Weather History Research Articles

Weather variability and malnutrition among farming households in Ethiopia

Unlike existing studies that examined the effects of weather variability by relying on the current weather conditions disregarding the long-term influence of historical weather patterns, we jointly estimate the effects of current and past weather variability on rural households’ nutritional status. Using three waves of nationally representative panel data from rural Ethiopia, we show that the nutritional status of farming households, measured by daily intakes of micro-and macronutrients, is more sensitive to past weather variability than the current weather condition. We also find that adverse weather history can trigger responses that are linked to the deterioration of nutritional status.

Spatial econometric modeling of malaria distribution in Burkina Faso

This study focuses on the spatial distribution of malaria rate in Burkina Faso. For this purpose, we used 2020 geographical and confirmed malaria cases data from the National Institute of Statistics and Demography of Burkina Faso. We also accessed climate data on the French weather history website. We deployed relevant spatial statistical tools to address the notions of neighborhood matrix, spatial autocorrelation, and spatial heterogeneity between geographical observations. Ordinary Least Squares (OLS) regression model, Spatial Error Model (SEM), Spatial Autoregressive model (SAR), and the spatial Durbin Model (SDM) were computed using cross-validation to ensure the reliability of our findings. The Akaike information criterion (AIC) was used to select the most appropriate model for our study. The specification tests conclude that there is a spatial dependence between the observations. The SDM was chosen as the best-fitting data for modeling geolocated malaria rates. This outcome reveals that environmental indicators, population literacy rate, and rural population size by region significantly affect the country’s geolocated malaria rate. Policymakers can use these findings to make informed decisions related to malaria spread.

A novel autonomous irrigation system for smart agriculture using AI and 6G enabled IoT network

Agriculture plays a vital role in the growth of humankind. The total world depends upon the agricultural contribution, and wherein the agriculture purely depends upon the necessity of irrigation. This paper develops an autonomous irrigation system using Artificial Intelligence (AI) and the 6G enabled Internet of Things (6G-IoT) transforming to create a hassle-free smart agriculture model. The developed autonomous irrigation system is purely based on prediction algorithm which is mount in a microprocessor, which uses weather history data using IoT to identify and predict rainfall patterns and any climatic changes; hence it creates an intelligent system which irrigates the field depending upon the outer environment. The developed system not only checks the environmental condition, but it also measures the moisture content of the soil to supply the required amount of water to the crops. The developed autonomous AI and 6G-IoT based smart agricultural irrigation system has been tested under control environment with an accuracy of 86.34%, sensitivity of 89.28% and precision of 91%. Hence, the proposed methodology could be the possible solution for autonomous irrigation of crops in future.

Delayed ettringite formation (DEF) and its effect on freeze-thaw damage in Finnish concrete façades

The strongest drivers causing delayed ettringite formation (DEF) and its impact as a cause of freeze-thaw damage in Finnish concrete façades is still poorly known as it has been only briefly touched as a part of one dissertation [1] and one master's thesis [2]. The conclusion of both studies was that DEF might slightly accelerate freeze-thaw damage of concrete, but how strongly or how rapidly was not stated. To complement the knowledge on that matter, this article analyses Finnish façade structures built between 1960 and 2003 using large case-study databases, weather history data and statistical methods.As a result, it was found that DEF occurs in more than half of the Finnish concrete façades, but it significantly increases the freeze-thaw damage only when it has spread widely in the pore structure of concrete. Such case has been quite rare, since the amount of widespread DEF covers only 4.4% of all observations. Wind-driven rain (WDR) was found to be one of the most significant drivers for DEF, and if the façades are either completely protected from it or oriented in the direction which are least exposed to WDR, DEF was not observed extensively at all. Façades where no DEF was detected had WDR exposure an average of 1852 mm per year. The facades where DEF was found extensively had 68% more exposure, an average of 3127 mm per year.

Evolving conceptions of silvopasture among farmers and natural resource professionals in Wisconsin, USA

Silvopasture has gained attention as an agroecological practice that may simultaneously meet farmer goals and provide environmental benefits, including climate change mitigation. At the same time there are significant concerns about the potential for livestock to damage trees and forest soils. Like other innovative agroecological systems, silvopasture combines management complexity with limited research knowledge. Unlike annual crops, the effects of silvopasture management can take decades to assess and require forestry as well as agronomic expertise. We conducted mixed-methods research on silvopasture attitudes and knowledge among farmers, agricultural advisors, and foresters in Wisconsin between 2014 and 2019. We asked: (1) How do farmers who practice grazing, agricultural advisors, and foresters perceive silvopasture? and (2) How did coverage of silvopasture change between 2009 and 2019 in a popular grazing publication? Perceptions of silvopasture were influenced by recent weather history, markets for forest and agricultural products, existing land uses, and other contextual factors. Some farmers and agricultural advisors were committed to silvopasture despite significant obstacles to implementing the practice. Over the course of the study period agricultural advisors increased their willingness to provide silvopasture advice to farmers and professional colleagues, and coverage of silvopasture increased in a popular grazing publication. Finally, a multi-county supportive community of practice was associated with greater enthusiasm for the practice. The greater acceptance of silvopasture among resource professionals follows an increase in silvopasture research and outreach in the region. This interest in silvopasture suggests both a need for, and openness to, greater collaboration among forestry and agricultural professionals and farmers to develop sustainable silvopasture standards.

Understanding weather futures based on the past: a case of Stornoway, Outer Hebrides

ABSTRACT We present a weather history for Stornoway in the Outer Hebrides, an island chain off the northwest coast of Scotland. It combines a new 164-year composite rainfall record representative of the settlement of Stornoway (1857–2019), alongside descriptive accounts of weather harvested from the school logbooks from the Nicolson Institute in Stornoway (1873–1974). The school logbooks record the experiences of the school, school children and wider community throughout the seasons. We describe the construction of the rainfall record for the period 1857–2019 and present analyses of long-term annual and seasonal variability, with a particular focus on wet/dry extremes. In examining instrumental and qualitative sources together, we consider not just climate, but also the impacts and responses of extreme weather on the communities of the Outer Hebrides and specifically Stornoway. The climate of the late nineteenth century in the Outer Hebrides was climatologically and meteorologically challenging, with harsh storms, severe cold, and droughts with notable societal impacts. School logbooks provide the opportunity to examine societal responses to past climate variability, enabling a better understanding of how future climates may be adapted and responded too.

Efficient data pruning using optimal KNN for weather forecasting in cloud computing

Established researchers may use cloud-deployed software, information sharing, collaborative effort devices, and cloud-based computer infrastructure to handle data and recreate models. This study examines cloud nomenclature in the meteorological local region to aid numerical weather forecast. Numerical weather prediction forecasts weather. Numerous machine-learning methods are suggested for numerical weather prediction equation evaluation (NWP). Despite these inaccuracies, the methodologies so far provide decent results. It is hard to manage weather history, but it is best to do so. Thus, we propose a novel k-nearest neighbour classifier to prune missing values. Despite these inaccuracies, the methodologies so far provide decent results. The new KNN, optimum k-nearest neighbour, uses an enhanced way to choose out the precise data required for prediction. Seattle rainfall datasets assess performance. We found that the optimum KNN method improves classification accuracy while reducing time.

Magnetic declination and climate change

Earth’s Magnetic Field is the magnetic field extended from inner Earth till out to space, meeting the solar wind. Its importance is crucial due to the deflection in most of solar wind having as a result the existence of ozone layer and the protection of Earth from UV radiation - a basic mechanism for preservation of life on Earth.Although the magnetic field is deeply studied for its contribution to life preservation, an important question is still unanswered - the possible relationship between Earth’s magnetic field and climate. Plenty of studies are focused on correlations involving time series and ionospheric variations but the physical mechanism remains unknown.Here, we propose an explanation based on time series (Historical Magnetic Declination) and the ionospheric variation over Europe as well as in other parts of the world, with a focus on zonal winds. We’ll examine a time series from Middle Ages till modern times in correlation with solar activity and weather history data, to support the hypothesis that there is a link between Earth’s climate changes and geomagnetic field variations. Finally, we’ll try to predict the climate characteristics of major parts of the globe for the following years.

Effect of global warming on dermatology practice: The increase in cases of cutaneous larva migrans in the eastern Black Sea region of Turkey.

Cutaneous larva migrans (CLM) is a parasitic disease seen in people in contact with soil in tropical countries. Almost all cases reported in regions without a tropical climate have a history of travel to a tropical region. In our study, we aimed to investigate the effect of climate change on CLM cases and the demographic characteristics of these cases. For this purpose, the climate information of the period in which we determined the case series and the characteristics of the patients were investigated. The study was designed retrospectively. Patient files and pre- and post-treatment photographic archives were reviewed retrospectively. In addition, the region's weather history over the last 50 years was examined. The records of patients diagnosed with CLM in 2018 were reviewed retrospectively. In total, 22 cases were detected. The common feature of all cases was that they worked as tea pickers. After examining the weather conditions of the period when the cases were commonly reported, it was determined that there was a sharp temperature increase compared with previous years. Due to climate change, an increase in the number of CLM cases is observed in non-tropical regions. Since it is considered a disease-specific to tropical regions, the diagnosis may be overlooked in cases outside these regions. However, CLM should be kept in mind as a diagnostic possibility by clinicians practicing in non-tropical regions as well, especially when treating patients who work with soil, such as tea harvesters.

Effects of large mammalian herbivory, previous fire, and year of burn on fire behavior in an African savanna

AbstractFire, herbivores, and climatic factors are all major drivers of savanna and grassland dynamics, and they interact in complex ways, which are still in the process of being explored. In particular, herbivores can reduce fire intensity by removal of biomass, and this could be reinforced by herbivores’ attraction to recently burned sites, although grassland resilience may limit the temporal depth of such effects. Fire temperature is the most common fire metric reported for grassland fire, but additional aspects of fire behavior can also be measured. Using a set of controlled, replicated experiments, we examined the effects of year of burn, herbivory by livestock and wildlife, previous burn, and weather history on fire behavior in an African savanna. Multiple measures of fire behavior (minimum fire temperature, flame front speed, fire residence time, maximum flame height, and flame length) in 36 controlled burns were positively intercorrelated. Burns conducted in 2018 were significantly cooler, especially at heights >0.5 m above the ground, than those in 2013, a wetter year with more grass fuel. Grass fuel loads and fire temperatures were reduced by the presence of livestock and wildlife. Our sampling methods did not for the most part reveal expected differences in fire temperatures or other behaviors between the reburned plots and those burned for the first time in 2018, with or without herbivores, suggesting strong postfire resilience in this semiarid savanna rangeland.

The Relationship Between Unexpected Outcomes and Lottery Gambling Rates in a Large Canadian Metropolitan Area

The purchase of lottery tickets is widespread in Canada, yet little research has directly examined when and why individuals engage in lottery gambling. By leveraging a large urban dataset of lottery sales in Toronto, Canada, and using a simple computational framework popular in psychology, we examined whether city residents gamble more when local outcomes are better than expected; for example, wins by local sports teams or amounts of sunshine based on recent weather history. We found that unexpectedly sunny days predict increased rates of fixed-prize lottery gambling. The number of local sports team wins also predicted increased purchase rates of fixed-prize lottery, but unexpected positive outcomes in sports did not. Our results extend previous findings examining the linkage between sunshine and gambling in metropolitan areas beyond the US, but do not fully replicate the previously observed relationships between unexpected sports outcomes and gambling in US cities. These results suggest that the observed malleability of lottery gambling in response to incidental events in the gambler’s environment may vary considerably across geographies.

Movement Control of Smart Mosque’s Domes using CSRNet and Fuzzy Logic Techniques

Mosques are worship places of Allah and must be preserved clean, immaculate, provide all the comforts of the worshippers in them. The prophet's mosque in Medina/ Saudi Arabia is one of the most important mosques for Muslims. It occupies second place after the sacred mosque in Mecca/ Saudi Arabia, which is in constant overcrowding by all Muslims to visit the prophet Mohammad's tomb. This paper aims to propose a smart dome model to preserve the fresh air and allow the sunlight to enter the mosque using artificial intelligence techniques. The proposed model controls domes movements based on the weather conditions and the overcrowding rates in the mosque. The data have been collected from two different resources, the first one from the database of Saudi Arabia weather's history, and the other from Shanghai Technology Database. Congested Scene Recognition Network (CSRNet) and Fuzzy techniques have applied using Python programming language to control the domes to be opened and closed for a specific time to renew the air inside the mosque. Also, this model consists of several parts that are connected for controlling the mechanism of opening/closing domes according to weather data and the situation of crowding in the mosque. Finally, the main goal of this paper has been achieved, and the proposed model has worked efficiently and specifies the exact duration time to keep the domes open automatically for a few minutes for each hour head.

Service design for climate-smart agriculture

Holistic information systems for climate-smart agriculture demands the seamless integration of various categories of climate, meteorological and weather data. Any actor in the agricultural value chain may harness weather forecasts at the short and medium-range, local weather history, and prevailing climatic conditions, to inform decision-making. Weather is fundamental to many day-to-day operations, especially at farm-level, influencing decision-making at various spatial and temporal scales. Many operational decisions ideally require hyper-localized service provision. In practice, integrating weather information into decision-support services demands a comprehensive understanding of various categories of weather-related data, their genesis, as well as the specific standards and data formats used by the meteorological community. This paper considers the weather as a crucial context for the delivery of farm-level operational services in smart agriculture, highlighting critical issues for reflection by system designers during the service design and implementation phases.

Investigating the Effects of Land Use and Land Cover on the Relationship between Moisture and Reflectance Using Landsat Time Series

To better understand the Earth system, it is important to investigate the interactions between precipitation, land use/land cover (LULC), and the land surface, especially vegetation. An improved understanding of these land-atmosphere interactions can aid understanding of the climate system and modeling of time series satellite data. Here, we investigate the effect of precipitation and LULC on the reflectance of the land surface in the northern U.S. Great Plains. We utilize time series satellite data from the 45 year Landsat archive. The length of the Landsat record allows for analysis of multiple periods of drought and wet conditions (reflecting climate, as well as weather), such that the precipitation-reflectance relationship can be investigated robustly for every individual pixel in the study area. The high spatial resolution of Landsat (30 m) allows for investigation of spatial patterns in weather (i.e., precipitation extremes) interactions with land surface reflectance at the scale of individual fields. Weather history is represented by a drought index that describes effective moisture availability, the Standardized Precipitation and Evaporation Index (SPEI). We find that effective moisture has a robust and consistent effect on reflectance over many types of land cover, with ∼90% of all pixels having significantly ( p < 0.01 ) higher visible reflectance during dry periods than during wet, occurring in nearly all regional, temporal, and LULC categories investigated. In grassland, the relationship is especially strong; there is an average reflectance increase of more than a third between very wet and very dry conditions (red band), and ∼99% of pixels have a significant relationship. In cropland, the effective moisture-reflectance relationship is more variable, suggesting that management decisions are an important factor in cropland-reflectance relationships.

Localness in Climate Change

Abstract Climate change is a global problem, yet it is experienced at the local scale, in ways that are both place-specific and specific to the accidents of weather history. This article takes the dichotomy between the global and the local as a starting point to develop a critique of the normative approach within climate science, which is global in various ways and thereby fails to bring meaning to the local. The article discusses the ethical choices implicit in the current paradigm of climate prediction, how irreducible uncertainty at the local scale can be managed by suitable reframing of the scientific questions, and some particular epistemic considerations that apply to climate change in the global South. The article argues for an elevation of the narrative and for a demotion of the probabilistic from its place of privilege in the construction and communication of our understanding of global warming and its local consequences.

Calibration of a simple 1D model for the hydraulic response of regional dykes in the Netherlands

A simple numerical model was set up to investigate the hydraulic behaviour of a regional dyke to improve understanding of the response under variable atmospheric conditions. The unsaturated hydraulic properties of the dyke body and the relevant foundation layers were calibrated either on the results of laboratory tests or on a national database, namely the Staringreeks, compiled for typical Dutch soils. The boundary conditions were imposed according to the weather history at the top, and to the pore pressures measured in the field at the bottom of the representative soil column. The results indicate that a simple 1D model is able to accurately reproduce the suction time history in the dyke core, provided the hydraulic conductivity and soil water retention properties are properly calibrated. The optimised hydraulic conductivities are typically two orders of magnitude higher than the saturated hydraulic conductivity from the laboratory tests, but comparable to the ones suggested in the database developed on field data. The work highlights that cautious evaluation of laboratory data is needed for field applications, and that direct information from the field should be used to validate numerical models in the presence of organic soils.

Clothing behaviour in Belgian homes

Clothing has a direct influence on the thermal comfort of an occupant and so, indirectly on the energy use of a building. Literary sources point out a lack of data about clothing behaviour in residential buildings. In order to assess the clothing behaviour two kinds of surveys are created: logbook surveys and online questionnaires. Both surveys are executed between March 11 and April 5, 2019. The mean clothing insulation worn during the investigation period is 0.58 clo. This clo-value differs from the clothing insulation values provided by Fanger, which are 1.0 clo for winter months and 0.5 clo for summer months. The influence of the indoor temperature, outdoor temperature, weather history memory, gender and age on the clothing behaviour is analysed. All variables have a small significant influence on the clo-value. It was found that occupants tend to wear the same clothes when they are at home. So, each participant clothes him/herself to be comfortable in their clothes and in the temperature of their own room. People who are used to live in lower indoor temperatures will, and are used to, wear more clothing insulation to be thermally comfortable than people living in warmer indoor temperatures. An adjustment in clothing behaviour can make a big impact on the energy use of residential buildings. A decrease in indoor temperature of 1°C can lead to heating energy savings of 10%. To remain thermally comfortable, the occupant must only wear an extra insulation value of 0.17 clo, which corresponds with a shirt. The question remains if occupants will effectively use the opportunity of changing clothes to lower their energy use.

Price Prediction for Agricultural Commodities in Bandung Regency Based on Functional Link Neural Network and Artifical Bee Colony Algorithms

In Indonesia, fluctuating agricultural commodity prices often impacts society negatively. In this study, farmers in Bandung Regency, West Java, Indonesia, were chosen as a case study. Fluctuating agricultural commodity prices can lead to farmers suffering losses due to the sale price being smaller or equal to the cost of planting. Price is influenced by crop productivity, while planting productivity is strongly influenced by weather. A system is developed in this study to predict the price of agricultural commodities based on price, productivity and weather history using a Functional Link Neural Network (FLNN) algorithm optimized with the Artificial Bee Colony (ABC) algorithm. The price prediction results can be used as recommendations for farmers as to whether they should plant or not. In addition, the prediction results are compared to the Artificial Neural Network (ANN) algorithm with Backpropagation algorithm as the learning algorithm. From the experimental result, the best Mean Absolute Percentage Error (MAPE) value was obtained with FLNN-ABC: 7.68% for the predicted price of chili and 10.59% for the predicted price of onion.

Assessing feasibility of the forest trafficability map for avoiding rutting – a case study

Information on forest trafficability (i.e. carrying capacity of the forest floor) is required before harvesting operations in Southern Boreal forest conditions. It describes the seasons when harvesting operations may take place without causing substantial damage to the forest soil using standard logging machinery. The available trafficability information have been based on subjective observations made during the wood procurement planning. For supporting forest operations, an open access map product has been developed to provide information on trafficability of forests. The forest stands are distributed into classes that characterize different harvesting seasons based on topographic wetness index, amount of vegetation, ground water height and ditch depth. The main goal of this case study was to evaluate the information of the static forest trafficability map in relation to the detected rutting within logging tracks measured in the field. The analysis concentrated on thinning stands since the effect of rutting is significant on the growth of the remaining trees. The results showed that the static trafficability map provided reliable and slightly conservative estimation of the forest trafficability. The majority (91.7%) of the evaluated stands were harvested without causing significant damage if harvesting was timed correctly compared to the trafficability information. However, it should be pointed out that the weather history at small scale, the skills of a driver, and effects of used machinery are not considered in the map product although they can have a considerable impact on the rutting.

Condition monitoring and predictive modelling of coating delamination applied to remote stationary and mobile assets

The ambiguous nature of meteorological parameters in uncontrolled environmental conditions makes it difficult to determine the structural integrity of stationary and mobile assets. The weather conditions for large vehicles, at The Tank Museum at Bovington, UK, which are operating in controlled and uncontrolled environmental conditions are investigated through weather history and corrosion monitoring techniques applied to large military vehicles by using linear polarisation resistance method. Corrosion initiation and propagation was found on several occasions during the operation of large vehicles within uncontrolled environment due to critical level of metrological parameters including salinity, relative humidity and rainfall. Comprehensive solutions have been proposed to detect damage initiation at the earliest possible stage to prompt maintenance professionals to take necessary actions to avoid damage. Early detection techniques will help to prolong the service life of large vehicles or metal structures which are operating or installed remotely. The analysis of diffusion of salt particles into coating during summer and winter season is also presented by estimating the salt concentration by taking linear relationship between wind speed and salt deposition rate based on ISO classification of airborne salinity. The proposed solutions can be applied to valuable assets operating in coastal, non-coastal and near the sea regions to predict and estimate the damage. The research will directly impact the maintenance and reliability of the automotive, oil and gas pipelines, aerospace and defence applications through remote condition monitoring technique.