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

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

Investigating ecosystem connections in the shelf sea environment using complex networks. 2024-02-08

DOI: https://doi.org/10.5194/bg-21-731-2024

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

DOI: https://doi.org/10.5194/gmd-16-6067-2023

Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses. 2023-07-27

DOI: https://doi.org/10.5194/gmd-16-4233-2023

The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system. 2023-06-27

DOI: https://doi.org/10.5194/tc-17-2509-2023

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

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

Hybrid ensemble-variational data assimilation in ABC-DA within a tropical framework. 2022-08-11

DOI: https://doi.org/10.5194/gmd-15-6197-2022

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

Balance conditions in variational data assimilation for a high‐resolution forecast model. 2021-07

DOI: https://doi.org/10.1002/qj.4106

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

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

DOI: https://doi.org/10.5194/gmd-13-3789-2020

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. 2020-01

DOI: https://doi.org/10.1002/qj.3652

Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project. 2019-03

DOI: https://www.mdpi.com/2073-4433/10/3/125

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

The ABC model: a non-hydrostatic toy model for use in convective-scale data assimilation investigations. 2017-12-05

DOI: https://doi.org/10.5194/gmd-10-4419-2017

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