Search results for: weather development

Authors: Babagana Abubakar, Alhaji Kuya

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Although the field of Space weather science is a young field among the space sciences, but yet history has it that activities related to this science began since the year 1859 when the great solar storm happened which resulted in the disruptions of telegraphs operations around the World at that particular time subsequently making it possible for the scientist Richard Carrington to be able to connect the Solar flare observed a day earlier before the great storm and the great deflection of the Earth’s Magnetic field (geometric storm) simultaneous with the telegraph disruption. However years later as at today with the advent of and the coming into existence of the Explorer 1, the Luna 1 and the establishments of the United States International Space Weather Program, International Geophysical Year (IGY) as well as the International Center for Space Weather Sciences and Education (ICSWSE) have made us understand the Space weather better and enable us well define the field of Space weather science. Despite the successes recorded in the development of Space sciences as a whole over the last century and the coming onboard of specialized bodies/programs on space weather like the International Space Weather Program and the ICSWSE, the majority of Africans including institutions, research organizations and even some governments are still ignorant about the existence of theSpace weather science,because apart from some very few countries like South Africa, Nigeria and Egypt among some few others the majority of the African nations and their academic institutions have no knowledge or idea about the existence of this field of Space science (Space weather).

Keywords: Africa, space, weather, education, science

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Authors: Nasser A. Al-Azri

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The effectiveness of passive cooling techniques is assessed based on bioclimatic charts that require the typical meteorological year (TMY) for a specified location for their development. However, TMYs are not always available; mainly due to the scarcity of records of solar radiation which is an essential component used in developing common TMYs intended for general uses. Since solar radiation is not required in the development of the bioclimatic chart, this work suggests developing TMYs based solely on the relevant parameters. This approach improves the accuracy of the developed TMY since only the relevant parameters are considered and it also makes the development of the TMY more accessible since solar radiation data are not used. The presented paper will also discuss the development of the TMY from the raw data available at the NOAA-NCDC archive of world weather data and the construction of the bioclimatic charts for some randomly selected locations around the world.

Keywords: bioclimatic charts, passive cooling, TMY, weather data

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Authors: Clarinda Vitorino Nhangumbe, Fredericks Ebrahim, Betuel Canhanga

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The price of an option weather derivatives can be approximated as a solution of the two-dimensional convection-diffusion dominant partial differential equation derived from the Ornstein-Uhlenbeck process, where one variable represents the weather dynamics and the other variable represent the underlying weather index. With appropriate financial boundary conditions, the solution of the pricing equation is approximated using a nonstandard finite difference method. It is shown that the proposed numerical scheme preserves positivity as well as stability and consistency. In order to illustrate the accuracy of the method, the numerical results are compared with other methods. The model is tested for real weather data.

Keywords: nonstandard finite differences, Ornstein-Uhlenbeck process, partial differential equations approach, weather derivatives

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Authors: Muhammad Luthfi, Sutikno Sutikno, Purhadi Purhadi

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Weather forecast has necessarily been improved to provide the communities an accurate and objective prediction as well. To overcome such issue, the numerical-based weather forecast was extensively developed to reduce the subjectivity of forecast. Yet the Numerical Weather Predictions (NWPs) outputs are unfortunately issued without taking dynamical weather behavior and local terrain features into account. Thus, NWPs outputs are not able to accurately forecast the weather quantities, particularly for medium and long range forecast. The aim of this research is to aid and extend the development of ensemble forecast for Meteorology, Climatology, and Geophysics Agency of Indonesia. Ensemble method is an approach combining various deterministic forecast to produce more reliable one. However, such forecast is biased and uncalibrated due to its underdispersive or overdispersive nature. As one of the parametric methods, Bayesian Model Averaging (BMA) generates the calibrated ensemble forecast and constructs predictive PDF for specified period. Such method is able to utilize ensemble of any size but does not take spatial correlation into account. Whereas space dependencies involve the site of interest and nearby site, influenced by dynamic weather behavior. Meanwhile, Geostatistical Output Perturbation (GOP) reckons the spatial correlation to generate future weather quantities, though merely built by a single deterministic forecast, and is able to generate an ensemble of any size as well. This research conducts both BMA and GOP to generate the calibrated ensemble forecast for the daily temperature at few meteorological sites nearby Indonesia international airport.

Keywords: Bayesian Model Averaging, ensemble forecast, geostatistical output perturbation, numerical weather prediction, temperature

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Authors: Debneil Saha Roy

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Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

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Authors: Johnstone Walubengo Wangusi

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Climate change is undeniably altering the frequency, intensity, and geographic distribution of extreme weather events worldwide. In this paper, we explore the complex interconnections between climate change and extreme weather phenomena, drawing upon research from atmospheric science, geology, and climatology. We examine the underlying mechanisms driving these changes, the impacts on natural ecosystems and human societies, and strategies for adaptation and mitigation. By synthesizing insights from interdisciplinary research, this paper aims to provide a comprehensive understanding of the multifaceted relationship between climate change and extreme weather, informing efforts to address the challenges posed by a changing climate.

Keywords: climate change, extreme weather, atmospheric science, geology, climatology, impacts, adaptation, mitigation

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Authors: Jerome T. Tolentino, Ma. Victoria Rejuso, Jara Kaye Villanueva, Loureal Camille Inocencio, Ma. Rosario Concepcion O. Ang

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Wind energy is rapidly emerging as the primary source of electricity in the Philippines, although developing an accurate wind resource model is difficult. In this study, Weather Research and Forecasting (WRF) Model, an open source mesoscale Numerical Weather Prediction (NWP) model, was used to produce a 1-year atmospheric simulation with 4 km resolution on the Ilocos Region of the Philippines. The WRF output (netCDF) extracts the annual mean wind speed data using a Python-based Graphical User Interface. Lastly, wind resource assessment was produced using a GIS software. Results of the study showed that it is more flexible to use Python scripts than using other post-processing tools in dealing with netCDF files. Using WRF Model, Python, and Geographic Information Systems, a reliable wind resource map is produced.

Keywords: wind resource assessment, weather research and forecasting (WRF) model, python, GIS software

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Authors: Jorge M. Uribe

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We propose a methodology for setting the price of an insurance contract targeted to manage the risk associated with weather conditions that affect variable renewable energy generation. The methodology relies on conditional quantile regressions to estimate the weather risk of a solar panel. It is illustrated using real daily radiation and weather data for three cities in Spain (Valencia, Barcelona and Madrid) from February 2/2004 to January 22/2019. We also adapt the concepts of value at risk and expected short fall from finance to this context, to provide a complete panorama of what we label as weather risk. The methodology is easy to implement and can be used by insurance companies to price a contract with the aforementioned characteristics when data about similar projects and accurate cash flow projections are lacking. Our methodology assigns a higher price to an insurance product with the stated characteristics in Madrid, compared to Valencia and Barcelona. This is consistent with Madrid showing the largest interquartile range of operational deficits and it is unrelated to the average value deficit, which illustrates the importance of our proposal.

Keywords: insurance, weather, vre, risk

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Authors: Zhaojun Wang, Zongdi Sun, Qinqin Cui, Xingwan Ren

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Based on multivariate statistical analysis theory, this paper uses the principal component analysis method, Mahalanobis distance analysis method and fitting method to establish the photovoltaic health model to evaluate the health of photovoltaic panels. First of all, according to weather conditions, the photovoltaic panel variable data are classified into five categories: sunny, cloudy, rainy, foggy, overcast. The health of photovoltaic panels in these five types of weather is studied. Secondly, a scatterplot of the relationship between the amount of electricity produced by each kind of weather and other variables was plotted. It was found that the amount of electricity generated by photovoltaic panels has a significant nonlinear relationship with time. The fitting method was used to fit the relationship between the amount of weather generated and the time, and the nonlinear equation was obtained. Then, using the principal component analysis method to analyze the independent variables under five kinds of weather conditions, according to the Kaiser-Meyer-Olkin test, it was found that three types of weather such as overcast, foggy, and sunny meet the conditions for factor analysis, while cloudy and rainy weather do not satisfy the conditions for factor analysis. Therefore, through the principal component analysis method, the main components of overcast weather are temperature, AQI, and pm2.5. The main component of foggy weather is temperature, and the main components of sunny weather are temperature, AQI, and pm2.5. Cloudy and rainy weather require analysis of all of their variables, namely temperature, AQI, pm2.5, solar radiation intensity and time. Finally, taking the variable values in sunny weather as observed values, taking the main components of cloudy, foggy, overcast and rainy weather as sample data, the Mahalanobis distances between observed value and these sample values are obtained. A comparative analysis was carried out to compare the degree of deviation of the Mahalanobis distance to determine the health of the photovoltaic panels under different weather conditions. It was found that the weather conditions in which the Mahalanobis distance fluctuations ranged from small to large were: foggy, cloudy, overcast and rainy.

Keywords: fitting, principal component analysis, Mahalanobis distance, SPSS, MATLAB

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Authors: G. N. Tiwari, Shyam

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Photovoltaic thermal (PVT) roof type greenhouse dryer installed above the wind tower of SODHA BERS COMPLEX, Varanasi has been analyzed for all types of weather conditions. The product to be dried has been kept at three different trays. The upper tray receives energy from the PV cover while the bottom tray receives thermal energy from the hot air of the wind tower. The annual energy estimation has been done for the all types of weather condition of composite climate of northern India. It has been found that maximum energy saving is observed for c type of weather condition whereas minimum energy saving is observed for a type of weather condition. The energy saving on overall thermal energy basis and exergy basis are 1206.8 kWh and 360 kWh respectively for c type of weather condition. The energy saving from all types of weather condition are found to be 3175.3 kWh and 957.6 kWh on overall thermal energy and overall exergy basis respectively.

Keywords: exergy, greenhouse, photovoltaic thermal, solar dryer

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Authors: Fan Ye

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Adverse weather conditions, particularly those with low visibility, are critical to the driving tasks. However, the direct relationship between visibility distances and traffic flow/roadway safety is uncertain due to the limitation of visibility data availability. The recent growth of deployment of Road Weather Information Systems (RWIS) makes segment-specific visibility information available which can be integrated with other Intelligent Transportation System, such as automated warning system and variable speed limit, to improve mobility and safety. Before applying the RWIS visibility measurements in traffic study and operations, it is critical to validate the data. Therefore, an attempt was made in the paper to examine the validity and viability of RWIS visibility data by comparing visibility measurements among RWIS, airport weather stations, and weather information recorded by police in crash reports, based on Ohio data. The results indicated that RWIS visibility measurements were significantly different from airport visibility data in Ohio, but no conclusion regarding the reliability of RWIS visibility could be drawn in the consideration of no verified ground truth in the comparisons. It was suggested that more objective methods are needed to validate the RWIS visibility measurements, such as continuous in-field measurements associated with various weather events using calibrated visibility sensors.

Keywords: RWIS, visibility distance, low visibility, adverse weather

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Authors: Clarinda V. Nhangumbe, Ercília Sousa

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Weather Derivatives are financial instruments used to cover non-catastrophic weather events and can be expressed in the form of standard or plain vanilla products, structured or exotics products. The underlying asset, in this case, is the weather index, such as temperature, rainfall, humidity, wind, and snowfall. The complexity of the Weather Derivatives structure shows the weakness of the Black Scholes framework. Therefore, under the risk-neutral probability measure, the option price of a weather contract can be given as a unique solution of a two-dimensional partial differential equation (parabolic in one direction and hyperbolic in other directions), with an initial condition and subjected to adequate boundary conditions. To calculate the price of the option, one can use numerical methods such as the Monte Carlo simulations and implicit finite difference schemes conjugated with Semi-Lagrangian methods. This paper is proposed two explicit methods, namely, first-order upwind in the hyperbolic direction combined with Lax-Wendroff in the parabolic direction and first-order upwind in the hyperbolic direction combined with second-order upwind in the parabolic direction. One of the advantages of these methods is the fact that they take into consideration the boundary conditions obtained from the financial interpretation and deal efficiently with the different choices of the convection coefficients.

Keywords: incomplete markets, numerical methods, partial differential equations, stochastic process, weather derivatives

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Authors: Yuqing Chen, Ying Xu, Renfa Li

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The development of matrix completion theory provides new approaches for data gathering in Wireless Sensor Networks (WSN). The existing matrix completion algorithms for WSN mainly consider how to reduce the sampling number without considering the real-time performance when recovering the data matrix. In order to guarantee the recovery accuracy and reduce the recovery time consumed simultaneously, we propose a new ALM algorithm to recover the weather monitoring data. A lot of experiments have been carried out to investigate the performance of the proposed ALM algorithm by using different parameter settings, different sampling rates and sampling models. In addition, we compare the proposed ALM algorithm with some existing algorithms in the literature. Experimental results show that the ALM algorithm can obtain better overall recovery accuracy with less computing time, which demonstrate that the ALM algorithm is an effective and efficient approach for recovering the real world weather monitoring data in WSN.

Keywords: wireless sensor network, matrix completion, singular value thresholding, augmented Lagrange multiplier

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Authors: Rebecca A. Deek

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There is evidence that erratic and extreme weather is becoming a common occurrence, and even predictions that this will become even more frequent and more severe. It also appears that the severity of earthquakes is intensifying. Some observers believe that human conduct has given reasons for such change; others attribute this to environmental and geological cycles. However, as some physicists, environmental scientists, politicians, and others continue to debate the connection between weather events, seismic activities, and climate change, other scientists, engineers, and urban planners are exploring how can our habitat become more responsive and resilient to such phenomena. There are a number of recent instances of nature’s destructive events that provide basis for the development of defensive measures.

Keywords: biomimicry, natural disasters, protection of human lives, resilient infrastructures

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Authors: Noriyuki Suyama

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This paper discusses purchasing behavior in retail stores, with a particular focus on the impact of weather changes on customers' purchasing behavior. Weather conditions are one of the factors that greatly affect the management and operation of retail stores. However, there is very little research on the relationship between weather conditions and marketing from an academic perspective, although there is some importance from a practical standpoint and knowledge based on experience. For example, customers are more hesitant to go out when it rains than when it is sunny, and they may postpone purchases or buy only the minimum necessary items even if they do go out. It is not difficult to imagine that weather has a significant impact on consumer behavior. To the best of the authors' knowledge, there have been only a few studies that have delved into the purchasing behavior of individual customers. According to Hirata (2018), the economic impact of weather in the United States is estimated to be 3.4% of GDP, or "$485 billion ± $240 billion per year. However, weather data is not yet fully utilized. Representative industries include transportation-related industries (e.g., airlines, shipping, roads, railroads), leisure-related industries (e.g., leisure facilities, event organizers), energy and infrastructure-related industries (e.g., construction, factories, electricity and gas), agriculture-related industries (e.g., agricultural organizations, producers), and retail-related industries (e.g., retail, food service, convenience stores, etc.). This paper focuses on the retail industry and advances research on weather. The first reason is that, as far as the author has investigated the retail industry, only grocery retailers use temperature, rainfall, wind, weather, and humidity as parameters for their products, and there are very few examples of academic use in other retail industries. Second, according to NBL's "Toward Data Utilization Starting from Consumer Contact Points in the Retail Industry," labor productivity in the retail industry is very low compared to other industries. According to Hirata (2018) mentioned above, improving labor productivity in the retail industry is recognized as a major challenge. On the other hand, according to the "Survey and Research on Measurement Methods for Information Distribution and Accumulation (2013)" by the Ministry of Internal Affairs and Communications, the amount of data accumulated by each industry is extremely large in the retail industry, so new applications are expected by analyzing these data together with weather data. Third, there is currently a wealth of weather-related information available. There are, for example, companies such as WeatherNews, Inc. that make weather information their business and not only disseminate weather information but also disseminate information that supports businesses in various industries. Despite the wide range of influences that weather has on business, the impact of weather has not been a subject of research in the retail industry, where business models need to be imagined, especially from a micro perspective. In this paper, the author discuss the important aspects of the impact of weather on marketing strategies in the non-food retail industry.

Keywords: consumer behavior, weather marketing, marketing science, big data, retail marketing

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Authors: Wei-Kuo Soong

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The forecasting of typhoon tracks remains a formidable challenge, primarily attributable to the paucity of observational data in the open sea and the intricate influence of weather systems at varying scales. This study investigates the case of Typhoon Hinnamnor in 2022, examining its trajectory and intensity fluctuations in relation to the interaction with a concurrent tropical cyclone and sea surface temperatures (SST). Utilizing the Weather Research and Forecasting Model (WRF), to simulate and analyze the interaction between Typhoon Hinnamnor and its environmental factors, shedding light on the mechanisms driving typhoon development and enhancing forecasting capabilities.

Keywords: typhoon, sea surface temperature, forecasting, WRF

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Authors: Muhammad Sameer Ahmed, Piotr Remlein, Tansal Gucluoglu

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The technique called as Generalized frequency division multiplexing (GFDM) used in the free space optical channel can be a good option for implementation free space optical communication systems. This technique has several strengths e.g. good spectral efficiency, low peak-to-average power ratio (PAPR), adaptability and low co-channel interference. In this paper, the impact of weather conditions such as haze, rain and fog on GFDM over the gamma-gamma channel model is discussed. A Trade off between link distance and system performance under intense weather conditions is also analysed. The symbol error probability (SEP) of GFDM over the gamma-gamma turbulence channel is derived and verified with the computer simulations.

Keywords: free space optics, generalized frequency division multiplexing, weather conditions, gamma gamma distribution

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Authors: Kalandi Charan Pradhan

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In this study, we investigate does weather variation determine temporary labour migration using 210 sample households from six Semi-Arid Tropic (SAT) villages for the period of 2005-2014 in India. The study has made an attempt to examine how households use temporary labour migration as a coping mechanism to minimise the risk rather than maximize the utility of the households. The study employs panel Logit regression model to predict the probability of household having at least one temporary labour migrant. As per as econometrics result, it is found that along with demographic and socioeconomic factors; weather variation plays an important role to determine the decision of migration at household level. In order to capture the weather variation, the study uses mean crop yield deviation over the study periods. Based on the random effect logit regression result, the study found that there is a concave relationship between weather variation and decision of temporary labour migration. This argument supports the theory of New Economics of Labour Migration (NELM), which highlights the decision of labour migration not only maximise the households’ utility but it helps to minimise the risks.

Keywords: temporary migration, socioeconomic factors, weather variation, crop yield, logit estimation

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Authors: Federica Di Marcantonio, Antoine Leblois, Wolfgang Göbel, Hervè Kerdiles

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Weather-related shocks can threaten the ability of farmers to maintain their agricultural output and food security levels. Informal coping mechanisms (i.e. migration or community risk sharing) have always played a significant role in mitigating the negative effects of weather-related shocks in Ethiopia, but they have been found to be an incomplete strategy, particularly as a response to covariate shocks. Particularly, as an alternative to the traditional risk pooling products, an innovative form of insurance known as Index-based Insurance has received a lot of attention from researchers and international organizations, leading to an increased number of pilot initiatives in many countries. Despite the potential benefit of the product in protecting the livelihoods of farmers and pastoralists against climate shocks, to date there has been an unexpectedly low uptake. Using information from current pilot projects on index-based insurance in Ethiopia, this paper discusses the determinants of uptake that have so far undermined the scaling-up of the products, by focusing in particular on weather data availability, price affordability and willingness to pay. We found that, aside from data constraint issues, high price elasticity and low willingness to pay represent impediments to the development of the market. These results, bring us to rethink the role of index insurance as products for enhancing smallholders’ response to covariate shocks, and particularly for improving their food security.

Keywords: index-based insurance, willingness to pay, satellite information, Ethiopia

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Authors: Chatrabhuti Pipop, Visessri Supattra, Charinpanitkul Tawatchai

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Durian is one of the top agricultural products exported by Thailand. There is the massive market potential for the durian industry. While the global demand for Thai durians, especially the demand from China, is very high, Thailand's durian supply is far from satisfying strong demand. Poor agricultural practices result in low yields and poor quality of fruit. Most irrigation systems currently used by the farmers are fixed schedule or fixed rates that ignore actual weather conditions and crop water requirements. In addition, the technologies emerging are too difficult and complex and prices are too high for the farmers to adopt and afford. Many farmers leave the durian trees to grow naturally. With improper irrigation and nutrient management system, durians are vulnerable to a variety of issues, including stunted growth, not flowering, diseases, and death. Technical development or research for durian is much needed to support the wellbeing of the farmers and the economic development of the country. However, there are a limited number of studies or development projects for durian because durian is a perennial crop requiring a long time to obtain the results to report. This study, therefore, aims to address the problem of durian production by developing an autonomous and precision irrigation system. The system is designed and equipped with an industrial programmable controller, a weather station, and a digital flow meter. Daily water requirements are computed based on weather data such as rainfall and evapotranspiration for daily irrigation with variable flow rates. A prediction model is also designed as a part of the system to enhance the irrigation schedule. Before the system was installed in the field, a simulation model was built and tested in a laboratory setting to ensure its accuracy. Water consumption was measured daily before and after the experiment for further analysis. With this system, the crop water requirement is precisely estimated and optimized based on the data from the weather station. Durian will be irrigated at the right amount and at the right time, offering the opportunity for higher yield and higher income to the farmers.

Keywords: Durian, precision irrigation, precision agriculture, smart farm

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Authors: Usama Mohamed Ahamed

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This research deals with the bond behavior of carbon FRP composite wraps adhered/bonded to the surface of the concrete. Four concrete mixes were designed to achieve a concrete compressive strength of 18, 22.5,25 and 30 MP after 28 days of curing. The focus of the study is on bond degradation when the hybrid structure is exposed to hot weather conditions. Specimens were exposed to 50 0C temperature duration 6 months and other specimens were sustained in laboratory temperature ( 20-24) 0C. Upon removing the specimens from their conditioning environment, tension tests were performed in the machine using a specially manufactured concrete cube holder. A lightweight mortar layer is used to protect the bonded carbon FRP layer on the concrete surface. The results show that the higher the concrete's compressive, the higher the bond strength. The high temperature decreases the bond strength between concrete and carbon fiber-reinforced polymer. The use of a protection layer is essential for concrete exposed to hot weather.

Keywords: concrete, bond, hot weather and carbon fiber, carbon fiber reinforced polymers

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Authors: S. Vavias, T. R. Brewer, T. S. Farewell

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Coastal floods have been identified as an important natural hazard that can cause significant damage to the populated built-up areas, related infrastructure and also ecosystems and habitats. This study attempts to fill the gap associated with the development of preliminary assessments of coastal flood vulnerability for compliance with the EU Directive on the Assessment and Management of Flood Risks (2007/60/EC). In this context, a methodology has been created by taking into account three major parameters; the maximum wave run-up modelled from historical weather observations, the highest tide according to historic time series, and the sea level rise projections due to climate change. A high resolution digital terrain model (DTM) derived from LIDAR data has been used to integrate the estimated flood events in a GIS environment. The flood vulnerability map created shows potential risk areas and can play a crucial role in the coastal zone planning process. The proposed method has the potential to be a powerful tool for policy and decision makers for spatial planning and strategic management.

Keywords: coastal floods, vulnerability mapping, climate change, extreme weather events

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Authors: Nadine Ayasha

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Hail happened in Sukabumi (August 23, 2020), Sekadau (August 22, 2020), and Bogor (September 23, 2020), where this extreme weather phenomenon occurred in the dry season. This study uses the ERA5 reanalysis model data, it aims to examine the vertical velocity impact on the hail occurrence in the dry season, as well as its relation to other weather parameters such as relative humidity, streamline, and wind velocity. Moreover, HCAI product satellite data is used as supporting data for the convective cloud development analysis. Based on the results of graphs, contours, and Hovmoller vertical cut from ERA5 modeling, the vertical velocity values in the 925 Mb-300 Mb layer in Sukabumi, Sekadau, and Bogor before the hail event ranged between -1.2-(-0.2), -1.5-(-0.2), -1-0 Pa/s. A negative value indicates that there is an upward motion from the air mass that trigger the convective cloud growth, which produces hail. It is evidenced by the presence of Cumulonimbus cloud on HCAI product when the hail falls. Therefore, the vertical velocity has significant effect on the hail event. In addition, the relative humidity in the 850-700 Mb layer is quite wet, which ranges from 80-90%. Meanwhile, the streamline and wind velocity in the three regions show the convergence with slowing wind velocity ranging from 2-4 knots. These results show that the upward motion of the vertical velocity is enough to form the wet atmospheric humidity and form a convergence for the growth of the convective cloud, which produce hail in the dry season.

Keywords: hail, extreme weather, vertical velocity, relative humidity, streamline

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Authors: S. M. Ali, A. Salam Khan, A. U. Khan, M. Tariq, M. S. Hussain, B. A. Abbasi, I. Hussain, U. Farid

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Environment and surrounding plays a pivotal rule in structuring life-style of the consumers. Living standards intern effect the energy consumption of the consumers. In smart grid paradigm, climate drifts, weather parameter and green environmental directly relates to the energy profiles of the various consumers, such as residential, commercial and industrial. Considering above factors helps policy in shaping utility load curves and optimal management of demand and supply. Thus, there is a pressing need to develop correlation models of load and weather parameters and critical analysis of the factors effecting energy profiles of smart grid consumers. In this paper, we elaborated various environment and weather parameter factors effecting demand of consumers. Moreover, we developed correlation models, such as Pearson, Spearman, and Kendall, an inter-relation between dependent (load) parameter and independent (weather) parameters. Furthermore, we validated our discussion with real-time data of Texas State. The numerical simulations proved the effective relation of climatic drifts with energy consumption of smart grid consumers.

Keywords: climatic drifts, correlation analysis, energy consumption, smart grid, weather parameter

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Authors: Clarinda Vitorino Nhangumbe, Ercília Sousa

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Weather derivatives are financial products used to cover non catastrophic weather events with a weather index as the underlying asset. The rainfall weather derivative pricing model is modeled based in the assumption that the rainfall dynamics follows Ornstein-Uhlenbeck process, and the partial differential equation approach is used to derive the convection-diffusion two dimensional time dependent partial differential equation, where the spatial variables are the rainfall index and rainfall depth. To compute the approximation solutions of the partial differential equation, the appropriate boundary conditions are suggested, and an explicit numerical method is proposed in order to deal efficiently with the different choices of the coefficients involved in the equation. Being an explicit numerical method, it will be conditionally stable, then the stability region of the numerical method and the order of convergence are discussed. The model is tested for real precipitation data.

Keywords: finite differences method, ornstein-uhlenbeck process, partial differential equations approach, rainfall derivatives

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Authors: Baharak Shakeri, Seyed Hashem Hosseini

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The weather in deserts is hot and dry in summers, and cold and dry in winters, and difference of temperature of nights and days sometimes reaches to 28°C. People of deserts have reached some solutions to cope with this climatic condition and to decrease its annoying features. Among these solutions are: constructing houses adjacent to each other, making tall walls, using mud brick and thatch cover, constructing domical arches, cellar, and wind catcher, which are together the devices to control the adversity of hot weather in summers and cold weather in winters. Using these solutions, the people of deserts have succeeded to make the best use with the least energy consumption, and to minimize the damage on the nature and environment, and in short, they are friends of the nature, which is a step toward the objectives of green buildings.

Keywords: desert house, green building, Iran, nature

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Authors: Zhichao Jiao, Craig Farnham, Jihui Yuan, Kazuo Emura

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It is common to use the outdoor design weather data to select the air-conditioning capacity in the building design stage. The outdoor design weather data are usually comprised of multiple meteorological elements for a 24-hour period separately, but the dependency between the elements is not well considered, which may cause an overestimation of selecting air-conditioning capacity. Considering the dependency between the air temperature and global solar radiation, we used the copula approach to model the joint distributions of those two weather elements and suggest a new method of selecting more credible outdoor design conditions based on the specific simultaneous occurrence probability of air temperature and global solar radiation. In this paper, the 10-year period hourly weather data from 2001 to 2010 in Osaka, Japan, was used to analyze the dependency structure and joint distribution, the result shows that the Joe-Frank copula fit for almost all hourly data. According to calculating the simultaneous occurrence probability and the common exceeding probability of air temperature and global solar radiation, the results have shown that the maximum difference in design air temperature and global solar radiation of the day is about 2 degrees Celsius and 30W/m2, respectively.

Keywords: energy conservation, design weather database, HVAC, copula approach

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Authors: Beatriz Martinez-Pastor, Maria Nogal, Alan O'Connor

Abstract:

During the last years, the number of extreme weather events has increased. A variety of extreme weather events, including river floods, rain-induced landslides, droughts, winter storms, wildfire, and hurricanes, have threatened and damaged many different regions worldwide. These events have a devastating impact on critical infrastructure systems resulting in high social, economical and environmental costs. These events have a huge impact in transport systems. Since, transport networks are completely exposed to every kind of climatological perturbations, and its performance is closely related with these events. When a traffic network is affected by a climatological hazard, the quality of its service is threatened, and the level of the traffic conditions usually decreases. With the aim of understanding this process, the concept of resilience has become most popular in the area of transport. Transport resilience analyses the behavior of a traffic network when a perturbation takes place. This holistic concept studies the complete process, from the beginning of the perturbation until the total recovery of the system, when the perturbation has finished. Many concepts are included in the definition of resilience, such as vulnerability, redundancy, adaptability, and safety. Once the resilience of a transport network can be evaluated, in this case, the methodology used is a dynamic equilibrium-restricted assignment model that allows the quantification of the concept, the next step is its improvement. Through the improvement of this concept, it will be possible to create transport networks that are able to withstand and have a better performance under the presence of climatological hazards. Analyzing the impact of a perturbation in a traffic network, it is observed that the response of the different links, which are part of the network, can be completely different from one to another. Consequently and due to this effect, many questions arise, as what makes a link more critical before an extreme weather event? or how is it possible to identify these critical links? With this aim, and knowing that most of the times the owners or managers of the transport systems have limited resources, the identification of the critical links of a transport network before extreme weather events, becomes a crucial objective. For that reason, using the available resources in the areas that will generate a higher improvement of the resilience, will contribute to the global development of the network. Therefore, this paper wants to analyze what kind of characteristic makes a link a critical one when an extreme weather event damages a transport network and finally identify them.

Keywords: critical links, extreme weather events, hazard, resilience, transport network

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Authors: Ahmed M. AlHasem

Abstract:

The Kuwaiti society has long been aware of climatic changes and their annual dates and trying to link them to astronomy in an attempt to forecast the future weather conditions. The reason for this concern is that many of the economic, social and living activities of the society depend deeply on the nature of the weather conditions directly and indirectly. In other words, Kuwaiti society, like the case of many human societies, has in the past tried to predict climatic conditions by linking them to astronomy or popular statements to indicate the timing of climate changes. Accordingly, this study was devoted to scientific investigation based on the statistical analysis of climatic data to show the accuracy and compatibility of some of the most important elements of the cultural heritage in relation to climate change and to relate it scientifically to precise climatic measurements for decades. The research has been divided into 10 topics, each topic has been focused on one legacy, whether by linking climate changes to the appearance/disappearance of star or a popular statement inherited through generations, through explain the nature and timing and thereby statistical analysis to indicate the proportion of accuracy based on official climatic data since 1962. The study's conclusion is that the relationship is weak and, in some cases, non-existent between the popular heritage and the actual climatic data. Therefore, it does not have a dependable relationship and a reliable scientific prediction between both the popular heritage and the forecast of weather conditions.

Keywords: astronomy, cultural heritage, statistical analysis, weather prediction

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Authors: Martin Kittel, Alexander Roth

Abstract:

The continuous integration of variable renewable energy sources (VRE) in the power sector is required for decarbonizing the European economy. Power sectors become increasingly exposed to weather variability, as the availability of VRE, i.e., mainly wind and solar photovoltaic, is not persistent. Extreme events, e.g., long-lasting periods of scarce VRE availability (‘VRE droughts’), challenge the reliability of supply. Properly accounting for the severity of VRE droughts is crucial for designing a resilient renewable European power sector. Energy system modeling is used to identify such a design. Our analysis reveals the sensitivity of the optimal design of the European power sector towards VRE droughts. We analyze how VRE droughts impact optimal power sector investments, especially in generation and flexibility capacity. We draw upon work that systematically identifies VRE drought patterns in Europe in terms of frequency, duration, and seasonality, as well as the cross-regional and cross-technological correlation of most extreme drought periods. Based on their analysis, the authors provide a selection of relevant historical weather years representing different grades of VRE drought severity. These weather years will serve as input for the capacity expansion model for the European power sector used in this analysis (DIETER). We additionally conduct robustness checks varying policy-relevant assumptions on capacity expansion limits, interconnections, and level of sector coupling. Preliminary results illustrate how an imprudent selection of weather years may cause underestimating the severity of VRE droughts, flawing modeling insights concerning the need for flexibility. Sub-optimal European power sector designs vulnerable to extreme weather can result. Using relevant weather years that appropriately represent extreme weather events, our analysis identifies a resilient design of the European power sector. Although the scope of this work is limited to the European power sector, we are confident that our insights apply to other regions of the world with similar weather patterns. Many energy system studies still rely on one or a limited number of sometimes arbitrarily chosen weather years. We argue that the deliberate selection of relevant weather years is imperative for robust modeling results.

Keywords: energy systems, numerical optimization, variable renewable energy sources, energy drought, flexibility

Procedia PDF Downloads 40