Atmosphere, Volume 15, Issue 5 (May 2024) – 104 articles

This paper is the first to examine the operation of the HawkEye satellite in the presence of dust aerosol. The study region is the Black Sea. Dust transport dates were identified using visual inspection of satellite imagery, back-kinematic HYSPLIT trajectory analysis, CALIPSO aerosol stratification and typing maps, and the global forecasting model SILAM. In a comparative analysis of in-situ and satellite measurements of the remote sensing reflectance, an error in the atmospheric correction of HawkEye measurements was found both for a clean atmosphere and in the presence of an absorbing aerosol. It is shown that, on average, the dependence of the atmospheric correction error on wavelength has the form of a power function of the form from λ⁻³ to λ⁻⁹. The largest errors are in the short-wavelength region of the spectrum (412–443 nm) for the dust and dusty marine aerosol domination dates. A comparative analysis of satellite and in situ measurements of the optical characteristics of the atmosphere, namely the AOD and the Ångström parameter, was carried out. It is shown that the aerosol model used by HawkEye underestimates the Angström parameter and, most likely, large errors and outliers in satellite measurements are associated with this. Full article

The escalating concern regarding increasing air pollution and its impact on the health risks associated with PM_2.5 in developing countries necessitates attention. Thus, this study utilizes the WRF-CMAQ model to simulate the effects of meteorological conditions on PM_2.5 levels in Changchun, a typical city in China, during January 2017 and January 2020. Additionally, it introduces a novel health risk-based air quality index (NHAQI) to assess the influence of meteorological parameters and associated health risks. The findings indicate that in January 2020, the 2-m temperature (T2), 10-m wind speed (WS10), and planetary boundary layer height (PBLH) were lower compared to those in 2017, while air pressure exhibited a slight increase. These meteorological parameters, characterized by reduced wind speed, heightened air pressure, and lower boundary layer height—factors unfavorable for pollutant dispersion—collectively contribute to the accumulation of PM_2.5 in the atmosphere. Moreover, the NHAQI proves to be more effective in evaluating health risks compared to the air quality index (AQI). The annual average decrease in NHAQI across six municipal districts from 2017 to 2020 amounts to 18.05%. Notably, the highest health risks are observed during the winter among the four seasons, particularly in densely populated areas. The pollutants contributing the most to the total excess risk (ERtotal) are PM_2.5 (45.46%), PM10 (33.30%), and O3 (13.57%) in 2017, and PM_2.5 (67.41%), PM10 (22.32%), and O3 (8.41%) in 2020. These results underscore the ongoing necessity for PM_2.5 emission control measures while emphasizing the importance of considering meteorological parameters in the development of PM_2.5 reduction strategies. Full article

In order to provide insights into how various page views are influenced by public engagement with weather information and to shed light on the patterns of warning issuance across different seasons and regions, this study analyzes the multi-dimensional characteristics of city weather forecast page views and the spatiotemporal characteristics of early warning information in China, from 1 March 2020 to 31 August 2023. This is achieved by utilizing the daily page views of city weather forecasts and meteorological warning data, comparing the public’s attention to weather during holidays versus regular days, assessing the public’s attention to weather under different meteorological warning levels, and performing statistical analysis of the spatiotemporal scale of meteorological disasters. Our analysis shows that compared to weekends and holidays, the public pays more attention to the weather on weekdays, and the difference between weekdays and national statutory holidays is more significant. Due to the widespread impact of heat waves, typhoons, severe convective weather, and geological disasters caused by heavy rainfall, public awareness and participation in flood season weather forecasting have significantly increased. Under red alerts, flash floods, typhoons, and geological risks are the primary concerns. Orange alerts predominantly feature flash floods, rainstorms, typhoons, snowstorms, and cold waves, while sandstorms attract the most attention during yellow alerts. Droughts, however, receive relatively less attention regardless of the warning level. Seasonal patterns in the issuance of meteorological warnings reveal a peak in summer, particularly with typhoons and rainstorms being the main concerns in July, followed by high temperatures and additional typhoon warnings in August. Heavy sea surface wind warnings exhibit a strong seasonal trend, with the majority issued during the winter months. Regionally, southern China experiences the highest frequency of severe convection weather warnings, with provinces such as Jiangxi, Guangxi, and Hunan being the most affected. Full article

Understanding and addressing the implications of extreme temperature-related events are critical under climate change, as they directly impact public health and strain energy infrastructure. This study delved into the critical assessment of deadly heat stress and extreme cold events in the Upper Midwestern United States (UMUS), from 1979 to 2021, recognizing the substantial and disparate impact these phenomena have on socially vulnerable communities. In the current study, the modified Mann–Kendall method was applied to understand the temporal trend of extreme heat stress, as well as extreme cold events, from 1979 to 2021 in the UMUS. The results showed that the average annual frequency of daytime extreme heat stress events was comparatively lower in the northern parts of the UMUS compared to the southern parts from 1979 to 2021. Furthermore, a significant increasing trend in daytime extreme heat stress was found in parts of Michigan, Wisconsin (around the lake region), Ohio, and lower parts of Indiana and Kentucky from 1979 to 2021. In contrast, a decreasing trend was noticed in western parts of the UMUS (parts of Minnesota, Iowa, and Missouri). A significant decreasing trend in extreme cold events was found throughout the UMUS from 1979 to 2021. However, an increasing trend was also noticed in Iowa and northern parts of Minnesota, Michigan, and Wisconsin. The results provide important insights for better understanding the unique risks posed by extreme temperature-related events, especially toward socially vulnerable communities in the UMUS, which is crucial for developing targeted interventions and fostering resilience in the face of escalating climate-related threats. Full article

Establishing reliable and effective prediction models is a major research priority for air quality parameter monitoring and prediction and is utilized extensively in numerous fields. The sample dataset of air quality metrics often established has missing data and outliers because of certain uncontrollable causes. A broad learning system based on a semi-supervised mechanism is built to address some of the dataset’s data-missing issues, hence reducing the air quality model prediction error. Several air parameter sample datasets in the experiment were discovered to have outlier issues, and the anomalous data directly impact the prediction model’s stability and accuracy. Furthermore, the correlation entropy criteria perform better when handling the sample data’s outliers. Therefore, the prediction model in this paper consists of a semi-supervised broad learning system based on the correlation entropy criterion (CC-SSBLS). This technique effectively solves the issue of unstable and inaccurate prediction results due to anomalies in the data by substituting the correlation entropy criterion for the mean square error criterion in the BLS algorithm. Experiments on the CC-SSBLS algorithm and comparative studies with models like Random Forest (RF), Support Vector Regression (V-SVR), BLS, SSBLS, and Categorical and Regression Tree-based Broad Learning System (CART-BLS) were conducted using sample datasets of air parameters in various regions. In this paper, the root mean square error (RMSE) and mean absolute percentage error (MAPE) are used to judge the advantages and disadvantages of the proposed model. Through the experimental analysis, RMSE and MAPE reached 8.68 μg·m⁻³ and 0.24% in the Nanjing dataset. It is possible to conclude that the CC-SSBLS algorithm has superior stability and prediction accuracy based on the experimental results. Full article

(1) Introduction: The subject of the present study concerns the analysis of the existence and long time evolution of the solar ~200 yr (de Vries/Suess) and ~2400 yr (Hallstadt) cycles during the recent part of the Wurm ice epoch and the Holocene, as well as their forcing on the regional East European climate during the last two calendar millennia. The results obtained here are compared with those from our previous studies, as well as with the results obtained by other authors and with other types of data. A possible scenario of solar activity changes during the 21st century, as well as different possible mechanisms of solar–climatic relationships, is discussed. (2) Data and methods: Two types of indirect (historical) data series for solar activity were used: (a) the international radiocarbon tree ring series (INTCAL13) for the last 13,900 years; (b) the Schove series of the calendar years of minima and maxima and the magnitudes of 156 quasi 11 yr sunspot Schwabe–Wolf cycles since 296 AD and up to the sunspot cycle with number 24 (SC24) in the Zurich series; (c) manuscript messages about extreme meteorological and climatic events (Danube and Black Sea near-coast water freezing), extreme summer droughts, etc., in Bulgaria and adjacent territories since 296 and up to 1899 AD, when the Bulgarian meteorological dataset was started. A time series analysis and χ²-test were used. (3) Results and analysis: The amplitude modulation of the 200 yr solar cycle by the 2400 yr (Hallstadt) cycle was confirmed. Two groups of extremely cold winters (ECWs) during the last ~1700 years were established. Both groups without exclusion are concentrated near 11 yr sunspot cycle extremes. The number of ECWs near sunspot cycle minima is about 2 times greater than that of ECWs near sunspot cycle maxima. This result is in agreement with our earlier studies for the instrumental epoch since 1899 AD. The driest “spring-summer-early autumn” seasons in Bulgaria and adjacent territories occur near the initial and middle phases of the grand solar minima of the Oort–Dalton type, which relate to the downward phases and minima of the 200 yr Suess cycle. (4) Discussion: The above results confirm the effect of the Sun’s forcing on climate. However, it cannot be explained by the standard hypothesis for total solar irradiation (TSI) variations. That is why another hypothesis is suggested by the author. The mechanism considered by Svensmark for galactic cosmic ray (GCR) forcing on aerosol nuclei was taken into account. However, in the hypothesis suggested here, the forcing of solar X-ray flux changes (including solar flares) on the low ionosphere (the D-layer) and following interactions with the Earth’s lithosphere due to the terrestrial electric current systems play a key role for aerosol nuclei and cloud generation and dynamics during sunspot maxima epochs. The GCR flux maximum absorption layer at heights of 35–40 km replaces the ionosphere D-layer role during the sunspot minima epochs. Full article

Exposure to radon gas in households presents serious health risks, including an increased likelihood of lung cancer. Following the COVID-19 pandemic, the change in individual habits has led to more time spent in indoor environments with remote activities; thus, the need to raise the awareness of air quality in dwellings and to mitigate the exposure of inhabitants to radon has emerged. This study investigated radon gas concentrations in the air of Latvian dwellings. RadTrack2 passive detectors were deployed in a representative sample of households across 106 municipalities of Latvia (98% of the territory), yielding data from 487 households (973 detectors). The data revealed a median radon concentration of 52 Bq/m³ (Q1 and Q3 were 29 and 93 Bq/m³), with the majority of samples (95.6%) falling below the national reference limit of 200 Bq/m³. The building type and presence of a cellar significantly impacted radon levels, with structures lacking cellars and older buildings exhibiting higher concentrations. Mechanical ventilation proved to be more effective in reducing radon levels, compared to natural ventilation. These findings emphasize the necessity of proactive measures to mitigate indoor radon exposure and to ensure the well-being of occupants. Additionally, the dissemination of research data on radon exposure through open-access scientific publications is vital for raising awareness and implementing effective mitigation strategies. Full article

The ECMWF‘s ensemble (ECEPS) predictions are documented for the lifecycles of six tropical cyclones (TCs) that formed during a long-lasting Rossby wave breaking event in the western North Pacific. All six TC tracks started between 20° N and 25° N, and between 136° E and 160° E. All five typhoons recurved north of 30° N, and the three typhoons that did not make landfall had long tracks to 50° N and beyond. The ECEPS weighted mean vector motion track forecasts from pre-formation onward are quite accurate, with track forecast spreads that are primarily related to initial position uncertainties. The ECEPS intensity forecasts have been validated relative to the Joint Typhoon Warning Center (JTWC) Working Best Track (WBT) intensities (when available). The key results for Tokage (11 W) were the ECEPS forecasts of the intensification to a peak intensity of 100 kt, and then a rapid decay as a cold-core cyclone. For Hinnamnor (12 W), the key result was the ECEPS intensity forecasts during the post-extratropical transition period when Hinnamnor was rapidly translating poleward through the Japan Sea. For Muifa (14 W), the key advantage of the ECEPS was that intensity guidance was provided for longer periods than the JTWC 5-day forecast. The most intriguing aspect of the ECEPS forecasts for post-Merbok (15 W) was its prediction of a transition to an intense, warm-core vortex after Merbok had moved beyond 50° N and was headed toward the Aleutian Islands. The most disappointing result was that the ECEPS over-predicted the slow intensification rate of Nanmadol (16 W) until the time-to-typhoon (T2TY), but then failed to predict the large rapid intensification (RI) following the T2TY. The tentative conclusion is that the ECEPS model‘s physics are not capable of predicting the inner-core spin-up rates when a small inner-core vortex is undergoing large RI. Full article

This study explores the spatiotemporal variability and determinants of rainfall patterns during the March to May (MAM) season in Rwanda, incorporating an analysis of teleconnections with oceanic–atmospheric indices over the period 1983–2021. Utilizing the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset, the study employs a set of statistical tools including standardized anomalies, empirical orthogonal functions (EOF), Pearson correlation, the Mann–Kendall (MK) trend test, and Sen’s slope estimator to dissect the intricacies of rainfall variability, trends, and their association with large-scale climatic drivers. The findings reveal a distinct southwest to northwest rainfall gradient across Rwanda, with the MK test signaling a decline in annual precipitation, particularly in the southwest. The analysis for the MAM season reveals a general downtrend in rainfall, attributed in part to teleconnections with the Indian Ocean Sea surface temperatures (SSTs). Notably, the leading EOF mode for MAM rainfall demonstrates a unimodal pattern, explaining a significant 51.19% of total variance, and underscoring the pivotal role of atmospheric dynamics and moisture conveyance in shaping seasonal rainfall. The spatial correlation analysis suggests a modest linkage between MAM rainfall and the Indian Ocean Dipole, indicating that negative (positive) phases are likely to result in anomalously wet (dry) conditions in Rwanda. This comprehensive assessment highlights the intricate interplay between local rainfall patterns and global climatic phenomena, offering valuable insights into the meteorological underpinnings of rainfall variability during Rwanda’s critical MAM season. Full article

The purpose of this first article is to provide a physical basis for atmospheric Rossby waves at the tropopause to clarify their properties and improve our knowledge of their role in the genesis of extreme precipitation and heat waves. By analogy with the oceanic Rossby waves, the role played by the pycnocline in ocean Rossby waves is replaced here by the interface between the polar jet and the ascending air column at the meeting of the polar and Ferrel cell circulation or between the subtropical jet and the descending air column at the meeting of the Ferrel and Hadley cell circulation. In both cases, the Rossby waves are suitable for being resonantly forced in harmonic modes by tuning their natural period to the forcing period. Here, the forcing period is one year as a result of the variation in insolation due to solar declination. A search for cause-and-effect relationships is performed from the joint representation of the amplitude and phase of (1) the velocity of the cold or warm modulated airflows at 250 mb resulting from Rossby waves, (2) the geopotential height at 500 mb, and (3) the precipitation rate or ground air temperature. This is for the dominant harmonic mode whose period can be 1/16, 1/32, or 1/64 year, which reflects the intra-seasonal variations in the rising and falling air columns at the meeting of the polar, Ferrel, and Hadley cell circulation. Harmonics determine the duration of blocking. Two case studies referring to extreme cold and heat waves are presented. Dual cyclone–anticyclone systems seem to favor extreme events. They are formed by two joint vortices of opposite signs reversing over a period, concomitantly with the involved modulated airflows at the tropopause. A second article will be oriented toward (1) the examination of different case studies in order to ascertain the common characteristics of Rossby wave patterns leading to extreme events and (2) a map of the globe revealing future trends in the occurrence of extreme events. Full article

The unprecedented reductions in anthropogenic emissions over the COVID-19 lockdowns were utilised to investigate the response of ozone (O₃) concentrations to changes in its precursors across various UK sites. Ozone, volatile organic compounds (VOCs) and NO_x (NO+NO₂) data were obtained for a 3-year period encompassing the pandemic period (January 2019–December 2021), as well as a pre-pandemic year (2017), to better understand the contribution of precursor emissions to O₃ fluctuations. Compared with pre-lockdown levels, NO and NO₂ declined by up to 63% and 42%, respectively, over the lockdown periods, with the most significant changes in pollutant concentrations recorded across the urban traffic sites. O₃ levels correspondingly increased by up to 30%, consistent with decreases in the [NO]/[NO₂] ratio for O₃ concentration response. Analysis of the response of O₃ concentrations to the NO_x reductions suggested that urban traffic, suburban background and suburban industrial sites operate under VOC-limited regimes, while urban background, urban industrial and rural background sites are NO_x-limited. This was in agreement with the [VOC]/[NO_x] ratios determined for the London Marylebone Road (LMR; urban traffic) site and the Chilbolton Observatory (CO; rural background) site, which produced values below and above 8, respectively. Conversely, [VOC]/[NO_x] ratios for the London Eltham (LE; suburban background) site indicated NO_x-sensitivity, which may suggest the [VOC]/[NO_x] ratio for O₃ concentration response may have had a slight NO_x-sensitive bias. Furthermore, O₃ concentration response with [NO]/[NO₂] and [VOC]/[NO_x] were also investigated to determine their relevance and accuracy in identifying O₃-NO_x-VOC relationships across UK sites. While the results obtained via utilisation of these metrics would suggest a shift in photochemical regime, it is likely that variation in O₃ during this period was primarily driven by shifts in oxidant (OX; NO₂ + O₃) equilibrium as a result of decreasing NO₂, with increased O₃ transported from Europe likely having some influence. Full article

Mediterranean water-stressed areas face significant challenges from higher temperatures and increasingly severe droughts. We assess the effect of climate change on rainfed barley production in the aridity-prone province of Almería, Spain, using the FAO AquaCrop model. We focus on rainfed barley growth by the mid-century (2041–2070) and end-century (2071–2100) time periods, using three Shared Socio-economic Pathway (SSP)-based scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Using the paired t-test, Spearman and Pearson correlation coefficient, Root Mean Squared Error, and relative Root Mean Squared Error, we verified AquaCrop’s ability to capture local multi-year trends (9 or more years) using standard barley crop parameters, without local recalibration. Starting with a reference Initial Soil Water Content (ISWC), different soil water contents within barley rooting depth were modelled to account for decreases in soil water availability. We then evaluated the efficiency of different climate adaptation strategies: irrigation, mulching, and changing sowing dates. We show average yield changes of +14% to −44.8% (mid-century) and +12% to −55.1% (end-century), with ISWC being the main factor determining yields. Irrigation increases yields by 21.1%, utilizing just 3% of Almería’s superficial water resources. Mulches improve irrigated yield performances by 6.9% while reducing irrigation needs by 40%. Changing sowing dates does not consistently improve yields. We demonstrate that regardless of the scenario used, climate adaptation of field barley production in Almería should prioritize limiting soil water loss by combining irrigation with mulching. This would enable farmers in Almería’s northern communities to maintain their livelihoods, reducing the province’s reliance on horticulture while continuing to contribute to food security goals. Full article

The prediction of typhoon tracks in the Northwest Pacific is key to reducing human casualties and property damage. Traditional numerical forecasting models often require substantial computational resources, are high-cost, and have significant limitations in prediction speed. This research is dedicated to using deep learning methods to address the shortcomings of traditional methods. Our method (AFR-SimVP) is based on a large-kernel convolutional spatio-temporal prediction network combined with multi-feature fusion for forecasting typhoon tracks in the Northwest Pacific. In order to more effectively suppress the effect of noise in the dataset to enhance the generalization ability of the model, we use a multi-branch structure, incorporate an atmospheric reconstruction subtask, and propose a second-order smoothing loss to further improve the prediction ability of the model. More importantly, we innovatively propose a multi-time-step typhoon prediction network (HTAFR-SimVP) that does not use the traditional recurrent neural network family of models at all. Instead, through fine-to-coarse hierarchical temporal feature extraction and dynamic self-distillation, multi-time-step prediction is achieved using only a single regression network. In addition, combined with atmospheric field reconstruction, the network achieves integrated prediction for multiple tasks, which greatly enhances the model’s range of applications. Experiments show that our proposed network achieves optimal performance in the 24 h typhoon track prediction task. Our regression network outperforms previous recurrent network-based typhoon prediction models in the multi-time-step prediction task and also performs well in multiple integration tasks. Full article

In this study, the constant temperature control system of a heating plate was established, ethanol–diesel fuel with different proportions was prepared, and a series of experiments were carried out. The experimental system was used to observe, summarize, and analyze four evaporation and crushing modes of mixed droplets, which were explosion, liquid filament stretching, exocytosis, and ejection mode. The evaporation process of four kinds of mixed droplets in their life cycle was analyzed by normalizing the diameter square. It was proposed that the evaporation process of droplets could be divided into the following three stages: a heating stage, a fluctuating evaporation stage, and an equilibrium evaporation stage. It was also pointed out that the expansion, ejection, and micro-explosion of droplets were the causes of fluctuating evaporation. The concept of expansion and crushing intensity was put forward and the expansion and crushing intensity of ethanol/diesel mixed droplets with different proportions were calculated. The reasons why expansion and crushing intensity first increased and decreased with the increase in ethanol blending ratio were analyzed. Finally, the time proportion of ethanol–diesel mixed droplets in each evaporation stage was calculated, which explained that the time proportion of the instantaneous heating stage showed a parabolic law with the increase in ethanol content. Full article

In this work, PM_2.5 was sampled at Dunkerque, a medium-sized city located in northern France. The mean concentration of PM_2.5 during the sampling period was 12.6 ± 9.5 μg·m⁻³. Samples were analyzed for elemental and organic carbon (EC/OC), water-soluble organic carbon (WSOC), humic-like substances (HULIS-C), water-soluble inorganic ions, and major and trace elements. The origin and the variations of species concentrations were examined using elemental enrichment factors, bivariate polar plot representations, and diagnostic concentration ratios. Secondary inorganic ions were the most abundant species (36% of PM_2.5), followed by OC (12.5% of PM_2.5). Secondary organic carbon (SOC) concentrations were estimated to account for 52% of OC. A good correlation between SOC and WSOC indicated that secondary formation processes significantly contribute to the WSOC concentrations. HULIS-C also represents almost 50% of WSOC. The determination of diagnostic ratios revealed the influence of anthropogenic emission sources such as integrated steelworks and fuel oil combustion. The clustering of 72 h air masses backward trajectories data evidenced that higher concentrations of PM_2.5, OC, and secondary inorganic aerosols were recorded when air masses came from north-eastern Europe and the French continental sector, showing the considerable impact of long-range transport on the air quality in northern France. Full article

Low-coverage line-belt-pattern protective forests offer significant advantages in terms of wind and sand control measures. It is important to study the windbreak effectiveness of sand-fixing forests with different spacing for the construction and optimization of plant shelterbelt configurations. The effect of plant spacing on the flow field around a row of trees was investigated using the k-ε turbulence model coupled with the porous media model. In order to accurately simplify the complex and stochastic plant constitutive features, we simplify the plant canopy to a circular platform geometry, which introduces a porous media model, and the plant trunk is simulated as a solid cylinder. The simulation results show that windbreaks only affect wind profiles up to 1.25-times the height of the tree; on the leeward side of the canopy, large-spaced shelterbelts provide greater protection in the near-wake zone, while small-spaced shelterbelts are more effective at reducing velocity in the re-equilibration zone. The flow field recovery properties of the trunk and canopy indicate that the canopy wake zone is longer. In this study, we also quantitatively analyze the relationship between average wind protection effectiveness as a function of plant spacing and streamwise distance from the leeward side of the canopy, and the given parameterized scheme shows a power exponential relationship between wind protection effectiveness and plant spacing and a logarithmic relationship with streamwise distance. This scheme can provide a predictive assessment of the effects during the implementation of the plant shelterbelt. Full article

An assessment of the projected changes in precipitation extremes for the 21st century is presented here for Greece and its individual administrative regions. The analysis relies on an ensemble of high-resolution Regional Climate Model (RCM) simulations following various Representative Concentration Pathways (RCP2.6, RCP4.5, and RCP8.5). The simulated changes in future annual total precipitation (PRTOT) under the examined scenarios are generally negative but statistically non-robust, except towards the end of the century (2071–2100) over high-altitude mountainous regions in Western Greece, Peloponnese, and Crete under RCP8.5. The pattern of change in the number of very heavy precipitation days (R20) is linked to the respective pattern of the PRTOT change with a statistically robust decrease of up to −5 days per year only over parts of the high-altitude mountainous regions in Western Greece, Peloponnese, and Crete for 2071–2100 under RCP8.5. Contrasting the future tendency for decrease in total precipitation and R20, the changes in the intensity of precipitation extremes show a tendency for intensification. However, these change patterns are non-robust for all periods and scenarios. Statistical significance is indicated for the highest 1-day precipitation amount in a year (Rx1day) for the administrative regions of Thessaly, Central Greece, Ionian Islands, and North Aegean under RCP8.5 in 2071–2100. The changes in the contribution of the wettest day per year to the annual total precipitation (RxTratio) are mainly positive but non-robust for most of Greece and all scenarios in the period 2021–2050, becoming more positive and robust in 2071–2100 for RCP8.5. This work highlights the necessity of taking into consideration high-resolution multi-model RCM estimates in future precipitation extremes with various scenarios, for assessing their potential impact on flood episodes and the strategic planning of structure resilience at national and regional level under the anticipated human-induced future climate change. Full article

Ozone pollution in chemical industrial parks is severe and complicated and is significantly influenced by pollutant emissions and meteorological parameters. In this study, we innovatively investigated the formation rules of ozone by using observation-based analyses and a gradient-boosting decision tree (GBDT) model, focusing on a typical chemical industrial park located in the Yangtze River Delta of China. The results revealed that ozone concentration was positively correlated with temperature while negatively correlated with NO₂ concentration and relative humidity (RH). Ozone pollution was predominantly observed from April to October (M_4–10). The optimized GBDT model was subsequently utilized to establish a specific and quantifiable relationship between each single dominant impact factor (RH, NO₂, temperature, and PM_2.5) and ozone within a complex and uncertain multi-factor context during M_4–10. Detailed discussions were conducted on the reaction rate of ozone-related to different levels of RH and temperature. The accumulation of ozone was favored by high temperature and low RH, with the maximum ozone concentration observed at the RH of 50% and the temperature of 35 °C. The NO₂-O₃ change curve exhibited distinct phases, including a period of stability, gradual increase, rapid increase, and equilibrium. During the second and third periods, the ratio of ozone production to NO₂ consumption was 0.10 and 2.73, respectively. Furthermore, there was a non-monotonic relationship between variations in ozone concentration and PM_2.5 concentration. Hence, it is imperative to implement fine control strategies in the park, such as adopting seasonal production strategies, implementing targeted measures for controlling NO_x and active VOCs, and employing special control methods during periods of high temperature. This study provides aid in achieving effective management of localized ozone pollution and ensuring compliance with air quality standards. Full article

Drawing upon a comprehensive global database of landslides and utilizing high-resolution IMERG satellite precipitation data, this study investigates the spatial and temporal variations of landslide occurrences across the member states of the Association of Southeast Asian Nations (ASEAN). This study constructs a region-specific, graded warning system by formulating an average effective intensity–duration (I–D) rainfall threshold curve for each ASEAN member. Examination of 1747 landslide events spanning from 2006 to 2018 illustrates a significant association between the frequency of landslides in ASEAN regions and the latitudinal movement of local precipitation bands. Incidences of landslides hit their lowest in March and April, while a surge is observed from October to January, correlating with the highest mortality rates. Geographical hotspots for landslide activity, characterized by substantial annual rainfall and constrained landmasses, include the Philippine archipelago, Indonesia’s Java Island, and the Malay Peninsula, each experiencing an average of over 2.5 landslides annually. Fatalities accompany approximately 41.4% of ASEAN landslide events, with the Philippines and Indonesia registering the most substantial numbers. Myanmar stands out for the proportion of large-scale landslide incidents, with an average casualty rate of 10.89 deaths per landslide, significantly surpassing other countries in the region. The I–D rainfall threshold curves indicate that the Philippines experienced the highest precipitation levels before landslide initiation, whereas Myanmar has the threshold set at a considerably lower level. Full article

Due to budget and sensor technology constraints, a single sensor cannot simultaneously provide observational images with both a high spatial and temporal resolution. To solve the above problem, the spatiotemporal fusion (STF) method was proposed and proved to be an indispensable tool for monitoring land surface dynamics. There are relatively few systematic reviews of the STF method. Bibliometrics is a valuable method for analyzing the scientific literature, but it has not yet been applied to the comprehensive analysis of the STF method. Therefore, in this paper, we use bibliometrics and scientific mapping to analyze the 2967 citation data from the Web of Science from 1991 to 2023 in a metrological manner, covering the themes of STF, data fusion, multi-temporal analysis, and spatial analysis. The results of the literature analysis reveal that the number of articles displays a slow to rapid increase during the study period, but decreases significantly in 2023. Research institutions in China (1059 papers) and the United States (432 papers) are the top two contributors in the field. The keywords “Sentinel”, “deep learning” (DL), and “LSTM” (Long Short-Term Memory) appeared most frequently in the past three years. In the future, remote sensing spatiotemporal fusion research can address more of the limitations of heterogeneous landscapes and climatic conditions to improve fused images’ accuracy. Full article

This study investigated the impact of the June 2015 geomagnetic storms on the Brazilian equatorial and low-latitude ionosphere by analyzing various data sources, including solar wind parameters from the advanced compositional explorer satellite (ACE), global positioning satellite vertical total electron content (GPS-VTEC), geomagnetic data, and validation of the SAMI2 model-VTEC with GPS-VTEC. The effect of geomagnetic disturbances on the Brazilian longitudinal sector was examined by applying multiresolution analysis (MRA) of the maximum overlap discrete wavelet transform (MODWT) to isolate the diurnal component of the disturbance dynamo (Ddyn), DP2 current fluctuations from the ionospheric electric current disturbance (Diono), and semblance cross-correlation wavelet analysis for local phase comparison between the Sq and Diono currents. Our findings revealed that the significant fluctuations in DP2 at the Brazilian equatorial stations (Belem, dip lat: −0.47° and Alta Floresta, dip lat: −3.75°) were influenced by IMF Bz oscillations; the equatorial electrojet also fluctuated in tandem with the DP2 currents, and dayside reconnection generated the field-aligned current that drove the DP2 current system. The short-lived positive ionospheric storm during the main phase on 22 June in the Southern Hemisphere in the Brazilian sector was caused by the interplay between the eastward prompt penetration of the magnetospheric convection electric field and the westward disturbance dynamo electric field. The negative ionospheric storms that occurred during the recovery phase from 23 to 29 June 2015, were attributed to the westward disturbance dynamo electric field, which caused the downward $\mathrm{E} \times \mathrm{B}$ drift of the plasma to a lower height with a high recombination rate. The comparison between the SAMI2 model-VTEC and GPS-VTEC indicates that the SAMI2 model underestimated the VTEC within magnetic latitudes of −9° to −24° in the Brazilian longitudinal sector from 6 to 17 June 2015. However, it demonstrated satisfactory agreement with the GPS-VTEC within magnetic latitudes of −9° to 10° from 8 to 15 June 2015. Conversely, the SAMI2 model overestimated the VTEC between ±10° magnetic latitudes from 16 to 28 June 2015. The most substantial root mean square error (RMSE) values, notably 10.30 and 5.48 TECU, were recorded on 22 and 23 June 2015, coinciding with periods of intense geomagnetic disturbance. Full article

Aerosol–cloud–precipitation interactions (ACI) are a known major cause of uncertainties in simulations of the future climate. An improved understanding of the in-cloud processes accompanying ACI could help in advancing their implementation in global climate models. This is especially the case for marine stratocumulus clouds, which constitute the most common cloud type globally. In this work, a dataset composed of satellite observations and reanalysis data is used in explainable machine learning models to analyze the relationship between the cloud droplet number concentration ($N_d$), cloud liquid water path (LWP), and the fraction of precipitating clouds (PF) in five distinct marine stratocumulus regions. This framework makes use of Shapley additive explanation (SHAP) values, allowing to isolate the impact of $N_d$ from other confounding factors, which proved to be very difficult in previous satellite-based studies. All regions display a decrease of PF and an increase in LWP with increasing $N_d$, despite marked inter-regional differences in the distribution of $N_d$. Polluted (high $N_d$) conditions are characterized by an increase of 12 gm⁻² in LWP and a decrease of 0.13 in PF on average when compared to pristine (low $N_d$) conditions. The negative $N_d$–PF relationship is stronger in high LWP conditions, while the positive $N_d$–LWP relationship is amplified in precipitating clouds. These findings indicate that precipitation suppression plays an important role in MSC adjusting to aerosol-driven perturbations in $N_d$. Full article

This study identified the practices of adaptation strategies to climate change in Jimma, Bedelle, Bonga, and Sokorru urban centers using a survey of 384 households, 55 key informant interviews, 4 focus group discussions, and field observations. A cross-sectional study design was employed from 2019 to 2021. The adaptive capacity of municipalities to reduce climate extreme events was rated as poor by the majority (51%), mostly reactive measures (76%). The climate hazards identified in four urban centers were riverine and flash floods, urban heat waves, landslides, and windstorms. The urban households practiced lifestyle modification, reduce paved surfaces, the use of air conditioner, planting trees, and multiple windows. The adaptation strategies practiced by municipalities include the relocation of prone areas, the support of basic amenities, the construction of protection walls, diversion ditches, the clearance of waterways and rivers, greenery, and park development. The adaptation actions were constrained by a lack of awareness, commitment, cooperation and coordination, adaptive capacity, and participation. Gray/physical infrastructures (costly but important) as adaptation actions were hampered by the low municipal capacity. We recommend that urban authorities should incorporate climate change adaptation strategies into urban planning and development proactively to ensure future resilient climate smart urban centers of southwest Ethiopia. Full article

Particular matter (PM) constitutes one of the major air pollutants. Human exposure to fine PM (PM with a median diameter less than or equal to 2.5 μm, PM_2.5) has many negative and diverse outcomes for human health, such as respiratory mortality, lung cancer, etc. Accurate air-quality forecasting on a regional scale enables local agencies to design and apply appropriate policies (e.g., meet specific emissions limitations) to tackle the problem of air pollution. Under this framework, low-cost sensors have recently emerged as a valuable tool, facilitating the spatiotemporal monitoring of air pollution on a local scale. In this study, we present a deep learning approach (long short-term memory, LSTM) to forecast the intra-day air pollution exceedances across urban and suburban areas. The PM_2.5 data used in this study were collected from 12 well-calibrated low-cost sensors (Purple Air) located in the greater area of the Municipality of Thermi in Thessaloniki, Greece. The LSTM-based methodology implements PM_2.5 data as well as auxiliary data, meteorological variables from the Copernicus Atmosphere Monitoring Service (CAMS), which is operated by ECMWF, and time variables related to local emissions to enhance the air pollution forecasting performance. The accuracy of the model forecasts reported adequate results, revealing a correlation coefficient between the measured PM_2.5 and the LSTM forecast data ranging between 0.67 and 0.94 for all time horizons, with a decreasing trend as the time horizon increases. Regarding air pollution exceedances, the LSTM forecasting system can correctly capture more than 70.0% of the air pollution exceedance events in the study region. The latter findings highlight the model’s capabilities to correctly detect possible WHO threshold exceedances and provide valuable information regarding local air quality. Full article

In addition to the detrimental health consequences, the early stages of the COVID-19 pandemic have yielded unforeseen benefits in terms of reducing air pollution emissions. This study investigated air pollution changes in Novi Sad, Serbia, during the COVID-19 lockdown (March–June 2020) and their correlation with acute exacerbations of chronic obstructive pulmonary disease (AECOPD) hospitalizations. Using quasi-Poisson generalized linear models (GLM) and distributed lag non-linear models (DLNM), we examined the relationship between the number of AECOPD hospitalizations and the concentrations of selected air pollutants (PM₁₀, PM_2.5, SO₂, and NO₂) from March to June of 2019, 2020, and 2021. During the COVID-19 lockdown, significant reductions in most air pollutant concentrations and the number of AECOPD hospitalizations were observed. However, neither the study year nor its interaction with air pollutant concentration significantly predicted AECOPD hospitalizations (p > 0.05). The 95% confidence intervals of the relative risks for the occurrence of AECOPD hospitalizations at each increase in the examined air pollutant by 10 μg/m³ overlapped across years, suggesting consistent effects of air pollution on the risk of AECOPD hospitalizations pre-pandemic and during lockdown. In conclusion, reduced air pollution emissions during the COVID-19 lockdown did not lead to a statistically significant change in the number of AECOPD hospitalizations. Full article

A recent study the detection of coseismic ionospheric disturbances or ionoquakes less than 400 s from the earthquake’s onset. The study also associates these rapid ionoquakes with the seismo-atmosphere–ionosphere (SAI) coupling mechanism energized by acoustic-gravity waves (AGWs) and the subsequent formation of coseismic thermospheric disturbances (CSTDs). The present study outlines a new analytical simulation code for AGWs that resolves the governing equations in the time–altitude and wavenumber domain and confirms the rapid arrival of AGWs in the thermosphere (earlier than the estimated arrival time from the ray-tracing simulation). The rapid arrivals of AGWs are associated with long wavelengths that connect to thermospheric altitudes and propagate with thermospheric sound speeds, avoiding averaging effects from the lower atmosphere. The fast simulation traces the rapid arrival of AGWs in the thermosphere and produces rapid CSTDs within 250–300 s from the earthquake’s onset. The simulation time is much shorter than the formation time of near-field CSTDs, a scenario favorable for the forecasting of CSTDs before observations of ionoquakes. In essence, the fast simulation offers an alternative tool for tracking the evolution of CSTDs. Full article

The high complexity of the parameter–simulation problem in land surface models over semiarid areas makes it difficult to reasonably estimate the surface simulation conditions that are important for both weather and climate in different regions. In this study, using the dense site datasets of a typical semiarid region over Tibet and the Noah land surface model with the constrained land parameters of multiple sites, an enhanced Kling–Gupta efficiency criterion comprising multiple objectives, including variable and layer dimensions, was obtained, which was then applied to calibration schemes based on two global search algorithms (particle swarm optimization and shuffled complex evaluation) to investigate the site-scale spatial complexities in soil temperature simulations. The calibrations were then compared and further validated. The results show that the Noah land surface model obtained reasonable simulations of soil moisture against the observations with fine consistency, but the negative fit and huge spatial errors compared with the observations indicated its weak ability to simulate the soil temperature over regional semiarid land. Both calibration schemes significantly improved the soil moisture and temperature simulations, but particle swarm optimization generally converged to a better objective than shuffled complex evaluation, although with more parameter uncertainties and less heterogeneity. Moreover, simulations initialized with the optimal parameter tables for the calibrations obtained similarly sustainable improvements for soil moisture and temperature, as well as good consistency with the existing soil reanalysis. In particular, the soil temperature simulation errors for particle swarm optimization were unbiased, while those for the other method were found to be biased around −3 K. Overall, particle swarm optimization was preferable when conducting soil temperature simulations, and it may help mitigate the efforts in surface forecast improvement over semiarid regions. Full article

Assessing indoor environmental quality (IEQ) is fundamental to ensuring health, well-being, and safety. A particular type of indoor compartment, land transport cabins (LTCs), specifically those of trains and buses, was surveyed. The global rise in commute and in-cabin exposure time gives relevance to the current study. This study discusses indoor climate (IC) in LTCs to emphasize the risk to the well-being and comfort of exposed occupants linked to poor IEQ, using objective assessment and a communication method following recommendations of the CEN-EN16798-1 standard. The measurement campaign was carried out on 36 trips of real-time travel on 15 buses and 21 trains, mainly in the EU region. Although the measured operative temperature, relative humidity, CO₂, and VOC levels followed EN16798-1 requirements in most cabins, compliance gaps were found in the indoor climate of these LTCs as per ventilation requirements. Also, the PMV-PPD index evaluated in two indoor velocity ranges of 0.1 and 0.3 m/s showed that 39% and 56% of the cabins, respectively, were thermally inadequate. Also, ventilation parameters showed that indoor air quality (IAQ) was defective in 83% of the studied LTCs. Therefore, gaps exist concerning the IC of the studied LTCs, suggesting potential risks to well-being and comfort and the need for improved compliance with the IEQ and ventilation criteria of EN16798-1. Full article

Cardiovascular diseases (CVDs) persist as a significant contributor to global morbidity and mortality despite advances in medical technology. Air pollution has emerged as a significant contemporary challenge due to increased energy consumption and rapid economic development. The study utilized multivariable Poisson regression and Distributed Lag Models (DLM) to assess the link between brief exposure to outdoor air pollutants (PM₁₀—particulate matter with a diameter ≤ 10 μm, NO₂—nitrogen dioxide, and O₃—ozone) and the risk of acute myocardial infarction with ST-segment elevation (STEMI) hospitalization, stratified by season. The research was conducted from January 2019 to December 2021 at the University Hospital in Timisoara, Romania, and daily records were collected for STEMI admissions, atmospheric pollutant levels, and meteorological parameters. The most pronounced impacts were observed with each 10 μg/m³ increase at lag 07 for PM₁₀ during summer, leading to a 2% increase in STEMI admissions, and for NO₂ during spring at lag 07, resulting in a 0.9% rise in CVD incidence. Men, middle-aged adults, and older adults exhibited greater susceptibility to elevated NO₂ and PM₁₀ concentrations than women and younger individuals. Brief exposure to diverse air pollutants heightens the likelihood of hospitalization due to STEMI, particularly among men and adults over 45. Effective measures must be implemented to mitigate these impacts, especially for vulnerable populations. Full article