Atmospheric aerosols play essential roles in regional energy balance, hydrological cycle, and air quality, thus greatly influencing the global climate and public health. Rapid economic expansion, industrialization, urbanization, and energy demand have significantly enhanced anthropogenic emissions over the Middle East (ME) that received the utmost scientific attention. Therefore, we present the temporal variability of atmospheric aerosols over the ME for a period of 15 years (2005–2019). Here, the long-term measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua, Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Clouds and the Earth’s Radiant Energy System (CERES) on Aqua are analyzed in order to understand the spatio–temporal variability of aerosols and their impacts on radiation budget over the ME. On average, a significant increase in aerosol optical depth (AOD) trend is observed by ~0.01 per year over ME. The peak aerosol loading was observed in summer (March–September) followed by the winter (October–February). A similar trend was observed in the CALIOP-derived extinct aerosol coefficients over ME. In addition, MODIS retrievals are validated against the AErosol RObotic NETwork (AERONET)’s ground-based sun photometers. Overall, MODIS AOD showed good agreement against AERONET AOD, with ~70% of the retrievals falling within the expected error and high correlation coefficient (R > 0.8). Furthermore, the associated changes in clear-sky Shortwave (SW) radiative flux indicates the enhanced aerosol loading over ME further increases the surface cooling (~1.2 W m⁻² per year) and atmospheric warming (~1.8 W m⁻² per year). Overall, the results suggest that enhanced aerosol emissions have significantly impacted the regional energy budget over ME during 2005–2019. The assessment also demonstrates the potential of synergetic use of multi-platform measurements for climate system studies. Full article

The work deals with identification/quantification of genetic material (RNA) of SARS-CoV-2 in air in different locations in Italy. This is an extremely important aspect to investigate the risks of airborne transmission and the role of this mechanism of transmission on the development of pandemic. Full article

Atmospheric blocking plays an important role in modulating mid-latitude weather, in particular in the Northern Hemisphere (NH). Trend analysis of atmospheric blocking for both hemispheres by using Şen’s Innovative Trend Analysis (ITA) is performed in this study. The blocking data archived in the University of Missouri covers the period of 1968–2019 for the NH and 1970–2019 for the Southern Hemisphere is used in the study. Block occurrence, duration and blocking intensity (BI) is analysed by classifying the NH (and SH) into three groups according to the preferred blocking locations: Atlantic, Pacific and Continental (Atlantic, Pacific and Indian). In the NH, blocking intensity showed mixed results. It showed a decreasing trend for the entire hemisphere and Atlantic Region, whilst a different trend was shown for different BI clusters. For blocking numbers and duration, the entire hemisphere and regions showed increasing trends. These increasing trend values were also statistically significant. In the SH, blocking intensity showed a decreasing trend for low clusters, whilst medium and high cluster increased for the entire hemisphere. Block duration showed an increasing trend for the entire SH. Block numbers showed increasing trends, except for one point in the low cluster. Blocking characteristics showed different trends for different preferred blocking locations. Increasing trends of blocking numbers for the overall SH and Pacific region are statistically significant at 95% level. Increasing trends of blocking duration for the overall SH, Atlantic and Pacific region are statistically significant at 90%, 95% and 95% level, respectively. Full article

Previous climatologies of blocking from this group using a one-dimensional index showed that in the Northern Hemisphere, blocking onsets primarily occurred in the Pacific Ocean region, the eastern Atlantic, and over continental Asia. In the Southern Hemisphere, the western and eastern Pacific are the two active regions for block onset. The development of technologies such as Geographic Information System (GIS) visualization software mean that ingesting data and then rendering it on a map has become easier and faster. This research group has previously not archived the latitude of onset, making a comparison of onset locations with 2-D indexes difficult. Now, the onset latitude is provided on the University of Missouri Blocking Archive. This work shows that the blocking onset regions are roughly consistent with those found by 2-D indexes, but there are some key differences based on the differences in the information presented. Here, block onset locations are slightly equatorward and upstream of those presented in 2-D indexes. Additionally, there are some differences in the onset locations for more persistent blocks and stronger blocks versus their less persistent and weaker counterparts. Full article

Data from an S-band polarimetric radar located at a mid-latitude, coastal location are used to compare two different methods for identifying stratiform and convective rain regions. The first method entails the retrievals of two (main) parameters of the rain drop size distributions using the radar reflectivity and the differential reflectivity. The second technique is a well-known texture-based method which utilizes the radar reflectivity and its spatial variability. A widespread event with embedded line convection was used as a test case. The two methods were compared using 500 m by 500 m pixel resolution gridded data constructed from the radar volume scans. Only 12% of the pixels showed disagreement between the two methods. Full article

This study investigated the carbonaceous species [elemental carbon (EC), organic carbon (OC), water soluble organic carbon (WSOC)] along with the trace elements (Al, S, Ti, Mn, Fe, Cu, Zn, As, Br, Pb, Cr, F, Cl, Na, K, Mg, Ca, P) in PM₁₀ over the megacity Delhi, India (collected from 2015–2019) to address certain significant scientific issues (i.e., what are the directionality or pathway of these emissions; what are the possible emission sources which are distressing the observation site; what are the periodical variations in these emissions; and whether the emissions are local, regional, or trans-boundary). Integration of these problems are addressed using various statistical approaches including potential source areas (PSA) [using hybrid modelling i.e., potential source contribution factor (PSCF)], the conditional bivariate probability function (CBPF), and principal component analysis (PCA). Furthermore, seasonal PSCF and CBPF indicate both local source (highly polluted residential areas, traffic congestions, and industrial emissions) and regional sources (Haryana, Punjab) dominancy during winter and post-monsoon seasons at the receptor site, whereas during summer and monsoon along with local source and the regional, trans-boundaries (Indo-Gangatic plane, Pakistan, Afghanistan, and Bay of Bengal) air parcel patterns also contribute to the aerosol loading at the sites. Moreover, the PCA approach framed four common sources [crustal/road dust (RD), industrial emission (IE), fossil fuel combustion + biomass burning (FCC+ BB), vehicular emission (VE)] with one mixed source over the sampling site of Delhi. Full article

Emissions from port operation and shipping have a significant impact on climate risk, affecting the environment and human health in coastal regions (UNEP). The port of Heraklion, located on the island of Crete over the eastern Mediterranean, plays a key role in the sustainability of Southeast Europe. The impact on its operation affects the socio-economic life and development of the region because Heraklion is not just a tourist destination but also a significant trade and transportation center for the eastern Mediterranean. This study investigates the impact of port operation on the air quality of Heraklion between two representative periods. The first, which is considered as the high port activity season, was from 02 August 2018 to 08 August 2018, while the second period was between 11 May 2018 and 16 May 2018. For the air quality measurements, a low-cost sensor was used, while the recordings were initially compared and finally evaluated based on the available data of the monitoring station of the Ministry of Environment and Energy. To investigate the air quality differences between the two studied periods, the correlation analysis, the hourly evolution of pollutants, and the mean differences between high and low periods for gaseous pollutants and particulate matter are studied. Moreover, the effect of meteorology on air quality is investigated. The results indicate that the high season is characterized by significantly higher concentrations of pollutants compared to the other period. In both seasons studied, the air pollution level increases during hours of high port-activity, indicating the impact of port activity on air quality in Heraklion. The analysis shows that meteorology affects the air quality. In particular, strong wind speeds are associated with lower concentrations of gaseous pollutants other than ozone, which is affected by atmospheric circulation. Finally, the analysis emphasizes the importance of further investigation of the impact of port operations on coastal air quality in the context of sustainable development. Full article

Government departments use the air quality index (AQI) to inform the public about how unhealthy the air is now or may become in the future. As the AQI increases, so do health threats. In addition to reporting daily air quality, it is also a measure of how air pollution impacts one’s health over a limited period of time. The AQI was created to assist people in understanding how local air quality affects their health. Therefore, the aim of this study was to assess the air quality of one day of 253 towns in Nigeria, thereby determining the health threat in these towns. The data were collected from the Tutiempo Network’s regular dataset by the Environmental Protection Agency (EPA). Data on all of the major pollutants (O₃, PM_2.5, PM₁₀, CO, NO, SO₂) were collected and statistical analysis was performed. Kura (Kano State), a town in northern Nigeria, recorded the highest level of 184, while Idiroko, a border town (Nigeria–Benin Republic) in Ogun State, had the lowest value of 41. Kura was portrayed as unhealthy, while Idiroko was portrayed as healthy, implying that Idiroko air poses little to no danger, while Kura air showed that certain people of the general public, as well as members of sensitive groups, could encounter more severe health effects. Full article

Various climatic and geographic parameters influence precipitation in Iran, which makes the interpretation of stable isotope signatures in precipitation very complicated. Thus, precipitation sampling stations for stable isotope analyses in Iran have been classified by cluster analysis (CA) into 10 clusters, based on their stable isotope characteristics. The classification of stations by CA also has a close correlation with the Koppen climatic zones across Iran. Finally, the stations in each cluster were plotted on the GMWL and EMMWL. This study shows that classifying precipitation sampling stations can simplify the interpretation of stable isotopes in the precipitation of regions with complicated climatic systems. Full article

The outbreak of the novel coronavirus SARS-CoV-2 (hereafter COVID-19) has changed the daily routines of people around the world. The first case of COVID-19 was confirmed in December 2019, whilst it was confirmed on 11 March 2020 in Turkey. After the number of cases reached 4500 per day by 10 April, the government declared more restrictive lockdown measures for 31 metropolitan cities, which were implemented for the following weekends and national and religious holidays. The changes in the concentrations of CO, NO_x, NO₂ and PM₁₀ during the period of these measures with respect to the pre-lockdown period and for different levels of measures for Samsun, the biggest city of the Karadeniz region, were investigated in this study. The daily mean concentrations of CO, NO_x, NO₂ and PM₁₀ were obtained from Tekkekoy station due to it having data completeness greater than 95% for all pollutants. The average CO, NO_x and NO₂ concentrations during the lockdown period declined with respect to the pre-lockdown period, whilst PM₁₀ increased by 3%. The average concentrations of all the pollutants decreased when the level of restrictions increased during the COVID-19 lockdown period. The number of days exceeding the WHO limit for PM₁₀ was decreased during the lockdown period to 16 days with respect to the pre-lockdown period at 19 days. There was only a positive weak relationship between the mobility decrease rate and NO₂ concentrations. Full article

The aim of this study was to evaluate the carbon dioxide (CO₂) and methane (CH₄) emissions during the composting and vermicomposting of sewage sludge under the effect of different proportions of straw pellets. Four treatments, including a control with three replicates, were designed to mix the initial sewage sludge with varying rates of pelletized wheat straw (0, 25%, 50%, and 75% (w/w)). Over a 60-day period, vermicomposting with Eisenia andrei treatments and composting were carried out. The results indicated that both composting and vermicomposting produce a significant (p < 0.001) amount of CO₂ and CH₄ emissions from all treatments. Vermicomposting significantly reduced CH₄ emissions by 18%, 34%, and 38% and increased CO₂ emissions by 75%, 64%, and 89% for the treatments containing 25%, 50%, and 75% straw pellets, respectively, compared to composting. However, CO₂ emissions decreased and CH₄ emissions increased during composting compared to vermicomposting. As a result of this finding, both composting and vermicomposting processes are recommended as an additive of pelletized wheat straw, depending on the target gas to be reduced. Full article

At the end of 2019, the first case of coronavirus (COVID-19) was reported in Wuhan, China. A month later, that epidemic turned into a national crisis, with infected individuals diagnosed all over China. In early March 2020, the World Health Organization (WHO) declared that the Wuhan epidemic had turned into a global pandemic. Many European countries had started to experience several cases affected by this coronavirus, which was known to be highly contagious. The WHO launched several recommendations to curb the spread of this virus and called for general confinement establishment in the affected countries. Tunisia quickly took this step on 22 March 2020 and announced immediate general confinement for two weeks, renewable according to the test results. Factories were closed to limit human damage. International flights were halted and the majority of government and private services were halted except minimum and emergency services. Following these successive events, the air quality improved markedly during the confinement period. NASA scientists say the reduction in nitrogen dioxide (NO₂) pollution first appeared near Wuhan, Northern Italy, and France, and they experienced a reduction of nearly 50% of their NO₂ emissions during this first confinement period (March–April 2020); NO₂ emissions were reduced by almost 30% in China. In Tunisia, NO₂, sulfur dioxide (SO₂) and carbon monoxide (CO) showed a remarkable decrease in the north and the center of Tunisia of more than 40% during this period, mainly linked to the reduction in emissions from road traffic and industries. Additionally, these pollutant gas concentrations were reduced by nearly 50% during the third pandemic wave, during the period of January–April 2021. Consequently, the air quality has improved significantly in Tunisia and around the world. Full article

Shipping contributions to atmospheric particulate matter were estimated by an approach based on high temporal resolution measurements of mass and number size distribution, correlated with meteorological and ship movements data, in two Adriatic harbours. Trends of contributions are discussed. Contribution to particle number concentrations (PNC) was 3–4 times larger than that to PM_2.5. In Venice, strategies for reduction of shipping emissions were effective in lowering the PM_2.5 primary impact, while PNC contribution was significant in Brindisi. The maximum contribution was found to ultrafine particles (UFP), followed by a minimum at diameters between 1 and 1.5 µm and a growth in the coarse range. Full article

Many research articles explore new designs and how to arrange barriers/obstacles to improve roadside air quality and ventilation within the urban street canyon. These obstacles are generally categorized into porous, non-porous and mixed type. Porous barriers include vegetated shrubs and trees; non-porous barriers include parked cars, low boundary walls, etc., while mixed barriers combine both porous and non-porous barriers. Moreover, new developments can benefit from added design flexibility using lift-up building design and building porosity as a promising way of improving ventilation. This short paper reviews the different research studies conducted on obstacles/barriers in an urban canyon which helps improve air quality and also highlights potential future research. Full article

We investigated the influence of natural and anthropogenic sources on bulk atmospheric deposition chemistry, from November 2017 until October 2019, in a Brazilian agricultural area. The pH mean value was 5.99 (5.52–8.46) and most deposition samples (~98%) were alkaline (pH > 5.60). We identified Ca²⁺ as the predominant species, accounting for 33% of the total ionic species distribution and the main precursor of atmospheric acidity neutralization (Neutralization Factor = 6.63). PMF analysis resulted in four factors, which demonstrated the influence of anthropogenic and natural sources, such as fertilizer application and production, marine intrusion/biomass burning, and biogenic emissions, and revealed the importance of atmospheric neutralization processes. Full article

Particulate matter (PM) is released in varying quantities from mobile sources depending on the type of fossil fuel used in combustion. According to the USEPA, PM exposure could cause a variety of problems such as premature deaths, nonfatal heart attacks, irregular heartbeat, asthma, reduced lung function, and respiratory issues. Therefore, it is necessary to predict the downwind concentrations near highways from mobile sources to protect the public from adverse health effects. The current study concentrates on developing an analytical line source dispersion model to account for different particle size ranges for particulate matter released from mobile sources. Available line source models do not explicitly consider different ranges of particle sizes present in the exhaust. The present study discusses the development of a dispersion model to predict downwind concentrations of PM by incorporating a range of particle sizes for an infinite and a finite-length mobile source. The dry deposition of particles is also considered during development. The emission rate, wind speed, wind direction, atmospheric turbulence, and dry deposition velocity of the particles are the model inputs. The sensitivity of the model is determined by simultaneously varying the independent input variables using Monte Carlo simulation by Crystal Ball software. The sensitivity analysis results generated using Crystal Ball are preliminary in nature and should be re-examined. Full article

Due to the urban heat island effect becoming more evident in the cities in Taiwan, the urban climate has become an essential factor in urban development. Taiwan is located on the border of tropical and subtropical climate zones, the climate condition is hot and humid, and the city shows high-density development. The dense urban development has increased the heat storage capacity of the ground and buildings. However, if only the climate stations set by the Central Meteorological Bureau to observe the climate data are applied, the predicted results differ from the actual urban climate conditions due to the small number of these stations and the too far distance between them. Therefore, this study employs the local climate zone (LCZ), which can classify the land features by considering both land use and land cover, and can be freely generated from satellite images. The LCZ classification method can view the type of the city through the height and density of obstacles. This study also combines the urban canopy model (UCM) of the mesoscale climate prediction model and weather research and forecasts (WRF). This approach can calculate vertical and horizontal planes of the city, such as building volume, road width, the influence of streets and roofs, roof heat capacity, building wall heat capacity, etc., to predict the climatic conditions in different lands in the study area. Simultaneously, to understand the actual distribution of urban climate more accurately, this study used the microclimate measurement network built in the research area to produce pedestrian-level temperature distribution and compared the estimated results with the actual measured values for urban climate assessment. This study can understand the cause of urban heat islands and assist urban planners more appropriately formulate heat island mitigation strategies in different regions. Full article

In this study the exposure levels from Quercus pollen in the greater area of Thessaloniki are estimated. The estimation is implemented with a modeling system, comprising the meteorological model WRF, the Natural Emissions Model (NEMO) for the calculation of the Quercus pollen emissions and the chemistry-transport model CAMx for the advection and the deposition of the pollen particles. The period of 2016 with the highest potential is selected, based on the available measurements for the area of interest. The modeling system is evaluated with meteorological and pollen measurements, as well on the expected exposure levels, indicating a satisfactory overall performance. The modeling system is finally utilized for the estimation of exposure levels in the greater area of Thessaloniki, showing that the city of is not going to experience significant number of days with high Quercus pollen concentrations, although other, smaller cities and towns might be affected. Full article

In this study, a methodological procedure combining a technique of meteorological normalisation, based on a random forest algorithm, with trend analysis and the change points detections in air quality time series is developed to analyse changes in pollutant concentrations levels. Data of air pollutants and meteorological parameters, collected over the period 2013–2019 in a rural area affected by anthropic sources of air pollutants, are used to test the procedure. The results appear to be promising in revealing, in a robust way, changes in pollutant levels not clearly observable in the original data. Full article

Using a feed-forward neural network, an inverse algorithm was developed to profile the vertical structure of temperature and specific humidity. The inverse algorithm (inverse model) was used to calculate temperature and humidity profiles, which were then compared with other existing methods. The inverse model is found efficient in profiling the vertical structure of temperature and humidity as compared to other existing methods. For example, the statistical methods notorious for their high computational cost, altitude-dependent error, and inability to accurately retrieve the vertical temperature and humidity profiles, are enhanced with an inverse model. The inverse model’s diurnal and seasonal cycle profiles are also found superior to those of other existing methods, which could be useful for assimilation in numerical weather forecast models. We suggest that incorporating such an inverse model into the ground-based microwave radiometer (GMWR) will enhance the accuracy of the vertical structure of temperature and humidity profiles, and so the improvement in weather forecasting. The developed inverse model has a resolution of 50 m between the surface to 500 m and 100 m between 500–2000 m, and 500 m beyond 2000 m. Full article

An overview of the meteorological and synoptic conditions for the formation of the severe drought which occurred in 2019–2020 in the southwest of Ukraine showed that the combination of several adverse factors influenced the evolution of the drought over a long period of time. Analysis of two types of drought indices and anomalies in the tropospheric pressure fields showed that the formation of a precipitation deficit began long before the occurrence of drought, due to the repetition of high pressure fields in the region, through which the intensity of the drought increased gradually. Full article

In this study, the moisture sources for the explosive cyclogenesis Miguel that occurred during 4–9 June 2019 in the North Atlantic were investigated. To determine the moisture sources, the Lagrangian FLEXPART particle dispersion model was used. The moisture uptake pattern revealed the western North Atlantic Ocean extending to north-western North America, the south-eastern coast of Greenland, and the central North Atlantic Ocean around 45° N and 50°–20° W as the main moisture sources for Miguel explosive cyclogenesis. Furthermore, the moisture uptake from these regions was higher than the climatology. During the intensification of Miguel, the moisture contribution from oceanic sources was higher than terrestrial sources. Although the total amount of atmospheric moisture achieved during the explosive intensification was similar to that absorbed the 24 h prior, they changed in intensity geographically, being more intense the local support over central and northern North Atlantic basin. Full article

In a series of collaborative Russian–American works (Levina and Montgomery, 2009–2015), we applied the fundamental ideas of self-organization in turbulence with broken mirror symmetry, the so-called “helical” turbulence. In this context, tropical cyclogenesis is considered as a threshold extreme event in the three-dimensional helical moist convective atmospheric turbulence of a vorticity-rich environment of a pre-depression zone. This allowed us to discover a large-scale vortex instability and answer the question “When will cyclogenesis commence given a favorable tropical environment?”. The new instability emerges against the background of seemingly disorganized convection, without a well-defined center of near-surface circulation and noticeably precedes the formation of a tropical depression. This can give the fundamental ground and quantitative substantiation for the term “Potential Tropical Cyclone” as a beginning of TC genesis. In the present work, we explore in detail the crucial role of special convective coherent structures of cloud scales—vortical hot towers (VHTs)—in the formation and maintenance of the secondary circulation and, therefore, of the whole mesoscale vortex system. On this basis, we propose how the onset of large-scale instability, i.e., the beginning of TC genesis, can be diagnosed exactly and distantly with VHTs patterns in the field of temperature (satellite data) and vertical helicity (cloud-resolving numerical analysis). The present research is intended to contribute to a recently initiated development of operational diagnosis of the beginning of TC genesis based on GOES Imagery and supported by cloud-resolving numerical modeling. Full article

Ongoing climate variations and variation of winter weather conditions in recent years in the Moscow region have strongly influenced the hill skiing industry. This paper calculates snowmaking hours and their variations in recent years to enable conclusions about ongoing changes and possible consequences for the future. Full article

This study evaluates the performance of the Numerical Tools for Hurricane Forecast (NTHF) system during the 2020 North Atlantic (NATL) tropical cyclones (TCs) season. The system is configured to provide 5-day forecasts with basic input from the National Hurricane Center (NHC) and the Global Forecast System. For the NTHF validation, the NHC operational best track was used. The average track errors for 2020 NATL TCs ranged from 62 km at 12 h to 368 km at 120 h. The NTHF track forecast errors displayed an improvement over 60% above the guidance Climatology and Persistence (CLIPER) model from 36 h to 96 h, although the NTHF was better than the CLIPER in all forecast periods. The forecast errors for the maximum wind speed (minimum central pressure) ranged between 20 km/h and 25 km/h (4 hPa to 8 hPa), but the NTHF model intensity forecasts showed only marginal improvement of less than 20% after 78 h over the baseline Decay Statistical Hurricane Intensity Prediction Scheme (D-SHIPS) model. Nevertheless, the NTHF’s ability to provide accurate intensity forecasts for the 2020 NATL TCs was higher than the NTHF’s average ability during the 2016–2019 period. Full article

One of the manifestations of severe local storms is strong linear winds, which are known as a downburst and which are capable of causing great losses to the country’s economy and society. Knowing which factors in the atmosphere are necessary for the occurrence of this phenomenon is essential for its better understanding and prediction. The objective of this study was to analyze the possible physical factors that accelerate downdrafts in the storm clouds in Cuba. To do so, 10 study cases simulated with the weather research and forecasting (WRF) model at 3 km of the spatial resolution were used. The factors capable of discriminating between downbursts and thunderstorms without severity were obtained. These were the absorption of latent heat by evaporation and fusion, the equivalent potential temperature difference between the level of maximum relative humidity in the low levels and of minimum relative humidity in the middle levels, the speed of the downdraft, and the downdraft available convective potential energy (DCAPE). Unlike previous research, they discriminated against updraft buoyancy and energy advection, both at the middle levels of the troposphere. Full article

This study examined the water budget of Hurricane Irma (2017) through a Lagrangian approach. To identify the moisture sources for the Hurricane Irma genesis and intensification the particle dispersion model FLEXPART was used. The North Atlantic Ocean between 15° and 30° North latitude and the South Atlantic Ocean were identified as the main moisture sources for Irma development. From the perspective of the water budget, the maximum accumulated precipitation along Irma’s trajectory coincides with the maximum water budget efficiency, which suggests that total precipitation depends largely on the water vapour supplied, rather than the storm intensity. Furthermore, the moisture supply from the surface under the area delimited by hurricane size is small, thus, the water vapour supply from the environment through the secondary circulation transports more moisture inward. Full article

Precipitation measurement over a complex topography and highly elevated regions has always been a great challenge in recent decades. On the other hand, satellite-based and numerical weather prediction model outputs can be an alternative to fill this gap. Hence, the goal of this study is to evaluate the spatiotemporal stability and hydrologic utility of four precipitation products (TMPA-3B42v7, IMERGHHFv06, ERA5, and PERSIANN) over a mountainous basin (Karasu basin) located in the eastern part of Turkey. Moreover, the Kling–Gupta efficiency (KGE), including its correlation, bias, and variability ratio components, are used for a direct comparison of precipitation products (PPs) with observed gauge data, and the Hansen–Kuiper (HK) score is utilized to assess the detectability strength of PPs for different precipitation events. In the same way, the hydrologic utility of PPs is tested by exploiting a conceptual rainfall–runoff model under Kling–Gupta efficiency (KGE) and Nash–Sutcliffe efficiency (NSE) metrics. Generally, all PPs show low performance for a direct comparison with observed data while their performance considerably increases for streamflow simulation. TMPA-3B42v7 has high reproducibility in streamflow (KGE = 0.84), followed by IMERGHHFv06 (KGE = 0.76), ERA5 (KGE = 0.75), and PERSIANN (KGE = 0.70), for the entire period (2015–2019) of this study. Full article

Soil erosion is one of the most critical environmental hazards of recent times. It broadly affects to agricultural land and reservoir sedimentation and its consequences are very harmful. In agricultural land, soil erosion affects the fertility of soil and its composition, crop production, soil quality and land quality, yield and crop quality, infiltration rate and water holding capacity, organic matter and plant nutrient and groundwater regimes. In reservoir sedimentation process the consequences of soil erosion process are reduction of the reservoir capacity, life of reservoir, water supply, power generation etc. Based on these two aspects, an attempt has been made to the present study utilizing Revised Universal Soil Loss Equation (RUSLE) has been used in integration with remote sensing and GIS techniques to assess the spatial pattern of annual rate of soil erosion, average annual soil erosion rate and erosion prone areas in the MAN catchment. The RUSLE considers several factors such as rainfall, soil erodibility, slope length and steepness, land use and land cover and erosion control practice for soil erosion prediction. In the present study, it is found that average annual soil erosion rate for the MAN catchment is 13.01-tons/ha/year, which is higher than that of adopted and recommended values for the project. It has been found that 53% area of the MAN catchment has negligible soil erosion rate (less than 2-tons/ha/year). Its spatial distribution found on flat land of upper MAN catchment. It has been detected that 26% area of MAN catchment has moderate to extremely severe soil erosion rate (greater than 10-tons/ha/year). Its spatial distribution has been found on undulated topography of the middle MAN catchment. It is proposed to treat this area by catchment area treatment activity. Full article

Atmospheric profiles are key inputs in correcting the atmospheric effects of thermal infrared (TIR) remote sensing data for estimating land surface temperature (LST). This study is a first insight into the feasibility of using the weather research and forecasting (WRF) model to provide high-resolution vertical profiles for LST retrieval. WRF numerical simulations were performed to downscaling NCEP climate forecast system version 2 (CFSv2) reanalysis profiles, using two nested grids with horizontal resolutions of 12 km (G12) and 3 km (G03). We investigated the use of these profiles in the atmospheric correction of TIR data applying the radiative transfer equation (RTE) inversion single-channel approach. The MODerate resolution atmospheric TRANsmission (MODTRAN) model and Landsat 8 TIRS10 (10.6–11.2 µm) band were taken for the method application. The accuracy evaluation was performed using in situ radiosondes in Southern Brazil. We included in the comparative analysis the atmospheric correction parameter calculator (ACPC; NASA) web tool and profiles directly from the NCEP CFSv2 reanalysis. The atmospheric correction parameters from ACPC, followed by CFSv2, had better agreement with the ones calculated using in situ radiosondes. When applied into the RTE to retrieve LST, the best results (RMSE) were, in descending order: CSFv2 (0.55 K), ACPC (0.56 K), WRF G12 (0.79 K), and WRF G03 (0.82 K). The findings suggest that increasing the horizontal resolution of reanalysis profiles does not particularly improve the accuracy of RTE-based LST retrieval. However, the WRF results are yet satisfactory and promising, encouraging further assessments. We endorse the use of the well-known ACPC and also recommend the NCEP CFSv2 reanalysis profiles for TIR remote sensing atmospheric correction and LST single-channel retrieval. Full article

The issue of the quantification of thermal comfort or heat stress on humans is in vogue nowadays. This is evident for indices, which are trying to quantify these effects. Most known indices are PET, modified PET, SET*, PT and UTCI. All thermal indices require the same thermo-physiological and meteorological parameters. Air temperature, air humidity, wind speed, and short and long wave radiation fluxes in terms of mean radiant temperature are the required meteorological parameters. For human thermo-physiology, information about heat production and clothing are required. The meteorological parameters have to be available in appropriate spatial and temporal scales depending on the target and the specific issues demanded. The appropriate spatial and temporal resolution data cannot only be delivered by measurement stations. Meso and micro scale models, which compute meteorological parameter and thermal indices, can be helpful in the development of mitigation and adaptation strategies in the era of climate change. Full article

Computational fluid dynamics (CFD) is an effective technique to investigate atmospheric processes at a local scale. For example, in near-source atmospheric dispersion applications, the effects of meteorology, air pollutant sources, and buildings can be included. A prerequisite is to establish horizontally homogeneous atmospheric conditions, prior to the inclusion of pollutant sources and buildings. This work investigates the modelling of the atmospheric surface layer under neutral and stable boundary layer conditions, respectively. Steady-state numerical solutions of the Reynolds averaged Navier–Stokes (RANS) equations were used, including the k-ε turbulence model. Atmospheric profiles derived from the Cooperative Atmosphere-Surface Exchange Study-99 (CASES-99) were used as reference data. The results indicate that the observed profiles of velocity and potential temperature can be adequately reproduced using CFD, while turbulent kinetic energy showed less agreement with the observations under the stable conditions. The results are discussed in relation to the boundary conditions and sources, and the observational data. Full article

Modelling the optical and radiative properties of atmospheric particles is governed by one of the key input parameters, i.e., the refractive index of aerosols. Availability of the region-specific refractive index data of aerosols is a major challenge for the atmospheric community. The refractive index of aerosols is a function of their physico-chemical properties. Uncertainty in the computation of the spectral refractive indices of aerosols leads to erroneous assessment of their optical and radiative properties. In the present work, the refractive indices of pure clay (kaolinite, illite) and polluted clay with anthropogenic hematite, AH (0.10 to 1.48%) submicron particles have been computed for the wavelength range of 0.38 to 1.2 µm. Anthropogenic hematite enhanced the overall absorption in the UV and visible range with maximum absorption at lower wavelengths (less than 0.55 µm). Full article

Due to the impact of global warming and extreme weather events, outdoor human thermal comfort conditions become tougher and harder to mitigate, especially for pedestrian movement and exercises. In order to better understand the thermal environment and thermal comfort, especially for outdoor sports, the 2021 National Intercollegiate Athletic Games held in Tainan, southern Taiwan, in May was selected as the research target. Both on-site, real-time environmental monitoring data and the Taiwan Climate Change Projection Information and Adaptation Knowledge Platform (TCCIP)’s Taiwan ReAnalysis Downscaling data (TReAD) were applied to estimate the modified physiologically equivalent temperature (mPET), the mean radiant temperature (Tmrt), and the wet bulb globe temperature (WBGT) for members participating in the relevant activities. The focus of this study was to analyze the thermal performance of (1) the torch relay around Taiwan from 20 April to 8 May and (2) the scheduled planning games held at the track and field stadium, at the National Cheng Kung University (NCKU), Taiwan, from May 15 to May 18, 2021. Full article

The short-term prediction of precipitation is a difficult spatio-temporal task due to the non-uniform characterization of meteorological structures over time. Currently, neural networks such as convolutional LSTM have shown ability for the spatio-temporal prediction of complex problems. In this research, we propose an LSTM convolutional neural network (CNN-LSTM) architecture for immediate prediction of various short-term precipitation events using satellite data. The CNN-LSTM is trained with NASA Global Precipitation Measurement (GPM) precipitation data sets, each at 30-min intervals. The trained neural network model is used to predict the sixteenth precipitation data of the corresponding fifteen precipitation sequence and up to a time interval of 180 min. The results show that the increase in the number of layers, as well as in the amount of data in the training data set, improves the quality of the forecast. Full article

The prediction of fog is a challenging task in operational weather forecast. Due to its dependency on small-scale processes, numerical weather models struggle to deal with under scale features, resulting in uncertainties in the fog forecast. Unawareness of the onset time and the duration of fog leads to disproportionate impact on open-air activities, especially in aviation. Nevertheless, in a small sized country such as Portugal mainland, the fog varies greatly. The traffic of the two busiest Portuguese international airports of Porto and Lisbon is affected by the occurrence of fog at different times of the year. The fog occurrence at Porto is a predominant winter phenomenon and a summer one at Lisbon. Observational variables and their trend are local indicators of favouring conditions to the fog’s onset, such as cooling, water vapour saturation and turbulent mixing. A dataset corresponding to 17 years of half-hourly METAR from the airports of Porto and Lisbon is used to diagnose the pre-fog conditioning. Two diagnostic models are proposed to assess pre-fog conditions. The first model is adapted from the statistical method proposed by Menut et al. (2014), which performs a diagnosis from key variables trend, such as temperature, wind speed and relative humidity. Thresholds are defined from the METAR samples in the 6 h period prior to the formation of fog. Due to the local character of fog, the presented thresholds are the most appropriate ones for each airport. The predictability of fog is then assessed using observations. The second approach consists of neural networks such as a fully connected (FC) network and a recurrent neural network (RNN), which are especially well suited for time series. By experimenting with different types of neural networks (NN), we will try to capture the connection between the temporal evolution of measured variables in the dataset and the fog onset. These experiments will include different time windows to measure its influence on prediction performance. Full article

During 2020, the Dominican Republic received the impact of several tropical organisms, among those that generated the greatest losses in the country, Tropical Storm Isaias stands out because of the significant precipitation and flooding it caused. The study analyzes the ability of the products of Flash Flood Guidance System (FFGS) and the Nowcasting and Very Short Range Prediction System (Spanish acronym SisPI) for the quantitative precipitation forecast (QPF) of the rains generated by Isaias on 30 and 31 July 2020 over the Dominican Republic. Various traditional verification methods are used in the study. The results show that both numerical weather-based systems are powerful tools for the QPF, and also to contribute to the prevention and mitigation of disasters caused by the extreme hydro-meteorological event analyzed. Full article

The evaluation of the Nowcasting and very short-range prediction system of the National Meteorological Service of Cuba is presented. The WRF numerical weather model is the primary tool employed in the system. The assessment is done for the relative humidity, precipitation, temperature, wind, and pressure during 2019 and for the simulation domain of highest spatial resolution (3 km). The measurements of the meteorological surface stations were used in the analysis. As result, the system has good ability to forecast the aforementioned variables, and its behavior is better in the pressure and temperature fields, while the worst results were obtained for precipitation. Although there was not much difference between the four initialization (0000, 0600, 1200, and 1800 UTC), the initialization at 1200 UTC stood out among the others because, in general, it had better performance in the forecast of the variables studied. Full article

A series of huge wildfires occurred in some regions of Lebanon in mid-October 2019, when the region witnessed a heat wave with high averages of minimum and maximum temperatures, accompanied by dry weather conditions. This study aimed to investigate the weather pattern that predominated over Lebanon between 10 and 18 October 2019, and to study the weather factors that ignited and spread the fire in several places. The study focused on the Chouf district, in Mount Lebanon Governorate, which witnessed the most severe wildfire outbreak, based on ERA5 atmospheric reanalysis data at the surface and upper levels between 10 and 18 October 2019. It was found that the existence of an atmospheric blocking system over the region for many days was the main factor in the creation of the dry and extremely hot weather, and that the breakdown of the ridge ignited the fire, reinforcing the wildfire’s intensity and spreading fire patches to other regions. Full article

The nowcasting and very short-term prediction system (SisPI, for its acronym in Spanish) is among the tools used by the National Meteorological Service of Cuba for the quantitative precipitation forecast (QPF). SisPI uses the WRF model as the core of its forecasts and one of the challenges to overcome is to improve the precision of the QPF. With this purpose, in this work we present the results of the application of a bias correction method based on artificial neural networks. The method is applied to the highest-resolution domain of SisPI (3 km), and the correction is made from the precipitation estimation of the GPM satellite product. Results shows higher correlation with the artificial neural network model in relation to the values predicted by SisPI (0.76 and 0.34, respectively). The mean square error when applying the artificial neural network model is 3.69, improving the performance of SisPI by 6.78. In general, the bias correction has a good ability to correct the precipitation forecast provided by SisPI, being less evident in cases where precipitation is reported and SisPI is not capable of forecasting it. In cases of overestimation by SisPI (which happens quite frequently), the correction achieves the best results. Full article

This study aims to investigate flash floods in Iraq by plotting the cartographic maps by using synoptic and dynamical analysis of meteorological reanalysis data obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) and statistical analysis of daily precipitation records from the Iraqi Meteorological and Seismology Organization for selected Iraqi stations (Mosul, Kirkuk, Khanaqin, Baghdad and Al-Rutba, Al-Hayy, Al-Nasiriyah, and Basra), as well as the use of geographic information system (GIS) techniques. Three models create to investigate and map flash floods in Iraq. The results of the first model (the longest period of time) shows that the station of Mosul record the longest period for a rainstorm, 9 days in 2014, while the lowest period was in Rutba, 6 days in 2012, and the other stations varied between these two stations. The results of the second model (the highest total rainfall), present that Kirkuk station recorded the highest amount of rain (117.2 mm in 2013), while Al-Rutba station, 47.2 mm in 2011, the lowest station. Finally, the results of the third model (the highest frequency of rainstorms per month) shows that the lowest frequency of rainstorms per month was in Basra, 29 rainstorms in 2009, while Mosul station has 40 rainstorms in 2007 and the other stations within these two values. Full article

The phenomenon of drought is one of the most dangerous for small islands because of its impacts on freshwater availability. Thus, in this study, the spatio-temporal evolution of meteorological drought that affected the main island of Puerto Rico in the period 1950–2019 was investigated. In doing so, the Standardized Precipitation–Evapotranspiration Index (SPEI), using monthly values of minimum and maximum temperatures and precipitation derived from Daymet Version 4 daily data at a 1 km × 1 km spatial resolution, was used. At a 1 month temporal scale, the SPEI showed great temporal variability, but there was a clear tendency towards wetting in the last years of the study period. A total of 85 meteorological drought episodes were identified. The spatial analysis also revealed that major affectation by moderate drought conditions occurred across the half west and south of the island, by severe drought also in the west half of the island but also along the eastern coast, and finally the extreme drought conditions, which were less frequent, principally affected the northeast of the country. A trend analysis of the area affected by moderate, severe, and extreme drought conditions revealed a tendency to decrease, which is reflected by the prevalence of positive spatial trends of the SPEI1 across the country. Full article

The main objective of the research was to determine the synoptic and mesoscale conditions, which, under the influence of two Upper Cold Lows (UCL), favor the occurrence of locally intense rain in Cuba. From the outputs of the Rapid Refresh (RAP) model, the characteristics of two UCL with four cases of locally intense rain associated with them were analyzed. The variables that were studied are relative humidity, temperature, geopotential height, vertical speed, wind force, divergence and vorticity. Mesoscale conditions were analyzed by using the Weather Research and Forecast System (WRF) model. Vertical cuts, numerical soundings and analysis of radar observations and satellite images were carried out. The CAPE shows extreme values higher than 6000 J/kg near the center of both systems and the LI reaches −13. The cases were developed in an environment of weak vertical wind shear in the surface layer −500 hPa and moderate to strong in the upper troposphere. High relative humidity values were identified at low levels, a dry layer was identified at medium levels and an increase in relative humidity was identified in the 300–200 hPa layer. Full article

This paper aims to highlight in a unique way the effects on sustainable agricultural systems due to the global growth of the development of the communications system. From the very beginning, we discussed the disruptions of climate change and, in particular, the risk of disaster, which causes long academic debates regarding electromagnetic radiation. During the study, we conducted empirical research using the wide range of receptors and a detailed determination bridge; we used quantitative methods to collect the processes and analyze the data and information incorporated to formulate observations and conclusions. The aim of this paper is to highlight an assessment for obtaining an answer to the possible causes of climate disturbances in agriculture, given the epistemic uncertainty. Mainly, we will reflect on the effects that interfere with the level of electromagnetic radiation produced by antennas. Opening advanced technologies with new satellite capabilities is expensive, so now, the density of high-power data transmissions useful in digital agriculture is the technical solution of operators. Our empirical research experimentally analyzes the data we collect in the statistical monitoring of electromagnetic waves, investigating to what extent the electromagnetic radiation affects agricultural systems concerned with trying to sequester C from the soil and reduce greenhouse effects. The paper defines and presents the evolution of the impact of new technologies developed in order to facilitate the implementation of intelligent agricultural solutions, admitting that the opening of new technologies facilitates the creation of the economically and socially interconnected global community (McLuchan, 1973). Taking into account the intensification of the use of digital agriculture, the analysis proposed for research is a topic that needs updating, and in this sense, it is necessary that this research be analyzed from environmental perspectives. Full article

Seasonal climatic prediction studies are a matter of wide debate all over the world. Cuba, a mainly agricultural nation, should greatly benefit from the knowledge, which is available months in advance of the precipitation regime and allows for the proper management of water resources. In this work, a series of six experiments were made with a mesoscale model WRF (Weather Research and Forecasting Model) that produced a 15-month forecast for each month of cumulative precipitation starting at two dates, and for three non-consecutive years with different meteorological characteristics: one dry year (2004), one year that started dry and turned rainy (2005), and one year where several tropical storms occurred (2008). ERA-Interim reanalysis data were used for the initial and border conditions and experiments started 1 month before the beginning of the rainy and the dry seasons, respectively. In a general sense, the experience of using WRF indicated that it was a valid resource for seasonal forecast, since the results obtained were in the same range as those reported by the literature for similar cases. Several limitations were revealed by the results: the forecasts underestimated the monthly cumulative precipitation figures, tropical storms entering through the borders sometimes followed courses different from the real courses inside the working domain, storms that developed inside the domain were not reproduced by WRF, and differences in initial conditions led to significantly different forecasts for the corresponding time steps (nonlinearity). Changing the model parameterizations and initial conditions of the ensemble forecast experiments was recommended. Full article

There have been several advances in understanding the North Atlantic Oscillation (NAO), but there are still uncertainties regarding its level of influence on the tropical climate. That is why this work determines the influence of the NAO on the main hydrometeorological events that affected Cuba in the 1999–2016 period. To comply with this, a regression analysis is carried out in the CurveExpert software where the combined influence of the NAO and El Niño-Southern Oscillation on hydrometeorological events is also examined. It was found that the NAO exerts a greater influence on Cuba when it is in its negative phase during the winter season. Full article

Measurements of atmospheric pressure by mesoscale transects of vehicle platforms such as the National Severe Storms Lab (NSSL) mobile mesonets have previously been collected in various targeted field campaigns. The challenges involved were specifically documented in the very different environments of tornadogenesis (Markowski et al., 2002) and orographic foehn winds (Raab and Mayr 2008). In recent years, the Jackson State University Mobile Meteorology Unit (MMU) has been developed with broad ranging applications in mind. Barometric pressure was originally expected only to be used for calculation of potential temperature over transects with significant elevation change. Previous studies have determined a dynamic change in measured pressure due to vehicle motion relative to the air that varies quadratically with speed, in agreement with theoretical expectations. This quadratic relationship is examined for the MMU under a variety of conditions. In order to consider least squares regression of this relationship, it was necessary to also have accurate speed and elevation data. Since even quite small elevation changes can produce measurable pressure changes, it was considered necessary to reduce pressures in each transect to the mean elevation using the methodology of Markowski et al. (2002). This required a combination of digital elevation model (DEM) and geographic positioning system (GPS) data to have sufficiently accurate elevations matched to the locations of the pressure measurements. Speed relative to ground from the GPS was used in place of actual air flow speed. Cases to be discussed include transects from approximately 20 to 200 km in length: approximately uniform conditions in flat terrain; crossing of orographic barriers; and cold fronts. Differences between pressure data collected with and without a pressure port are also considered. The impacts for determination of mesoscale pressure gradients, potential temperature, and other derived quantities will be evaluated. Full article

NEXRAD radars detect biological scatterers in the atmosphere, i.e., birds and insects, without distinguishing between them. A method is proposed to discriminate these bird and insect echoes. Multiple scans are collected for mass migration of birds (insects) and coherently averaged along their different aspects to improve the data quality. Additional features are also computed to capture the dependence of bird (insect) echoes on the observed aspect, range, and local regions of space. Next, ridge classifier and decision tree machine learning algorithms are trained on the collected data. For each method, classifiers are trained, first with the averaged dual pol inputs and then different combinations of the remaining features are added. The performance of both methods, are analyzed using metrics computed on a held-out test data set. Further case studies on roosting birds, bird migration, and insect migration cases, are conducted to investigate the performance of the classifiers when applied to new scenarios. Overall, the ridge classifier using only dual polarization variables was found to perform consistently well on both the test data and in the case studies. This classifier is recommended for operational use on the US Next-Generation Radars (NEXRAD) in conjunction with the existing Hydrometeor Classification Algorithm (HCA). The HCA would be used first to separate biological from non-biological echoes, then the ridge classifier could be applied to categorize biological echoes into birds and insects. To the best of our knowledge, this study is the first to train a machine learning classifier that can detect diverse patterns of bird and insect echoes, based on dual polarization variables at each range gate. Full article