Noncollocation can obscure small constellation-dependent differences in Global Navigation Satellite System (GNSS) precipitable water vapor (PWV). We assessed five constellation configurations processed with a common precise point positioning strategy at the same four physical station groups surrounding the Beijing radiosonde site during June–September 2023–2025. In the original year-specific matched samples, BeiDou Navigation Satellite System (BDS)-only Root Mean Square Error (RMSE) was 1.3%, 5.4%, and 7.9% lower than GPS-only RMSE in 2023, 2024, and 2025, respectively. Because these percentages were descriptive, we added a direct paired analysis on strict-common samples (n = 225, 231, and 216). Paired BDS-minus-GPS RMSE differences were −0.19 mm (95% CI: −0.41 to −0.03), −0.39 mm (−0.61 to −0.16), and −0.44 mm (−0.65 to −0.23), respectively. Fixed-geometry nearest-station, alternative inverse-distance weighting, and elevation-sensitive tests showed that the small constellation difference was not invariant to spatial transfer. Residuals became progressively more negative with increasing radiosonde PWV, and high-moisture (>50 mm) biases were −10.75 mm for BDS and −11.36 mm for GPS. Full-season leave-one-year-out PWV percentile achieved Area Under Curve (AUC) = 0.846, but its transferred threshold yielded False Alarm Ratio (FAR) = 92.2% and Critical Success Index (CSI) = 7.6%. The common five-configuration processing provides a controlled comparison; the rainfall result supports retrospective regional moisture monitoring, not stand-alone operational prediction.
To characterize summertime ozone (O3) pollution at the Dianshan Lake suburban site, hourly surface observations and 15 min ozone-lidar profiles were collected during June–August 2022. Monthly mean O3 increased progressively from June through August (89.0 ± 47.4 to 102.1 ± 59.3 μg/m3), while VOCs averaged 33.9 ± 22.4 μg/m3, dominated by alkanes (53.1%), followed by aromatics (30.3%), alkenes (14.4%) and alkynes (2.3%). A representative high-O3 episode revealed NO + HO2 as the dominant production pathway (60.0%), whereas O3 loss was dominated by the NO2 + OH pathway forming HNO3 (75.9%). Sensitivity analysis supported a VOC-limited response during the modeled 9–14 August episode; the transition in the tested joint-reduction scenarios occurred near a VOCs/NOx ratio of 0.80 but was model-sensitive. Integrating concentrations, ozone formation potential (OFP), and OH Loss Rates (LOH) prioritized isoprene, propylene, ethylene, m/p-xylene, toluene, and i-pentane for control. The reproduced five-factor PMF solution attributed 25.7% to gasoline vehicle exhaust, 24.6% to LPG/NG use, 21.2% to biogenic emissions, 15.1% to solvent use, and 13.4% to diesel vehicle exhaust.
The article analyzes the spatiotemporal structure of thunderstorm activity and local electric-field changes in the Northern Tien Shan region based on data from the global WWLLN network and local measurements of the atmospheric electric field obtained by the ELIS-TS complex during 2013–2025. The main thunderstorm season from April to October is considered. During this period, the ELIS-TS complex recorded 3603 events, of which 74.6% were accompanied by negative electric-field changes and 25.4% by positive electric-field changes. Peak activity occurs from June to July, which coincides with the period of maximum convection in this region. A comparison of local measurements with WWLLN data showed that the largest number of recorded electric-field disturbances corresponded to WWLLN-detected events located within the first tens of kilometers from the station. Spatial analysis revealed an increased density of lightning discharges in mountainous and foothill areas, as well as a seasonal shift in thunderstorm activity due to the orographic influence of the terrain. Dividing the study area into lowland and mountainous zones revealed differences in the diurnal activity pattern: the peak occurs earlier in the mountains, indicating earlier convective development over high-altitude terrain.
The sporadic-E (Es) layer is a recurrent feature of the mid-latitude E region, yet the eastern Tibetan Plateau remains poorly sampled by routine ionosonde observations. We analyze 28,771 automatically scaled top-of-hour Digisonde soundings recorded at Ganzi (31.17° N, 100.44° E) from September 2015 to August 2019 during the declining phase of solar cycle 24. We examined monthly and local solar time (LST) variations in Es occurrence, foEs, and hpEs and evaluated the PyIRI foEs specification. Es occurs in 53.1% of interpretable soundings, with a summer-daytime maximum and a late-autumn minimum (about 77% in June and 37% in November). The layers are comparatively weak, with a median foEs of 3.35 MHz and only 2.1% of soundings exceeding 7 MHz. Adjusted daytime Es occurrence and mean foEs differ among the F10.7 quintiles, but neither varies monotonically across the groups. The global Kp tests are not significant for either outcome, while the Dst groups differ only in adjusted mean foEs. For Es-present soundings, PyIRI gives a small overall bias of −0.17 MHz, but the month–LST decomposition reveals summer-daytime overestimation of 1–1.5 MHz and a weaker winter underestimation. Leave-one-analysis-year-out validation reduces the pooled RMSE from 1.44 to 1.39 MHz, with improvement in three of the four held-out years.
Air quality modeling of sulfur dioxide (SO2) concentrations remains challenging due to the high variability of both natural and anthropogenic emission sources, as well as the complexities associated with its multiphase chemistry. The data assimilation (DA) of satellite observations is a promising technique for constraining model uncertainties by combining the strengths of high-resolution and dense satellite retrievals with physical consistent model outputs. However, existing SO2 DA applications have primarily focused on volcanic events while this study addresses them together with other emissions. The implementation of an SO2 DA framework within the MINNI regional chemical transport model using an Ensemble Adjusted Kalman Filter (EAKF) via the DART framework is presented. The performances of the DA assimilation framework were tested using Sentinel-5P/TROPOMI SO2-COBRA total column retrievals over continental Europe for August 2023. The filter constrained the ensemble variance to capture plumes from power plants and volcanic activity. The ensemble considered 20 members and perturbations of emissions and boundary conditions. On a monthly basis, the mean correction for the total column averaged over the domain was 2 × 10−5 mol m−2, with localized maximum adjustments reaching 3.3 × 10−4 mol m−2. At the surface level, domain-averaged corrections of concentrations reached up to 2.6 µg m−3. Despite current limitations related to ensemble size, static vertical localization, and the typical temporal fading of initial condition corrections, validation against in situ data confirmed the system’s ability to transfer column information to near-surface levels. These results demonstrate the feasibility and added value of integrating mixed-source SO2 satellite retrievals into regional air quality simulations, contributing to more accurate, observation-driven atmospheric monitoring.
This study aims to characterize the seasonal variability and optical properties of aerosols and to investigate their potential sources, transport pathways, and radiative impacts over a rural site in Southeast India. Ground–based MICROTOPS–II Sunphotometer observations during April 2021–December 2023 were integrated with trajectory–based source analysis and OPAC–SBDART radiative–transfer simulations to examine the links between aerosol characteristics, meteorological conditions, source regions, and radiative effects. The annual mean aerosol optical depth at 500 nm (AOD500) was found to be 0.56 ± 0.22, peaking during pre–monsoon (0.66 ± 0.19) and winter (0.64 ± 0.23), and lowering during the rainy monsoon (0.49 ± 0.21). Enhanced aerosol loading during the dry seasons was associated with local emissions and long–range continental transport, whereas monsoon conditions favored marine influence, atmospheric ventilation and wet scavenging, as supported by trajectory analyses using potential source contribution function (PSCF) and concentration weighted trajectory (CWT) models. Higher Ångström exponent (AE) values during winter and pre–monsoon (1.30 ± 0.24) indicated dominance of fine–mode continental aerosols, while the lower monsoon values (0.83 ± 0.37) reflected increased contribution of coarse particles. Negative values of spectral curvature further confirmed fine–mode dominance during dry seasons. The estimated precipitable water vapor increased markedly from winter (2.00 ± 0.37 cm) to monsoon (4.35 ± 0.35 cm), likely influencing aerosol optical properties through hygroscopic growth. Meteorological parameters significantly modulated aerosol loading and size distribution across seasons. AOD–AE relationships revealed predominance of fine anthropogenic aerosols in all seasons except monsoon, while aerosol classification indicated substantial fine–mode contributions under turbid atmospheric conditions. OPAC–SBDART simulations estimated significant aerosol–induced surface cooling (−41 to −42 W m−2) and atmospheric warming (38–41 W m−2) under high aerosol loading conditions, leading to atmospheric heating rates of 1.1–1.2 K day−1. However, lower aerosol loading in monsoon reduced heating rates to 0.3–0.4 K day−1. Current findings highlight the critical role of monsoon flow and meteorological dynamics in regulating aerosol characteristics and regional radiative forcing over Southeast India.
Episodic air pollution events (EAPEs) remain significantly less investigated than pollution generated by continuous emission sources, despite their potential to produce intense short-term concentration peaks and acute exposure conditions. Among these events, fireworks displays represent one of the most intense and short-lived anthropogenic sources of atmospheric pollution in urban environments. The research presents and discusses the use of reference air quality monitoring approaches to characterize transient pollution episodes, highlighting the limitations of conventional monitoring systems and regulatory frameworks, which rely on hourly or daily averaged concentrations and may therefore underestimate the real temporal evolution, peak intensity, and short-term exposure associated with EAPEs. Experimental field investigations were conducted over two consecutive years during New Year’s Eve celebrations, using an integrated monitoring approach combining certified reference instrumentation and calibrated low-cost sensors. PM10, NO2, CO, and O3 concentrations were monitored before, during, and after the events, enabling comparisons between daily, hourly, and minute-resolution measurements. Results demonstrated that conventional monitoring successfully identifies the occurrence of the pollution episode but fails to adequately characterize its peak dynamics. Minute-resolution PM10 measurements revealed concentrations up to 215 µg m−3, approximately three times higher than the corresponding hourly values (Peak Amplification Factor, PAF = 2.90–2.93), while transient variations in gaseous pollutants were consistently identified. The findings provide experimental evidence supporting the integration of high-temporal-resolution measurements with conventional monitoring networks to improve the characterization of episodic air pollution events, thereby supporting environmental authorities in enhancing monitoring strategies, exposure assessment, air quality management, and the protection of public health.
Prescribed fire is an effective tool for reducing wildfire risk but emits pollutants such as carbon monoxide (CO) and fine particulate matter (PM2.5) that negatively impact both indoor and outdoor air quality. While emissions from prescribed fires have been widely characterized, there remains limited understanding of how emissions vary across combustion conditions, fuel types, and regions. More specifically, there has been little work that has looked at both emissions of CO and PM2.5 as well as other compounds such as elemental carbon (EC), organic carbon (OC), and speciated organic compounds. This work quantifies the variability of emission factors (EFs) for CO, PM2.5, EC, OC, and speciated organic compounds across 19 prescribed fires in Colorado and southeastern Georgia using low-cost sensors. This allowed us to have highly temporally resolved EF datasets across multiple monitors and multiple burns. Results show that CO EFs are strongly driven by combustion phases, with higher emissions associated with lower modified combustion efficiency, or smoldering combustion (+3260%). However, PM2.5 EFs exhibit weaker and more variable relationships with combustion phase and are more strongly influenced by fuel type (+108% for Colorado-based fuels) and burn characteristics (+8.3%). Broadcast burns generally exhibited higher CO and PM2.5 EFs than pile burns, while daytime burning conditions were associated with lower emissions for both CO and PM2.5. The results provide improved emission factor estimates across multiple conditions and can inform both prescribed fire management practices and the development of more representative emissions inventories.
This article proposes an enhanced quality control (QC) method based on Spatiotemporal Graph Convolutional Networks (STGCN) to identify potential outliers in surface temperature observations. The STGCN model employs a graph structure to simultaneously capture temporal and spatial dependencies, with the adjacency matrix constructed using spatial distances and topography-assisted elevation priors. Compared to baseline methods, experimental results indicate that STGCN achieves superior overall performance across evaluation metrics, effectively balancing Type I and Type II errors. The findings demonstrate that the proposed framework is an effective QC method for detecting observational anomalies in surface temperature datasets.
Odor management requires consideration of both compound concentrations and olfactory impacts determined by odor threshold concentrations. This study evaluated missing-data structures, interpolation performance, odor activity value (OAV)-based odor contributions, and seasonal compositional changes using 20 min monitoring data for 22 designated odor compounds collected from a livestock farm, a wastewater treatment facility, and an anonymized organic waste treatment facility in Eumseong, Republic of Korea (Site C), from 1 August 2022 to 30 June 2023. Because the dataset contained multi-week to multi-month block missing periods, compound-specific interpolation performance was assessed using block holdout validation with BiLSTM, BiGRU, TCN, Transformer, and HistGradientBoostingRegressor models. OAV, summed odor activity value (SOAV), and odor contribution (OC) were calculated primarily from observed concentrations, while fully interpolated series were additionally used for sensitivity comparison to evaluate interpolation-induced bias. Ammonia was excluded from OAV analysis owing to its low valid observation rate. Among the analyzable compounds, odor contribution was dominated by volatile fatty acids and trimethylamine rather than hydrogen sulfide. The livestock farm and wastewater treatment facility showed n-valeric-acid-dominated profiles, whereas Site C showed a mixed profile involving valeric acids and trimethylamine. Seasonal analysis indicated relatively consistent fatty-acid-dominated compositions at the livestock farm and wastewater treatment facility, while Site C showed a possible shift from fatty-acid dominance in warm seasons to trimethylamine dominance in cold seasons. These results provide an OAV-based framework for identifying odor management priorities in long-term continuous odor datasets with severe missingness.
Atmospheric effects substantially influence remote-sensing reflectance retrieval in optically complex inland waters. This study evaluated seven atmospheric correction approaches (QUAC, FLAASH, Sen2Cor, LaSRC, 6S, C2RCC, and ACOLITE) for Sentinel-2 MSI and Landsat-8/9 OLI imagery over the Danjiangkou and Luhun reservoirs. The evaluation used 67 quality-controlled, temporally matched in situ spectral observations and satellite matchups. Performance was quantified using the squared Pearson correlation coefficient (r2), root mean square error (RMSE), and average unsigned relative error (AURE). Laboratory-measured chlorophyll-a (Chl-a) concentrations were used to develop sensor-specific retrieval models and to examine how atmospheric-correction differences propagated into Chl-a estimates and spatial patterns. Because residual aerosol and sun-glint effects may remain after atmospheric correction, an exploratory SWIR-based adjustment was evaluated for the C2RCC visible-band outputs. In the pooled-band analysis, C2RCC yielded the most favorable balance of the evaluated metrics for both sensor datasets. However, performance varied among bands, and the Landsat-8/9 B5 output showed very weak covariation with the in situ measurements. Within the model-development dataset, Sen2Cor achieved the highest Sentinel-2 r2 (0.762), whereas C2RCC achieved the lowest Sentinel-2 RMSE (2.30 mg/m3). C2RCC achieved both the highest Landsat-8/9 r2 (0.689) and the lowest RMSE (3.18 mg/m3). Independent temporal validation used 14 Luhun observations from 2024. Sen2Cor yielded the lowest Sentinel-2 RMSE and AURE (1.658 mg/m3 and 29.28%). For Landsat-8/9 OLI, C2RCC yielded the highest r2 (0.536), lowest RMSE (3.468 mg/m3), and lowest AURE (44.08%). Relative errors increased in weak-signal near-infrared bands, underscoring the need for band-specific interpretation. The SWIR-based adjustment improved both RMSE and AURE for Sentinel-2 MSI but did not provide a consistent improvement for Landsat-8/9 OLI. An exploratory comparison of quality-screened imagery from 2016 to 2025 showed broadly similar reservoir-scale Chl-a patterns in C2RCC-derived products from the two sensors. These results provide reservoir-specific evidence for atmospheric-correction selection and Chl-a retrieval under the sampled conditions.
Convection-permitting simulations resolve the deep convective cells that organise Mediterranean tropical-like cyclones. They also generate localised pressure minima that can capture a conventional cyclone tracker and pull it away from the synoptic-scale centre. We introduce High-Resolution Multilevel Python-Based Algorithm for Cyclones’ Centroid Tracking (HIMPACT), an open-source Python algorithm developed by the corresponding author within the CETEMPS framework, that stabilises cyclone-centre identification by combining three elements: a multi-level geopotential analysis restricted to the 800–950 hPa layer, a percentile-based threshold that isolates the vortex core from convective perturbations, and a convex-hull centroid that depends on the geometry of a percentile-defined core rather than on a single extreme grid point, so that an isolated convective pressure deficit cannot displace the estimate by more than a fraction of the core radius. HIMPACT was evaluated in four tracking experiments across three Mediterranean cyclones at grid spacings from 2 to 28 km using WRF, ICON-DREAM and ERA5, while MPAS was additionally used to test portability and computational scaling on an unstructured Voronoi mesh. Across the three experiments in which the driving data resolve a coherent lower-tropospheric cyclone structure, the best five-level configurations reduce root-mean-square displacement errors by approximately 16–48% relative to the corresponding single-level configurations. Activating the absolute minimum alongside the centroid more than doubles the error variance when the pressure field is multi-modal. The 800–950 hPa window avoids both surface extrapolation artefacts below 950 hPa and mid-tropospheric steering signatures above 800 hPa. A counterexample with an extratropical storm exposes a data-quality threshold: when the driving dataset does not resolve a vertically coherent cyclone structure, the multi-level weighted mean diverges, and single-level tracking becomes the safer choice. HIMPACT is model-agnostic, requires no format conversion, and runs on a single CPU core at approximately 9.8–41.3 s per time step for the recommended five-level configuration across the tested back-ends; substantially larger costs occur for high-level-count MPAS configurations.
Mutual interactions exist between tropical cyclones and the East Asian mid-latitude trough (EAMT). This study mainly used statistical analysis and numerical experiments to investigate the influence of TCs on the zonal movement of EAMT. Composite results indicate that TCs can induce an average of 11.82 degrees in the EAMT meridional displacement, and the anomalous remote geopotential height (HGT) triggered by TCs serves as an important factor driving the zonal movement of the EAMT. The EAMT tends to move towards the region of negative HGT difference and away from the region of positive HGT difference. The temperature anomalies induced by TCs are a critical factor leading to the HGT anomalies. For the TC Maria case, it induces a maximum meridional displacement of 0.76 degrees of the EAMT at 450 hPa. TC Maria first triggers anomalous cold advection in the mid-latitude regions of the East Asia–Northwest Pacific area, which then leads to an anomalous decrease in HGT within the EAMT trough region under the constraint of hydrostatic equilibrium. Consequently, the anomalous negative HGT caused by the TC results in the zonal movement of the EAMT line. The results of this study provide evidence that remote disturbances induced by TCs in the tropical WNP can affect weather circulation in the mid-latitudes of East Asia.
Drought monitoring in arid regions commonly relies on climatic indices, yet the accumulation timescale that best represents surface soil moisture drought may vary spatially. This study evaluated the Standardized Precipitation Evapotranspiration Index (SPEI) at 1-, 3-, and 6-month accumulation periods against satellite-derived surface soil moisture across Saudi Arabia during 2003–2024. Temporal correspondence, grid-cell Spearman correlation, and receiver operating characteristic area under the curve (ROC-AUC) were evaluated. The analysis included 1462 grid cells meeting the minimum paired-observation criterion, representing 53.4% of the 2736 cells in the national domain. SPEI-1 consistently showed the strongest overall performance, with the highest temporal correspondence with surface soil moisture drought extent (ρ=0.50, versus 0.37 for SPEI-3 and 0.30 for SPEI-6), median grid-cell correlation (ρ˜=0.290, versus 0.255 and 0.175), and median ROC-AUC (0.629, versus 0.618 and 0.580). SPEI-1 was also the nominal highest-AUC timescale in 52.5% of analyzed cells, compared with 29.5% for SPEI-3 and 17.9% for SPEI-6. However, the median AUC difference between the first- and second-ranked timescales was only 0.033, and paired bootstrap comparisons showed that 94.8% of cells had no uniquely supported AUC winner. Regionally, nine of the 13 administrative regions showed a nominal majority preference for SPEI-1, whereas four had no single-timescale majority. The overall SPEI-1 > SPEI-3 > SPEI-6 ordering was also preserved under alternative soil moisture drought thresholds and temporal out-of-sample validation. These findings indicate that shorter SPEI accumulation periods generally provide the closest representation of near-surface soil moisture drought within the observed Saudi domain. The results provide practical guidance for selecting SPEI accumulation periods according to the land-surface drought process being monitored.
Near-surface ozone (O3) pollution is a growing environmental concern, particularly in the Beijing–Tianjin–Hebei (BTH) region, one of China’s most densely populated megacity clusters experiencing increasingly severe O3 episodes. Existing data-driven forecasting models systematically underestimate high-concentration events and offer limited lead times. To reveal the meteorological drivers of extreme O3 episodes, we conducted composite anomaly analysis over 2019–2023 and identified the dominant meteorological mechanism as a coupled pattern of mid-tropospheric anticyclonic circulation with high temperature, low humidity, and deep subsidence inversion, which suppresses vertical diffusion while southerly advection drives rapid near-surface O3 accumulation. Motivated by meteorological diagnostics, we proposed ARC-Net, a Transformer-encoder-based Adaptive Residual Correction Network that ingests numerical weather prediction data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and air quality observations to produce hourly O3 forecasts up to 240 h (10 days) ahead. The model features a dual-branch regression-classification architecture enhancing feature discrimination at high concentrations and an Adaptive Residual Correction module that dynamically calibrates outputs through a triple-gating mechanism conditioned on pollution-level priors. In independent forecast tests for the year 2023 across 13 cities in the BTH region, ARC-Net achieved R2 = 0.879 and a root mean square error (RMSE) of 17.03 μg/m3 at 0–24 h, retaining R2 = 0.749 and RMSE = 24.57 μg/m3 at 0–240 h. For extreme episodes (maximum daily 8 h average ozone (MDA8_O3) ≥ 215 μg/m3), the Critical Success Index improved by 63.9% over the baseline, and RMSE decreased by 33.15% within the 215–265 μg/m3 range in a representative case. These results indicate that meteorology-guided predictors combined with adaptive residual correction can partially alleviate high-O3 underestimation and provide practically useful medium-range warning skill.
Wildfires represent a major environmental hazard with significant impacts on ecosystems, climate, biodiversity, and human activities. The increasing frequency and intensity of wildfire events have highlighted the need for reliable and timely detection techniques based on satellite remote sensing. This study investigates the application of Proper Orthogonal Decomposition (POD) to thermal observations acquired from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite for wildfire anomaly detection. A wildfire event that occurred on 8 August 2021 in Calabria, Southern Italy, was selected as the primary case study. To assess the consistency of the POD response beyond the primary case, the analysis was further extended to two additional wildfire events, Viggianello–Abate and Pazzano–Montestella, using the 15 × 15 pixel extraction window. Middle Infrared (MIR, 3.9 μm) observations collected at 15 min intervals over a complete day were analyzed using four different spatial extraction windows (3 × 3, 15 × 15, 30 × 30, and 45 × 45 pixels). POD was employed to separate dominant background thermal variability from localized fire-induced anomalies. The analysis focused on higher-order POD modes, particularly the 6th, 7th, and 8th modes, which exhibited enhanced sensitivity to wildfire activity. Results showed that POD successfully identified thermal anomalies corresponding to wildfire occurrence times independently detected by the RST-FIRES methodology. The comparison of extraction window sizes revealed that the 15 × 15 pixel window provided the best balance between anomaly enhancement, spatial localization, and noise reduction. Larger windows introduced excessive spatial smoothing and reduced localization capability, whereas the smallest window was more affected by noise. The findings demonstrate the potential of POD as an effective complementary approach for wildfire detection and monitoring using geostationary satellite observations.
Parsimonious emulators (PEs) trained on complex climate models (CCMs) are useful when global variables like global mean surface temperature and climate-system energy content are sought. CCM runs over millennia extracted from the LongRunMip repository are used to construct and test PEs for global mean temperature and net incoming radiation flux. For the temperature, the PE is a linear impulse response in the form of a superposition of k decaying exponentials, comprising k weight coefficients and k decay times to be estimated by least-square fitting to the temperature from CCM runs with abrupt step-function forcing. The model fit for k≥3 is good on all time scales, and the fitted model seems to perform even better for smoother forcing scenarios, suggesting that it reflects essential features of the CCM to which it is fitted. Data for radiation flux are combined with temperature data to produce low-order polynomial fits to Gregory plots and analytic expressions for the evolution of the effective feedback parameter, the radiation fluxes, the evolution of climate-system energy content, and an effective system heat capacity. The analysis reveals four stages of the ocean heat uptake, characterised by increasing effective heat capacity. From these Pes, one can compare the global performance of CCMs under different forcing scenarios, highlighting distinguishing features, such as evolution of albedo feedback and cloud radiative effect. Producing Gregory plots for all-sky and clear-sky outgoing long-wave and short-wave radiation, varying cloud albedo is identified as the main contributor to model spread of equilibrium climate sensitivity.
High-resolution wind-field prediction over complex terrain is important for wind-energy assessment, grid safety, and hazard mitigation. This study uses a coupled Weather Research and Forecasting–Computational Fluid Dynamics (WRF–CFD) workflow for the Askervein Hill benchmark. Driven by the fifth-generation European Centre for Medium-Range Weather Forecasts Reanalysis dataset, WRF provides mesoscale atmospheric conditions, and CFD resolves the terrain-induced flow features at high spatial resolution. Because repeated WRF and CFD simulations are computationally expensive, we develop a time-aware neural-network surrogate model, termed the multilayer perceptron-prior gated temporal correction (MLP-GTC) model, to predict the resulting three-component wind field. The model first learns the local wind response associated with terrain and inlet conditions, and it then applies a gated temporal correction based on recent inlet conditions and their corresponding pointwise predictions. The data are divided chronologically into 683 training, 146 validation, and 147 test time steps, with 5187 fixed spatial points evaluated at each time step. The coupled WRF–CFD simulations reproduce the observed wind structure with a maximum Pearson correlation coefficient of 0.96 and a 10 m wind-speed root mean square error of 0.84 m/s. For the three-component velocity field, MLP-GTC achieves validation/test root mean square errors of 0.282/0.310 m/s and mean absolute errors of 0.179/0.199 m/s. It improves on the multilayer perceptron model (0.486/0.516 m/s) and the matched five-step single-layer long short-term memory network (0.334/0.438 m/s).
Welding technology, extensively utilized in modern industry, poses significant health risks due to metal dust exposure, which can lead to respiratory discomfort, neurological issues, and an increased risk of lung cancer and pneumoconiosis. Enhancing ventilation within factory buildings has proven to be an economical approach to mitigating these risks. This study employs computational fluid dynamics (CFD) to model the airflow and dust transport within a large welding workshop measuring 300 m in length, 28 m in width, and 21 m in height. The impact of the exhaust-to-supply air ratio (ESR) and the height of the side exhaust port (SEP) on dust removal efficiency is investigated. Comparative analysis of transport dynamics between low-density aluminum alloy welding fume and high-density carbon steel welding fume reveals optimal dust exhaust designs. The study identifies two peaks in workshop air velocity at 0–2 m and 8–12 m above the ground, with the top exhaust port (TEP) outperforming the SEP in dust removal. An increased ESR accelerates the upward migration of welding fume, reducing lateral dispersion. An improperly set SEP height can lead to airflow short-circuiting or excessive lateral dispersion, hindering effective dust removal. Optimal SEP height for aluminum alloy and carbon steel dust are determined to be 5 m and 6 m, respectively.
Regional 10 m wind-speed products extend coverage beyond sparse station networks, but their errors vary with wind regime and location. This study evaluates a station-trained LightGBM correction of a 5 km WRF/HNR wind-speed product over Hainan Island. Raw and corrected fields were collocated with stations using a four-neighbour inverse-distance operator. A spatially separated 2022 holdout retained 67 stations after five coordinate-overlap exclusions. Across 582,610 common hourly pairs, corrected RMSE decreased from 3.198 to 1.211 m s−1 and Pearson R increased from 0.368 to 0.651. The paired RMSE reduction was 1.986 m s−1 [95% CI 1.839, 2.122]. Improvement was largest below 3.4 m s−1 (2.203 m s−1) and remained positive for 3.4–7.9 m s−1 (0.566 m s−1). In contrast, corrected RMSE increased by 0.990 m s−1 for 8.0–10.7 m s−1 and by 1.656 m s−1 for ≥10.8 m s−1. A time-only adjustment reduced pooled RMSE to 1.870 m s−1 but also degraded the two upper strata. The 2016–2022 maps show a lower corrected product climatology, with a mean corrected-minus-raw increment of −2.273 m s−1 across the Hainan buffer. Temporal partitions and AWS-selected station groups show variation across season, time of day, and setting. The correction reduces station-sampled errors for light-to-moderate wind speeds; it changes speed magnitude while retaining the raw-product wind direction.
