ECMWF system
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024,https://doi.org/10.5194/gmd-17-6489-2024, 2024
Short summary
Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows
Giorgio Veratti, Alessandro Bigi, Sergio Teggi, and Grazia Ghermandi
Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024,https://doi.org/10.5194/gmd-17-6465-2024, 2024
Short summary
AeroMix v1.0.1: a Python package for modeling aerosol optical properties and mixing states
Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and Damu Bala Subrahamanyam
Geosci. Model Dev., 17, 6379–6399, https://doi.org/10.5194/gmd-17-6379-2024,https://doi.org/10.5194/gmd-17-6379-2024, 2024
Short summary
Impact of ITCZ width on global climate: ITCZ-MIP
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev., 17, 6365–6378, https://doi.org/10.5194/gmd-17-6365-2024,https://doi.org/10.5194/gmd-17-6365-2024, 2024
Short summary

Cited articles

Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities, Mon. Weather Rev., 139, 3887–3905, 2011. a
Bannister, R.: A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143, 607–633, https://doi.org/10.1002/qj.2982, 2017. a, b
Bannister, R. N.: A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances, Q. J. Roy. Meteor. Soc., 134, 1951–1970, 2008a. a
Bannister, R. N.: A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics, Q. J. Roy. Meteor. Soc., 134, 1971–1996, 2008b. a, b, c, d, e
Bannister, R. N.: ABC-DA model software, GitHub available at: https://github.com/rossbannister/ABC-DA_1.4da (last access: 24 August 2020), 2019. a
Download
Short summary
Forecasting models start from initial conditions, and data assimilation (DA) is the way that initial conditions are found from a combination of previous model data and latest observations. The ABC model is a simplified convective-scale model developed previously, and ABC-DA is the version of this system that includes the DA capability. This system is described in the present paper, and its performance is demonstrated with a range of options that control how the data assimilation is done.