An airborne cloud seeding experiment was conducted over the eastern coast of Zhejiang, China, on 4 September 2016 during a major international event held in Hangzhou. In an attempt to reduce the likelihood of rainfall onset, a major airborne experiment for weather modification took place by seeding hygroscopic agents to warm clouds to reduce cloud droplet size. The effectiveness of seeding is examined, mainly for stratiform clouds with patchy small convective cells. A radar-domain-index (RDI) algorithm was proposed to analyze the seeding effect. The threshold strategy and the tracking radar echo by correlation (TREC) technique was applied in the domain selection. Factors analyzed include echo reflectivity parameters such as the mean and maximum echo intensity, the anomaly percentage of the grid number of effective echoes, the fractional contribution to the total reflectivities, and the vertically integrated liquid (VIL) water content during and after the seeding process. About 12 min after seeding ended, the composite reflectivity of seeded clouds decreased to a minimum (< 10 dBz) and the VIL of seeded clouds was ∼0.2 kg m⁻³. The echo top height dropped to ∼3.5 km, and the surface echoes were also weakened. By contrast, there was no significant variation in these echo parameters for the surrounding non-seeded clouds. The seeded cell appeared to have the shortest life cycle, as revealed by applying the cloud-cluster tracking method. The airborne Cloud Droplet Probe (CDP) measured cloud number concentration, effective diameter, and liquid water content, which gradually increased after the start of cloud seeding. This is probably caused by the hygroscopic growth of agent particles and collision–coalescence of small cloud droplets. However, these parameters sampled at ∼40 min after seeding decreased significantly, which is probably due to the excessive seeding agents generating a competition for cloud water and thus suppressing cloud development and precipitation. Overall, the physical phenomenon was captured in this study, but a more quantitative in-depth analysis of the underlying principle is needed.

In this paper, multi-source observation, such as aircraft, ground-based remote sensing, and satellite-retrieved data, has been utilized to compare and analyze the vertical characteristics of aerosol optical properties and the planetary boundary layer height (H_PBL) over the North China Plain (NCP) region during May–June 2016. Aircraft observations show the vertical profiles of aerosol absorption coefficients (σ_abs), scattering coefficients (σ_sca), and extinction coefficients (σ_ext) gradually decrease with altitude, with their maximum values near H_PBL. The vertical profiles of σ_ext depended most on the vertical distribution of measured σ_sca, indicating a significant contribution of scattering aerosols. In addition, the prominent characteristic of the inverse relationship between σ_ext and moisture profile could serve as a reference for predicting air quality in the NCP region. The lower layer pollution during the field experiment was likely caused by the accumulation of fine-mode aerosols, characterized by the vertical distribution of the Ångström exponent and the Aerosol Robotic Network (AERONET) products. Typically, H_PBL derived from aircraft and surface Micro Pulse Lidar (MPL) was approximate, while the predicted H_PBL by meteorological data indicates an underestimation of ~192 m. Aerosol optical depth (AOD) calculated from aircraft and ground-based remote sensing (such as MPL and AERONET) experienced a strong correlation, and both of them exhibited a similar tendency. However, the AOD retrieved from satellites was significantly larger than that from aircraft and ground-based remote sensing. Overall, the inversion algorithm, cloud identification algorithm, representativeness of the space, and time of the observation may lead to an overestimation or underestimation of AOD under certain circumstances. This study may serve as a re-evaluation of AOD retrieved from multi-source observations and provide a reference to uncover the actual atmospheric environment in the NCP regions.

Robust water management is in intense demand in many water scarcity areas, such as arid and semi-arid regions in the world. As part of the regional water management strategy, rain enhancement is vital to replenish groundwater reservoirs, and the key challenge is how to assess its effectiveness. Some recent weather modification experiments attained cloud seeding effect through advanced in situ measurement coupled with accurate numerical simulation. However, there is still a lack of an objective and scientific approach to quantitatively evaluate the rain enhancement effect, especially for many non-randomized operational cloud seeding activities in China. In this study, we proposed a composite evaluation approach by analyzing two operational aircraft cloud seeding cases in stratus clouds in Shaanxi, China. By calculating the aircraft cloud seeding agent plumes, the target areas (as well as the control areas) of cloud seeding were dynamically and roughly determined. Physical properties, such as radar reflectivity and precipitation, were individually quantified in these areas. The cloud seeding effect was then evaluated by calculating the difference in parameter variation between target and control areas. This approach can be applied to qualitative analysis in a single aircraft cloud seeding operation and can also provide quantitative statistical results from multiple cloud seeding cases. We found that the average precipitation enhancement percentage of 18 operational aircraft cloud seeding cases is ~4.84%. Note that the homogeneity hypothesis of the seeding cloud, the error in the calculation of the target area, and the selection of control areas are the major uncertainties likely in the evaluation of the cloud seeding effect by this approach.

Haze is a majorly disastrous type of weather in China, especially central and eastern of China. The development of haze is mainly caused by highly concentrated fine particles (PM_2.5) on a regional scale. Here, we present the results from an autumn and winter study conducted from 2013 to 2020 in seven highly polluted areas (27 representative stations) in central and eastern China to analyze the growth mechanism of PM_2.5. At the same time, taking Beijing Station as an example, the characteristics of aerosol composition and particle size in the growth phase are analyzed. Taking into account the regional and inter-annual differences of fine particles (PM_2.5) distribution, the local average PM_2.5 growth value of the year is used as the boundary value for dividing slow, rapid, and explosive growth (only focuses on the hourly growth rate greater than 0). The average value of PM_2.5 in the autumn and winter of each regional representative station shows a decreasing trend as a whole, especially after 2017, whereby the decreasing trend was significant. The distribution value of +ΔPM_2.5 (PM_2.5 hourly growth rate) in the north of the Huai River is lower than that in the south of the Huai River, and both of the +ΔPM_2.5 after 2017 showed a significant decreasing trend. The average PM_2.5 threshold before the explosive growth is 70.8 µg m⁻³, and the threshold that is extremely prone to explosive growth is 156 µg m⁻³ to 277 µg m⁻³ in north of the Huai River. For the area south of the Huai River, the threshold for PM_2.5 explosive growth is relatively low, as a more stringent threshold also puts forward stricter requirements on atmospheric environmental governance. For example, in Beijing, the peak diameters gradually shift to larger sizes when the growth rate increases. The number concentration increasing mainly distributed in Aitken mode (AIM) and Accumulation mode (ACM) during explosive growth. Among the various components of submicron particulate matter (PM₁), organic aerosol (OA), especially primary OA (POA), have become one of the most critical components for the PM_2.5 explosive growth in Beijing. During the growth period, the contribution of secondary particulate matter (SPM) to the accumulated pollutants is significantly higher than that of primary particulate matter (PPM). However, the proportion of SPM gradually decreases when the growth rate increases. The contribution of the PPM can reach 48% in explosive growth. Compared to slow and rapid growth, explosive growth mainly occurs in the stable atmosphere of higher humidity, lower pressure, lower temperature, small winds, and low mixed layers.

The FAO56 Penman–Monteith model (FAO56-PM) is widely used for estimating reference crop evapotranspiration (ET₀). However, key variables such as shortwave radiation (R_s) and net longwave radiation (R_ln) are often unavailable at most weather stations. While previous studies have focused on calibrating R_s, the influence of large R_ln, particularly in high-altitude regions with thin air, remains unexplored. This study investigates this issue by using observed data from Bange in central Tibet to identify the optimal methods for estimating R_s and R_ln to accurately calculate ET₀. The findings reveal that the average daily R_ln was 8.172 MJ m⁻² d⁻¹ at Bange, much larger than that at the same latitude. The original FAO56-PM model may produce seemingly accurate ET₀ estimates due to compensating errors: underestimated R_ln offsetting underestimated net shortwave radiation (R_sn). Merely calibrating R_s does not improve ET₀ accuracy but may exacerbate errors. The Liu-S was the empirical model for R_s estimation calibrated by parameterization over the Tibetan Plateau and the Allen-LC was the empirical model for R_ln estimation calibrated by local measurements in central Tibet. The combination of the Liu-S and Allen-LC methods showed much-improved performance in ET₀ estimation, yielding a high Nash–Sutcliffe Efficiency (NSE) of 0.889 and a low relative error of −5.7%. This strategy is indicated as optimal for ET₀ estimation in central Tibet. Trend analysis based on this optimal strategy indicates significant increases in ET₀ in central Tibet from 2000 to 2020, with projections suggesting a continued rise through 2100 under climate change scenarios, though with increasing uncertainty over time. However, the rapidly increasing trends in precipitation will lead to decreasing trends in agricultural water use for highland parley production in central Tibet under climate change scenarios. The findings in this study provide critical information for irrigation planning to achieve sustainable agricultural production over the Tibetan Plateau.

Accurate short-term weather forecasting plays a vital role in disaster response, agriculture, and energy management, where timely and reliable predictions are essential for decision-making. Graph neural networks (GNNs), known for their ability to model complex spatial structures and relational data, have achieved remarkable success in meteorological forecasting by effectively capturing spatial dependencies among distributed weather stations. However, most existing GNN-based approaches rely on pairwise station connections, limiting their capacity to represent higher-order spatial interactions. Moreover, their dependence on supervised learning makes them vulnerable to spatial heterogeneity and temporal non-stationarity. This paper introduces a novel spatial–temporal pretraining framework, Hypergraph-enhanced Meteorological Pretraining (HyMePre), which combines hypergraph neural networks with self-supervised learning to model high-order spatial dependencies and improve generalization across diverse climate regimes. HyMePre employs a two-stage masking strategy, applying spatial and temporal masking separately, to learn disentangled representations from unlabeled meteorological time series. During forecasting, dynamic hypergraphs group stations based on meteorological similarity, explicitly capturing high-order dependencies. Extensive experiments on large-scale reanalysis datasets show that HyMePre outperforms conventional GNN models in predicting temperature, humidity, and wind speed. The integration of pretraining and hypergraph modeling enhances robustness to noisy data and improves generalization to unseen climate patterns, offering a scalable and effective solution for operational weather forecasting.