Search Results (31)

Atmospheric aerosols significantly impact air quality, human health, and regional climate, with regions like the Caribbean Basin affected by various aerosol types, including marine, anthropogenic, and desert dust particles. This study utilizes Agglomerative Hierarchical Clustering (AHC) to analyze more than a decade of Aerosol Robotic Network (AERONET) data (2007–2023) from four Caribbean islands: Barbados, Guadeloupe, Puerto Rico, and Cuba. We examined sixteen physical parameters, including Aerosol Optical Depth (AOD), Angstrom Exponent (AE), and Volume Particle Size Distribution (VPSD), to identify distinct aerosol regimes and groups of daily measurements displaying similar aerosol optical properties. The originality of this work lies in the significant number of parameters considered to achieve a classification free of arbitrary orientation. The clustering method identified specific periods and aerosol characteristics, revealing seasonal patterns of background marine aerosols and Saharan dust events. By referring to existing research and using analysis tools such as VPSD and AE versus AOD representation, we aimed to define value ranges of physical parameters attributable to marine, dust, and mixed aerosols in the Caribbean region. The results underscore the diversity of aerosol sources and their seasonal variations across the Caribbean, providing critical insights for improving regional air quality management. This classification approach integrates comprehensive aerosol properties and is reinforced by the analysis of atmospheric circulation using the HYSPLIT model. These findings not only advance the characterization of aerosol regimes but also contribute to sustainable air quality management practices by providing actionable data to mitigate the adverse health and environmental impacts of aerosols. Full article

Mineral dust from desert areas accounts for a large portion of aerosols globally, estimated at 3–4 billion tons per year. Aerosols emitted from arid and semi-arid areas, e.g., from parched lakes or rivers, are transported over long distances and have effects on a global scale, affecting the planet’s radiative balance, atmospheric chemistry, cloud formation and precipitation, marine biological processes, air quality, and human health. Desert dust transport takes place in the atmosphere as the result of a dynamical sequence beginning with dust uplift from desert areas, then followed by the long-range transport and terminating with the surface deposition of mineral dust in areas even very far from dust sources. The Mediterranean basin is characterized by frequent dust intrusion events, particularly affecting Spain, France, Italy, and Greece. Such events contribute to the increase in PM₁₀ and PM_2.5 concentration values, causing legal threshold values to be exceeded. In recent years, these events have shown a non-negligible increase in frequency and intensity. The present work reports the results of an analysis of the dust events that in recent years (2018–2023) affected the Mediterranean area and in particular central Italy, focusing on the more recurrent meteorological configurations leading to long-range transport and on the consequent increase in aerosol concentration values. A method for desert intrusion episodes identification has been developed using both numerical forecast model data and PM₁₀ observed data. A multi-year dataset has been analyzed by applying such an identification method and the resulting set of dust events episodes, affecting central Italy, has been studied in order to highlight their frequency on a seasonal basis and their interannual variability. In addition, a first attempt at a meteorological classification of desert intrusions has been carried out to identify the most recurrent circulation patterns related to dust intrusions. Understanding their annual and seasonal variations in frequency and intensity is a key topic, whose relevance is steeply growing in the context of ongoing climate change. Full article

The traditional power prediction methods cannot fully take into account the differences and similarities between units. In the face of the complex and changeable sea climate, the strong coupling effect of atmospheric circulation, ocean current movement, and wave fluctuation, the characteristics of wind processes under different incoming currents and different weather are very different, and the spatio-temporal correlation law of offshore wind processes is highly complex, which leads to traditional power prediction not being able to accurately predict the short-term power of offshore wind farms. Therefore, aiming at the characteristics and complexity of offshore wind power, this paper proposes an innovative short-term power prediction method for offshore wind farms based on a Gaussian mixture model (GMM). This method considers the correlation between units according to the characteristics of the measured data of units, and it divides units with high correlation into a category. The Bayesian information criterion (BIC) and contour coefficient method (SC) were used to obtain the optimal number of groups. The average intra-group correlation coefficient (AICC) was used to evaluate the reliability of measurements for the same quantized feature to select the representative units for each classification. Practical examples show that the short-term power prediction accuracy of the model after unit classification is 2.12% and 1.1% higher than that without group processing, and the mean square error and average absolute error of the short-term power prediction accuracy are reduced, respectively, which provides a basis for the optimization of prediction accuracy and economic operation of offshore wind farms. Full article

Wetland ecosystems in the Qinghai–Tibet Plateau are pivotal for global ecology and regional sustainability. This study investigates the dynamic changes in wetland ecosystems within the Chaidamu Basin and their response to drought, aiming to foster sustainable wetland utilization in the Qinghai–Tibet Plateau. Using Landsat TM/ETM/OLI data on the Google Earth Engine platform, we employed a random forest (RF) method for annual long-term land cover classification. Standardized precipitation evapotranspiration indices (SPEI3, SPEI6, SPEI9, and SPEI12) on different time scales were used to assess meteorological drought conditions. We employed a Pearson correlation analysis to examine the relationship between wetland changes and various SPEI scales. The BFASAT method was used to evaluate the impact of SPEI12 trends on the wetlands, while a cross-wavelet analysis explored teleconnections between SPEI12 and atmospheric circulation factors. Our conclusion is as follows: The wetlands, including lake, glacier, and marsh wetlands, exhibited a noticeable increasing trend. Wetland expansion occurred during specific periods (1990–1997, 1998–2007, and 2008–2020), featuring extensive conversions between wetlands and other types, notably the conversion from other types to wetlands. Spatially, lake and marsh wetlands predominated in the low-latitude basin, while glacier wetlands were situated at higher altitudes. There were significant negative correlations between the SPEI at various scales and the total wetland area and types. SPEI12 displayed a decreasing trend with non-stationarity and distinct breakpoints in 1996, 2002, and 2011, indicating heightened drought severity. Atmospheric circulation indices (ENSO, NAO, PDO, AO, and WP) exhibited varying degrees of resonance with SPEI12, with NAO, PDO, AO, and WP demonstrating longer resonance times and pronounced responses. These findings underscore the significance of comprehending wetland changes and drought dynamics for effective ecological management in the Chaidamu Basin of the Qinghai–Tibet Plateau. Full article

The atmospheric circulation, not only near the surface but also at high altitudes, is probably the main factor determining the weather and climate of a given area, along with its latitude, altitude, the shape of the relief of the area and its surroundings, and the proximity of water basins of different sizes. The main objective of this study is to investigate the relationship between anticyclonic circulation types in the middle troposphere at the 500 hPa level and the seasonal summer temperature over the region of the central Balkan Peninsula, particularly Bulgaria. A previously compiled classification of atmospheric circulation is used, and the frequencies of the circulation types are correlated with the mean seasonal (monthly) temperature, where the extreme seasons and months are defined as the 10th percentile for cold summer seasons and months and the 90th percentile for warm ones. A positive and statistically significant correlation was found for the anticyclones located southwest of Bulgaria and a negative one for those located southeast of it. A comparison between the last two 30-year climatological periods (1961–1990 and 1991–2020) was also made, and an irrefutable decrease in the number of cold summer seasons from 257 to just 17 was found in the last 30 years, respectively, as well as a rapid increase in the number of extreme warm summer seasons from 26 to 263, encompassing all 15 meteorological stations studied. Full article

Thin ice with a thickness of less than half a meter produces strong salt and heat fluxes which affect deep water circulation and weather in the polar oceans. The identification of thin ice areas is essential for ship navigation. We have developed thin ice detection algorithms for the AMSR2 and FY-3C MWRI radiometer data over the Arctic Ocean. Thin ice (<20 cm) is detected based on the classification of the H-polarization 89–36-GHz gradient ratio (GR8936H) and the 36-GHz polarization ratio (PR36) signatures with a linear discriminant analysis (LDA) and thick ice restoration with GR3610H. The brightness temperature ($T_B$) data are corrected for the atmospheric effects following an EUMETSAT OSI SAF correction method in sea ice concentration retrieval algorithms. The thin ice detection algorithms were trained and validated using MODIS ice thickness charts covering the Barents and Kara Seas. Thin ice detection is applied to swath $T_B$ datasets and the swath charts are compiled into a daily thin ice chart using 10 km pixel size for AMSR2 and 20 km for MWRI. On average, the likelihood of misclassifying thick ice as thin in the ATIDA2 daily charts is 7.0% and 42% for reverse misclassification. For the MWRI chart, these accuracy figures are 4% and 53%. A comparison of the MWRI chart to the AMSR2 chart showed a very high match (98%) for the thick ice class with SIC > 90% but only a 53% match for the thin ice class. These accuracy disagreements are due to the much coarser resolution of MWRI, which gives larger spatial averaging of $T_B$ signatures, and thus, less detection of thin ice. The comparison of the AMSR2 and MWRI charts with the SMOS sea ice thickness chart showed a rough match in the thin ice versus thick ice classification. The AMSR2 and MWRI daily thin ice charts aim to complement SAR data for various sea ice classification tasks. Full article

The detection of Martian polar ice cap change patterns is important for understanding their effects on driving Mars’s global water cycle and for regulating atmospheric circulation. However, current Martian ice cap identification using optical remote sensing data mainly relies on visual interpretation, which makes it difficult to quickly extract ice caps from multiple images and analyze their fine-scale spatiotemporal variation characteristics. Therefore, this study proposes an automatic Martian polar ice cap extraction algorithm for remote sensing data and analyzes the dynamic change characteristics of the Martian North Pole ice cap using time-series data. First, the automatic Martian ice cap segmentation algorithm was developed based on the ice cap features of high reflectance in the blue band and low saturation in the RGB band. Second, the Martian North Pole ice cap was extracted for the three Martian years MY25, 26, and 28 using Mars Orbiter Camera (MOC) Mars Daily Global Maps (MDGMs) data, which had better spatiotemporal continuity to analyze its variation characteristics. Lastly, the spatiotemporal variation characteristics of the ice cap and the driving factors of ice cap ablation were explored for the three aforementioned Martian years. The results indicated that the proposed automatic ice cap extraction algorithm had good performance, and the classification accuracy exceeded 93%. The ice cap ablation boundary retreat rates and spatiotemporal distributions were similar for the three years, with approximately 10⁵ km² of ice cap ablation for every one degree of areocentric longitude of the Sun (Ls). The main driving factor of ice cap ablation was solar radiation, which was mainly related to Ls. In addition, elevation had a different effect on ice cap ablation at different Ls in the same latitude area near the ablation boundary of the ice cap. Full article

The Mediterranean area is considered a hot spot on our planet because it represents the crossroads of various aerosols. Several studies have shown that the weather in the region is affected by the North-Atlantic Oscillation, which, in turn, is well connected with the El Niño–Southern Oscillation (ENSO) phenomenon. Nevertheless, no study has investigated the ENSO effect on the solar radiation and atmospheric aerosols in this region. The present study considers a greater area around the Mediterranean Sea over the period 1980–2022. The results show that there exists a loose but significant dependence, in some cases, of the optical properties of aerosols (aerosol optical depth, Ångström exponent, cloud optical depth) and solar radiation (net short-wave and net long-wave radiation, direct aerosol radiative forcing) on ENSO events. The results of this study provide motivation for further investigations, since such results can increase the accuracy of general circulation models that deal with climate change. Besides the ENSO effect, the enrichment of the Mediterranean atmosphere in suspended particles from great volcanic eruptions is shown. The inter-annual variation of the examined parameters is presented. A classification of the existing aerosols over the area is also provided. Full article

The main goal of the present study is to identify the prevailing atmospheric circulation patterns (circulation types) that are associated with the occurrence of precipitation (both mean and extreme) over southern Romania. A daily circulation type calendar derived from an automatic and objective classification scheme is used in synergy with the daily precipitation time series from five weather stations in the study area for a sixty-year period (1961–2020). Both mean and extreme precipitation do not show statistically significant trends, except for the annual precipitation at Constanța, for the value with daily precipitation totals greater than the 95th percentile at Craiova and the number of days exceeding the 99th percentile at Buzău and Râmnicu -Vâlcea, where significant negative trends were noticed. Moreover, the precipitation trends were analyzed in relation to the atmospheric circulation types. Non-significant positive trends were observed for the precipitation amounts (annually, winter, spring, and autumn) corresponding to very rainy circulation types (C, Cwsw), while for summer, the equivalent trends were negative. Moreover, it became evident that during extreme precipitation events, the predominant circulation types (C, Cwsw) are associated with western or almost western atmospheric circulation and Mediterranean- or Atlantic-originated depressions. Full article

The variation of PM_2.5 concentration in the atmosphere is closely related to the variation in weather patterns. The change in weather pattern is accompanied by the corresponding change in atmospheric circulation characteristics. It is necessary to explore the relationship between PM_2.5 concentration changes and atmospheric circulation characteristics during pollution periods. In this paper, Lamb-Jenkinson objective classification method is applied to classify daily atmospheric circulation. The pollution periods are calculated and the atmospheric circulation variation rule is obtained. Combined with the physical parameter field (humidity, potential temperature, and potential height), a typical pollution period is analyzed. Additionally, the influence of atmospheric circulation type variation on PM_2.5 concentration and transport channel during the pollution period was obtained. The results show that atmospheric circulation types in the study period are dominated by A-type (anticyclonic), N-type (north), and NE-type (northeast), indicating obvious seasonal differences, and the proportion of C-type (cyclonic) circulation was increased significantly in summer. During the pollution period analysis from 2 to 4 January 2019, atmospheric circulation type changed from N-type to NE-type (northeast), the wind direction changed from southeast wind, and the change of pressure gradient was consistent with the trend of the wind field. Moreover, the physical parameter field assisted in verifying the process of the pollution period from the conducive to the accumulation of PM_2.5 to conducive to the deposition of pollutants and external transport. The research results would provide theoretical support for PM_2.5 prediction during the pollution period and also supply a theoretical and technical basis for the establishment of ecological compensation standards for air pollution and atmospheric environmental control. Full article

A study of the climatic characteristics and annual runoff of the Volga and Severnaya Dvina rivers demonstrates that, on the East European Plain (EEP), Early Twentieth Century Warming (ETCW) manifested in a multiyear drought between 1934 and 1940; this drought has no analogues in this region in terms of intensity and duration according to Palmer’s classification, and caused extreme hydrological events. The circulation conditions during this event were characterized by an extensive anticyclone over Eastern Europe, combined with a cyclonic anomaly in the circumpolar region. An analysis of the spatial features of sea surface temperature (SST) anomalies indicate that the surface air temperature (SAT) anomalies in July on the EEP during ETCW were related not only to the North Atlantic (NA) warming and positive AMO phase, but also to a certain spatial pattern of SST anomalies characteristic of the 1920–1950 period. The difference between the SST anomalies of the opposite sign in the different NA zones, used as the indicator of the obtained spatial pattern, shows the quite close relations between the July SAT anomalies on the EEP and the atmospheric circulation patterns responsible for them. The positive phase of the Atlantic Multidecadal Oscillation (AMO) and the expansion of the subtropical high-pressure belt to the north and to the east can be considered as global-scale drivers of this phenomenon. The AMO also impacts the sea ice cover in the Barents–Kara Sea region, which, in turn, could have led to specific atmospheric circulation patterns and contributed to droughts on the EEP in the 1930s. Full article

Mountains are one of the most sensitive regions in terms of climate changes. This also concerns water balance, which plays an important role, especially in the context of the ecological state. Furthermore, numerous studies indicate the atmospheric circulation as one of the crucial factors affecting climate conditions. Therefore, the goal of the paper is to examine the impact of the atmospheric circulation on the changes in climate water balance (CWB) in the Sudetes Mountains and their foreland. The analysis was carried out based on the 1981–2020 data derived from the Polish and Czech meteorological stations. The impact of the circulation factor was examined using the Lityński classification, while the calculation of evapotranspiration based on the Penman–Monteith equation. The results showed that despite positive trends for evapotranspiration, the changes in CWB in 1981–2020 were generally statistically insignificant. The only exception was the increase in CWB under the eastern circulation and its negative tendency for the western and southern sectors for some of the stations. This corresponds to the changes in the frequency of the circulation types. The results of the study can be used in further research on water balance in the region. Full article

Analyzing and classifying atmospheric circulation patterns (CPs) is useful for studying climate variability. These classifications can effectively identify the links between large-scale and regional-local scale processes. This work uses the historical (1975–2014) and projected (2015–2054) simulations of the MPI-ESM1-2-HR model to reproduce the CPs over the Middle East and Iran. Eighteen CPs were identified based on the geopotential height (GPH) of 500 hPa data from Coupled Model Intercomparison Project Phase 6 (CMIP6) in SSP1-2.6, SSP3-7.0, and SSP5-8.5. The method of principal component analysis (PCA) and k-means clustering was used. Then, the possible variability of each pattern in the surface and mid-level of the atmosphere and their expected changes in the frequency of CPs in global warming scenarios were investigated. This research showed that CPs 3, 6, and 11 happen during warm months of the year. The surface thermal low pressure is associated with the subtropical high in the atmosphere mid-level. According to the intensity of the low and the northward development, or the orbital expansion of the subtropical high, this pattern has an increasing (CPs 3 and 6) or decreasing (CP11) trend in the future period. CPs 1 and 12 occur during cold months. In CP1, dynamic high pressure prevails over Iran. However, in CP12, Iran is affected by high pressure from southeastern Europe. They will decrease in future projections. CPs 7 and 16, which often occur in the transition season (spring), show an increase in the projected patterns. CP 18 occurs throughout the year, but its highest frequency is in autumn, and the frequency of occurrence decreases. An increase in 500 hPa geopotential height over the Arabian Sea in all 18 classes and all three SSPs is predicted for future periods. Analysis of the obtained weather types indicates the identification of all effective atmospheric circulation patterns in the study area so that the behavior and frequency of each pattern explain the prevailing atmospheric phenomena in this region. Full article

An extensive climatological and synoptic analysis of the winter cold spells that occurred in the Balkan Peninsula over a 59-year study period (1961–2019) is the aim of the present study. Winter cold spells (WCSPs hereafter) are defined as periods of at least three consecutive days when the daily minimum temperature is below the 5% of the empirical winter distributions. This diagnostic index is used to detect the occurrence of cold events during the study period, while the duration, frequency, and intensity of these extreme climate events are further analyzed. Moreover, in order to investigate the relation of the WCSPs with the atmospheric circulation, two daily circulation type calendars, derived from an advanced automatic flexible classification, were utilized. The automatic daily circulation type calendars were used, aiming at identifying the atmospheric conditions that prevail before or during WCSPs. The climatological analysis showed that the spatial distribution of the extreme minimum temperatures in the Balkan Peninsula presents a positive gradient from north to south, whereas coastal areas present more moderate minimum temperatures than inland areas of the region. In terms of WCSPs, the winter of 1962–1963 was the one with the longest WCSPs, for most of the stations under study. In general, a decreasing trend in the frequency of WCSP occurrence has been found towards the end of the study period. The circulation type investigation revealed that, during WCSPs in the Balkan region, the associated circulation at the 500 hPa is the Cne (cyclonic northeastern) and at the 1000 hPa is the Anw (anticyclonic northwest). Full article

This study presents the assessment of the quantitative influence of atmospheric circulation on the pollutant concentration in the area of Kraków, Southern Poland, for the period 2000–2020. The research has been realized with the application of different statistical parameters, synoptic meteorology tools, the Random Forests machine learning method, and multilinear regression analyses. Another aim of the research was to evaluate the types of atmospheric circulation classification methods used in studies on air pollution dispersion and to assess the possibility of their application in air quality management, including short-term PM₁₀ daily forecasts. During the period analyzed, a significant decreasing trend of pollutants’ concentrations and varying atmospheric circulation conditions was observed. To understand the relation between PM₁₀ concentration and meteorological conditions and their significance, the Random Forests algorithm was applied. Observations from meteorological stations, air quality measurements and ERA-5 reanalysis were used. The meteorological database was used as an input to models that were trained to predict daily PM₁₀ concentration and its day-to-day changes. This study made it possible to distinguish the dominant circulation types with the highest probability of occurrence of poor air quality or a significant improvement in air quality conditions. Apart from the parameters whose significant influence on air quality is well established (air temperature and wind speed at the ground and air temperature gradient), the key factor was also the gradient of relative air humidity and wind shear in the lowest troposphere. Partial dependence calculated with the use of the Random Forests model made it possible to better analyze the impact of individual meteorological parameters on the PM₁₀ daily concentration. The analysis has shown that, for areas with a diversified topography, it is crucial to use the variability of the atmospheric circulation during the day to better forecast air quality. Full article