{"id":631,"date":"2023-10-24T09:40:02","date_gmt":"2023-10-24T08:40:02","guid":{"rendered":"https:\/\/research.reading.ac.uk\/earth-system-prediction\/?page_id=631"},"modified":"2024-10-30T11:11:37","modified_gmt":"2024-10-30T11:11:37","slug":"previous-projects","status":"publish","type":"page","link":"https:\/\/research.reading.ac.uk\/earth-system-prediction\/previous-projects\/","title":{"rendered":"Previous Projects"},"content":{"rendered":"

The use of machine-learning to improve localization of background covariances in Variational Data Assimilation.<\/span><\/b>\u00a0<\/span>\u00a0<\/span><\/p>\n

Supervisor: Dr Alison Fowler<\/span><\/i>\u00a0<\/span><\/p>\n

How can we use machine learning (ML) to reduce sampling errors in ensemble-based data assimilation (DA)?\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n

Data assimilation is the process of combining imperfect models with observations. Many operational weather centres use ensembles to quantify imperfections in model forecasts to help determine how the forecasts should be corrected with observations. Ensemble information is extremely valuable but does introduce statistical noise into the DA process. A method called localization helps solve this problem, but it can be expensive in practice. It is proposed that ML can improve efficiency and help to solve this problem.\u00a0<\/span>\u00a0<\/span><\/p>\n

As an example application, we propose using ML to mimic advection processes to describe how localization should change over time. This is important when the localization length scale is shorter than the propagation of error structures through the DA time window.\u00a0 Just as ML can emulate forecast models, it should be able to take account of differences in advection velocities over time and space.\u00a0<\/span>\u00a0<\/span><\/p>\n

We plan to use the state-of-the-art Joint Centre Satellite Data Assimilation (JCSDA) technical infrastructure and the incredibly flexible background error covariance framework with the System Agnostic Background Error Representation (SABER) repository. This is fully in line with the Met Office\u2019s system development framework.\u00a0<\/span>\u00a0<\/span><\/p>\n

A simple model will be used for the initial investigation and optimal localization theories will be used to train the ML schemes. Thereafter extensions to the project could involve using it with a scale-dependent localization scheme (Caron, 2023) and moving to more realistic models of the atmosphere.\u00a0<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n

Making better use of near-surface observations in coupled atmosphere-ocean prediction<\/span><\/b>\u00a0<\/span>\u00a0<\/span><\/p>\n

Supervisor: Professor Amos Lawless<\/span><\/i>\u00a0<\/span><\/p>\n

In the last few years operational weather forecasting centres, such as the Met Office, have started to use coupled atmosphere-ocean models to produce their regular weather forecasts. Using a coupled model allows a better representation of the influence of the ocean on the atmosphere, which is important for predicting high-impact weather events such as storms and tropical cyclones. The models are initialised using the latest observations of the atmosphere and ocean with a mathematical technique known as data assimilation, which combines the observations with the model, taking into account their respective uncertainties. Some observations that are close to the surface (such as satellite measurements of sea-surface temperature) give useful information about both the atmosphere and ocean state, but current data assimilation methods are not adequate for exploiting this information fully. In this project we will address this challenge in two stages. First, we will improve the mathematical mapping between the model state and the observations in the data assimilation process, known as the observation operator. We will design an improved operator that incorporates more completely the physical relationship between the measurements and the coupled atmosphere-ocean state. We will then investigate how we can best generate ensembles of coupled model forecasts to obtain information about the uncertainty in our predictions by a better representation of the uncertainty in near-surface observations. New methods will be developed mathematically and tested using idealised models. The most promising ideas will then be tested in a research mode of the Met Office\u2019s forecasting system.<\/span>\u00a0<\/span><\/p>\n

Where do we need the high resolution in the ocean?<\/span><\/b>\u00a0<\/span>\u00a0<\/span><\/p>\n

Supervisor: Professor Bryan Lawrence<\/span><\/i>\u00a0<\/span><\/p>\n

Most of the European community uses coupled global ocean-atmosphere models (GCMs) which include an ocean component with a globally uniform mesh, even though we know that we need finer meshes in some regions than others. We do this because the impact of using variable resolution on climate is hard to understand, and both running and analysing variable resolution models is not easy. The overall purpose of this project is to address some of those concerns by experimenting with models which focus higher resolution in selected regions, to address the question \u201cCan we use variable resolution in the ocean component of very high resolution coupled model systems in such a way that the simulations can be made enough cheaper to afford more of them, and hence better sample climate variability?\u201d\u00a0<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n

The exact models to be used are not yet known but are likely to involve ones where either the ocean resolution is natively variable or there are one or more embedded high-resolution regions. Many different directions are possible ranging between very scientific and quite computational, addressing questions such as: \u201cHow much does atmospheric variability depend on ocean variability in regions with large-scale eddies or low eddy activity?\u201d; \u201cCan we resolve those regions at lower resolution than active regions with small-scale eddies and still get realistic and useful climate simulations?\u201d; \u201cCan this approach lead to simulations that can be made cheap enough to generate usefully larger ensembles?\u201d; \u201cHow can we best analyse models to answer these sorts of questions?\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n

Cloud feedbacks on tropical climate at subseasonal scales<\/span><\/b>\u00a0<\/span>\u00a0<\/span><\/p>\n

Supervisor: Professor Chris Holloway<\/span><\/i>\u00a0<\/span><\/p>\n

\u00a0<\/span>In the tropics, clouds and the large-scale circulation are intimately coupled. The large-scale circulation controls the location of clouds, which in turn feedback on the circulation though latent and radiative heating and transport of air parcels. Consequently, clouds play a crucial role in numerous tropical features, including the Intertropical Convergence Zone, the Madden-Julian Oscillation, and the El Nino Southern Oscillation. Cloud-circulation coupling also contributes to uncertainty in cloud responses to climate warming.\u00a0<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n

Despite its importance, the coupling between clouds and circulation remains relatively poorly understood. This is at least in part due to the fact that representing this coupling in numerical models requires a computationally expensive combination of high resolution to resolve the convective and cloud scales and a large domain to capture circulation features.\u00a0<\/span>\u00a0<\/span><\/p>\n

This project will exploit kilometre-scale simulations to investigate the role of clouds in shaping tropical climate dynamics on subseasonal timescales. Clouds and circulation features will be evaluated against observations to understand how well these are captured by the model. Cloud locking experiments will be used to identify the role of clouds in tropical dynamics in the model. Based on this work and expertise from AFESP modelling centre partners, further experiments will study how perturbing specific model parameters change tropical cloud properties and consequently circulation with the aim of identifying physical processes where there is potential for improvement.\u00a0<\/span>\u00a0<\/span><\/p>\n

Downstream impacts of embedded convection in warm conveyor belts\u00a0<\/span><\/b>\u00a0<\/span><\/p>\n

Supervisor: Dr Oscar Martinez-Alvarado<\/span><\/i>\u00a0<\/span><\/p>\n

Recent research has shown that embedded convection within warm conveyor belts (WCBs) can influence the occurrence of heavy precipitation and the development of heatwaves. The misrepresentation of WCB embedded convection can importantly impact the development of forecast errors, affecting weather prediction skill. However, further research is needed to understand case-to-case variability.\u00a0\u00a0<\/span>\u00a0<\/span><\/p>\n

Separate research on convection-permitting climate simulations has shown more intense precipitation and damaging winds than their lower-resolution counterparts, likely produced by processes, such as WCB embedded convection, that are not well represented at low resolutions. These results highlight the need to understand the effects of these processes on downstream HIW under different model resolutions.\u00a0<\/span>\u00a0<\/span><\/p>\n

This project will investigate the following research questions:\u00a0\u00a0<\/span>\u00a0<\/span><\/p>\n