For numerical weather prediction (NWP), data assimilation (DA) combines short-term forecasts and various atmospheric observations to achieve optimal initial conditions, based on which subsequent forecasts are launched. With the rapid advancements in numerical models and observing systems, DA has been significantly evolved. Modern methods now can account for uncertainties of state variables across various spatiotemporal scales, incorporate multiscale observation error statistics, and enforce dynamical constrains and model balances. Meanwhile, observations from various platforms, such as ground-based, aircraft, and satellite, have been assimilated. These include data from polar-orbiting and geostationary satellites, radar-derived radial winds and reflectivity, Global Navigation Satellite System (GNSS) radio occultations, etc. To further utilize the advanced observing systems and DA techniques for high-impact weather predictions, target observation strategies have been developed to identify areas where additional observations can yield the greatest predict improvements. Based on the advancements of DA theories and methods, China's operational systems have made significant progress, establishing advanced operational DA systems. Over the past decade, the forecast skill of 5 d global weather prediction has improved by approximately 15%. The article reviews a century of development in DA, and discusses future directions, including the advanced DA methods, operational frameworks, integration of novel observations, and the synergy between DA and artificial intelligence.

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.

For numerical weather prediction (NWP), data assimilation (DA) combines short-term forecasts and various atmospheric observations to achieve optimal initial conditions, based on which subsequent forecasts are launched. With the rapid advancements in numerical models and observing systems, DA has been significantly evolved. Modern methods now can account for uncertainties of state variables across various spatiotemporal scales, incorporate multiscale observation error statistics, and enforce dynamical constrains and model balances. Meanwhile, observations from various platforms, such as ground-based, aircraft, and satellite, have been assimilated. These include data from polar-orbiting and geostationary satellites, radar-derived radial winds and reflectivity, Global Navigation Satellite System (GNSS) radio occultations, etc. To further utilize the advanced observing systems and DA techniques for high-impact weather predictions, target observation strategies have been developed to identify areas where additional observations can yield the greatest predict improvements. Based on the advancements of DA theories and methods, China’s operational systems have made significant progress, establishing advanced operational DA systems. Over the past decade, the forecast skill of 5-day global weather prediction has improved by approximately 15%. The article reviews a century of development in DA, and discusses future directions, including the advanced DA methods, operational frameworks, integration of novel observations, and the synergy between DA and artificial intelligence.

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.

The flow-dependent background error covariance between variables is important, yet traditional climatic background errors in variational systems are difficult to characterize such kind of information. Although 4DVar can implicitly evolve the initial background error covariance by tangent linear and adjoint models, it suffers from complex development, maintenance, and expensive computational costs, which are particularly prominent in high-precision scalable global atmospheric models. To avoid the tangent linear and adjoint models, the four-dimensional ensemble forecast error is introduced into the CMA global data assimilation system, and the H-4DEnVar assimilation scheme is developed. The batch cycling forecast experiments and typhoon forecast experiments are conducted and compared with the 4DVar scheme. Batch forecast experiments indicate that the introduction of the four-dimensional ensemble forecast error improves the analysis and significantly improves the global forecast performance; typhoon forecast experiments show that the flow-dependent background error in H-4DEnVar is the main reason for the reduction in typhoon track forecast error; the comparison with 4DVar reveals that H-4DEnVar exhibits essentially comparable forecast capability at 26% of the computational cost of 4DVar when the IO cost of the ensemble forecast error is considered. The H-4DEnVar assimilation scheme shows good analysis and forecast effects while avoiding the tangent linear and adjoint models, and provides a reference for achieving four-dimensional assimilation scheme without the tangent linear and adjoint models.