Accurate representation of the atmospheric Boundary Layer Height (BLH) remains challenging in numerical weather and climate modeling, especially when turbulent processes occur at scales below the model grid resolution. This thesis investigates whether Quantum Machine Learning (QML) models can be used to build compact BLH parameterizations from coarse-grained ICON-XPP atmospheric columns. The work develops a complete offline pipeline for QML-compatible BLH prediction. Starting from high-dimensional atmospheric profiles, several dimensionality reduction and feature-engineering strategies are evaluated to obtain compact representations suitable for Quantum Neural Networks (QNNs). Two complementary 8-dimensional input representations are selected: SAE8D, a supervised autoencoder latent space optimized for predictive performance, and FtEng8D, a physically interpretable feature-engineered space based on stability, humidity-gradient and wind-shear diagnostics. Several QNN architectures are then trained and compared against parameter-matched classical Multi-Layer Perceptron (MLP) baselines. The QNN experiments analyze the effect of circuit depth, ansatz family, readout, data re-uploading and training-set size. The final full-dataset evaluation shows that the selected QNN remains competitive with a fixed MLP under the same parameter budget. The MLP achieves slightly better average RMSE, R2 and MAE, while the QNN shows lower seed-to-seed variability and a bias closer to zero. The error analysis reveals that the final QNN performs best in the dominant low and moderate BLH regimes, but systematically underestimates rare deep-boundary-layer cases. SHAP-based interpretability analysis indicates that the QNN mainly relies in physically meaningful thermodynamic, stability and humidity-gradient features, supporting the plausibility of the learned parameterization. Overall, the results do not demonstrate a systematic quantum advantage over classical machine learning. However, they show that QML-compatible BLH parameterization is feasible when high-dimensional atmospheric columns are first compressed into carefully designed low-dimensional representations. The proposed QNN therefore provides a compact and physically motivated prototype for future quantum-compatible atmospheric parameterizations, with future work focusing on richer interpretable feature spaces, rare high-BLH regimes and online testing in ICON-XPP.
Accurate representation of the atmospheric Boundary Layer Height (BLH) remains challenging in numerical weather and climate modeling, especially when turbulent processes occur at scales below the model grid resolution. This thesis investigates whether Quantum Machine Learning (QML) models can be used to build compact BLH parameterizations from coarse-grained ICON-XPP atmospheric columns. The work develops a complete offline pipeline for QML-compatible BLH prediction. Starting from high-dimensional atmospheric profiles, several dimensionality reduction and feature-engineering strategies are evaluated to obtain compact representations suitable for Quantum Neural Networks (QNNs). Two complementary 8-dimensional input representations are selected: SAE8D, a supervised autoencoder latent space optimized for predictive performance, and FtEng8D, a physically interpretable feature-engineered space based on stability, humidity-gradient and wind-shear diagnostics. Several QNN architectures are then trained and compared against parameter-matched classical Multi-Layer Perceptron (MLP) baselines. The QNN experiments analyze the effect of circuit depth, ansatz family, readout, data re-uploading and training-set size. The final full-dataset evaluation shows that the selected QNN remains competitive with a fixed MLP under the same parameter budget. The MLP achieves slightly better average RMSE, R2 and MAE, while the QNN shows lower seed-to-seed variability and a bias closer to zero. The error analysis reveals that the final QNN performs best in the dominant low and moderate BLH regimes, but systematically underestimates rare deep-boundary-layer cases. SHAP-based interpretability analysis indicates that the QNN mainly relies in physically meaningful thermodynamic, stability and humidity-gradient features, supporting the plausibility of the learned parameterization. Overall, the results do not demonstrate a systematic quantum advantage over classical machine learning. However, they show that QML-compatible BLH parameterization is feasible when high-dimensional atmospheric columns are first compressed into carefully designed low-dimensional representations. The proposed QNN therefore provides a compact and physically motivated prototype for future quantum-compatible atmospheric parameterizations, with future work focusing on richer interpretable feature spaces, rare high-BLH regimes and online testing in ICON-XPP.
Long-term climate projections require running global Earth system models on timescales of hundreds of years and have relatively coarse resolution (from 40 to 160 km in the horizontal) due to their high computational costs. Unresolved subgrid-scale processes, such as clouds, are described in a semi-empirical manner by so called parameterizations, which are a major source of uncertainty in climate projections. Machine learning models trained on short high-resolution climate simulations are promising candidates to replace conventional parameterizations. In this work, we explore the potential of quantum machine learning (QML), and in particular quantum neural networks (QNNs), to develop cloud cover parameterizations. QNNs differ from their classical counterparts, and their potentially high expressivity turns them into promising tools for accurate data-driven schemes to be used in climate models. We perform an extensive comparative analysis between several QNNs and classical neural networks (NNs), by training both ansatzes on data coming from high-resolution simulations with the ICOsahedral Non-hydrostatic weather and climate model (ICON). Our results show that the overall performance of the investigated QNNs is comparable to that of classical NNs of similar size, i.e., with the same number of trainable parameters, with both ansatzes outperforming standard parameterizations used in climate models. Our study includes an analysis of the generalization ability of the models as well as the geometrical properties of their optimization landscape. We also investigate the effects of finite sampling noise, and show that the training and the predictions of the QNNs are stable even in this noisy setting. These results demonstrate the applicability of QML to learn meaningful patterns in climate data, and are thus relevant for a broad range of problems within the climate modeling community.
The advent of quantum computing has opened unprecedented promises to tackle humanity' s most pressing challenges: computationally intractable problems might come within reach through quantum advantages. It is imperative for the field of climate modeling to explore possible uses of this emerging technology. Global climate models have successfully projected different future scenarios, but the spread in projections remains large, with subgrid-scale parameterizations being the main origin of these uncertainties. While machine learning models improve these parameterizations, quantum computing could bring decisive further improvements. Atmospheric turbulence affects the climate because it determines the rates of exchange of heat, moisture, and momentum between the Earth surface and the atmosphere, but has been especially hard to predict and model. Here, we develop a quantum machine learning-based subgrid-scale parameterization for the turbulent vertical heat flux. The training data are coarse-grained data from large-eddy-simulations of a dry convective boundary layer in an idealized setup without topography. Training various quantum and classical neural network models, we find that quantum models based on parametrized circuits with just 2 or 3 qubits achieve accuracies similar to classical models with the same number of trainable parameters. In contrast, the Smagorinsky closure applied to the coarse target resolution deviates strongly from the highly resolved flux. Both our quantum and classical models generalize well to unseen atmospheric conditions. Finally, the feature importances in our quantum models are on average more stable with respect to the random initial parameters, highlighting the possibility to exploit quantum advantages in climate and Earth system models.
Cloud cover parameterizations play an important role in climate models, influencing radiative transfer, atmospheric dynamics, and hydrological cycles. However, their correct formulation remains a major challenge in climate modeling, as subgrid-scale cloud processes must be approximated. Classical and quantum Neural Networks have been proposed as potential tools for improving these parameterizations. While previous studies suggest that classical neural networks can produce physically meaningful results, it remains unclear whether quantum neural networks (QNNs) exhibit similar capabilities or rely on spurious correlations. This study begins by comparing a classical neural network and a quantum neural network to assess whether they capture comparable physical dependencies when predicting cloud cover, before exploring alternative architectures for each approach. Using explainable AI (XAI) techniques, specifically SHapley Additive exPlanations (SHAP), the learned feature dependencies in both types of models are analyzed. This approach enables us to evaluate not only predictive performance but also the extent to which each model captures the underlying physics of cloud cover. Our results show that both classical and quantum models exhibit similar learning patterns, extracting comparable relationships from the data. While the QNN does not outperform the classical network, it achieves comparable results, suggesting that quantum machine learning (QML) could be a viable approach in this domain. These findings contribute to the ongoing exploration of QML in climate science and highlight the potential of quantum methods for atmospheric modeling. More broadly, this study supports the integration of machine learning into climate science while ensuring physical consistency and interpretability.
Compact quantum neural networks have been explored for cloud-cover parameterization, but their predictive value depends on how physical knowledge is incorporated and which classical models provide the reference. We investigate an offline residual formulation that retains a fitted \XR\ cloud-cover estimate and learns a correction to its logit in transformed target space. Using released ICON/DYAMOND-derived data, we compare ten model variants in six- and eight-input settings, with three matched replicates and 10,000 training samples per replicate. All quantum circuits use local Z readout and noiseless exact expectation values. Under a common 30-epoch protocol, residual prediction reduced physical-space test mean squared error relative to direct QNN prediction by 54.3\% and 38.8\% for six-input XYZ and ZZXY circuits, and by 49.2\% and 38.9\% for their eight-input versions. Residual QNNs also outperformed the selected compact residual multilayer perceptron in every paired replicate. A larger, longer-trained Grundner-Q1 adaptation nevertheless achieved lower error: residual Q1 reached mean MSEs of 0.00778 and 0.00791, compared with 0.01124 and 0.01082 for residual XYZ. The results support residual prediction for compact simulated QNNs within the tested budget and establish a clear boundary set by stronger classical references. They do not establish computational quantum advantage or online climate-model skill.
Weather forecasting plays a critical role across various sectors, supporting strategic planning and reducing the impact of hazardous climatic conditions. However, the inherently chaotic and nonlinear nature of atmospheric systems limits the performance of conventional forecasting approaches, often resulting in prediction inaccuracies and heightened risk. This study proposes a quantum-driven predictive framework that exploits the computational strengths of Quantum Machine Learning (QML) and hybrid quantum–classical optimization to enhance the accuracy and efficiency of modern forecasting models. The framework employs Quantum Neural Networks (QNNs) combined with Variational Quantum Circuits (VQCs) to learn complex spatial–temporal dynamics embedded within large meteorological datasets. Through quantum feature encoding, key atmospheric indicators—including temperature, humidity, wind velocity, and barometric pressure—are projected into high-dimensional Hilbert spaces, enabling more expressive pattern extraction and robust predictive behavior. Simulation results reveal that the quantum-augmented approach surpasses traditional deep learning architectures in both training convergence and forecast precision when applied to extensive weather records. The hybrid design further supports scalability by intelligently distributing computational workloads between classical processors and quantum hardware. Overall, this research demonstrates the transformative potential of quantum computing in atmospheric modeling, offering a foundation for future real-time forecasting systems capable of managing the increasing complexity of global climate patterns.
The use of computational intelligence has become commonplace for accurate wind speed and energy forecasting, however the energy-intensive processes involved in training and tuning stands as a critical issue for the sustainability of AI models. Quantum computing emerges as a key player in addressing this concern, offering a quantum advantage that could potentially accelerate computations or, more significantly, reduce energy consumption. It is a matter of debate if purely quantum machine learning models, as they currently stand, are capable of competing with the classical state of the art on relevant problems. We investigate the efficacy of quantum neural networks (QNNs) for wind speed nowcasting, comparing them to a baseline Multilayer Perceptron (MLP). Utilizing meteorological data from Bahia, Brazil, we develop a QNN tailored for up to six hours ahead wind speed prediction. Our analysis reveals that the QNN demonstrates competitive performance compared to MLP. We evaluate models using RMSE, Pearson’s R, and Factor of 2 metrics, emphasizing QNNs’ promising generalization capabilities and robustness across various wind prediction scenarios. This study is a seminal work on the potential of QNNs in advancing renewable energy forecasting, advocating for further exploration of quantum machine learning in sustainable energy research.