Search Results (543)

This study investigates the use of a Random Forest (RF), an artificial intelligence (AI) model, to estimate the planetary boundary layer height (PBLH) over Central Amazonia from climatic elements data collected during the GoAmazon experiment, held in 2014 and 2015, as it is a key metric for air quality, weather forecasting, and climate modeling. The novelty of this study lies in estimating PBLH using only surface-based meteorological observations. This approach is validated against remote sensing measurements (e.g., LIDAR, ceilometer, and wind profilers), which are seldom available in the Amazon region. The dataset includes various meteorological features, though substantial missing data for the latent heat flux (LE) and net radiation (Rn) measurements posed challenges. We addressed these gaps through different data-cleaning strategies, such as feature exclusion, row removal, and imputation techniques, assessing their impact on model performance using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and $r^2$ metrics. The best-performing strategy achieved an RMSE of 375.9 m. In addition to the RF model, we benchmarked its performance against Linear Regression, Support Vector Regression, LightGBM, XGBoost, and a Deep Neural Network. While all models showed moderate correlation with observed PBLH, the RF model outperformed all others with statistically significant differences confirmed by paired t-tests. SHAP (SHapley Additive exPlanations) values were used to enhance model interpretability, revealing hour of the day, air temperature, and relative humidity as the most influential predictors for PBLH, underscoring their critical role in atmospheric dynamics in Central Amazonia. Despite these optimizations, the model underestimates the PBLH values—by an average of 197 m, particularly in the spring and early summer austral seasons when atmospheric conditions are more variable. These findings emphasize the importance of robust data preprocessing and higtextight the potential of ML models for improving PBLH estimation in data-scarce tropical environments. Full article

The safety of lithium-ion batteries during transportation is critically important. However, current standards exhibit limitations, as their environmental testing parameter thresholds fail to fully encompass actual transportation conditions. To enhance both safety and standard applicability, in this study, we focused on four representative environmental conditions: temperature, vibration, shock, and low atmospheric pressure. Field measurements were conducted across road, rail, and air transport modes using a self-developed data acquisition system based on the NearLink communication technology. The measured data were then compared with the threshold values defined in current international and national standards. The results reveal that certain measured values exceeded the upper limits prescribed by existing standards, indicating limitations in their applicability under extreme transport conditions. Based on these findings, we propose revised testing parameters that better reflect actual transport risks, including a temperature cycling range of 72 ± 2 °C (high) and −40 ± 2 °C (low), a shock acceleration limit of 50 gn, adjusted peak frequencies in the vibration PSD profile, and a minimum pressure threshold of 11.6 kPa. These results provide a scientific basis for optimising safety standards and improving the safety of lithium-ion battery transportation. Full article

The WIVERN mission promises to deliver the first global observations of the three-dimensional wind field and the associated cloud and precipitation structure in a wide range of atmospheric phenomena, including isolated thunderstorms, tropical cyclones, mid-latitude frontal systems, and polar lows. A critical element in the development of the mission’s wind products is the differentiation between stratiform and convective regions. Convective regions are defined as those where vertical wind velocities exceed 1 m/s. This work introduces CONSTRAINN, a family of U-Net-based neural network models that utilise all of WIVERN observables—including vertical profiles of reflectivity and Doppler velocity, as well as brightness temperatures—to reconstruct convective wind activity within the Earth’s atmosphere. Results show that the retrieved convective/stratiform masks are well reconstructed, with an equitable threat score exceeding 0.6. Ablation experiments further reveal that Doppler velocity signals are the most informative for the reconstruction task. Full article

In recent years, China has experienced growing impacts from extreme weather events, emphasizing the importance of understanding regional atmospheric moisture dynamics, particularly Precipitable Water Vapor (PWV), to support sustainable environmental and urban planning. This study utilizes ten years (2013–2022) of Global Navigation Satellite System (GNSS) observations in typical cities in eastern China and proposes a comprehensive multiscale frequency-domain analysis framework that integrates the Fourier transform, Bayesian spectral estimation, and wavelet decomposition to extract the dominant PWV periodicities. Time-series analysis reveals an overall increasing trend in PWV across most regions, with notably declining trends in Beijing, Wuhan, and southern Taiwan, primarily attributed to groundwater depletion, rapid urban expansion, and ENSO-related anomalies, respectively. Frequency-domain results indicate distinct latitudinal and coastal–inland differences in the PWV periodicities. Inland stations (Beijing, Changchun, and Wuhan) display annual signals alongside weaker semi-annual components, while coastal stations (Shanghai, Kinmen County, Hong Kong, and Taiwan) mainly exhibit annual cycles. High-latitude stations show stronger seasonal and monthly fluctuations, mid-latitude stations present moderate-scale changes, and low-latitude regions display more diverse medium- and short-term fluctuations. In the short-term frequency domain, GNSS stations in most regions demonstrate significant PWV periodic variations over 0.5 days, 1 day, or both timescales, except for Changchun, where weak diurnal patterns are attributed to local topography and reduced solar radiation. Furthermore, ERA5-derived vertical temperature profiles are incorporated to reveal the thermodynamic mechanisms driving these variations, underscoring region-specific controls on surface evaporation and atmospheric moisture capacity. These findings offer novel insights into how human-induced environmental changes modulate the behavior of atmospheric water vapor. Full article

The impacts of the solar eclipse that occurred on 8 April 2024 over the United States on various atmospheric parameters are investigated. We analyzed surface and vertical profiles of temperature and humidity to understand how this eclipse affected the atmosphere from the ground to the stratosphere. Our findings show a significant response throughout the atmospheric range. The eclipse caused a decrease in shortwave radiation, leading to cooler Earth surfaces and a subsequent drop in surface temperature. This cooling effect also resulted in high relative humidity and lower wind speeds at the surface. Furthermore, GPS radio occultation data from COSMIC-2 revealed a decrease in tropospheric temperature and increase in stratospheric temperature during the eclipse. We also observed a reduction in both the temperature and height of the tropopause. The uniqueness of the present investigations lies in delineating the solar eclipse’s effects on the land and ocean. Our analysis indicates that land regions experienced a more pronounced temperature change compared to ocean regions. Full article

The knowledge of the radiative properties of clouds and the atmospheric state is of fundamental importance in modelling phenomena in numerical weather predictions and climate models. In this study, we show the results of the retrieval of cloud properties, along with the atmospheric state and the surface temperature, from far-infrared spectral radiances, in the 100–1000 cm⁻¹ range, measured by the Far-Infrared Radiation Mobile Observation System-Balloon version (FIRMOS-B) spectroradiometer from a stratospheric balloon launched from Timmins (Canada) in August 2022 within the HEMERA 3 programme. The retrieval study is performed with the Optimal Estimation inversion approach, using three different forward models and retrieval codes to compare the results. Cloud optical depth, particle effective size, and cloud top height are retrieved with good accuracy, despite the relatively high measurement noise of the FIRMOS-B observations used for this study. The retrieved atmospheric profiles, computed simultaneously with cloud parameters, are in good agreement with both co-located radiosonde measurements and ERA-5 profiles, under all-sky conditions. The findings are very promising for the development of an optimised retrieval procedure to analyse the high-precision FIR spectral measurements, which will be delivered by the ESA FORUM mission. Full article

Straw biomass is a renewable but problematic fuel due to its high alkali and chlorine content, which can cause slagging and corrosion during combustion. To mitigate these issues, this study investigates the influence of aluminosilicate additives on the thermal behavior and combustion characteristics of straw biomass. Laboratory-scale testing is carried out using thermogravimetric analysis under atmospheric air, showing the TG, DTG, and DSC profiles of samples (kaolinite, halloysite, straw biomass, and straw biomass with 4 wt.% of halloysite). Additionally, the main combustion parameters, like the ignition temperature, the maximum peak temperature, the burnout temperature, and some combustion indexes, are presented. The results show the effect of a heating rate in the range of 5–20 °C/min. Moreover, in this study, two non-isothermal model methods (Kissinger and Ozawa) are used to estimate energy activation. While halloysite slightly affects the combustion indexes and marginally reduces energy activation, its overall influence does not significantly alter combustion efficiency. These findings support the potential and safe use of halloysite for the biomass combustion process. Full article

The atmospheric boundary layer (ABL) and tropopause play critical roles in weather formation and climate change. This study initially focuses on the ABL height (ABLH), tropopause height (TPH), and temperature (TPT) retrieved from the integrated radio occultation (RO) profiles from FY-3E, FY-3F, and FY-3G satellites during September 2022 to August 2024. All three FY-3 series satellites are equipped with the RO payload of Global Navigation Satellite System Radio Occultation Sounder-II (GNOS-II), which includes open-loop tracking RO observations from the BeiDou navigation satellite system (BDS) and the Global Positioning System (GPS). The wavelet covariance transform method was used to determine the ABL top, and the temperature lapse rate was applied to judge the tropopause. Results show that the maximum ABL detection rate of FY-3/GNOS-II RO can reach up to 76% in the subtropical eastern Pacific, southern hemisphere Atlantic, and eastern Indian Ocean. The ABLH is highly consistent with the collocated radiosonde observations and presents distinct seasonal variations. The TPH retrieved from FY-3/GNOS-II RO profiles is in agreement with the radiosonde-derived TPH, and both TPH and TPT from RO profiles display well-defined spatial structures. From 45°S to 45°N and south of 55°S, the annual cycle of the TPT is negatively correlated with the TPH. This study substantiates the promising performance of FY-3/GNOS-II RO measurements in observing the ABL and tropopause, which can be incorporated into the weather and climate systems. Full article

This study aims to develop a high-resolution temperature dataset from 40 km to 110 km over China by machine learning techniques, with a horizontal resolution of 0.5° × 0.5° and vertical resolution of 1 km, utilizing measurements from SABER onboard the Thermosphere, Ionosphere, Mesosphere Energetics, and Dynamics (TIMED) and Fengyun 4A (FY-4A) satellites. Accurate temperature profiles play a critical role in understanding the atmospheric dynamics and climate change. However, because of the limitation of traditional detecting methods, the measurements of the upper stratosphere and mesosphere are rare. In this study, a new method is developed to construct a high-resolution temperature dataset over China in the middle atmosphere based on the XGBoost technique. The model’s performance is also validated based on rocket observations and ERA5 reanalysis data. The results indicate that the model effectively captures the characteristics of the vertical and seasonal variations in temperature, which provide a valuable opportunity for further research and improvement of climate models. The model demonstrates the highest accuracy below 80 km with RMSE < 12 K, while its performance decreases above 100 km, where RMSE can exceed 20 K, indicating optimal performance in the upper stratosphere and lower mesosphere regions. Full article

The sterility of ophthalmic drugs is a fundamental requirement for ensuring patient safety, and as such, it is subject to stringent regulatory standards. However, significant gaps remain regarding the effect of sterilization techniques on the impurity profile and relative content of active pharmaceutical ingredients (API). Previous research involving a set of five APIs used in ophthalmic preparations (dexamethasone, methylprednisolone, aciclovir, tetracycline hydrochloride, and triamcinolone) demonstrated that gamma irradiation led to the formation of specific impurities in the corticosteroids, dexamethasone and methylprednisolone. This study aims to further explore the effect of both gamma and electron beam (E-beam) irradiation on the impurity profiles of these APIs under varying conditions, with and without dry ice. The analyses were conducted using high-performance liquid chromatography with ultraviolet/visible light (UV/VIS) detection and the effect of sterilization conditions was assessed in accordance with the assay and related substances test outlined in the European Pharmacopoeia (Ph. Eur.). Additionally, this study investigated whether exposure in a controlled atmosphere with reduced oxygen or water content could mitigate the formation of impurities and influence the stability of the compounds. The results indicated a protective effect of low-temperature and low-oxygen environments during both gamma and E-beam irradiation but no effect of dry conditions. Full article

The temperature of the middle atmosphere is of great significance in the coupled study of the upper and lower layers. A pure rotational Raman–Rayleigh scattering LiDAR system was developed for profiling the middle atmospheric temperature at daytime and nighttime continuously by employing an ultra-narrow band interferometer. The comparisons between LiDAR detections and radiosonde data show that the LiDAR system has temperature detection capabilities of 80 km and 60 km at night and during the day, respectively. The results demonstrate that our method can reliably detect the atmospheric temperature in the middle atmosphere. The significant non-uniformity in the horizontal distribution of temperature in the middle atmosphere and the vertical gradient of atmospheric temperature could be observed by using the developed LiDAR. Full article

Understanding optical turbulence within the atmospheric boundary layer (ABL) is essential for refining atmospheric motion analyses, enhancing numerical weather prediction models, and improving light propagation assessments. This study develops an optical turbulence model for the boundary layer over the South China Sea (SCS) by integrating multiple observational and reanalysis datasets, including ERA5 data from the European Center for Medium-Range Weather Forecasts (ECMWF), radiosonde observations, coherent Doppler wind lidar (CDWL), and ultrasonic anemometer (CSAT3) measurements. Utilizing Monin–Obukhov Similarity Theory (MOST) as the theoretical foundation, the model’s performance is evaluated by comparing its outputs with the observed diurnal cycle of near-surface optical turbulence. Error analysis indicates a root mean square error (RMSE) of less than 1 and a correlation coefficient exceeding 0.6, validating the model’s predictive capability. Moreover, this study demonstrates the feasibility of employing ERA5-derived temperature and pressure profiles as alternative inputs for optical turbulence modeling while leveraging CDWL’s high-resolution observational capacity for all-weather turbulence characterization. A comprehensive statistical analysis of the atmospheric refractive index structure constant ($C_n^2$) from November 2019 to September 2020 highlights its critical implications for optoelectronic system optimization and astronomical observatory site selection in the SCS region. Full article

Atmospheric parameter acquisition along the vertical profile of the troposphere across different locations on the Earth is of primary importance in gaining knowledge of the evolution of large-scale meteorological systems and the relative movements of air masses. Normally, this happens thanks to the launch, into the atmosphere, of radiosondes connected to balloons filled with helium gas. However, on a small scale, and in particular geomorphological contexts, different and peculiar meteorological situations may arise, in which the air column in the lower layers can behave differently from normal, giving rise to the so-called thermal inversions. In this work, in a particular sinkhole in the Apulia region, the use of a multi-rotor UAV (Unmanned Aerial Vehicle) equipped with a temperature data logger was tested. The flight along the vertical, starting from the lowest point of the sinkhole, made it possible to archive the temperature data of the air column in the first 80 m of altitude. The data validation confirmed the goodness of the UAV acquisitions and their subsequent processing made it possible to extrapolate the vertical temperature profile of the sinkhole during the winter thermal inversion phenomenon. In addition to confirming the predisposition of this sinkhole to strong thermal inversions, the preliminary results of this work have highlighted the efficiency of this new methodology. It has proved to be useful in assessing small-scale vertical profiles of atmospheric variables in a relatively low altitude range. Furthermore, this methodology can represent a strong scientific and technological innovation applicable in the meteorological field and in that of environmental monitoring. Full article

Thermodynamic parameters play a crucial role in analyzing and optimizing crystallization processes. In this investigation, the solubility profiles of erythromycin thiocyanate dihydrate were determined gravimetrically under atmospheric pressure (0.1 MPa) across six monosolvent systems (methanol, n-propanol, methyl acetate, ethyl acetate, propyl acetate, and water) and two binary solvent mixtures (water–methanol and water–n-propanol), spanning a temperature range of 278.15–318.15 K. The results showed that the solubility of erythromycin thiocyanate dihydrate is apparently affected by temperature and solvent type. For pure solvents, erythromycin thiocyanate dihydrate has higher solubility in alcohol solvents, and lower solubility in ester solvents and water. In mixed solvent systems, erythromycin thiocyanate dihydrate exhibits reduced solubility with higher water content. The experimental solubility values in monosolvent systems were correlated using the Apelblat, Yaws, and Van’t Hoff models, with the Apelblat model showing the best fitting effect. The Apelblat model, Apelblat Jouyban Acre model, and CNIBS/R-K model were employed for data correlation in binary solvent systems, with the Apelblat model and CNIBS/R-K model showing better fitting results. Full article

Accurate prediction of Cloud Liquid Water Content (CLWC) is critical for understanding and forecasting weather phenomena, particularly in regions with complex microclimates. This study integrates high-resolution ERA5 climatic data from the European Centre for Medium-Range Weather Forecasts (ECMWF) with radiosonde observations from the Hong Kong area to address data accuracy and resolution challenges. Machine learning (ML) models—specifically Fine Tree regressors—were employed to interpolate radiosonde data, resolving temporal and spatial discrepancies and enhancing data coverage. A metaheuristic algorithm was also applied for data cleansing, significantly improving correlations between input features (temperature, pressure, and humidity) and CLWC. The methodology was tested across multiple ML algorithms, with ensemble models such as Bagged Trees demonstrating superior predictive accuracy and robustness. The approach substantially improved CLWC profile reliability, outperforming traditional methods and addressing the nonlinear complexities of atmospheric data. Designed for scalability, this methodology extends beyond Hong Kong’s unique conditions, offering a flexible framework for improving weather prediction models globally. By advancing CLWC estimation techniques, this work contributes to enhanced weather forecasting and atmospheric science in diverse climatic regions. Full article