Precise joint prediction of wind and solar energy is significant for the running of renewable power grid. Nevertheless, either wind or solar energy is affected by various factors, exhibiting distinct characteristics, which pose great challenges for prediction. Therefore, this paper proposes a Temporal Decoupling and Distribution Learning Joint Prediction Model (TDDL-JP), which analyzes complex time series data by time series decomposition method and extracts spatial distribution features by combining distribution learning mechanism, aiming to achieve precise regional joint prediction of wind and photovoltaic power. A multi-level full-band decomposition module is used for high and low frequency iterative decomposition to extract timing features at different frequencies. In view of the mapping relationship between the dynamic mode of subsequences and meteorological features, a feature mode decoupling module is used to learn the meteorological feature extraction and fusion scheme of each subsequence. A cross-scale, dual-branch feature extraction module is proposed to sense the local and global spatial feature weights, enhancing predictive performance. To verify the effectiveness of the model, ablation experiments, comparative experiments, and seasonal sensitivity experiments were conducted on the research area dataset. Compared with the current optimal benchmark model, the comprehensive MAPE decreased by 12.4%, effectively improving the joint prediction accuracy of wind and solar power.
The microphysical properties of stratus cloud systems vary significantly across different regions and stages of development. This study investigates the microphysical characteristics of a postfrontal stratus cloud system with snowfall based on aircraft observations conducted on 1–2 March 2025 over Xingtai, Hebei Province, China. Distinct variability is observed, with thinner supercooled liquid clouds at the periphery and thicker clouds near the surface front, characterized by mixed-phase cloud below and ice cloud above. Clear differences are observed between the mixed-phase and ice-phase layers, as the mixed-phase layer shows higher RH and lower ice crystal number concentrations (Ni) and ice water content (IWC). Distinct vertical variations in supercooled cloud droplet properties, such as cloud droplet number concentrations (Nc), effective radius (re), and liquid water content (LWC), are also observed across different regions. Additionally, the ice crystal habit varies with altitude, with small dendritic and plate-like crystals aloft and large irregular aggregates dominating the mid- and lower layers, where small crystals possibly produced by second ice production are also observed. During the dissipating stage, decreasing RH and rising temperature within cloud correspond to reductions in Ni and IWC, along with broader particle spectra in both ice crystals and supercooled cloud droplets. These findings highlight the pronounced spatiotemporal variability of stratus cloud microphysics within the observed postfrontal system and suggest potential mechanisms in cloud evolution.
The rotating triboelectric nanogenerator (R-TENG) has emerged as a core technology in micro-energy harvesting and micro-signal detection. However, the high impedance, high-voltage and low-current output characteristics of R-TENG structures, lead to bottlenecks such as impedance mismatch, difficult topological adaptation and weak anti-interference capabilities. Under controlled bench-test conditions with 1-3 Hz mechanical excitation, customized rectification and energy storage circuits improve R-TENG energy harvesting efficiency from 60% to 70%-95%, a 10%-35% gain primarily by mitigating impedance mismatch. Sensing accuracy for rotational and wind speed is ±0.5%-±2% of full scale, varying with methods and environmental interference. This paper systematically reviews the research progresses of the micro-energy conversion and micro-signal processing technologies for R-TENG. Regarding micro-energy harvesting, the key technologies such as rectification, energy storage optimization, and DC-DC buck conversion are analyzed systematically. For micro-signal processing, in-depth analysis is conducted on signal processing methods such as filtering, noise reduction, and feature extraction in typical application scenarios such as motor speed estimation and wind speed measurement. The core contributions include: established a comprehensive analysis framework that bridges theoretical foundations and engineering applications; highlighted the performance metrics and practicality through structured comparisons; identified the research gaps in circuit stability, model generalizability and adaptation to extreme environments. Finally, this article analyzes the development directions of R-TENG such as intelligent adaptability, anti-interference ability, and universal modularization, which will help promote the innovative application of R-TENG in micro-energy harvesting and micro-signal processing.
Particulate nitrate (NO3−) in coastal air affects the nitrogen cycle and cloud condensation nuclei, yet its chemical formation processes, especially with respect to air mass transport and local emissions, are poorly understood. To clarify NO3− sources and formation mechanisms, nitrogen and oxygen isotopic compositions (δ15N and Δ17O) for NO3− and Δ17O of ozone (O3) were measured at a mountain site located in Tai Mo Shan (640 m a.s.l.) and δ15N and Δ17O of NO3− were measured at an urban site located in Tsim Sha Tsui (60 m a.s.l.) in Hong Kong, a coastal megacity of southern China. Average δ15N-NO3−, Δ17O-NO3−, and Δ17O-O3 at the mountain site were −1.1 ± 2.1‰, 22.4 ± 1.1‰, and 24.1 ± 1.4‰, respectively, while urban δ15N-NO3− and Δ17O-NO3− were 0.8 ± 1.3‰ and 21.1 ± 0.9‰. Bayesian modeling identified the NO2 + OH reaction as the dominant nitrate formation pathway at both sites, highlighting the role of photochemical processes in coastal environments. N2O5 hydrolysis was more prevalent at the humid mountain site, and HC/DMS/XNO3 pathway influenced urban areas through hydrocarbon emissions and marine air masses. Natural gas and coal combustion emerged as the predominant contributors to nitrate aerosols at Tai Mo Shan, where the absence of local emissions underscored regional transport of these aerosols, while NOx emissions from ships and vehicles dominated urban NO3− sources. Transitioning to clean fuels and electric vehicles is vital for reducing urban NOx emissions and associated health risks from nitrate particles.
