Date: 6th January 2026, Tuesday (11:00am – 12:00pm)
Presenter: Adam Clayton (CCRS)
Topic: Two data assimilation developments that improved high-resolution UK forecasts

Abstract:
After giving an overview of my career as a data assimilation scientist, first at the UK Met Office, and then at the Korea Institute of Atmospheric Prediction Systems (KIAPS), I will summarize two projects I was involved in to improve the performance of the UK Met Office’s “UKV” regional variational data assimilation (DA) system, the UK counterpart of Singapore’s SINGV DA system. The first project was the development of “hybrid-EnVar” methods for the UKV, where ensemble data is used to improve the representation of flow-dependent error structures in background error covariance estimates. This hybrid-EnVar approach led to some clear improvements to forecast skill, and was later tested within the SINGV system by Joshua Lee at CCRS. However, there were problems related to the presence of large-scale structures within the ensemble perturbations, so the first project was suspended, and focus was moved to a second project aimed at improving the handling of large scales with the UKV DA cycle. This second project delivered a system for blending large-scale structures from global model forecasts into UKV background forecasts, culminating in a configuration that significantly improved UKV forecasts. This “background blending” system, which differs from the “analysis blending” system used in the SINGV system, was implemented in the operational UKV system in 2022. I will finish by summarising the relevance of the two projects to the future development of CCRS DA systems.

Speaker Profile:
Adam Clayton is the head of CCRS’s Data Assimilation and Ensembles branch, having joined CCRS in November 2025. After completing a Batchelor’s degree in Mathematics at Imperial College, London, Adam started a 27-year career at the UK Met Office in 1993, where he focused mainly on the development of the Met Office’s variational data assimilation (DA) systems, specializing in issues such as control of gravity-wave noise (the subject of his MSc dissertation), use of ensemble-based background error covariances, development of ensemble forecasting systems, and blending of large-scale structures into limited-area DA systems. His time working for the Met Office included a 3-year secondment to the Earth Simulator Center in Yokohama, where he worked on the production and analysis of high-resolution climate integrations, and a 7-year secondment to the Korea Meteorological Administration (KMA), where he worked on collaborative DA projects between KMA and the Met Office. In 2020, Adam joined the Korea Institute of Atmospheric Prediction Systems (KIAPS), where he led the DA Techniques team, and contributed to the development of KIAPS DA systems and the associated technical infrastructure.

Date: 20th January 2026, Tuesday (11:00am – 12:00pm)
Presenter: Jianyu Liang (CCRS)
Topic: A Machine-Learning Observation Operator for Satellite Observations Without Radiative Transfer Models

Abstract:
In data assimilation, observation operators convert model variables into what instruments observe. The development of observation operators usually relies on writing computer code to represent physical relationships, which can be time-consuming and slow down the use of new observations. In this study, I will present how to use machine learning to build an observation operator for satellite observations without prior physical knowledge of the radiative transfer process. Using this ML-based observation operator to assimilate additional satellite microwave observations can reduce the atmospheric temperature error in the model.

Speaker Profile:
Dr Jianyu Liang is a Research Scientist at CCRS. At CCRS, his main research includes radar quality control, Quantitative Precipitation Estimation, and nowcasting. He obtained his MSc in Atmospheric Sciences at the Institute of Atmospheric Physics, China, in 2008 and his PhD from York University, Canada, in 2019. Before joining CCRS, he was a postdoctoral researcher at RIKEN R-CCS, Japan. His postdoctoral work mainly focuses on data assimilation, including assimilating satellite observations, developing machine-learning-based observation operators, investigating non-Gaussian data assimilation methods, and applying data assimilation techniques to study the Venus atmosphere.

Date: 27th January 2026, Tuesday (11:00am – 12:00pm)
Presenter: Yi Ming (Boston College)
Topic: Relative Contributions of Hadley vs. Walker Circulation to Tropical Circulation Weakening

Abstract:
The tropical time-mean circulation is widely expected to slow down in a warmer climate. An often cited explanation for this is the thermodynamic scaling argument proposed by Held and Soden (2006), which was later invoked by Vecchi and Soden (2007) to explain the projected weakening of the Walker circulation (the zonal component of the tropical circulation). Drawing upon three papers currently being developed within my group, I will discuss the physical meanings and utilities of three commonly used metrics of circulation strength. I will then present a more complete picture of how the Hadley (the meridional component) and Walker circulations respond to warming—an issue central to understanding the future large-scale conditions that govern extreme weather events in Singapore and Southeast Asia.

Speaker Profile:
Prof Yi Ming is the Institute Professor of Climate Science and Society at Boston College. He uses climate models, observations and theories to elucidate the physical mechanisms governing Earth’s climate system and applies the fundamental understanding to practical issues of societal and policy importance.