Search results for: weather index

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: 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: 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: 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: Patience Muchini, Electdom Matandiroya, Emmanuel Mashonjowa

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A ground-end manifestation of space weather phenomena is known as geomagnetically induced currents (GICs). GICs flow along the electric power transmission cables connecting the transformers and between the grounding points of power transformers during significant geomagnetic storms. Geomagnetically induced currents have been studied in other regions and have been noted to affect the power grid network. In Zimbabwe, grid failures have been experienced, but it is yet to be proven if these failures have been due to GICs. The purpose of this paper is to characterize geomagnetically induced currents with a power grid network. This paper analyses data collected, which is geomagnetic data, which includes the Kp index, DST index, and the G-Scale from geomagnetic storms and also analyses power grid data, which includes reactive power, relay tripping, and alarms from high voltage substations and then correlates the data. This research analysis was first theoretically analyzed by studying geomagnetic parameters and then experimented upon. To correlate, MATLAB was used as the basic software to analyze the data. Latitudes of the substations were also brought into scrutiny to note if they were an impact due to the location as low latitudes areas like most parts of Zimbabwe, there are less severe geomagnetic variations. Based on theoretical and graphical analysis, it has been proven that there is a slight relationship between power system failures and GICs. Further analyses can be done by implementing measuring instruments to measure any currents in the grounding of high-voltage transformers when geomagnetic storms occur. Mitigation measures can then be developed to minimize the susceptibility of the power network to GICs.

Keywords: adverse space weather, DST index, geomagnetically induced currents, KP index, reactive power

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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: Muhammad Rasyid Angkotasan

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The soil is a dense granules and arrangement of the pores that are related to each other, so that the water can flow from one point which has higher energy to a point that has lower energy. The flow of water through the pores of the porous ground is urgently needed in water seepage estimates in ground water pumping problems, investigate for underground construction, as well as analyzing the stability of the construction of Weirs. Climate change resulted in long-term changes in the distribution of weather patterns are statistically throughout the period start time of decades to millions of years. In other words, changes in the average weather circumstances or a change in the distribution of weather events, on average, for example, the number of extreme weather events that increasingly a lot or a little. Climate change is limited to a particular regional or can occur in all regions of the Earth. Geographical location between two continents and two oceans and is located around the equator is klimatologis factor is the cause of flooding and drought in Indonesia. This caused Indonesia' geographical position is on a hemisphere with a tropical monsoon climate is very sensitive to climatic anomaly El Nino Southern Oscillation (ENSO). ENSO causes drought occurrence in sea surface temperature conditions in the Pacific Equator warms up to the middle part of the East (El Nino). Based on the analysis of the climate of the last 30 years show that there is a tendency, the formation of a new pattern of climate causes the onset of climate change. The impact of climate change on the occurrence of the agricultural sector is the bergesernya beginning of the dry season which led to the above-mentioned pattern planting due to drought. The impact of climate change (drought) which is very extreme in Indonesia affect the shrinkage dimensions grain land and reduced the value of a percentage of the soil Plasticity Index caused by climate change.

Keywords: climate change, soil shrinkage, plasticity index, shrinking dimensions

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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: U. Mary

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Let be a graph with a finite, nonempty set of objects called vertices together with a set of unordered pairs of distinct vertices of called edges. The vertex set is denoted by and the edge set by. Given two graphs and the wiener index of, wiener index for the splitting graph of a graph, the first Zagreb index of and its splitting graph, the 3-steiner wiener index of, the 3-steiner wiener index of a special graph are explored in this paper.

Keywords: complementary prism graph, first Zagreb index, neighborhood corona graph, steiner distance, splitting graph, steiner wiener index, wiener index

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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: Kwa-Sur Tam, Byung O. Kang

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By using different ranges of daily sky clearness index defined in this paper, any day can be classified as a clear sky day, a partly cloudy day or a cloudy day. This paper demonstrates how short-term forecasting of solar irradiation can be improved by taking into consideration the type of day so defined. The source of day type dependency has been identified. Forecasting methods that take into consideration of day type have been developed and their efficacy have been established. While all methods that implement some form of adjustment to the cloud cover forecast provided by the U.S. National Weather Service provide accuracy improvement, methods that incorporate day type dependency provides even further improvement in forecast accuracy.

Keywords: day types, forecast methods, National Weather Service, sky cover, solar energy

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Authors: Abir Touati, Syed Jawad Hussain, Farid Touati, Ammar Bouallegue

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Although there has been a growing interest in the hybrid free-space optical link and radio frequency FSO/RF communication system, the current literature is limited to results obtained in moderate or cold environment. In this paper, using a soft switching approach, we investigate the effect of weather inhomogeneities on the strength of turbulence hence the channel refractive index under Qatar harsh environment and their influence on the hybrid FSO/RF availability. In this approach, either FSO/RF or simultaneous or none of them can be active. Based on soft switching approach and a finite state Markov Chain (FSMC) process, we model the channel fading for the two links and derive a mathematical expression for the outage probability of the hybrid system. Then, we evaluate the behavior of the hybrid FSO/RF under hazy and harsh weather. Results show that the FSO/RF soft switching renders the system outage probability less than that of each link individually. A soft switching algorithm is being implemented on FPGAs using Raptor code interfaced to the two terminals of a 1Gbps/100 Mbps FSO/RF hybrid system, the first being implemented in the region. Experimental results are compared to the above simulation results.

Keywords: atmospheric turbulence, haze, hybrid FSO/RF, outage probability, refractive index

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Authors: Reza Ziaie Moayed, Seyed Abdolhassan Naeini, Peyman Nouri, Hamed Yekehdehghan

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The shear strength of soils is a significant parameter in the design of clay structures, depots, clay gables, and freeways. Most research has addressed the shear strength of saturated soils. However, soils can become partially saturated with changes in weather, changes in groundwater levels, and the absorption of water by plant roots. Hence, it is necessary to study the strength behavior of partially saturated soils. The shear vane test is an experiment that determines the undrained shear strength of clay soils. This test may be performed in the laboratory or at the site. The present research investigates the effect of liquidity index (LI), plasticity index (PI), and saturation degree of the soil on its undrained shear strength obtained from the shear vane test. According to the results, an increase in the LI and a decrease in the PL of the soil decrease its undrained shear strength. Furthermore, studies show that a rise in the degree of saturation decreases the shear strength obtained from the shear vane test.

Keywords: liquidity index, plasticity index, shear strength, unsaturated soil

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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: Vito Telesca, Giuseppina A. Giorgio, M. Ragosta

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The effects of extreme heat events are increasing in recent years. Humans are forced to adjust themselves to adverse climatic conditions. The impact of weather on human health has become public health significance, especially in light of climate change and rising frequency of devasting weather events (e.g., heat waves and floods). The interest of scientific community is widely known. In particular, the associations between temperature and mortality are well studied. Weather conditions are natural factors that affect the human organism. Recent works show that the temperature threshold at which an impact is seen varies by geographic area and season. These results suggest heat warning criteria should consider local thresholds to account for acclimation to local climatology as well as the seasonal timing of a forecasted heat wave. Therefore, it is very important the problem called ‘local warming’. This is preventable with adequate warning tools and effective emergency planning. Since climate change has the potential to increase the frequency of these types of events, improved heat warning systems are urgently needed. This would require a better knowledge of the full impact of extreme heat on morbidity and mortality. The majority of researchers who analyze the associations between human health and weather variables, investigate the effect of air temperature and bioclimatic indices. These indices combine air temperature, relative humidity, and wind speed and are very important to determine the human thermal comfort. Health impact studies of weather events showed that the prevention is an essential element to dramatically reduce the impact of heat waves. The summer Italian of 2012 was characterized with high average temperatures (con un +2.3°C in reference to the period 1971-2000), enough to be considered as the second hottest summer since 1800. Italy was the first among countries in Europe which adopted tools for to predict these phenomena with 72 hours in advance (Heat Health Watch Warning System - HHWWS). Furthermore, in Italy heat alert criteria relies on the different Indexes, for example Apparent temperature, Scharlau index, Thermohygrometric Index, etc. This study examines the importance of developing public health policies that protect the most vulnerable people (such as the elderly) to extreme temperatures, highlighting the factors that confer susceptibility.

Keywords: heat waves, Italy, local warming, temperature

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Authors: Jiemiao Chen, Shuoxun Xu

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The main goal of our research is to apply the Vine copula measuring dependency between price, temperature, and precipitation indices to calculate a fair crop insurance premium. This research is focused on Worth, Iowa, United States, over the period from 2000 to 2020, where the farmers are dependent on precipitation and average temperature during the growth period of corn. Our proposed insurance considers both the natural risk and the price risk in agricultural production. We first estimate the distributions of crops using parametric methods based on Goodness of Fit tests, and then Vine Copula is applied to model dependence between yield price, crop yield, and weather indices. Once the vine structure and its parameters are determined based on AIC/BIC criteria and forecasting price and yield are obtained from the ARIMA model, we calculate this crop insurance premium using the simulation data generated from the vine copula by the Monte Carlo Simulation method. It is shown that, compared with traditional crop insurance, our proposed insurance is more fair and thus less costly for the farmers and government.

Keywords: vine copula, weather index, crop insurance premium, insurance risk management, Monte Carlo simulation

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Authors: Imèn Khanchouch, Malika Charrad, Mohamed Limam

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The interpretation of the quality of clusters and the determination of the optimal number of clusters is still a crucial problem in clustering. We focus in this paper on multi-SOM clustering method which overcomes the problem of extracting the number of clusters from the SOM map through the use of a clustering validity index. We then tested multi-SOM using real and artificial data sets with different evaluation criteria not used previously such as Davies Bouldin index, Dunn index and silhouette index. The developed multi-SOM algorithm is compared to k-means and Birch methods. Results show that it is more efficient than classical clustering methods.

Keywords: clustering, SOM, multi-SOM, DB index, Dunn index, silhouette index

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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: Nato Kutaladze, George Mikuchadze, Giorgi Sokhadze

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Precipitation forecasts are one of the most demanding applications in numerical weather prediction (NWP). Georgia, as the whole Caucasian region, is characterized by very complex topography. The country territory is prone to flash floods and mudflows, quantitative precipitation estimation (QPE) and quantitative precipitation forecast (QPF) at any leading time are very important for Georgia. In this study, advanced research weather forecasting model’s skill in QPF is investigated over Georgia’s territory. We have analyzed several convection parameterization and microphysical scheme combinations for different rainy episodes and heavy rainy phenomena. We estimate errors and biases in accumulated 6 h precipitation using different spatial resolution during model performance verification for 12-hour and 24-hour lead time against corresponding rain gouge observations and satellite data. Various statistical parameters have been calculated for the 8-month comparison period, and some skills of model simulation have been evaluated. Our focus is on the formation and organization of convective precipitation systems in a low-mountain region. Several problems in connection with QPF have been identified for mountain regions, which include the overestimation and underestimation of precipitation on the windward and lee side of the mountains, respectively, and a phase error in the diurnal cycle of precipitation leading to the onset of convective precipitation in model forecasts several hours too early.

Keywords: extremal dependence index, false alarm, numerical weather prediction, quantitative precipitation forecasting

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Authors: Rosyidah, Permata Wulandari

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We investigate the price effect of addition and deletions from the Indonesia Sharia Stock Index (ISSI) and Jakarta Islamic Index (JII). Using event study methodology, we measure abnormal returns for firms over the period June 2019 - to December 2021. Through the sample of 107 additions and 95 deletions, we find evidence to support the theory of Muslim country investment behavior. We find that additions to the Islamic index led to a significant positive stock market reaction and deletions to the Islamic index led to a negative stock market reaction on Jakarta Islamic Index (JII) and there is no significant reaction of addition and deletion on Indonesia Sharia Stock Index (ISSI).

Keywords: abnormal return, abnormal volume, event study, index changes, sharia index

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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: Adian Fatchur Rochim, Abdul Muis, Riri Fitri Sari

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H-index has been widely used as a performance indicator of researchers around the world especially in Indonesia. The Government uses Scopus and Google scholar as indexing references in providing recognition and appreciation. However, those two indexing services yield to different H-index values. For that purpose, this paper evaluates the difference of the H-index from those services. Researchers indexed by Webometrics, are used as reference’s data in this paper. Currently, Webometrics only uses H-index from Google Scholar. This paper observed and compared corresponding researchers’ data from Scopus to get their H-index score. Subsequently, some researchers with huge differences in score are observed in more detail on their paper’s publisher. This paper shows that the H-index of researchers in Google Scholar is approximately 2.45 times of their Scopus H-Index. Most difference exists due to the existence of uncertified publishers, which is considered in Google Scholar but not in Scopus.

Keywords: Google Scholar, H-index, Scopus, performance indicator

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Authors: Višnjica Vučetić, Mislav Anić, Jelena Bašić, Petra Sviličić, Ivana Tomašević

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The Agroclimatic Atlas of Croatia (Atlas) for the periods 1981–2010 and 1991–2020 is monograph of six chapters in digital form. Detailed descriptions of particular agroclimatological data are given in separate chapters as follows: agroclimatic indices based on air temperature (degree days, Huglin heliothermal index), soil temperature, water balance components (precipitation, potential evapotranspiration, actual evapotranspiration, soil moisture content, runoff, recharge and soil moisture loss) and fire weather indices. The last chapter is a description of the digital methods for the spatial interpolations (R and GIS). The Atlas comprises textual description of the relevant climate characteristic, maps of the spatial distribution of climatological elements at 109 stations (26 stations for soil temperature) and tables of the 30-year mean monthly, seasonal and annual values of climatological parameters at 24 stations. The Atlas was published in 2021, on the seventieth anniversary of the agrometeorology development at the Meteorological and Hydrological Service of Croatia. It is intended to support improvement of sustainable system of agricultural production and forest protection from fire and as a rich source of information for agronomic and forestry experts, but also for the decision-making bodies to use it for the development of strategic plans.

Keywords: agrometeorology, agroclimatic indices, soil temperature, water balance components, fire weather index, meteorological and hydrological service of Croatia

Procedia PDF Downloads 96

Authors: P. Góes, J. Manzi

Abstract:

This paper focuses on the development of an energy efficiency index that will be applied to reactive systems, which is based in the First and Second Law of Thermodynamics, by giving particular consideration to the concept of maximum entropy. Among the requirements of such energy efficiency index, the practical feasibility must be essential. To illustrate the performance of the proposed index, such an index was used as decisive factor of evaluation for the optimization process of an industrial reactor. The results allow the conclusion to be drawn that the energy efficiency index applied to the reactive system is consistent because it extracts the information expected of an efficient indicator, and that it is useful as an analytical tool besides being feasible from a practical standpoint. Furthermore, it has proved to be much simpler to use than tools based on traditional methodologies.

Keywords: energy, efficiency, entropy, reactive

Procedia PDF Downloads 375

Authors: Yassine Jadi, Lin Jie

Abstract:

In order to serve the society in an electronic manner, many developing countries have launched tremendous e-government projects. The strategies of development and implementation e-government system have reached different levels, and to ensure consistency of development, the governments need to evaluate e-government performance. The United nation has design e-government development ranking index (EGDI) that rely on three indexes, Online service index (OSI), Telecommunication Infrastructure index (TII), and human capital index( HCI) which are not reflecting the interaction between a government and their citizens. Based on data envelopment analyses (DEA) technique, we are using E-participating index (EPI) as an output of government effort to evaluate the performance of e-government system. Therefore, the ranking index can be achieved in efficiency manner.

Keywords: e-government, DEA, efficiency measurement, EGDI

Procedia PDF Downloads 341