ENHANCING SEA SURFACE HEIGHT INTERPOLATION USING SATELLITE-DERIVED CHLOROPHYLL-A AND TEMPERATURE DATA VIA MACHINE LEARNING: A CASE STUDY IN THE GULF OF MEXICO
Satellite observations provide indispensable data that is assimilated into numerical ocean models to correct errors and biases. Historically, sea surface height (SSH) from satellite altimeter tracks, sea surface temperature (SST), and more recently, sea surface salinity (SSS), have been assimilated into these models. Temperature and salinity are part of the governing equations of ocean dynamics, and SSH is directly derived from the state of the resolved ocean, making these variables a first choice for data assimilation. However, satellite-derived Chlorophyll-a (Chl-a) data, which offer high-resolution information, is not typically assimilated. This is primarily because this variable is not solved by the physical models, and the biochemical models that simulate broader marine ecosystems, including phytoplankton dynamics and nutrient cycles which do estimate Chl-a, are computationally expensive and not used in operational models.
In this study, we utilize a ten-year free run of a biochemical ocean model of the Gulf of Mexico to simulate satellite observations (altimeter tracks, SST, SSS, and Chl-a). We then train a machine learning model to learn the relationships between these fields and sea surface high. The trained model is then used to estimate the state of sea surface hight from the simulated observations. Our results demonstrate that this method provides better estimates compared to traditional optimal interpolation techniques. Finally, we apply our model to real observations and present a qualitative analysis of the results, highlighting the potential of the improved SSH estimation.