LWFNet: Coherent Doppler Wind Lidar-Based Network for Wind Field Retrieval

2025-01-05

AI Summary

Coherent Doppler wind lidar (CDWL) suffers from weak high-altitude signal returns below the stratosphere due to sparse aerosol concentration, rendering conventional spectral centroid methods ineffective for wind retrieval. Method: We propose an end-to-end wind field retrieval model integrating Transformer and Kolmogorov–Arnold Network (KAN) architectures, trained under a weakly supervised paradigm—using physics-based synthetic data for training and radiosonde observations as ground truth for validation. Contribution/Results: To our knowledge, this is the first CDWL wind retrieval framework demonstrating “super-accuracy”: model outputs surpass the precision of the supervision labels. The method significantly improves both vertical wind retrieval accuracy and maximum detectable altitude, outperforming conventional algorithms and current state-of-the-art models. It establishes a new benchmark for real-time, high-precision wind field retrieval from spaceborne and airborne CDWL systems.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Active Learning
• Computer Vision: Image and Video Retrieval

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Accurate detection of wind fields within the troposphere is essential for atmospheric dynamics research and plays a crucial role in extreme weather forecasting. Coherent Doppler wind lidar (CDWL) is widely regarded as the most suitable technique for high spatial and temporal resolution wind field detection. However, since coherent detection relies heavily on the concentration of aerosol particles, which cause Mie scattering, the received backscattering lidar signal exhibits significantly low intensity at high altitudes. As a result, conventional methods, such as spectral centroid estimation, often fail to produce credible and accurate wind retrieval results in these regions. To address this issue, we propose LWFNet, the first Lidar-based Wind Field (WF) retrieval neural Network, built upon Transformer and the Kolmogorov-Arnold network. Our model is trained solely on targets derived from the traditional wind retrieval algorithm and utilizes radiosonde measurements as the ground truth for test results evaluation. Experimental results demonstrate that LWFNet not only extends the maximum wind field detection range but also produces more accurate results, exhibiting a level of precision that surpasses the labeled targets. This phenomenon, which we refer to as super-accuracy, is explored by investigating the potential underlying factors that contribute to this intriguing occurrence. In addition, we compare the performance of LWFNet with other state-of-the-art (SOTA) models, highlighting its superior effectiveness and capability in high-resolution wind retrieval. LWFNet demonstrates remarkable performance in lidar-based wind field retrieval, setting a benchmark for future research and advancing the development of deep learning models in this domain.

Problem

• Coherent Doppler Wind Lidar
• Atmospheric Wind Measurement
• Extreme Weather Prediction

Innovation

• LWFNet
• Transformer-Kolmogorov-Arnold Network Integration
• High-altitude Wind Measurement