Clouds cause key uncertainty in climate forecasts. Since previous assessments overlooked how warming changes clouds, we identified a cloud cover trend mode linked to global warming. Testing 13 climate models showed that while they capture historical warming, they miss this trend mode, underestimating its magnitude and misplacing the pattern. This reveals fundamental limits in current models. Resolving this will improve predictions of cloud behavior, making climate projections more reliable.

Sea fog disrupts shipping but remains hard to forecast due to a lack of measurements inside fog. We flew a drone with a novel cloud sensor over the Yellow Sea, obtaining the first vertical profiles of temperature, humidity, and fog droplets through a full fog life cycle. Our results show how turbulent mixing and a stable layer above the fog trap moisture, causing fog to thicken. These findings provide a solid foundation to improve fog forecasts and maritime safety.

We focused on CO₂ and CH₄ characteristics in urban atmospheric boundary layer (ABL). Results showed that CO₂ and CH₄ in the ABL were consistently higher than those in the free atmosphere. The inversion jump values of CO₂ and CH₄ over Beijing were both larger than those over Nanjing. Based on CH₄ : CO₂, we found that inventory might  have overlooked the energy transition occurring in cities, while also demonstrating that one-dimensional slab model is inadequate for urban greenhouse gas budgets.

This study shows how temperature inversions (warm air above cold) trap pollution in China. Using six years of national data, we find these "lids" frequently cause severe haze, especially in winter, by increasing pollution probability and intensity. Impacts differ by region and inversion type/strength. These findings help improve air quality forecasts and regional pollution control strategies.

This study employed the numerical model to simulate an intense blowing snow event near Zhongshan Station, East Antarctica, from 15–17 July 2022. While primarily driven by a mid-latitude cyclone, the event’s evolution was strongly modulated by complex local topography, which influenced airflow and enhanced snow transport. Terrain-induced uplift intensified snowfall and prolonged blizzard conditions, underscoring the importance of high-resolution modeling for Antarctic weather research.

Ground-based microwave radiometers (GMWRs) offer high temporal resolution observations with strong sensitivity to the lower atmosphere, making them valuable for data assimilation. However, their assimilation has traditionally focused on retrieved profiles. This study implemented the direct assimilation of brightness temperatures from GMWRs with a machine learning-based bias correction scheme. The results show improvements in the low-level atmospheric structure and precipitation predictions.

Integrated Multi-source Polar Mesoscale Cyclone Tracks (IMPMCT) is a dataset containing a 24-year record (2001–2024) of polar storms in the Nordic Seas. These storms, called Polar Mesoscale Cyclones (PMCs), sometimes cause extreme winds and waves, threatening marine operations. IMPMCT combines remote sensing measurements and reanalysis data to construct a comprehensive PMCs archive. It includes 1110 PMCs tracks, 16 001 cloud patterns, and 4472 wind records, providing fundamental data for advancing our understanding of their development mechanisms.

Because of the limitations of past data-based models and the high cost of NWP computing, accurate precipitation proximity forecasting remains challenging. A dual encoder-decoder framework is proposed to enhance short-term forecasting and reduce underestimation in extreme precipitation by using spatio-temporal information conversion equations and adaptive weighted gradient loss. Experiments on SEVIR datasets show greater accuracy than existing deep learning methods.

This study utilized the IPDA (integrated path differential absorption) lidar on board the DQ-1 satellite to monitor emissions from localized strong point sources and, for the first time, observed the diurnal variation in CO₂ emissions from a high-latitude power plant. Overall, power plant CO₂ emissions were largely consistent with local electricity consumption patterns, with most plants emitting less at night than during the day and with higher emissions in winter compared to spring and autumn.

The Three-Dimensional Precipitation Particle Imager (3D-PPI) was introduced as a new instrument to measure the three-dimensional shape, size, and falling velocity of precipitation particles. Field experiments of the 3D-PPI were conducted in Tulihe, China, during the winter of 2023 to 2024. More than 880 000 snowflakes in a typical snowfall case lasting 13 h were recorded. It shows potential applications in atmospheric science, polar research, and other fields.

Understanding lightning activity is important for meteorology and atmospheric chemistry. However, the occurrence of lightning activity in clouds is uncertain. In this study, we quantified the difference between isolated thunderstorms and non-thunderstorms. We showed that lightning activity was more likely to occur with more graupel volume and/or riming. A deeper Z_DR column was associated with lightning occurrence. This information can aid in a deeper understanding of lighting physics.

To improve the retrieval speed of the AERI optimal estimation (AERIoe) method, a fast-retrieval algorithm named Fast AERIoe is proposed on the basis of the findings that the change in Jacobians during the retrieval process had little effect on the performance of AERIoe. The results of the experiment show that the retrieved profiles from Fast AERIoe are comparable to those of AERIoe and that the retrieval speed is significantly improved, with the average retrieval time reduced by 59 %.

Influenced by the representativeness of ice crystal scattering models, the existing terahertz ice cloud remote sensing inversion algorithms still have significant uncertainties. We developed an ice cloud remote sensing retrieval algorithm of the ice water path and particle size from aircraft-based terahertz radiation measurements based on the Voronoi model. Validation revealed that the Voronoi model performs better than the sphere and hexagonal column models.

We apply the 3-D global chemical transport model (GEOS-Chem) to simulate co-polluted days by O₃ and PM_2.5 (O₃–PM_2.5PDs) in Beijing–Tianjin–Hebei in 2013–2020 and investigate the chemical and physical characteristics of O₃–PM_2.5PDs by composited analyses of such days that are captured by both the observations and the model. We report for the first time the unique features in vertical distributions of aerosols during O₃–PM_2.5PDs and the physical and chemical characteristics of O₃–PM_2.5PDs.

A neural-network-based approach was suggested to estimate CO₂ emissions using satellite-based net primary productivity (NPP) and XCO₂ retrievals. XCO₂ anomalies were calculated for each year using OCO-2 retrievals. A Generalized Regression Neural Network (GRNN) model was then built; NPP, XCO₂ anomalies, and ODIAC CO₂ emissions from 2015 to 2018 were used as a training dataset; and, finally, CO₂ emissions were predicted for 2019 based on the NPP and XCO₂ anomalies calculated for the same year.

In this work, an airborne experiment was carried out to validate a newly developed CO₂ monitoring IPDA lidar against the in situ measurements obtained from a commercial CO₂ monitoring instrument installed on an aircraft. The XCO₂ values calculated with the IPDA lidar measurements were compared with the dry-air CO₂ mole fraction measurements obtained from the in situ instruments, and the results showed a good agreement between the two datasets.