Mitigating masked pixels in climate-critical datasets

Remote sensing observations of the Earth's surface are frequently stymied by clouds, water vapour, and aerosols in our atmosphere. These degrade or preclude the measurementof quantities critical to scientific and, hence, societal applications. In this study, we train a natural language processing (NLP) algorithm with high-fidelity ocean simulations in order to accurately reconstruct masked or missing data in sea surface temperature (SST)--i.e. one of 54 essential climate variables identified by the Global Climate Observing System. We demonstrate that the Enki model repeatedly outperforms previously adopted inpainting techniques by up to an order-of-magnitude in reconstruction error, while displaying high performance even in circumstances where the majority of pixels are masked. Furthermore, experiments on real infrared sensor data with masking fractions of at least 40% show reconstruction errors of less than the known sensor uncertainty (RMSE < ~0.1K). We attribute Enki's success to the attentive nature of NLP combined with realistic SST model outputs, an approach that may be extended to other remote sensing variables. This study demonstrates that systems built upon Enki--or other advanced systems like it--may therefore yield the optimal solution to accurate estimates of otherwise missing or masked parameters in climate-critical datasets sampling a rapidly changing Earth.