Our People

Dr Ranjini Swaminathan

NCEO UKESM Core Scientist
EO Data-Model Evaluation

Research interests

I am a member of the UK Earth System Model Core Development (UKESM) team. As an NCEO researcher in the team, I am interested in using EO and observational data with applied machine learning for the purposes of Earth System model evaluation and towards developing the next generation of such models.

Recent publications

A glimpse into the future: The 2023 temperature extremes in the North Atlantic in the context of longer-term climate change. 2025-01-20

DOI: https://doi.org/10.5194/egusphere-egu24-3457

Regional impacts poorly constrained by climate sensitivity . 2025-01-20

DOI: https://doi.org/10.5194/egusphere-egu24-5527

Regional Impacts Poorly Constrained by Climate Sensitivity. 2024-12

DOI: https://doi.org/10.1029/2024EF004901

Regional impacts poorly constrained by climate sensitivity. 2024-10-27

URI: https://centaur.reading.ac.uk/119178/

A machine learning framework to evaluate vegetation modeling in Earth system models. 2024-07-19

URI: https://centaur.reading.ac.uk/117412/

A Machine Learning Framework to Evaluate Vegetation Modeling in Earth System Models. 2024-07

DOI: https://doi.org/10.1029/2023MS004097

A glimpse into the future: the 2023 ocean temperature and sea-ice extremes in the context of longer-term climate change. 2024-01-11

URI: https://centaur.reading.ac.uk/114727/

Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciarán. 2024

URI: https://centaur.reading.ac.uk/115971/

Scenario choice impacts carbon allocation projection at global warming levels. 2023-12-13

DOI: https://doi.org/10.5194/esd-14-1295-2023

Scenario choice impacts carbon allocation projection at global warming levels. 2023-12-13

URI: https://centaur.reading.ac.uk/100609/

Evaluating Vegetation Modeling in Earth System Models with Machine Learning Approaches. 2023-11-20

DOI: https://doi.org/10.22541/essoar.170049918.89868321/v1

Evaluating Vegetation Modelling in Earth System Models with Machine Learning Approaches. 2023-05-15

DOI: https://doi.org/10.5194/egusphere-egu23-3457

Choice of Forecast Scenario Impacts the Carbon Allocation at the Same Global Warming Levels. 2023-01-04

DOI: https://doi.org/10.5194/egusphere-2022-1483

The Physical Climate at Global Warming Thresholds as Seen in the U.K. Earth System Model. 2022

http://www.scopus.com/inward/record.url?eid=2-s2.0-85120056184&partnerID=MN8TOARS

Earth System Model Evaluation Tool (ESMValTool) v2.0 – An extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP. 2020

http://www.scopus.com/inward/record.url?eid=2-s2.0-85089404860&partnerID=MN8TOARS

UKESM1: Description and Evaluation of the U.K. Earth System Model. 2019

http://www.scopus.com/inward/record.url?eid=2-s2.0-85074861901&partnerID=MN8TOARS

A computational framework for modelling and analyzing ice storms. 2018

http://www.scopus.com/inward/record.url?eid=2-s2.0-85093013056&partnerID=MN8TOARS

Modeling ice storm climatology. 2015

http://www.scopus.com/inward/record.url?eid=2-s2.0-84952690952&partnerID=MN8TOARS

Expanding the point-automatic enlargement of presentation video elements. 2011

http://www.scopus.com/inward/record.url?eid=2-s2.0-84455188418&partnerID=MN8TOARS

Improving and aligning speech with presentation slides. 2010

http://www.scopus.com/inward/record.url?eid=2-s2.0-78149488622&partnerID=MN8TOARS

Studying on the move – Enriched presentation video for mobile devices. 2009