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Ecological monitoring is an important part of environmental protection. To monitor the movement and abundance of animals in the airspace, an Aerial Ecological Monitoring System (AEMS) is developed by CMA Meteorological Observation Center for China's next-generation weather radar (CINRAD) network. Characteristics of weather radar clear air echo data and airborne biological scattering data are studied to identify biological echoes through fuzzy logic algorithm, and the system can monitor real-time ecological activities of insects such as biological density, migration path and space-time distribution.Weather Radar Airborne Ecological Monitoring System has been put into trial operation since May 2022. During the real-time monitoring period, it's found that the insect activity shows obvious spatial and temporal distribution characteristics. From August to September, pests are of large quantity and wide range, indicating urgent need of insect disaster prevention and control. In May, June, September, and October, insect activity gradually increases from 2000 BT every day, reaches its peak from 2200 BT to 2300 BT, gradually decreases thereafter, and disappears mostly by 0600 BT. In July and August, insect activity gradually increases from 2000 BT every day, with a peak from 2100 BT to 2200 BT. Insect activity begins during the daytime, increases at 0600 BT, becomes more frequent at 1300 BT, and gradually decreases thereafter. From May to July, there is a significant shift from south to north (i.e., northward migration process), and in late August, it quickly changes into a to southward migration. The southward migration process of insects is larger and more numerous than the northward migration process. It's verified that the system can effectively monitor real-time aerial ecology, providing technology and data support for precise pest control.However, characteristics of pests need further research and clear distribution of pests is an urgent need. Therefore, in-depth research will be carried out on aerial ecological classification technology, combined with other direct observation means such as real-time monitoring by drones to explore the relationship between radar detection and different pests, and improve the ability to identify different kinds of insects.

Synthesizing or retrieving the radial velocity of weather radar can obtain a three-dimensional wind field, which is an important research direction in radar meteorology. A fine three-dimensional wind field helps to study the structure and motion characteristics of small-scale and meso-scale weather systems. Array weather radar (AWR) consists of three-phased array transmit-receive subarrays (referred as transceiver subarrays), which is used for synchronous detection. AWR data are of high temporal and spatial resolution, thus ensuring the correctness of wind field synthesis and retrieval.According to domestic and aboard research, three-dimensional variational data assimilation (3DVAR) wind field retrieval algorithm is quite mature. Using AWR data of 10 rainfall cases at Changsha airport from April to September in 2019, the wind field is retrieved and evaluated. In the three-dimensional fine detection area of the AWR, detection data of a L-band boundary layer wind profile radar and the AWR synthetic wind field are used as reference value to evaluate the retrieved wind field.Results show that the retrieved wind field, the synthetic wind field, and wind profile radar product are more consistent and reasonable in stable precipitation process. In addition, the result error is larger in convective precipitation. The unevenness of the environmental wind field in convective precipitation can reduce the accuracy of wind measurement, and therefore it is not enough to explain the rationality of the AWR retrieved wind field. The wind profile radar is quite different from the AWR retrieved and the synthetic wind field. For different precipitation types, the wind field structure retrieved by AWR and the wind field obtained by AWR synthetic wind field are consistent with the basic characteristics of various weather systems. The spatial distribution and size direction of the horizontal wind field of two algorithms are very close. Error results show that the relative deviation of horizontal wind speed in the stable and convective precipitation is less than 19% and 29%, and the difference of horizontal wind direction is lower than 14.92° and 26.35°, respectively. The error is within the acceptable range. Compared with the AWR synthetic wind field, the retrieved wind field result during stable precipitation process is better than that during convective precipitation process.

S-band weather radar can often detect a large number of clear air echoes at night. However, there are different views on the mechanism of clear air echoes. According to characteristics of biological migration, combined with L-band radiosonde data and Beijing S-band weather radar data from 1 March to 18 October in 2018, changes of clear air echo reflectivity factor in different seasons and wind directions are analyzed to discuss causes of clear air echo. Firstly, characteristics of time variation of clear air echo are analyzed. The intensity of clear air echo in Beijing increases after sunset, weakens before sunrise, and changes little at night. The intensity of echo gradually increases from March to the middle of May, then weakens from July to the first ten days in August, and maintains at a high level since September. In vertical profiles, the fluctuation of high-level echoes, up to the altitude of 2 kilometers, is larger than that of low-level echoes, with an obviously seasonal variation. Secondly, by comparing the relationship between the wind direction and radar data in different periods of time, it is found that the echo is not consistent with the biological activity regularity. The entropy of radar data gray level co-occurrence matrix is also calculated, and there is no rule to follow the biology. Therefore, it is considered that the turbulence is the dominant cause of night clear air echo in Beijing. And then, radiosonde data show that the temperature lapse rate and wind shear are consistent with the seasonal variation of combined reflectivity factor at the altitude from 1 to 2 km. Finally, possible influence factors of clear air echo are inferred based on some phenomena observed.

In order to obtain more detailed small-scale weather system data, Meteorological Observation Center of China Meteorological Administration (CMA) designed and developed X-band array weather radar (AWR), cooperating with relevant manufacturers. In March of 2018, the first prototypeis deployed at Changsha Airport for field experiments. Combing advantages of networked radars and a distributed phased array technology, the AWR has a highly coordinated scanning mode and high spatial and temporal resolutions to acquire fine echo intensity and wind field data. Compared with conventional parabolic antenna weather radars, a phased array antenna has wider beams and stronger side lobes, so that more ground clutter will appear in radar echoes. If the ground clutter cannot be effectively detected and removed, the accuracy of radar products will be affected seriously.Data collected by the X-band AWR at Changsha Airport are used to study the ground clutter detection algorithm for the AWR. According to the research progress all over the world, characteristic parameters of reflectivity factors, radial velocity and velocity spectrum width are extracted. In addition, time variability of reflectivity factor (TVR), a new parameter, is added due to the high temporal and spatial resolution of the AWR. Based on analyzing statistical characteristics of each feature parameter, membership functions are determined. The contribution of TVR to the clutter detection algorithm and the performance of the algorithm on different weather conditions are analyzed. Results show that the accuracy of ground clutter detection for Changsha Airport AWR can be maximally increased by 4% by adding TVR, and the error rate of detecting the precipitation echo as clutter echo can be decreased by 2%. The accuracy of the proposed ground clutter detection algorithm reaches 96% in the detection of ground clutter when no precipitation processes happen. In the precipitation weather, the accuracy is 92%, and the error rate of detecting the precipitation echo as clutter echo is about 10%. The algorithm can basically detect and remove the ground clutter echoes from precipitation echoes.

Underwater acoustic target recognition has always played a pivotal role in ocean remote sensing. By analyzing and processing ship-radiated signals, it is possible to determine the type and nature of a target. Historically, traditional signal processing techniques have been employed for target recognition in underwater environments, which often exhibit limitations in accuracy and efficiency. In response to these limitations, the integration of artificial intelligence (AI) methods, particularly those leveraging machine learning and deep learning, has attracted increasing attention in recent years. Compared to traditional methods, these intelligent recognition techniques can autonomously, efficiently, and accurately identify underwater targets. This paper comprehensively reviews the contributions of intelligent techniques in underwater acoustic target recognition and outlines potential future directions, offering a forward-looking perspective on how ongoing advancements in AI can further revolutionize underwater acoustic target recognition in ocean remote sensing. [ABSTRACT FROM AUTHOR]

Clear-air echo studies are usually based on isotropic turbulence theory. But the theory has been considered incomplete by modern turbulence theory. The intermittence of turbulence can reveal obvious shortcomings in the existing studies of clear-air echoes. The mechanism of clear-air echo scattering needs to be supplemented. This paper introduces the troposcatter theory, normally used in over-the-horizon communication, to fill the gap left by Bragg scattering. By treating radar as a self-transmitted and self-received device, the equivalent transmission loss of weather radar is established and compared with the recommendations of the Radiocommunication Sector of the International Telecommunication Union (ITU-R). The results show that the S-band radar transmission loss aligns with ITU-R recommendations. There is also a linear regression relationship between the radar transmission loss and height, which conforms to the troposcatter theory. This means that the theory of troposcatter scattering is a supplement to the theory of Bragg scattering. Tropospheric scattering can be thought of as general Bragg scattering. Meanwhile, based on ITU-R recommendations, this study also provides a new way for the recognition of biological echoes. [ABSTRACT FROM AUTHOR]

The mobile ocean weather observation system, named Marine Weather Observer (MWO), developed by the Institute of Atmospheric Physics (IAP), consists of a fully solar-powered, unoccupied vehicle and meteorological and hydrological instruments. One of the MWOs completed a long-term continuous observation, actively approaching the center of Typhoon Sinlaku from 24 July to 2 August 2020, over the South China Sea. The in situ and high-temporal-resolution (1 min) observations obtained from MWO were analyzed and evaluated through comparison with the observations made by two types of buoys during the evolution of Typhoon Sinlaku. First, the air pressure and wind speed measured by MWO are in good agreement with those measured by the buoys before the typhoon, reflecting the equivalent measurement capabilities of the two methods under normal sea conditions. The sea surface temperature (SST) between MWO and the mooring buoys is highly consistent throughout the observation period, indicating the high stability and accuracy of SST measurements from MWO during the typhoon evolution. The air temperature and relative humidity measured by MWO have significant diurnal variations, generally lower than those measured by the buoys, which may be related to the mounting height and sensitivity of sensors. When actively approaching the typhoon center, the air pressure from MWO can reflect some drastic and subtle changes, such as a sudden drop to 980 hPa, which is difficult to obtain by other observation methods. As a mobile meteorological and oceanographic observation station, MWO has shown its unique advantages over traditional observation methods, and the results preliminarily demonstrate the reliable observation capability of MWO in this paper. [ABSTRACT FROM AUTHOR]

Deep learning models can produce unstable results by introducing imperceptible perturbations that are difficult for humans to recognize. This can have a significant impact on the accuracy and security of deep learning applications due to their poorly understood interpretability. As a field critical to security research, this problem clearly exists in underwater acoustic target recognition for ocean sensing. To address this issue, this article investigates the reliability of state-of-the-art deep learning models by exploring adversarial attack methods that add small, exquisite perturbations on acoustic Mel-spectrograms to generate adversarial spectrograms. Experimental results based on real-world datasets reveal that these models can be forced to learn unexpected features when subjected to adversarial spectrograms, resulting in significant accuracy drops. Specifically, when employing the iterative attack method, the overall accuracy of all models experiences a significant decrease of approximately 70% for two datasets under stronger perturbations. [ABSTRACT FROM AUTHOR]

The wavenumber integration model is the most precise approach for assessing arbitrary horizontally stratified media within the sphere of computational ocean acoustics. Unlike the normal-mode approach, it considers not only discrete spectra but also continuous spectral components, resulting in fewer model approximation errors for horizontally stratified media. Traditionally, the depth-separated wave equation in the wavenumber integration model has been solved using analytical and semianalytical methods, and numerical solutions have been primarily based on the finite difference and finite element methods. This paper proposes an algorithm for solving the depth equation via the Chebyshev–Tau spectral method, combined with a domain decomposition strategy, resulting in the development of a numerical program named WISpec. The algorithm can simulate the sound field excitation not only from a point source but also from an infinite line source. To that end, the depth equations for each layer are first discretized through the Chebyshev–Tau spectral method and subsequently solved simultaneously by incorporating boundary and interface conditions. Representative numerical experiments are presented to validate the accuracy and speed of WISpec. The high degree of consistency of results obtained from different software tools running the same configuration provides ample evidence that the numerical algorithm described in this paper is accurate, reliable, and numerically stable. [ABSTRACT FROM AUTHOR]

It is commonly believed that clear-air echoes detected by weather radars are caused by atmobios migration. However, clear-air echoes are sometimes inconsistently related to the activity of living creatures. In some cases, the characteristics of radar products seem to conform to biological scattering, but the movement of echoes cannot be observed. For these reasons, we sought to expand the cause of clear-air echoes from a Chinese Doppler S-band Weather Radar (CINRAD/SA) in Beijing. Some contradictions were discovered in a case which diverged from previous conclusions. It was found that the progression and movement of clear-air echoes do not conform to the rules of biological activities. The frequency distribution of dual-wavelength ratio peaks is 21.5 dB, which is in accordance with Villars–Weisskopf's turbulence theory. From 1 May to 20 May, the 58% dual-wavelength ratio between the S-band and the X-band was distributed between 18 dB and 24 dB. These results show that more than half of the clear-air echoes of CINRAD/SA at night were caused by turbulence in Beijing. A new model of troposcatter propagation, the reflecting-layers model, was then introduced to explain the radar observations. According to the reflecting-layers model, the echoes' diurnal variation and reflectivity characteristics are attributed to the effects of turbulent mixing. Excessive turbulent mixing affects the generation of the reflective layer, thereby weakening the echo signal. It is necessary to re-examine the position of turbulence in clear-air echoes. [ABSTRACT FROM AUTHOR]