China Meteorological Administration

Abstract Tropical cyclone (TC) intensity is controlled by both environmental factors and its internal structure. This paper investigates the influence of inner core features derived from satellite data on TC intensity changes and explores effective methods to improve TC intensity forecast accuracy. We construct models for forecasting TC intensity over the next 5 days using the light gradient boosting machine (LGBM) algorithm in the northwestern Pacific. The models integrate climatological and persistence predictors, large-scale environmental variables at the initial time, inner core features extracted from satellite data, and variables related to the forecasted TC location. The models are trained on TC data from 2005 to 2019, tested using best track data from 2020 to 2022 and real-time data in 2023. Results show that the inner core features derived from satellite data are helpful for intensity forecasts within 36 h, and variables related to the forecasted location are helpful for improving TC intensity forecast accuracy in most cases, especially for the forecasts beyond 48 h. Compared with the National Centers for Environmental Prediction (NCEP) and European Centre for Medium-Range Weather Forecasts (ECMWF) models, our models demonstrate 4%–48% and 19%–70% decreases in mean absolute errors (MAEs) in real-time intensity forecasts. The most important predictors identified include potential future intensity change, intensity changes during the previous 12 h, vertical wind shear, and sea–land ratio. The inner core feature extracted from satellite data is also among the top 10 important variables for short-time intensity forecasts. Significance Statement This study advances tropical cyclone (TC) intensity forecasting by integrating climatological and persistence predictors, large-scale environmental variables at the initial time, satellite-derived inner core features, and variables related to the forecasted TC location into the light gradient boosting machine (LGBM) models. Our models significantly improve forecast accuracy, reducing mean absolute errors by 4%–48% compared with the National Centers for Environmental Prediction (NCEP) and 19%–70% compared with the European Centre for Medium-Range Weather Forecasts (ECMWF). Results denote that the inner core features improve the intensity prediction in the short time, and variables related to the forecasted TC location improve the intensity forecasts in most cases, especially for the forecasts beyond 48 h. This study provides valuable insights for enhancing operational TC intensity forecasting.

Abstract Prolonged compound hot–dry events (CHDEs) have been linked to surging emergency medical demand, especially among vulnerable populations, making them a growing public health concern in a warming climate. However, the seasonal and regional drivers of high health-risk CHDEs (HHR–CHDEs) remain unclear, limiting effective public health responses. Here, we integrate a temperature–humidity-based health-risk index, ambulance dispatch records, and distributed lag nonlinear models to obtain high health-risk temperature–humidity thresholds. During HHR–CHDEs exposure, particularly higher emergency dispatch demands are observed among males, the elderly, and individuals suffering from trauma or alcohol poisoning. We further use ERA5 reanalysis data to examine the dominant modes and physical mechanisms of HHR–CHDEs across China during the early and late warm seasons. In the early warm season, HHR–CHDEs are concentrated in northwest China and are jointly driven by enhanced surface heating (51% contribution) and intensified moisture loss (37%), underpinned by the synergistic effect of an upstream Rossby wave source over eastern Europe and anomalous northwest Pacific sea surface temperature (SST) warming. This coupling promotes a quasi-stationary high pressure anomaly that blocks synoptic disturbances and reinforces regional heat–dryness feedbacks. By contrast, in the late warm season, HHR–CHDEs are centered over the middle–lower Yangtze River region and are dominated by persistent heat accumulation (65%) with a secondary drought contribution (25%). These events are driven by large-scale circulation anomalies resulting from upstream Rossby wave energy originating in the North Atlantic and enhanced by downstream SST warming over the subtropical northwest Pacific. This Atlantic–Pacific coupling induces pronounced adjustments in the Walker and Hadley circulations, promoting subsidence and suppressed monsoonal moisture transport, and sustaining a health-threatening hot–dry atmosphere over densely populated regions. These findings reveal seasonally distinct remote forcing pathways and highlight the value of integrating large-scale diagnostics into climate–health early warning systems. Significance Statement High health-risk compound hot–dry events (HHR–CHDEs) are a growing climate–health threat, yet their physical drivers remain poorly understood. This study identifies seasonally distinct large-scale atmospheric and oceanic precursors of HHR–CHDEs across China, revealing key Rossby wave pathways and land–atmosphere feedbacks that amplify these events. By integrating climate diagnostics with health-relevant thresholds, our findings bridge the gap between meteorological extremes and public health outcomes, offering a foundation for targeted early warning systems under climate change.

This study analyzes ground-based observations and multi-source remote sensing data from eight dust storm events in 2024 at two sites: the Tazhong (TZ) station in the Taklamakan Desert interior and the Xiaotang (XT) station on its northern margin, systematically investigates the interrelationships among dust particle size, friction velocity (U*), and dust flux, and evaluates the applicability of remote sensing data in dust monitoring. The results indicate that particle size significantly influences both horizontal fluxes (Q) and vertical dust fluxes (F). Fine particles (d[0.5]) enhance surface dust flux, while coarse particles (D[4,3]-due to their greater gravitational settling-are less capable of sustained suspension, limiting their long-distance transport. A positive correlation exists between friction velocity (U*) and Q, whereas its impact on F is weaker, suggesting that vertical transport is regulated primarily by particle size, gravitational settling, and turbulent structures. Regarding remote sensing data, MODIS Aerosol Optical Depth (AOD) shows strong consistency with ground-based dust flux measurements, especially at the Xiaotang (XT) station, where AOD closely follows the variation trends of both Q and F. This reflects the effectiveness of remote sensing data in capturing changes in dust activity. Additionally, the Aerosol Absorbing Index (AAI) from Sentinel-5P exhibits a highly significant positive correlation with ground-level dust concentrations, effectively reflecting the vertical structure of dust events. This research provides valuable data support and theoretical foundation for dust warning systems and desertification control projects.

Abstract Atmospheric chemistry encapsulates the emission of various pollutants, the complex chemistry reactions, and the meteorology dominant transport, which form a dynamic system that governs air quality. While deep learning (DL) models have shown promise in capturing intricate patterns for forecasting individual atmospheric components—such as and ozone—the critical interactions among multiple pollutants and the combined influence of emissions and meteorology are often overlook. This study introduces a DL‐based framework–Zeeman for atmospheric chemistry forecasting. Our model effectively captures the nuanced relationships among these constituents and while achieving a 68.5‐fold increase in computational speed compared to traditional numerical model. Evaluations demonstrate that our approach rivals numerical model, offering an efficient solution for atmospheric chemistry forecasting. In the future, this model could be further integrated with data assimilation techniques to facilitate efficient and accurate atmospheric emission estimation and concentration forecast.

Abstract. The Lightning Mapping Imager (LMI) aboard the Fengyun-4A (FY-4A) satellite, once one of the only two geostationary lightning detection payloads operating in space, has accumulated a substantial volume of observational data. Extensive efforts have been made to correct lightning geolocation deviations, including payload misalignment correction, cloud-top height parallax correction, and thermal deformation correction. These measures have substantially improved the geolocation accuracy of LMI observations. However, individual correction schemes are not necessarily applicable across the entire LMI field of view; furthermore, a comprehensive, unified correction dataset has yet to be established, which has limited the wider utilization of LMI data. To address the remaining systematic geolocation deviations in current LMI lightning products, we propose a new correction method based on reference data from the World Wide Lightning Location Network (WWLLN). Using ground-based lightning observations as a benchmark, the LMI field of view is subdivided into 400 subregions arranged in a 20 × 20 grid. Within each subregion, sensitivity experiments are conducted to match spaceborne LMI detections with ground-based lightning events, thereby quantifying the systematic deviation for each subregion. A weighted curve-fitting approach is then applied to the coordinate deviations derived from the matched events to obtain a correction curve for each subregion. These subregional correction curves are subsequently mapped back to the image coordinates, enabling an analysis of the spatiotemporal variability of lightning geolocation deviations across the full LMI coverage. Finally, the fitted curve values are applied as correction terms to the original data, resulting in the construction of a new, refined correction dataset. Building upon the existing Level-2 lightning products, this method significantly enhances the geolocation accuracy of LMI observations. The coordinate deviations between LMI detections and ground-based lightning network observations exhibit pronounced convergence in both the zonal and meridional components, indicating a substantial improvement in geolocation performance. In addition, a domain-wide assessment of geolocation accuracy reveals that, except for regions such as Xinjiang and Mongolia where lightning occurrence is too sparse to support robust curve fitting, the geolocation accuracy across most of the LMI field of view is relatively stable, with an average error of approximately 15 km (about 1.5 pixels), achieving high practical accuracy. The corrected dataset is publicly available at https://doi.org/10.11888/Atmos.tpdc.303312 (Zhang et al., 2026).

The transportation sector powered by fossil fuels is a significant contributor to PM2.5 pollution. Systematic evaluation of traffic-related PM2.5 on acute coronary syndrome (ACS) onset is not yet characterized, and the health benefits of reduced traffic-related PM2.5 and evidence-based mitigation strategies are currently absent. We investigated the association of ACS onset with short-term traffic-related PM2.5 exposure in a nationwide time-stratified case-crossover study, using 627,828 ACS cases extracted from the China Cardiovascular Association. We evaluated the risks of ACS onset associated with traffic-related PM2.5, calculated the attributable onsets, and then conducted cluster analysis using the K-means algorithm to identify priority cities for emission reduction. We found a robust association between traffic-related PM2.5 and ACS onset, i.e., an increased risk of 2.39% (95% CI: 1.79–3.00%) for same-day (lag 0) PM2.5 per 10 μg/m3 increase. The greatest health benefits due to a 1 μg/m3 PM2.5 reduction were achieved by traffic-related PM2.5, reaching 10,267 (95%CI: 4503–16,063) onsets per year. In addition, three obvious categories were clustered, and traffic-polluted cities were highlighted (cluster 2, not belonging to megacities), with high emissions and concentrations of traffic-related PM2.5. Therefore, intensified efforts to mitigate traffic-related PM2.5 emissions should be promoted, and the prioritization of emission reduction strategies in these traffic-polluted cities is imperative for safeguarding public health.

Accurate cloud base height (CBH) over the Tibetan Plateau—Earth's Third Pole—is essential for constraining Asian monsoon dynamics, glacial melt projections, and water security, affecting 1.9 billion people downstream. However, ERA5 reanalysis systematically underestimates CBH by up to 5.20 km in southern regions, propagating errors into climate models and hydrological forecasts. Here, we present a two-step machine learning framework that progressively eliminates this hidden bias. Step 1 refines the ERA5 retrieval algorithm using three years of ground-based lidar observations (October 2021–December 2024), reducing the site-level mean bias error from 1.8 km to 0.1 km and improving the regional correlation with CALIPSO from 0.25 to 0.40. Step 2 applies an Optuna-optimized XGBoost model trained on high-confidence CALIPSO observations (N=106,718), fusing the refined ERA5 data with vertical atmospheric profiles and surface attributes. The final product achieved a test-set RMSE of 1.87 km (R²=0.71, MBE=−0.02 km), with seasonal correlations reaching 0.72–0.86 and southern plateau bias reduced from −5.20 km to −0.11 km, a 97.9% improvement. This scalable approach enables reliable, long-term CBH reconstruction, which is critical for advancing climate model parameterizations and water resource assessments across High Mountain Asia.