Current Weather Conditions Research Articles

Simulating the impact of climate change on energy consumption and yield in Canadian greenhouse horticulture

Greenhouse horticulture is a very energy-intensive industry in cold regions such as Canada due to heating and lighting needs. It is still largely unknown how climate change will impact the energy profile and productivity of this vital industry. In this work, a greenhouse producing tomatoes has been simulated in eight Canadian cities under current and 2080 climates based on climatic trajectory RCP8.5, in order to determine how the energy consumption and tomato yield would be affected. Results show that, on average, energy consumption decreases by 11 % due to a reduction of the heating needs, whereas yield decreases by 17 % due to a higher canopy temperature. The use of light-emitting diodes (LED) resulted in a lower energy consumption than that of high-pressure sodium (HPS) lighting in both current and future weather conditions. This work suggests that the greenhouse industry is likely to require some adaptations to climate change and that reaching a balance between energy consumption and productivity will be a challenge. As an example, the addition of a mechanical cooling and dehumidification system was simulated and allowed to increase the yield compared to the current situation, even in the 2080 climate change scenario, at the expense of higher energy consumption. However, a more in-depth analysis is required to identify the best adaptative strategies for mitigating the impacts of climate change on greenhouse production.

Internet of things weather monitoring system

The IoT Weather Monitoring System aims to develop a comprehensive and efficient solution for real-time weather data collection, analysis, and dissemination using the Internet of Things (IoT) technology. With the increasing need for accurate and up-to-date weather information, this project leverages interconnected sensors, data processing techniques, and cloud computing to create a reliable and accessible weather monitoring system. The system comprises a network of weather sensors strategically deployed in various locations to capture meteorological parameters such as temperature, humidity, air pressure, and rainfall. These sensors are integrated with microcontrollers and wireless communication modules to transmit data to a central data hub. The data hub employs advanced data processing algorithms to clean, aggregate, and analyze the incoming data streams, ensuring the accuracy and quality of the information. To enhance user accessibility, the processed weather data is made available through a user- friendly web application and mobile interface. Users can view current weather conditions, historical trends, and receive alerts for severe weather events based on the analyzed data. Additionally, the system's cloud-based architecture allows for scalability and remote management, ensuring seamless operation and easy expansion to accommodate growing demands.

Classification of Rainfall Intensity and Cloud Type from Dash Cam Images Using Feature Removal by Masking

Weather Report is an initiative from Weathernews Inc. to obtain sky images and current weather conditions from the users of its weather app. This approach can provide supplementary weather information to radar observations and can potentially improve the accuracy of forecasts However, since the time and location of the contributed images are limited, gathering data from different sources is also necessary. This study proposes a system that automatically submits weather reports using a dash cam with communication capabilities and image recognition technology. This system aims to provide detailed weather information by classifying rainfall intensities and cloud formations from images captured via dash cams. In models for fine-grained image classification tasks, there are very subtle differences between some classes and only a few samples per class. Therefore, they tend to include irrelevant details, such as the background, during training, leading to bias. One solution is to remove useless features from images by masking them using semantic segmentation, and then train each masked dataset using EfficientNet, evaluating the resulting accuracy. In the classification of rainfall intensity, the model utilizing the features of the entire image achieved up to 92.61% accuracy, which is 2.84% higher compared to the model trained specifically on road features. This outcome suggests the significance of considering information from the whole image to determine rainfall intensity. Furthermore, analysis using the Grad-CAM visualization technique revealed that classifiers trained on masked dash cam images particularly focused on car headlights when classifying the rainfall intensity. For cloud type classification, the model focusing solely on the sky region attained an accuracy of 68.61%, which is 3.16% higher than that of the model trained on the entire image. This indicates that concentrating on the features of clouds and the sky enables more accurate classification and that eliminating irrelevant areas reduces misclassifications.

Building A Simple Weather Forecast Application using Python Django

Weather forecasting plays a crucial role in various sectors such as agriculture, aviation, transportation, and disaster management. Accurate and timely weather predictions are essential for making informed decisions and mitigating potential risks. This project presents a comparative study of different Application Programming Interfaces (APIs) for weather data retrieval and their effectiveness in weather forecasting. The objective is to evaluate the performance, reliability, and accuracy of various APIs in providing weather information for accurate forecasting. This project presents the development of a sophisticated weather forecasting application using the Python Django framework. In response to the growing demand for accurate and accessible weather information, this application provides users with timely updates on current weather conditions, temperature variations, and detailed forecasts for the upcoming six hours. The application dynamically adapts its interface to reflect hourly changes in weather patterns, ensuring that users receive up-to-date and reliable information at all times. One of the key features of this application is its user-friendly interface, which allows users to conveniently search for weather data anytime and anywhere. Whether planning a trip, scheduling outdoor activities, or simply staying informed about local weather conditions, users can rely on this application to deliver precise and relevant information tailored to their needs. Furthermore, the application enhances user experience by providing comprehensive coverage of global weather data. Users can easily access weather information for any location worldwide, enabling them to plan travel itineraries, explore new destinations, and make informed decisions about their future holiday plans.

A HARDWARE PLATFORM FOR A MARITIME COLLISION AVOIDANCE SYSTEM

A maritime collision avoidance system built on a database is being developed. Different information important for navigation, like electronic charts, bathymetric data, sea current and forecast weather conditions, etc., as well as knowledge about weather and load-dependent maneuvering capabilities of the involved vessels, are entered into the database tables. When using the system in real-time, the targeted data must be found very quickly in the database. In parallel, for the prediction of ship courses and for the calculation of evasive courses, the system must be capable of solving very many calculations in the shortest possible time, and these calculations often use database tables. Fast communication for updating database tables with the latest data is equally important in such a system. This article briefly presents the hardware developed for the project. We designed such data models for the efficient application of the Postgres database, capable of real-time calculation.

Nature-based Solutions (NbS) for increasing urban greening and reducing runoff flows in narrow streets

When we explore Nature-based Solutions (NbS) and their implementation in small cities with narrow streets and sidewalks as a feature of their historical cores, we can discover several challenges in terms of sustainability and microclimate regulation. The small cities in the Global South differs significantly from that in the Northern Hemisphere, because their financial reality; it is critical for the community, public authorities, and academia to collaborate to find accessible and novel approaches. Fighting floods and enhancing the environment in these tiny cities can begin with the reintroduction of vegetated spaces and their integration with Blue and Green Infrastructures, thereby assisting grey infrastructures in urban landscape planning. This article is an exercise result of the insertion of NbS in Cachoeira do Sul (RS) - Brazil, city located in the Guarani aquifer zone. The use of NbS was intended to increase drainage area, reduce runoff flows, and provide others ecosystem services in an urban street with nearly no vegetation, or spaces for this. The efficacy of the concept was investigated through the LID-TTT program, allowing to suggest its application in others streets and avenues in the same city. We were also able to use it to model other scenarios in the studied section, seeking an increase in permeability, modifying street coverings and walkways. This surge could also occur by building eco-corridors to enhance connectedness among isolated remnant patches in the city (green areas in backyards, squares, and urban edges), resulting in an increase in biodiversity. This paper intends to propose tiny solutions to dealing with recent flooding caused by current extreme weather conditions.

Evaluating the Growth and Development Ability of 5 Late Ripening Varieties of Coffee (Coffea Canephora Pierre) in Lam Dong – Vietnam.

Purpose: To improve the quality of Vietnamese coffee, many synchronous solutions are needed can solve problems such as: Technical, economic, management... In which, changing the varieties of coffee to suit current weather conditions has contributed significantly to improving yields and quality.
 Methodology: Experiments and the comparison among 5 late ripening varieties of coffee was implemented in Lam Dong province (TR10; TR14; TR15; TR16; TR6) .The experiment was carried out RCBD with 5 treatments, and 3 replications and three replication. TR10, TR16, TR14, TR15 are suitable for the conditions of Bao Loc, Lam Dong.
 Findings: The TR10, TR14, TR15 Robusta in the commercial stage have a yield of > 4 tons of core/ha, the yield between trees in the same line is quite uniform, the rate of R1 type seeds is > 99%, and there is no rust disease.
 Unique Contributor to Theory, Policy and Practice: It is recommended that the authorities consider and allow large-scale trial production in some areas with suitable conditions in Lam Dong province

Winds of Change: Enhancing Wind Power Generation Forecasting with LSTM Models and Advanced Techniques in a Single Turbine Wind Farm

This article presents a comprehensive analysis of wind power generation using a Long Short-Term Memory (LSTM) model in the context of a wind farm with a single turbine. The novelty of the research lies in the integration of a technical model, predictive model, heat map analysis, and LSTM architecture to enhance forecasting accuracy. The performance of the forecasted model is evaluated using standard metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). In the estimation phase, wind power generation is predicted based on current weather conditions and historical trends. Leveraging multivariate time series forecasting with LSTM through the Kera library, different look-back values are explored to optimise prediction accuracy. In particular, the study achieves favourable performance metrics, including MSE of 27.781, RMSE of 5.271, MAE of 3.281, and variance of 0.886 with a 70% training and 30% test split. Furthermore, a 60% training and 40% test split yield improved accuracy with MSE of 28.791, RMSE of 5.366, MAE of 3.376, and variance of 0.888. In the prediction phase, the LSTM model is employed to forecast power generation without relying on future weather information. Through various experiments, the optimal look-back period and number of neurones for the LSTM model are determined. Notably, the achieved mean absolute percent errors (MAPE) of 11.433% and 11.158% for 24 and 48 hours of data respectively showcase the impact of varying neuron counts on prediction accuracy. To further enhance predictive capabilities, optimisation techniques are implemented, involving adjustments to the LSTM model, including increased input batch size, batch normalisation, and dense layers. This optimisation leads to a substantial decrease in MAPE from 92.53% to 87.14% in a monthly prediction experiment. Furthermore, forward predictions are made for multiple days in the future, where LSTM successfully captures patterns and fluctuations, resulting in MAPEs of 70.74% for 24 hours, 39.26% for 2 days, and 51.48% for 1 week. Additionally, a comparison of two LSTM-based models, Autoencoder LSTM and FFT-Encoder-Decoder-LSTM, demonstrates the superior performance of the latter in terms of RMSE. This study emphasizes the potential of LSTM models in wind power generation prediction while introducing novel insights into optimizing model performance for accurate forecasting. The integration of technical and predictive models, along with heat map analysis and LSTM architecture, sets the groundwork for advancing wind power prediction methodologies.

Severe storm damage and short‐term weather stresses on corn: A review

AbstractAdverse weather conditions from acute events (e.g., storms causing lodging, flooding, or hail) or short‐duration weather patterns (i.e., periods of cold events; extended waterlogged field conditions) can result in yield losses, though management practices may play key roles in aiding with crop recovery or avoidance of these stress events. This review summarizes current knowledge (with emphasis placed on the US Midwest) related to corn response to short‐term weather stresses of (i) cold temperature, (ii) excess water, (iii) hail/defoliation damage, and (iv) wind damage. Each section presents summaries of how corn growth and yield are affected, provides context into past events experienced, identifies agronomic or production recommendations to correct or alleviate the stress condition, and proposes areas where future research is needed. This review also highlights challenges associated with controlled simulation work on these stressors, and also identifies key areas to expand future research efforts. In general, yield losses associated with strong storms and short‐term weather events often ranged from 5% to 35%, but extreme cases could result in up to 80%–100% yield loss. Much of the literature on these topics was published prior to 1995, though it still forms the basis for modern agronomic guidance, which is problematic given the changes in agriculture in the last 20 years in management practices, available genetics and technologies, and changing environmental conditions. Revisiting these foundational studies and expanding them to examine current and future weather conditions are critical for better informing agronomic recommendations, for devising mitigation strategies, and for determining accurate yield loss expectations following these stresses.

Velocity estimation of thunderstorm movement and dealiasing of single Doppler radar during convective events

The use of meteorological radars in monitoring current weather conditions is crucial regarding the observation of the evolution and dissipation of thunderstorms. Thus, Doppler velocities being measured in each radar scan and velocity vectors derived from Numerical Weather Prediction (NWP) models—that are usually not as highly resolving as radar scans—are combined, as a monitoring utility during the evolution of a convective weather event. The objective of this study is to develop a new method that allows the implementation of a thunderstorm movement velocity estimation technique combining block matching and optical flow techniques. This new method constitutes a nowcasting (NWC) application that enables the use of a single Doppler radar without the need of using NWPs. The method relies on the estimation of the thunderstorm movement vector velocities (Doppler velocity) for each constant altitude plan position indicator (CAPPI) and through correction for aliasing errors to obtain 3D vector velocity fields for convective systems. The performance of the method is evaluated for selected case studies of convective thunderstorms under different synoptic scale conditions over Greece, a geographical area with challenges in forecasting due to its sharp relief and the need for optimization of the use of radar products.

Weather-Based Arthritis Tracking: A Mobile Mechanism for Preventive Strategies

Arthritis is a common joint disorder characterised by symptoms such as swelling, pain, stiffness, and limited joint movement. It primarily affects older individuals, women, and athletes. The advent of information technology has created opportunities for patients to manage their health conditions more effectively. Research indicates that weather can affect arthritis symptoms, with many patients experiencing severe discomfort during rainy weather due to the expansion of already inflamed tissues. However, there is currently no mobile application mechanism available that combines weather forecasting with health recommendations for arthritis patients, which means that patients may not have access to important information that could help them manage their symptoms. Furthermore, few research workflows have focused on weather conditions in online arthritis treatment systems. This research aims to develop a weather-based mobile system for arthritis tracking that provides health advice and alerts based on current and forecast weather conditions, as well as features to help patients track how weather affects their arthritis. This system utilises several tools for its development. The Flutter Framework is used for creating mobile apps, while Firebase is chosen as the cloud-hosted database. Visual Studio Code and Android Studio are utilised as the code editor and Android emulator, respectively. Information about weather forecasts is retrieved via the OpenWeather API. The application mechanism will feature a user-friendly interface to help users stay updated on weather forecasts, and it will collect data in a reliable and user-centric manner for generating robust evidence on health outcomes.

ShopSculpt : Crafting Your Cart with Intelligent Ingenuity

In the present world from small companies to giant company’s product recommendation system plays a very major role, also people are very much interested in online shopping these days, so Recommendations are utilized to make the customer's job easier and faster. The majority of these recommendations are made based on their previous transaction history and association rules generated from it. Apriori Algorithm is the most widely used method for generating association rules based on frequent product sets. But it has a drawback, while generating association rules it uses the whole transactions list and frequent product sets, ignoring the seasonal transactions for generating rules. Hence seasonal transactions are not considered for mining rules, if a person is buying a cake for Christmas, it’s very likely that recommender system recommends birthday balloons, birthday caps as recommendations. But actually, it should recommend Christmas related products for recommendation. To solve this problem, we proposed a model using chi square test with improvised Apriori algorithm. This research paper introduces ShopSculpt, an innovative automated shopping cart system designed to enhance the user shopping experience by leveraging intelligent algorithms. ShopSculpt employs a multifaceted approach, considering user shopping interests, patterns, seasonal trends, and current weather conditions to provide personalized and context-aware product recommendations. The aim is to create a dynamic and responsive shopping environment that adapts to individual preferences and external factors, thereby optimizing the user's shopping journey.

Simulation of a solar cooling system

. In this technical and economical analysis of a building air-conditioning system, the main goal is to analyse what variables have effect on the system. To obtain the wanted temperature in the room we work, it is necessary to overcome the external conditions of temperature and radiation. However, this external conditions depend on the geographical situation, they change constantly and it is obvious that engineers cannot have an influence on current weather conditions during the making decision process. For all these reasons, once the architectural design of a building is decided, the engineer can only take decisions regarding some parameters like area of solar collectors , storage tank volume and input temperature of the absorption chiller. This article focusses on the execution of simulations in the aim of determining the optimun value of these parameters, paying attention to technical and economical goals.

Fieldwork in an Increasingly Variable Climate: The Kites in Context Project 2023 Field Season

ABSTRACT Archaeologists commonly include climatic data in their analyses of living in the past, but rarely do current weather conditions achieve mention in our professional considerations. The Kites in Context Project (KiC) focuses on a multi-scalar investigation of desert kites in the eastern badia region of Jordan. During the Neolithic (7000–5000 b.c.) period, people began building large animal traps, known as desert kites, across a wide swath of southwestern Asia. This multi-year project is designed to provide novel insights into the chronology and function of these animal traps through an intensive study incorporating remote sensing with boots-on-the-ground excavation and survey. The 2023 season began like any other—a combination of aerial survey using drones, walking the landscape in a pedestrian survey, and excavation under sunny skies. During our 2023 field season in Jordan, we experienced dramatic, atypical weather, likely driven by climate change. In this photo essay, we present images from this surprising rain and flooding event in the Black Desert of eastern Jordan that caused us to consider on a more personal level the challenges that may have faced the hunters and herders who constructed the kites and who managed the water thousands of years ago.

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.

Показатель пожарной опасности для лесной среды с использованием метеорологических и GPS-данных на территории Забайкальского национального парка

The main indicators of potential fire hazards are temperature, relative humidity, precipitation, and wind speed readings. Prompt detection of fire risk should be an integral part of forest protection systems. For this purpose, it is essential to use automatic instruments based on comprehensive meteorological observations. In recent years, most forest fires in the Republic of Buryatia have started due to natural rather than anthropogenic causes. To assess the risk of forest fire based on current weather conditions, the Nesterov index is used. Continuous GPS measurements, combined with highprecision geodetic techniques, allow us to regularly obtain data on tropospheric signal delays, which provide information about the total water vapor content in the atmosphere (IWV). The possibility of using the IWV values obtained to assess the fire risk of forest areas is demonstrated, taking into account data from the Ust-Barguzin weather station located on the territory of the Trans-Baikal National Park. Creating an online resource based on Nesterov's complex indicator using GPS measurements and meteorological data would allow for real-time monitoring of fire risk in forests.

XGBoost sequential system for the prediction of Persian lemon crop yield

AbstractToday, Mexico ranks second worldwide in total lemon production; however, the yield per hectare is only ranked eighth, which makes evident a low productive potential in the country. This work provides an impact analysis of certain physical and climatic variables on the yield of Persian lemon (Citrus latifolia) harvest; its objective is to determine which variables are controllable to guarantee a higher crop yield in different uncontrollable climatic conditions. For this, we developed a yield prediction system. The system consists of three sequential Extreme Gradient Boosting (XGBoost) algorithms fed with historical information, which allow for prediction of Persian lemon crop in its three stages: flowering, budding, and fruiting, having as input variables planting density, number of trees in production, pruning and cleaning, and pest control, in addition to uncontrollable weather elements such as rain and wind. The variables with the greatest impact on crop yield are pruning, cleaning, and pest control. The algorithms have a performance of 0.87, 0.93, and 0.97, respectively, for each stage. The analysis provides the farmer with a tool that allows him to make data‐driven decisions based on the manipulation of good agricultural practices considering the current weather conditions, thus promoting a higher harvest yield and thus a higher profit margin.

CO2 Emissions from Soils under Different Tillage Practices and Weather Conditions

CO2 emissions are one of the greenhouse gases that significantly contribute to climate change. The use of reduced soil tillage practices could contribute to the mitigation of CO2 emissions from soils under ongoing climate change conditions. The use of reduced and no-tillage practices in the summer period, the most critical period for CO2 and for water loss from soils, would contribute to the mitigation of CO2 emissions that is required by the European Union. The aim of this research was to contribute to the specification of CO2 emission factors, following different soil tillage practices in the summer period under variations in weather. Gentler tillage practices were defined in terms of reducing CO2 emissions from the soil. This research was carried out as a long-term field experiment. The effects of soil tillage practices on CO2 emissions were studied over a six-year period as a long-term field experiment and concerned the use of different soil tillage practices for over 20 years (established in 1995), with these including conventional tillage (CT; plowing to 20–22 cm), reduced tillage (RT; chiseling to 10 cm), and no-tillage (NT; without tillage). The crop rotation was winter wheat–winter oilseed rape–winter wheat–pea. CO2 emissions were measured at least 7–10 times during the summer–autumn period in the years 2017–2022 after agrotechnical operations following the winter wheat harvest. Soil moisture was determined in all the treatments. Weather conditions were measured by means of the meteorological station of the Crop Research Institute. The CO2 emissions were the highest in the summer period under CT in comparison with RT and NT. Reduced tillage and no-tillage practices, with mulch on the surface of the soil, decreased CO2 emissions by a 6-year average of 45% and 51%, respectively. The mean CO2 emissions were 6.1, 3.1, and 2.9 μmol CO2 m−2 s−1 for CT, RT, and NT. The highest CO2 emissions and the largest differences among different tillage practices were measured in 2019, with high temperatures and repeated rainfall. CO2 emissions under CT reached 22 μmol CO2 m−2 s−1, which was 7.5 and 5.8 times higher than under RT and NT, respectively. Current weather conditions, mainly temperature and precipitation, played an important role in CO2 emissions. The hot and dry weather in 2018 decreased overall CO2 emissions, with CO2 emissions, even under conventional tillage, reaching only 2.5 μmol CO2 m−2 s−1 on average. As a result of climate change, the temperatures also gradually increased in the later stages of the year, with more summer days being expected during autumn and higher CO2 emissions from soils being expected as a result.

Weather, weekday, and vacation effects on webcam recorded daily visitor numbers in the alpine winter season

Winter tourism is an important economic factor in the European Alps, which could be exposed to severely changing meteorological conditions due to climate change in the future. The extent to which meteorology influences winter tourism figures has so far been analyzed mainly based on monthly or seasonal data and in relation to skier numbers. Therefore, we record for the first time daily visitor numbers at five Bavarian winter tourism destinations based on 1518 webcam images using object detection and link them to meteorological and time-related variables. Our results show that parameters such as temperature, cloud cover or sunshine duration, precipitation, snow depth, wind speed, and relative humidity play a role especially at locations that include other forms of winter tourism in addition to skiing. In the ski resorts studied, on the other hand, skiing is mostly independent of current weather conditions, which can be attributed mainly to artificial snowmaking. Moreover, at the webcam sites studied, weekends and vacation periods had an equal or even stronger influence on daily visitor numbers than the current weather conditions. The extent to which weather impacts the (future) visitor numbers of a winter tourism destination must therefore be investigated individually and with the inclusion of non-meteorological variables influencing human behavior.

Weather shocks and movie recreation demand in China

Understanding the impacts of weather shocks on various economic sectors is crucial for designing effective climate policies. While previous studies have focused mainly on the agricultural and industrial sectors, there has been limited exploration of weather effects on the service sector, particularly in emerging economies. This study addresses this research gap by analyzing high-frequency movie-viewing records of 49 major cities in China between 2015 and 2017 to examine the effects of weather shocks on in-theater movie recreation. The findings reveal that both extreme temperatures and pouring rains significantly reduce movie demand. We also investigate the relationship between weather and movie supply at both extensive and intensive margins, and confirm the weather-movie demand results are not driven by supply-side dynamics. The back-of-the-envelope calculation indicates that extreme temperatures led to a loss of 5.14 million moviegoers and a 311.32 million Chinese Yuan loss in box office revenue for the Chinese film market in 2017, while losses due to pouring rains amounted to 1.28 million audiences and 69.16 million Chinese Yuan in revenues. This paper highlights the significant damage caused by current extreme weather conditions to China's film market and emphasizes that such damage is expected to worsen in the future with the intensification of climate change.