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There are three lateral dynamic attitudes, delineated by rolling, yawing, and sideslipping. It is possible to solve for the pressures on the rolling wing by quasi‐steady analysis. This approach is, however, inapplicable for the yawing or sideslipping wing, and it is with the latter two cases that this paper deals.

The purpose of this paper is to propose an improved reinsurance pricing framework, which includes a crop yield forecasting model that integrates weather variables and crop production information from different geographically correlated regions using a new credibility estimator, and closed form reinsurance pricing formulas. A yield restatement approach to account for changing crop mix through time is also demonstrated.

The new crop yield forecasting model is empirically analyzed based on detailed farm-level data from Manitoba, Canada, covering 216 crop varieties from 19,238 farms from 1996 to 2011. As well, corresponding weather data from 30 stations, including daily temperature and precipitation, are considered. Algorithms that combine screening regression, cross-validation and principal component analysis are evaluated for the purpose of achieving efficient dimension reduction and model selection.

The results show that the new yield forecasting model provides significant improvements over the classical regression model, both in terms of in-sample and out-of-sample forecasting abilities.

The empirical analysis is limited to data from the province of Manitoba, Canada, and other regions may show different results.

This research is useful from a risk management perspective for insurers and reinsurers, and the framework may also be used to develop improved weather risk management strategies to help manage adverse weather events.

This is the first paper to integrate a credibility estimator for crop yield forecasting, and develop a closed form reinsurance pricing formula.

Weather affects consumer decision-making. However, academic research on how weather factors affect specific takeaway foods is limited. This paper aims to fill in the gap and therefore to contribute to online marketing and operation.

Web crawler techniques were first exploited to collect takeaway food ordering data from Meituan, the world’s largest GMV platform. Then statistics models and a time series regression model were selected to study the weather impact on online orders.

The findings highlight that certain weather factors, such as temperature, air quality and rainfall have clear effects on most category takeaway orders.

Quantitative analysis of weather impacts on the takeaway ordering business will help to guide the online service platforms for marketing promotion and the settled businesses to make reasonable arrangements for inventory and marketing tactics.

Retailers have long been aware that weather affects the sales of a myriad of products, but until now, most were not in a position to manage the risks weather presents. Rising weather variability combined with advances in weather-index financial instruments have prompted new interest in investigating the relationship between sales and weather. The purpose of this paper is to explore the impact of changes in weather on UK retail sales, to estimate the contribution of weather to sales, and evaluate the maximum potential loss caused by adverse weather, for each season and retail sector.

The authors present a methodology to identify and quantify the extent to which a company is exposed to weather risks, in order to incorporate them into its risk management policy and take actions to mitigate these risks. For each season and each retail category, the authors provide a measure of the impact of weather on sales that can be used as a benchmark to analyse sales performance.

The authors propose a new risk assessment indicator to evaluate the potential losses caused by adverse weather (WeatherRisk). The authors show that intra-annual changes in weather significantly affect retail sales. The exposure of retail categories to weather are not the same depending on the season, and the response of individual retail categories to the same change in weather varies considerably. Although temperature is a predominant explanatory variable, the authors show that weather-sensitivity analysis should include precipitation, humidity rate and wind.

One limitation of this study is that the authors individually compute WeatherRisk for each significant weather variable. Further research could explore new approaches to evaluate Total WeatherRisk, which take into account potential multicollinearity issues between weather variables.

The methodology allows retailers to measure the effects of weather on sales performance, evaluate the risks at stake, and protect sales and margins from weather risks, with newly available index-based financial instruments. Managers may now actively use weather as a differential advantage, and at the same time focus their efforts on improving resiliency to increasing climate variability.

In this paper, the authors produce a detailed analysis of the exposure of each retail sectors to unseasonal weather. This is the first time all retail sectors are analysed and ranked per season at a national level. The authors provide managers with actionable information to improve their understanding of how weather impact sales over each season, and to allow them to structure weather-index-based instruments with financial partners.

Weather is often referred as an uncontrollable factor, which influences customer’s buying decisions and causes the demand to move in any direction. Such a risk usually leads to loss to industries. However, only few research studies about weather and retail shopping are available in literature. The purpose of this paper is to develop a model and to analyze the relationship between weather and retail shopping behavior (i.e. store traffic and sales).

The data set for this research study is obtained from two food retail stores and a fashion retail store located in Lower Bavaria, Germany. All these three retail stores are in same geographical location. The weather data set was provided by a German weather service agency and is from a weather station nearer to the retail stores under study. The analysis for the study was drawn using multiple linear regression with autoregressive elements (MLR-AR). The estimated coefficients of weather variables using MLR-AR model represent corresponding weather impacts on the store traffic and the sales.

The snowfall has a significant effect on the store traffic and the sales in both food and fashion retail stores. In food retail store, the risk due to snowfall varies depending on the location of stores. There are also significant lagging effects of snowfall in the fashion retail store. However, the rainfall has a significant effect only on the store traffic in the food retail stores. In addition to these effects, the sales in the fashion retail store are highly affected by the temperature deviation.

Limitations in availability of data for the weather variables and other demand influencing factors (e.g. promotion, tourism, online shopping, demography of customers, etc.) may reduce efficiency of the proposed MLR-AR model. In spite of these limitations, this study can be able to quantify the effects of weather variables on the store traffic and the sales.

This study contributes to the field of retail distribution by providing significant evidence of relationship between weather and retail business. Unlike previous studies, the proposed model tries to consider autocorrelation property, main and interaction effects between weather variables, temperature deviation and lagging effects of snowfall on the store traffic or the sales. The estimated weather impacts from this model can act as a reliable tool for retailers to explain the importance of different non-catastrophic weather events.

The paper aims to examine theoretically valuation of weather derivatives and their hedging roles in corporate risk management.

The paper introduces an extended financial market model in which the weather risk is included as an independent random process and examines the effectiveness of weather derivatives and traditional price forwards in a unified theoretical framework. It also provides a no‐arbitrage approach to price weather derivatives, which theoretically combines the actuarial and financial paradigms.

The results document that corporate leverage level is an essential factor determining the choice between price forwards and weather derivatives. In some cases; weather derivatives outperform price forwards, while in some other cases; a joint use of both instruments is optimal, depending on the firm's risky leverage level. Interestingly, the paper identifies the case when the leverage level is very high, the positive roles of both instruments diminish and the firm is unhedgeable.

The paper provides important insights to investors and hedgers and extends the literature on corporate risk management.

The purpose of this paper is to investigate the influence of external factors on the Facebook dialogue. As both weather and point in time substantially. As both weather and point in time substantially influence people’s lives, it can be assumed that both factors may also affect communication on Facebook. To the best of the authors’ knowledge, this is the first study focusing on the impact of the external factors “weather” and “point in time” on a public utility’s Facebook communication.

The potential influence is explored through the case study of an Austrian public utility. The study focuses on 321 postings, published via the company’s official Facebook account between August 2016 and February 2018.

The empirical results confirm the influence of “weather” and “point in time” indicators on the stakeholder dialogue. The findings highlight how the relevant items affect the posting behavior of a utility, as well as stakeholders’ reactions, comments and shares.

By introducing both external factors to the social media literature, this paper broadens the understanding of Facebook communications beyond the sender and receiver of digital information. In this way, the research contributes to a more holistic view of Facebook dialogue. It provides practical advice on how social media managers of public utilities may use weather forecasts and “point in time” considerations to more strategically foster stakeholder dialogue in social media.

This paper aims to study the effect of ultraviolet (UV) light on the corrosion behavior of BC550 weathering steel in simulated marine atmospheric environment.

The effect of UV light on the corrosion behavior of BC550 weathering steel in simulated marine atmospheric environments were investigated by the corrosion weight gain experiment, in situ electrochemical noise, scanning electron microscope and X-ray diffraction.

UV light accelerated the corrosion process of BC550 weathering steel in the simulated marine atmospheric environment during the first 168 h. The maximum influence factor of UV light was 0.32, and it was only 0.08 after 168 h of corrosion process.

As the extension of corrosion time, the thickness and density of the corrosion product layer increased, which weakened the acceleration effect of UV light.

Weather derivatives have emerged as a generally acknowledged, if not widely utilized, risk management product within the past 5 to 10 years. The authors of this article compare the costs and benefits of weather derivatives in relation to insurance contracts for hedging weather risk, within the context of long‐term trends in hedging demand due to global warming. The article finds that, as global warming results in increased climactic variation and greater frequency and intensity of climatic anomalies (i.e., higher volatility), derivatives may provide coverage at a lower cost than standard insurance.

One of the sectors most affected by the variable weather events caused by climate change and global warming is the aviation sector. Especially in aircraft accidents, weather events increasing with climate change and global warming are effective. The purpose of this study is to determine how much the change in weather conditions caused by global warming and climate changes affect the aircraft in the world between the years 2010 and 2022.

In this study, it was investigated which weather events were more effective in aircraft crashes by determining the rates of air events and aircraft crashes in aircraft crashes with a passenger capacity of 12 or more that occurred between 2010 and 2022.

It is clearly seen that increasing weather conditions with global warming and climate change increase the effect of weather conditions in aircraft crashes.

The difference of this study from other studies is the evaluation of the data of the past 12 years, in which the increasing consequences of global warming and climate change have been felt more. It also reveals the necessity of further research on the effects of weather conditions on aircraft.