Global navigation satellite system (GNSS) timing modules from 5G base stations enable precipitable water vapor (PWV) retrieval, offering a promising approach to overcome the limitations of conventional meteorological stations in atmospheric monitoring. In this study, we retrieved PWV values (referred to as 5G-PWV) based on GNSS observations from 5G base stations in Zhoushan City, Zhejiang Province, China, using data from September to November 2024. The results were compared with ERA5 reanalysis data (ERA5-PWV) and observations from a dedicated GNSS station in Zhoushan (ZJZS-PWV). The 5G PWV data showed consistent temporal variability with both ERA5-PWV and ZJZS-PWV. After applying a bi-weight quality control algorithm, comparisons against ERA5-PWV revealed a mean bias of −1.07 mm, a root mean square error of 8.03 mm, and a correlation coefficient of 0.828 for 5G-PWV. We further conducted assimilation and forecast experiments using the CMA MESO (China Meteorological Administration Mesoscale Model) coupled with its three-dimensional variational data assimilation system for two typhoon cases: Bebinca and Krathon. Validation of precipitation forecasts—with and without assimilation of 5G PWV—showed that the assimilation of 5G-PWV improved the threat score (TS). After assimilating 5G-PWV, both false alarms and missed alarms across different precipitation intensity thresholds were slightly reduced. The improved forecast performance is attributed to effective adjustments in the initial humidity field through the assimilation of 5G-PWV, as well as subsequent enhancements in the model's dynamic conditions via precipitation feedback. Our study demonstrates that GNSS derived PWV from 5G base stations has clear potential to improve typhoon-related precipitation forecasting.

In this study, we investigate the impact of a scale-aware convective parameterization scheme (CPS) on simulating two high-impact extreme precipitation events during the Meiyu season at a 1 km resolution. Compared with explicit resolving simulation, applying scale-aware CPS mostly affects the atmospheric environment before convection is triggered and the regions outside the convective cell (e.g., stratiform regions) by consuming convective available potential energy (CAPE), which further inhibits precipitation. For the 2019 event, since the explicit resolving simulation underestimates precipitation, applying scale-aware CPS further inhibits precipitation and reduces the skill for the heavy rainfall category. In contrast, for the 2020 event, as the explicit resolving simulation overestimates precipitation, using scale-aware CPS inhibits precipitation and improves the skill of the model. When scale-aware CPS is applied, increasing σ 1 , which represents the effect of horizontal resolution, the CPS precipitation is reduced and the grid-scale precipitation is increased, but the overall effect inhibits the total precipitation. However, with the increase in σ 1 , although the number of stations with heavy rainfall (≥50 mm) is reduced, the average precipitation among these stations is increased. Further analysis of those stations with the largest 24 h precipitation and cross sections over regions with large radar reflectivity shows that increasing σ 1 reduces the CAPE consumed by CPS and provides a more favorable environment for strong convections and strengthens precipitation. The results of this study may provide useful information for operational model application and may be beneficial to society.

Atmospheric temperature and humidity profiles are essential for numerical weather prediction and severe weather monitoring. To effectively utilize data from the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the FY-4 satellite, this study proposes a retrieval method based on a radial basis function (RBF) neural network, which integrates numerical model background profiles with GIIRS simulated radiance errors to construct a mapping from these two inputs to background profile errors. A channel selection strategy is developed using correlations between background errors and radiance errors to identify channels sensitive to temperature and humidity variations at different pressure levels. Experiments are conducted using data from land stations in Zhejiang Province, China, from August to December 2024, including 829 clear-sky and 2109 cloudy profiles. Under clear-sky conditions, the method reduces temperature and humidity root mean square error (RMSE) by approximately 39% and 22.3% compared to background profiles. Under cloudy conditions, despite severe radiation interference, RMSE reductions of 38.5% for temperature and 15.3% for humidity are achieved, with notable improvements below 900 hPa and above 750 hPa for humidity. Compared with the multilayer perceptron (MLP) method, RBF shows superior performance under all test conditions, especially in cloudy-sky humidity retrieval. The proposed approach provides an effective, physically constrained framework for operational GIIRS data application in temperature and humidity retrieval.

Assimilating satellite-retrieved profiles is an important approach to improving numerical model initial conditions and typhoon forecasting skill. This study uses temperature and humidity retrieved profile products from the Geostationary Interferometric Infrared Sounder (GIIRS) onboard Fengyun-4B for three typhoons that affected Zhejiang Province in July 2025 with different track types. Sensitivity experiments are conducted under clear-sky, cloudy-sky, and all-sky conditions. Vortex relocation experiments are designed to separate the contributions of typhoon vortex structure and environmental field to track forecasts. Results show that assimilation systematically adjusts temperature and humidity in the typhoon inner core, rainband, and outer environment. All schemes produce lower–middle tropospheric warming and upper tropospheric cooling. Humidity decreases in the low-level outer environment but increases around the typhoon vortex and along the Western Pacific Subtropical High edge. All-sky assimilation is optimal for track forecasts, whereas clear-sky or cloudy-sky assimilation alone can be adverse. For 72-h precipitation, all-sky assimilation increases the heavy rain threat score by 0.09 versus control and the torrential rain threat score by 0.09 versus cloudy-sky assimilation. This study reveals the comprehensive advantages of all-sky GIIRS profile assimilation for typhoon forecasting and supports the operational application of GIIRS-retrieved temperature and humidity profiles.