Aerosol observation data assimilation is key to atmospheric environmental prediction. The original CMA chemistry–weather (CMA-CW) 3DVar data assimilation system uses simply fine and coarse particulate matter (PM_2.5 and PM_2.5–10) as control variables, with only concentrations of PM_2.5 and PM₁₀ being assimilated. In this study, a new 3DVar assimilation framework is developed, which considers seven aerosol species (black carbon, organic carbon, soil-dust, sea salt, sulfate, nitrate, and ammonium, instead of PM_2.5 and PM_2.5–10) in refined size segments as control variables, aiming to direct assimilate more aerosol related variables such as aerosol optical depth and lidar extinction and to provide accurate chemical initial fields for the CMA-CW model. Moreover, in the new assimilation framework, a novel equal-proportion distribution and compensation for background error increment is proposed and implemented, which produces physically more reasonable background error and thereby more reliable atmospheric–chemical analysis increments. The new assimilation framework is validated by idealized and real-case assimilation experiments, with reasonable performance. Based on the CMA-CW coupled model with the new assimilation framework and a rapid update cycle configuration, five-day CMA-CW simulation experiments for a widespread heavy fog–haze event in winter 2016 are carried out. The results demonstrate that assimilation of the surface aerosol observation data significantly improves the short-time forecast of atmospheric pollutants, due to refined and more precise information on aerosol compositions brought by the new assimilation framework. Meanwhile, the surface aerosol data assimilation also makes a positive contribution to visibility forecasting, significantly improving the visibility forecast in the heavy pollution areas.

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.

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 (4D-Var) 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 4D-Var 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.

In this study, the adaption of a novel three-point multi-moment constrained finite-volume transport scheme for uniform points with center constraints (MCV3_UPCC) to cubed sphere geometry is implemented and described. For the MCV3_UPCC scheme, the three equidistant solution points are located within a single cell and a polynomial of 4th degree can be built by imposing the multi-moment center constraints. The resultant scheme has third-order accuracy and guarantees the exact numerical conservation. The Fourier analysis of MCV3_UPCC scheme demonstrates that the novel MCV3_UPCC has better numerical dissipation and dispersion than the original 3rd order Multi-moment Constrained finite Volume (MCV3) scheme. Then it is applied to quasi-uniform cubed-sphere grid, which is designed to avoid the polar problem on the traditional latitude–longitude grid. To suppress the non-physical numerical oscillations, a bound-preserving (BP) algorithm to constrain the conserved advected tracer to within the initial maxi-mum and minimum values is also implemented. The scheme is validated with several widely used benchmarks involving prescribed non-divergent two-dimensional flow on the sphere and different initial tracer distributions. The resulting conservative transport model with high-order accuracy and positive preserving property is comparable to other high-order schemes and has the potential for the numerical simulation of various traces in the atmosphere.

This paper reviews the development of ensemble weather forecast and the primary techniques employed in the main ensemble prediction systems (EPSs) designed by China and other countries. Here, the emphasis is placed on the advancements in the China Meteorological Administration (CMA) global and regional ensemble prediction systems (i.e., CMA-GEPS and CMA-REPS), with particular attention to operational technologies such as initial and model perturbation methods and the applications of ensemble forecast. Through comparative verification with EPSs from other leading international numerical weather prediction (NWP) centers, CMA’s EPSs demonstrate forecast skills comparable to its global counterparts. As EPSs progress to convective scales and coupled systems between sea, land, air, and ice, the paper addresses some key challenges in ensemble forecast technologies across the aspects of operation, science, integration of artificial intelligence (AI), merging of weather and climate models, and challenging user requirements. Finally, a summary of conclusions and future perspectives on ensemble forecast are provided.

A classical two-time level semi-implicit semi-Lagrange scheme (SISL) is used in CMA-GFS. Lagrange advection velocity and nonlinear terms are calculated by temporal extrapolation, which can cause computational instability and even integration interruption in large gradient areas such as the areas of jet. A SISL/P-C (predictor-corrector) algorithm is developed in CMA-GFS to reduce the impact of temporal extrapolation and construct a quasi-second order precision time discretization scheme by reducing the semi-implicit coefficients from 0.72 to 0.55. Results of idealized and real-data experiments show that this new scheme can effectively improve forecast accuracy, stability and conservation. The integration time step can be increased from 300 s to 450 s at 0.25° horizontal resolution, and the model calculation efficiency can be increased by 20%.

A warm-sector torrential rain under weak synoptic scale forcing occurred in Guangzhou on 7 May 2017. The precipitation process developed rapidly and locally, and the precipitation intensity is extremely high. Many operational numerical weather prediction models failed to forecast this storm. To study the model uncertainty in the forecast of this precipitation process, the Conditional Nonlinear Optimal Perturbation related to Parameters (CNOP-P) is adopted to select key physical parameters which can best represent the nonlinear error growth characteristics of the meso-micro scale system. A new model perturbation scheme CNOP-P-RP is constructed based on these key parameters. Convective-permitting ensemble prediction experiment is carried out based on the CMA-Meso model. Finally, the physical mechanism behind the influences of key parameters selected by CNOP-P on local convection in different stages is investigated. The result shows that the key parameters selected by CNOP-P are mainly related to vertical diffusion, auto-conversion from cloud to rain and conversion from other hydrometeors to raindrops. Compared with Stochastic Perturbed Parameterization Tendencies (SPPT) scheme which is widely utilized in operational ensemble prediction systems, the ensemble prediction experiment based on the CNOP-P-RP scheme is more skillful and reliable for probability forecast of precipitation and surface elements in this process. Further analysis shows the variation of piedmont temperature gradient and surface cold pool caused by the uncertainty of vertical diffusion plays an important role in convective triggering and rainstorm development. From 00:00 BT to 04:00 BT 7 May, the enhancement of vertical diffusion near the center of heavy precipitation in Huadu strengthened the vertical transport of heat, momentum and water vapor. The melting of snow and graupel particles is the main reason for the increase of precipitation, indicating that although the formation of raindrops is mainly caused by condensation near the top of boundary layer and collision of cloud water, the effect of ice particles cannot be ignored. From 04:00 BT to 08:00 BT 7 May, with the strengthening of water vapor transport and upward movement, a more active warm rain process dominated the increase of precipitation in the heavy precipitation center at Zengcheng. This study preliminarily proves the feasibility of the CNOP-P-RP scheme in describing the uncertainties in convection-permitting ensemble prediction systems, and provides some references for the improvement of warm-sector torrential rain forecast in South China.

The atmospheric motion is inherently nonlinear. The high-impact weather events that people concern are generally determined by small- and medium-scale systems overlaid on the large-scale circulation. The accumulation of seemingly minor computational errors can significantly impact the model’s predictive capabilities. When solving these equations, the flow field is commonly separated into basic flow and perturbation flow through the introduction of a reference state. This approach solves the problem of “small differences between large numbers” in terms such as the pressure gradient force (PGF) and improves the spatial discretization accuracy of the model. This paper first reviews the development of zero-dimensional (0D), one-dimensional (1D), two-dimensional (2D), three-dimensional (3D), and four-dimensional (4D) reference state deduction methods. Then, it details the implementation of these different dimensional reference state deduction methods within the context of the Global Regional Assimilation and Prediction System Global Forecast System (GRAPES_GFS) model of China Meteorological Administration (CMA). Furthermore, the accuracy of the different dimensional reference states is tested through multiple benchmark tests. The results demonstrate that the high-dimensional reference state provides a closer approximation to the real atmosphere across various altitudes and latitudes, resulting in a more comprehensive and effective improvement in discretization accuracy. Finally, the paper offers suggestions on issues related to reference state deduction.