Accurate sea level pressure (SLP) data are critical for the forecasting and monitoring of tropical cyclones (TCs). Previous studies have explored SLP retrieval for TCs using passive microwave observations in a single oxygen band (60 or 118 GHz). Leveraging the Fengyun-3 (FY-3) satellite’s capability to simultaneously observe radiation in both 60 and 118 GHz bands, this study proposes a neural network-based algorithm to retrieve TC SLP fields from combined observations of brightness temperature (TB) and warm TB anomalies in both frequency bands. Application studies were carried out using the joint observations from the Microwave Temperature Sounder-2 (MWTS-2) and Microwave Humidity Sounder-2 (MWHS-2) onboard the FY-3D satellite. Optimal frequency channels for the SLP retrieval were selected based on an information content analysis. SLP retrievals within the TC core regions (within a 2° radius from the centers) were compared with SLP data from the Global Data Assimilation System Final Analysis (GDAS/FNL). The results showed root mean square errors (RMSEs) of 2.78 hPa for tropical depressions and storms, 4.23 hPa for hurricanes or typhoons, and 6.15 hPa for major hurricanes or severe typhoons. Retrievals were also compared within situobservations, and the corresponding RMSEs were 3.82, 4.86, and 5.14 hPa, respectively. Furthermore, comparative experiments between the new algorithm and the previous algorithm that used only observations from a single oxygen band demonstrated that the new algorithm provides more comprehensive SLP information and achieves higher accuracy.

The development of time-based flow management has significantly enhanced the safety, reliability, and predictability of air traffic control (ATC). Actual flight paths often deviate from these standard terminal arrival routes due to pilots requesting shortcut arrivals or ATC officers implementing holding procedures to alleviate congestion. These deviations exacerbate the dynamic complexity of air traffic management (ATM). To address these challenges, we propose a novel online learning Transformer-bidirectional gated recurrent unit (Transformer-BiGRU) framework for tactical spatiotemporal flight trajectory prediction. BiGRU further obtains bidirectional sequence information to improve the Transformer’s spatiotemporal prediction. The proposed research utilises image processing techniques to produce ATC aeronautical holding instructions from historical automatic dependent surveillance-broadcast data. The framework significantly improves real-time prediction ability and environment adaptability by integrating holding instructions and online learning. Experiment results demonstrate that incorporating holding instructions with the proposed Transformer-BiGRU reduces the mean absolute error by approximately 10% in latitude, 8.9% in longitude, and 13.1% in flight level compared to the best baseline model. Furthermore, the mean deviation error of horizontal distance decreases from 0.49 to 0.42 nautical miles (a 13% improvement). These results confirm that the methodology benefits real-time ATC decision-making in various ATM scenarios and provides valuable insights to assure airspace safety.

Highly variable wind fields (HWFs), which usually have drastically changing velocities over time, can seriously impact aviation safety, wind energy assessment, and so on. A recently proposed deep learning method can well predict the wind fields in ordinary cases, but its performance deteriorates when HWFs are involved. In this letter, a nowcasting method taking into account the impact of HWFs is proposed. First, standard deviations (SDs) of the lidar observations within a time interval are used to identify highly variable events. Second, a loss function weighted by the SDs is adopted to train the nowcasting neural network. Additionally, a new dimensionless metric is introduced to quantitatively measure the nowcasting performance with emphasis on HWFs. Experimental results demonstrate that the weighted loss function can efficiently improve the nowcasting performance on HWFs such as the initiation and dissipation processes of wind shear. Compared with the original nowcasting method, the network with the weighted loss function can reduce the nowcasting errors by an improvement of more than 15.2% in terms of the new metric.

To better describe the main features of the complex airflow in the atmospheric boundary layer, a hybrid wind field retrieval method based on machine learning (ML) and data assimilation (DA) is proposed. Based on the joint measurement of lidar andin situmeasurements, a 3-D variational data assimilation (3DVAR) method is used to retrieve the fine-scale wind field. To address the iterative interpolation problem in the traditional DA methods, this article isolates the interpolation from the optimization and uses the regression methods in ML to estimate the interpolated observations on analysis grids. More specifically, the supervised regression and semisupervised regression are, respectively, used for lidar andin situobservations according to their heterogeneity. Simulation and field measurement results indicate that, compared with the traditional DA methods, the proposed method can better estimate both 2-D and 3-D velocities, by an improvement of more than 42.3% on average.

The uniform and linear wind models have been commonly used for wind field retrieval in meteorological community. However, the accuracy and robustness of the retrieval results can be quite unsatisfactory due to the mismatch between these models and the real wind distribution, especially under complex wind conditions. In this article, a nonlinear model based on high-order Taylor expansion is proposed to deal with this limitation, and the combination of ridge regression and decomposition-iteration process (denoted as Ridge-DI method) is further introduced to solve the model with high accuracy and robustness. A case study on simulation and field experiment shows that the proposed method with the third-order Taylor expansion can reduce the mean root-mean-square errors (RMSEs) of the retrieved velocities by more than 16.84% in comparison with traditional methods.