Leveraging Deep Learning for Downscaling GOME-2 Atmospheric Data Using TROPOMI Observations

The Global Ozone Monitoring Experiment 2 (GOME-2) and the TROPOspheric Monitoring Instrument (TROPOMI) are two significant satellite-based instruments dedicated to monitoring Earth’s atmosphere. GOME-2, part of the MetOp platform, has been operational since 2006, and was originally developed to monitor the ozone layer in the atmosphere. However, its onboard spectrometer can also detect pollutant gases, including NO₂, which we will use as an initial example in this study.

GOME-2 spatial resolution is very coarse: a single data point is representative of an area of approximately 40 km x 80 km, which provides a broad view of atmospheric composition at global scale but limits its effectiveness in capturing fine-scale variations over cities and other human activity areas.

This study investigates whether TROPOMI high-resolution data can be utilized to downscale GOME-2 observations, potentially yielding insights into atmospheric changes dating back to 2006. We explore the implications of this process on spatial and radiometric accuracy and consider its broader significance for the future of satellite observations.

Given the abundance of available training data, we propose a novel approach involving deep learning. In particular, we used a combination of Residual Dense Blocks (RDBs) which state-of-the-art studies have shown to outperform similar Convolutional Neural Networks (CNNs) and Generative Neural Networks (GNNs) but still relies on the convolution operation, unlike transformers architectures (e.g., Vision Transformers ViTs). Then, to effectively train our model, we addressed challenges such as the resolution disparity between GOME-2 and TROPOMI (approximately a factor of 10), which requires working with a large pixel space, significantly increasing the memory needed for training. And the significant issue of missing data in atmospheric acquisition, e.g., due cloud cover.

Aside from the technical challenges of developing such model, the output validation plays a crucial role in ensuring the reliability and scientific utility of our results. We therefore evaluated our model performance on an independent dataset to verify the consistency of absolute reported NO₂ values.

The approach involved training the model on one year of data (2023) over 10 selected locations and evaluate its performance using the ground-based Pandonia Global Network (PGN), a network of well-calibrated instruments designed to provide high-quality measurements of atmospheric trace gases at specific locations.

Results show an improvement not only limited to the reconstruction of fine details but also on the agreement of the absolute reported NO₂ value between PGN data and the output from our model. We are currently working on expanding the dataset to further test the limits of our approach at global scale. Another active research area is the extension of the proposed approach to other common trace gases common between the two instruments. We hope to enhance the utility of this approach for broader applications in atmospheric science and to highlight the potential of leveraging deep learning downscaling for atmospheric data.