Ocean data buoys are a critical means of automatically acquiring offshore oceanographic and meteorological data, offering advantages of long-term, fixed-position, continuous, and real-time monitoring. The turbulence generated by the buoy’s structure is a significant factor affecting wind speed measurement accuracy. In this study, a 10 m-diameter buoy was analyzed to evaluate the influence of structural turbulence and shielding effects on wind speed measurements. The Reynolds-averaged Navier-Stokes (RANS) equations, combined with the RNG k–ε turbulence model, were employed to simulate the flow field and turbulence characteristics. Numerical simulations were conducted to calculate wind fields around the buoy and measured wind speeds at two sensor heights with varying pitch angles. The shielding effects on wind measurements were examined across different wind directions. To assess the impact of photovoltaic panels, wind fields and wind speed measurements were also analyzed for a configuration without these panels. Results indicate that shielding effects can cause substantial wind speed measurement errors, particularly when sensors are located on the leeward side, due to the formation of low -wind -speed zones in the wake region. Measurement error increases with higher incident wind speeds. Sensors positioned at greater elevations exhibit improved accuracy, as they are less affected by near-surface turbulence. Removing photovoltaic panels reduces measurement error; however, the shielding effects caused by the buoy body itself remain significant and cannot be neglected.

Abstract Shipboard meteorological instruments are onboard instruments used to monitor real-time meteorological elements, such as temperature, humidity, air pressure, wind speed, and direction, in the navigation environment. It is crucial for ensuring maritime navigation safety, improving ship meteorological navigation capabilities, and supporting marine climate research. Currently, miniaturization and portability are the primary requirements for ship meteorological instruments in ocean monitoring. This article proposes an integrated portable small ship meteorological instrument solution: temperature, humidity, pressure, wind speed, and wind direction are synchronously measured through a multi module sensor group, and combined with attitude sensors, electronic compasses, and GPS modules to obtain ship spatial attitude angle, speed, and heading data, respectively; Then, the wind field measurement error is corrected by synthesizing the real wind vector and the rotation matrix attitude compensation algorithm. The experiment shows that the temperature, humidity, and pressure measurements of this scheme are stable and in line with the actual environment. The actual wind measurement error (wind speed ≤ 0.7 m/s, wind direction ≤ 20.5°) can meet the requirements of small ship maritime meteorological monitoring.

In recent years, with the development of technologies such as the Internet of Things (IoT), big data and cloud computing, digital twin technology has gradually been applied in marine research. The digital twin realizes real-time monitoring, analysis and optimization of the state and behavior of a physical object or system by creating a virtual model. Research shows that digital twin technology has extensive application potential in ship design, marine resource development, marine equipment engineering design and optimization, marine ecological protection and early warning of disasters. Although digital twin technology has great potential in marine research, it also faces many challenges, including the complexity of data acquisition and processing, the accuracy and real-time performance of model construction, and the need for multidisciplinary cross-integration. An in-depth analysis of the technical bottlenecks and future development directions will provide an important reference for subsequent research and promote the further application and development of digital twin technology in marine research.

Duo to fluctuations in atmospheric turbulence and yaw control strategies, wind turbines are often in a yaw state. To predict the far wake velocity field of wind turbines quickly and accurately, a wake velocity model was derived based on the method of momentum conservation considering the wake steering of the wind turbine under yaw conditions. To consider the shear effect of the vertical incoming wind direction, a two-dimensional Gaussian distribution function was introduced to model the velocity loss at different axial positions in the far wake region based on the assumption of nonlinear wake expansion. This work also developed a “prediction-correction” method to solve the wake velocity field, and the accuracy of the model results was verified in wake experiments on the Garrad Hassan wind turbine. Moreover, a 33-kW two-blade horizontal axis wind turbine was simulated using this method, and the results were compared with the classical wake model under the same parameters and the computational fluid dynamics (CFD) simulation results. The results show that the nonlinear wake model well reflected the influence of incoming flow shear and yaw wake steering in the wake velocity field. Finally, computation of the wake flow for the Horns Rev offshore wind farm with 80 wind turbines showed an error within 8% compared to the experimental values. The established wake model is less computationally intensive than other methods, has a faster calculation speed, and can be used for engineering calculations of the wake velocity in the far wakefield of wind turbines.

The sea surface wind field is an important physical parameter in oceanography and meteorology. With the continuous refinement of numerical weather prediction, air-sea interface materials, energy exchange, and other studies, three-dimensional (3D) wind field distribution at local locations on the sea surface must be measured accurately. The current in-situ observation of sea surface wind parameters is mainly achieved through the installation of wind sensors on ocean data buoys. However, the results obtained from this single-point measurement method cannot reflect wind field distribution in a vertical direction above the sea surface. Thus, the present paper proposes a theoretical framework for the optimal inversion of the 3D wind field structure variation in the area where the buoy is located. The variation analysis method is first used to reconstruct the wind field distribution at different heights of the buoy, after which theoretical analysis verification and numerical simulation experiments are conducted. The results indicate that the use of variational methods to reconstruct 3D wind fields is significantly effective in eliminating disturbance errors in observations, which also verifies the correctness of the theoretical analysis of this method. The findings of this article can provide a reference for the layout optimization design of wind measuring instruments in buoy observation systems and also provide theoretical guidance for the design of new observation buoys in the future.

As an important component ofbiogeochemical cyclein coastal ecosystems, sediments are the sink of heavy metals. Therefore, distribution and dynamics of heavy metals in sediments could assess ecological quality and predict ecological risks. In the new era, rapid and green technology are highly needed, especially that could determine multi-parameters simultaneously Here, we explored a new method to rapidly determine concentrations of heavy metals in sediments by visible and near infrared reflectance spectroscopy (VIRS).We sampled sediments in the Jiaozhou Bay, China, collected their reflectance spectra, and measured concentrations of four heavy metals (As, Cr, Cu, and Zn). Heavy metal models were established and evaluated using substances highly correlated with heavy metals. This study provides an effective reference for rapid analysis of As, Cr, Cu, and Zn simultaneously in sediments, at least in the Jiaozhou Bay, and for ecological environment protection and resource development of the Jiaozhou Bay.

Accurate measurement of wind speed on maritime vessels is crucial for weather and sea condition forecasting, safe navigation, power generation, hydrological simulation, and other applications. However, the precision of measurements may be subject to certain errors due to factors such as vessel motion and environmental conditions. To enhance the precision of shipborne wind speed measurement, this paper introduces an innovative approach based on contrastive learning. Through proficient feature extraction and the application of a self-supervised contrastive learning algorithm, this method extracts features of varying granularity from marine observational data to predict and correct shipborne wind speed measurements. To the best of our knowledge, this study represents the first attempt to validate contrastive learning in the intelligent analysis of marine observational data. Validation experiment results demonstrate the outstanding performance of this method in both single-step and multi-step predictions, showcasing higher efficacy and robustness compared to alternative approaches.

Monitoring wind field changes is of great importance for real-time weather forecasting, military environmental forecasting and space weather situation analysis. Compared with weather balloons, cup wind speed sensors, thermal wind speed sensors, ultrasonic anemometers, wind profilers and other wind measurement tools, laser wind lidar has the significant advantages of high measurement accuracy, meticulous measurement time detection distance. As the research continues, lidar gradually in the civil field as well as the military field has more and more broad application prospects. This paper briefly introduces the working principle of laser wind lidar. Highlights the development history of laser wind lidar. The various types of laser wind lidars are compared, and their respective characteristics are listed. Finally, the development trend and characteristics of laser wind lidar technology are briefly summarized.

针对某航次过程中船舶气象仪测风数据出现异常的问题,建立故障树模型;通过故障定性分析,确定出故障原因和故障位置,并通过实证排查和测试对故障原因及位置进行确认,采取措施对故障进行消除;利用实船航行试验方法对所采取措施进行验证.试验结果表明:所分析的故障原因及定位准确,采取措施有效,表明该方法可提高船舶气象仪测风故障的诊断与维修效率.

In order to quantitatively analyze the data measurement accuracy of ocean buoys under normal and extreme sea conditions, in this study, we simulated the six-degree-of-freedom motion response of self-designed ocean buoys under different sea conditions based on a separated vortex simulation and the fluid volume method and analyzed the impact of the unsteady motion of buoys on data measurement. The results indicate that under normal sea conditions, the deviation between the numerical method used in this paper and the experimental results is less than 10%. The heaving motion of a buoy is most sensitive to changes in wave conditions. The fluctuation intensity of buoy motion is modulated by the height and wavelength of waves. When the wave height and wavelength are similar to the overall geometric size of a buoy, the wave characteristics of the buoy’s heave, yaw, and pitch motion are significant. In addition, under extreme sea conditions, the movement of the buoy can also cause a deviation in the measured velocity in the transverse flow direction, but the overall deviation is less than 10%. In extreme sea conditions, the wind speed measurement results should be corrected to improve the measurement accuracy of a buoy.

文章根据原理不同将测风传感器分为皮托管式、机械式、热式、超声式和硅压阻固态式测风传感器,并对每一类测风传感器的测风方法、技术原理和工作环境进行了分析和总结;最后依据传感器技术的发展及使用需求的变化情况,对测风传感器的现状与发展进行了研究.研究成果对测风传感器的研制、选型及使用提供了参考.

Clouds are visible aggregates of small water droplets, supercooled water droplets, ice crystals or their mixtures suspended in the atmosphere; sometimes they also contain some larger raindrops, ice particles and snow crystals whose bottoms do not touch the ground[1]. The observation of clouds is an important part of meteorological observation, and the accurate acquisition of cloud information is of great importance for climate research, weather forecasting, and water resources management, among many other fields. Cloud amount, cloud type and cloud base height are the three elements of cloud observation in meteorological operations, and are also important statistics when analyzing cloud data[2]. Currently, only the cloud height measurement has been achieved, while there are no mature technologies and instruments for observing cloud type and cloud amount, and is still achieved through manual observation. In this paper, the ground-based observation technologies of cloud type and amount have been summarized, and the research status of cloud type identification and cloud amount observation have been analyzed and compared. On the basis of the image processing technologies, the development trend of cloud type and amount observation technologies are prospected by considering the number, quality, and feature extraction methods of samples.

In this paper, a method based on different similarity algorithms is proposed to find and compare the salient features in the spatio-temporal evolution map of aerosols. The pixel number difference of similar features calculated using this method in different aerosol spatio-temporal evolution maps is equivalent to the movement time. By analyzing the design principles, advantages and disadvantages of four different algorithms: SSIM, Corr2, Ahash and Dhash, the simulation analysis experiment is designed. From the aspects of theoretical analysis and experimental test, the images with different pixel numbers are layered and solved, and the solution effects of different similarity algorithms are comprehensively compared. The results of simulation and comparison show that among the four similarity algorithms, Ahash and Dhash algorithms perform well and have less fluctuation in accuracy, and the above conclusions can provide reference for the feature finding comparison methods of similarity algorithms in aerosol density mapping.

In view of the difficulty to realize the meteorological and hydrological observation on the unmanned islands or reefs, the complexity of using wireless sensor network cannot be too high and the requirement of reliability is higher, so a master-slave dual-cluster head network trust model based on adaptive weighted D-S evidence theory fusion calculation is proposed. At the same time, the reliability is adaptively adjusted according to the time sliding statistics of the node measurement data. The base station determines whether to receive data and determines the master-slave cluster head in the next cycle according to the fusion result difference and their historical data evaluation, which reduces the complexity of trust calculation and develops the remote unattended Islands and reefs meteorological and hydrological wireless sensor monitoring technology. The test results show that the technology is effective and feasible, with the advantages of convenient layout and reliable data transmission.

Marine monitoring instruments and equipments are crucial for understanding and managing the ocean. In recent years, significant achievements have been obtained in the technologies and application of marine monitoring instruments and equipments in China. However, China still lags behind developed countries in terms of core technologies and equipment for marine monitoring. This study analyzes the development requirements and development status of China’s marine monitoring instruments and equipment from the aspects of global ocean stereoscopic observation system, national nearshore operational observation system, and technologies and core equipment for marine environment monitoring and detection. Moreover, it elaborates on the problems existing in China’s marine monitoring instruments and equipment in terms of policies and mechanisms, original innovation and basic scientific research, common key technologies, technical standards and testing, as well as cincization and industrialization. Furthermore, we propose key development directions and several suggestions including (1) establishing an innovative system of marine monitoring instruments, (2) expanding the marine monitoring instrument industry, and (3) building a marine public test infrastructure, hoping to provide a reference for the development and research of China’s operational marine stereoscopic monitoring system.

With the development of meteorological observation refinement, the polar regions and other extremely cold regions also need to carry out real-time wind observation, but the existing various types of wind measurement sensors are difficult to adapt to such extremely cold environment. In this paper, we developed a wind measurement and control system based on the principle of ultrasonic wind measurement with time difference method for extremely cold environment, and designed the basic circuit consisting of STM32 microcontroller minimum system, transducer driving circuit, echo signal receiving and conditioning circuit, etc. Based on this, we designed a fuzzy PID temperature control circuit to protect the sensitive element transducer at constant temperature, and also carried out the thermal insulation and protection design for the measurement and control part of the shell. And through the software simulation of the designed PID temperature control mode to select the verification. The wind sensor measurement and control circuit designed in this paper is applicable to the extremely cold environment, which has a guaranteed role in the development of wind measurement sensors applicable to the extremely cold environment and is of great significance for the acquisition of wind element information in the polar region.

The difference in the nutrient content and harmful heavy metal content of different regions and types of soils can have a certain impact on the crops grown on them. Different crop varieties have their own ecologically suitable areas for planting. Identification of the types of agricultural soil from different regions, can quickly providing references for the selection and cultivation of crops on different types/regions of soil, which is conducive to ensuring the safety of agricultural products. This study is carried out using a laser-induced breakdown spectra (LIBS) device with a frequency-doubled 532 nm excitation wavelength, a laser pulse width of 8 ns, a repetition frequency of 5 Hz, and a pulsed laser energy of 15 mJ. The LIBS data of nine standard agricultural soils collected from different regions. Each sample is collected 60 sets of spectra and a total of 540 sets of data are obtained. At first, the all obtained spectral data are normalized to compensate for the spectral changes in the measurement process. Then the spectral data in the 200–800 nm band are analyzed. Randomly select 30 %, 50 %, and 70 % of the spectral data according to the Kennard-Stone (KS) classification as the training set, and the remaining spectral data as the test set. The Support Vector Machine (SVM) model is used to classify and identify the soil samples of agricultural soil in different areas. The results show that when 30 % of the spectral data are selected as the training set, the accuracy of identifying different agricultural soil types is 88.6 %, and the accuracy of identifying all nine types of agricultural soils is above 76.7 %. The accuracy of soil identification for the two types of agricultural soil is 100 %, and the identification accuracy for the seven types of soil is above 86 %. When 50 % spectral data are selected as training set, the overall recognition accuracy can reach 95.9 %, the recognition accuracy of 8 types of agricultural soil samples is above 90.5 %, and the recognition accuracy of 4 types of soils can reach 100 %, The recognition accuracy of only one type of soil is less than 90 %, which is 86.7 %. When the selected training set contains 70 % of spectral data, the overall recognition accuracy can reach 96.3 %, the recognition accuracy of 8 types of agricultural soil samples is above 90 %, and the accuracy of 1 type of agricultural soil recognition is less than 90 %, which is 89.19 %. The results show that LIBS technology can be applied to the rapid identification of soil types in agricultural soils.

降雨量的测量对气象预报及防汛减灾具有重要作用,因此当降雨量传感器发生故障时需要能快速修复.而目前应用最广泛的翻斗式降雨量传感器其故障类型多样,致使故障诊断难度大.为此,本文提出一种基于故障树理论与灰色关联法的降雨量传感器故障诊断新方法,采用故障树理论及灰色关联分析法建立了专家系统,通过故障知识获取、故障树建立、灰色关联度获取、知识库构建和推理机推理设计,实现了降雨量传感器的故障诊断.试验验证表明,该方法故障诊断快速准确,实现系统自动诊断替代人工检测与排查,准确率可达到90％以上,提高了降雨量传感器的诊断与维修效率.

船舶相对风是指船舶在海面上系泊或航行时测风传感器所测得的风速风向,大中型船舶往往安装2个以上的测风传感器以克服单传感器的局限性.为了综合利用多个传感器的测量值,提出一种基于动态权值的数据融合算法.在此基础上为进一步提高融合数据精度,将参考基准值首先采用卡尔曼滤波进行优化处理,然后再将其代入公式参与融合计算.采用实船航行试验测风数据验证表明,该融合算法能够区分测量值的优劣,倚重更有利的测量信息,有效降低相对风的测量误差,优于目前广泛采用的算数平均值方法.