Adverse Weather Effects Research Articles

Enhancing yellow maize production for sustainable food and nutrition security in Kenya

Maize is a strategic staple crop serving both as human food and feed in the livestock sector. It therefore has a critical socioeconomic value safeguarding against food and nutrition insecurity. Maize yields are however low especially among smallholder farmers who rely on this crop for their livelihood and sustenance. This has rendered most Sub-Saharan Africa countries such as Kenya to be net importers and depended on food aid mainly in form of maize. Even though white maize variety is nutritionally inferior, it is the most produced and consumed with little regard placed on yellow maize. We reviewed 154 articles and reports to highlight challenges facing maize production and sustainable agricultural practices that should be embraced to overcome them, nutritional benefits of yellow maize, factors hindering its consumption and research gaps that need to be addressed to enhance its production and utilization. Key production challenges identified include shrinking land sizes, declining soil fertility, adverse and unpredictable weather patterns and the devastating striga weed. Intercropping especially cereals with legumes have great potential for efficient land, water and nutrient resource utilization, manage weeds and minimize crop failure and adverse weather effects. This combined with integrated soil fertility management will ensure increased yields. It was found that yellow maize has higher carotenoid content hence superior to white maize and when taken with grain legumes provides a low-cost balanced diet. Despite this, yellow maize utilization is low because it is regarded as poor man’s crop, associated with food aid and reserved as livestock feed. This negative perception can be changed through educational campaigns on its nutritional value in order to enhance local production and encourage social acceptability to aid alleviate vitamin A deficiency, a key limiting micronutrient. In conclusion, tapping in the nutritional superiority of yellow maize through legume intercropping should be enhanced.

Analogical Image Translation for Fog Generation

Image-to-image translation is to map images from a given style to another given style. While exceptionally successful, current methods assume the availability of training images in both source and target domains, which does not always hold in practice. Inspired by humans' reasoning capability of analogy, we propose analogical image translation (AIT) that exploit the concept of gist, for the first time. Given images of two styles in the source domain: A and A', along with images B of the first style in the target domain, learn a model to translate B to B' in the target domain, such that A:A' :: B:B'. AIT is especially useful for translation scenarios in which training data of one style is hard to obtain but training data of the same two styles in another domain is available. For instance, in the case from normal conditions to extreme, rare conditions, obtaining real training images for the latter case is challenging. However, obtaining synthetic data for both cases is relatively easy. In this work, we aim at adding adverse weather effects, more specifically fog, to images taken in clear weather. To circumvent the challenge of collecting real foggy images, AIT learns the gist of translating synthetic clear-weather to foggy images, followed by adding fog effects onto real clear-weather images, without ever seeing any real foggy image. AIT achieves zero-shot image translation capability, whose effectiveness and benefit are demonstrated by the downstream task of semantic foggy scene understanding.

Response of black currant plants to abnormalities in water and temperature regime 2020

The water-temperature regime features in the Central Chernozem region in 2020 were reviewed. The main deviations from the mean perennial values of air temperature and humidity, as well as the precipitation distribution in spring and summer were determined. It was revealed that the distinctive feature of the entire 2020 growing season was significant instability of weather conditions - dry periods with high air temperatures were followed by wet and cool periods. In addition, a high amplitude of the daily air temperature difference was noted, which was significantly exceeding the average perennial values. The phenotypic, biological and physiological responses of black currant plants to anomalies of the water-temperature regime were studied; in particular, monitoring of photosynthetic activity dynamics of black currant plants revealed differences in response depth to damaging effects of adverse weather, depending on the variety and age of the plants. It was found that in black currant plants older than 5 years, the reaction to temperature anomalies is weaker than in three-year-old’s. The reaction depth of phenological, biological and economic characteristics to the effect of water-temperature abnormalities firstly depends on the variety’s genotypic features.

Uneven Effects of Adverse Weather Conditions on Participation in Leisure-Time Physical Activities Across Income Levels.

To examine how the effect of adverse weather on participation in leisure-time physical activities (LTPA) varies with income. Cross-sectional study. 14,394 individuals from 56 Canadian cities, surveyed in 1992, 1998, and 2005. The adverseness level of daily weather is measured by the number of hours with precipitation or strong winds (wind speeds in excess of 38 km/hour) between 6 am and 11 pm. Probit and multinomial logit models are used to examine the variation in weather-LTPA correlations across income levels. At the mean income level, when the weather quality deteriorated from all-day nice weather to all-day adverse weather, the probability of participating in LTPA decreased by 24.54% (from 0.2424 to 0.1829, P < 0.01). As income increased by $10,000, the same deterioration in weather quality led to a 17.06% decrease in LTPA (from 0.2508 to 0.2080, P < 0.01). The smaller decrease is mainly because the $10,000 increase in income is associated with a 14.49% increase in indoor LTPA, which partly offsets the decrease in outdoor LTPA. Interventions and policies that increase indoor physical activity options, such as providing easier access to indoor facilities and offering subsidies for purchasing or renting home exercise equipment, are promising for effectively promoting LTPA, especially for individuals in lower-income groups or from regions that frequently experience adverse weather.

Non-Parametric Association Rules Mining and Parametric Ordinal Logistic Regression for an In-Depth Investigation of Driver Speed Selection Behavior in Adverse Weather using SHRP2 Naturalistic Driving Study Data

Human error is considered to be one of the major causes of crashes, especially in inclement weather. Although many studies have investigated the effect of adverse weather on traffic safety and operations, there is a lack of research into the differences in driving behavior and performance during adverse weather, particularly at a trajectory level. With this research gap in mind, this study presents a novel approach for an in-depth investigation of driver speed selection behavior in adverse weather utilizing trajectory-level data acquired from the SHRP2 Naturalistic Driving Study using a promising association rules data mining technique. The preliminary analysis revealed that drivers reduced their speeds by 3.9% in the presence of light rain, by 10.2% in heavy rain, 15.2% in light snow, 29.8% in heavy snow, 1.8% with distant fog, and 7.4% with near fog. The findings from the association rules mining approach indicated that driving more than 5 mph above the speed limit was closely associated with clear weather as well as young and inexperienced drivers; whereas a reduction in speed to more than 5 mph below the speed limit was closely associated with snowy road surfaces combined with affected visibility. These findings are also in line with the results from the ordered logistic regression, which revealed that drivers were 1.4 times more likely to reduce their speeds in light rain, 1.7 times in heavy rain, 4.3 in light snow, 12.2 in heavy snow, 1.7 with distant fog, and 2.0 with near fog. The findings from this study provide an unprecedented opportunity to develop a Human-in-the-Loop Variable Speed Limit algorithm.

Decoupling of impact factors reveals the response of German winter wheat yields to climatic changes

Yield development of agricultural crops over time is not merely the result of genetic and agronomic factors, but also the outcome of a complex interaction between climatic and site-specific soil conditions. However, the influence of past climatic changes on yield trends remains unclear, particularly under consideration of different soil conditions. In this study, we determine the effects of single agrometeorological factors on the evolution of German winter wheat yields between 1958 and 2015 from 298 published nitrogen (N)-fertilization experiments. For this purpose, we separate climatic from genetic and agronomic yield effects using linear mixed effect models and estimate the climatic influence based on a coefficient of determination for these models. We found earlier occurrence of wheat growth stages, and shortened development phases except for the phase of stem elongation. Agrometeorological factors are defined as climate covariates related to the growth of winter wheat. Our results indicate a general and strong effect of agroclimatic changes on yield development, in particular due to increasing mean temperatures and heat stress events during the grain-filling period. Except for heat stress days with more than 31°C, yields at sites with higher yield potential were less prone to adverse weather effects than at sites with lower yield potential. Our data furthermore reveal that a potential yield levelling, as found for many West-European countries, predominantly occurred at sites with relatively low yield potential and about one decade earlier (mid-1980s) compared to averaged yield data for the whole of Germany. Interestingly, effects related to high precipitation events were less relevant than temperature-related effects and became relevant particularly during the vegetative growth phase. Overall, this study emphasizes the sensitivity of yield productivity to past climatic conditions, under consideration of regional differences, and underlines the necessity of finding adaptation strategies for food production under ongoing and expected climate change.

Analysis of LiDAR and Camera Data in Real-World Weather Conditions for Autonomous Vehicle Operations

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Autonomous vehicle technology has the potential to improve the safety, efficiency, and cost of our current transportation system by removing human error. With sensors available today, it is possible for the development of these vehicles, however, there are still issues with autonomous vehicle operations in adverse weather conditions (e.g. snow-covered roads, heavy rain, fog, etc.) due to the degradation of sensor data quality and insufficiently robust software algorithms. Since autonomous vehicles rely entirely on sensor data to perceive their surrounding environment, this becomes a significant issue in the performance of the autonomous system. The purpose of this study is to collect sensor data under various weather conditions to understand the effects of weather on sensor data. The sensors used in this study were one camera and one LiDAR. These sensors were connected to an NVIDIA Drive Px2 which operated in a 2019 Kia Niro. Two custom scenarios (static and dynamic objects) were chosen to collect sensor data operating in four real-world weather conditions: fair, cloudy, rainy, and light snow. An algorithm developed herein was used to provide a method of quantifying the data for comparison against the other weather conditions. The results from these performance algorithms show that sensor data quality degrades by an average of 13.88% for static objects and 16.16% for dynamic objects while operating in these conditions, with operations in rain proving to have the most significant effect on sensor data degradation. From this study, it is hypothesized that advancements in data processing algorithms can improve the usability of this degraded data. In future work, we seek to explore fault-tolerant sensor fusion algorithms that can overcome the effects of adverse weather.&lt;/div&gt;&lt;/div&gt;

CNN-Based Lidar Point Cloud De-Noising in Adverse Weather

Lidar sensors are frequently used in environment perception for autonomous vehicles and mobile robotics to complement camera, radar, and ultrasonic sensors. Adverse weather conditions are significantly impacting the performance of lidar-based scene understanding by causing undesired measurement points that in turn effect missing detections and false positives. In heavy rain or dense fog, water drops could be misinterpreted as objects in front of the vehicle which brings a mobile robot to a full stop. In this paper, we present the first CNN-based approach to understand and filter out such adverse weather effects in point cloud data. Using a large data set obtained in controlled weather environments, we demonstrate a significant performance improvement of our method over state-of-the-art involving geometric filtering. Data is available at https://github.com/rheinzler/PointCloudDeNoising.

Uticaj klimatskih uslova na rast i razvoj CichoriumEndivia(L) u leto-jesen u plastenicima u Lenjingradskoj oblasti, Rusija

The paper discusses the results of the study of the effect of climatic factors on the yield and biometric indicators of various varieties of endive chicory (Cichorium endivia (L.)) over a two-year study grown in the summer-autumn period in the plastic greenhouses on the experimental plots of the St. Petersburg Agrarian University, Pushkin, Russia. The plants of the genus Chicory are known and valued for their unique medicinal and nutritional properties. Total of 14 varieties of domestic and foreign Cichorium endivia breeding were studied. The study was carried out in 2015 and 2016years that were sharply differed in the amount of precipitation and temperature. The climate of Leningrad region is characterised with humid and long-lasting cold season. The aim of the research was to identify the variety of endive chicory that is most suitable and tolerant for the climatic conditions of Leningrad region, Russia. During the growing season the plant biometric parameters such as plant height (cm), rosette diameter of leaves (cm), and number of leaves (pieces) were measured. During harvesting, additional parameters such as biomass of plants and leaves, the area of the assimilation surface were determined. The decrease in plant biometric parameters during the phenological observations in September 2016 was associated with an increased amount of precipitation in July-August. The results showed that the variety "Broad Betavian fullhearted" was the most sensitive to the adverse effects of weather, showing the largest variation in biometrics over the studied years. In plants of this variety the mass of leaves decreased by 9.2 times, the area of the assimilation surface was reduced by 11.6 times, while the crop shortfall in 2016 was almost 90%. The varieties "Green curled" and "Scarola bionda" were the most tolerant to weather conditions. These varieties were characterized by the maximum yield in both years of research: "Green curled"-7.90 kg/m 2 and 2.87 kg/m 2 , "Scarola bionda"-8.06 kg/m 2 and 2.84 kg/m 2 in 2015 and 2016, respectively. Generally, the result of the study showed that most of the studied varieties were sensitive to the amount of precipitation and the number of sunny days. The study made it possible to identify the most sensitive and most tolerant varieties of endive chicory for growing in the Leningrad region.

A spatial panel regression model to measure the effect of weather events on freight truck traffic

Truck drivers adhere to delivery schedules making them more likely to reroute rather than cancel a trip when faced with inclement weather. While previous studies modeled the direct effects of adverse weather on total traffic volumes, none considered the particular implications for trucks. The ability to predict spatial and temporal shifts in truck traffic resulting from adverse weather is novel and useful for decision makers tasked with long-range freight planning and for the trucking industry. With deeper insights into rerouting around adverse weather, the trucking industry will be able to more efficiently plan and accurately estimate billable miles. Thus, this study applied dynamic spatial panel regression that captures rerouting behavior of trucks due to adverse weather conditions. Results showed that changes in truck traffic volume due to adverse weather conditions, e.g. surface runoff, snow mass, and humidity, exhibited spatial (direct and indirect) and temporal shifts (short and long term effects).

Anti-freezing of natural draft dry cooling system of power generation by water re-distribution during winter

The freezing risk of heat exchangers of the natural draft dry cooling system (NDDCs) in winter has great threaten on the safe and efficient operation of thermal power generating unit. This study concentrated on the potential of waterside re-distribution to relieve the adverse weather effects during winter. Based on the requirements of the thermal cycle and cooling system, a global control anti-freezing method with water flow distribution for NDDCs under crosswinds during winter was proposed. Taking the operation cost as the objective function, the Lagrange multiplier method combined with the so-called entransy dissipation analysis was employed to obtain the optimal results. A 660 MW supercritical coal-fired power generating unit was taken as an example to show the applications. The results indicated that energy saving potential of cooling water re-distributions increased with ambient natural wind, but also with the increase of total costs during hasher freezing environment. The saved consumption cost of coal would cover the growth of pump power. Compared with the previous research, the maximum savings of operation cost can reach 1.11 g/kWh after optimization.

Soft Computing Techniques for Weather Change Predictions in Delhi

Weather forecasting and warning is the application of science and technology to predict the state of the weather for a future time of a given location. The emergence of adverse effects of weather has endangered the life of general public in previous years. The unpredicted flood and super cyclone in many places have created havoc. The government and private agencies are working on its behaviours but still it is challenging and incomplete. But, the application of soft computing techniques in weather prediction has made a significant perfomance now a days. This research work presents the comparative study of soft computing techniques like MultiLayer Perceptron(MLP), Support Vector Machine(SVM) and J48 Decision Tree for forecasting the weather of Delhi with ten years data comprising of temperature, dew, humidity, air pressure, wind speed and visibility. This paper tries to describe the comparison among above models using four different error values like Relative Absolute Error(RAE), Mean Absolute Error(MAE), Root Mean Squared Error(RMSE) and Root Relative Squared Error(R2) with a proposed model by defining new algorithm. Further the performance can be enhanced if textmining will be applied in this proposed model.

Post-disaster power system restoration planning considering sequence dependent repairing period

In this paper, a post-disaster restoration planning (PDRP) model for critical power system infrastructure is proposed. The model intends to find the optimal repair and activation schedule for damaged components so that the system load accommodation capability is maximized and the makespan of restoration process is minimized. The model takes into account the effect of adverse weather on the component repair process and a time-varying repair velocity function model is proposed to obtain the time-varying repair duration (TVRD) of the damaged component. The sequence-dependent repairing period (SDRP) of damaged components can be formed by combining TVRD and the transportation time of resources, and embedded into the PDRP model. The proposed model also allows the repair units to perform repair work on arbitrary damage component, not just the damage components in their routine service area. As a result, the maintenance efficiency is maximized. A two-stage bi-pheromone heuristic method is developed to efficiently solve the proposed PDRP model. The sequence position adherence information of each repair task and the relationship between two adjacent tasks are used in the search so that the solution time of the PDRP model is avoided from increasing exponentially with the problem size. The IEEE-118 and IEEE-30 bus test system are used for case studies to validate the proposed model and method. In addition, advantages of considering the sequence dependent repairing period are presented.

Examining the effect of adverse weather on road transportation using weather and traffic sensors.

Adverse weather related to reduced visibility caused by fog and rain can seriously affect the mobility and safety of drivers. It is meaningful to develop effective intelligent transportation system (ITS) strategies to mitigate the negative effects of these different types of adverse weather related to reduced visibility by investigating the effect of rain and fog on traffic parameters. A number of previous researches focused on analyzing the effect of adverse weather related to reduced visibility by using simulated traffic and weather data. There are few researchers that addressed the impact of adverse weather instances using real-time data. Moreover, this paper conducts comprehensive investigation to clearly compare the changes of driving behavior and traffic parameters in adverse weather including fog and rain using real-time traffic and weather data collected by advanced vehicle-based traffic sensors and weather sensors. After some preliminary analysis, the analysis of variance method (ANOVA) was applied to further compare the significance of effects of these two kinds of adverse weather on traffic parameters. The conditional regression models were employed finally to explore the relationship between these two types of adverse weather and traffic parameters. The results would be beneficial to develop effective intelligent traffic control countermeasures under these different types of adverse weather conditions related to reduced visibility.

The economic impacts of technology and climate change: New evidence from U.S. corn yields

AbstractOver the past century, U.S. farmers have been offered a steady stream of new agricultural technologies, and more recently, experienced climate change. Because these two events have been occurring simultaneously, identifying their separate effects is difficult, and misimputation is easy. This article explicitly examines the economics of technical change and the interaction between weather and technology as revealed in a half century of panel data on U.S. Midwest rainfed state‐average corn yields. Observed yields reflect two components: yield potential and damage to the potential caused by weather and pests. Yield potential is modeled as a stochastic production frontier where nitrogen fertilization, public corn research, and introduction and adoption of biotech corn seeds impact yield potential and excess heat impacts nitrogen productivity. The yield‐damage/damage‐control function permits biotech corn plants to abate adverse effects of weather and pest events. Results include the following: nitrogen use, public corn research, and biotech seed‐corn adoption increase yield potential; soil moisture stress reduces yield potential, and excess heat severely reduces nitrogen productivity. Biotech corn plants abate yield damage caused by soil moisture stress but not excess heat.

Feedback Control Engineering for Cooperative Community Development: Tools for Financial Management Advice for Low-Income Individuals

Individuals who manage their financial life to make ends meet have a complicated decision-making task. Their challenge is especially arduous if they live in an underprivileged community and have very low income (that is, they are part of the 10.7% of the world's population that survives on under $1.90 per day [1]). Most individuals in this situation are self-employed and face uncertain income levels (that is, they are day laborers and only hired periodically). They have inefficient or even nonexistent health services in their community and possibly poor available educational training. Furthermore, they may not have the financial services like insurance to help them cope with sudden and unpredictable events or shocks (the effects of adverse weather on crops).

How Do African Farm Households Respond to Changes in Current and Past Weather Patterns? A Structural Panel Data Analysis from Malawi

We use three waves of nationally representative household‐level panel data from Malawi to estimate a structural model characterizing the response of smallholder farm households to current and past weather patterns, and the subsequent impacts on household net income. We also quantify heterogeneity among households along the wealth spectrum regarding their ability to adapt to evolving weather patterns. This approach yields two key findings. First, adverse weather history prompts households to devote more time to maize cultivation on their own farms, to the detriment of other, possibly more remunerative income sources. Households also reduce application of productivity‐enhancing inputs, such as fertilizer and improved maize varieties, in response to adverse weather history. Our results are robust to different clustering structures and falsification tests aimed at ruling out alternative explanations to observed trends. Second, we find that, by maintaining a more diversified income structure, wealthier households are better able to adapt to adverse weather history. Consequently, adverse changes in past weather may be regressive in nature, creating a “climate‐induced” poverty trap that locks poor smallholder households into low‐value maize cultivation from season to season. This finding suggests that developing more weather‐resilient maize varieties and promoting smallholder livelihood diversification strategies may help mitigate the effects of adverse weather on the most vulnerable households.

Prioritization Scheme for Proposed Road Weather Information System Sites: Montana Case Study

Weather presents considerable challenges to highway agencies both in terms of safety and operations. From a safety standpoint, snow, ice and other forms of precipitation may reduce pavement friction, increasing the potential for crashes when vehicles are traveling too fast for the conditions. From an operations standpoint, heavy snow storms may affect the connectivity of the highway network due to closures that need to be cleared in an efficient and timely fashion. Further, travelers should be informed about unusual pavement conditions and road closures on time to minimize the effect of adverse weather on the safety and mobility of the traveling public. For the aforementioned reasons, road weather information has become increasingly important for highway agencies particularly in regions that experience harsh winter weather conditions. Road Weather Information System (RWIS) consists of the hardware, software, and communications interfaces to collect and transfer road weather observations from or near the roadway to a display device at the user’s location (1). Today, most environmental sensor stations (ESS) for RWIS include various atmospheric sensors, some form of pavement sensor, and camera imaging. Additional sensors are also being added to some ESS locations to measure traffic volumes, traffic speeds, and vehicle classifications and weights (2). Road weather information has been used by highway agencies in many applications such as winter maintenance, traveler information, and other weather-related intelligent transportation systems (ITS) applications. Data adequacy, reliability, and geographic coverage are critical attributes to consider for these transportation applications. Highway agencies are often faced with the challenge of selecting a limited number of ESS sites from a larger pool of proposed sites given the limited budgets available. This process has been largely subjective in practice, relying on the expertise and judgement of the agency staff involved. Therefore, there is a need for an objective prioritization scheme for proposed ESS stations, which should guide future RWIS expansion and ensure maximum utility (benefits) from new ESS installations.

Household cereal crop harvest and children\u2019s nutritional status in rural Burkina Faso

BackgroundReduction of child undernutrition is one of the Sustainable Development Goals for 2030. Achievement of this goal may be made more difficult in some settings by climate change through adverse impact on agricultural productivity. However, there is only limited quantitative evidence on the link between household crop harvests and child nutrition. We examined this link in a largely subsistence farming population in rural Burkina Faso.MethodsData on the middle-upper arm circumference (MUAC) of 975 children ≤5 years of age, household crop yields, and other parameters were obtained from the Nouna Health and Demographic Surveillance System. Multilevel modelling was used to assess the relationship between MUAC and the household crop harvest in the year 2009 estimated in terms of kilocalories per adult equivalent per day (kcal/ae/d).ResultsFourteen percent of children had a MUAC <125 mm (a value indicative of acute undernutrition). The relationship between MUAC and annual household food energy production adjusted for age, sex, month of MUAC measurement, household wealth, whether a household member had a non-agricultural occupation, garden produce, village infrastructure and market presence, suggested a decline in MUAC below around 3000 kcal/ae/d. The mean MUAC was 2.49 (95% CI 0.45, 4.52) mm less at 1000 than at 3000 kcal/ae/d.ConclusionsLow per capita household crop production is associated with poorer nutritional status of children in a rural farming population in Burkina Faso. This and similar populations may thus be vulnerable to the adverse effects of weather on agricultural harvest, especially in the context of climate change.

Impacts of adverse weather on Arctic road transport

Arctic regions are geographically peripheral and characterized by cold climate, constantly changing weather conditions and strong seasonal variations. This article examines variations in road traffic volume due to adverse weather in an arctic region, a topic that has received little attention in the transportation literature. The subject of the case study is northern Norway's Saltfjellet mountain pass, which is part of European Highway 6 (Ev6). A succinct econometric structural equation model was used to test hypotheses regarding the impacts of fluctuations in temperature, precipitation and wind speed on passenger and freight traffic volumes. The findings indicate that there was some reduction in traffic volume during adverse weather, particularly with respect to passenger traffic. However, the day-to-day variations in traffic volumes were relatively low at the studied section of road, although it is known that generalized transport costs increase significantly in adverse weather due to delays related to poor driving conditions and closed roads. The studied region is rural with limited access to alternative routes or transport modes, thus making this portion of the road particularly important for the communities in the region. Hence, the road users have few other options than using this high-cost road in order to maintain their activities. The use of standardized parameters in transport models to predict the effect of adverse weather on traffic volume, would not be appropriate in the studied context. However, it is recommended that the benefits of reducing the extra costs that adverse weather impose on traffic are estimated and included in road-project assessment tools to capture the burden and strain imposed on road users. This to ensure that appropriate decisions are made regarding the development and improvement of transportation facilities, particularly in rural areas.