Example of Future Projects
Title: The use of machine-learning to improve localization of background covariances in Variational Data Assimilation.
How can we use machine learning (ML) to reduce sampling errors in ensemble-based data assimilation (DA)?”
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.
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. Just as ML can emulate forecast models, it should be able to take account of differences in advection velocities over time and space.
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’s system development framework.
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.
Title: Making better use of near-surface observations in coupled atmosphere-ocean prediction
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’s forecasting system.
Title: The role of soil hydro-thermal processes and parameters in sub-seasonal to seasonal predictability
The predictive skill of sub-seasonal to seasonal forecasting (S2S; 2 weeks to 2 months) remains relatively low compared to shorter-range weather predictions and seasonal climate predictions. One source of predictability on these timescales is from land-surface anomalies which integrate atmospheric variability and can persist for several weeks.
A common shortcoming of the models used in S2S predictions is their deficiency in establishing correct anomalies in ‘skin’ temperature, Tskin (a complex mix of vegetation and soil surface temperatures). Tskin plays a pivotal role in the energy- and water balance and therefore in land-atmosphere (LA) interactions. However, S2S model deficiency is typically diagnosed using 2-m air temperatures. Herein, the distinct role of Tskin, and its dependence on sub-surface conditions, flows and properties, gets bypassed, hampering understanding and model improvement. If the effect of the soil system is considered, emphasis is on soil moisture, via its implicit effect on turbulent heat fluxes e.g., in studies on exacerbated heatwaves through LA coupling “hot spots”.
Using open-access databases generated by WCRP projects (S2S, LS4P), model sensitivity studies, and (sub-)surface verification data, the candidate will develop and test novel diagnostic metrics based on the soil thermal regime (e.g., persistence, coherence and phase relationships for soil enthalpy). These metrics will consider: coupled soil heat-and water transfer; the intricate interactions between soil temperatures, hydro-thermal properties, soil heat flux, Tskin and lower tropospheric temperatures; the influence of basic soil properties, such as texture and profile depth, on these metrics, and subsequent impact on sub-seasonal variability and predictive skill.
Title: Where do we need the high resolution in the ocean?
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 “Can 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?”
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: “How much does atmospheric variability depend on ocean variability in regions with large-scale eddies or low eddy activity?”; “Can we resolve those regions at lower resolution than active regions with small-scale eddies and still get realistic and useful climate simulations?”; “Can this approach lead to simulations that can be made cheap enough to generate usefully larger ensembles?”; “How can we best analyse models to answer these sorts of questions?”
Title: Cloud feedbacks on tropical climate at subseasonal scales
In the tropics, clouds and the large-scale circulation are intimately coupled. The large-scale circulation controls the location of clouds, which in turn feed back 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.
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.
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.
Title: Examining the influence of tropical SST drifts on sub-seasonal forecast
There are substantial predictability gaps from weeks 3-4 of sub-seasonal forecasts, after the predictability arising from atmospheric initial conditions has substantially reduced. After initialisation, coupled forecast models tend to show distinct and pervasive SST drifts – or growing biases relative to observations – and this is particularly notable in the tropical oceans. However, it is not clear how these drifts in tropical SST impact the associated forecasts. In this project, we will examine the contributions of these pervasive tropical SST drifts on sub-seasonal forecasts. We will also aim to identify and quantify specific areas of possible improvement.
Our specific focus will be on examining how the tropical SST drifts affect teleconnection patterns to the extratropics on sub-seasonal timescales, including how these affect the influence of the MJO and ENSO. The project will involve analysing historical reforecasts from coupled sub-seasonal forecast systems. With the student, we will test specific hypotheses shaped by the analysis of the reforecasts by performing a series of dedicated new experiments. The first set of experiments will involve nudging mixed-layer ocean temperatures towards observations is specified areas within coupled forecast simulations. Another set of experiments will make use of prescribed SSTs in a more idealised setup to examine the response to targeted SST drifts in different regions, which will help to isolate the response and understand the subsequent influence the forecasts.
The student will develop an understanding of atmospheric/ocean dynamics, ocean-atmosphere coupling, and associated teleconnection mechanisms and develop experience of using coupled general circulation models and running forecast simulations.
Title: Short range sea ice forecasting, using advanced models and new data
Over recent decades, the sea ice cover of the Arctic Ocean has reduced in extent, area, thickness and age. Climate models suggest the Arctic will become seasonally ice free within 20-30 years. Navigating the Arctic Ocean can considerably shorten trade routes, reducing carbon emissions and saving money. Shipping navigability has improved in recent years and this will continue, with increased interest in sea ice weather forecasts being inevitable.
This PhD project will investigate and develop a prototype for an unprecedentedly skilful sea ice forecast system, up to subseasonal timescales at various spatial resolutions (<50 km). This project will combine the latest developments in sea ice-ocean modelling such as melt ponds, form drag and floe size distribution (largely from UoR) with the latest satellite sea ice products, including CryoSat-2, SMOS, IceSat-2 and, for the first time, estimates of summer sea ice thickness. We will use a data assimilation scheme we developed based on the existing Parallel Data Assimilation Framework.
We will (i) improve the underlying sea ice-ocean model using assimilation of new, ground-truthed satellite sea ice data. This is done through examination of reanalysis increments in individual terms in the sea ice mass and momentum budgets, allowing model shortcomings to be identified and rectified; (ii) identify sources of predictive skill throughout the year but particularly in the navigable summer and shoulder seasons, building on our previous success using melt pond fraction to predict the sea ice minimum; and (iii) develop and assess a prototype sea ice forecast system.
Title: Large ensembles of machine learning forecasts for advanced nonlinear filters in atmospheric data assimilation
Recently, machine learning (ML) weather forecasting models have shown deterministic forecast skill approaching that of physics-based models, at a small fraction of the computational cost. This provides the opportunity to create very large ensembles of ML forecasts, with the potential to improve data assimilation (DA), the process of optimally combining forecasts and observations to estimate the state of the atmosphere.
Many atmospheric processes are known to be highly nonlinear and non-Gaussian, especially at convective scales. However, DA algorithms currently used in operational centres are only optimal in the linear, Gaussian setting. Advanced nonlinear DA algorithms such as particle filters can estimate the true state of the system without such restrictions, and there is thus great interest in their application to the atmosphere. However, their use has so far been considered unfeasible due to the large ensembles required.
The aim of this project is to test the use of ML ensembles for enabling advanced nonlinear DA methods, following previous work that has shown that a large ML ensemble can augment a small ensemble of physics-based forecasts. This will involve:
- Assessing the probabilistic skill of ML ensembles, and investigating how this skill can be improved during training or with transfer learning.
- Developing a particle filter algorithm which incorporates both physics-based and ML forecasts, following previous work on the multi-model ensemble Kalman filter.
- Experiments with this particle filter algorithm and large ML ensembles on models of increasing complexity, ultimately testing in a close-to-operational setting at the ECMWF.
Title: Prediction of high impact extratropical cyclone clustering events
Clusters of extratropical cyclones, which follow one another in close succession, can result in rain falling on already saturated ground leading to costly flooding events. For example, in the UK, in December 2015 with storms Desmond, Eva and Frank, and more recently in February 2022 with storms Dudley, Eunice and Franklin. These cyclones cannot be treated as isolated meteorological events, because they have the potential to interact with each other – either directly or indirectly through feedbacks onto the background flow.
The PhD project aims firstly to assess the predictability of extratropical cyclone clusters by comparing global ECMWF ensemble forecasts with reanalysis data. Cyclone clustering outside the North Atlantic storm track has been understudied. The project will then focus on their interaction and feedback mechanisms, which are often underestimated in models. The recent introduction of a 100-member extended range ensemble at ECMWF provides an opportunity to test the hypothesis that model inadequacies in representing these feedbacks weaken responses to local and remote processes, ultimately limiting predictability. Leveraging data from the ensemble will enable discernment of differences between well-performing and underperforming members. Additionally, the research will evaluate model errors in a cyclone-centered framework, offering an innovative approach to identifying poorly represented physical processes in the forecast model.
Title: Data Assimilation of atmospheric and marine tracers – how good are we and how good can we be?
The project considers assimilation of atmospheric or marine tracers. In case of a single tracer, relevant models are linear (yet infinite dimensional) transport models. In principle, optimal (Bayesian) assimilation into such models is accomplished through the Kalman filter, although deploying it in practice is difficult for two reasons. Firstly, an infinite dimensional version of the Kalman Filter is needed, and secondly the Kalman filter relies on appropriate statistics of the dynamical and observational error covariances which need to be estimated. In addition, the ambient atmospheric or ocean velocity field is required which is available only up to a certain degree of error.
This project will explore different ways of estimating error covariances (or Gain Matrices) and assess the resulting assimilation performance. Particular focus will be on
- Approaches from Machine Learning; Combining those with Data Assimilation has recently received considerable attention but mostly to reconstruct the underlying dynamics. In contrast we want to reconstruct model and observational error statistics, based on unbiased estimates of DA performance as in [1].
- Other approaches based on conventional DA such as in [2].
The project is very flexible with regards to theory vs application. The project could thus be adapted to the student’s interest.
[1] Mallia-Parfitt, N. and Jochen Bröcker, J. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(10):2016.
[2] Fowler, A.M., Skákala, J. and Ford, D. Quarterly Journal of the Royal Meteorological Society, 2022.
Title: Developing Smoother methods for post-processing Earth System reanalyses
Analyses of historical atmospheric and climate properties (including eg. ocean, sea-ice, land surface) rely on assimilation methods which combine available (possibly sparse) observational data with a time evolving computer model, to produce the complete state of the system, – “maps-without-gaps”. Current methods however use algorithms developed for weather forecasting and therefore incorporate only a short window of recent observations (the model then projects the forecast). However a better reconstruction of past weather and climate events should be possible by incorporating “future” observations made soon after the events of interest have occurred, especially in the pre-satellite era when fewer observations were available. Better reconstructions of past weather and climate states using all relevant observations is of great interest for assessing climatic trends. “Kalman smoothing” algorithms are capable of incorporating both past and future data. You will investigate and develop new methods of “post-processed” smoothing, which are efficient and computationally cheap to implement by taking current historical reanalyses and further enhancing them using the stored data products. Examples of applications include; better reconstruction of historical storms by incorporating more hours-to-days of observational data, improved analysis of past sea-ice states using more days-to-months of data, to improved ocean circulation states by using some future months-to-years of data (as ocean memory timescales are long). You will work with an experienced team of assimilation scientists at Reading, the Met Office and ECMWF and will have access to current state-of-the-art reanalysis systems and their data products using local and national computing infrastructures.
Title: Tropical Waves and the Sub-seasonal Prediction of Tropical Cyclones
Tropical cyclones (TCs) are a major source of extreme weather in the tropics, sub-tropics and can even influence higher latitudes. It is therefore scientifically and societally important they be accurately predicted up to a few weeks (sub-seasonal).
An aspect of the tropical circulation that has potential to improve TC forecasts is an improved understanding of the interaction of TCs with pre-existing tropical waves, including equatorial waves (EQWs). While the statistical relationship between EQWs and TCs has been studied previously, the nature of the interaction and how this influences TC genesis and intensification is largely unknown. Furthermore, there is still significant scope for identifying the full potential of tropical waves in affecting TCs (time of emergence). Improving our understanding of the underpinning processes that influence TC-wave interactions and how they are represented in forecast models, will lead to improved TC forecasts on the sub-seasonal timescale.
The research questions are: (1) what processes control the TC-tropical wave interaction and do tropical waves enhance or suppress TC formation and intensification? (2) Does the representation of the TC-wave interaction in forecast models affect the predictability of TC genesis, track and intensity on sub-seasonal time scales? (3) Do large scale modes, e.g., El Niño Southern Oscillation (ENSO), Madden Julian Oscillation (MJO), affect the TC-wave interaction and TC predictability?
This project will make use of readily available databases of TCs and tropical waves produced from the UK Met Office and ECMWF systems.
Title: Predicting high impact storms across East Africa
At-risk communities across East Africa regularly suffer the consequences of high impact storms and lack sufficient early warning information to anticipate for them. This project will advance our understanding of the causal chain of processes by with large-scale drivers of sub-seasonal variability (e.g. the Madden-Julian Oscillation; MJO) modulate the environmental conditions and influence predictability of local convective storms. The representation of identified mechanisms will be explored in a range of state-of-the-art weather and climate models. Answering the research question: What are the MJO-modulated environmental sources of East African storm predictability?
The MJO controls the large-scale including wind shear, moisture availability, atmospheric instability, and land surface characteristics, which modulate convective initiation and subsequent storm development. Observations and reanalysis of individual storms, and existing convection-permitting modelling simulations, will be used to identify the relative roles of these MJO-modulated environmental conditions for storm initiation and growth. Further, the project will explore how MJO-modulated convection depends on modes of interannual variability and pre-existing land-surface conditions. The predictability of these processes will be evaluated in operational sub-seasonal prediction models to understand their potential to support better early warning.
Through collaborative partnerships with the UK Centre for Ecology and Hydrology (UKCEH) and regional forecasting institutions, Kenya Meteorological Department (KMD) and East African Climate Prediction and Applications Centre (ICPAC), the scientific research will support national and international efforts to improve the prediction and communication of high impact storms in the region, aligning well with international efforts to support sub-seasonal prediction for applications (WMO SAGE agenda).
Title: Modelling Tree Species Distribution from Space to Combat Climate Change .
Changes in tree species and forest cover have indirect biophysical effects on the climate, e.g., by impacting low-level convective cloud formation1 , which may have ramifications for the planetary hydrological cycle. Therefore, accurate monitoring of forest composition is essential for understanding these biophysical effects for devising informed strategies. Several studies exist which make use of remote sensing and EO data to systematically quantify forest cover changes2,3, but we have little information on potential changes in tree species composition. Knowing this is important to predict future climate change effects and track forest exploitation where the most valuable species are removed or replanted preferentially. Most studies in this context focus on investigating the impact of climate change on a few commercially important tree species and often rely on forest inventory data of individual stands to extract ground truth4,5. Moreover, the spatial resolution of existing datasets is also coarse, e.g., the current global state-of-the-art dataset “EU-Trees4F”6 with the highest number of 67 tree species in Europe, provides the tree distribution maps at 10 km spatial resolution. Within this project’s scope, we intend to overcome these shortcomings and aim at enabling large-scale tree species classification maps and deriving their future projections. Later, we will fuse these maps and associated projections with the available climate data, e.g., the cloud fractional cover obtained from a climatology of satellite observations7 , to yield crucial insights about key geoinformation, such as changes in tree species, to allow a more holistic understanding of the subtlety of global changes in response to climate change.
References: 1 Duveiller, G., et al., 2021. Revealing the widespread potential of forests to increase low level cloud cover, Nature Communications, 12, 4337. 2 Wernick, I.K., et al., 2021. Quantifying forest change in the European Union. Nature, 592, E13–E14. 3 Decuyper, M., et. al., 2022. Continuous monitoring of forest change dynamics with satellite time series, Remote Sensing of Environment, 269, 112829. 4 Axelsson A., et al., 2021, Tree species classification using Sentinel-2 imagery and Bayesian inference, International Journal of Applied Earth Observation and Geoinformation, vol. 100. 5 Hemmerling, J., et al., 2021. Mapping temperate forest tree species using dense Sentinel-2 time series, Remote Sensing of Environment, vol. 267, 112743. 6 Mauri, A., et al., 2022. EU-Trees4F, a dataset on the future distribution of European tree species, Scientific Data, 9, 37. 7 Stengel, M., et al., 2017. Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project, Earth System Science Data, 9, pp. 881–904.
Title: Downstream impacts of embedded convection in warm conveyor belts
Recent research has shown that embedded convection within warm conveyor belts (WCBs) can influence the occurrence of heavy precipitation1 and the development of heatwaves2. The misrepresentation of WCB embedded convection can importantly impact the development of forecast errors, affecting weather prediction skill3,4,5. However, further research is needed to understand case-to-case variability.
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 resolutions6. These results highlight the need to understand the effects of these processes on downstream HIW under different model resolutions.
This project will investigate the following research questions:
- What processes determine the effects of WCB embedded convection on downstream impact?
- How do differences in process representation in numerical models across different resolutions lead to divergent evolutions and forecast error?
For example, the student can investigate the dynamics by which WCB embedded convection interacts with the jet stream, triggering Rossby waves and downstream impacts. Storm Daniel, which caused catastrophic flooding in Greece and Libya in 2023, constitutes a potential case study. Its genesis was driven by a “cascade of events” (convection over Iberia, development of blocking and a downstream cut-off low). Effects of climate change can also be investigated under the frameworks of CANARI, which investigates Arctic-North Atlantic climate change impacts on the UK, the MetOffice’s K-Scale programme within the path-to-high-resolution R&I theme, and ECMWF’s digital twin initiative, DestinE, through the supervisors’ involvement in these programmes.
References
- Oertel, A., Boettcher, M., Joos, H., Sprenger, M., and Wernli, H. (2020). Potential vorticity structure of embedded convection in a warm conveyor belt and its relevance for large-scale dynamics, Weather Clim. Dynam., 1, 127–153.
- Oertel, A., Pickl, M., Quinting, J. F., Hauser, S., Wandel, J., Magnusson, L., … & Grams, C. M. (2023). Everything hits at once: How remote rainfall matters for the prediction of the 2021 North American heat wave. Geophysical Research Letters, 50, e2022GL100958.
- Martínez-Alvarado, O., Madonna, E., Gray, S.L. and Joos, H. (2016) A route to systematic error in forecasts of Rossby waves. Quarterly Journal of the Royal Meteorological Society, 142, 196–210.
- Grams, C.M., Magnusson, L. and Madonna, E. (2018) An atmospheric dynamics perspective on the amplification and propagation of forecast error in numerical weather prediction models: A case-study. Quarterly Journal of the Royal Meteorological Society, 144, 2577–2591.
- Clarke, S. J., Gray, S. L., & Roberts, N. M. (2019). Downstream influence of mesoscale convective systems. Part 1: influence on forecast evolution. Q. J. R. Meteorol. Soc., 145, 2933-2952.
- Manning, C., Kendon, E. J., Fowler, H. J., Roberts, N. M., Berthou, S., Suri, D., & Roberts, M. J. (2022). Extreme windstorms and sting jets in convection-permitting climate simulations over Europe. Clim. Dyn., 58, 2387-2404.
Title: Understanding (and predicting) tropical cyclone genesis and evolution in the Indian Ocean at coupled km-scale
Future projected changes in tropical cyclones (TCs) are uncertain due to our limited understanding of the processes that drive TC precursors, genesis and intensification and how these processes interact with the large-scale environment. Existing literature suggests that, at least in some regions, TC precursor systems evolve from organised convective systems, which are typically unresolved or poorly represented in climate model simulations. TC intensification rates are also strongly influenced by model resolution, but as demonstrated by hurricane Otis, limitations in current modelling predictions can lead to poor forecasts and devastating impacts.
In this project we will use the Met Office’s new regional, coupled km-scale modelling capability to answer the research question: What are the processes that drive TC genesis and intensification in the Indian Ocean, how might these change in future and what are the implications for TCs landfalling Africa? Despite their devasting impacts, little is known about how TCs will change in the region. The CP4A (Convection-Permitting 4km-model for Africa) domain will be extended across the Indian Ocean basin to encompass TC genesis regions. With km-scale resolution and atmosphere-ocean coupling, the model should be capable of representing the key processes for TC evolution, and hence enable new insights and understanding. Simulations using present-day and future climate conditions will be used to test how these processes and interactions may change in future. These new simulations will complement atmosphere-only CP4A simulations to identify the impacts of coupling on TC evolution, including potential re-intensification in the Mozambique channel, evidenced recently by Cyclone Freddy.
Title: Developing the first sub-seasonal clear-air turbulence forecasts
Clear-air turbulence (CAT) is a significant hazard to aviation. Operational forecasts of CAT currently extend out to lead times of 18 hours and are used for flight planning. Projections show that climate change is strengthening CAT over periods of decades. However, there are currently no forecasts of CAT in between these two extremal time scales, despite demand from the aviation sector.
This project will design, implement, and test the world’s first sub-seasonal forecasting system for CAT. The project will first develop a CAT forecast product for the IFS, by using a large multi-diagnostic ensemble to calculate eddy dissipation rates. A recent survey of ECMWF forecast users found that this product was high on their wish list. The project will then produce 12-hourly global probabilistic CAT forecasts out to four weeks using the 51 ensemble members of the SEAS5 forecasting system each month. The skill of the forecasts will be tested using automated in-flight eddy dissipation rate measurements from the WMO’s Aircraft Meteorological Data Relay (AMDAR) system.
The end result will be a validated and verified CAT forecasting system suitable for operational use. It will allow probabilistic CAT forecasts out to four weeks to be produced and made available by ECMWF, updated monthly. It is known that there is demand for such a product from the aviation sector, which would use the forecasts for medium-term fuel strategy and for air-traffic controller and flight dispatcher workload planning. The product would also be suitable for inclusion in the next ECMWF climate reanalysis, ERA6.
Title: Machine learning driven balance relationships for next generation data assimilation systems .
This proposal is about km-scale data assimilation (DA). Effective DA improves predictability by extending the time range of useful forecasts, and eliminating the presence of spin-down effects (transient error growth rates at the initialisation time). Doing this well – using imperfect observations – requires knowledge of how each model grid point and variable should be properly coupled to other (neighbouring) grid points and variables (the ‘forecast error statistics’). In this respect, current DA systems were designed with large-scale systems in mind, and so a different approach is needed for high-resolution models. The goal of this project is to study machine learning (ML) techniques in the context of this problem. This is a new frontier in DA science.
There are exciting research questions relevant to km grid length models (regional and global models). How to estimate the ‘true’ forecast error statistics? How do they change in time? Which ML methods can be applied to this problem to efficiently reproduce the true statistics? How can these methods be trained? How do the outcomes of the ML compare with traditional methods? What are the implications for forecasting?
The key innovation will be in the combination of ML with DA in order to determine and efficiently use flow-dependent forecast error statistics. Such knowledge is useful for the current application of DA to state-of-the-art high-resolution models, but may also help guide purely ensemble-based DA methods for the purpose of reducing sampling noise. The method could also be applied to the representation of flow-dependent atmosphere-ocean coupled covariances.
Title: How skilful are AI-based forecasts of monsoon weather?
Artificial-intelligence-based forecasts may revolutionise weather prediction, yet limited understanding of how well, and when and why, these new models work creates unease when applying them operationally. There is a pressing need to learn how to use these models safely and evaluate their usefulness to ensure they meet the needs of forecasters and contribute to public safety (Ebert-Uphoff and Hilburn, 2023).
Our overarching question is: how confident can we be in AI-based forecasts of monsoon weather at lead times between a few days and four weeks ahead? The project will consider:
- How does the skill of AI models in predicting monsoon weather patterns compare to that of traditional NWP models as a function of lead time?
- How does skill depend on large-scale extra/tropical forcing, e.g., phases of the BSISO and Silk Road Pattern, stage of the monsoon front progression?
- How can we best create AI ensemble forecasts, what is the probabilistic skill, and can it be improved with post-processing?
- How well are extreme precipitation events and their precursors (TCWV, circulation) forecast?
We will run novel AI models (PanguWeather (Huawei), GraphCast (Google DeepMind), FourCastNet (Nvidia), potentially ECMWF’s AIFS) to produce 30-day hindcasts for monsoon seasons on the UoR or JASMIN cluster. One week of global 0.25° forecasts takes about one minute so we will be able to create large ensembles by perturbing initial conditions. We will target seasons after the AI models’ training period (1979-2020) and use conventional methods and explainable AI to identify how and where wind and humidity biases develop.
Ebert-Uphoff, I., & Hilburn, K. (2023). The outlook for AI weather prediction. Nature, 619(7970), 473–474.
Title: Realising the benefits of next generation sub-km ensemble weather forecasts
While ensemble weather forecasts with grid scales of a few kms are state-of-the-art, the Met Office are exploring the next generation of sub-km scale ensembles. Ensembles are sets of repeated weather forecasts that help gauge the reliability and skill of predictions by showing a range of possible outcomes. The ensemble approach is essential for sub-km scale forecasts due to the scales of interest being small compared to the unpredictable (convective and larger) scales being forecast. Ensembles with 300-m grid length have been trialled in summer 2022 (London) and summer 2023 (Wessex). Looking ahead, they will also be routinely run in Paris for the summer 2024 Olympics ahead of operational implementation as a “trailblazer” configuration in 2025/26. These sub-km ensembles potentially provide benefits in situations where surface heterogeneity and convective-scale dynamics are important, but their extraordinary computational costs raise questions about whether they are the most effective use of resources.
The main research question is “what is the most effective way to use km-scale ensembles to gain the benefits of sub-km scale models?”. The student will investigate the weather conditions and forecast variables for which sub-km ensembles provide additional value compared to km-scale ensembles and how the ensemble skill-to-spread ratio compares. Additional questions relate to the potential to exploit machine learning approaches. Are sub-km forecasts able to simulate different forecast evolutions to the km-scale “parent” forecast, or do they just add realistic “noise” that could be emulated? Can the parent ensemble forecasts be clustered into representative groups, limiting the number of necessary downscaled forecasts?
Title: Maritime Continent Precipitation Processes and Predictability in ECMWF Sub-seasonal Forecasts .
The Maritime Continent (MC) region is highly vulnerable to high impact weather associated with heavy precipitation. Improved predictions on sub-seasonal timescales (2-4 weeks) could provide early warnings to allow action to mitigate the impacts of these events. Precipitation over the region is strongly modulated by the Madden-Julian Oscillation (MJO), the dominant mode of intra-seasonal variability in the tropics, and the MJO should provide a significant source of predictability on sub-seasonal timescales. Realizing this predictability depends on two factors. Firstly, can we predict the MJO? Secondly, given a prediction of the MJO can we predict the local precipitation response? The ECMWF sub-seasonal forecasts has good skill for prediction of the large-scale evolution of the MJO, however, MJO-related precipitation skill is lower over MC land than over ocean regions. MC precipitation is driven by complex interactions between the large-scale variability (including the MJO), and variability on smaller scales, including diurnal land-sea-mountain breezes. Higher model resolution, and particularly explicit convection, has generally been found to improve the representation of these processes. Using observations, output from ECMWF operational monthly forecasts, and high resolution simulations, this project will address the causes of the relatively low skill for sub-seasonal precipitation forecasts over the land regions in the MC. In particular:
- How well does the ECWMF model capture the MJO-related precipitation anomalies over the MC and its modulation of more localised land-sea breezes and mesoscale systems?
- Does the simulated relationship between the MJO and precipitation depend on atmospheric resolution and/or the representation of convection?
Title: S2S prediction of marine heat waves and associated compound events .
Marine heat waves (MHWs) are an extreme event type of growing scientific interest, because they have important effects on ecosystems and fisheries (Rodrigues et al. 2019). The frequency and intensity of MHWs are expected to increase with global warming, as is evident in recent observed trends. From an S2S perspective the trend is a complicating factor: using a fixed SST threshold means the same statistical event reflects a different configuration of causal factors and is thus a different physical event, whilst using a moving threshold means that the impacts will be different. The main research question in this project is how to meaningfully evaluate and communicate MHW predictions on the S2S timescale in a non-stationary climate. This question lies at the intersection of statistical and physical reasoning. Our working hypothesis is that this can be done by treating MHWs as short-term (probabilistic) events riding on top of both long-term (trend) and medium-term (low-frequency variability) components, both of which can be regarded as known in the S2S context. This combination of causal factors is important because compound aspects of the event, e.g. drought over adjacent land areas associated with anti-cyclonic atmospheric blocking conditions, will have a different relationship with the MHW on the different timescales. The spatial structure of the MHW within the ocean would be an important reflection of these differences. In this project, the student would compare S2S predictions of MHWs in the Mediterranean and in the western south Atlantic (off southeast Brazil), to provide contrasting situations.
Title: Tropical Atlantic TC risk: complementing observations and models with Artificial Intelligence
What is the sub-seasonal predictability of TCs in the Western North Atlantic and what governs it; is it changing due to meridional migration of the storm track and/or the lengthening of the active hurricane season? While there is currently no indication that numerical weather prediction of TC tracks, intensity and intensification possesses robust skill beyond 3-5 days, there is untapped potential in making use of (high-resolution) predictions, as examples of physically plausible unfolding’s of classes of hazardous events. As such, it is sensible to conceive of an investigation based on combining collections of NWP products for specific case studies (large samples, short duration) with seasonal products, which do possess limited skill, in terms of predictability of the second kind, despite being unable to address the sub-seasonal scale. Targeted use of advanced statistical methods can augment the sample size of such event “catalogues”, and enable a more systematic and robust investigation of the role of large-scale and local circulations (e.g. orography effects around coastlines of key islands in the Caribbean region). Likely methods and tools comprise physically based bootstrapping, and the use of storylines to systematically test physical hypotheses against ensembles of low-resolution models. The ultimate usefulness of this exploratory exercise is in aiding the planning and deployment of anticipatory efforts by authorities and NGOs. This project is also closely aligned with NERC-NSF Huracán, which investigates the role of large-scale drivers of TC variability in the North Atlantic basin in a climate context, using the same data sources.