Quantum Machine Learning Parameterization of the Boundary Layer Height

Kurzfassung

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.