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This study investigates the characteristics and intensity of El Niño–Southern Oscillation (ENSO) events from January 1875 to December 2023, employing an advanced method for intensity determination based on various ENSO indices defined as a continuous five-month period with temperatures exceeding 0.5 °C for warm events or falling below −0.5 °C for cold events. A total of 40 warm and 41 cold events were identified, with further classification revealing seven extreme warm events and five extreme cold events. The analysis shows a positive skewness in frequency distribution, indicating a predominance of strong warm events. The primary mode of variability is found to be interannual oscillation in the 3–8 year range, with significant decadal oscillations in the 10–16 year range. This study highlights the importance of methodological rigor in evaluating ENSO dynamics, contributing to a more comprehensive understanding of climate variability and offering a reliable framework for future research.

Frequent heavy snowfall in Xinjiang plays an important role in the land water cycle. In this study, 18 groups of simulation experiments are conducted on the heavy snowfall event in Xinjiang during 9–13 December of 2015 using the Weather Research and Forecasting (WRF) model. In these experiments, the combination of six land surface parameterization schemes (the Noah scheme, Noah-MP scheme, RUC scheme, CLM4 scheme, PX scheme, and TD scheme) with three microphysical parameterization schemes (the WSM6 scheme, Thompson scheme, and Lin scheme) are adopted, where the observed snowfall data are used for performance evaluation. Results show that the simulated snowfall intensity and snowfall range in different areas are very sensitive to the selection of the land surface scheme. The snowfall in southern Xinjiang is overestimated by almost all six schemes, where the Noah-MP scheme performs more reasonably than the others. The Noah scheme shows its advantage in northwestern Xinjiang. The three different microphysical schemes vary significantly in producing snowfall amount. The WSM6 scheme produced the largest snowfall amount, and the Lin scheme resulted in the smallest snowfall amount. In addition, the accumulated snowfall amounts above 10 mm are generally underestimated by all six land surface schemes, while the accumulated snowfall amounts below 10 mm are overestimated by most of the schemes. The Noah-MP scheme performs the best in the simulation of the snowfall amount in the whole region. However, the Noah scheme shows an advantage in areas with a large snowfall amount.

Airborne LiDAR has been adopted as a powerful survey tool for overhead power transmission line (TL) cruising so that the operator can quickly search for, locate and eliminate the risk objects at the power line corridor scene. However, the TLs are moving objects, which positions are dynamically affected by working conditions (e.g. temperature variation, wind-induced conductor motion) while they are acquired by airborne LiDAR. The point clouds data acquired by airborne LiDAR only reflect the geometric relationship between the TL and its surrounding objects at the transient moment of the data acquisition. In order to overcome the shortcomings of the instantaneously acquired laser scanning data, this article presents an approach for simulating the dynamic TL shape under different working conditions based on mechanical computation of the overhead line. The proposed approach considers the tension variation of TL resulting from weather conditions, such as temperature, wind and ice, while simulates TL sag curve using the parabolic catenary equation combined with the tension. A performance evaluation was conducted over the TLs data with multiple voltage levels. Experiments results show that the proposed approach is effective for simulating the 3D shape of TL under different working conditions. The simulation error achieved was less than 0.65m and the maximum Diff-Ratio was about 1.57%. This provides a scientifically sound predicting and modeling approach for TL risk assessment and warning along the corridor.

Accurately estimating the ocean’s interior structures using sea surface data is of vital importance for understanding the complexities of dynamic ocean processes. In this study, we proposed an advanced machine-learning method, the Light Gradient Boosting Machine (LightGBM)-based Deep Forest (LGB-DF) method, to estimate the ocean subsurface salinity structure (OSSS) in the South China Sea (SCS) by using sea surface data from multiple satellite observations. We selected sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), sea surface wind (SSW, decomposed into eastward wind speed (USSW) and northward wind speed (VSSW) components), and the geographical information (including longitude and latitude) as input data to estimate OSSS in the SCS. Argo data were used to train and validate the LGB-DF model. The model performance was evaluated using root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R2). The results showed that the LGB-DF model had a good performance and outperformed the traditional LightGBM model in the estimation of OSSS. The proposed LGB-DF model using sea surface data by SSS/SST/SSH and SSS/SST/SSH/SSW performed less satisfactorily than when considering the contribution of the wind speed and geographical information, indicating that these are important parameters for accurately estimating OSSS. The performance of the LGB-DF model was found to vary with season and water depth. Better estimation accuracy was obtained in winter and autumn, which was due to weaker stratification. This method provided important technical support for estimating the OSSS from satellite-derived sea surface data, which offers a novel insight into oceanic observations.

Ocean salinity variability provides a new way to study the evolution of the the El Niño-Southern Oscillation (ENSO). Comparisons between the salinity variation and related processes responsible for sea surface temperature anomaly (SSTA) were extensively examined for the two strong El Niño (EN) events in 1997/1998 and 2015/2016, and a special EN event in 2014/2015. The results show that the development of EN is significantly correlated with a sea surface salinity anomaly (SSSA) in the tropical western-central Pacific. In the spring of 1997 and 2015 with strong EN events, the western-central equatorial Pacific exhibited significant negative SSSA that propagated eastward to the west of the dateline. The negative SSSA induced increased barrier layer thickness (BLT) which enhanced sea surface temperature (SST) warming in the tropical central Pacific. In contrast, although a negative SSSA occurred during April of the 2014/2015 weak EN event in the western-central equatorial Pacific, this SSSA was mainly confined to between 160° E and 180° E without significant eastward movement, resulting in a weakened BLT thickening process and a weak modulation effect on SST. We also confirm that the surface forcing associated with fresh water flux (FWF: evaporation (E) minus precipitation (P)) plays a prominent role in the surface salinity tendency in the tropical Pacific during EN events. Moreover, the negative FWF anomaly leads a strong negative SSSA by two months. Compared with the two strong ENs, the early negative FWF anomaly in the weak 2014/2015 EN did not present distinct development and eastward propagation and weakened rapidly in the summer of 2015. We demonstrate that change in salinity can modulate the ENSO, and the variation of SSSA and associated physical processes in the tropical western-central Pacific and could be used as an indicator for predicting the development of ENSO.

Encoding and processing quantum information in the time-of-arrival of photons offer significant advantages for quantum information science and technology. These advantages include ease of experimental realization, robustness over photon state transmission, and compatibility with existing telecommunication infrastructure. Additionally, time-of-arrival encoding has the potential for high-rate quantum communication and holds promise for the future development of quantum internet. This review explores the generation, processing, and applications of time-encoded quantum states, focusing on both single-photon states, energy–time entanglement, and time-bin entanglement. We summarize the nonlinear optics platforms and advanced laser and modulation techniques utilized for photon sources that enable quantum information encoding onto the photons' time-of-arrival. We also highlight advanced quantum state processing methods in the time domain, including the Franson interferometry, optical switch-based schemes, and state-of-the-art measurement and detection schemes that allow for high-speed and multi-dimensional quantum operations. Finally, we review the mainstream implementations mainly including the quantum communication demonstrations and outline future directions for developing practical quantum networks leveraging time-encoded photon states. [ABSTRACT FROM AUTHOR]

Quantum memory, which maps photonic quantum information into a stationary medium and retrieves it at a chosen time, plays a vital role in the advancement of quantum information science. In particular, the scalability of a quantum memory is a central challenge for quantum network that can be overcome by using integrated devices. Quantum memory with an integrated device is highly appealing since it not only expands the number of memories to increase data rates, but also offers seamless compatibility with other on-chip devices and existing fiber network, enabling scalable and convenient applications. Over the past few decades, substantial efforts have been dedicated to achieving integrated quantum memory using rare earth ions doped solid-state materials, color centers, and atomic gases. These physical platforms are the primary candidates for such devices, where remarkable advantages have been demonstrated in achieving high-performance integrated quantum memory, paving the way for efficiently establishing robust and scalable quantum network with integrated quantum devices. In this paper, we aim to provide a comprehensive review of integrated quantum memory, encompassing its background and significance, advancement with bulky memory system, fabrication of integrated device, and its memory function considering various performance metrics. Additionally, we will address the challenges associated with integrated quantum memory and explore its potential applications. By analyzing the current state of the field, this review will make a valuable contribution by offering illustrative examples and providing helpful guidance for future achievements in practical integrated quantum memory. [ABSTRACT FROM AUTHOR]