Applications of Data Assimilation and Parameter Calibration with Multi-Resolution Measurements of Seawater Temperature for Hydrodynamic Modeling of Shallow, Tidal Environments

This study investigates the integration of seawater temperature data acquired through in-situ and remote sensing methods, aiming to enhance the accuracy of hydrodynamic models. Our focus is on a shallow bay influenced by semidiurnal tides, where continuous thermal discharges from a coastal power plant significantly impact the local temperature field. The investigation addresses the model's uncertainty in capturing the variability of thermal effluents, particularly regarding the input descriptions for the discharge rate (Q) and excess temperature (ΔT) added to the ambient waters. These parameters display seasonal variations that reflect the energy consumption trends of the local population, introducing complexity into seawater temperature modeling. We aim to assess the effectiveness of using different data types in two key application areas: (A) generating better initial conditions through data assimilation with the Ensemble Kalman Filter (EnKF) technique, and (B) automating the calibration of the model parameters for the description of the thermal discharge. To explore these applications, we conduct a twin experiment that replicates the bay's real-world conditions, allowing for a comprehensive evaluation of the impact of integrating temperature data of varying resolutions on the assimilation and calibration processes. Our goal is to determine the most effective spatiotemporal scales for these applications, and to provide recommendations for modeling approaches in similar tidal environments.