Atmospheric Weather Conditions Research Articles

Machine learning-based models for forecasting radio refractivity over the coastal area of South Africa

Surface refractivity is a crucial parameter that determines the bending of radio signals as they propagate within the troposphere. It is greatly influenced by the atmospheric weather conditions and changes rapidly, especially in the coastal areas. This research utilized 50 years (1974-2023) surface temperature, pressure, and humidity data from six coastal stations in South Africa to forecast radio refractivity in the Mediterranean climate. Five machine learning models: Gated Recurrent Unit (GRU), Light Gradient Boosting Machine (LightGBM), Long-Short Term Memory (LSTM), Prophet, and Random Forest were trained for future prediction of surface refractivity at any coastal area in South Africa. The stations latitude, longitude, altitude, surface refractivity and date were applied as the input parameters to train the models. The models were optimized through the randomized searchCV hyperparameter tuning to improve their efficiency. The LightGBM outperformed other models with RMSE and adjusted determination coefficients of 1.67 and 0.96, respectively. The model is recommended for future prediction of surface refractivity needed for the improvement of point-to-point wireless communication, terrestrial radio and television transmissions, and mobile communication networks in the coastal sub-tropical regions.

Design analysis of a sustainable techno-economic hybrid renewable energy system: Application of solar and wind in Sigulu Island, Uganda

Sustainable energy is central to achieving the global Sustainable Development Goals (SDGs), particularly in electrifying underserved communities. This study examines Sigulu Island, which lacks grid electricity and relies on costly, polluting diesel generators. On-site assessments revealed a daily load demand of 1,455.705 kWh. This study designed and analyzed a Sustainable Techno-economic Hybrid Renewable Energy System (STHRES) combining solar photovoltaics and wind turbines, with battery backup, to meet the island's energy needs. The research adopted both qualitative and quantitative methods, gathering atmospheric weather conditions specific to Sigulu Island. Solar panels and wind turbines were identified as the most viable options, with the system incorporating 677 units of 1 kW solar panels and 27 units of 1 kW wind turbines, generating 839.97 kW and 640.08 kW daily, respectively. Additionally, 527 Li-ion batteries were used to store approximately 1480.05 kW of surplus energy demand to manage fluctuations capable of sustaining the island for 8 hours during a total blackout. The initial installation costs are estimated at $90,393.04 for solar PV, $27,729.82 for wind turbines, $159,169.81 for batteries, and $92,407.00 for the inverter. The STHRES is projected to save $56,917.93 annually, covering 15.4 % of the installation costs compared to diesel operations. Moreover, this system will reduce Uganda's carbon footprint by 436,035.6 kgCO₂ annually, equivalent to a 0.01 % reduction in national emissions. The proposed system decreases the Net Present Cost (NPC) from $426,617.60 to $369,699.67 and the Cost of Energy (COE) from $32.12/kWh to $ 27.79/kWh. With a 9 % Internal Rate of Return (IRR) and a 3 % Return on Investment (ROI), STHRES has a payback period of 8.2 years, demonstrating its financial and environmental benefits for Sigulu Island.

Investigating the influence of cosmic ray and solar activities on atmospheric weather dynamics within the equatorial electrojet region (Nigeria)

The association between atmospheric weather conditions along the equatorial electrojets and complexity in emergence flux in solar magnetic activity involved in the weather fluctuation processes may be substantial such that it affect the weather conditions particularly in the earth’s equatorial regions. In this study, we have analysed relevant parameters influencing the atmospheric weather conditions across the major cities and zones in Nigeria with solar magnetic activity and cosmic ray intensity covering five solar cycles from 19 to 24. Our investigation was principally based on solar magnetic activity which include solar parameters; flare index, cosmic ray, and atmospheric weather parameters including temperature, relative humidity, and precipitation. We utilized the cross-correlation and wavelet coherence techniques. The study found that the Northern region (Guinea and Sahel Savannah) located along 8.92 to 13.70 degrees latitudes and the Southern regions (Coastal and Derived) located within latitudes 4.5 and 8.9 degree latitudes responded differently to various atmospheric and solar parameters. The temperature and relative humidity in the northern parts is comparatively higher than in the southern region, while the southern region experienced higher precipitation. The flare index and cosmic ray exhibit different patterns as well negatively correlated. The relationships between the precipitations and relative humidity across Nigeria show a distinct behaviour which could be attributed to concentrations of ions influencing cloud properties across the country. There are strong and positive correlations between the two indices, with high coefficients specifically during the examined solar cycles. The difference in responses between Southern and Northern Nigeria could be attributed to mechanism driving the atmospheric weather through the Earth latitudinal couplings.

Анализ зависимости между погодными условиями и качеством атмосферного воздуха в городах Казахстана за 11-летний период (2010–2020 гг.) как основа эпидемиологических рисков

В статье был проведен ретроспективный анализ зависимости между погодными условиями и присутствием поллютантов в воздушном пространстве города Караганды и Петропавловска за 11-летний период (2010–2020 гг.). Динамика кода загрязненности атмосферы исследуемых городов свидетельствовала о более высоком уровне загрязнения воздуха в г. Караганде на протяжении всех сезонов, по сравнению с показателями г. Петропавловска. В газообразных выбросах по г. Караганде достоверно преобладали вещества, относящиеся к первой категории опасности: диоксид азота в зимний период, озон весной и летом. Фенолы и формальдегид присутствовали в воздухе г. Караганды на протяжении всех сезонов, достоверно превышая ПДК. Анализ корреляционных зависимостей между погодными и экологическими факторами позволил обнаружить средние и слабые прямые и обратные корреляции между загрязнением атмосферы, погодными условиями и сезонами. Было обнаружено, что в зимний сезон синергизм мог проявляться вследствие одновременного присутствия диоксида азота и формальдегидов, содержание которых в атмосфере достоверно превышало ПДК. В летний сезон синергизм можно было ожидать вследствие одновременного присутствия в воздухе больших концентраций озона и формальдегида. Комплексное воздействие погодных условий, уровня и характера загрязненности атмосферного воздуха в Карагандинском регионе по сравнению с г. Петропавловском свидетельствует о вероятности значительного усиления или видоизменения негативных последствий для здоровья человека.

Development of a Low-cost IoT-based Remote Sensing Device for Real-time Atmospheric Data Acquisition and Weather Monitoring

The knowledge of basic meteorological parameters is needful in terms of understanding the atmospheric behavior for a given period and consequently also for creating the weather forecast. In this study, an IOT based atmospheric weather data acquisition device is designed to ease and improve on the current measuring technique of acquiring Temperature, Pressure, and Relative Humidity measurement at different altitude levels. This design aims at solving the problem of inadequate atmospheric data by monitoring atmospheric weather conditions using sensors while the microcontroller processes the data collected and relays it to the user. This research was carried out at the University of Uyo, between September 2021 and January, 2023. Taking cognizance of the fact that weather forecasting is of great relevance in our world, this system has been designed using a BME280 module for the atmospheric parameters capturing, an ESP8266 as the microcontroller for Data processing, and a wireless module for processing and transfer of the data from the BME module, a NEO6M GPS module for longitude and latitude, a Li-ion cell to power the components and then a TP4056 circuit to recharge the Li-ion cell. A web server application known as Thingspeak.com was incorporated to help the user interact and access the acquired data to make for ease of understanding and real-time logging of the data obtained. The device successfully transmitted real-time weather data of the target atmospheric parameters captured by it to its users remotely. The results prove that this device is optimally effective and viable for capturing atmospheric parameters for real-time monitoring purposes. This work is relevant in the weather forecasting sector, agricultural sector, as well as to individuals who may have need of gathering information about the atmosphere for proactive planning of daily activities.

Outdoor airborne allergens: Characterization, behavior and monitoring in Europe

Aeroallergens or inhalant allergens, are proteins dispersed through the air and have the potential to induce allergic conditions such as rhinitis, conjunctivitis, and asthma. Outdoor aeroallergens are found predominantly in pollen grains and fungal spores, which are allergen carriers. Aeroallergens from pollen and fungi have seasonal emission patterns that correlate with plant pollination and fungal sporulation and are strongly associated with atmospheric weather conditions. They are released when allergen carriers come in contact with the respiratory system, e.g. the nasal mucosa. In addition, due to the rupture of allergen carriers, airborne allergen molecules may be released directly into the air in the form of micronic and submicronic particles (cytoplasmic debris, cell wall fragments, droplets etc.) or adhered onto other airborne particulate matter. Therefore, aeroallergen detection strategies must consider, in addition to the allergen carriers, the allergen molecules themselves. This review article aims to present the current knowledge on inhalant allergens in the outdoor environment, their structure, localization, and factors affecting their production, transformation, release or degradation. In addition, methods for collecting and quantifying aeroallergens are listed and thoroughly discussed. Finally, the knowledge gaps, challenges and implications associated with aeroallergen analysis are described.

FSO-Based Space-Air-Ground Integrated Vehicular Networks: Cooperative HARQ With Rate Adaptation

Space-air-ground integrated vehicular networks (SAGIVN) have been widely envisioned as a promising network architecture for 6 G to support the Internet of Vehicles (IoV). In SAGIVN, free-space optics (FSO)-based, high altitude platform (HAP) relay-assisted low Earth orbit (LEO) satellite systems have recently attracted research efforts worldwide. Critical challenges in designing and implementing FSO-based SAGIVN include atmospheric turbulence, weather conditions, and pointing misalignment. This paper offers a comprehensive cross-layer design framework of error-control protocols with rate adaptation for FSO burst transmissions in HAP-aided SAGIVN. Remarkably, we propose a design of link-layer cooperative incremental redundancy (IR) hybrid automatic repeat request (HARQ)-based sliding window mechanism. An analytical channel model for HAP-aided LEO satellite to emerging unmanned aerial vehicles (UAVs) FSO links is provided. At the same time, the analysis can also be applied to other kinds of vehicles. The queuing behavior induced by both the cooperative IR-HARQ protocol and the rate adaptation scheme is analyzed with a Markov model. Several performance metrics are analytically obtained, including the average round-trip frame delay, throughput, and energy efficiency. The results quantitatively demonstrate the impact of atmospheric turbulence and weather conditions on the system performance and support the optimal selection of system parameters. Additionally, the effectiveness of the proposed system is numerically confirmed. Monte Carlo simulations are also performed to validate the accuracy of theoretical derivations.

Robust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0

Abstract. The climate impact of non-CO2 emissions, which are responsible for two-thirds of aviation radiative forcing, highly depends on the atmospheric chemistry and weather conditions. Hence, by planning aircraft trajectories to reroute areas where the non-CO2 climate impacts are strongly enhanced, called climate-sensitive regions, there is a potential to reduce aviation-induced non-CO2 climate effects. Weather forecast is inevitably uncertain, which can lead to unreliable determination of climate-sensitive regions and aircraft dynamical behavior and, consequently, inefficient trajectories. In this study, we propose robust climate-optimal aircraft trajectory planning within the currently structured airspace considering uncertainties in standard weather forecasts. The ensemble prediction system is employed to characterize uncertainty in the weather forecast, and climate-sensitive regions are quantified using the prototype algorithmic climate change functions. As the optimization problem is constrained by the structure of airspace, it is associated with hybrid decision spaces. To account for discrete and continuous decision variables in an integrated and more efficient manner, the optimization is conducted on the space of probability distributions defined over flight plans instead of directly searching for the optimal profile. A heuristic algorithm based on the augmented random search is employed and implemented on graphics processing units to solve the proposed stochastic optimization computationally fast. An open-source Python library called ROOST (V1.0) is developed based on the aircraft trajectory optimization technique. The effectiveness of our proposed strategy to plan robust climate-optimal trajectories within the structured airspace is analyzed through two scenarios: a scenario with a large contrail climate impact and a scenario with no formation of persistent contrails. It is shown that, for a nighttime flight from Frankfurt to Kyiv, a 55 % reduction in climate impact can be achieved at the expense of a 4 % increase in the operating cost.

Identification of Terahertz Link Modulation in Atmospheric Weather Conditions

With the rapid increase of wireless connectivity, current spectrum resources are not enough for significant requirements for large data capacity. Research interests are moving towards the high-frequency band in the terahertz range for wider bandwidth. However, multipath scattering and induced time delay, suffered by terahertz links propagating in outdoor weather, lead inevitably to increasing expenses in baseband signal processing. This trades away the advantage of low time latency and high stability, which are commonly considered as important merits of terahertz wireless communication techniques. To reduce the burden in signal processing and explore the feasibility of modulation identification in the terahertz band, a method of wireless link modulation identification is considered as a potential solution. In this work, it is investigated theoretically by employing two kinds of neural networks: the convolutional neural network (CNN) and the long short-term memory network (LSTM). Link deterioration caused by different atmospheric weather is introduced into the theoretical model, and the performance of this method is evaluated. Results show that the identification accuracy of the constructed neural networks can be up to 99%, which means such a method is efficient for identification of the modulation format of terahertz wireless links under different weather conditions. This work demonstrates the feasibility of modulation identification in outdoor terahertz communication scenarios and provides specific references.

Performance analysis of the coherent FMCW photonic radar system under the influence of solar noise

The real-time high-resolution target detection in radar applications has increased the demand for photonic radar systems due to their higher bandwidth and faster processing capabilities over conventional microwave radar. Additionally, photonic-based radar technology can revolutionize the limited performance of conventional microwave radar caused by various atmospheric attenuations. This article presents a frequency-modulated continuous-wave photonic radar system using coherent detection. The performance of the proposed system is theoretically investigated under the effect of various real-time atmospheric weather attenuations and the influence of solar background noise, which is further verified on the simulation platform. This study is conducted under different atmospheric weather conditions, such as clear, haze, and fog, to highlight the practical limitation of free-space links in the presence of solar background noise. The detected signal at different target ranges with and without solar noise is measured and then compared in terms of signal-to-noise ratio. Furthermore, the impact of solar noise on the system performance when the Sun is under different sky conditions and zenithal angles is also analyzed. The results presented here provide insights into designing photonic radar systems for practical applications like autonomous vehicle radar systems, self-traffic control, and navigation.

The oscillatory effect of chemical constituents, dynamics, barometric, and thermodynamics on weather over Accra, and Bissau

A sharp contrast in atmospheric weather conditions prevailed over Accra and Bissau and that led to the oscillatory nature of weather conditions between the two cities. During the latter part of July 2022, Bissau experienced persistent and continuous rains whilst Accra remained fair and dry. It was realized that when an air parcel rises, low-pressure system is created at the surface but when the air parcel sinks, high pressure forms. Pressures were predominantly high over Accra and low over Bissau during that time. Models from CAMS were used to study chemical compositions to determine the concentration and effect of Sulphur Dioxide (SO2) over the two cities. Wind behavior over various levels in the atmosphere was considered using model products from ECMWF, satellite imagery from EUMETSAT, and t-phi gram from the Air Resources Laboratory of NOAA to study the thermodynamics of the skies. METAR, hourly observations made by the National Meteorological Offices from the two cities were retrieved from the AADS and Ogimet to study the barometric nature during the period. Results show that the atmosphere over Bissau had been less polluted as compared to that of Accra which makes Bissau to be persistently cloudy. Atmospheric chemical constituents would have to compete for moisture if the concentration is high and does not support the sustenance of massive clouds capable of producing rain. It has been realized that the development of anticyclone, an anticlockwise circulation at the 200 hPa level contributed to the cloud sustenance over Bissau during that time.

Design and Implementation of a Microcontroller-based Weather Acquisition Device

An atmospheric data acquisition device is designed to ease and improve on the current method of acquiring Temperature, Pressure, and Relative Humidity measurement at different altitudes. The proposed work aims to solve the problem of inadequate atmospheric data by monitoring atmospheric weather conditions using sensors while the microcontroller processes the data collected and relays it to the user. This research was carried out at the University of Uyo, between September 2018 and January, 2023. Considering that weather forecasting is of the utmost importance in our current society, the system has been built using a BME280 module for the atmospheric parameters acquisition, an ESP8266 as the microcontroller for Data processing, and a wireless module for processing and transfer of the data from the BME module, a NEO6M GPS module for longitude and latitude, a Li-ion cell to power the components and a TP4056 circuit to recharge the Li-ion cell. A web application Thingspeak.com was incorporated to help the user interact and access the data to enable ease of understanding and real-time logging of the data collected. This work is targeted toward the weather forecasting sector, agricultural sector, and individuals which may wish to gather information about the atmosphere for knowledge consumption. The results show that this device has a good performance for capturing atmospheric parameters for real-time monitoring purposes.

Consequence modeling of liquefied petroleum gas accidental release from retail outlets in an African city residential environment

AbstractThe drive by the Nigerian government to increase the usage of liquefied petroleum gas (LPG) within the country has led to the rapid growth of refill stations without adherence to international safety and regulatory guidelines on gas handling. This has increased associated hazards such as explosions and fires leading to loss of life and damage to properties. This study employed the Areal Locations of Hazardous Atmospheres software to model the consequences of explosion and fire due to accidental leakage of LPG into the environment in four selected locations in the Ilorin metropolis under four scenarios. The longest threat zones were observed in Scenario II in all the selected refill stations. The flammability hazard for gas station 1 was predicted to be 98 and 238 m for the red and yellow threat zones, respectively. For thermal radiation, the predicted threat zones are 17, 27, and 43 m for 10, 5, and 2 kW/m2, respectively. January was observed to have the longest threat zones, particularly in the flammability hazard which indicates the influence of atmospheric weather conditions on dispersion. To reduce the accidental release of LPG, continuous enlightenment on safety rules is mandatory for all.

The Influence of Varying Atmospheric and Space Weather Conditions on the Accuracy of Position Determination.

Today's technological developments make it possible to use machines to perform specific tasks instead of humans. However, the challenge for such autonomous devices is to precisely move and navigate in constantly changing external environments. In this paper, the influence of varying weather conditions (air temperature, humidity, wind speed, atmospheric pressure, type of satellite systems used/satellites visible, and solar activity) on the accuracy of position determination was analyzed. To reach the receiver, a satellite signal must travel a great distance and pass through all layers of the Earth's atmosphere, the variability of which causes errors and delays. Moreover, the weather conditions for receiving data from satellites are not always favorable. In order to investigate the impact of delays and errors on position determination, the measurements of the satellite signal were conducted, the motion trajectories were determined, and the standard deviations of these trajectories were compared. The results obtained show that it is possible to achieve high precision in determining the position, but varying conditions, such as solar flares or satellites' visibility, meant that not all measurements are able to achieve the required accuracy. The use of the absolute method of satellite signal measurements contributed to this to a large extent. To increase the accuracy of positioning by GNSS systems, it is first of all proposed to use a dual-frequency receiver that eliminates ionospheric refractions.

The Impact of Atmospheric Synoptic Weather Condition and Long-Range Transportation of Air Mass on Extreme PM10 Concentration Events

Atmospheric synoptic weather patterns have a significant impact on the concentration, dispersion, and transportation of air pollution in various regions and times around the world. To assess the impact of atmospheric synoptic weather patterns and long-range air mass transportation, we used weather classification techniques from the BP training model and the HYSPLIT model. Our research uncovered four weather conditions linked to PM10 concentration categories ranging from normal to extreme. Weather conditions 3 and 4 are the most significant conditions supporting the occurrence of extreme concentration events that are heavily influenced by anti-cyclones. Despite weather conditions influencing high concentrations, 60% of long-distance air mass transport to Secunda from Mpumalanga province increased to extreme PM10 concentrations. Furthermore, long-term weather shifts have been observed to positively impact reducing the concentration of PM10 extreme events.

SAR and Optical Image Registration Based on Uniform Feature Points Extraction and Consistency Gradient Calculation

Synthetic aperture radar (SAR) satellites have an active sensor on board, which emits electromagnetic signals and measures the strength and time delay of the returned signal backscattered from ground objects. Optical images have rich spectral information, but it is easily affected by atmospheric attenuation and weather conditions. Thus, the study of the registration between these two images is of great significance. We present a novel method for SAR and optical image registration. In the stage of feature points extraction, the method combines phase consistency intensity screening and scale space grid division to obtain stable and uniform feature points from the image. During the stage of feature description, the method employs the extended phase consistency method to calculate the gradient amplitude and direction of the image, and improves the correctness of the main direction calculation and descriptor construction. Experimental results demonstrate its superior matching performance with respect to the state-of-the-art methods.

Effect of Atmospheric Weather Condition on the performance of FSO

Effect of Atmospheric Weather Condition on the performance of FSO

Comparative analysis of different atmospheric Weather condition in free space optical Communication system

Comparative analysis of different atmospheric Weather condition in free space optical Communication system

Performance investigation of 2 × 20 Gb/s MDM-RoFSO link incorporating spiral-phased Hermite–Gaussian modes under strong weather conditions

This research paper presents a novel high bit rate and spectral efficient 20 Gb/s mode division multiplexed (MDM) radio over free space optical communication (MDM-RoFSO) system. 10 Gb/s MZM/QAM-16-modulated data streams, each from two distinct channels, are transported over two spiral-phased Hermite–Gaussian (HG) laser modes, HG00 and HG01, respectively. These two HG channels are multiplexed using MDM at a 10 GHz RF signal, amplified and transmitted over the atmospheric link at an optical wavelength of 1550 nm. The proposed MDM-RoFSO system is simulated for MZM modulation over two distinct HG modes in clear sky, dense haze, dense fog, and strong rain weather conditions. The performance of the system is evaluated using performance metrics such as BER, eye diagram, Q factor, and link transmission range. The simulation results show that the HG00 mode is more robust and the extended transmission range is achieved from (7 to 24.4) km for (24.4 to 2.98) Q factor for clear sky to strong rain conditions. This transmission range is further extended by the factor of (2.9 to 1.7) km when the QAM-16 modulation is incorporated in composite (HG00 + HG01) mode and then transmitted in similar weather conditions for the acceptable BER of < 10–5. Finally, the simulation results confirm that the QAM-16 modulated composite HG mode is able to mitigate the atmospheric weather conditions and travel a longer transmission distance compared to individual HG mode transmission.

Estimation of solar irradiance and solar power based on all-sky images

The ramp event of solar irradiance is prone to occur due to atmospheric conditions, and makes it difficult to integrate solar power into regional power grid. The atmospheric aerosol, cloud occlusion, temperature and weather conditions especially influence the daily solar irradiance, and cause the uncertain of the solar power which would leads to the regional grid overloaded or lower-loaded. To integrate the solar power into the regional grid reliably, the estimation of solar irradiance and solar power output plays an important role. In the study, a system is proposed to perform accurate solar irradiance and solar power estimation. Through the all-sky image feature extractions, the regional and global weights are obtained. The long short-term memory (LSTM) is used as the training model with the weights as inputs to estimate the solar irradiance. The power curve is made by using the estimated solar irradiance and the solar power output to estimate the solar power. Several performance indices are used to evaluate the proposed method, including mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2). The experimental results show that the performance of the solar irradiance estimation of the proposed method is remarkable with the lower MAE and RMSE and the higher R2. Furthermore, the performance of the solar power estimation using the proposed method is very close to the actual estimation performance.