Background normalization algorithms attempt to suppress the ambient and self-noise during the measurements of sonar, which enhance the detection performance and the display effect of weak signals. Conventional background normalization methods are usually sensitive to the accuracy of prior set filtering interval and threshold, while significant noise is still detected in low frequency. In this paper, an improved background normalization algorithm is proposed by thresholding the processing interval between several local peak values and local valley values. Compared to the existing scenarios, the proposed approach automatically calculates the filtering interval and threshold, with substantial resilience to the noise level in low frequency. Experimental results illustrate the effectiveness of our algorithm.

Intraseasonal oscillation of the evaporation duct, lasting 30–60 days, has been identified over the South China Sea (SCS) summer monsoon region based on multiple reanalyses and observational data. The boreal summer intraseasonal oscillation (BSISO) causes anomalies at the air–sea boundary and thus plays a dominant role in modulating the variation of the evaporation duct. The height and strength of the duct enhance/suppress during the negative/positive phase of the BSISO over the SCS. This results from the fact that active BSISO convection reduces solar radiation reaching the sea surface by increasing cumulus cloud cover, whereupon precipitation and water vapor transported by the enhanced southwest jet increase humidity over the air–sea boundary. Reduced air–sea temperatures and humidity differences lead to a weaker evaporation duct. Usually, the temporal evolution of the evaporation duct lags 2–4 days behind the BSISO, with the center of evaporation duct anomalies farther south than the BSISO. Simulated electromagnetic fields substantively influence the condition of the evaporation duct, with obvious over-the-horizon and radar blind spot effects in the typical negative phase of the BSISO, which is very different from standard atmospheric conditions.

The precise interpolation of oceanic temperature and salinity is crucial for comprehending the dynamics of marine systems and the implications of global climate change. Prior neural network-based interpolation methods face constraints related to their capacity to delineate the intricate spatio-temporal patterns that are intrinsic to ocean data. This research presents an innovative approach, known as the Discretized Spatial Encoding Neural Network (DSE-NN), comprising an encoder–decoder model designed on the basis of deep supervision, network visualization, and hyperparameter optimization. Through the discretization of input latitude and longitude data into specialized vectors, the DSE-NN adeptly captures temporal trends and augments the precision of reconstruction, concurrently addressing the complexity and fragmentation characteristic of oceanic data sets. Employing the North Atlantic as a case study, this investigation shows that the DSE-NN presents enhanced interpolation accuracy in comparison with a traditional neural network. The outcomes demonstrate its quicker convergence and lower loss function values, as well as the ability of the model to reflect the spatial and temporal distribution characteristics and physical laws of temperature and salinity. This research emphasizes the potential of the DSE-NN in providing a robust tool for three-dimensional ocean temperature and salinity reconstruction.

Ocean satellite data are often impeded by intrinsic limitations in resolution and accuracy. However, conventional data reconstruction approaches encounter substantial challenges when facing the nonlinear oceanic system and high-resolution fusion of variables. This research presents a Discrete Satellite Gridding Neural Network (DSGNN), a new machine learning method that processes satellite data within a discrete grid framework. By transforming the positional information of grid elements into a standardized vector format, the DSGNN significantly elevates the accuracy and resolution of data fusion through a neural network model. This method’s innovative aspect lies in its discretization and fusion technique, which not only enhances the spatial resolution of oceanic data but also, through the integration of multi-element datasets, better reflects the true physical state of the ocean. A comprehensive analysis of the reconstructed datasets indicates the DSGNN’s consistency and reliability across different seasons and oceanic regions, especially in its adept handling of complex nonlinear interactions and small-scale oceanic features. The DSGNN method has demonstrated exceptional competence in reconstructing global ocean datasets, maintaining small error variance, and achieving high congruence with in situ observations, which is almost equivalent to 1/12° hybrid coordinate ocean model (HYCOM) data. This study offers a novel and potent strategy for the high-resolution reconstruction and fusion of ocean satellite datasets.

From 18 to 21 August 2024, short-duration torrential rains hit Northeastern China. A severe marine heatwave occurred in China’s offshore simultaneously, significantly affecting water vapor transport and precipitation system evolution. From the air-sea interaction perspective, this study analyzes how offshore marine heatwaves regulate atmospheric rivers—weather systems triggering heavy precipitation—to reveal the mechanism by which sea surface temperature anomalies influenced this torrential rain. First, the study identified physical characteristics of atmospheric rivers and marine heatwaves using multi-source meteorological and oceanographic data plus relevant algorithms, and analyzed their correlation with North China’s rainstorm. Then, sea surface temperature sensitivity experiments were conducted via the Weather Research and Forecasting model to diagnose how sea surface temperature anomalies indirectly affect precipitation intensity and distribution through atmospheric rivers. Key conclusions are: (1) Statistical analysis shows atmospheric rivers exist in East Asia in August, with trajectories mostly along the East Asian coast and obvious spatiotemporal variations. On 20 August 2024, a strong atmospheric river extended from South China to Northeast China, moving mainly along China’s eastern coast and providing abundant water vapor for precipitation. (2) Favorable circulation patterns combined with the marine heatwaves jointly drove the formation of atmospheric rivers and precipitation. The extreme precipitation was synergistically driven by an extremely long-persisted marine heatwave over the East China Sea, which provided a continuous moisture source for the atmospheric river. The interaction between upper-level and lower-level jet streams enhanced moisture transport and atmospheric baroclinic instability, thereby promoting the triggering of convective precipitation. (3) Experiments with the WRF+3DPWP show the long-persisted marine heatwave affects lower atmospheric circulation, strengthening the southeasterly jet along China’s eastern coast to boost atmospheric river intensity and related precipitation. Cyclonic circulation anomalies near the Yellow Sea also promote trough and cold vortex formation. Besides, higher sea surface temperature increases low-level atmospheric humidity and water vapor flux, enhances atmospheric instability, and promotes convective system development. From the novel “marine heatwave–atmospheric river–heavy rainfall” perspective, this study clarifies the 2024 summer North China torrential rain’s physical mechanisms and influencing factors, highlights local air-sea interaction’s role in short-term weather and climate, and provides new references for improving heavy rainfall prediction.