Our People

Dr Ross Bannister

NCEO Research Fellow
Data Assimilation

Research interests

I am interested in using satellite information to support the generation of climate services, with a particular interest over Africa. As an Earth Observation scientist and TAMSAT operations lead, I have helped develop the TAMSAT satellite-derived rainfall and soil moisture operational products for Africa while supporting stakeholders build their capacity to utilise such data.

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