DL-FIRE: a deep learning model for spatiotemporal prediction of the lidar-like aerosol profile over Southeast China.

Authors

Chen Yang, Shengcheng Cui, Chenglei Pei, Zhi Qiao, Mingzhou Li, Mengying Zhai, Huiqiang Xu, Debao Dong

Published in

Journal of the Optical Society of America. A, Optics, image science, and vision. Volume 43. Issue 8. Pages 1252-1261. Aug 01, 2026.

Abstract

Vertical profiles of the aerosol extinction coefficient (AEC) offer height-resolved information crucial for quantifying aerosol-radiation interactions and aerosol-cloud processes. However, continuous observation of such profiles remains limited due to sparse ground-based lidar coverage and the discontinuous nature of satellite retrievals. To mitigate this spatiotemporal data gap, we present DL-FIRE, a novel, to our knowledge, deep learning framework for spatiotemporal prediction of lidar-like aerosol profiles. The model predicts vertical AEC profiles at hourly lead times from 0 to 48 h using lagged ERA5 meteorological features. Its architecture centers on a specialized transformer encoder that utilizes a learnable aggregation token to distill integrated profile representations and implicitly capture vertical aerosol dynamics. We further introduce a masked loss function augmented with pairwise ordering constraints, enabling effective learning from sparse and incomplete profiles. Evaluated on the full dataset, our approach achieves R=0.5308 and MSE=0.0090, reducing the prediction error by 9.1% and 11.8% compared to the multilayer perceptron (MLP) and convolutional neural network (CNN) baselines, respectively, while demonstrating enhanced stability. Importantly, the model demonstrates strong generalization capability, independently validated against ground-based lidar measurements at Guangzhou (R=0.58, MSE=0.0067) and Shouxian (R=0.53, MSE=0.0189) during 2024-2025, confirming its practical reliability. Collectively, DL-FIRE provides a reliable tool for atmospheric sensing and supports intelligent environmental decision-making, with potential applications in long-term regional aerosol prediction studies.

PMID:
42863008
Bibliographic data and abstract were imported from PubMed on 13 Oct 2026.

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