An unprecedented persistent heavy precipitation occurred in Henan province during 17—22 July 2021, causing huge economic losses. Currently, extreme precipitation forecasting is still a hotspot and a difficult issue in sub-seasonal climate prediction research. Regional climate models provide a new way to further improve sub-seasonal precipitation forecasting in China with finer spatial resolution and better parameterization of physical processes compared to that of the global models. This study uses the regional Climate-Weather Research and Forecasting model (CWRF) nested with the China Meteorological Administration Climate Prediction System version 3 (CMA_CPSv3) to improve prediction capabilities for this persistent heavy precipitation event. It is shown that the spatial distribution, magnitude, and forecast accuracy of precipitation predicted by CWRF are improved compared to that predicted by CMA_CPSv3. Although both models underestimate the amount of precipitation, the CWRF forecasts larger accumulated precipitation and spatial distribution of precipitation is more consistent with observation. CWRF forecasts initialized on 26 June and 29 June are better than that of CMA_CPSv3 on the same initial dates. The CWRF significantly improves the forecast of low-level wind fields and low-level jets in East Asia compared with the CMA_CPSv3. The CWRF is particularly effective in improving the simulation of directions of low-level jets and water vapor fluxes, allowing water vapor to converge on the windward slopes of mountain ranges and providing favorable water vapor conditions for precipitation. The CWRF better forecasts the water vapor flux convergence and ascending motions over Zhengzhou, and all these improvements lead to higher precipitation forecasting skill of CWRF.

Atmosphere-wave interaction is a crucial dynamic process at the air-sea interface, with the sea surface momentum roughness length being a key variable in the coupled atmosphere-wave modelling system. The Global-Regional Integrated Forecast System (GRIST), a next-generation unstructured-grid unified weather and climate modelling system, has been independently designed and developed in China in recent years. By employing the ESMF/NUOPC framework, GRIST has been integrated with the WW3 model to create the coupled atmosphere-wave modelling system (GRIST-WW3). In this system, the atmospheric model provides 10 m wind fields over the sea surface to drive the wave model, while the sea surface roughness, derived from a wave parameterization scheme, is fed back into the atmospheric model. Preliminary results show that the GRIST-WW3 system accurately captures spatial distribution of sea surface wind field and significant wave height, both of which agree well with observations. However, in regions such as the Southern Hemisphere's westerlies and areas near typhoons, where the wind speeds are notably high, the model tends to overestimate 10 m wind speed and significant wave height. The two-way coupling process increases the average and dispersion of sea surface roughness, reducing wind speed biases in areas of high wind speed. In terms of typhoon simulation, improvements in the simulation of typhoon trajectory and 10 m maximum wind speed are evident with the atmosphere-wave coupled modelling system, although the minimum sea level pressure remains unaffected. In the coupled atmosphere-wave modelling system, the wave parameterization scheme of sea surface roughness is essential for accurately simulating high wind speed areas. The optimization of the scheme should be guided by the atmospheric model's bias characteristics, with the primary goal of reducing bias in high wind speed regions.

To obtain high precision wind profile and turbulence products and fully understand the application potential of wind lidar, quality control of Doppler wind lidar with five-beam swing (DBS5) mode is investigated in this work using radial velocity and signal-to-noise ratio (SNR). Three-dimensional wind and turbulence measurements by the DBS5 mode of wind lidar are systematically evaluated based on measurements of a three-dimensional ultrasonic anemometer mounted on a tower. The results show that the wind lidar exhibits excellent observational accuracy with root mean square errors (RMSEs) as low as 0.4 m/s, 0.1 m/s, 0.1 m/s, 0.1 m/s, and 0.5 m²/s² for horizontal wind speed (WS), vertical velocity (w), standard deviation of vertical velocity (σ_w), friction velocity (u_*), and turbulent kinetic energy (TKE), respectively at the height of 140 m. Moreover, the impacts of time scale, elevation angle, and spatial scale on observational accuracy of wind lidar are investigated. The results indicate that variations in time scale have little impact on observational accuracy, while elevation angle may affect the accuracy of TKE. Additionally, the RMSEs of WS and TKE gradually increase with increasing spatial scale, while the accuracy of w, σ_w, and u_* remain relatively stable. Further investigation of profiles of turbulence and vertical velocity under clear-sky conditions in Beijing indicates that the power spectra of vertical velocity is consistent with the classical −5/3 scaling law at different periods and heights, whereas white noise appears in the high-frequency region and intensifies with increasing height, especially above the boundary layer. Finally, the vertical turbulence of low-level jets (LLJs) observed by Doppler wind lidar is stronger below the jet height and weaker in and above the jet height.

To meet the need for weather forecasting, strong convection monitoring and warning services, an hourly 1 km wind gust product across China has been produced using hourly wind gust observations and model forecast data. These data are adopted after the procedure of quality control, space-time matching and fusion analysis. This product is updated at a 5 min lag with the overall RMSEs of 1.9 m/s for independent test and 0.68 m/s for the non-independent test. The results indicate that as the wind speed increases, the number of samples continuously declines, there is a relatively pronounced tendency for the product's error to rise, and the accuracy gradually decreases. However, the quality of this product improves effectively compared to model predictions of high speed of wind gusts. Specifically, the accuracy of wind speed above magnitude 9 has improved by 89.3%, and the relative error has dropped significantly with a reduction ratio of about 27.4%. The development of this product can support disaster mitigation and decision-making related to catastrophic gales and typhoons.

Retrievals of satellite-observed emissions of atmospheric pollutants and greenhouse gases provide essential information and data for understanding the sources of these key atmospheric compositions and for implementing precise emission control measures. Over the past two decades, significant progress has been made in the field of emission inversion, with Chinese researchers playing a substantial role. In celebration of the 100th anniversary of the Chinese Meteorological Society and Acta Meteorologica Sinica, this paper systematically reviews the advances in satellite-based emission inversion research by Chinese scientists during this period. (1) Several widely used inversion methodologies, including data assimilation, local mass balance, Gaussian models, two-dimensional (2D) models, and machine learning, are briefly summarized. (2) Emission inversion studies focusing on major atmospheric pollutants— such as nitrogen oxides (NO_x), ammonia (NH₃), formaldehyde (HCHO), glyoxal (CHOCHO), sulfur dioxide (SO₂), and carbon monoxide (CO)—as well as greenhouse gases like carbon dioxide (CO₂) and methane (CH₄), are systematically elaborated. (3) Finally, the historical evolution of inversion methods and target species, challenges in current satellite-based emission inversion, and future research directions are discussed to promote more accurate quantification of atmospheric pollutants and greenhouse gas emissions. It is worth noting that contributions from Chinese researchers have provided critical scientific support to environmental protection and carbon neutrality efforts in China.

Hong Kong is a coastal city in southern China. In the nineteenth century, Hong Kong's shipping industry had been well developed, serving as an important entrepot in the region. The Hong Kong Observatory (HKO) was established in 1883. Early operations of the HKO were all related to the shipping industry at the time, including meteorological observations and tropical cyclone warning service. The HKO has been conducting meteorological measurements at its Headquarters in Tsimshatsui since 1884. The long-term weather observations document the variations of climate in Hong Kong caused by global climate change and local urbanization. In 2017, the HKO Headquarters received the World Meteorological Organization's recognition as one of the first batches of centennial observing stations in the world. In more than a century, the HKO's services have evolved in pace with the increasing expectations and requirements of the modern society. During the period, the HKO made use of advanced technologies from time to time in tandem with its operational developments. Looking ahead, the HKO will further strengthen the cooperation with meteorological authorities in mainland China and the Greater Bay Area, playing the role of connecting the world to promote close meteorological cooperations regionally and internationally, and improving the ability to prevent natural disasters and respond to emergencies.