Transformer model improves planetary boundary layer height estimates from satellite data
PBLH Estimation from Satellite Radiances via a Dual-Encoder Transformer
Computer Vision and Pattern RecognitionMachine Learning
Summary
Estimating how high the lowest part of the atmosphere reaches (called the boundary layer) using satellite data is tricky because satellites see indirect signals. The authors worked with a big dataset matching satellite readings to known boundary heights and tested multiple methods to find the best one. They designed a special artificial intelligence model that looks at the data in two ways and can handle missing or unclear satellite readings caused by clouds or weather. Their model made more accurate height predictions worldwide than previous methods, including on new data taken during a recent weather campaign.
What this means in practice
- •For weather forecasting teams: Improve near-surface atmospheric height estimates from satellites under varying weather conditions using the dual-encoder Transformer model.
- •For climate monitoring agencies: Use the dual-encoder Transformer to produce more accurate global maps of atmospheric boundary layer height for climate analysis.
Authors
Lorenzo Innocenti, Luca Catalano, Edoardo Arnaudo, Claudio Rossi, Salvatore Larosa, Domenico Cimini, Paolo Garza
Abstract
Estimating the Planetary Boundary Layer Height (PBLH) from satellite observations is a challenging regression problem due to the indirect relationship between top-of-atmosphere radiances and near-surface atmospheric structure. Progress has been limited both by the lack of architectures capable of handling the multimodal, spatially incomplete nature of satellite overpasses, and by the scarcity of suitable datasets. In this paper, we build upon the large-scale dataset pairing MetOp radiances with ERA5 PBLH labels that we introduced in our previous work, making three contributions. First, we establish a benchmark across eight approaches spanning pixel-wise regression, swath-wise sequence models, and convolutional and Transformer models operating on the full orbital passage. Second, we quantify what the resulting model actually relies on, using grouped Shapley decomposition over the input blocks. Third, we present the best-performing architecture found: a dual-encoder Transformer whose masked-input handling lets it operate in all weather conditions. The proposed model achieves MAE = 155.8 m on the held-out global test set, outperforming all baselines on every evaluation subset. On 30 out-of-distribution granules acquired on two days overlapping the TEAMx observational campaign, it achieves MAE = 165.3 m, outperforming a pixel-wise baseline trained on the same data (MAE = 197 m).