Analysis Of Weather Data Research Articles

How a non harmonic-like tremor at a volcanic lake could be caused by sustained wind: A case of study of Taupō volcano

Taupō volcano lies underneath the largest lake in New Zealand. It undergoes unrest roughly every ten years, has minor eruptions every few hundred years, and has supereruptions. Understanding volcanic seismic signals provides insights into the volcano dynamics and helps to anticipate eruptions. Harmonic and non-harmonic volcanic tremor are common signals that indicate fluid mobilisation in volcanic systems. We observed a signal that resembled non-harmonic tremor during 2019 at Taupō volcano. Careful time-frequency analysis of seismic data and weather data from around the lake revealed that the signals were not magmatic in origin, but were most likely from lake microseisms caused by special conditions of the wind, which generates wind-driven waves in the lake. The frequency range of lake microseisms at Taupō starts and ends with a dominant frequency of 0.7–1.0 Hz, and during the maximum energy peak, the dominant frequency is of 0.50–0.56 Hz. Such signals may be common in caldera lakes, which need to be distinguished from tremor caused by volcanic activity, so as not to exaggerate the probability of eruptions.

Determining the Effective Meteorological Parameters on Potato Yield in Niğde Province and Yield Prediction with Machine Learning and Deep Learning Models

In this study, we explore the impact of meteorological parameters on potato yield in Nigde province, a key agricultural region in Turkey for potato production. Understanding the relationship between weather conditions and crop yield is crucial for optimizing agricultural practices and ensuring food security, especially in regions susceptible to climate variability. The study includes all meteorological parameters observed and recorded in Nigde Directorate of Meteorology, covering the period between 1990 and 2023. Through the analysis of historical weather data and potato yield records, we aim to identify the most influential meteorological factors affecting potato production. Then, artificial intelligence techniques such as Random Forest, Gradient Boost, Convolutional Neural Networks and Recurrent Neural Networks are utilized to predict the potato yield in order to provide valuable insights into how different weather patterns influence crop performance. The findings suggest that potato yield is correlated with meteorological parameters to some degree and AI techniques can predict the potato yield but lacks the precision. The reason is that potato production in Nigde is mostly done with irrigation. However, this further increases the risk that could result from the changes in groundwater levels and pollution in irrigation ponds for agricultural practices in Nigde.

Measuring climate vulnerability of tourism-dependent livelihoods: The case of Lamgtang National Park

Climate change poses significant challenges to tourism-dependent communities in mountain regions, threatening their livelihoods and well-being. However, there is a lack of comprehensive assessments that consider the multidimensional nature of vulnerability and the specific socio-cultural contexts of these communities. This study assesses the livelihood vulnerability of tourism-dependent communities in Lamtang National Park exposed to climate change using the Livelihood Vulnerability Index (LVI) and the Livelihood Vulnerability Index -Intergovernmental Panel on Climate Change (LVI-IPCC) framework. A mixed-methods approach was employed, including household surveys (n=119), vulnerability index calculations, and analysis of weather data. The purposive stratified sampling based on ecological gradient and proximity to trekking trails ensured the representativeness of the sample. One hundred nineteen households were surveyed, with respondents from the Hill Janajati ethnic group. Results showed that the majority of households (63%) were tourism-dominant, followed by agriculture-dominant (17%) and mixed livelihood (13%). The LVI results revealed a moderate vulnerability (0.365), with financial and natural capitals being the most vulnerable. The LVI-IPCC analysis showed that the community's adaptive capacity (0.537) is slightly lower than its exposure (0.564), and sensitivity is comparatively low (0.296), resulting in a low LVI-IPCC index (0.01). Weather data analysis, including the Mann-Kendall trend test, Sen's slope analysis, and multi-model ensemble projections, indicated increasing precipitation trends and a warmer, wetter future for the region. The triangulation of LVI, LVI-IPCC, forecast data, and weather station data strengthens the findings and highlights the need for targeted interventions. The projected changes in temperature and precipitation patterns for Rasuwa district and vulnerability status of tourism-dependent communities highlight the urgency of implementing climate change adaptation measures, which may include diversifying livelihoods, improving access to education and training, strengthening social support systems, and promoting sustainable land and water management practices.

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.

A practical guide to climate econometrics: Navigating key decision points in weather and climate data analysis

A practical guide to climate econometrics: Navigating key decision points in weather and climate data analysis

Crop Yield Prediction using Machine Learning

Weather profoundly impacts agricultural outcomes, making accurate crop prediction vital for farmers' decision-making. This abstract presents a comprehensive overview of weather-based crop prediction, emphasizing its significance, key components, and methodologies. The process begins with the collection and analysis of historical weatherdata encompassing variables such as temperature, precipitation, humidity, and sunlight. Utilizing Python programming and data visualization libraries like Pandas and Matplotlib facilitates the exploration and visualization of this data, revealing trends and patterns. Machine learning algorithms, including regression and ensemble methods, are employedto develop predictive models. These models leverage historical weather data to forecast future crop yields accurately. Python's extensive libraries, such as Scikit-learn and TensorFlow, offer robust tools for model development and evaluation. Incorporating advanced technologies like remote sensing and satellite imagery further refines the prediction process. These tools provide real-time insights into crop health and growth, enhancing the precision of forecasts. Ultimately, weather-based crop prediction serves as a valuable decision support tool for farmers, enabling informed choices regarding planting, irrigation, and harvesting practices. By harnessing historical weather data, machine learning algorithms, and innovative technologies, stakeholders can optimize agricultural productivity, mitigate risks, and contribute to global food security

Integrated Hydrological and Hydrochemical Analysis of Arsenic and Iron Behavior in Waters of a Decommissioned Tin Mine in Ehrenfriedersdorf, Germany

Arsenic contamination poses significant challenges to environmental and public health, with mining activities contributing to its wider distribution. This study investigates the fate of arsenic and iron in mine waters at a decommissioned tin mine, now a visitor mine, located in Ehrenfriedersdorf, Germany, situated in the Free State of Saxony. Despite the general shortage of data, which is common for old mining sites, we explored the complex interplay of climatic conditions, hydrological processes, and arsenic and iron behavior in the mine waters through a comprehensive approach encompassing general site characterization, mine water monitoring, and analysis of local weather data. Over a period of three years, we conducted 14 sampling campaigns, collecting a total of 95 water samples, each consisting of three filtration subsamples, resulting in the analysis of 285 water samples. These samples were collected both aboveground and underground. Aboveground samples included mine outflows, a tailing outflow, and an adjacent creek, while underground sampling points were scattered throughout the mine initially and later focused on the identified “main” mine water system. The chemical data from the analyses were correlated with local climatic water balances to reveal distinctive patterns in arsenic and iron concentrations at various locations within the mine system. Our findings shed light on the hydrological behavior of the mine, helping to elucidate the impact of precipitation and potential evapotranspiration on arsenic and iron concentrations in a tailing outflow, in the flooded mine body, and at the portal of the main dewatering gallery. Our findings emphasize the importance of sustained monitoring and the utilization of local weather data to comprehend metalloid and metal contamination risks in similar mining environments.

Growth unveiled: decoding the start of grassland seasons in Austria

ABSTRACT The start of the growing season (SOS) in grasslands is a critical factor that significantly affects grassland dynamics, production and quality. In the context of grassland fodder production, the exact detection of the start of the growing season allows farmers and land managers to precisely plan harvest schedules for hay production or silage making. However, the NDVI time-series based detection of the SOS for grassland poses challenges due to the gradual onset of greenness and the wide variety of environmental conditions. These challenges emphasize the need for novel approaches to improve satellite-based SOS detection accuracy. This study establishes a new methodology for identifying the SOS specifically tailored to grassland regions in Austria. To accomplish this, a synergistic approach combining remote sensing (MODIS NDVI at 250 m) and weather data analysis was adopted. Results indicate that the integrated approach outperforms alternative methods in estimating SOS. The study demonstrated a more robust and accurate estimates of SOS that were generally consistent with the phenological observations of a reference species (Forsythia suspensa) and in agreement with the observed SOS dates based on visual interpretation of 10 m resolution Sentinel-2 images. For future studies, the integration of additional validation techniques is crucial.

Identifying Challenges to 3D Hydrodynamic Modeling for a Small, Stratified Tropical Lake in the Philippines

Three-dimensional hydrodynamic modeling for small, stratified tropical lakes in the Philippines and in Southeast Asia in general is not deeply explored. This study pioneers investigating the hydrodynamics of a small crater lake in the Philippines with a focus on temperature simulation using a Fantom Refined 3D model that has been tested mostly for temperate and sub-tropical lakes. The lake’s monthly temperature during the dry season served as a reference for the model’s initial condition and validation. For the simulation to proceed, input data such as weather, inflow, and bathymetry were prepared. In the absence of hourly meteorological data from local weather stations, this paper adopted the satellite weather data from Solcast. Simple correlation analysis of daily weather data between local stations and Solcast showed valid and acceptable results. Inflow values were estimated using the rational method while the stream temperature was estimated from a regression equation using air temperatures as input. The validated satellite-derived data and runoff model can therefore be employed for 3D modeling. The simulations resulted in extremely higher temperatures compared with those observed when using previous default model settings. Direct modifications were then applied to weather parameters, compromising their integrity but resulting in reasonable profiles. By adding scaling factors to heat flux equations and multiplying their components by 0.75 (shortwave), 1.35 (longwave), 0.935 (air temperature), and 0.80 (wind), better results were achieved. This study identifies several challenges in performing 3D hydrodynamic modeling, such as paucity in input hydro-meteorologic and limnologic data and the need for heat flux model improvement. Overall, this study was successful in employing 3D hydrodynamic modeling in a tropical lake, which can pave directions and serve as an excellent reference for future modeling in the same region.

Analysis of space weather and small satellite data in polar orbit using machine learning

This publication explores the dependencies between factors of space weather (geomagnetic indices Dst, Kp, and Ap, Wolf solar activity index, and solar wind parameters) and the telemetry of the small satellite Cubebel-1. The importance of maintaining the functionality of such satellites in orbit is described based on the research results of specialists in this field. A correlation analysis is presented, conducted using Python language tools. The results are presented in the form of a correlation matrix. A comparison is made between the results of this study and similar results of correlation analysis conducted with data from the satellite Siriussat-1. Suggestions for further research using the telemetry of the Cubebel-1 satellite are provided.

Real-time weather data analysis by the solar fuzzy logic-based MPPT controller

Real-time weather data analysis by the solar fuzzy logic-based MPPT controller

Climate Change and Crop Adaptation: Its Impacts and Opportunities for Crops Production

Climate change is the departure of a region's weather patterns from the norm or from conditions that have been historically documented as usual. The term "climate" refers to long-term weather patterns, whereas the term "climate change" refers to changes in weather patterns through time. Climate, which can refer to a county, state, continent, or the entire world, is simply the pattern of weather that prevails on average in a location. Despite rigorous analysis of weather data conducted, it is difficult to forecast the effects of climate change for a specific agricultural operation because of the numerous human and environmental elements influencing it as well as the increased variability in weather over time and across space. After more than a decade of disagreement, the scientific community has now come to the conclusion that climate change is one of the planet's most pressing environmental issues of this century. The issue has evolved from a scientific to a political and economic matter, with dire economic repercussions, according to conventional thinking. Climate change has numerous impacts on the production of crops. The productivity of crops is subjected to a variety of stress and potential yields are rarely achieved. In order to sustain crop production with present-day challenges, there have to be packages to manage stresses caused by climate change. If properly managed, some sectors such as the horticulture sector could be might benefit from the impact of climate change in terms of energy, productivity, and market advantages. This review paper, therefore, reviews the impact opportunity and challenges of climate change on the productivity of crops.

Використання метеосервісів, дистанційних і польових методів для обґрунтування температурних показників розвитку пшениці озимої

Goal. To justify the method of determining the phases of the development of winter wheat for obtaining reliable information using information technologies and remote sensing of the Earth. Methods. The data of remote monitoring of temperature indicators on the field and field observations and remote monitoring of the field using automatic meteorological equipment were used. Results. Research conducted on plots in vil. Khlybodarske (Odesa oblast) (46°47’96” N, 30°59’09” E) and vil. Huta Chuhorska (Khmelnytskyi oblast) (48°70’38” N, 27°04’08” E) confirmed mutual complementarity and some interchangeability of field monitoring information and information from weather forecasting services, the need to create an inexpensive service for monitoring and analyzing weather data, which could give forecasts and recommendations regarding the phases of development of crops. Conclusions. The proposed comprehensive approach to monitoring and forecasting the temperature indicators of winter wheat development enables agronomists to effectively monitor the development phases of crops. The use of inexpensive field monitoring stations by farmers will improve the quality of the received information about temperature indicators in the field. Determination of the phases of agricultural plant development is important for high-quality irrigation management, fertilizer application planning, and chemical plant protection. In the future, it is proposed to test the forecast of the onset of phenological phases with the help of the proposed integrated approach using the service of monitoring and analysis of weather data.

Review of Methods to Create Meteorological Data Suitable for Moisture Control Design by Hygrothermal Building Envelope Simulation

Hygrothermal simulations have become essential for sustainable and resilient building design because moisture is the major cause of problems in buildings. Appropriate meteorological input data are important to obtain meaningful simulation results. Therefore, this article reviews different methods to create Hygrothermal Reference Years (HRY) as severe or average climate inputs. The current standards define HRYs solely based on outdoor temperature, although moisture problems are caused by a combination of climate parameters, including driving rain and other loads. Therefore, there are also methods considering several impact parameters. The existing methods can be classified into two categories: construction-independent and construction-dependent methods. The former determines HRY based on a weather data analysis and is useful for large-scale parametric studies comprising many climatic parameters acting on buildings. The latter is based in addition to computer simulations to verify the HRY also in the context of specific construction types. It is a more comprehensive approach since the moisture responses of constructions are the decisive outcome for performance predictions. The advantages and disadvantages of the different methods are summarized and compared. Lastly, further research questions and simplifications aimed at practitioners are pointed out to arrive at reliable hygrothermal building performance predictions.

The air temperature change effect on water quality in the Kvarken Archipelago area

The Kvarken Archipelago is Finland's World Heritage site designated by UNESCO. How climate change has affected the Kvaken Archipelago remains unclear. This study was conducted to investigate this issue by analyzing air temperature and water quality in this area. Here we use long-term historical data sets of 61 years from several monitoring stations. Water quality parameters included chlorophyll-a; total phosphorus; total nitrogen; coliform bacteria thermos tolerant; temperature; nitrate as nitrogen; nitrite-nitrate as nitrogen, and Secchi depth and correlations analysis was conducted to identify the most relevant parameters. Based on the correlation analysis of weather data and water quality parameters, air temperature showed a significant correlation with water temperature (Pearson's correlations = 0.89691, P < 0.0001). The air temperature increased in April (R2 (goodness-of-fit) = 0.2109 &P = 0.0009) and July (R2 = 0.1207 &P = 0.0155) which has indirectly increased the chlorophyll-a level (e.g. in June increasing slope = 0.39101, R2 = 0.4685, P < 0.0001) an indicator of phytoplankton growth and abundance in the water systems. The study concludes that there might be indirect effects of the likely increase in air temperature on water quality in the Kvarken Archipelago, in particular causing water temperature and chlorophyll-a concentration to increase at least in some months.

Estimation of genotype by temperature-humidity index interactions on milk production and udder health traits in Montbeliarde cows

BackgroundHeat stress negatively influences cattle welfare, health and productivity. To cope with the forecasted increases in temperature and heat waves frequency, identifying high-producing animals that are tolerant to heat is of capital importance to maintain milk production. This study, based on the joint analysis of on-farm performance and weather data, had two objectives: (1) to determine the response in production performances (milk, fat and protein yields, fat and protein contents) and udder health (somatic cell score) to temperature-humidity index (THI) variations in Montbeliarde cows, and (2) to estimate the interactions between genotype and THI, to enable the identification of the most adapted animals for facing the expected increases in temperature.ResultsTest-day records from first and second lactations from 2016 to 2020 were associated with the average THI during the three days before the test-day record. In total, 446,717 test-day records from 55,650 cows in first lactation and 457,516 test-day records from 58,229 cows in second lactation were analysed. The optimal THI was below 55 (i.e. ~ 12–13 °C) for all traits. Individual responses to THI were estimated by random regression models, which also included individual responses to days in milk. Regardless of the stage of lactation, genetic correlations along the THI gradient were above 0.80, which suggests that genotype-by-THI interactions were weak for production and udder health traits. Nevertheless, a variability in the individual slope of decay could be highlighted at high THI. The genetic correlation between production level at moderate THI and the slope at high THI was negative, while for somatic cell score, it was positive, indicating that heat stress amplifies the susceptibility to mastitis.ConclusionsThe optimal THI for French Montbeliarde cows is below 55 for production and udder health traits. Genetic-by-THI interactions are weak in French Montbeliarde cows for production and udder health traits, but not all animals react in the same way to high temperatures. Even if there is little room for improvement, using a heat tolerance index in cattle selection would be relevant to anticipate the expected increases in temperature. Further investigations are needed to interpret this variability on production traits. However, the current selection for mastitis resistance seems appropriate to adapt cattle to rising temperatures.

The Effect of Climate Change on Food Crop Production in Lagos State.

Climate change is set to be particularly disruptive in poor agricultural communities. This study examines the effects of, and farmer's perceptions of, climate change on farming practices for cassava and maize in Lagos, Nigeria. Analysis of weather data from 1998 to 2018 (the most recent available) reveals little impact on cassava yield but a significant impact on maize yield. Furthermore, survey results indicate that farmers in this area are currently implementing techniques to adapt to changes in climate based on the type of crop grown. Agriculture in Lagos, Nigeria, is largely rain-fed and climate change negatively impacts crop productivity by decreasing crop yield and soil fertility, limiting the availability of soil water, increasing soil erosion, and contributing to the spread of pests. A decline in crop production due to climate change may be further exasperated by a lack of access to farming technology that reduces over-reliance on the rain-fed farming system and subsistence agriculture. This study indicates that there is a need for initiatives to motivate young and older farmers through access to credits, irrigation facilities, and innovative climate change adaptive strategies.

Deep Learning and Neural Network-Based Wind Speed Prediction Model

This paper aims to develop a wind speed prediction model by utilizing deep learning and neural networks. The analysis of weather data using a neural network architecture has been completed. The Long Short-Term Memory (LSTM) architecture is a type of artificial Recurrent Neural Network (RNN) used in deep learning is the first method plots the predicting Wind Speed based on the dataset and predicts the future spread. A dataset from a real-time weather station is used in the implementation model. The dataset consists of information from the weather station implements of the recurrent neural network model that plots the past spread and predicts the future stretch of the weather. The performance of the recurrent neural network model is presented and compared with Adaline neural network, Autoregressive Neural Network (NAR), and Group Method of Data Handling (GMDH). The NAR used three hidden layers. The performance of the model is analyzed by presenting the Wind Speeds of Erbil city. The dataset consists of the Wind Speed of (1992-2020) years, and each year consist of twelve months (from January to December).

Development of NCL equivalent serial and parallel python routines for meteorological data analysis

The NCAR Command Language (NCL) is a popular scripting language used in the geoscience community for weather data analysis and visualization. Hundreds of years of data are analyzed daily using NCL to make accurate weather predictions. However, due to its sequential nature of execution, it cannot properly utilize the parallel processing power provided by High-Performance Computing systems (HPCs). Until now very few techniques have been developed to make use of the multi-core functionality of modern HPC systems on these functions. In the recent trend, open-source languages are becoming highly popular because they support major functionalities required for data analysis and parallel computing. Hence, developers of NCL have decided to adopt Python as the future scripting language for analysis and visualization and to enable the geosciences community to play an active role in its development and support. This study focuses on developing some of the widely used NCL routines in Python. To deal with the analysis of large datasets, parallel versions of these routines are developed to work within a single node and make use of multi-core CPUs to achieve parallelism. Results show high accuracy between NCL and Python outputs and the parallel versions provided good scaling compared to their sequential counterparts.

SYSTEM OF FORECASTING THE SMART HOME SOLAR BATTERY POWER

Controlling household appliances in automatic mode according to the specified logic and the needs of the residents of a smart home allows efficient use of electricity and saves time and money. The paper proposes a system of forecasting the smart home solar battery power based on the analysis of weather data. The cloudiness degree values obtained using the OpenWeatherMap online service for a given area and time are used. Forecasting is carried out by methods of correlation analysis. The Pearson coefficient and parameters of the regression line were determined using the experimentally measured power of the Buheshui JZ-110X69 photovoltaic module under different weather conditions. The close to one value of Pearson coefficient modulus indicates a close relationship and high correlation between cloudiness and solar panel power. Correction coefficients were introduced to eliminate the influence of the angle between sun rays and the normal to the panel surface on the photovoltaic module power. The correction coefficients were determined on a cloudless day on March 20, 2022, in Lviv for each hour as the ratio of the solar module power at the measurement time to the measured at 1 pm power. It was established that adjusting the photovoltaic module power value according to the time of day increases the degree of linear correlation and the angle of inclination of the regression line. The weather forecast and obtained linear regression equation were used to estimate the future power of the solar battery. The use of correction coefficients according to the time of day increases the accuracy of the smart home solar battery power forecast. The obtained results can be used to form recommended scenarios for the rational use of energy resources. Choosing the optimal scenarios for using household devices in accordance with the forecasted solar battery power makes it possible to balance the comfort necessary level and frugal energy management of a smart home. Key words : smart home, energy management, energy efficiency, correlation, regression line, forecasting.