Our People

Dr Ross Bannister

NCEO Research Fellow
Data Assimilation

Research interests

I am interested in inverse problems relating to the Earth system using data assimilation, which helps to infer valuable information about the environment, which might otherwise be difficult or impossible to measure directly. Data assimilation combines models with measurements of things that can be observed. I work on theoretical aspects of data assimilation including model error statistics and the general methodology. I apply data assimilation to a range of models from simplified to complex, including weather models (to estimate the initial conditions) and chemical transport models (to estimate surface fluxes of trace gases).

Recent publications

EnviFlux (v1.0): a simplified surface flux inversion tool based on four-dimensional variational data assimilation (4D-Var). 2026-10-12

DOI: https://doi.org/10.5194/gmd-19-9751-2026

Model-resolution impacts on all-sky infrared radiance simulation and cloud-dependent observation errors over Indonesia. 2026-09-28

URI: https://centaur.reading.ac.uk/132019/

EnviFlux (v1.0): a simplified surface flux inversion tool based on four-dimensional variational data assimilation (4D-Var). 2026-09-24

URI: https://centaur.reading.ac.uk/131811/

Regularisation of the 4DEnVar Data Assimilation method for Calibration of Land Surface Models. 2026-03-23

DOI: https://doi.org/10.5194/egusphere-2026-132

Machine Learning-Driven Background Error Covariances for High-Resolution Data Assimilation. 2026-03-13

DOI: https://doi.org/10.5194/egusphere-egu26-1685

EnviFlux (v1.0): a simplified surface flux inversion tool based on four-dimensional variational data assimilation (4D-Var). 2026-03-10

DOI: https://doi.org/10.5194/egusphere-2025-6352

A Reanalysis of the Arctic sea ice cover over the satellite era utilising summertime observations of SIT. 2026-02-23

DOI: https://doi.org/10.5194/egusphere-2026-742

Hybrid machine learning data assimilation for marine biogeochemistry. 2026-01-12

DOI: https://doi.org/10.5194/bg-23-315-2026

Hybrid machine learning data assimilation for marine biogeochemistry. 2025-11-15

URI: https://centaur.reading.ac.uk/127198/

Exploring a hybrid ensemble–variational data assimilation technique (4DEnVar) with a simple ecosystem carbon model. 2025-03

URI: https://reading-pure-test.eprints-hosting.org/121204/

Exploring a hybrid ensemble–variational data assimilation technique (4DEnVar) with a simple ecosystem carbon model. 2025

https://www.scopus.com/inward/record.url?eid=2-s2.0-85217282040&partnerID=MN8TOARS

Hybrid machine learning data assimilation for marine biogeochemistry. 2025

https://www.scopus.com/inward/record.url?eid=2-s2.0-105004774456&partnerID=MN8TOARS

Inverse modelling for surface methane flux estimation with 4DVar: impact of a computationally efficient representation of a non-diagonal B-matrix in INVICAT v4. 2024-03-07

DOI: https://doi.org/10.5194/egusphere-2024-655

Variable-dependent and selective multivariate localization for ensemble-variational data assimilation in the tropics. 2024-02-12

URI: https://centaur.reading.ac.uk/115233/

Exploring a Hybrid Ensemble-Variational Data Assimilation Technique (4denvar) with a Simple Ecosystem Carbon Model. 2024

https://www.scopus.com/inward/record.url?eid=2-s2.0-85209460824&partnerID=MN8TOARS

Investigating ecosystem connections in the shelf sea environment using complex networks. 2024

https://www.scopus.com/inward/record.url?eid=2-s2.0-85186069390&partnerID=MN8TOARS

Variable-Dependent and Selective Multivariate Localization for Ensemble-Variational Data Assimilation in the Tropics. 2024

https://www.scopus.com/inward/record.url?eid=2-s2.0-85189858026&partnerID=MN8TOARS

The Hydro-ABC model (version 2.0): a simplified convective-scale model with moist dynamics. 2023-10-31

URI: https://centaur.reading.ac.uk/113325/

The effects of assimilating a sub-grid scale sea ice thickness distribution in a new Arctic sea ice data assimilation system. 2023-06

URI: https://centaur.reading.ac.uk/111572/

Inverse modelling for trace gas surface flux estimation, impact of a non-diagonal B-matrix. 2023-05-15

DOI: https://doi.org/10.5194/egusphere-egu23-14826

Ecosystem connections in the shelf sea environment using complex networks. 2023-04-17

DOI: https://doi.org/10.5194/egusphere-2023-475

Supplementary material to "Ecosystem connections in the shelf sea environment using complex networks". 2023-04-17

DOI: https://doi.org/10.5194/egusphere-2023-475-supplement

Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses. 2023-03-15

DOI: https://doi.org/10.5194/egusphere-2023-337

A satellite era reanalysis of the Arctic sea ice cover utilising year-round observations of sea ice thickness. 2023-02-22

DOI: https://doi.org/10.5194/egusphere-egu23-3302

The “Hydro-ABC model” (Vn 2.0): a simplified convective-scale model with moist dynamics. 2023-02-07

DOI: https://doi.org/10.5194/egusphere-2022-1436

On the Stochasticity of Reanalysis Outputs of 4D-Var. 2023

https://www.scopus.com/inward/record.url?eid=2-s2.0-85153728069&partnerID=MN8TOARS

Progress, challenges, and future steps in data assimilation for convection-permitting numerical weather prediction: Report on the virtual meeting held on 10 and 12 November 2021. 2023

https://www.scopus.com/inward/record.url?eid=2-s2.0-85137574924&partnerID=MN8TOARS

Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses. 2023

https://www.scopus.com/inward/record.url?eid=2-s2.0-85170856115&partnerID=MN8TOARS

The Hydro-ABC model (Version 2.0): A simplified convective-scale model with moist dynamics. 2023

https://www.scopus.com/inward/record.url?eid=2-s2.0-85178224091&partnerID=MN8TOARS

The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system. 2023

https://www.scopus.com/inward/record.url?eid=2-s2.0-85164247333&partnerID=MN8TOARS

The effects of assimilating a sub-grid scale sea ice thickness distribution in a new Arctic sea ice data assimilation system. 2022-10-25

DOI: https://doi.org/10.5194/egusphere-2022-982

Utilising Cryosat-2 observations of the Arctic sea ice cover to produce a new Arctic sea ice reanalysis. 2022-03-27

DOI: https://doi.org/10.5194/egusphere-egu22-3764

Hybrid ensemble-variational data assimilation in ABC-DA within a tropical framework. 2022-03-18

DOI: https://doi.org/10.5194/gmd-2022-3

Hybrid ensemble-variational data assimilation in ABC-DA within a tropical framework. 2022

https://www.scopus.com/inward/record.url?eid=2-s2.0-85136824324&partnerID=MN8TOARS

Balance conditions in variational data assimilation for a high-resolution forecast model. 2021-08-03

URI: https://reading-pure-test.eprints-hosting.org/98194/

Dynamically informed covariance modelling in data assimilation. 2021-03-03

DOI: https://doi.org/10.5194/egusphere-egu21-1778

The effects of assimilating a sub-grid scale sea ice thickness distribution in a new Arctic sea ice data assimilation system. 2021-03-03

DOI: https://doi.org/10.5194/egusphere-egu21-2657

Balance conditions in variational data assimilation for a high-resolution forecast model. 2021

https://www.scopus.com/inward/record.url?eid=2-s2.0-85107539355&partnerID=MN8TOARS

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. 2020-08-27

URI: https://reading-pure-test.eprints-hosting.org/92546/

Response to referee 1. 2020-06-09

DOI: https://doi.org/10.5194/gmd-2019-318-AC1

Response to referee 2. 2020-06-09

DOI: https://doi.org/10.5194/gmd-2019-318-AC2

Response to short comment 1.. 2020-06-09

DOI: https://doi.org/10.5194/gmd-2019-318-AC3

Supplementary material to "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". 2020-02-03

DOI: https://doi.org/10.5194/gmd-2019-318-supplement

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. 2020-02-03

DOI: https://doi.org/10.5194/gmd-2019-318

Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales. 2020

https://www.scopus.com/inward/record.url?eid=2-s2.0-85076365455&partnerID=MN8TOARS

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. 2020

https://www.scopus.com/inward/record.url?eid=2-s2.0-85091820611&partnerID=MN8TOARS

Improvements in forecasting intense rainfall: Results from the FRANC (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) project. 2019

https://www.scopus.com/inward/record.url?eid=2-s2.0-85063488311&partnerID=MN8TOARS

Response to editor. 2018-02-05

DOI: https://doi.org/10.5194/gmd-2017-260-AC3

Response to reviewer 1. 2018-02-05

DOI: https://doi.org/10.5194/gmd-2017-260-AC1

Response to reviewer 2. 2018-02-05

DOI: https://doi.org/10.5194/gmd-2017-260-AC2

Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble. 2017-11-20

DOI: https://doi.org/10.5194/gmd-2017-260

Reply to Reviewer 1. 2017-08-27

DOI: https://doi.org/10.5194/gmd-2017-68-AC1

Reply to Reviewer 2. 2017-08-27

DOI: https://doi.org/10.5194/gmd-2017-68-AC2

The "ABC model" (Vn 1.0): a non-hydrostatic toy model for use in convective-scale data assimilation investigations. 2017-04-25

DOI: https://doi.org/10.5194/gmd-2017-68

A review of operational methods of variational and ensemble-variational data assimilation. 2017-01

URI: https://reading-pure-test.eprints-hosting.org/68685/

A review of operational methods of variational and ensemble-variational data assimilation. 2017

https://www.scopus.com/inward/record.url?eid=2-s2.0-85015325888&partnerID=MN8TOARS

The ABC model: A non-hydrostatic toy model for use in convective-scale data assimilation investigations. 2017

https://www.scopus.com/inward/record.url?eid=2-s2.0-85037674745&partnerID=MN8TOARS

How is the balance of a forecast ensemble affected by adaptive and non-adaptive localization schemes?. 2015-09

URI: https://reading-pure-test.eprints-hosting.org/142165/

How is the balance of a forecast ensemble affected by adaptive and nonadaptive localization schemes?. 2015

https://www.scopus.com/inward/record.url?eid=2-s2.0-84945129969&partnerID=MN8TOARS

Representation of model error in a convective-scale ensemble prediction system. 2014

https://www.scopus.com/inward/record.url?eid=2-s2.0-84891759910&partnerID=MN8TOARS

A new floating model level scheme for the assimilation of boundary-layer top inversions: The univariate assimilation of temperature. 2012

https://www.scopus.com/inward/record.url?eid=2-s2.0-84859764796&partnerID=MN8TOARS

A method for merging flow-dependent forecast error statistics from an ensemble with static statistics for use in high resolution variational data assimilation. 2011

https://www.scopus.com/inward/record.url?eid=2-s2.0-79954843134&partnerID=MN8TOARS

Correlations of control variables in variational data assimilation. 2011

https://www.scopus.com/inward/record.url?eid=2-s2.0-79955437609&partnerID=MN8TOARS

Ensemble prediction for nowcasting with a convection-permitting model – II: Forecast error statistics. 2011

https://www.scopus.com/inward/record.url?eid=2-s2.0-79954528298&partnerID=MN8TOARS

Ensemble prediction for nowcasting with a convection-permitting model-I: Description of the system and the impact of radar-derived surface precipitation rates. 2011

https://www.scopus.com/inward/record.url?eid=2-s2.0-79954558836&partnerID=MN8TOARS

Issues in the assimilation of the sources of a trace gas by inverse modelling. 2011

URI: https://reading-pure-test.eprints-hosting.org/137492/

Issues in the assimilation of the sources of a trace gas by inverse modelling. 2011

https://www.scopus.com/inward/record.url?eid=2-s2.0-79954842539&partnerID=MN8TOARS

Characterising the background errors for the boundary-layer capping inversion. 2010

URI: https://reading-pure-test.eprints-hosting.org/145115/

Characterising the background errors for the boundary-layer capping inversion. 2010

https://www.scopus.com/inward/record.url?eid=2-s2.0-80054971104&partnerID=MN8TOARS

The role of balance in data assimilation. 2010

URI: https://reading-pure-test.eprints-hosting.org/151764/

Modelling of forecast errors in geophysical fluid flows. 2008-03

URI: https://reading-pure-test.eprints-hosting.org/129663/

Modelling of forecast errors in geophysical fluid flows. 2008-03

URI: https://reading-pure-test.eprints-hosting.org/130493/

Modelling of forecast errors in geophysical fluid flows. 2008-03

URI: https://reading-pure-test.eprints-hosting.org/131391/

Modelling of forecast errors in geophysical fluid flows. 2008-03

URI: https://reading-pure-test.eprints-hosting.org/131678/

Modelling of forecast errors in geophysical fluid flows. 2008-03

URI: https://reading-pure-test.eprints-hosting.org/134892/

A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances. 2008

URI: https://reading-pure-test.eprints-hosting.org/129750/

A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances. 2008

URI: https://reading-pure-test.eprints-hosting.org/130634/

A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances. 2008

URI: https://reading-pure-test.eprints-hosting.org/131797/

A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances. 2008

URI: https://reading-pure-test.eprints-hosting.org/135022/

A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances. 2008

https://www.scopus.com/inward/record.url?eid=2-s2.0-58049201686&partnerID=MN8TOARS