The present study aims to identify the parameters from Consortium for Small-scale Modelling in CLimate Mode (COSMO-CLM) regional climate model that strongly control the prediction of extreme events, in particular, heat waves, extreme rainfall and cyclonic storms over West Bengal and the adjoining areas observed between 2013 to 2018. Metrics, namely Performance Score, Performance Index and Skill Score are employed to identify the parameters that most strongly influence the model output variables out of the 25 chosen tunable parameters corresponding to six parameterization schemes of the COSMO-CLM model. The sensitivity metrics are evaluated for three meteorological variables such as 2m-temperature, precipitation and cloud cover, simulated by the model corresponding to the different parameters for eleven extreme events over simulation domain and four inner sub-domains. It is evident from the results that only a subset of model parameters exhibits significant changes in model behaviour for distinct parameter values. In this particular study and region, no parameter is found to be sensitive from the soil parameterization scheme. Furthermore, in almost all input model parameters, the model performance reveals opposite character in different domains. Performance Index calculated from observations and model simulations with the default parameter confirms that model performance worsens when domain size reduces.

Dust storms are one of the most frequent weather phenomena in the Middle East and North Africa (MENA) region. Therefore, the daily forecast of dust events is a vital tool for the different sectors. There are many regional models used to forecast atmospheric dust storms. Here, the ICTP regional climate model (RegCM4) was used to simulate atmospheric dust emission, transportation, and deposition, with the optical properties of the dust particles, over the MENA region. In this paper, forecasted Dust Optical Depth (DOD) by RegCM4 has been compared with the Aerosol Optical Depth (AOD) measured by AERONET over different stations and by MODIS. The first run used two datasets (NCEP/GFS and ERA-Interim) for the meteorological initial and boundary conditions, whereas the second experiment used GFS with two dust emission schemes. The last run used GFS with two different values for the erodibility factor (1 and 0.5). The RegCM4 forecast with GFS and Scheme1 resulted in higher values of DOD than that measured by AERONET. However, when using the ERA-Interim or Scheme2, they did not make a significant difference, but the erodibility factor decreasing has led to reducing the overestimation values.

The Municipality of Florianópolis is located in the State of Santa Catarina (SC), at the South Region of Brazil, in the coastal zone. The place is considered the capital of the State or Island of Santa Catarina. According to the census accomplished out in 2022 by the Brazilian Institute of Geography and Statistics the Municipality had 537.217 habitants, being the second most populous in the State, distributed in a territorial area of 674,844 km². The State of SC is recognized by the numbers of disasters that frequently occurs, whether caused by precipitations – such as flood or drought, the landslides, windstorms, hail and even the occurrence of the first storm in the South Atlantic Ocean – Hurricane Catarina. As described by Monteiro (2001) the State is one of those with the best precipitation distribution when were compere with the other States in Brazil, this happens because to a series of factors and different situations as cold fronts, the South Atlantic Convergence Zone (SACZ), the medium level troughs, maritime circulation, tropical convection, cyclonic vortices and the relief.

The municipality’s economy is based on tourism with several tourist attractions and beaches, in the technology sector, commerce, civil constructions, mariculture and aquaculture. In accordance with Murara (2012) the average annual rainfall of Florianopolis is 1.734 mm. This study pretends to analyze and show the precipitation rates – high or not, which triggered the flooding episodes, that took place in December 2022 in the Municipality of Florianopolis, in addition to compare with other extremes situations recorded previously. For this, information and data were collected at the National Institute of Meteorology (INMET), satellites images, synoptics charts, facts on the Civil Defense website, as well as photographs and images belonging to state and municipal newspapers.

The fashion industry is one of the world's largest and third most polluted industry. It produces a carbon footprint of around 10% annually which is much higher than the footprint produced by flights and shipping. Nowadays, there is an increase in demand for different and new products for all ages of people due to which fast-changing fashion become a trend. But there is a hidden cost in the manufacturing of each material which is ignored by the people and which costs the environment and eventually the health of the people. It not only pollutes the air due to the emission of greenhouse gases but also consumes plenty of water along with the increase in plastic and some other wastes which pollute our environment. The solution to the problem is to avoid and move away from this fast fashion trend and subsequently by buying a few clothes that are good in quality and which do not pose a threat to the environment. But this will lower the sale as well as the revenue of the fashion industry which eventually affect our economy. The purpose of this study is to construct a fractional mathematical programming model which caters to both the objectives i.e., minimizing environmental pollution and maximizing revenue of the fashion industry with respect to the constraints based on the industry and environment side. A case study on the Indian Fashion Industry is done to illustrate the proposed model and the results indicated that the model is a good fit for catering to both the objectives and for controlling the pollution in the fashion industry.

Tropospheric ducts are an abnormal condition of the atmosphere that affects the propagation of electromagnetic waves, which has a direct impact on the performance of various radio systems, including mobile telephony, which is especially sensitive to these interferences in communications. Knowing what type of meteorological situations help the formation of this phenomenon allows alerting and taking measures in time, which helps to correctly manage the cuban telecommunications system. Products such as the ERA5 reanalysis are an important tool for studying tropospheric ducts as they provide high spatial and temporal resolution historical data, as well as global coverage of weather conditions. These data include three-dimensional information of temperature, humidity, and atmospheric pressure, which makes it possible to identify patterns and trends in the formation of refractive anomalies in different regions and times. Taking the study period 1981-2022, it was found that during the rainy season (May to October), surface ducts predominate, while during the dry season (November to April), elevated ducts are more common. The reanalysis data made it possible to verify that the presence of stable atmospheric conditions and the combination of a humid layer at low levels followed by a dry layer at altitude favors the formation of this type of phenomenon. This synoptic configuration mainly occurs when the Subtropical anticyclone of the Atlantic interacts with migratory anticyclones during the quarter of March - May and also interacts with tropical East waves in July. These are the periods of the year in which the most significant events occur, and the periods in which they also tend to present the greatest thickness of the duct layers, mainly in the seas to the north of the Cuban archipelago.

Numerous studies relating to the characterization of rain drop size distributions (DSD) have been conducted in the past several decades. Data include measurements from disdrometers as well as those retrieved from dual polarization and/or dual frequency weather radars. In this paper, we examine several different features of DSDs based on data and observations from two mid-latitude coastal locations: (a) Delmarva peninsula, USA, and (b) Incheon, ROK. In each case, the full DSD spectra were obtained from two collocated disdrometers, namely, Meteorological Particle Spectrometer (MPS) and 2D video disdrometer (2DVD). Two events from location (a) and one event from location (b) will be presented. For (a), observations and retrievals from NASA’s S-band polarimetric radar will also be included in the analyses as well as retrieved DSD parameters from the dual-wavelength precipitation radar onboard the Global Precipitation Measurement satellite. For (b), the MPS and 2DVD based DSD data will be compared with measurements from a collocated Precipitation Observation Sensor System (POSS). In all cases, emphasis will be on the underlying shape of the DSDs and its representation by the generalized gamma model, including for DSDs which correspond to light-rain.

Protected Areas as a carbon storage have evidently high value over the World. At the same time, they can play a significant role for climate change adaptation as nature-based solutions. In huge adaptation deficit, which results average in 60 billion rubles of losses in Russia from extremal weather events annually, the importance of protective ecosystem services is underestimated. Conservation of intact vegetation serves to stability of in several times larger territory, than within a Protected Area. In mountainous regions forests and grasslands prevent mudflows. In tundra and high mountains vegetation keep permafrost from fast degradation in warming climate. Forests maintain hydrological regime of rivers over the country: increase minimum low flow in droughts and decrease magnitude and pace of floods, what influence all territory downstream in the river basin. Protection of fields and settlements from strong winds by forests and tree-strips is using more than 100 years. For indigenous people, who are especially vulnerable for adverse climate change impacts, Protected Areas provide territory and natural resources for maintenance of traditional lifestyle. To highlight the value of Protected Areas as nature-based solutions it is critically important to asses the costs of provided ecosystem services and avoided losses. That can be used in planning of adaptation activities at National and local levels.

In recent years, Computational Fluid Dynamics has become a method widely used by the scientific community to study the dispersion of air pollutants in urban areas, these models allow to accurately quantify values of concentration of pollutants and how they are distributed spatially. This article seeks to analyze the effectiveness of computational fluid dynamics models and their validation methods used to estimate pedestrian exposure to Traffic Related Air Pollutants. This work proposes an exploratory methodology based on a literary review. A total of 28 articles were selected and analyzed from 455 literature published in the Scopus database between 2018-2023. The results show the effects of meteorological variables such as wind speed and wind direction on the dispersion of pollutants, especially that at higher wind speeds, they tend to disperse more quickly, which reduces the concentration of these hazards pollutants at the level of the pedestrian respiratory zone, another strategy widely used is tree planting near avenues, however, this must be done with careful analysis, since having more green infrastructure does not always translate into a reduction in pollutants near avenues. There is a tendency to use experimental data with wind tunnel, as validation of results of computer simulations, of the 28 selected articles 14 were based on this technique. Computational fluid dynamics is an advantageous tool, however, most of the estimates obtained in the literature only quantify exposure to the entry of the respiratory system, therefore, it is necessary to complement, with other models that consider the physical activity of people and thus evaluate more specifically the effect of inhaled pollutants on the entire respiratory system of pedestrians.

Temperature, as one of the most important factors in meteorological data analysis, is a variable parameter with severe changes in different periods. The trend of temperature changes over time is also of particular importance to investigating climate change. In this research, using the data from the TRY Project, which includes meteorological data with an accuracy of 1 km grid and a time accuracy of 1 hour, the temperature parameter of the city of Berlin is selected and the average temperature of the urban area of Berlin was calculated at different temporal scales. In addition to finding the linear regression trend of average annual temperature increase, Fourier transforms analysis and the least squared error fitting method was used to investigate harmonic temperature fluctuations to find the main sinusoidal period. Further, with the statistical analysis of data in daily averages and 1-hour intervals by considering medians of data as the benchmark for classification, months from April to October were determined as the hot months of the year, and hours from 9 to 19 were determined as daytime. Based on the mentioned classification, it was found that while the median difference between hot and cold months is 12.5 ˚C, the median difference between days and nights for the data related to the hot months and cold months is 5 ˚C and 2.1 ˚C, respectively. With this classification, the probability distribution of temperature was studied for each group, and the degree of similarity of this distribution with probability distribution functions such as normal, beta, gamma, and cosine were investigated. The separate analysis of the data categorized by this method had the highest degree of similarity with beta and normal functions.

Volcanoes are not traditionally considered to be significant sources of black carbon particles for the stratosphere. The main reason for this well-established view is the absence of appreciable traces of black carbon in volcanic emissions. Recently (Zuev et al., 2014), proposed a new hypothesis of formation and injection of nanodispersed carbon into the stratosphere during powerful explosive volcanic eruptions due to conversion of carbon-containing volcanic gases into black carbon particles. Critical analysis of this hypothesis and new observational data showed that it does not contradict the existing ideas about the principal possibility of this process, but can and should be significantly supplemented and corrected. Analysis of physicochemical processes in volcanic column showed the possibility of formation of the following types of black carbon particles: single highly dispersed particles or their small fractal aggregates, particles of fibrous or encapsulate types associated with pyroclasts, graphite-like pyrocarbon particles.Moreover, the formation of particles of the second and third types is energetically more accessible than that of the first type. Data on the detection of carbonаceous particles in the stratosphere and volcanic ash confirm the possibility of formation of all types of predicted particles and their identity with particles produced by known technological processes and detected after powerful volcanic eruptions in Kamchatka (Russia). The main limiting factors determining both the possibility and the lower boundary of the conditions for the formation of particles of different types have been identified: temperature and concentration of carbon-bearing gases in the volcanic column. For plinian-type eruptions, these parameters appear to be insufficient for the formation of black carbon particles in appreciable amounts and their accumulation in the stratosphere, which contradicts the previously mentioned hypothesis. All potentially formed black carbon remains in volcanic ash and sediments.