Search Results (192)

The Huaihe River Basin (HRB) experiences pronounced seasonal circulation and recurrent aerosol pollution, yet continuous observations of wind structure above the atmospheric boundary layer remain limited. We integrate rotary Rayleigh Doppler wind lidar (RRDWL) measurements and radiosonde profiles with ERA5 re-analysis, MERRA-2 aerosol optical depth (AOD), surface PM_2.5 observations, and selected HYSPLIT trajectories to characterize free-atmospheric wind variability and assess its statistical relationships with regional aerosol conditions. During periods of concurrent observation, RRDWL, radiosonde, and ERA5 profiles consistently capture the dominant features of the wind structure between 11 and 30 km, although agreement weakens near the tropopause. ERA5 data for 2017–2023 reveal pronounced seasonal variability, with stronger upper-tropospheric winds in spring, autumn, and winter than in summer. Enhanced boundary-layer ventilation generally coincides with lower surface PM_2.5 concentrations, but the strength and direction of this relationship vary among cities and time periods. The observed probability of elevated-PM_2.5 episodes is highest under westerly and northwesterly flow, while selected-event trajectories indicate air-mass pathways originating from, or passing through, western and northern sectors. Seasonal AOD distributions reveal substantial spatial and temporal variability in column-integrated aerosol loading. These results provide a vertically resolved, multi-dataset characterization of atmospheric wind variability over a monsoon-influenced basin and establish an observational framework for distinguishing statistical wind–aerosol relationships from mechanistic interpretations of aerosol transport. Full article

Accurate atmospheric temperature and humidity profiles are fundamental to weather monitoring and prediction. Geostationary imagers such as the Advanced Meteorological Imager (AMI) provide continuous observations and enable profile retrievals through radiative transfer–based algorithms; however, these products remain affected by systematic biases associated with the limited number of spectral channels and reliance on background fields from numerical weather prediction models. This study presents a data-driven post-processing framework to generate reanalysis-consistent profiles by refining AMI-retrieved temperature, mixing ratio, and relative humidity profiles using Light Gradient Boosting Machine (LGBM) models trained with ERA5 reanalysis data. Using four years (2020–2023) of hourly observations, the refined profiles were evaluated against both ERA5 and independent radiosonde measurements. Relative to ERA5, the refinement yields modest but consistent reductions in root mean square error (RMSE), including approximately 0.04 g kg⁻¹ (6–7%) for mixing ratio and 1.9 percentage points (≈14%) for relative humidity, while temperature shows a smaller error reduction of about 0.02 K (2–3%). When compared with radiosondes, temperature RMSE shows a marginal increase overall (<1%) with a larger increase in the lower troposphere, whereas improvements are observed for mixing ratio (2–3%) and relative humidity (6–7%). Seasonal and diurnal analyses reveal systematic error structures in the original AMI profiles, particularly wet-bias patterns in summer moisture fields, which are partially mitigated by the refinement. Feature-importance analysis using Shapley Additive Explanations (SHAP) identifies the dominant contribution of AMI water vapor channels, consistent with their known vertical sensitivity. Overall, this long-term evaluation demonstrates the feasibility of machine learning-based refinement for geostationary imager atmospheric profiles, while also highlighting inherent limitations related to the information content of current-generation imagers. Full article

The atmospheric weighted mean temperature ($T_m$) is critical for converting a zenith wet delay ($ZWD$) to precipitable water vapor ($PWV$). However, the existing $T_m$ models still have certain limitations: Those driven by surface-measured parameters achieve high accuracy but depend heavily on in situ instruments, incurring high costs and lacking forecasting capability. Empirical models avoid measured data but fail to capture short-term $T_m$ variations, leading to lower accuracy. Daily weather forecast data—which are low-cost, readily available, and reflective of short-term changes—offer a promising alternative. This study develops a gridded $T_m$ model named CGTV-Tm, which couples temperature and water vapor pressure, using ERA5 reanalysis data over China (2019–2023). The model can be driven by daily weather forecast data. A dual vertical correction method is also proposed to improve performance. Validation against 2024 ERA5 and radiosonde data shows that CGTV-Tm achieves RMSEs of 2.38 K (vs. ERA5) and 2.64 K (vs. radiosonde), significantly outperforming the Bevis (3.61 K, 3.67 K), PTm (3.19 K, 2.94 K), and CGT-Tm (2.71 K, 3.08 K) models. When driven by daily weather forecast data, CGTV-Tm achieves an RMSE of 2.90 K, improving accuracy by 29.6% and 21.2% over the state-of-the-art empirical models GPT3 and HGPT2, respectively. These results demonstrate that CGTV-Tm not only surpasses traditional linear $T_m$ models that rely solely on surface temperature but also, by using weather forecast data, it removes dependence on in situ instruments, offering a superior low-cost solution for real-time GNSS (Global Navigation Satellite System) $PWV$ retrieval. Full article

The mixing layer height (MLH) is a key meteorological parameter governing the dispersion and accumulation of air pollutants in the atmospheric boundary layer. Over the Zagreb urban area, low MLH values are associated with stable atmospheric conditions and temperature inversions that inhibit vertical air mixing and promote pollutant accumulation near the ground. This study investigates the influence of MLH on concentrations of particulate matter with an aerodynamic diameter smaller than 10 µm (PM₁₀) and particle-bound polycyclic aromatic hydrocarbons (PAHs) using a five-year dataset of radiosonde measurements and air quality observations. Daily MLH values at 12:00 UTC were compared with simultaneous measurements of PM₁₀ and PAH concentrations. Inversion conditions (MLH < 1000 m) occurred on 44% of days, with approximately 80% of events recorded during the cold season. Elevated PM₁₀ and PAH concentrations were strongly associated with these stable conditions. Of 69 daily exceedances of the PM₁₀ limit value (50 µg m⁻³), 62 occurred during inversion events. Statistical tests confirmed significantly higher concentrations during stable atmospheric conditions (p < 0.05), with further increases observed during prolonged inversion periods. Empirical relationships between MLH and pollutant concentrations were developed and may support future machine-learning-based air quality forecasting. The results highlight atmospheric stability as a critical factor for urban air quality management. Full article

Accurate estimation of the weighted mean temperature (Tm) is essential for retrieving precipitable water vapor (PWV) from ground-based Global Navigation Satellite System (GNSS) observations. Machine learning (ML) techniques excel in modeling nonlinear relationships among Tm time series, station geographic coordinates, and surface meteorological parameters, and recent studies have demonstrated that ML and neural network models outperform conventional linear Tm models. However, the full potential of surface meteorological measurements at GNSS stations for high-precision Tm retrieval remains to be fully explored. This study develops regional Tm empirical models using two ML methods—random forest (RF) and Temporal Mixture of Experts with Sequential Attention (TMESA)—to generate reliable real-time Tm estimates and enhance the accuracy of operational GNSS-PWV retrievals over China. A traditional linear model is adopted as the baseline to evaluate the performance improvements of the proposed models. The models are trained and tested using 10-year (2014–2023) hourly ERA5-derived Tm products and in-situ surface pressure, temperature, and relative humidity from 2377 meteorological stations, with Tm diurnal variations, station coordinates, and day of year integrated as auxiliary predictive features. Validation is conducted using 2024 ERA5 reanalysis data and radiosonde profiles from 120 stations across China. Results show that the RF model yields a bias (RMSE) of −0.11 K (2.67 K) against ERA5 and −0.21 K (2.67 K) against radiosonde data, while the TMESA model achieves superior performance with bias (RMSE) of −0.02 K (2.34 K) and 0.09 K (2.46 K), respectively, whose performance levels comparable to state-of-the-art studies. Compared with the traditional linear model, the RF model reduces Tm RMSE by 32% against ERA5 and 25% against radiosonde data, while the TMESA model achieves reductions of 40% and 33%, respectively. These findings confirm that the proposed ML models can provide high-accuracy Tm estimates for reliable GNSS-PWV retrieval. Future work will focus on the operational application of these models for near-real-time GNSS-PWV estimation. Full article

The performance of the meteorological measurements of an Unmanned Aircraft System (UAS) is studied in this paper by comparison with the simultaneous data collected by a wind mast, a radiosonde sensing package and ground-based, remote-sensing meteorological instruments at the radiosonde station of King’s Park, Hong Kong. They are found to meet the “breakthrough” level requirement of the World Meteorological Organization. The UAS is then used to collect meteorological data for the first time at a sandbox project location in Hong Kong for low altitude economy (LAE), namely, an area of complex terrain at an isolated island called Peng Chau. Some interesting features are identified in the vertical profiling flight of wind speed and turbulent kinetic energy (TKE), which forms the basis for developing meteorological support for LAE at this site in the future. High-resolution numerical weather prediction (NWP) simulation is then performed and evaluated statistically by comparison with UAS measurements at these two locations. The simulation of wind direction and TKE appears to be rather challenging as demonstrated in this comparison exercise. The root-mean-square-error of the simulated TKE is found to be of a similar order of magnitude as the absolute value itself, and the wind direction from the outer domain is found to have limited “correction” with the use of high-resolution terrain data in the NWP simulation with the mesoscale model. Further research directions for the simulation are discussed, with the objective of providing weather forecasting services for supporting LAE developments in Hong Kong. Full article

Accurate measurements of wind fields in the troposphere and stratosphere are essential for advancing atmospheric dynamics research, improving weather prediction, and supporting aerospace operations. However, a single Doppler lidar technique usually has limited capability to provide vertically extended wind profiles across both aerosol-rich lower altitudes and molecular-dominated higher altitudes. In this paper, we present a hybrid Doppler lidar system that combines a 355 nm scanning incoherent Rayleigh Doppler lidar with a 1550 nm coherent aerosol Doppler lidar for multi-scale wind field detection. The coherent Doppler lidar is used for boundary-layer wind retrievals, while the Rayleigh Doppler lidar, based on the double-edge technique, extends wind profiling from the upper boundary layer to approximately 40 km. Field deployments demonstrate continuous wind profiling from 50 m to 40 km, extending from the boundary layer to the stratosphere. Comparisons with radiosonde measurements show good agreement during the field campaigns, supporting the feasibility of this hybrid configuration for vertically extended wind profiling. The resulting high-resolution wind measurements across multiple atmospheric regions provide valuable data sources for studies of multi-scale circulation research, gravity wave dynamics, and climate-related atmospheric processes. Full article

Quantifying the distribution and spatiotemporal variation of water vapor and liquid water is of great significance for understanding the atmospheric thermodynamic processes during extreme meteorological events. The water vapor and liquid water data obtained from ground-based measurements by three MP-3000A microwave radiometers (MWRs) over the middle reaches of the Yangtze River Basin were analyzed. Firstly, a comparison between MWRs and radiosonde was conducted, and the co-located observation results indicated that MWRs used in this study feature high detection accuracy and favorable consistency. The integrated water vapor (IWV) measured by one of MWRs (Serial No. 3115) was with the best performance for IWV observation, and the bias and RMSE were 0.22 cm and 0.18 cm. In addition, the detection biases of integrated liquid water (ILW) between three MWRs in pre-precipitation were smaller than those in non-precipitation. All three instruments captured the diurnal variation characteristics of vapor density (VD) and liquid water content (LWC) profiles. The variation in ILW and IWV in different stations showed that ILW maintained low values before precipitation and increased sharply during the pre-precipitation stage, indicating strong indicative significance for rainfall occurrence. The ILW increment was more remarkable in Wuhan station, where mostly covered with urban and water body underlying surfaces. However, the magnitude of IWV variation before precipitation was smaller than that of ILW, especially in Jingzhou station. Under non-precipitation condition, VD and LWC vertical profiles at the three stations were relatively stable. Before precipitation, they exhibited substantial increases with obvious spatial discrepancies: sharp growth in Wuhan, moderate enhancement in Xianning, and slight increment in Jingzhou. Overall, atmospheric water vapor and liquid water increase significantly before precipitation, and their distribution spatiotemporal differences are closely related to local underlying surfaces and precipitation characteristics, which can provide meaningful references for short-term precipitation forecasting. Full article

Accurate forecasting of near-surface PM_2.5 concentrations remains challenging due to the complex coupling between atmospheric vertical structure, thermodynamic stability, and pollutant accumulation processes. Most existing surface-based statistical and deep learning approaches struggle to represent the three-dimensional state of the atmosphere, which limits their robustness under complex meteorological conditions. In this study, we propose a multi-source spatiotemporal learning framework(MST-Net) to enhance PM_2.5 forecasting accuracy by integrating vertically resolved atmospheric information from lidar and radiosonde observations. The proposed approach incorporates vertical profile features together with surface measurements to provide complementary information on atmospheric vertical structure and its temporal evolution. Experimental results demonstrate that MST-Net consistently outperforms conventional time-series models across multiple forecast horizons. Notably, at extended lead times (12–24 h), the proposed framework exhibits enhanced stability and slower error growth. For 24 h forecasts, MST-Net reduces RMSE by approximately 13% and MAE by about 19%. These results indicate that leveraging multi-source vertical atmospheric information can effectively improve the reliability of urban air quality forecasting. Full article

Upper troposphere (UT) humidity records are crucial for climate studies. To maximize temporal representativeness and enhance the lidar signal, pseudo-monthly averaging—limited to nighttime measurement—is applied, yielding water vapor mixing ratio (WVMR) profiles up to 16 km. This study evaluates 11 years (2013–2023) of WVMR profiles from a UV Raman lidar (Li1200) at Réunion Island, comparing them with MLS-Aura satellite retrievals, ERA5 reanalysis data, and GRUAN-processed M10 radiosondes. The results reveal a systematic dry shift in MLS of up to 30% above 12 km, particularly during the wet season. The lidar exhibits a slight downward shift in WVMR, approximately 5% lower than ERA5 throughout the UT, with the largest deviations occurring above 14 km and greater variability during the wet season. Calibration-related challenges during the dry season result in lidar WVMR profiles that are up to 10% drier than ERA5. Additionally, comparisons with GRUAN-processed radiosondes show a substantial dry shift relative to the lidar, exceeding 30% above 12 km. We investigate the effect of GNSS-based lidar calibration by applying an alternative calibration method, which produces higher WVMR values. This reveals a dry shift in ERA5 relative to the lidar, increasing with altitude in the UT up to 25%. These measurements contribute to the global effort to monitor and validate tropical and subtropical upper tropospheric humidity. Full article

Propagating easterly waves (EW) are analyzed here, within the dynamical environment of the Caribbean Low-Level Jet (CLLJ) using radiosondes from the Organization of Tropical East Pacific Convection (OTREC) field campaign, ERA5 reanalysis, and lightning from the World Wide Lightning Location Network (WWLLN) over $5^{\circ }$–${20}^{\circ }$ N, ${60}^{\circ }$–${100}^{\circ }$ W during 21 August–30 September 2019. Radiosondes resolve the vertical structure of the waves at San Andrés (Colombia), Limón and Santa Cruz–Guanacaste (Costa Rica), while ERA5 provides spatial–temporal continuity and vertically integrated diagnostics—namely, the vertically integrated moisture flux divergence (VIMFD) and the vertically integrated geopotential flux divergence (VIGFD). Lightning from WWLLN and precipitation from ERA5 and the Integrated Multi-satellite Retrievals for the Global Precipitation Measurement mission (GPM IMERG) offer independent convective proxies to track disturbances. Mean profiles from radiosondes and ERA5 show strong agreement at Limón and Guanacaste and some differences at San Andrés, yet all datasets capture coherent, phase-locked anomalies in zonal wind, meridional wind, temperature, humidity, vertical velocity and vorticity used to diagnose EW–CLLJ interactions. VIMFD, VIGFD, lightning and precipitation exhibit westward-propagating cores that align with the above anomalies, indicating that organized convection is coupled to the disturbances, whereas the mean state preconditions the environment to enable wave-induced upward motion. A robust vertical adjustment of the CLLJ is documented: the core shifts from near 925 hPa over the Caribbean Sea to about 700 hPa over the Eastern Tropical Pacific ($\Delta p\sim 150$ hPa). This feature is reproduced by a 30-year ERA5 climatology, consistent with jet-exit forcing and enhanced boundary-layer coupling over land. Conditions favorable for barotropic instability using the Rayleigh–Kuo criterion, were present over most of the period. A qualitative barotropic conversion proxy, computed from the eddy momentum covariance $⟨u^{\prime }{v}^{\prime }⟩$, shows positive values in the lower troposphere at Guanacaste and in the layer 850–700 hPa at San Andrés, suggesting mean-to-eddy momentum transfer, whereas the signal at Limón is weaker. Together, these results provide a physically consistent view of EW–CLLJ interactions across the IAS; therefore, a schematic of those mechanisms is proposed here. The results highlight the need for high-resolution modeling and full energy-budget analyses. Full article

This study presents an intercomparison of planetary boundary layer height (PBLH) estimates derived from three distinct approaches: the Morphological Image Processing Approach (MIPA) algorithm applied to ground-based lidar measurements, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) and Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) reanalysis model outputs, and radiosonde (RS) observations, this latter being taken as reference. The intercomparison was conducted during three measurement episodes, encompassing a total of 153 h (6 days), as part of the Boundary Layer Extensive Campaign with muLti-instrumentaL Analysis (BELLA), carried out in spring and early summer 2024 at the CNR-IMAA Atmospheric Observatory (CIAO) in southern Italy (40.60N, 15.72E). The study provides insights into the performance and reliability of these PBLH estimation approaches under diverse atmospheric scenarios. Visual and statistical analyses of selected case studies indicate that MIPA often tracked the aerosol layering structure and diurnal PBLH evolution more closely than ERA5 and MERRA-2, particularly during convective growth and evening transitions. On the other hand, it is found that ERA5 provides more accurate estimates of the nighttime PBLH, where MIPA shows poor nighttime estimation capabilities. Quantitative comparison against radiosonde data reveals that MIPA reaches a weighted root mean square error ($RMS{E}_w$) of $380\pm 41$ m with a coefficient of determination ($R^2$) of $0.68\pm 0.16$, while ERA5 shows an $RMS{E}_w$ of $292\pm 72$ m and an $R^2$ of $0.81\pm 0.11$; and MERRA-2 shows an $RMS{E}_w$ of $631\pm 124$ m and an $R^2$ of $0.34\pm 0.21$. By combining MIPA daytime and ERA5 nighttime PBLH, the overall results are improved, obtaining an $R^2=0.86\pm 0.08$ and an $RMS{E}_w$ of $213\pm 40$ m. This intercomparison highlights the strengths and limitations of each method and demonstrates the benefits of combining complementary PBLH retrieval techniques. The findings contribute to refining boundary layer monitoring methodologies and provide guidance for operational atmospheric observation networks. Full article

This study presents an operational approach to atmospheric wind profiling using a purpose-built meteorological uncrewed aerial vehicle (UAV) and an orientation-based wind estimation method that does not rely on dedicated onboard anemometers. The quadrotor platform, designed and developed by our team, has a maximum take-off mass of 2.45 kg and is capable of acquiring vertical atmospheric profiles up to 3000 m under a wide range of weather conditions. Within the framework of the World Meteorological Organization’s (WMO) global demonstration campaign for evaluating the use of uncrewed aircraft systems in operational meteorology and associated field activities, twelve vertical wind profiles were collected in parallel with radiosonde observations. UAV-based wind estimates were evaluated against radiosonde data using the WMO OSCAR (Observing Systems Capability Analysis and Review) performance framework. Across most wind speed regimes, the central 50% of UAV–radiosonde wind speed differences remain within OSCAR threshold requirements, indicating operationally relevant accuracy. Systematic deviations are physically interpretable and arise primarily in strongly sheared boundary-layer flows. A representative low-level jet case is used as a stress test, demonstrating that the UAV system remains safe and that wind estimates remain reliable even under extreme wind conditions, supporting robust performance in less demanding regimes. These results establish UAV-based wind profiling as a viable and complementary observing technique in the lower atmosphere and provide a practical pathway toward high-resolution, operational boundary-layer wind measurements. Full article

A key component necessary to improve the performance of climate and weather forecasting models is understanding the physical mechanisms controlling tropical deep convection. In this study, the thermodynamic variables linked to deep convection within this equatorial sea-breeze convective regime are analyzed. A range of data sets are employed: GNSS-based PWV and surface precipitation data, lightning and daily radiosonde observations, and GOES-13/16 and GPM satellite products. Our results indicate that the convective indices of CAPE and CIN, often used as predictors of deep convection, do not clearly distinguish deep-convective and non-convective days. In contrast, the variables representative of the atmospheric water vapor content, PWV and vertical water vapor distribution as well as an entrainment-based buoyancy measure, are better markers of potential deep convection. For this region, however, the water vapor/deep convection relationship with precipitation does not appear as strong as over tropical oceans and tropical continental regions. Finally, our results show that there is no strong link between daily average precipitation intensity and daily lightning count. However, deep-convective days without lightning had higher water vapor at the beginning of the day, as compared to days with lightning, which resulted in convective showers earlier in the day. Full article

Meteorological measurements from Unmanned Aircraft Systems (UASs) increase the volume of observations available for validating and improving high-spatiotemporal-resolution models. Accurate model forecasts for UAS operations are essential to the successful development of the low-altitude economy (LAE). In this study, two UAS test flights were analyzed to assess the consistency between UAS measurements and Regional Atmospheric Modeling System model outputs, thereby evaluating model forecast skill. UAS measurements were compared with ground-based anemometer and radiosonde observations to meet the World Meteorological Organization observational requirements at both the Threshold and Goal levels. Model-forecast turbulence exhibited strong agreement with atmospheric turbulence derived from high-frequency UAS wind data. The numerical weather prediction model at high spatial and temporal resolution is found to have sufficiently accurate forecasts to support UAS operation. A computational fluid dynamics model was also tested for high-resolution wind and turbulence forecasting; however, it did not yield improvements over the meteorological model. This work represents the first study of its kind for LAE applications in Hong Kong, and further statistical analyses are planned. Full article