arXiv subjects

Bobby Antonio

Publications and source records attributed to Bobby Antonio.

12 recordsLinked to original sources

arXiv2026

ACE2-NEMO: Coupling an ML atmospheric emulator to a full-depth dynamical ocean model

Understanding how fast atmospheric variability shapes slow climate variability and sensitivity remains a central challenge in Earth-system science. Recent advances in machine-learned (ML) atmospheric models have demonstrated remarkable skill on weather timescales, but their emergent behaviour in a fully coupled climate system remains largely unexplored. We present early results from a new hybrid modelling framework, in which the ACE2 ML atmospheric emulator is interactively coupled to the NEMO ocean model. We report on a set of 70-year coupled simulations (1950-2020 historical forcing and fixed-1950s control). These experiments represent, to our knowledge, the first multi-decadal integrations of a machine-learned atmosphere interacting with a full-depth dynamical ocean. Several historical and fixed-1950s control simulations from the fully dynamic global coupled climate model EC-Earth, which has the same ocean component used in ACE2-NEMO, are also considered for comparison. We assess the behaviour of the coupled system, with particular focus on low-frequency tropical variability and the climate response to greenhouse-gas forcing. Analysis of potentially emergent El Ni\~{n}o-like variability reveals realistic fast timescale air-sea coupling in the tropical Pacific, but the temporal variability is unrealistic, with very low amplitude oscillations; this appears to be due to weak atmospheric feedback in the tropical Pacific. The response to CO2 forcing shows initial agreement with EC-Earth3P, but deviates due to reduced downward short-wave radiation in ACE2. These results provide a unique test of physical realism for atmospheric emulators, and evaluate the possible role of entirely machine-learned components in next-generation Earth system models.

physics.ao-ph↗

arXiv2026

Investigating How the Fractions Skill Score and Brier Divergence Skill Score Reflect Forecast Error

Meaningful scores for forecast verification are essential for developing reliable forecasts, and there has been much effort to develop scores that align well with human perceptions of forecast quality. Whilst many of these scores have intuitive interpretations, relatively little is known about how these scores rank different forecasts, and how scores reflect forecast error. We theoretically explore the behaviour of two scores that fall within the `neighbourhood' paradigm of spatial verification; the Fractions Skill Score (FSS) and Brier Divergence Skill Score (BDnSS). We investigate how each score ranks forecasts with two types of error; errors in the mean frequency (corresponding to intensity or shape errors) and errors in the standard deviation (corresponding to errors in spatial structure, such as blurring or excess noise). We find that under many situations the FSS assigns higher scores to forecasts that over-predict mean frequency, thus theoretically confirming the need to use the FSS with percentile thresholds. Both scores generally assign higher scores to forecasts with lower neighbourhood standard deviation, a reflection of the `double penalty' problem; however, we observe that size of this effect is larger for the BDnSS than the FSS, showing that the FSS under some situations is less susceptible to the double penalty problem than the BDnSS.

physics.ao-ph↗

arXiv2026

Role of the ocean for fast atmospheric evolution revealed by machine learning

There have recently been many efforts to create machine learnt atmospheric emulators designed to replace physical models. So far these have mainly focused on medium-range weather forecasting, where these `Machine Learnt Weather Prediction' (MLWP) models can outperform leading operational forecasting centres. However, because of this focus on shorter timescales, many of these emulators ignore the effects of the ocean, and take no ocean variables as inputs. We hypothesise that such MLWP models have learnt a best-guess of the evolution of the atmosphere, by implicitly inferring ocean conditions from atmospheric states, with no access to ocean data. Turning this limitation into a strength, we use it as a means to study the role of the oceans on the evolution of the atmosphere. By exploring how model forecast errors relate to properties of the air-sea interface, we infer what ocean information these atmospheric emulators are able to derive from atmospheric data alone, and what they cannot. This highlights the regions and processes through which the ocean independently influences the atmosphere on fast timescales. We perform this analysis for GraphCast, finding clear relationships between air-sea properties and the forecast errors over the ocean, including clear seasonal effects. We then explore what this reveals about GraphCast's internal representation of the ocean. In addition to understanding real-world ocean-atmosphere interactions, this analysis provides guidance for improving forecast skill and physical realism in MLWP models, and for informing how future machine learning models should use ocean information on short timescales.

physics.ao-ph↗

arXiv2026

Error in ERA5 2m Temperature identified using GraphCast

Reanalyses such as ERA5 have long been foundational for weather and climate science. They have also found a new use case, as training and verification data for machine-learnt weather prediction (MLWP) models. Here we compare short-lead time (6h) forecasts from the MLWP model GraphCast against ERA5. In doing so, we identify a recurrent, spatially coherent error in 2m Temperature centred on the Ethiopian Highlands, that occurs predominantly at 0600 UTC. We show that these error events are not an error in the forecast from GraphCast, but are in fact an error in ERA5, and are also present in the ECMWF operational analysis. They arise from the 2D optimal interpolation procedure, when surface reports are assimilated that are temporally displaced compared to the background forecast. This produces spuriously warm analysis increments over Ethiopia on approximately 7\% of dates at 0600 UTC across the reanalysis record. The spread from the ensemble of data assimilation partially flags these cases but is underdispersive. We assess the impact on GraphCast, which was trained on ERA5. While GraphCast can largely ignore these unphysical error events, a small systematic degradation in forecast skill over the region is observed. We discuss implications for using reanalysis as truth in machine learning training and verification, and recommend simple changes to reduce such artefacts in future analyses.

physics.ao-ph↗

arXiv2025

Improved rainfall forecasts in daily use over East Africa

Ensemble forecasting has proven to be a vital tool for predicting extreme, life-threatening or only partially predictable weather events. As well as providing probabilistic products, individual ensemble members provide context and realisations of possible extreme weather. However, many National Meteorological Services in East Africa do not have the computing resources to enable them to run their local area models in ensemble mode over the full period of the two-week medium range. In this paper we test the performance of a forecast system, comprising the global ECMWF ensemble forecast, post-processed using cGAN, a neural network model, and forecasts calibrated using IDR, a recent statistical method, against the probabilistic climatology. cGAN provides comparable levels of probabilistic skill as IDR applied to the ECMWF ensemble or IDR applied to the machine-learning models FuXi and GraphCast. These methods improve the raw ECMWF ensemble forecast substantially, which is itself an improvement on deterministic forecasts. The ability of cGAN to produce individual realisations of future rainfall was important when assessed in an operational context by the National Meteorological and Hydrological Services of Kenya and Ethiopia. Moreover, in common with IDR, cGAN is cheap to train/run and requires no additional post-processing. It is run on laptops to generate many thousands of ensemble members, making it suitable for Meteorological Services with limited computational facilities.

physics.ao-ph↗

arXiv2025

Seasonal forecasting using the GenCast probabilistic machine learning model

Machine-learnt weather prediction (MLWP) models are now well established as being competitive with conventional numerical weather prediction (NWP) models in the medium range. However, there is still much uncertainty as to how this performance extends to longer timescales, where interactions with slower components of the earth system become important. We take GenCast, a state-of-the-art probabilistic MLWP model, and apply it to the task of seasonal forecasting with prescribed sea surface temperature (SST), by providing anomalies persisted over climatology (GenCast-Persisted) or forcing with observations (GenCast-Forced). The forecasts are compared to the European Centre for Medium-Range Weather Forecasts seasonal forecasting system, SEAS5. Our results indicate that, despite being trained at short timescales, GenCast-Persisted produces much of the correct precipitation patterns in response to El Ni\~{n}o and La Ni\~{n}a events, with several erroneous patterns in GenCast-Persisted corrected with GenCast-Forced. The uncertainty in precipitation response, as represented by the ensemble, compares favourably to SEAS5. Whilst SEAS5 achieves superior skill in the tropics for 2-metre temperature and mean sea level pressure (MSLP), GenCast-Persisted achieves significantly higher skill in some areas in higher latitudes, including mountainous areas, with notable improvements for MSLP in particular; this is reflected in a higher correlation with the observed NAO index. Reliability diagrams indicate that GenCast-Persisted is overconfident compared to SEAS5, whilst GenCast-Forced produces well-calibrated seasonal 2-metre temperature predictions. These results provide an indication of the potential of MLWP models similar to GenCast for the `full' seasonal forecasting problem, where the atmospheric model is coupled to ocean, land and cryosphere models.

physics.ao-ph↗

arXiv2023

How to derive skill from the Fractions Skill Score

The Fractions Skill Score (FSS) is a widely used metric for assessing forecast skill, with applications ranging from precipitation to volcanic ash forecasts. By evaluating the fraction of grid squares exceeding a threshold in a neighbourhood, the intuition is that it can avoid the pitfalls of pixel-wise comparisons and identify length scales at which a forecast has skill. The FSS is typically interpreted relative to a `useful' criterion, where a forecast is considered skillful if its score exceeds a simple reference score. However, the typical reference score used is problematic, as it is not derived in a way that provides obvious meaning or that scales with neighbourhood size, and forecasts that do not exceed it can have considerable skill. We therefore provide a new method to determine forecast skill from the FSS, by deriving an expression for the FSS achieved by a random forecast, which provides a more robust and meaningful reference score to compare with. Through illustrative examples we show that this new method considerably changes the length scales at which a forecast would be regarded as skillful, and reveals subtleties in how the FSS should be interpreted.

physics.ao-ph↗

arXiv2023

Further analysis of cGAN: A system for Generative Deep Learning Post-processing of Precipitation

The conditional generative adversarial rainfall model "cGAN" developed for the UK \cite{Harris22} was trained to post-process into an ensemble and downscale ERA5 rainfall to 1km resolution over three regions of the USA and the UK. Relative to radar data (stage IV and NIMROD), the quality of the forecast rainfall distribution was quantified locally at each grid point and between grid points using the spatial correlation structure. Despite only having information from a single lower quality analysis, the ensembles of post processed rainfall produced were found to be competitive with IFS ensemble forecasts with lead times of between 8 and 16 hours. Comparison to the original cGAN trained on the UK using the IFS HRES forecast indicates that improved training forecasts result in improved post-processing. The cGAN models were additionally applied to the regions that they were not trained on. Each model performed well in their own region indicating that each model is somewhat region specific. However the model trained on the Washington DC, Atlantic coast, region achieved good scores across the USA and was competitive over the UK. There are more overall rainfall events spread over the whole region so the improved scores might be simply due to increased data. A model was therefore trained using data from all four regions which then outperformed the models trained locally.

physics.ao-ph↗

arXiv2016

Quantum computation and communication in strongly interacting systems

Each year, the gap between theoretical proposals and experimental endeavours to create quantum computers gets smaller, driven by the promise of fundamentally faster algorithms and quantum simulations. This occurs by the combination of experimental ingenuity and ever simpler theoretical schemes. This thesis comes from the latter perspective, aiming to find new, simpler ways in which components of a quantum computer could be built. We first search for ways to create quantum gates, the primitive building blocks of a quantum computer. We find a novel, low-control way of performing a two-qubit gate on qubits encoded in a decoherence-free subspace, making use of many-body interactions that may already be present. This includes an analysis of the effect of control errors and magnetic field fluctuations on the gate. We then present novel ways to create three-qubit Toffoli and Fredkin gates in a single step using linear arrays of qubits, including an assessment of how well these gates could perform, for quantum or classical computation, using state-of-the-art ion trap and silicon donor technology. We then focus on a very different model from the normal circuit model, combining ideas from measurement-based quantum computation (MBQC) and holonomic quantum computation, showing that all MBQC patterns with a property called \emph{gflow} can be converted into a holonomic computation. The manifestation of the properties of MBQC in this adiabatically driven model is then explored. Finally, we investigate ways in which quantum information can be communicated between distant parties, using minimally engineered spin chains. The viability of using 1D Wigner crystals as a quantum channel is analysed, as well as schemes using ideal uniform spin chains with next-neighbour interactions, and edge-locking effects.

quant-ph↗

arXiv2015

Self assembled Wigner crystals as mediators of spin currents and quantum information

Technological applications of many-body structures that emerge in gated devices under minimal control are largely unexplored. Here we show how emergent Wigner crystals in a semiconductor quantum wire can facilitate a pivotal requirement for a scalable quantum computer, namely transmitting quantum information encoded in spins faithfully over a distance of micrometers. The fidelity of the transmission is remarkably high, faster than the relevant decohering effects, independent of the details of the spatial charge configuration in the wire, and realizable in dilution refrigerator temperatures. The transfer can evidence near unitary many-body nonequilibrium dynamics hitherto unseen in a solid-state device. It could also be useful in spintronics as a method for pure spin current over a distance without charge movement.

cond-mat.str-el↗

arXiv2014

Adiabatic graph-state quantum computation

Measurement-based quantum computation (MBQC) and holonomic quantum computation (HQC) are two very different computational methods. The computation in MBQC is driven by adaptive measurements executed in a particular order on a large entangled state. In contrast in HQC the system starts in the ground subspace of a Hamiltonian which is slowly changed such that a transformation occurs within the subspace. Following the approach of Bacon and Flammia, we show that any measurement-based quantum computation on a graph state with \emph{gflow} can be converted into an adiabatically driven holonomic computation, which we call \emph{adiabatic graph-state quantum computation} (AGQC). We then investigate how properties of AGQC relate to the properties of MBQC, such as computational depth. We identify a trade-off that can be made between the number of adiabatic steps in AGQC and the norm of $\dot{H}$ as well as the degree of $H$, in analogy to the trade-off between the number of measurements and classical post-processing seen in MBQC. Finally the effects of performing AGQC with orderings that differ from standard MBQC are investigated.

quant-ph↗

arXiv2013

Two-qubit gates for decoherence-free qubits using a ring exchange interaction

It is known that it is possible to encode a logical qubit over many physical qubits such that it is immune to the effects of collective decoherence, and it is possible to perform universal quantum computation using these `decoherence-free' qubits. However, current proposed methods of performing gates on these encoded qubits could be difficult to implement, or could take too much time to perform. Here we investigate whether exploiting ring-exchange interactions, which may be naturally present, can simplify the implementation of these gates in any way. Using a ring exchange interaction, we have found a way to create a controlled-Z gate on the 4-qubit decoherence-free subspace and the 3-qubit decoherence-free subsystem using a sequence with 5 pulses. This could be useful in situations where simplicity is important or where ring exchange interactions are prominent. We also investigate how timing errors and magnetic field fluctuations affect the fidelity of this gate.

quant-ph↗