This work presents the first lidar observations of a Quasi‐Biennial Oscillation (QBO) in the interannual variations of stratospheric gravity wave potential energy density (E_pm in 30–50 km) at McMurdo (77.84°S, 166.67°E), Antarctica. This paper also reports the first identification of QBO signals in the distance between McMurdo and the polar vortex edge. Midwinter stratospheric gravity wave activity is stronger during the QBO easterly phase when the June polar vortex expands and the polar night jet shifts equatorward. During the QBO westerly phase, gravity wave activity is weaker when the polar vortex contracts and the polar night jet moves poleward. Nine years of lidar data (2011–2019) exhibit the mean E_pm winter maxima being ~43% higher during QBO easterly than westerly. The June polar vortex edge at 45 km altitude moves equatorward/poleward during QBO easterly/westerly phases with ~8° latitude differences (39.7°S vs. 47.7°S) as revealed in 21 years of MERRA‐2 data (1999–2019). We hypothesize that an equatorward shifted polar vortex corresponds to less critical level filtering of gravity waves and thus higher E_pm at McMurdo. The critical level filtering is characterized by wind rotation angle (WRA), and we find a linear correlation between the WRA and E_pm interannual variations. The results suggest that the QBO is likely controlling the interannual variations of the E_pm winter maxima over McMurdo via the critical level filtering. This observationally based study lays the groundwork for a rigorous numerical study that will provide robust statistics to better understand the mechanisms that link the tropical QBO to extratropical waves.

Thermal infrared (TIR) whole-sky imaging enables continuous cloud observations during both daytime and nighttime, making it an important tool for ground-based cloud monitoring. However, existing infrared cloud detection methods mainly rely on manually selected thresholds or handcrafted image features, which typically require instrument-specific calibration and exhibit limited generalization capability. Moreover, wide-field imaging covers not only the zenith region but also the peripheral areas at large zenith angles, which are susceptible to the combined effects of strong atmospheric background emission, thermal radiation contamination from the surface and surrounding environment, and imaging geometric distortion, resulting in reduced cloud–background contrast and making cloud segmentation considerably more challenging than in conventional infrared sky imagery. To address these limitations, this study proposes a deep-learning framework for automatic cloud detection and cloud cover retrieval from wide-field TIR whole-sky images. The framework adopts a two-stage strategy in which infrared images are first classified as clear-sky or cloudy, followed by semantic segmentation of cloudy images for cloud cover estimation. To support model training and evaluation, a dataset containing 3000 wide-field TIR whole-sky images was established using a semi-automatic labeling procedure. Experimental results demonstrate that the proposed method effectively identifies cloud structures, including those located near the edge of wide-field whole-sky images. The classifier achieved an overall accuracy of 98.44%, the segmentation network attained a mean pixel accuracy of 90.44%, and the derived cloud cover showed excellent agreement with the manually annotated reference masks with a correlation coefficient of 0.92. Ablation studies further validate the effectiveness of the proposed deep-learning framework in improving the segmentation of complex cloud structures. The truncated EfficientNet-B0 encoder yields the most pronounced contribution, while the ASPP module improves the delineation of complex sky and cloud structures. When applied to the observations from the ground-based TIR all-sky camera, the retrieved cloud cover agreed well with the FY4B/AGRI cloud mask product, with a correlation coefficient ranging from 0.81 to 0.89 across the four seasonal months in 2023. These results prove that the proposed framework provides an effective and robust solution for long-term ground-based cloud monitoring using wide-field TIR whole-sky imagery. It also offers a practical foundation for applications such as cloud climatology, solar radiation assessment, and atmospheric observation.

Real-time, non-contact velocity measurement of hot-rolled bars is critical for metallurgical process control, but conventional laser Doppler velocimetry (LDV) systems often fail in these environments. The intense broadband thermal radiation from targets up to 1000 °C, coupled with severe surface depolarization, overwhelms weak scattered signals in high-speed (up to 40 m/s) rolling zones. To address this issue, we developed a fully integrated, thermal-radiation-resistant LDV sensing system. Hardware optimization was achieved by eliminating polarized-light transmission and adopting a parallel-beam design, which significantly enlarges the laser overlap area and increases detection depth. Furthermore, a 1550 nm laser (100 mW) was coaxially combined with a 10 nm narrow-band filter to isolate the thermal background and boost signal strength. A customized workflow utilizing continuous Fourier transform (CFT) spectral refinement and energy centroid estimation was implemented to precisely extract the true Doppler shift. Performance evaluations show the system achieves an excellent signal-to-noise ratio (SNR) of 29,532. Allan variance analysis confirms a stable detection sensitivity of 0.003 m/s (0.1 s integration time), a local short-to-medium-term optimal limit of 1.6 × 10⁻⁴ m/s, and a statistical accuracy of 0.005 m/s. Finally, the system was successfully deployed on an industrial rolling mill production line. It provided reliable velocity feedback for mill speed adjustment, achieving a near-zero-tension rolling process and fundamentally resolving workpiece dragging, squeezing, and steel pile-up.

To address severe noise interference in high-temperature H₂O absorption spectra in metallurgical environments and the limited performance of traditional filtering algorithms, this study proposes an enhanced U-Net neural-network filtering method based on residual connections and smoothness constraints, termed Residual & Smooth U-Net (RSU-Net). Based on the classical U-Net architecture, the proposed model incorporates dilated convolutions, residual connections, a smoothness constraint in the loss function, and endpoint smoothing to suppress boundary fluctuations while preserving spectral line profiles. For simulated spectra, RSU-Net increased the signal-to-noise ratio (SNR) from 16.35 dB to 50.43 dB, outperforming traditional filters and exceeding a feedforward neural network-assisted Savitzky–Golay (S-G) filtering method by 6.19 dB. Quantitative spectral-fidelity evaluation showed that RSU-Net achieved an integrated absorbance relative error of 0.11% and a peak position shift of 1.2 × 10⁻⁴ cm⁻¹, indicating effective preservation of the main absorption peak characteristics. In experiments at 800–1010 °C, RSU-Net effectively reduced noise. At 1000 °C, it reduced the concentration standard deviation by 8.83 ppm compared with wavelet filtering. Allan deviation analysis was used as a relative stability indicator and showed improved stability over wavelet filtering.