Conference Agenda

Severe convective rainfall, triggered by multi-scale atmospheric interactions, poses a critical forecasting challenge in coastal cities like Shanghai, where monsoon, topography, and sea-land breeze amplify extremes. Conventional methods, constrained by scale separation and model biases, struggle to predict convection. This study develops the Synergistic Framework for Convective Rainfall Forecasting (SSF-CRF) by integrating three modules: (1) Adaptive S/X-band radar remote sensing, dynamically capturing mesoscale convective structures; (2) Gated Vertical Information Propagation (GVIP) network, machine learning on vertical energy propagation to capture convection; (3) Precipitation Ordinal Distribution Autoencoder (PODA), correcting numerical weather prediction (NWP) biases with ordinal precipitation classification. Verification against Radar data and European Centre for Medium-Range Weather Forecasts (ECMWF) model indicates that SSF-CRF improves heavy rainfall (≥50 mm/h) Critical Success Index (CSI) by 33% versus operational forecasts. It offers a potential solution for convective forecasting in climate-vulnerable coastal regions, advancing remote sensing-driven atmospheric applications.

Monitoring atmospheric water vapour is essential for weather forecasting and climate studies. GNSS networks can retrieve Precipitable Water Vapour (PWV) continuously at each station location, but the accuracy depends on the quality of the satellite orbit and clock corrections used in the processing. This study evaluates PWV retrieval from 478 stations of the French Centipede low-cost GNSS network using four levels of correction products with decreasing latency: GFZ Final ($\sim$2 weeks), Rapid ($\sim$1 day), Ultra-rapid (3--9 hours), and broadcast ephemerides (real-time). Validation against ERA5 reanalysis shows that the Final and Rapid products achieve similar performance (RMSE $\approx$ 2~mm, $r^2$ = 0.84), confirming that near-real-time processing introduces no significant accuracy loss. Ultra-rapid products remain usable (RMSE = 3.4~mm), while broadcast ephemerides show larger errors (RMSE = 5.8~mm) but still capture the spatial moisture pattern. In addition, a real-time experiment using the freely available Galileo High Accuracy Service (HAS) demonstrates that stable tropospheric estimates (ZTD $\pm$ 1.4~mm, PWV $\pm$ 0.2~mm) can be obtained in real time, even before the positioning solution has fully converged. These results suggest that combining the spatial density of low-cost networks with real-time HAS corrections could enable high-resolution PWV monitoring that is not achievable with existing systems.

Fengyun-3G, launched in 2023, carries Ku- Ka dual-frequency precipitation measurement radar (PMR) providing new opportunities for monitoring the fine three-dimensional structure of typhoon precipitation over the ocean. This study first validate the FY-3GPMR data by using the ground-based data, then utilizes PMR to analyze the precipitation during the rapid intensification phase of Super Typhoon Yagi in the year of 2024. The analysis reveals the horizontal and vertical distribution characteristics of precipitation during Yagi's RI phase based on the FY-3G PMR data, and discusses the associated dynamical-microphysical coupling mechanism. Overall, FY-3G PMR offers critical insights for understanding cloud and precipitation process involved in the RI.

Atmospheric water vapor plays a fundamental role in regional climate regulation and precipitation formation, yet its vertical structure and spatiotemporal evolution over mainland China remain insufficiently understood due to complex terrain and diverse climatic conditions. Using Fengyun-4A layered precipitable water (LPW) products from 2020 to 2023, this study provides a comprehensive assessment of the vertical distribution, spatiotemporal variability, and key environmental drivers of water vapor across China. Results show pronounced spatial gradients and seasonal contrasts: total precipitable water (TPW) exhibits a slight overall decline, primarily driven by reductions in low layer; spatially, TPW is highest in the southeast and lowest in the northwest; seasonally, water vapor peaks in summer and reaches its minimum in winter, with spring and autumn representing monsoon-transition phases. Vertically, approximately 75% of atmospheric water vapor is concentrated within the lowest 4 km, with the middle layer contributing most to regional differences, while high layer remains relatively uniform and minimally influenced by terrain. Environmental correlations indicate that TPW is positively associated with 2m temperature, relative humidity, surface pressure, total cloud cover, and precipitation, but negatively associated with DEM and evaporation. Layer-dependent responses indicate that the lower layer is strongly influenced by surface processes, the middle layer by both surface moisture transport and large-scale circulation, and the high layer primarily by thermodynamic structure and synoptic background. These findings, derived from high-resolution satellite observations, enhance understanding of atmospheric water vapor stratification and its controlling mechanisms, providing essential support for water vapor transport diagnosis, precipitation evolution, and operational forecasting improvement.

In recent decades, global climate change has triggered a rise in extreme environmental phenomena, including prolonged droughts, intensified precipitation events, and shifts in tidal patterns. This study focuses on the application of the observations from Global Navigation Satellite System (GNSS) signals for monitoring and classifying climatic conditions, with particular emphasis on drought.

Using daily vertical displacement data from a GNSS station in California (2005–2023), we developed a robust analysis framework. It includes data cleaning (removing outliers, filling gaps, detecting offsets, and modeling noise), trend and seasonal pattern extraction through Singular Spectrum Analysis (SSA), feature generation (like amplitude, energy, and dominant frequency), labeling

based on the Standardized Precipitation-Evapotranspiration Index (SPEI), and classification using a hybrid CNN-Transformer model. The results demonstrate the model’s capability to accurately detect drought periods (SPEI > -1) characterized by diminished amplitudes in seasonal components and heightened noisy fluctuations, as well as wet periods (SPEI < 1) marked by elevated energy in semi-annual signals. The model was evaluated with an overall accuracy of 83.3 percent, an F1-score of 0.90 for the drought class, and successful application to future data (2024–2029). This approach, independent of traditional meteorological data, underscores the potential of GNSS as a geodetic tool for environmental monitoring, albeit with limitations such as reliance on single stations and the need for supplementary datasets. The methodology holds promise for enhancing early warning systems and climate models.

Carbon dioxide (CO₂) and methane emissions (CH₄) are primary greenhouse gases whose rising atmospheric levels intensify global climate change. Wetlands, despite covering only 5–8% of Earth’s land area, contribute nearly 30% of global methane emission while storing up to 30% of global soil organic carbon. This makes wetlands both sinks and sources of greenhouse gases, though their seasonal CO₂ and CH₄ dynamics in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) remain poorly understood.

Ground-based instruments offer high accuracy but limited spatial coverage, whereas satellite missions, such as Sentinel-5P/TROPOMI for XCH₄ and OCO-2 for XCO₂, enable wide-area monitoring. This study investigates the seasonal dynamics of CH₄ and CO₂ across different wetland ecosystems in the GBA using satellite observations and ERA5-Land climate variables. Seasonal means were computed in Google Earth Engine for Winter, Spring, Summer, and Autumn from 2019 to 2025.

Results show a consistent rise in atmospheric CH₄ from 1856 ppb (2019) to 1939 ppb (2025), with the highest levels in Autumn and Winter. CO₂ increased from 404 ppm to 424 ppm, peaking in Winter and Spring. Non-wetland regions and mangroves emerged as the primary contributors to greenhouse gas accumulation, while salt marshes and other wetlands showed lower values.

Pearson correlation analysis indicated strong influence of temperature, dew point, and precipitation on CO₂, while CH₄ showed variable sensitivity to rainfall and wind. Findings emphasize the impact of land-cover type and climate in shaping seasonal greenhouse gas dynamics, supporting SDG 13 and SDG 15, and necessitating hyperspectral data integration for climate policies.

Urban air quality remains a critical concern, as NO₂ emissions from transport and industrial activities frequently exceed healthy limits in major cities. Urban vegetation can help reduce pollution by enhancing natural filtration and cooling, while higher land surface temperatures (LST) tend to intensify pollutant accumulation. Using satellite-based remote sensing, this study investigates how vegetation health (NDVI) and surface temperature influence NO₂ levels in two distinct urban environments: Blackburn/Arlington Road in England and District No. 3 in Tehran, Iran, across pre-, during-, and post-COVID-19 lockdown periods. Both cities experienced notable environmental improvements in 2020: NDVI increased from approximately 0.45–0.48 to around 0.54–0.61, while NO₂ dropped significantly from about 0.46–0.50 to roughly 0.13–0.35. LST also declined from pre-lockdown values near 0.46–0.48 to as low as 0.12–0.38. During the lockdown, vegetation levels showed a clear negative relationship with NO₂ concentrations, and pollution trends displayed a strong positive association with higher temperatures, highlighting the linked benefits of greener and cooler environments. However, as human activities resumed after 2021, these relationships became inconsistent or weakened, with occasional shifts in direction depending on seasonal conditions and external drivers such as traffic recovery and industrial intensity. Overall, the results reinforce that increasing vegetation coverage and mitigating urban heating can meaningfully reduce NO₂ levels. By revealing how urban form, vegetation dynamics, and thermal conditions collectively shape pollution patterns, this research provides insights for city planners, environmental managers, and public health authorities working to design more sustainable and healthier urban environments.