ASPECT (Adaptation-oriented Seamless Predictions of European ClimaTe)
ASPECT (Adaptation-oriented Seamless Predictions of European ClimaTe)
ASPECT aims for the setup and demonstration of a seamless climate information system with a time horizon up to 30yr, accompanied by underpinning research and utilisation of climate information for sectoral applications. The goal is to improve existing climate prediction systems and merge their outputs across timescales together with climate projections to unify a seamless climate information system as a standard for sectoral decision-making. The focus will be on European climate information but we will also look more widely where there is a policy interest and in regions of European interest. We will maintain a strong link into an exploit learning from the WCRP lighthouse activities on explaining and predicting earth system change.
Funders: Horizon Europe, UKRI
Duration: Jan 2023 to Dec 2026
People:
Machine Learning for Early Warning Systems
Machine Learning for Early Warning Systems
Skillful early warnings and weather forecasts delivered by national and regional meteorological centres like KMD, EMI and ICPAC play a vital role in any effective disaster risk management system. The technical support from ECMWF, coupled with their global medium-range weather forecasting model and the longstanding collaboration with WFP, forms the foundation of this project’s objective. With partners in place, support from Google.org and the collective interest to challenge the status quo, the project entails training meteorological centres to run, adapt, and optimize Generative Adversarial Network (GAN) models to the complex rainfall patterns in eastern Africa.
Funder: World Food Programme (WFP)
Collaborators: ICPAC, Kenya Meteorological Department (KMD), ECMWF and Ethiopia Meteorological Institute (EMI)
Duration: October 2023 to March 2026
People:
Forecast-based attribution of extreme weather and climate events
The forecast-based attribution approach utilises state-of-the-art high resolution operational weather and climate forecast models that successfully predicted the specific extreme event in question to study the role of certain climate drivers for the characteristics of the extreme.
People:
Seasonal forecasting, teleconnections and multi-decadal variations in skill
This has been a topic of much interest and covers a wide range of studies.
People:
HURACAN (HUrricane Risk Amplification and Changing north Atlantic Natural disasters)
HURACAN (HUrricane Risk Amplification and Changing north Atlantic Natural disasters)
Huracán is a strategic collaboration between the UK and US with the overarching objective to deliver a new, physically based understanding of the risks posed to the British Isles/Western Europe and the Northeast United States by Cyclones of Tropical Origin (CTOs) in a changing climate. The risk of CTOs is currently poorly quantified, owing to a fundamental lack of evidence; yet these events are high-impact and are expected to become more frequent in the future. Poor theoretical understanding impedes confident prediction. Huracán brings together world-leading expertise—from both sides of the Atlantic—to study, for the first time, the full life cycle of CTOs, a key requirement for scientific progress. Huracán will address fundamental knowledge gaps exploiting diverse observations, theoretical advances and hypothesis-driven analysis of a wealth of numerical simulations, to provide actionable information to decision-makers. Huracán will explore physically plausible scenarios, given the predictable components of future climate and the conditional dependence of cyclone processes on those components. Huracán will experiment with the simulation of CTO-specific impacts and investigate worst-case configurations of the physical climate system. This concerted effort will transform the assessment of CTO risks across the North Atlantic mid-latitudes.
Funders: NERC, NSF
Duration: March 2023 to Feb 2027
People:
- Dr Stella Bourdin
- Dr Nick Leach
- Callum Pemberton (MPhys student 2023/24, AOPP)
DART (Dengue Advanced Readiness Tool)
DART (Dengue Advanced Readiness Tool)
DART aims at developing an integrated digital system for dengue outbreak prediction and monitoring. Researchers at the University of Oxford and Oxford University Clinical Research Unit in Vietnam will develop an automated forecasting system that integrates datasets from across Hanoi and Ho Chi Minh. The team are to build a mobile and desktop application capable of supporting predictions of dengue within cities; the application will integrate weather and disease forecasts in order to improve understanding of the relationship between them.
Funder: Wellcome Trust
Duration: Dec 2022 to Nov 2025
People:
- Dr Iago Perez Fernandez (Department of Engineering Science)
- Lucy Main (MPhys student 2022/23 in AOPP)
- Dr Sarah Sparrow (PI, Department of Engineering Science)
- Prof David Wallom (Department of Engineering Science)
- Prof Sophie Yacoub (Oxford University Clinical Research Unit, Centre for Tropical Medicine and Global Health)
Publications:
Main, L., S. Sparrow, Weisheimer, A. and M. Wright (2024). Verification of ECMWF probabilistic precipitation and temperature forecasts over Vietnam for use in dengue warning system Meteorolog. Apps., submitted
Northwest European Seasonal Weather Prediction from Complex Systems Modelling
Northwest European Seasonal Weather Prediction from Complex Systems Modelling
Funder: NERC
Duration: 2021-2024
People:
- Prof Edward Hanna (University of Lincoln, PI)
- Dr Laura Baker (University of Reading)
- Prof Len Shaffrey (University of Reading)
- Prof Ed Hawkins (University of Reading)
openIFS@home [this is the project name, not an email address ..]
This ongoing project provides a platform to run large ensemble experiments with ECMWF's easy-to-use version of its Integrated Forecasting System openIFS on volunteers computers. As a citizen science project it relies on the distributed computing infrastructure of Climateprediction.net (CPDN).
People:
- Dr Sarah Sparrow (Oxford e-Research Centre)
- Prof David Wallom (Oxford e-Research Centre)
- Andy Bowery (Oxford e-Research Centre)
- Dr Glenn Carver (previously ECMWF)
- Dr Marcus Köhler (ECMWF)
- Tim Hempel (AOPP)
- Dr Jamie Towner (AOPP)
- Dr Pirkka Ollinaho (FMI)
- Dr Florian Pappenberger (ECMWF)
Publications:
- Towner, J., S. Sparrow, D. Wallom, A. Bowery, A. Weisheimer, G. Carver, F. Pappenberger & H. Cloke (2023). OpenIFS@home: Using land surface uncertainties and large ensembles for seasonal heatwave prediction. ECMWF Newsletter, 175, 8-9. https://www.ecmwf.int/en/newsletter/175/news/openifshome-using-land-surface-uncertainties-and-large-ensembles-seasonal
- Sparrow, S., A. Bowery, G.D. Carver, M.O. Koehler, P. Ollinaho, F. Pappenberger, D. Wallom and A. Weisheimer (2021). OpenIFS@home version 1: a citizen science project for ensemble weather and climate forecasting. Geosci. Model Dev., 14, 3473-3486, https://doi.org/10.5194/gmd-14-3473-2021
- Launch of openIFS@home: https://www.ecmwf.int/en/about/media-centre/news/2019/boost-research-ecmwf-provides-openifs-oxford-based-volunteer