Accurate measurements of upper troposphere humidity are essential to enhance understanding of contrail formation and guiding mitigation efforts. This study evaluates the ability of the IPRAL Raman Lidar, located south of Paris, to provide high-resolution water vapour mixing ratio (WVMR) profiles at contrail-relevant [...] Read more.

Accurate measurements of upper troposphere humidity are essential to enhance understanding of contrail formation and guiding mitigation efforts. This study evaluates the ability of the IPRAL Raman Lidar, located south of Paris, to provide high-resolution water vapour mixing ratio (WVMR) profiles at contrail-relevant altitudes. Raman signals are screened on hourly bases, and a universal calibration method, independent of acquisition mode, is proposed towards operational Lidar water vapour profiles, using co-located ERA5 data. Calibration factors are derived from comparisons between 4 and 6 km, and nightly coefficients determined from hourly factors. Instrumental stability is monitored through the temporal evolution of calibration factors, and stable-period medians are adopted as final values. The uncertainty of calibrated WVMR profiles is assessed by comparison with GRUAN processed Meteomodem M10 radiosondes and ERA5 data. Results show a high agreement (>90%), with IPRAL exhibiting a small negative bias (~10%) below 8 km, reducing to ~5% up to 10.5 km to radiosondes. ERA5 systematically underestimates water vapour at cruise altitudes, with a dry bias increasing from 10% at 9 km to >20% at 11 km. Recent IAGOS corrections to ERA5, improving supersaturation representation, are validated over Paris. This calibrated Lidar data set supports improved atmospheric modelling and contributes to future air traffic management strategies.Full article

Numerical simulation studies of the dispersion of dimethyl sulfide (DMS) in the air have increased over the last two decades in parallel with the interest in understanding its role as a precursor of non-sea salt aerosols in the lower to middle levels of [...] Read more.

Numerical simulation studies of the dispersion of dimethyl sulfide (DMS) in the air have increased over the last two decades in parallel with the interest in understanding its role as a precursor of non-sea salt aerosols in the lower to middle levels of the troposphere. Here, we review recent numerical modeling studies that have included DMS emissions, their atmospheric oxidation mechanism, and their subsequent impacts on air quality at regional and global scales. In addition, we discuss the available methods for estimating sea–air DMS fluxes, including parameterizations and climatological datasets, as well as their integration into air quality models. At the regional level, modeling studies focus on the Northern Hemisphere, presenting a large gap in Antarctica, Africa, and the Atlantic coast of South America, whereas at the global scale, modeling studies tend to focus more on polar regions, especially the Arctic. Future studies must consider updated climatologies and parameterizations for more realistic results and the reduction in biases in numerical simulations analysis.Full article

Statistical climate forecast systems typically do not use preceding global gridded sea surface temperature (SST) data directly; instead, they extract a single predictor (e.g., the Niño3.4 index) or multiple predictors (e.g., time series of several SST spatial modes). In this study, four different [...] Read more.

Statistical climate forecast systems typically do not use preceding global gridded sea surface temperature (SST) data directly; instead, they extract a single predictor (e.g., the Niño3.4 index) or multiple predictors (e.g., time series of several SST spatial modes). In this study, four different SST predictor extracting methods (one single-predictor method and three multiple-predictor methods) are comparatively analyzed within the same climate forecast platform incorporating either the linear regression (LR) model or the neural network (NN) forecast model. Rolling forecast experiments with the LR model show that, compared to a single strong SST predictor, only multiple predictors with more high-quality information (high signal-to-noise ratio) could improve the forecast skill. Sensitivity experiments also show that the influence of multiple-predictor extracting methods on forecast skill from the NN model is much weaker than that from the LR model. Moreover, whether or not multiple SST predictors are orthogonal might also affect the forecast skill. The above analyses provide a reference for establishing statistical climate forecast system based on preceding SST data.Full article

The DC global electric circuit, GEC, was conceived by C.T.R. Wilson more than a century ago. Powered by thunderstorms and electrified shower clouds, an electric current I ~1 kA flows up into the ionosphere, maintaining the ionospheric potential V ~250 kV with respect [...] Read more.

The DC global electric circuit, GEC, was conceived by C.T.R. Wilson more than a century ago. Powered by thunderstorms and electrified shower clouds, an electric current I ~1 kA flows up into the ionosphere, maintaining the ionospheric potential V ~250 kV with respect to the Earth’s surface. The circuit is formed by the current I, flowing through the ionosphere all around the world, down through the atmosphere remote from the current sources (J ~2 pA/m² through a resistance R ~250 Ω), through the land and sea surface, and up to the thunderstorms as point discharge currents. This maintains a downward electric field E of magnitude ~130 V/m at the Earth’s surface away from thunderstorms and a charge Q ~−6.10⁵ C on the Earth’s surface. The theoretical modelling of ionospheric currents and the miniscule geomagnetic field perturbations (ΔB ~0.1 nT) which they cause, as derived by Denisenko and colleagues in recent years, are reviewed. The time constant of the GEC, τ = RC, where C is the capacitance of the global circuit capacitor, is estimated via three different methods to be ~7 to 12 min. The influence of stratus clouds in determining the value of τ is shown to be significant. Sudden excitations of the GEC by volcanic lightning in Iceland in 2011 and near the Tonga eruption in 2022 enable τ to be determined, from experimental observations, as ~10 min and 8 min, respectively. It has been suggested that seismic activity, or earthquake precursors, could produce large enough electric fields in the ionosphere to cause detectable effects, either by enhanced radon emission or by enhanced thermal emission from the earthquake region; a review of the quantitative estimates of these mechanisms shows that they are unlikely to produce sufficiently large effects to be detectable. Finally, some possible links between the topics discussed and human health are considered briefly.Full article

Stormwater drainage from urbanised areas has gained importance due to progressing land surface sealing and climate change. More frequent extreme rainfall events lead to overloaded drainage systems and flash floods, particularly in industrial zones experiencing rapid development. The study analysed the sewage system [...] Read more.

Stormwater drainage from urbanised areas has gained importance due to progressing land surface sealing and climate change. More frequent extreme rainfall events lead to overloaded drainage systems and flash floods, particularly in industrial zones experiencing rapid development. The study analysed the sewage system operation in the Special Economic Zone (SEZ) in Lower Silesia, Poland to assess the impact of climate-induced rainfall changes. Three rainfall scenarios were used: model rainfall using historic rainfall intensities, model rainfall using actual intensities, and real precipitation recorded in June 2022. Findings indicate that climate change has negatively affected the stormwater drainage system, resulting in increased overloads and flooding. Particularly, the II scenario showed a significant rise in rainwater inflow to retention reservoirs by 53.1% for ZR-1 and 44.5% for ZR-2 (compared to the I scenario). To address these issues, adaptations are needed for increased rainwater flows, including additional retention facilities, blue–green infrastructure, or rainwater harvesting for the SEZ needs.Full article

Supplementary material:
Supplementary File 1 (ZIP, 959 KiB)

Ammonia (NH₃) emissions, which are key precursors of fine particulate matter, pose significant environmental challenges. This study investigated the spatiotemporal variations in NH₃ emissions across the eastern German lowlands from 2013 to 2022 using IASI-B satellite data. Five major Land [...] Read more.

Ammonia (NH₃) emissions, which are key precursors of fine particulate matter, pose significant environmental challenges. This study investigated the spatiotemporal variations in NH₃ emissions across the eastern German lowlands from 2013 to 2022 using IASI-B satellite data. Five major Land Cover Classes (LCC) –tree, grassland, cropland, built-up areas, and water bodies– were analyzed. The results showed distinct diurnal variations, with nighttime NH₃ concentrations exceeding 2.0 × 10¹⁶ molecules cm⁻² in the peak months. Seasonal patterns indicated significant emissions in March (1.2 × 10¹⁶ molecules cm⁻²), April (1.1 × 10¹⁶ molecules cm⁻²), and August (9.6 × 10¹⁵ molecules cm⁻²), while the lowest concentrations occurred in September (0.6 × 10¹⁵ molecules cm⁻²). Persistent hotspots were identified in the northwestern region, where emissions peaked in spring (1.8 × 10¹⁶ molecules cm⁻²⁾ and summer (1.3 × 10¹⁶ molecules cm⁻²), primarily due to agricultural activities. Over the study period, the annual NH₃ concentration peaked in 2015, 2018, and 2022. Using k-means clustering, three distinct emission zones were identified, with Cluster 3 showing the highest NH₃ emission values, particularly in urban centers, and agricultural zones were identified, covering less than 20% of the study area, where cropland predominates (8%). Meteorological factors significantly influenced NH₃ levels, with negative correlations obtained for precipitation, wind speed, and evaporation, while solar radiation, boundary layer height, and instantaneous moisture fluxes showed positive correlations. A case study from March 2022, employing the HYSPLIT trajectory model, confirmed that agricultural practices are the dominant NH₃ source, with emissions reaching 3.2 × 10¹⁶ molecules cm⁻² in hotspot regions.Full article

In modern communication, the electromagnetic spectrum serves as the carrier for information transmission, and the only medium enabling information exchange anywhere, anytime. To adapt to the changing dynamics of a complex electromagnetic environment, electromagnetic spectrum allocation algorithms must not only meet the demands [...] Read more.

In modern communication, the electromagnetic spectrum serves as the carrier for information transmission, and the only medium enabling information exchange anywhere, anytime. To adapt to the changing dynamics of a complex electromagnetic environment, electromagnetic spectrum allocation algorithms must not only meet the demands for efficiency and intelligence but also possess anti-jamming capabilities to achieve the best communication effect. Focusing on intelligent wireless communication, this paper proposes a multi-agent hybrid game spectrum allocation method under incomplete information and based on the Monte Carlo counter-factual regret minimization algorithm. Specifically, the method first utilizes frequency usage and interference information from both sides to train agents through extensive simulations using the Monte Carlo Method, allowing the trial values to approach the expected values. Based on the results of each trial, the counterfactual regret minimization algorithm is employed to update the frequency selection strategies for both the user and the interferer. Subsequently, the trained agents from both sides engage in countermeasure communication. Finally, the probabilities of successful communication and successful interference for both sides are statistically analyzed. The results show that under the multi-agent hybrid game spectrum allocation method based on the Monte Carlo counter-factual regret minimization algorithm, the probability of successful interference against the user is 32.5%, while the probability of successful interference by the jammer is 37.3%. The average simulation time per round is 3.06 s. This simulation validates the feasibility and effectiveness of the multi-agent hybrid game spectrum allocation module based on the Monte Carlo counter-factual regret minimization algorithm.Full article

This study analyzes changes in NO_x concentrations due to the aerodynamic and dry deposition effects of roadside trees in the Jongno area, a central business district of Seoul, Republic of Korea, using a computational fluid dynamics (CFD) model. The simulation results indicate [...] Read more.

This study analyzes changes in NO_x concentrations due to the aerodynamic and dry deposition effects of roadside trees in the Jongno area, a central business district of Seoul, Republic of Korea, using a computational fluid dynamics (CFD) model. The simulation results indicate that the on-road NO_x concentration was slightly increased (2.09%) due to the aerodynamic effect of roadside trees. However, the dry deposition effect of roadside trees had a greater impact on reducing NO_x concentrations (−2.77%) along sidewalks. It was observed that the reduction in NO_x concentration due to the dry deposition effect of roadside trees was likely to offset the increase in NO_x concentrations due to the aerodynamic effect of roadside trees, resulting in an overall decrease in NO_x concentrations. Furthermore, sensitivity tests showed that the increase in NO_x concentrations due to the aerodynamic effects of roadside trees was intensified along sidewalks when ambient wind speeds were high, while the decrease in NO_x concentration was proportional to the deposition velocity of roadside trees. Therefore, roadside trees should be planted where aerodynamic effects do not significantly increase NO_x concentrations in order to improve near-road air quality.Full article

This paper offers a new perspective on the explanation of the poleward mass flux in the stratosphere. This mass flux represents the upper leg of the so-called Brewer–Dobson circulation. This new perspective is based on the following hypothesis. A positive potential vorticity anomaly, [...] Read more.

This paper offers a new perspective on the explanation of the poleward mass flux in the stratosphere. This mass flux represents the upper leg of the so-called Brewer–Dobson circulation. This new perspective is based on the following hypothesis. A positive potential vorticity anomaly, centered over the North Pole, exists in the stratosphere during the winter half-year. This positive potential vorticity anomaly is associated with a negative isentropic density anomaly, which forms due to cross-isentropic downwelling associated with radiative cooling. Isentropic potential vorticity mixing due to breaking planetary waves weakens this potential vorticity anomaly while zonal-mean thermal wind balance is maintained. This requires a weakening of the negative Polar cap isentropic density anomaly, which in turn requires a poleward isentropic mass flux. Support for this hypothesis is found in a case study of a major Sudden Stratospheric Warming event, as an example of intense potential vorticity mixing. It is shown that the stratosphere, both before and after this event, is very close to zonal-mean thermal wind balance, despite the disruptive potential vorticity mixing, while mass is shifted poleward during this event. Solutions of the potential vorticity-inversion equation, which is an expression of thermal wind balance, for zonal-mean potential vorticity distributions before and after the Sudden Stratospheric Warming, demonstrate that mass must shift poleward to maintain zonal-mean thermal wind balance when the positive potential vorticity anomaly is eliminated by mixing. This perspective on the reasons for the poleward stratospheric mass flux also explains the observed isobaric warming as well as the Polar cap zonal-mean zonal wind reversal during a major Sudden Stratospheric Warming.Full article

Air pollution in Chiang Mai during the dry winter season is extremely severe. During this period, high levels of fine particles are primarily generated by open biomass burning in Thailand and neighboring countries. In this study, ambient VOC(Volatile Organic Compounds) samples were collected [...] Read more.

Air pollution in Chiang Mai during the dry winter season is extremely severe. During this period, high levels of fine particles are primarily generated by open biomass burning in Thailand and neighboring countries. In this study, ambient VOC(Volatile Organic Compounds) samples were collected using an adsorbent tube from 13 March to 26 March 2024, with careful consideration of sampling uncertainties to ensure data reliability. Furthermore, while interannual variability exists, the findings reflect atmospheric conditions during this specific period, allowing for an in-depth VOC assessment. A comprehensive approach to VOCs was undertaken, including benzene, toluene, ethylbenzene, m,p-xylene (BTEX); biogenic volatile organic compounds (BVOCs); and carbonyl compounds. Regression analysis was performed to analyze the correlation between isoprene concentrations and wind direction. The results showed a significant variation in isoprene levels, indicating their high concentrations due to biomass burning originating from northern areas of Chiang Mai. The emission sources of BTEX and carbonyl compounds were inferred through their ratio analysis. Additionally, correlation analyses between PM_2.5, BTEX, and carbonyl compounds were conducted to identify common emission pathways. The ratio of BTEX among compounds suggested that long-range pollutant transport contributed more significantly than local traffic emissions. Carbonyl compounds were higher during the episode period, which was likely due to local photochemical reactions and biological contributions. Previous studies in Chiang Mai have primarily focused on PM_2.5, whereas this study examined individual VOC species, their temporal trends, and their interrelationships to identify emission sources.Full article

On 1 December 2023, a strong geomagnetic storm was triggered by an interplanetary shock caused by a coronal mass ejection (CME). This study used data from 193 Global Navigation Satellite System (GNSS) observation stations in China to study the three-dimensional morphological total electron [...] Read more.

On 1 December 2023, a strong geomagnetic storm was triggered by an interplanetary shock caused by a coronal mass ejection (CME). This study used data from 193 Global Navigation Satellite System (GNSS) observation stations in China to study the three-dimensional morphological total electron content (TEC) disturbances during this storm. By analyzing GNSS TEC data from 15 GNSS stations along the magnetic field lines, it was found that TEC disturbances spread from low to high latitudes, confirmed by ionosonde NmF2 data. The TEC disturbance first appeared at the LJHP station, (21.68° N) at 11:30 UT and propagated to the BJFS station (39.60° N) at 13:30 UT with a propagation speed of about 217 m/s and maximum amplitude of ±0.2 m. The TEC disturbance lasted the longest, approximately 4 h, between latitudes 25° N and 32° N. Additionally, this study investigated the ionosphere’s three-dimensional electron density distribution in the Guangxi region using an ionospheric tomography algorithm. Results showed that the TEC disturbances were mainly concentrated between 450 and 580 km in altitude. At 12:00 UT, the maximum change in electron density occurred at a 580 km height at 26° N, 112° E, increasing by 20.54 total electron content unit (TECU). During the main phase of the geomagnetic storm, the electron density expanded from higher to lower layers, while during the recovery phase, it recovered from the lower layers to the higher layers.Full article

In contemporary times, wastewater treatment plants (WWTPs) were recognized as substantial sources of odorous emissions, potentially impacting nearby communities’ sensory experience. This study investigates the half-lives (T½) of odorous compounds emitted from WWTPs and their degradation due to atmospheric hydroxyl radicals (•OH) in [...] Read more.

In contemporary times, wastewater treatment plants (WWTPs) were recognized as substantial sources of odorous emissions, potentially impacting nearby communities’ sensory experience. This study investigates the half-lives (T½) of odorous compounds emitted from WWTPs and their degradation due to atmospheric hydroxyl radicals (•OH) in different environmental settings. The calculated half-lives of specific odorants in rural areas ranged from 31.36 min to 517.33 days, in urban areas from 42.50 min to 1550 days, and in the marine boundary layer from 42.50 min to 129,861 days. These results show that compounds with high reactivity and short T½, such as methanethiol and ethanethiol, degrade rapidly and are less likely to contribute to long-term odor nuisances. In contrast, compounds with longer half-lives, such as carbonyl sulfide and ammonia, persist longer in the atmosphere, with higher potential for sustained odor issues. The findings suggest that •OH plays a significant role in degrading odorous compounds. These insights into odorant–oxidant kinetics may aid in predicting atmospheric half-lives and their contribution to secondary aerosol formation, thus informing regulatory and mitigation strategies to improve air quality.Full article

Climate change and air pollution are intrinsically interconnected as carbon dioxide and air pollutants are co-emitted during fossil fuel combustion. Low-carbon policies, aimed at mitigating carbon emissions, are also anticipated to yield co-benefits for air quality; however, the extent to which regional low-carbon [...] Read more.

Climate change and air pollution are intrinsically interconnected as carbon dioxide and air pollutants are co-emitted during fossil fuel combustion. Low-carbon policies, aimed at mitigating carbon emissions, are also anticipated to yield co-benefits for air quality; however, the extent to which regional low-carbon policies can effectively achieve significant reductions in air pollutant levels remains uncertain. In China, the implementation of the low-carbon city pilot (LCCP) policy has reduced carbon emissions, but further research is needed to examine its effectiveness regarding achieving air quality co-benefits. Adopting a difference-in-differences model with a 19-year national database of air quality, this study examines whether the LCCP policy improves air quality in China’s metropolitan areas and explores how these policy initiatives address their air pollution challenges. The results indicate that, following the implementation of the LCCP policy, the mean, maximum, and standard deviation of the AQI in pilot cities decreased significantly by 9.3%, 20.8%, and 19.8%, respectively, compared to non-pilot cities. These results suggest that the LCCP policy significantly improves air quality and provide evidence that this improvement is facilitated by advancements in green technology, industrial restructuring, and the optimization of urban planning and landscape design.Full article

Kármán vortex streets are quintessential phenomena in fluid dynamics, manifested by the periodic shedding of vortices as airflow interacts with obstacles. The genesis and characteristics of these vortex structures are significantly influenced by various atmospheric parameters, including temperature, humidity, pressure, and wind velocities, [...] Read more.

Kármán vortex streets are quintessential phenomena in fluid dynamics, manifested by the periodic shedding of vortices as airflow interacts with obstacles. The genesis and characteristics of these vortex structures are significantly influenced by various atmospheric parameters, including temperature, humidity, pressure, and wind velocities, which collectively dictate their formation conditions, spatial arrangement, and dynamic behavior. Although deep learning methodologies have advanced the automated detection of Kármán vortex streets in remote sensing imagery, existing approaches largely emphasize visual feature extraction without adequately incorporating critical atmospheric variables. To overcome this limitation, this study presents an innovative auxiliary network framework that integrates essential atmospheric physical parameters to bolster the detection performance of Kármán vortex streets. Utilizing reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF-ERA5), representative atmospheric features are extracted and subjected to feature permutation importance (PFI) analysis to quantitatively evaluate the influence of each parameter on the detection task. This analysis identifies five pivotal variables: geopotential, specific humidity, temperature, horizontal wind speed, and vertical air velocity, which are subsequently employed as inputs for the auxiliary task. Building upon the YOLOv8s object detection model, the proposed auxiliary network systematically examines the impact of various atmospheric variable combinations on detection efficacy. Experimental results demonstrate that the integration of horizontal wind speed and vertical air velocity achieves the highest detection metrics (precision of 0.838, recall of 0.797, mAP50 of 0.865, and mAP50-95 of 0.413) in precision-critical scenarios, outperforming traditional image-only detection method (precision of 0.745, recall of 0.745, mAP50 of 0.759, and mAP50-95 of 0.372). The optimized selection of atmospheric parameters markedly improves the detection metrics and reliability of Kármán vortex streets, underscoring the efficacy and practicality of the proposed methodological framework. This advancement paves the way for more robust automated analysis of atmospheric fluid dynamics phenomena.Full article

Using chaos theory, maximum entropies are calculated for 108 time series, each consisting of 28,463 hourly data of urban meteorology and pollutants. The series were measured with standardized and certified instruments (EPA) in six locations at different heights and in three periods (2010/2013, [...] Read more.

Using chaos theory, maximum entropies are calculated for 108 time series, each consisting of 28,463 hourly data of urban meteorology and pollutants. The series were measured with standardized and certified instruments (EPA) in six locations at different heights and in three periods (2010/2013, 2017/2020, and 2019/2022) in a basin geomorphology. Each urban meteorology series corresponds to relative humidity (RH), temperature (T), and wind speed magnitude (WS), and each pollutant series corresponds to 10 µm particulate matter (PM₁₀), 2.5 µm particulate matter (PM_2.5), and carbon monoxide (CO). These pollutants are in the top three places of presence in the studied geomorphology and in incidence in population diseases. From the calculated entropies, a quotient is constructed between the entropies of each of the first two urban meteorology variables (RH and T) and the sum of maximum entropies of the time series of anthropogenic pollutants, demonstrating the gradual decay in time of the quotient that is dominated by the maximum entropies of the pollutants. The latter leads to a more excited and warm boundary layer, due to thermal transfers, which makes it more unpredictable, increasing its capacity to contain water. It is verified that the diffusion is anomalous with alpha < 1 and that the contamination has a high probability, using a heavy-tailed probability function, of causing extreme events by influencing urban meteorology.Full article

Where and at what altitudes electromagnetic wave ducts within the atmosphere are likely to occur is important for a variety of communication and military applications. We examined the modified refractivity profiles and wave duct characteristics derived from nearly 50,000 observed upper air soundings [...] Read more.

Where and at what altitudes electromagnetic wave ducts within the atmosphere are likely to occur is important for a variety of communication and military applications. We examined the modified refractivity profiles and wave duct characteristics derived from nearly 50,000 observed upper air soundings obtained over four years from seven tropical and subtropical islands, as well as middle latitude sites at four US coastal locations, three sites near the Great Lakes, and four US inland sites. Across all location types, elevated ducts were found to be more common than surface-based ducts, and the median duct thicknesses were ~100 m. There was a weak correlation between duct thickness and strength and, essentially, no correlation between the duct strength and duct base height. EM ducts more frequently occurred at the tropical and subtropical island locations (~60%) and middle latitude coastal locations (70%) as compared to the less than 30% of the time that occurred at the Great Lake and US inland sites. The tropical and subtropical island sites were more likely than the other location types to have ducts at altitudes higher than 2 km, which is above the boundary layer height.Full article

Supplementary material:
Supplementary File 1 (ZIP, 105 KiB)

This study establishes a dataset of ancient military defense system sites in the Hexi Corridor area from the Han Dynasty to the Qing Dynasty to analyze the temporal changes and spatial distribution characteristics of the military defense system in different periods. In addition, [...] Read more.

This study establishes a dataset of ancient military defense system sites in the Hexi Corridor area from the Han Dynasty to the Qing Dynasty to analyze the temporal changes and spatial distribution characteristics of the military defense system in different periods. In addition, it compares the climate characteristics of the Hexi Corridor area though the past 2000 years. It also discusses the possible relationship between the construction of the Hexi military defense system and climate change. We found that the Han and Ming Dynasties were the main periods for constructing the regional military defense system. Furthermore, the Wei, Tsin, and Southern and Northern Dynasties expanded the scale based on the previous period. As a result, the spatial distribution was highly concentrated. During this time, multiple cold–dry and warm–humid periods occurred in the region. Moreover, significant climate change coincided with the heyday of building military facilities and the period of frequent warfare. Environmental factors have an impact on the spatial distribution of military sites. Therefore, the northern border war was the direct cause of the construction of the military defense system. However, the transformation of the environment caused by climate change was the fundamental driving force for this process, evolving across different eras.Full article

This study provides a comprehensive evaluation of dust events over Iran, using synoptic data from 286 meteorological stations. The dust events are classified according to synoptic dust codes as suspended dust and others (i.e., blowing dust, dust storms) and based on their intensity [...] Read more.

This study provides a comprehensive evaluation of dust events over Iran, using synoptic data from 286 meteorological stations. The dust events are classified according to synoptic dust codes as suspended dust and others (i.e., blowing dust, dust storms) and based on their intensity with horizontal visibility ≤1, 3, 5, and 10 km. Severe events (visibility ≤ 1 km) of suspended dust (code 06) occurred primarily in the western parts of Iran, while blowing dust events of moderate or severe intensity dominated over the south and eastern Iran, thus revealing a contrasting spatial distribution regarding the type and frequency of dust events. Furthermore, a distinct seasonality is revealed in the number of dust events, since suspended dust maximized in SW Iran from March to July, highly associated with Shamal winds, while blowing dust storms over south and east Iran maximized from April to August. Zabol city, east Iran, and some stations along the coast of the Arabian Sea are highly impacted by this type of dust storm throughout the year. Trend analysis revealed a notable increase in frequency of dust events during the period 1994–2023, particularly in the western part of Iran, mostly attributed to transboundary dust from the Mesopotamian plains. The large increase in dust activity during 1994–2009 was followed by a decrease during the 2010s at many stations, while notable differences were observed in the spatial distribution of the trends in suspended and blowing dust. An inverse correlation between dust events and precipitation anomalies was observed, since years with abnormal precipitation (e.g., 2019; 138% increase) were related to a substantial decrease in dust occurrence. Over an 11-year period, surface dust concentrations exceeded the annual PM₁₀ threshold of 50 µg/m³ on more than 800 days, with maximum concentrations reaching up to 1411 µg/m³. This highlights the urgent need for effective management strategies to mitigate the impacts of dust storms on air quality and public health in Iran.Full article

Accurate daily precipitation forecasting is crucial for the rational utilization of water resources and the prediction of flood disasters. To address the low reliability and low prediction accuracy of existing daily precipitation prediction models based on deep learning which arise from the nonlinear [...] Read more.

Accurate daily precipitation forecasting is crucial for the rational utilization of water resources and the prediction of flood disasters. To address the low reliability and low prediction accuracy of existing daily precipitation prediction models based on deep learning which arise from the nonlinear and non-stationary characteristics of surface precipitation data, this paper first employs the principal component analysis (PCA) method to extract the principal components of the original data. Given that the convolutional neural network (CNN) is adept at capturing spatial dependencies, bidirectional long short-term memory (Bi-LSTM, a variant of long short-term memory (LSTM)) can capture the long-term dependence of time-series data, and the attention mechanism allows the model to focus on the more important features of the input data. A PCA-CNN-BiLSTM-Attention fusion neural network was constructed. Taking Kunming, China as the study area, the experimental results demonstrate that the Nash efficiency coefficient of the proposed model reaches 0.993, which is 15.3% and 12.6% higher than that of the CNN-LSTM and CNN-BiLSTM models, respectively. This indicates high prediction accuracy and provides an effective and feasible method for daily precipitation prediction.Full article