Numerical weather prediction (NWP) is the core technology for weather forecast and disaster prevention and mitigation. The research and operational applications of NWP have always been highly valued in China, and have achieved great progress with an appreciable international influence in the theories, algorithms, and operational system developments. This paper first summarizes the scientific and technological evolution of NWP in China, and then focuses on the current status and recent updates of the two homemade global NWP systems: GRAPES (Global/Regional Assimilation and PrEdiction System) and YHGSM (YinHe Global Spectral Model). (1) GRAPES possesses both deterministic and ensemble forecast systems, with global (regional) model versions running on 12–50 km (3–10 km) resolutions. Significant improvements have been made on its dynamic core, four-dimensional variational (4DVar) assimilation, satellite and radar data assimilation, ensemble forecast, and cloud microphysics schemes, and so on. It is capable to perform subseasonal to seasonal forecast and has incorporated an atmospheric chemistry model, typhoon numerical forecast model, and ocean wave model. (2) YHGSM continues to follow the development route of spectral models, featured prominently with a dry-mass conserved spectral dynamical core, ensemble 4DVar assimilation, coupled ocean-land-atmosphere ensemble forecast, and the medium-term and monthly-extended global high-resolution forecast as the baseline. These NWP systems autonomouly developed by the China Meteorological Administration and the national defense insitution benefit from long-term adherence to the national science and technology development strategies and close research to operation practices.

The Advanced Radiative Transfer Modeling System (ARMS), a computationally efficient satellite observation operator, has been successfully integrated into the YinHe four-dimensional variational data assimilation (YH4DVAR) system. This study investigates the impacts of assimilating Advanced Microwave Sounding Unit-A (AMSU-A) observations from the Meteorological Operational Satellite-C (MetOp-C) on the performance of YH4DVAR. Through a month-long global statistical analysis and a case study of Typhoon Hinnamnor, we evaluate the benefits of AMSU-A data assimilation under clear sky conditions. Key findings are as follows. (1) ARMS achieves simulation accuracy comparable to RTTOV (Radiative Transfer for the Television and InfraRed Observation Satellite Operational Verti-cal sounder) version 11.2, demonstrating only a 0.5% discrepancy in data retention after quality control. (2) Implementation of ARMS as an operator in YH4DVAR enhances forecast accuracy for the 850-hPa temperature and 500-hPa geopotential height in the tropical region. (3) Compared to RTTOV, ARMS has improved the intensity forecast of Typhoon Hinnamnor and reduced mean wind speed errors by approximately 2% and central pressure errors by approximately 1%. ARMS has now been operationally adopted as an alternative observational operator wi-thin YH4DVAR, demonstrating exceptional numerical stability, computational efficiency, and promising potential for future satellite data assimilation applications.

Aviation turbulence is an essential factor threatening flight safety. However, due to its complex mechanism, which study has been one of the key issues faced by the aviation industry. In recent years, with the development of large eddy numerical simulation (LES), which has become an important method to solve aviation turbulence problems. This study reviews the research progress of LES technology in the past few decades, focusing on the impact of aircraft trailing vortex wakes and low-level turbulence during the takeoff and landing phase, as well as convective induced turbulence, mountain wave turbulence, and clear air turbulence during the cruise phase on aircraft turbulence. It also summarizes and prospects the urgent problems to be solved in the application of LES technology and future key research directions. Overall, LES simulation research on aviation turbulence has got much achievement, which can clarify the source and lifecycle of aviation turbulence more clearly, significantly improving the mechanism cognition, quantitative diagnosis, prediction and warning capabilities. However, in terms of mechanism, the interaction mechanism of various complex turbulent processes is still unclear; in terms of numerical model techniques, the predictive skill of LES of aviation turbulence is still limited by errors in initial conditions, boundary conditions, and the models themselves (e.g., parameterizations, dynamical methods). In the future, the development of nesting and dynamic grid technology between LES and mesoscale regional models, high-resolution ensemble prediction methods and probability prediction approaches, as well as the combination with deep learning methods will further improve the computational efficiency and prediction ability of LES on aviation turbulence simulation and forecasting.