Estimation of anthropogenic carbon dioxide (CO₂) emission sources and natural sinks (i.e., CO₂ fluxes) is essential for the development of climate policies. Satellite observations provide an opportunity for top-down inversion of CO₂ fluxes, which can be used to improve the results of bottom-up estimation. This study proposes to develop a new top-down CO₂ flux estimation method based on deep learning, as well as satellite observations, and an atmospheric chemical transport model. This method utilizes two deep learning models: the concentration correction model and the concentration–flux inversion model. The former optimizes the GEOS-Chem-simulated CO₂ concentration using Orbiting Carbon Observatory-2 (OCO-2) satellite observations, while the latter establishes the complicated relationship between CO₂ concentration and CO₂ flux. Results showed that both deep learning models demonstrated excellent prediction performance, with a mean bias of 0.461 ppm for the concentration correction model and an annual mean correlation coefficient of 0.920 for the concentration–flux inversion model. A posterior CO₂ flux was obtained through a two-step optimization process using these well-trained models. Our findings indicate that the posterior estimations of CO₂ flux sources in eastern China and northern Europe have been significantly reduced compared to the prior estimations. This study provides a new perspective on top-down CO₂ flux inversion using satellite observation. With advancements in deep learning algorithms and increased satellite observations, this method may become an effective approach for CO₂ flux inversion in the future.

Deep-learning-based convection schemes have garnered significant attention for their notable improvements in simulating precipitation distribution and tropical convection in Earth system models. However, these schemes struggle to capture the stochastic nature of moist physics, which can degrade the simulation of large-scale circulations, climate means, and variability. To address this issue, a stochastic parameterization scheme called DIFF-MP, based on a probabilistic diffusion model, is developed. Cloud-resolving data are coarse-grained into resolved-scale variables and subgrid contributions, which serve as conditional inputs and outputs for DIFF-MP. The performance of DIFF-MP is compared with that of generative adversarial networks and variational autoencoders. The results demonstrate that DIFF-MP consistently outperforms these models in terms of prediction error, coverage ratio, and spread–skill correlation. Furthermore, the standard deviation, skewness, and kurtosis of the subgrid contributions generated by DIFF-MP more closely match the test data than those produced by the other models. Interpretability experiments confirm that DIFF-MP’s parameterization of moist physics is physically consistent.

Orographic cirrus clouds frequently occur over mountainous regions and can influence the radiative balance of the upper troposphere, yet their characteristics and regional variability remain insufficiently understood on a global scale. In this study, we investigate the occurrence, vertical structure, and microphysical and optical properties of orographic cirrus over four major mountainous regions, namely the Rocky Mountains, the Andes, the Alps, and the Himalayas, using the Identification and Classification of Cirrus (IC-CIR) framework together with satellite observations from MODIS, CloudSat, and CALIPSO. The results reveal clear regional differences in both occurrence and structure. Cloud cover is higher over the Himalayas and the Alps and lower over the Andes, while seasonal variability is strongest over the Himalayas and the Alps and weakest over the Andes. In terms of vertical structure, cirrus over the Andes reaches higher cloud tops and exhibits a bimodal distribution. The Andes also show smaller values of ice water path, optical depth, and cirrus reflectance. These results provide a unified comparison of orographic cirrus clouds across four representative major mountainous regions and highlight substantial regional differences in their characteristics and potential radiative influence under different topographic and dynamical environments.

Deep-learning-based convection schemes receive wide attention due to its impressive im-provement on precipitation distribution and tropical convections of earth system simulation. But they cannot represent the stochasticity of moist physics, which will degrade the simulation of large-scale circulations, climate mean, and variability. To solve this problem, a stochastic pa-rameterization scheme based on probabilistic diffusion model named DIFF-MP is developed. The cloud-resolving data from GRIST model is coarse-grained into resolved-scale variables and sub-grid contributions due to moist physics to form the training data. DIFF-MP’s performance is compared against generative adversarial network and variational autoencoder. Results show that DIFF-MP is consistently better than the other two models on prediction error, coverage ratio, and spread-skill correlation. The standard deviation, skewness, and kurtosis of subgrid contributions generated by DIFF-MP is also closer to the testing data than the others. Interpretability experiment shows that DIFF-MP’s parameterization of moist physics is physically reasonable.

Low-level jets (LLJs) play an important role in the transport of heat, water vapor, and atmospheric pollutants. Based on one year (1 September 2023 to 31 August 2024) of tropospheric wind profiler radar (RWP) observations at the Wenjiang Meteorological Observation Base in Chengdu, this study systematically investigates the wind field structure in the middle and lower troposphere over the Chengdu region and the vertical distribution and evolution characteristics of LLJs. The effective detection height of the RWP reaches at least 7.4 km throughout the year, demonstrating good consistency with concurrent radiosonde data. Horizontal wind speed accelerates markedly above 3 km, with the strongest vertical gradient observed in winter. In the lower layer, the prevailing wind direction is primarily controlled by mountain-valley breezes; with increasing altitude, the westerly belt gradually becomes the dominant wind system. Within the atmospheric boundary layer (below 1 km), the wind field exhibits a distinct diurnal cycle: easterly winds dominate in the afternoon, shifting to northerly winds at night. Surface wind speed peaks in the afternoon, whereas upper-level wind speed peaks at night. The occurrence frequency of LLJs is highest in July (26.3% for LLJ-1), followed by April (17.8%). The prevailing wind directions of LLJs are north-northeasterly and northeasterly, and jet core heights are mainly distributed between 0.7 and 1.9 km. For weaker LLJs (LLJ-1 and LLJ-2), both frequency and intensity are higher at night than during the day, peaking at 22:00. These findings deepen our understanding of boundary layer dynamics over complex basin terrain and provide a high-resolution observational benchmark for model improvements and weather warnings.

Low‑level jets (LLJs) play an important role in the transport of heat, water vapor, and pollutants. Based on one year of tropospheric wind profiler radar (RWP) observations, this study systematically analyzes the wind field structure in the middle and lower troposphere over the Chengdu region, as well as the vertical distribution and evolution characteristics of LLJs. The results show that the effective detection height of the wind profiler radar reaches 7.4 km throughout the year, demonstrating good consistency with radiosonde data. Horizontal wind speed accelerates significantly above 3 km, with the highest vertical gradient of wind speed occurring in winter. The prevailing wind direction in the lower layer is mainly influenced by mountain‑valley breezes; with increasing altitude, the westerly belt gradually becomes the dominant wind system. Within the atmospheric boundary layer (below 1 km), the wind field exhibits a distinct diurnal variation: easterly winds dominate in the afternoon, shifting to northerly winds at night. The peak surface wind speed occurs in the afternoon, while the peak upper‑level wind speed occurs at night. The occurrence frequency of LLJs is highest in July, followed by April. The prevailing wind directions of LLJs are north‑northeasterly and northeasterly, and the jet core heights are mainly distributed between 0.7 and 1.9 km. Both the occurrence frequency and intensity of LLJs are higher at night than during the day. These findings deepen our understanding of the boundary layer structure over complex basin terrain.