In this review, the concept of the aviation and aerospace transition zone (AATZ), that is, the area between the highest altitude of aviation vehicles and the lowest altitude of space vehicles, is introduced. It is 50–150 km away from the Earth’s surface, and the coupling between the AATZ and its upper and lower atmosphere causes complex and unique physical and chemical processes. As a transition zone between space weather and Earth weather, the AATZ has great scientific research value and broad application prospects. Focusing on the dynamics, chemistry, and modeling studies of the atmosphere in this region, we comprehensively analyze and discuss the current research status, current challenges, and future prospects of this region. The propagation, dissipation, and momentum deposition of the atmospheric wave force change the surrounding wind field and temperature structure, forming complex dynamical processes. The mutation enhancement and redistribution of metal atoms and metal ions, as well as the occurrence of airglow, directly or indirectly show the changes in composition and energy transfer caused by complex chemical processes. The modeling studies of the above phenomena are conducive to the deepening of understanding, and the defects of the simulation also reflect the lack of observation on understanding. The discussion in this review encourages more research on the AATZ as well as the development and enrichment of existing observational tools and techniques to fill in the knowledge gaps and enhance the ability to apply the region in the aerospace industry.

The initial field has a crucial influence on numerical weather prediction (NWP). Data assimilation (DA) is a reliable method to obtain the initial field of the forecast model. At the same time, data are the carriers of information. Observational data are a concrete representation of information. DA is also the process of sorting observation data, during which entropy gradually decreases. Four-dimensional variational assimilation (4D-Var) is the most popular approach. However, due to the complexity of the physical model, the tangent linear and adjoint models, and other processes, the realization of a 4D-Var system is complicated, and the computational efficiency is expensive. Machine learning (ML) is a method of gaining simulation results by training a large amount of data. It achieves remarkable success in various applications, and operational NWP and DA are no exception. In this work, we synthesize insights and techniques from previous studies to design a pure data-driven 4D-Var implementation framework named ML-4DVAR based on the bilinear neural network (BNN). The framework replaces the traditional physical model with the BNN model for prediction. Moreover, it directly makes use of the ML model obtained from the simulation data to implement the primary process of 4D-Var, including the realization of the short-term forecast process and the tangent linear and adjoint models. We test a strong-constraint 4D-Var system with the Lorenz-96 model, and we compared the traditional 4D-Var system with ML-4DVAR. The experimental results demonstrate that the ML-4DVAR framework can achieve better assimilation results and significantly improve computational efficiency.

Underwater acoustic signal denoising technology aims to overcome the challenge of recovering valuable ship target signals from noisy audios by suppressing underwater background noise. Traditional statistical-based denoising techniques are difficult to be applied effectively in complex underwater environments, especially in the case of extremely low signal-to-noise ratios (SNRs). To address these problems, we propose a noise-aware deep learning model with fullband-subband attention network (NAFSA-Net) for underwater acoustic signal denoising. NAFSA-Net adopts an encoder to extract the feature representation of the input audio. Subsequently, the noise subnet and the target subnet are designed to estimate the noise component and the target component simultaneously. Specifically, some stacked fullband-subband attention (FSA) blocks are deployed in each subnet to capture both global dependencies and fine-grained local dependencies of features. Furthermore, we introduce an interaction module to transmit auxiliary information from the noise subnet to the target subnet. Finally, we propose an improved weight SI-SNR loss function to optimize the training of our model. Experimental results show that our proposed NAFSA-Net substantially outperforms traditional methods and competitive DNN-based solutions in denoising underwater noisy signals with very low SNRs. More importantly, our proposals achieve equally excellent performance on both unseen datasets, which indicates that NAFSA-Net can be a more robust choice for real-world underwater acoustic denoising systems.

The quality and accuracy of remote sensing instruments continue to increase, allowing geoscientists to perform various quantitative retrieval applications to observe the geophysical variables of land, atmosphere, ocean, etc. The explosive growth of time-series remote sensing (RS) data over large-scales poses great challenges on managing, processing, and interpreting RS ‘‘Big Data.’’ To explore these time-series RS data efficiently, in this paper, we design and implement a high-performance framework to address the time-consuming time-series quantitative retrieval issue on a graphics processing unit cluster, taking the aerosol optical depth (AOD) retrieval from satellite images as a study case. The presented framework exploits the multilevel parallelism for time-series quantitative RS retrieval to promote efficiency. At the coarse-grained level of parallelism, the AOD time-series retrieval is represented as multidirected acyclic graph workflows and scheduled based on a list-based heuristic algorithm, heterogeneous earliest finish time, taking the idle slot and priorities of retrieval jobs into account. At the fine-grained level, the parallel strategies for the major remote sensing image processing algorithms divided into three categories, i.e., the point or pixel-based operations, the local operations, and the global or irregular operations have been summarized. The parallel framework was implemented with message passing interface and compute unified device architecture, and experimental results with the AOD retrieval case verify the effectiveness of the presented framework.

Quantitative precipitation nowcasting (QPN) is a highly challenging task in weather forecasting. The ability to provide precise, immediate, and detailed QPN products is necessary for a variety of situations, including storm warnings, air travel, and large gatherings. To address this challenge, this paper proposes a new transformer LPT(Lightweight Physics-informed Transformer)-QPN for QPN tasks, utilizing vertical cumulative liquid water content (VIL) products. This model adopts novel transformer modules to model long-term evolution of precipitation and incorporates multi-head squared attention (MHSA) to model its highly non-linear relationships while reducing computational complexity. The results of experimental evaluations demonstrate the superiority of LPT-QPN when compared to existing state-of-the-art QPN models. In particular, the LPT-QPN model demonstrates greater accuracy for long lead time and in high-intensity areas, confirmed in both quantitative and qualitative evaluations. Additionally, through three customized fine-tuning schemes, we are able to further improve the predictability of the LPT-QPN model for specific precipitation events. By incorporating physical constraints of convection diffusion equation, our approach offers novel perspectives for future explorations that combine physical prior knowledge and deep learning techniques.

Underwater acoustic signal denoising is a challenging task due to the complexity of the underwater environment. Most of the existing methods cannot effectively cope with the problem of underwater acoustic signal (UWAS) denoising at low signal-to-noise ratios (SNRs). According to the characteristics of UWAS, a novel idea is proposed to simultaneously model latent features from both the time and frequency dimensions of complex-valued spectrum in a dual-branch self-attention network, namely DBSA-Net. In this model, both magnitude and phase information in the complex spectrum are enhanced from different dimensions by two branches. Specifically, DBSA-Net is an encoder-decoder based network with several global-local-self-attention (GL-SA) blocks distributed on dual branches between encoder and decoder. Each GL-SA block incorporates global self-attention and local self-attention to capture distant context and fine-grained local dependencies along the temporal and frequency dimensions. Moreover, we also design an information interaction module between two branches to exchange complementary information. This interaction module together with a merge block fuse features extracted from different dimensions, thus enhancing the capability of our model to learn the target signal features. Extensive experiments are conducted to evaluate our model on a publicly available dataset. Results of the ablation experiments show that the different modules of DBSA-Net play their respective roles in improving denoising performance and are empirically valid. In both the seen ships and unseen ships scenarios, the proposed DBSA-Net outperforms existing approaches by a large margin on various evaluation metrics.