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The turbulent standard deviations and the turbulent third-order and fourth-order moments are the key turbulence dispersion parameters in Lagrangian dispersion models. However, the characteristics of these parameters under heavy haze conditions in urban areas have not been fully investigated, and the commonly used similarity relations of these parameters in models were based on observations in highly flat and sparsely populated areas. In this paper, the vertical profiles of these parameters and their local similarity relations under heavy haze conditions in the wintertime of Beijing have been analyzed by using data collected at a 325-m meteorological tower. The heavy haze process has been divided into three stages: transport stage (TS), cumulative stage (CS), and dispersion stage (DS). Results show that the turbulent dispersion parameters behave differently during three stages. In the TS and DS, the maxima appear in the profiles of the turbulent standard deviations above the urban canopy; in the CS, the turbulent standard deviation are almost constant with height. The analysis of the third and fourth order moments shows that the wind velocities above the urban canopy in the TS deviate from the Gaussian distribution more significantly than those in the CS and DS. The local similarity relations of the turbulent dispersion parameters in the TS, especially for the longitudinal wind components, are normally different from those in the CS and DS. Thus, different from the common assumptions in Lagrangian models, the turbulence dispersion in horizontal directions is anisotropic and should be parameterized by multiple similarity relations under heavy haze conditions.

The main purpose of this paper is to observe the effect PV variation of solar temperature and irradiance on different conditions and on the inverter output for a grid-connected system. Majorly temperature& solar irradiation effects the performance of a grid connected inverter, also on the photo-voltaic (PV) electric system. The simulation based study was carried out in order to evaluate the variation of inverter output with the variation of solar temperature and irradiance with the variation in climate. The analysis of Grid-connected inverter and their performance at various seasons and conditions is investigated. Solar power plant for a year. In solar power plant efficiency of inverter is also considered to calculate overall losses so, the inverter efficiency and plant performance are considered in this paper using MAT Lab software. In summer season the inverter performed efficiency is decreased because of peak temperature value and slightly increased with the increase in irradiance.

Forest fires are very intense deadly destroying the homes, wildlife, timber also polluting the atmosphere with hazardous compounds of pollutants. Forest fire produces various ill effects and increases the global temperature; it has a prolonged impact on landscapes and deduces the production of oxygen as it destroys past spreading. As wildfire is a part of nature its intensity cannot be handled after crossing a threshold level. The proposed system detects the forest fire at minimal stage with assistance of cameras fixed at the towers. With real time fire detection algorithm programmed, fire alerts the respective forest department with unique node set location with Long Range network (LoRa). Proposed Fire detection system is placed at the end of node networks with gateway connected and intern with interneted to local forest control areas. The fire detection system consumes very low power as they are energised with localised solar systems. Forest ranges for long dimensions therefore these LoRa can transmit data to a larger range.

An indoor thermal environment is affected by various heat elements, such as heat transfer through walls, solar radiation, and heat emissions from people, lighting and equipment. To promote both local thermal comfort and building energy efficiency, demand-oriented ventilation (such as personalized ventilation) has been developed. When using this method, a good understanding on indoor temperature distribution becomes necessary. For this purpose, an index known as Contribution Ratio of Indoor Climate (CRI) has been developed through extraction from the calculation results of Computational Fluid Dynamics (CFD). This index can be used to analyze the independent contribution of each heat element to indoor temperature distribution. In this paper, a complete and detailed introduction of the CRI is given, including its basic premises, definitions, and mathematical meaning. Particularly, calculation method of the CRI in natural convection airflow fields is further developed. Two cases (forced and natural convection airflow fields) have been carried out in different scenarios, with results showing that the CRI of a heat source had higher values in the area around itself. Also, it had a larger influence range in forced convection airflow field because of the convective airflow, while relatively larger CRI values only appear in the area above the heat source in the natural convection airflow field because of the heat plume. As a useful index for understanding the form of indoor temperature field, the CRI has guiding significance for regulating air-conditioning/ventilation systems to build better indoor thermal environment.

There are prototypes of social housing massively built in contrasting climatic conditions, generating thermal comfort needs that are difficult to satisfy by the users themselves. Variation of indoor air volume in living spaces where the use of air conditioning and natural ventilation strategies provides elements to improve thermal comfort conditions. This research shows the thermal performance located in a representative social housing according to Mexico's National Housing Commission. Operative temperature results from a benchmark case were compared to six Virtual Evaluation Models, using the Dynamic Thermal Simulation tool Design Builder® from the perspective of probability. The main objective was to determine the minimum use of active systems with different indoor air volumes and improve comfort conditions to promote sustainable living in social housing. The analysis was conducted under an adaptive comfort range according to three different climate conditions in Mexico adopting a Numerical Theoretical Method. The main findings can be divided into two parts: a) the impact of the indoor air volume on thermal performance was evidenced in a proportion of time in three representative climates of the central region of Mexico, and b) no relationship was found between indoor air volume and thermal comfort in sub-humid cold climate; in sub-humid temperate climate, the same number of comfort hours was found in two different models, and in sub-humid warm climate, an inversely proportional relationship was found between indoor air volume and the comfort hours. This findings implies a greater knowledge relative to what we know about sub-humid cold, temperate and warm climates.

This paper presents the results of thermal comfort and air quality studies in naturally ventilated residential buildings in Kampala City, Uganda. Questionnaire surveys were used for obtaining occupant subjective thermal sensation votes. Indoor and outdoor measurements for air temperature (Ta), Mean Radiant Temperature (MRT), relative humidity, air speed, and air quality were done for seven buildings over fifteen days during the month of June 2019. DesignBuilder software was used to develop the reference building model and to simulate strategies for improvement of thermal comfort. Survey results showed that 67.8% of the respondents were comfortable with the indoor thermal environment. The indoor air temperature range was 22.7°C - 27.9°C which lies within the 80% acceptable limits set by ASHRAE 55. The measured indoor MRT range was 24.24°C - 25.57°C. Measured levels of indoor PM2.5 were double the World Health Organization recommended limits, while the levels of PM10 were thrice the recommended limits of ASHRAE 62.1. The CO2 concentrations were within the limits set by ASHRAE 62.1. The developed model predicted comfortable indoor conditions with a temperature range of 23.6°C - 25.3°C based on the 80% acceptable limits set by ASHRAE 55. The results show that majority of occupants preferred cooler temperatures during the day which is justified by the use of adaptive measures to obtain thermal comfort.

Thermal comfort inside classrooms located in an institutional building is examined as a function of air temperature, relative humidity and wind velocity. The effect of east-west orientation on the thermal comfort inside the classrooms located in different floor levels is understood based on field experiments and validated using computational fluid dynamics simulations. The air temperature inside the classrooms shows an increasing trend with the floor level. Air temperature and relative humidity inside the classrooms reveal an inverse relationship as a function of east-west orientation with different floor level. As time progresses, the air temperature inside classrooms located on the eastern side of the building is apparently higher than rooms situated on the western side of the building, both field experiments and simulation acknowledge the same. Appropriate reasons have been proposed behind the discussed phenomenon using computational fluid dynamics simulation.

Around the world people are rapidly moving to cities. The rapid urbanization has led to changes in land use and land cover, which (i) modify the urban surface energy balance making the cities hotter than the surrounding rural area, (ii) exacerbate the impact of extreme weather events like heatwaves and (iii) lead to poor quality of life. City administrations around the world are undertaking drastic measures to mitigate extreme heat. Here, in this study we test a wearable sensing platform to both study pedestrian-level microclimate boundary conditions as well as the impact of urban greening on moderating excess heat in dense urban areas. The experiment presented here was conducted in New York City. Typically, the urban thermal state is estimated by means of direct observations from ground-based sensors, satellite based remote sensing techniques, and high-resolution urban climate modeling, all of which are too coarse to resolve pedestrian-level impacts, a key parameter in determining heat stress for citizens occupying the outdoors. Here a wearable and a portable sensing apparata were used to monitor key environmental parameters - air temperature, relative humidity, wind speed, mean radiant temperature and solar radiation. The coupled monitoring platform included multiple sensors and a wireless data logging system which were all prototyped by the authors on open source technologies. The sensor platform was able to accurately map the thermal environment of multiple dense urban spaces, being to reproduce the spatial variability in key microclimate parameters, and the performance was comparable to traditional stationary ground-based weather stations. The results, which may be of key help also for validating microclimate forecasting models, indicate high spatial variability in temperature, humidity and solar radiation within the same urban parks. Our findings also indicate that on average small urban parks in a dense urban setting were able to reduce the air temperature by 3–7 °C in New York City with major gains during the mid-afternoon periods.

The ability to forecast the concentration of air pollutants in an urban region is crucial for decision-makers wishing to reduce the impact of pollution on public health through active measures (e.g. temporary traffic closures). In this study, we present a machine learning approach applied to forecasts of the day-ahead maximum value of ozone concentration for several geographical locations in southern Switzerland. Due to the low density of measurement stations and to the complex orography of the use-case terrain, we adopted feature selection methods instead of explicitly restricting relevant features to a neighborhood of the prediction sites, as common in spatio-temporal forecasting methods. We then used Shapley values to assess the explainability of the learned models in terms of feature importance and feature interactions in relation to ozone predictions. Our analysis suggests that the trained models effectively learned explanatory cross-dependencies among atmospheric variables. Finally, we show how weighting observations helps to increase the accuracy of the forecasts for specific ranges of ozone’s daily peak values.

The national lockdown policies have drastically disrupted socioeconomic activities during the COVID-19 pandemic in China, which provides a unique opportunity to investigate the air quality response to such anthropogenic disruptions. And it is meaningful to evaluate the potential health impacts of air quality changes during the lockdown, especially for PM2.5 with adverse health effects. In this study, by using PM2.5 observations from 1388 monitoring stations nationwide in China, we examine the PM2.5 variations between the COVID-19 lockdown (February and March in 2020) and the same period in 2015-2019, and find that the national average of PM2.5 decreases by 18 μg m−3, and mean PM2.5 for most sites (about 75%) decrease by 30%-60%. The anthropogenic and meteorological contributions to these PM2.5 variations are also determined by using a stepwise multiple linear regression (MLR) model combined with the Kolmogorov-Zurbenko filter. Our results show that the change of anthropogenic emissions is a leading contributor to those widespread PM2.5 reductions, and meteorological conditions have the negative influence on PM2.5 reductions for some regions, such as Beijing-Tianjin-Hebei (BTH). Additionally, the avoided premature death due to PM2.5 reduction is estimated as a predicted number based on a log-linear concentration-response function. The total avoided premature death is 9952 in China, with dominant contribution (94%) from anthropogenic emission changes. For BTH, Yangtze River Delta, Pearl River Delta and Hubei regions, the reductions of PM2.5 are 24.1, 24.3, 13.5 and 29.5 μg m−3, with the avoided premature deaths of 1066, 1963, 454 and 583, respectively., Graphical abstract Image, graphical abstract

Earth Air Tunnel Heat Exchanger (EATHE) systems are not so much popular in India because of restricted accessibility of ground space in majority of residential and commercial sector. In the present article, thermal performance of newly designed helical shaped EATHE system has been compared with conventional U–shaped EATHE system by keeping similar geometrical and operational configuration for the summer (April–June) and winter (December–February), respectively. In this regard, therefore, numerical model of both EATHE systems have been designed and experimentally validated. The thermal performance of both EATHE systems has been compared in terms of outlet air temperature and effectiveness. The present numerical study suggested that the newly designed helical EATHE system exhibits better thermal performance with lower ground requirement as compared to conventional U-shaped EATHE system. To evaluate the thermal comfort potential of EATHE system in office buildings of India, composite climate specific adaptive thermal comfort boundaries has been used. The detailed thermal performance behaviour of the EATHE system suggests that an additional 80% of the time; ambient air conditions became thermally comfortable during peak summer season. In addition, analysis of the results revealed that EATHE system has more potential during heating operation mode to maintain indoor comfort in winter season of composite climate in India.

According to the few researches on Nearly zero energy residential buildings (NZERB) in hot-summer and cold-winter zone, although it could reduce the cooling load of buildings due to its high thermal insulation and air tightness, it still needed for certain cooling in summer. This paper studied indoor environment of NZERB under three kinds of air-conditioners (split-type air-conditioner, multi-line air-conditioner and ceiling radiant air-conditioner). Firstly, a simulation model of NZERB was established based on Nanjing, a typical city in hot-summer and cold-winter zone. Secondly, variation of indoor air temperature and building load characteristics with outdoor air temperature were studied. Thirdly, indoor environment and energy consumption under three selected conventional air-conditioners in summer were simulated. Finally, the discussion was given, and an air-conditioner combining with convective and radiant cooling were proposed. The results indicated that the air-conditioner needed to be turned on in NZERB in hot-summer and cold-winter zone due to the room air temperature in off-air condition ranged from 32 °C to 36 °C, which was higher than designed indoor environment temperature in summer, but the indoor environment of NZERB under three selected conventional air-conditioners could not meet the requirements of energy saving and comfort at the same time, and a proposed convective-radiant air-conditioner could be fast, stable, and energy saving. The findings can provide a reference for conducting active technology in NZERB.

The cyclone separatoris an important separation device. This paper presents a new type of embedded two-stage cyclone, which includes a 2nd-stage cyclone (internal traditional cyclone) with multiple inlets and a 1st-stage cyclone (outer cylinder) that unifies the 2nd-stage cyclone inlets into one inlet. The Taguchi experimental method was used to studythe two-stage cyclone separator's inlet area on its performance. Studies have shown that the increase of the 1st-stage cyclone inlet area and the increase in the number of 2nd-stage cyclone inlets (N) positively affect reducing the pressure drop and a negative effect on efficiency. It is recommended to use 2S (the original 1st-stage cyclone inlet area) of the 1st-stage cyclone inlet area and 2N of the 2nd-stage cyclone inlets when separating fine particles. Compared with a traditional cyclone, the pressure drop is reduced by 1303 Pa, the mass separation efficiency (Eq) is increased by 0.56%, and the number separation efficiency (En) is increased by 2.05%. When separating larger particles, it is recommended to use 2S of the 1st-stage cyclone inlet area and 4N of the 2nd-stage cyclone inlets. Compared with a traditional cyclone, although En decreases slightly, the pressure drop is reduced by 3055 Pa, and the Eq is increased by 0.56%. The research results provide new insight into the design of the cyclone.

This paper examines if wind might be an additional energy source for solar powered, crewed Mars missions during dusty days. Wind data from Viking Lander 2 is analyzed from a wind engineering point of view, as it is done on Earth, to evaluate wind turbine sites. It is assumed that wind energy will not be the primary energy source used on a crewed Mars mission but an additional option during times of global dust storms, when solar power is suffering from an increase of atmospheric optical depth, which is seen as one of the largest drawbacks of solar arrays. In order to evaluate if wind energy might be a reliable energy source during dust storms, when the energy yield of solar cells is reduced, it will be investigated whether wind strength has a positive correlation with increased atmospheric optical depth. This is, in case wind speed is generally capable of providing a significant amount of power, a further requirement if wind turbines should compensate the power loss of solar arrays during increased atmospheric optical depth. For a first estimate of the amount of wind turbines needed to maintain energy output during a storm a case study is done which regards power production during the 1977b global dust storm.

In the current work we have used the deep learning based long short-term memory model to predict the strength and peak time of solar cycle 25 by employing the monthly smoothed sunspot number data obtained from WDC-SILSO, Royal Observatory of Belgium, Brussels. We have used the stacked LSTM forecasting model to predict the upcoming cycle 25. From our analysis it has been shown that our proposed model is capable of capturing long term dependencies as well as trend within the data. For cycle 20 and 21 the error difference between predicted as well as observed peak value is 2.3 and 0.7 respectively while the peak prediction error is 1.47% and 0.30%. The RMSE of the model for cycle 20 and 21 is 3.97 and 4.34 respectively. For cycle 22, the AE and RE is 4.6 and 2.16% while the RMSE of the model for this case is 4.50. The predicted peak amplitude of solar cycle 23 and 24 from our proposed model has a relative error of 1.75% and 1.99% respectively from the observed value while the RMSE is 3.4 for cycle 23 and 4.2 for cycle 24. Our projected prediction of cycle 25 using the proposed LSTM model, says that it will be stronger than cycle 24 and weaker than cycle 23. The solar cycle 25 will peak with an amplitude of sunspot number at 171.9 ± 3.4 and will be 47 % stronger than cycle 24. The solar cycle 25 will reach its peak in August 2023 ± 2 months.

To mitigate uncertainties in wind resource assessments and to improve the estimation of energy production of a wind project, this work uses a decision tree machine learning model to assess the effectiveness of hub-height wind speed, rotor-equivalent wind speed, and lapse rate as variables in power prediction. Atmospheric data is used to train regression trees and correlate the power outputs to wind profiles and meteorological characteristics to be able to predict power responses according to physical patterns. The decision tree model was trained for four vertical wind profile classifications to showcase the need for multiple calculations of wind speed at various levels of the rotor layer. Results indicate that when compared to traditional power curve methods, the decision tree combining rotor-equivalent wind speed and lapse rate improves prediction accuracy by 22% for the given data-set, while also proving to be the most effective method in power prediction for all classified vertical wind profile types. Models incorporating lapse rate into predictions performed better than those without it, showing the importance of considering atmospheric criteria in wind power prediction analyses.

International audience; This study is a follow up on a previous one carried out within the frame of the French project SMARTEOLE, during which, a ground-based scanning LiDAR measurement campaign was conducted in the onshore wind farm of Sole du Moulin Vieux. That previous study focused on the wakes of two wind turbines that experienced different degrees of interaction depending on the incoming wind direction, through the processing of LiDAR measurements. The measurement duration (7 months) ensured the statistical convergence of the ensemble-averaged flow fields obtained after holding a categorisation process based on the wind speed at hub height, wind direction, and atmospheric stability, where only near-neutral stability conditions were considered. The present study focuses on integrating the operational data of the wind turbines through SCADA processing to complement the LiDAR wake field observations and to be used as an input for analytical wake models. First, the correlation between the atmospheric stability, deduced from MERRA-2 dataset, and the free-stream turbulence intensity, measured by the wind turbines’ anemometers, is studied for different wind speed ranges. It is observed that the turbulence intensity tends towards a consistent value as the atmospheric stability approaches near-neutral stability conditions, giving confidence into the applied strategy of data categorisation based on MERRA-2 outputs. The influence of the degree of wake interaction on the wake added turbulence, the velocity and power deficits between both turbines is assessed. Clear trends between the wake added turbulence and both the velocity and power deficits are detected. Consequently, two fitting laws are proposed. Then, different analytical wake models and wake superposition methods are fed with the operational data deduced from the processed SCADA data, and are used for predicting the evolution of the velocity deficit within the wake. Some statistical metrics are used for error quantification of the different engineering wake models compared to the scanning LiDAR measurements, used as reference, and Blondel and Cathelain produces the closest results to the field measurements.

The problem of disturbances in the parameters of the F2 layer and upper ionosphere during the days preceding onset of a magnetic storm is known already for many decades. Many publications have been dedicated to analysis of these disturbances both in the critical frequency and height of the F2 layer and in the total electron content. The majority of researchers have drawn a conclusion that such disturbances are observed prior almost to every storm, positive events (an excess of the values in the prestorm period over the corresponding values on a geomagnetically quiet day) prevailing. Moreover, some authors claimed that the aforementioned disturbances could be considered as precursors of the coming magnetic storm. A concept of the so-called Q-disturbances, according to which the disturbances during the prestorm days are not related to the coming storm but are a result of several processes (meteorological influence on the ionosphere, changes in the magnetosphere-ionosphere interaction) which sometimes cause deviations of ionospheric parameters is an alternative to the precursor concept. Arguments are presented in favor of the precursor concept. These arguments are, first of all, based on the existence of a dependence of parameters of the prestorm disturbances on parameters of the coming storm. The dependencies on the magnetic storm intensity, local time of the storm onset SO, and time from the disturbance to SO present the strongest arguments against the Q-disturbance concept. Numerous examples of these dependencies are presented. The recent publications with new experimental data on the foF2 and TEC behavior in the prestorm period are discussed. Arguments in favor of the prestorm concept presented in these publications are briefly considered.

The efficiency of the photovoltaic thermal (PVT) system is affected by the meteorological and climatic conditions. Studies based on mathematical modeling has been applied to optimize the PVT system using absorber shape factor ( Z ) and mass flow rate ( m ). Mathematical modeling of heat transfer coefficients and their simulation in MATLAB is presented in the current study using the meteorological data of three major cities (Ahmedabad, Delhi, and Pilani) of India. The scope of the present work is not only limited to finding the effect of solar radiation intensity and ambient temperature but also to establish the optimized value for Z and the best suitable shape for the absorber plate. The detailed analysis of results obtained shows higher values of m gives better total efficiency. For m = 0.02 kg/s but it is observed Z = 1.3 gives maximum efficiency, 48% at noon, in comparison to Z = 2 which is 43% for m = 0.1 kg/s. Hence curved grooved absorber plate is predicted to give better performance for various geographical locations.

In this study, single and aggregated forecasters of half-hourly global horizontal irradiance are assessed. The models are the standard persistent model and four newly proposed static, dynamic, moving average, and amplified persistent models. These sub-forecasters are aggregated using equal, annual optimal, and monthly optimal weights. A particle swarm optimizer was used to find those weights. Measured data, obtained from two desert sites for the years 2015–2018, was used for fitting and training the different models, while the data of the year 2019 was used to test their prediction capabilities. For the single forecasters, the dynamic model is the most accurate, followed by the static and average models. When the aggregated model of annual optimal weights was tested, the three contributing forecasters were the dynamic, average, and amplified models. The dynamic forecaster held the largest weight due to its prediction superiority during overcast and partially cloudy days. When monthly optimal weights were used, all forecasters contributed, and the dynamic model held the largest weight during winter but not in the summer when the clear sky condition is dominant. The aggregated model was the most precise, with relative mean square errors lower than 15.0% and coefficients of determination higher than 98.8%.

India is a tropical country with diverse climates, and thermal adaption plays a significant role in sustaining thermal comfort in non-conditioned buildings. Indoor air temperature is taken as the main parameter for thermal comfort in buildings, which is very high in western India and creates uncomfortable for occupant. Thermal comfort is a primary need inside the buildings from a human health perspective. Roof structure and the color of the exterior surface affect the thermal comfort of the buildings in a hot-dry climate. Roof contributes maximum heat gain towards the outdoor temperature wave amplitude and time lag in non-conditioned buildings during summer. Three commercially available reflective paints or coatings were selected and used in the simulation to assess the performance of a non-conditioned building. In this paper, we present the evaluation of the discomfort index (DI) and Tropical Summer Index (TSI) for the summer season (April and May).The comfort hours achieved using cool roof coatings were 12–17 hours more than the base case or conventional roof used in a hot-dry climate. The tropical summer index (TSI) was found adequate within its comfort range (27.5 °C) for roofs with surface coatings than a base case roof slab.

Vehicles parked under blazing sun increases the cabin temperature to a broiling point as greenhouse effect comes into play. Similar is found in winter season when vehicle is parked in open, cabin temperature falls drastically. This extreme rise and fall in the vehicle cabin temperature, results in more fossil fuel consumption to power air-conditioner for high loads, which eventually leads to increase in generation of harmful gases. Additionally, the drastic condition inside vehicle cabin makes it profusely miserable for human comfort. Thus, relevant renewable energy powered cooling/heating system is required for the vehicle cabin when parked in open areas. Therefore, in this study initially, a 3D heat and mass transfer model is developed for evaluating temperature variations in vehicle cabin for summer and winter seasons. Then, a solar powered thermoelectric cooling-heating system is proposed to resolve the extreme rise and fall of vehicle cabin temperature without running the engine or using any power from the primary source. And, the thermal performance of the proposed system is evaluated and assessed in the numerical study. The proposed system was able to reduce the average temperature of the cabin space by 17 °C in summer season and the average temperature rise of the cabin space is found by 15 °C in winter season. The solar simulator for the vehicle rooftop solar panel investigated annual averages of normalized energy per day and loss in solar energy collection due to variation in tilt angle, which was around 20%. Thus, the present study demonstrates the feasibility of proposed novel solar assisted thermoelectric cooling/heating system in both summer and winter seasons. Hence, study demonstrates that the thermoelectric technology with solar assistance is effective and viable for cooling and heating of vehicle cabin during parking.

The performance of FSO links gets degraded significantly due to fog weather conditions. This paper aims to perform the link budget analysis for different locations of India by analyzing the impact of foggy weather on the operation of FSO links. The meteorological data of daily visibilities of potential locations of India, have been analyzed carefully to calculate the probability of exceeding visibility, ranging from 1 to 5 km, for each of the locations. It has been observed that Ludhiana has recorded maximum fog events having visibility less than 1 km and Chennai has recorded maximum events having visibility more than 5 km. In addition to this, the average attenuation coefficients due to fog have been calculated for each of the locations, based on the meteorological data. Further, the maximum feasible link distances for various locations of India have been computed for three different practical configurations of FSO links by using the link margin equation. The coastal area of Chennai has delivered maximum feasible link ranges as compared to other considered locations of India.

Yearly sunspot cycle 24 (SC24) activity reached a minimum in December 2019. Timeline for SC25 is explored with data for sunspot numbers (SSNs) and geomagnetic indices (aa/Ap) using our time-tested heuristic methodology not based on any model. We infer SC25 may be as active as SC24, reaching its peak (Rmax) in February 2024 ± 6 months, ruling out Dalton-like minimum in 21st century as well as McIntosh et al. [2020] forecast that “SC25 could have a magnitude that rivals the top few since records began.” Wide range of SC25 forecasts challenges our understanding of solar dynamo operation and reliability of future long-term space weather / climate forecasts. We cannot anticipate whether SC25 may have multiple peaks; no theory is tuned to predict fine structure of a SC. During ascending phase of SC24 a CME from the Sun barely missed the Earth. A direct hit by it could have caused damage of trillions of dollars, shutting down power grid and rendering satellites blind.

Here we present a prognostic model of horizontal spread of bacterial drug resistance genes in spacecraft crews. The model takes into account the value of gene transfer frequency during conjugation and mobilization of plasmids, adjusted for its variability under the influence of space flight factors, the frequency of occurrence and concentration of microbial communities, which form a pool for the propagation of antibiotic resistance genes in space flight. To develop a phenomenological model of antibiotic resistance, two stages of a scientific experiment were carried out. The first stage consisted of identifying the biotope of the nasopharynx of cosmonauts participating in the Salyut-7 space program and the number of antibiotic resistance determinants in bacteria identified in nasopharynx. The second stage of the experiment was carried out on the Russian Segment of the International Space Station and consisted in determining the values of mobilization and conjugation in space flight conditions, under normal conditions on Earth and in post-flight research. In these investigations the archive data was chosen, since while Salyut flights only few visiting crew missions were undertaken, so the flights were mostly isolated and because of that were more likely as planned interplanetary missions and thus are of most our interest. The phenomenological model, based on the experiments, made it possible to predict a multiple decrease in antibiotic resistance in space conditions, against the background of increased resistance of R-plasmids.

In the present study, pollutants levels from March 1, 2020, to August 30, 2020, were compared with similar periods of 2019 to assess the impact of lockdown due to COVID 19 on the quality of air in 3 different cities of Gujarat, India named Ahmedabad, Gandhinagar, and Valsad. Data was collected from AccuWeather and Central Pollution Control Board website for study period. Data was analyzed by comparing air quality taking 2019 as reference and correlation matrix were developed for study sites. Lockdown resulted from COVID-19 has shown decline trends in pollutant concentration resulting in improved air quality in the study area. In the first city Ahmedabad, Gujarat an air pollutant parameter such as CO, PM2.5, PM10, NO2, and SO2 were reduced by 54%, 181%, 70%, 33%, and 103% respectively. Almost similar trends were observed in the rest of the two cities also i.e., Gandhinagar and Valsad. NO2 level got reduced by a factor of 100% which can be associated with ban on vehicular movement. Correlation between the air pollutant and metrological variables with COVID-19 variables were also studied be developing correlation matrix. In Gandhinagar, a strong correlation was observed between carbon monoxide, and PM10 with COVID-19, a moderate correlation between NO2, and SO2 with the COVID-19 variables, a week correlation was found between temperature and COVID-19, and no correlation is found between wind speed and COVID-19 variables.

The Italian Riviera was, from the second half of the nineteenth century to the Second World War, one of the most famous, elitist, climatic, winter, international tourist destinations. In Bordighera, in particular, the British community was so important that a typical English ‘environmental bubble’ was created, and still today there is not only physical, but also cultural evidence of it. Among the latter, the polyglot Journal de Bordighera (1883–1935), a large number of issues of which are still held at the local Museum Bicknell, is the privileged witness of the carefree life of the tourists spending their winter on the Riviera in that period. One of its weekly columns, the ‘Bulletin Météorologique’, contains precise data, collected by the tourists themselves, about the temperatures of the resort, while comments on the special climate of the region are scattered across different issues. That information is unique for Bordighera because, even if the location has always been appreciated for its mild climate, surprisingly no other meteorological data have ever been recorded for such a long period. The aim of this paper is twofold: to reconstruct this historical climatological series, which appears to have reasonable historical reliability, if verified with the series of other weather stations of the Riviera, and to consider the observations about the climate published by the tourist meteorologists on the Journal de Bordighera as a positioned way to observe and to narrate the climate of the Riviera.

Detection of volcanic hazards are important for public health and aircraft safety. In this study, we investigate the ability to detect volcanic plume using a simple analytic approach based on Global Navigate Satellite System (GNSS) observations, numerical weather model (NWM) fields and ground-based automatic weather stations. The GNSS data obtained from the network located around the Sakurajima volcano in Japan was used to estimate zenith tropospheric delays (ZTD) in October 2014, when the volcano showed an increase in activity. For our analysis, the ZTDs reconstructed in the ray tracing approach were applied to ERA5 reanalysis, and then compared with the ZTDs estimated by the Natural Resources Canada web GNSS processing software. Four analytical techniques were employed: target, space, time, and eventually, time and space differentiation. Meteorological data from nearby weather stations, as well as ERA5 reanalysis, helped separate the ZTD increments connected to weather conditions and volcanic activity. The results revealed that our approach correctly demonstrates Sakurajima activity in four cases out of five. We also noted a correlation between the results of our calculations and plume spreading direction data in 25% of cases.

NTCM is a relatively simple climatological ionosphere model. However, its weather ionosphere characteristic specification capability is limited. Also, its nowcasting and forecasting performance of the East-African equatorial ionosphere has not yet been tested. Therefore, this paper demonstrates techniques of changing NTCM to an ionospheric weather model that nowcasts and forecasts VTEC for East Africa. It has been done by adapting the model to quiet day VTEC from nineteen GNSS receivers in East Africa in 2013 and 2014. Then, performances of the model in nowcasting and forecasting VTEC, before and after the adaption, have been investigated compared to observed VTEC. We have found that NTCM, after adaptation, represents observed features of diurnal variations of VTEC and equatorial ionization anomaly (EIA) much better than before adaptation. After adaptation, the mean and standard deviation of daily mis-modelings are comparable with the mean and standard deviation of yearly mis-modelings. The study showed that NTCM, after adaptation to a reference station, performs better than before ingestion at the stations nearby the reference station; however, its performance decreases at the locations far away from the reference station. In addition, after ingestion, one hour ahead prediction of NTCM is found to be the best compared to longer time predictions.

This paper exposes the risk of generalization of wind conditions from a single met-mast measurement to be representative of the actual flow field in a wind farm situated in complex terrain. As a case study, Large-Eddy Simulation (LES) of the neutral Atmospheric Boundary Layer (ABL) flow for a mid-western Sweden wind farm is performed. The site-specific complex topography and the forest properties like the Plant Area Density and the tree heights are extracted from the Airborne Laser Scanning (ALS) 3D data, thus the forest is heterogeneous. To emphasize the impact of the local topography and surface roughness on the wind field, the wind turbines are not included in the numerical simulations. The predicted wind speeds using LES are compared to wind speed from the nacelle-mounted anemometers taken from the wind farm's turbine SCADA data, focusing on the wake-free turbines. A sufficient degree of match is observed, supporting the accuracy of the numerical simulations. The results show that inflow variables i.e., mean wind speed, shear exponent and turbulence intensity vary at each wind turbine location justifying the need for turbine-specific assessment of the wind resource in a wind farm located in forested complex terrain. The inter-turbine (between turbines in the wind farm) differences in wind resource is quantified in terms of the difference in turbine-specific structural and mechanical loads by running wind turbine mechanical simulations using the extracting the wind fields predicted by the LES. The results show that not only inter-turbine loads varying significantly in the wind farm, but the turbine loads also differ significantly if a homogeneous assumption is made for the forest. Most importantly, it was found that the homogeneous forest assumption predicted a higher turbulence intensity compared to a heterogeneous forest resulting.

Wind Generating Resources (WGRs) are variable, uncontrollable, and uncertain compared to traditional generating resources. As Wind Generating Resources (WGRs) have the intermittent nature of WGRs and uncertain characteristics according to the weather condition, the accurate prediction of WGRs' capacity factor is an essential factor associated with integrating a large amount of wind generating resources into the grid. As wind farm outputs depend on natural wind resources that vary over space and time, spatial correlation analysis is also needed to estimate power outputs of wind generation resources. In this paper, we propose the potential capacity factor estimates of new wind generating resources using the augmented spatial analysis and modelling of power outputs produced by wind farms that are geographically distributed in windy areas. To validate the proposed spatial prediction model, we use the empirical data from the Jeju Island's wind farms in South Korea.

The purpose of the data assimilation is to use all the available information to determine as accurately as possible the state of the atmosphere. In this context we consider the assimilation of the GPS-ZTD (Global Positioning System – Zenith Total Delay) by a nudging scheme in Regional Atmospheric Modeling System at Institute of Atmospheric Sciences and Climate (RAMS@ISAC) at medium horizontal resolution (5 km). The water vapour mixing ratio ( q v ) given by the RAMS@ISAC is perturbed according to the differences between observed and simulated ZTD. To verify the impact of the GPS-ZTD data assimilation on the representation of the atmospheric humidity field, especially at the local scale, a numerical experiment was performed over central Italy area, over a one-month time frame. The studied network consists of 41 geodetic receivers and 3 single frequency receivers, this latter category of receivers has turned up more and more in marketing for the very low cost with respect to high level of performance. For what concerns all devices, processing was carried out in PPP (Precise Point Positioning) using goGPS, an Open Source Program Package for GNSS Positioning. For the application of PPP processing to single-frequency receivers, goSEID method (an alternative implementation of the SEID approach integrated in the goGPS open source software) was applied; the algorithm, starting from the observations of a unique (or more) dual frequency “reference” receiver, is able to generate L2 synthetic observations for any single frequency receiver located in its surroundings (15–20 km). Results show that the assimilation of GPS-ZTD has a positive impact on the simulation of the Integrated Water Vapour over the areas where GPS receivers are located. The results obtained, using data assimilation, were indeed compared with those obtained by the model without data assimilation of GPS-ZTD. The two outputs were subsequently evaluated against observations obtained from two geodetic receivers (ROUN and INGR) not used in the assimilation process. The result showed that the independent observation was more consistent with the model output in which also the single-frequency receivers had been assimilated. The assimilation of GPS-ZTD improves also the simulated precipitation, especially by reducing false alarms.

The focus of this study is to develop a highly accurate formulation to estimate the day number (DN), solar declination angle (SOLDEC), solar altitude angle (SOLALT) and also to predict the diffuse horizontal irradiance (DHI), direct normal irradiance (DNI) and global horizontal irradiance (GHI) for any location around the world at any time of the day for both short term and long term periods. Regression analysis is done using continuous 12 years of satellite measured historical solar irradiance, weather and solar angle data in the temporal resolution of 10 min for 12 cities around the world such as Kuala Lumpur, Auckland, Tokyo, Riyadh, London, Accra, Antananarivo, Brasilia, Lima, Quito, Ottawa and Honolulu. The models generated through the regression analysis perform better than existing models in predicting the solar irradiance, hence, these models are efficient and reliable for universal global application.

Offshore wind energy has seen a steady increase in the latest years as it serves as an ideal alternative energy source to meet our renewable energy goals. Due to this increase in interest and in installations of offshore wind farms a better understanding of the impact of waves on the power production of wind farms in necessary. This wave-wind farm power production interaction has scarcely been examined, however, some exceptions of small scale large-eddy-simulations exist. Unfortunately, these studies are not able to take multiple wind farms under real weather conditions into account. Here, we show the power production and wake lengths of 1250 offshore wind turbines located in the German Bight simulated by a stand-alone atmospheric (WRF) model and a coupled atmosphere-wave (WRF-SWAN) model. The coupled atmosphere-wave model estimates larger (smaller) power production in case of waves and wind traveling in the same (opposed) direction compared to the stand-alone atmospheric model. The relative difference between the two models can be as much as 20% for the two-week averaged grid power production, while the difference in total power over this two-week period is equal to 9%. Moreover, the wind farm wake lengths of waves and wind traveling in the same (opposed) direction are longer (shorter) for the coupled atmosphere-wave model compared to the stand-alone atmospheric model. Here, the relative difference of the mean wake length between the two models can be up to 25%. This shows the importance of waves as a part of the offshore wind environment and can be an important factor in future wind assessment or power production estimation studies.

Forest fires preparedness strategies require the assessment of spatial and temporal variability of fire danger. While several tools have been developed to predict fire occurrence and behaviour from weather data, it is acknowledged that fire danger models may benefit from direct assessment of live fuel condition, as allowed by Earth Observation technologies. In this study, the performance of pre-fire observations of land surface temperature (LST) anomaly and of the Perpendicular Moisture Index (PMI) in predicting fire characteristics was evaluated against the Canadian Forest Fire Weather Index (FWI) System, a fire danger model adopted in several areas worldwide. To this purpose, a database of forest fires recorded in Campania (13,595 km2), Italy, was combined with MODIS retrievals of LST anomaly and PMI, and with FWI maps from NASA’s Global Fire Weather Database. Fires were grouped in decile bins of LST anomaly, PMI and FWI System components, and probability distribution functions of burned area, fire duration and rate of spread were fitted in each bin. The dependence of probability model parameters on LST anomaly, PMI and FWI System components was assessed by means of trend analysis (coefficient of determination and p-value of the linear fit, Sen’s slope and Mann-Kendall test) and likelihood ratio test versus the corresponding unconditional probability model. Finally, the probability of an extreme event, conditional to ignition, was modelled as a function of LST anomaly and PMI. Results show that the probability distribution function of burned area has a strong dependence on both LST anomaly and PMI, that the probability distribution function of fire duration has a strong dependence on LST anomaly but not on PMI, and that the probability distribution function of rate of spread has a weak dependence on LST anomaly and a strong dependence on PMI. These results are in line with expectations from models of the combustion and flames propagation processes. Trend analyses and likelihood ratio tests showed that the FWI System components are good predictors of burned area and fire duration, but not of rate of spread. They also confirmed that, where LST anomaly and PMI are covariates of the considered fire characteristic, their performance is similar or better than the FWI System components. Finally, the probability of an extreme event in terms of burned area as a joint function of LST anomaly and PMI shows a wider dynamic range than the same probability modelled as a function of these remote sensing variables individually.

High penetration of photovoltaics (PV) has been observed in the energy market over the last decade. However, its integration into electrical grids is challenging, as solar energy is highly fluctuating given its dependence on different weather variables. Consequently, short-term forecasting of solar irradiance provides a pivotal solution to ensure optimal use of the produced energy and reduce its uncertainty. This study proposes a hybrid convolutional neural network and Multilayer perceptron (CNN–MLP) model to forecast the global irradiance 15 min ahead. The model uses images from a hemispherical sky imager, time series of GHI, and weather variables collected from a ground meteorological station in Morocco. The evaluation of the proposed model under clear, mixed, and overcast days shows that the proposed model performs better than the persistence model. The root mean square error (RMSE) varies between 13.05 W/m2 and 49.16 W/m2 for CNN–MLP and between 45.76 W/m2 and 114.19 W/m2 for persistence. The coefficient of determination (R2) varies between 0.99 and 0.94 for the MLP–CNN and between 0.98 and 0.79 for persistence. The results show that the proposed model could be an appropriate choice for short-term forecasting even under cloudy conditions.

A major hurdle in increasing the economic feasibility of solar photovoltaic (SPV) plants is the ever-increasing share of location-dependent costs (land, transmission, labor, etc.) in total installation costs. Such costs are geospatial in nature, due to spatial socio-economics affecting them. Present geolocation methods, for locating SPV installation sites, do not consider the effect of location-dependent costs in installation. We use a spatial parameterization model for examining the factors causing spatial variation of the installation costs of land, labor, transmission and supply chains for suburban SPV plants, within a geographic boundary. The model is applied to Kolkata city, India, and the spatial variation of the costs are checked in a 2500 km2 suburban boundary. The spatial variation of the location-dependent costs is mainly caused by the distance from an economic focal point of the city. The variations significantly optimize at minima points in the 2500 km2 boundary, where the location-dependent costs increase by 10% with an average 2.6 km deviation and an average 6.7 km deviation from the global minima, for small and large plants, respectively. The spatial minima is mainly caused by variance of land and transmission costs. This minima location lies on the extrapolation of a line that connects the city focal point with the substation. The capacity of the SPV plants at the optima increases with increasing transmission voltage (11 kV to 66 kV), ranging from 4 MW to 257 MW in the case-study (small to large scale), while the minima shift away from the city focal point (ranging 29 km to 48 km) with increasing capacity. This study provides a perspective on how the spatial variation of installation costs can play a role in the geolocation of SPV plants. Furthermore, the empirical and spatial variation of location-dependent costs can enable energy planners to evaluate the economic feasibility of solar power and promote better land-use near cities.

Power system operations are unavoidably influenced by forecast uncertainties. The forecast uncertainty's effect on operations was quantified in individual systems, however, was seldom investigated in a hybrid system. This study aims to quantify the effect of forecast uncertainties on the hybrid hydro–photovoltaic system operation. First, the operation model is built considering streamflow and photovoltaic forecast uncertainties with long- and short-time scales. Second, total release uncertainty is determined from the sum of squares for releases under different forecast scenarios. Finally, a variance partitioning method is used to divide the total release uncertainty into the uncertainties induced by individual forecast errors. Longyangxia hybrid hydro–photovoltaic power plant from China was selected as a case study. Results provide following insights: (1) long-term inflow forecast uncertainty accounts for 98.89% of the contribution to the long-term release uncertainty, while photovoltaic uncertainty accounts for 1.11%; (2) long-term inflow forecast uncertainty still dominates the short-term release uncertainty, with the contribution of 62.06%; and (3) the total release uncertainty decreases in long-term hybrid operation compared with the reservoir operation, indicating the complementary relationship between long-term hydropower and photovoltaic power. These implications highlight the importance of improving forecast accuracies for varied energy sources at different time scales.

Lightning is a sudden release of electrostatic energy that plays a significant role in the ionospheric-magnetospheric coupling process. A short-term increase in the particle count rates (CR) in the Van Allen Radiation Belt (VAB) is known as the particle bursts. Due to lightning events, the increase in the number of high-energy particles in the inner region of the VAB (L 2) is a fascinating research to implement. This paper tries to determine the number of lightning-induced particles, which can successfully establish a connection between the two regions of the atmosphere (the ionosphere and the magnetosphere). Lightning-generated electromagnetic radiation propagates through the Earth Ionosphere Waveguide (EIWG), where some energy leaks through and scatters particles in the radiation belt. By lowering the pitch angle, some particles precipitate into the ionosphere and cause secondary ionization. This excess particle count caused by lightning strokes is detected using data from the NOAA-15 satellite and data from the World Wide Lightning Location Network (WWLLN) is used to verify the relationship between lightning stroke and particle count. In this paper, we process our work by using lightning locations over a specific region of India and the geographic/geomagnetic conjugate locations. The monthly variation shows a high correlation between the lightning stroke number and the particle counts within the conjugate geomagnetic location, while the correlation with the particle counts within the conjugate geographic location is significantly lower. In the second half of the paper, we use an automated energy filtration method, for the computation of the particle counts induced by maximum energetic lightning (having lightning energy value > 104 J and the particle counts induced by minimum energetic lightning (having lightning energy value 104 J). The results of this analysis demonstrate that the variation of PCs in the conjugate geomagnetic location is more closely related to the maximum energetic lightning variation than the lowest energetic lighting variation. This paper will demonstrate the coupling process between the ionosphere and the magnetosphere based on counting the particles produced by lightning.

In response to the rapid growth of energy consumption in urban cities, rooftop wind turbines (RWT) have emerged as a powerful technique for providing sustainable energy and strategically minimizing the carbon footprint of buildings. However, significant knowledge gaps exist regarding how RWT respond to the complex urban environmental flows modified by the coexisting buildings and trees. This study conducts idealized numerical experiments using an open-source large-eddy simulation model to investigate the interactions between actuator-disc turbines, street trees and buildings. We found that trees taller than the mean building height modifies the existing roof-level strong shear layer by extracting energy from the mean momentum. A significant change in the mean kinetic energy budget is induced, drastically increasing turbulence production of the flow, which leads to lower power output of RWT. Trees lower than the buildings hardly alter the mean flow field but they reduce turbulence production near the roof level. As a result, improved power output (16%) and decreased normalized power fluctuation (5.2%) are observed compared to the control case without trees. The results highlight that it is important to assess effects of different street tree morphologies on the performance of RWT in their design and implementation processes.

An effort is made to examine the cloud parameters over few stations in India for one year from October 2016 to September 2017. This study includes the investigation of cloud base height, cloud top height, zero degree isotherm height, number of cloud layers, cloud thickness, number of days cloud detected, cloud liquid water content and cloud attenuation at 6 GHz, 12.5 GHz and 24 GHz. Salonen Uppala (SU) model and Water Vapor Pressure-Nanyang Technological University (WVP-NTU) model are used for cloud detection using the radiosonde data at Bangalore, Hyderabad, Mumbai, Thiruvananthapuram and Chennai. The cloud parameters found using both the models are compared. WVP-NTU model shows enhanced values for most of the cloud parameters over SU model. Cloud base height calculated using these models are compared with METAR data and found that Cloud base height from WVP-NTU model matches well with METAR data. Cloud liquid water content and cloud attenuation values are calculated using the ITU-R model. The cumulative distributions (CDF) of cloud attenuation for one year are compared for the places considered. The seasonal CDFs of cloud attenuation using the WVP model are compared with the rain statistics. In this study, the maximum cloud attenuation of 1.67 dB/km is measured in Hyderabad by WVP-NTU combined ITU-R model at 24 GHz.

The intensification of extreme events, storm surges and coastal flooding in a climate change scenario increasingly influences human processes, especially in coastal areas where sea-based activities are concentrated. Predicting sea level near the coasts, with a high accuracy and in a reasonable amount of time, becomes a strategic task. Despite the developments of complex numerical codes for high-resolution ocean modeling, the task of making forecasts in areas at the intersection between land and sea remains challenging. In this respect, the use of machine learning techniques can represent an interesting alternative to be investigated and evaluated by numerical modelers. This article presents the application of the Long-Short Term Memory (LSTM) neural network to the problem of short-term sea level forecasting in the Southern Adriatic Northern Ionian (SANI) domain in the Mediterranean sea. The proposed multi-model architecture based on LSTM networks has been trained to predict mean sea levels three days ahead, for different coastal locations. Predictions were compared with the observation data collected through the tide-gauge devices as well as with the forecasts produced by the Southern Adriatic Northern Ionian Forecasting System (SANIFS) developed at the Euro-Mediterranean Center on Climate Change (CMCC), which provides short-term daily updated forecasts in the Mediterranean basin. Experimental results demonstrate that the multi-model architecture is able to bridge information far in time and to produce predictions with a much higher accuracy than SANIFS forecasts.

In Poland, especially since 2018, a dynamic increase in installed capacity in photovoltaics has been observed. In households with a photovoltaic installation (of which there were about 350,000 in Poland at the end of 2020), a phenomenon called ”duck curve” is observed, consisting in low energy consumption from the power grid (correlated with the simultaneous delivery of the produced PV energy to the grid) during the day and relatively high energy consumption in the evening. This case concerns days with relatively high insolation values. The aim of the work is to analyse data from 608 PV installations, including the estimation of the difference (VDC) between maximum hourly electricity overproduction and maximum hourly net electricity consumption from the grid. The maximum monthly VDC value (calculated as the average of daily values) in 2019 reached the level of 2.9 kW per one prosumer household. Calculation for each day of 2019 allowed to determine the maximum VDC value of 3.9 kW, counting on a prosumer household. The median of this value (for 365 days) was 2.08 kW. It is estimated that the maximum power fluctuation in the national power grid due to the connection of 400,000 prosumer households in the grid is approximately 1.6 GW.

The determination of zenith hydrostatic delay (ZHD) is a key step to obtain water vapor information using GNSS technique, and it can be used to separate zenith wet delay (ZWD) with high accuracy from GNSS measurements. Estimates of ZHD are generally of higher accuracy when obtained from accurate pressure information and using the Saastamoinen model rather than when obtained from climatology. To improve this model, we analyzed the differences between the integrated ZHD and the Saastamoinen modeled ZHD, explored the relationship between the ZHD differences and meteorological parameters, and proposed a calibration model based on a global 2.5°×2° grid using the 5 years of pressure levels from the ERA5 reanalysis data provided by European Center for Medium-Range Weather Forecasts (ECMWF). The performance of the calibration model was assessed against the Saastamoinen model using different data sources in 2016, i.e., the ERA5 data and globally distributed radiosonde data. The numerical results show that the calibration model outperforms the Saastamoinen model with a global 59%/89% improvement of RMSE and bias in the ECMWF comparison. In comparison with the radiosonde data, the mean bias/RMSE of the calibration model improves by 5.14/3.24, 1.73/1.47 and 3.15/2.84 mm against the Saastamoinen model in the latitude zones of 60°, respectively.

The presence of clouds is widely identified as the primary uncertainty in current surface solar global horizontal irradiance (GHI) forecasts. Despite a wealth of historical satellite-derived irradiance observations, only limited research has investigated this problem from a purely data-driven perspective, something that has seen tremendous success in related domains such as radar- and satellite-based precipitation short-term forecasting. This paper presents IrradianceNet, a novel satellite-based neural network for spatiotemporal forecasting of surface solar irradiance up to 4 h in the future over Europe. Our method is fully data-driven and needs no post-processing or calibration based on sparse ground-based measurements of irradiance. We demonstrate superior forecasting performance compared to several persistence models, the TV - L 1 algorithm, and ERA5 reanalysis data for satellite-derived solar irradiance using the European SARAH-2.1 dataset. We also validate these results using ground-based pyranometer observations from the Baseline Surface Radiation Network. Our conclusions remain unchanged when we account for hourly and monthly seasonality. Finally, applying a simple cloud mask scheme, we demonstrate that our performance improvement arises due to a considerable reduction in cloudy pixel errors. This is initial evidence that purely data-driven methods might better approximate and infer future cloud dynamics and their impact on surface solar irradiance.

It has been claimed that hydrogen aviation may still significantly contribute to global warming through jets contrails, as hydrogen jets produce more contrails than jets fueled with kerosene. The claim, based on modeling exercises, can be tested via the Covid-19 experiment. Since March 2020, air traffic has been severely disrupted because of the pandemic. This has been true especially in Australia, the hermit country where citizens have been practically prevented from leaving or returning, with international air travel dramatically reduced. Analysis of the surface temperature data about international airports and air traffic from Australia provides the opportunity to test one of the hypotheses formulated about jet contrails' contribution to global warming. It is shown as opposed to previous claims, jet contrails are not responsible for reducing the difference between the maximum and minimum daily temperatures close to airports. Compared to the last 9 months of 2019, with air traffic normal, during the last 9 months of 2020 of highly disrupted flights, the difference in between the maximum and minimum temperatures in Sydney and Melbourne airports, has not been larger, but smaller by about 1 °C. The reduction in the difference between temperatures has been also smaller in Brisbane airport, but only at about 0.22 °C. We conclude as the claim adoption of hydrogen as an aviation fuel may produce global warming through the contrails mechanism is likely wrong. The contrails effect is everything but well understood and quantified with accuracy, and poorly represented in climate models. The contrails contribution to global warming is expected to be minor, and it should not void the value of hydrogen as a future renewable fuel free of CO2 emissions.

Demand for solar energy is increasing due to its clean nature in contrast to conventional fossil fuel sources and lowering of the cost harvesting solar energy. Consequently, it’s important to assess the spatio-temporal distribution and future projection of global horizontal irradiance (GHI) under future climate scenarios. However, due to the absence of high-quality ground observations, whether related to the measuring site accessibility or the associated high cost of equipment, it’s generally difficult to get GHI at the desired spatial interval. Empirical calibrated models may be used for estimating GHI using other local meteorological parameters available at finer spatial interval. The present study compared the performance of four popular empirical models including a newly proposed MND model with an intention to reduce model complexity. The models are calibrated and validated over nineteen locations spread over four climatic zones of India. The results reveal that the MND model performs more efficiently than the other three models. Statistical parameters also confirm the accuracy level of MND. Four sets of common coefficients are generated for projection of daily GHI in any location of the respective four zones. Finally, temperature and rainfall data sets projected by the CMIP5 models, corresponding to 23 locations across the West Bengal State, are used to produce GHI for four future periods under four Representative Concentration Pathway scenarios. Simulated results support the view of having less GHI availability in the near future. In general, the whole State might experience a deficit in annual mean daily GHI by ≥1 MJ m-2day−1.

This work examines the geometrical parameters of the solar chimney power plant using three dimensional CFD simulation. The collector design considers the ground slope, the cover tilt angle, and the inlet opening height. The performance indicators are the temperature, pressure, and velocity with emphasis on the chimney inlet temperature and the incident solar intensity for the estimation of monthly plant performance. The considered collector slopes reflect the alpine nature of the ground for the research area in the suburbia of the city of Erbil on 36th parallel, to the North of Iraq. With a fixed plant height, the actual chimney length is reduced with the increase in the ground slope as the collector inclination increases from the periphery to the center of the collector. At 35° optimum ground slope, there is 18% reduction in horizontal span and 36% in chimney height for the same system volume on flat ground. For this ground slope, a collector cover tilt of zero (parallel to the ground) has shown better results. The inlet height yields little change in temperature and velocity. The pressure profile is unfamiliar and pushes the turbine location into the chimney. Sloped collector solar chimney can play a role in alpine regions with electricity shortages. The simulation results for output power peaked in July with 47.7 kW.

Summary We provide the first quantitative assessment of power production and wake generation from offshore wind energy lease areas along the U.S. east coast. Deploying 15-MW wind turbines, with spacing equal to the European average, yields electricity production of 116 TWh/year or 3% of current national supply. However, power production is reduced by one-third due to wakes caused by upwind wind turbines and wind farms. Under some flow conditions whole wind-farm wakes can extend up to 90 km downwind of the largest lease areas, and the frequency-weighted average area with a 5% velocity deficit is 2.6 times the footprint of the lease areas. Simulations including maritime corridors demonstrate reduction in the wake effects leading to power-efficiency gains and may offer contingent benefits. First-order scaling rules are developed that describe how “wake shadows” from large offshore wind farms scale with prevailing meteorology and wind turbine installed densities.