Weather Elements Research Articles

Geochemistry and U–Pb geochronology of detrital zircon grains in beach sediments from the northwestern gulf of Mexico, Tamaulipas, Mexico: Implication for provenance

The mineralogy, bulk geochemical composition, chemistry and U–Pb ages of detrital zircons in sediments from the La Pesca (LP) and Tesoro Altamira (TA) beaches, NW Gulf of Mexico are analyzed. The aim of this study is to infer the weathering history and provenance of sediments, and to identify the potential source terranes that are contributing sediments to the LP and TA beach areas. The beach sediments are rich in quartz and aluminosilicates. The Chemical Index of Weathering (CIW') and trace elements such as Th, Sr, U, and Ba reveal high weathering intensity for the LP and TA beach sediments. Major-element based diagrams and trace elemental ratios based on Co, Cr, Sc, La, and Th contents in sediments indicate felsic provenance, which is further supported by a negative europium anomaly and rare earth element (REE) patterns. The Th/U ratios (>0.3) together with positive cerium and negative europium anomalies of zircons in the LP and TA beaches indicate igneous origin. The comparison of zircon U–Pb ages of this study with ages reported from the adjacent terranes revealed that the Proterozoic (Paleoproterozoic: 1607.07–1943.45 Ma and Mesoproterozoic: 1021.19–1586.52 Ma) zircons are derived from the Oaxaquia, Mesa Central, Sierra Madre Oriental Provinces of Mexico and Mazatzal–Yavapai Province of the USA. On the other hand, Mesozoic (Jurassic 146.49–199.43 Ma and Cretaceous 68.46–136.82 Ma) and Cenozoic (Eocene 33.97–51.46 Ma and Oligocene 23.39–33.86 Ma) zircons are contributed by the Mexican volcanic rocks, Mesa Central and Sierra Madre Oriental Provinces in Mexico, and Mogollon-Datil Volcanic field and Colorado Plateau in USA. The rivers and their tributaries draining from the source areas are considered as a carrier and agent of distributing sediments along the northwestern Gulf of Mexico coastal areas, which are subsequently mixed by littoral currents.

IMPLEMENTATION OF VECTOR AUTOREGRESSIVE (VAR) AND VECTOR ERROR CORRECTION MODEL (VECM) METHOD IN PNEUMONIA PATIENTS WITH WEATHER ELEMENTS IN PANGKALPINANG CITY

The news about Covid-19 is no longer as scary as in previous years. As COVID-19 cases decrease, health protocols are becoming more relaxed, making it easier for the virus to spread. Besides COVID-19, ARI is one of Indonesia's leading causes of death for children under five. Around 20-40% of hospital admissions are children due to ARI, with around 1.6 million deaths due to pneumonia alone in children under five per year. Currently, ARI dominates the diseases most suffered by the people of Bangka Belitung. Based on this, using the VAR and VECM method to analyze pneumonia sufferers in toddlers regarding weather elements in the Pangkalpinang City. The VAR model has a simpler structure with a minimal number of variables where all the variables are endogenous, with the independent variable being the lag. Meanwhile, VECM can be used to model cointegrated and non-stationary time series data. The data used in this research is the number of monthly cases of toddlers suffering from pneumonia and data on climate conditions, namely rainfall, air temperature, air humidity and duration of sunlight during 2019-2021 in Pangkalpinang City. The results of the Granger Causality test show that the pneumonia variables regarding rainfall, temperature, duration of sunlight and humidity only have a one-way causality pattern. The VAR estimation results show that weather elements (rainfall, temperature and duration of sunlight) do not significantly affect pneumonia in the short term. Meanwhile, the VECM estimation results show that in the long-term pattern, humidity variables affect pneumonia. For this reason, it is recommended that the relevant agencies carry out outreach to the public, especially to pneumonia sufferers, to avoid damp weather. Because the lower the humidity value, the greater the potential for pneumonia in Pangkalpinang City, Bangka Belitung Islands Province.

Weather Station Based Rasberry Pi

Research on weather elements is very necessary for the benefit and welfare of mankind, the observed weather elements will become material for predicting the weather in the future. This data is also used to prevent the risk of adverse impacts caused by the weather itself. To measure the weather with this automatic system, a tool called AWS is needed. The AWS design requires a Rasberry Pi. By using this Rasberry Pi we can monitor weather changes originating from BMKG via the web in the form of an Analog Gauge.

ARTICLE A Review and Case Analysis of the Impact of Weather Factors on Public Transportation in Singapore

As one of the most popular modes of public transportation around the world, taxi services often see their effectiveness impeded by weather elements such as rainfall, which makes it difficult for people to get a taxi. Existing literature has examined the relationship between taxi demand and rainfall, revealing a lack of understanding of the connection between rainfall and taxi supply. Moreover, Singapore remains underrepresented in studies on this relationship. This study contributes to the literature by providing nuance to the commonly held belief that rain value is negatively correlated with lower taxi availability. Further research may consider exploring the alternative causes of decreased taxi availability and incorporating private ride-hailing services in their investigations.

Storm Gust Prediction with the Integration of Machine Learning Algorithms and WRF Model Variables for the Northeast United States

Abstract Wind gusts are often associated with severe hazards and can cause structural and environmental damages, making gust prediction a crucial element of weather forecasting services. In this study, we explored the utilization of machine learning (ML) algorithms integrated with numerical weather prediction outputs from the Weather Research and Forecasting (WRF) Model, to align the estimation of wind gust potential with observed gusts. We have used two ML algorithms, namely, random forest (RF) and extreme gradient boosting (XGB), along with two statistical techniques: generalized linear model with identity link function (GLM-Identity) and generalized linear model with log link function (GLM-Log), to predict storm wind gusts for the northeast (NE) United States. We used 61 simulated extratropical and tropical storms that occurred between 2005 and 2020 to develop and validate the ML and statistical models. To assess the ML model performance, we compared our results with postprocessed gust potential from WRF. Our findings showed that ML models, especially XGB, performed significantly better than statistical models and Unified Post Processor for the WRF (WRF-UPP) Model and were able to better align predicted with observed gusts across all storms. The ML models faced challenges capturing the upper tail of the gust distribution, and the learning curves suggested that XGB was more effective than RF in generating better predictions with fewer storms.

Seasonal Electrical Load Forecasting Using Machine Learning Techniques and Meteorological Variables

Accurate forecasting of seasonal power consumption is crucial for effective grid management, especially with increasing energy demand and renewable energy integration. Weather patterns significantly influence energy usage, making load prediction a challenging task. This study employs machine learning algorithms, including Random Forest (RF), Artificial Neural Networks (ANN), and Decision Tree (DT) models, to forecast electricity consumption using meteorological variables such as solar irradiance, humidity, and ambient temperature. The impact of weather elements on load prediction accuracy across different seasons is explored using seasonal forecasting techniques. The results demonstrate the superior performance of ANN and RF models in forecasting summer and winter loads compared to the rainy season. This discrepancy is attributed to the abundance of data for the summer and winter seasons, and the ability of the models to capture complex patterns within the data for these particular seasons. The study highlights the potential of machine learning techniques, particularly ANN and RF, in conjunction with meteorological data analysis, for enhancing the accuracy of seasonal electrical load forecasting. This can contribute to more effective power grid management and support the transition towards a more sustainable energy landscape. The findings underscore the importance of data quality, quantity, and appropriate model selection for different seasonal conditions.

Analyzing the Improvement Effect of the k-Distribution Method on the Radiation Parameterization for WRF Model

To address the need for the accurate parameterization of radiative absorption by gasses (for predicting atmospheric warming), Chou et al. developed a new k-distribution method. In this study, we compared the improved k-distribution method (hereinafter referred to as the NEW method) with the New Goddard radiation schemes (hereinafter referred to as the OLD method) for the WRF (the weather research and forecasting) model. The results of radiative flux calculations by the NEW and OLD methods of k-distribution using the New Goddard Radiation Scheme were compared with the results of the line-by-line (LBL) method, and the results showed that the radiative flux calculated by the NEW was accurate to within 1.00 Wm−2 with respect to the LBL, while the OLD showed large differences at altitudes above the upper troposphere and near the surface. Therefore, in this study, we selected clear-sky and cloudy-day conditions and compared the weather elements prediction results of WRF using the NEW and OLD methods. For the clear-sky days, the downward shortwave radiation at the surface and the temperature at 2 m above the surface (hereinafter referred to as T2) over land and ocean were reversed in sign due to the highly sensitive absorption coefficients of gasses. For cloudy days, the absorption effect by gasses harmonized with the scattering effect induced by cloud droplets; the differences in the shortwave and longwave radiations and radiative heating rate between the NEW and OLD methods were obvious. Thus, it was analyzed that the proposed NEW method could lead to significant improvements in forecasting weather elements.

Are turbulence effects on droplet collision–coalescence a key to understanding observed rain formation in clouds?

Rain formation is a critical factor governing the lifecycle and radiative forcing of clouds and therefore it is a key element of weather and climate. Cloud microphysics-turbulence interactions occur across a wide range of scales and are challenging to represent in atmospheric models with limited resolution. Based on past experiments and idealized numerical simulations, it has been postulated that cloud turbulence accelerates rain formation by enhancing drop collision-coalescence. We provide substantial evidence for significant impacts of turbulence on the evolution of cloud droplet size distributions and rain formation by comparing high-resolution observations of cumulus congestus clouds with state-of-the-art large-eddy simulations coupled with a Lagrangian particle-based microphysics scheme. Turbulent coalescence must be included in the model to accurately represent the observed drop size distributions, especially for drizzle drop sizes at lower heights in the cloud. Turbulence causes earlier rain formation and greater rain accumulation compared to simulations with gravitational coalescence only. The observed rain size distribution tail just above cloud base follows a power law scaling that deviates from theoretical scalings considering either a purely gravitation collision kernel or a turbulent kernel neglecting droplet inertial effects, providing additional evidence for turbulent coalescence in clouds. In contrast, large aerosols acting as cloud condensation nuclei ("giant CCN") do not significantly impact rain formation owing to their long timescale to reach equilibrium wet size relative to the lifetime of rising cumulus thermals. Overall, turbulent drop coalescence exerts a dominant influence on rain initiation in warm cumulus clouds, with limited impacts of giant CCN.

A marine accident analysis based on data-driven Bayesian network considering weather conditions and its application to Taiwanese waters

Few marine accident studies contemplate the weather effects on the places where and dates when shipping casualties occur. This study conducts a marine accident analysis based on Bayesian Network (BN) considering the weather characteristics of the accident spots and occurrence dates. The weather element is adopted as a node in the BN that comprises a group of five clusters illustrating the states of current speed, wave height and wind speed. A total of 431 accidents occurring in the western coast of Taiwan during the period from 2014 to 2022 are analyzed. Wave with height greater than slight sea has a certain effect on the accident frequency and severity. Mishaps are likely to suffer severe consequences if encountering the weather conditions featured by strong current, moderate/fresh breeze and slight/moderate sea. In addition, Accident Type is the most influential node of Accident Severity among the parameters, followed by Ship Type, Gross Tonnage (GT), Weather Cluster, Location and Ship Age. Finally, the three most crucial parameters leading to Very Serious Marine Casualty are Accident Type, Ship Type and GT.

Stories with the terrain, stories with the ice: Storytelling as a socio-material practice

This article argues for the importance of understanding storytelling as a socio-material practice growing from engagements with the environment and its constituents. Based on an ethnography of guided tours on Svalbard, the article reveals how storytelling arises from encounters and engagements with particular constituents of the environment, such as terrain or ice, as well as from bodily experiences such as walking, feeling, touching, looking or (not) hearing. The practice of storytelling then shifts looking at and receiving information about something to seeing and knowing it through embodied experience – a shift that is, however, limited by incommensurable temporalities of diverse constituents of the environment. Storytelling as a practice thus contains not only verbal information about miners or (glacier) ice but also arises from and shapes the ways of being with (constituents of) the environment, knowing them and seeing them. Consequentially, relations between the tour group and the terrain, ice or weather elements that are emerging, are all a configurative part of the socio-material practice of storytelling.

Impact of weather patterns and meteorological factors on PM2.5 and O3 responses to the COVID-19 lockdown in China

Abstract. Haze events in the North China Plain (NCP) and a decline in ozone levels in Southern Coast China (SC) from 21 January to 9 February 2020 during the COVID-19 lockdown have attracted public curiosity and scholarly attention. Most previous studies focused on the impact of atmospheric chemistry processes associated with anomalous weather elements in these cases, but fewer studies quantified the impact of various weather elements within the context of a specific weather pattern. To identify the weather patterns responsible for inducing this unexpected situation and to further quantify the importance of different meteorological factors during the haze event, two approaches are employed. These approaches implemented the comparisons of observations in 2020 with climatology averaged over the years 2015–2019 with a novel structural SOM (self-organising map) model and with the prediction of the “business as usual” (hereafter referred to as BAU) emission strength by the GBM (gradient-boosting machine) model, respectively. The results reveal that the unexpected PM2.5 pollution and O3 decline from the climatology in NCP and SC could be effectively explained by the presence of a double-centre high-pressure system across China. Moreover, the GBM results provided a quantitative assessment of the importance of each meteorological factor in driving the predictions of PM2.5 and O3 under the specific weather system. These results indicate that temperature played the most crucial role in the haze event in NCP, as well as in the O3 change in SC. This valuable information will ultimately contribute to our ability to predict air pollution under future emission scenarios and changing weather patterns that may be influenced by climate change.

The Synoptic Relationship Between Days of Thermal Extremes and Pressure Systems During the Year 2020 in Iraq (Baghdad as An Example)

Temperature is one of the most important elements of weather and climate that directly and indirectly affects other weather and climatic phenomena. The study aims to daily detect thermal extreme days during the year 2020 over the months of the year for the maximum temperature (hot extreme days) at the Baghdad Climatic Station as well. The pressure systems varied in their strength or shallowness in each case. The study concluded that the number of days (cases) of thermal extremes during one month, with one day being unique in recording the highest or lowest extreme values, according to the prevailing lows or highs in the atmosphere, as the maximum extremes in the average maximum temperature (extreme hot days) during the study year were evident on the twenty-eighth day of July at a rate of (51.8) °C, followed by the high temperature extremes on the thirtieth day of the month. August, around (47.3)°C, and the cause of the extremes on the two days mentioned was the strongest pressure system controlling Iraq’s climate during the summer months, represented by the Indian seasonal low, and its deep appearance at the level of 850 millibars, accompanied by tropical air in the middle of the troposphere, that is, at the level of 500 millibars, in the form of a dent or barrier. My pressure, which increases the severity of the daily thermal extremes for that month, citing in our study the daily weather maps on each thermally extreme day.

Evaluation of the bioclimate of the Piatra-Neamț tourist resort

Abstract: Background: The study responds to real needs of knowing the bioclimate of some tourist resorts in order to develop different types of tourism, in which the bioclimate plays a significant role. In Romania, a similar study was carried out for the resort towns of Vatra Dornei and Tg. Ocna. Methods: The research is based on time series of weather elements from 1961-2021. These series were used for the calculation of the bioclimatic index of the physiological equivalent temperature (PET) which allows the analysis of the temporal peculiarities of the bioclimate. In order to highlight in a new perspective the relationship between biocli-mate and tourism in the Piatra Neamț resort, we created climate-tourism schemes (CTIS). Results: PET values ranged between a maximum of 35.7°C (severe thermal stress due to heating) and a minimum of - 29.3°C (extreme thermal stress due to cooling). The mean PET value was 8.8°C (indicating moderate cold stress). The period of the year with biocli-matic comfort, for hiking, visiting, outdoor sports, climatotherapy, balneotherapy cor-re-sponds to the interval April (3rd decade) - October (1st decade). Conclusions: Tourists who want to plan their vacation in Piatra Neamț will benefit from non-restrictive bioclimatic conditions from the end of April to the beginning of October. During the cold season, with a layer of snow, winter sports can be practiced.

PENGARUH LA NINA DAN EL NINO TERHADAP PENYAKIT DEMAM BERDARAH DENGUE DAN MALARIA DI INDONESIA

The phenomena of El nino and La nina are closely related to rainfall and its impact on various fields. Rainfall is an element of weather and climate that greatly influences all aspects of life including health, especially mosquito vector-borne diseases in Indonesia such as Dengue hemorrhagic fever (DHF), malaria, chikungunya, zika, and others. In this regard, an analytical study was carried out on the effect of El nino and La nina on DHF and malaria cases in Indonesia. This paper is the result of data analysis from the study "The Influence of El nino and La nina on Seasonal Variability in South Kalimantan" and the data of DHF and malaria cases obtained from the Data and Information Center of the Ministry of Health of Indonesia. Descriptive analysis was carried out on the trend of DHF and malaria cases fluctuation associated with El nino and La nina phenomena that occurred throughout the time period 2007-2017. El nino and La nina phenomena affect fluctuations in DHF and malaria cases related to increased and decreased of rainfall which affect the growth and development and also the activity patterns of mosquito as vectors of transmitting DHF and malaria. The increase of DHF cases in Indonesia was influenced by "strong" El nino and "moderate" La nina while the increase of malaria cases in Indonesia was more influenced by "strong" and "moderate" La nina events.

Influence of weather conditions and elements of harvest structure on yield of spring triticale cultivated in organic agriculture on southern chernozem of northern Kazakhstan

The article presents the results of research on the influence of weather conditions and organic fertilizers on the elements of the crop structure and the productivity of spring triticale. The studies were carried out in the LLP " SPCGF named after A. Barayev" on southern carbonate chernozems in 2018-2021. Triticale was cultivated according to the falow. Dry above-ground mass of sweet clover, sainfoin, alfalfa, brome and wheatgrass was introduced into the fallow field as organic fertilizers. An annual decrease in the grain yield of spring triticale was noted over the years of research from 1.95 t/ha in 2018 to 1.01 t/ha in 2021. The weight of 1000 grains did not change significantly over the years of the study and ranged from 37.0-45.7 g. The number of triticale grains per unit area gradually decreased over the years of the study from a maximum in 2018 of 6951 pieces/m2 to a minimum in 2021 of 2739 pcs/m2. Variants of application of aboveground biomass of various perennial grasses as organic fertilizers had an equivalent effect on the yield of triticale and crop structure elements in comparison with the control (aboveground biomass of sweet clover). A positive correlation was obtained between the yield of triticale and HTC (according to Selyaninov) for the months of June and August, where the dependence on the variants of the experiment changed from medium to high (with June r=0.62 ... 0.94, with August r=0.64 ... 0.80). A moderate correlation was obtained between the grain yield and the HTC (according to Selyaninov) for the period June-August (r=0.59…0.78). The influence of the mass of 1000 grains on the yield was ambiguous, in some cases the relationship was weak (r=-0.21 ... -0.27) in others - medium (r=0.33 ... 0.36). A positive correlation was established between the grain yield and the number of grains per 1 m2 (r=0.61…0.94).

Modeling influence of weather variables on energy consumption in an agricultural research institute in Ibadan, Nigeria

<abstract> <p>Climate change is having a significant impact on weather variables like temperature, humidity, precipitation, solar radiation, daylight duration, wind speed, etc. These weather variables are key indicators that affect electricity demand and consumption. Hence, understanding the significance of weather elements on energy needs and consumption is important to be able to adapt, strategize, and predict the effect of the changing climate on the required energy of an organization. This study aims to investigate the relationship between changing weather elements and electricity consumption, employing Multivariate Linear Regression (MLR), Support Vector Regressions (SVR), and Artificial Neural Network (ANN) models to predict the effect of weather changes on energy consumption. The following approaches were engaged for this study: Creating a catalog of weather elements and parameters of energy need or its consumption; analyzing and correlating electrical power consumption to weather factors; and developing prediction models—MLR, SVR, and ANN to predict the significance of the change in the variables of weather on the electrical energy consumption. Among the weather variables considered, temperature emerged as the most influential factor affecting electricity consumption, displaying the highest correlation. The monthly total pattern for electricity use for the case study area followed a similar pattern as the mean apparent temperature. Of the three models (MLR, SVR, and ANN) developed in this study, the ANN model yielded the best predictive performance, with Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of 2.733%, 1.292%, and 4.66%, respectively. Notably, the ANN model outperformed the other models (MLR and SVR) by more than 20% across the predictive performance metrics employed.</p> </abstract>

Relationship between Winter Air Quality and Weather Elements in Harbin

Relationship between Winter Air Quality and Weather Elements in Harbin

Weather Forecasting Using Artificial Neural Network (ANN): A Review

Extreme weather occurrences provide issues that necessitate the development of technology capable of accurate analysis and exact prediction in order to successfully reduce their effects. Artificial neural networks (ANNs) have appeared in the last few years as a promising weather forecasting technology because of their capacity to manage intricate and nonlinear weather factors. The use of ANNs in weather element prediction has shown considerable improvements in forecasting precision and accuracy. Factors influencing an ANN model's efficacy include input Data Type and Volume for Training, Hidden Layer Neuron Count, network architecture, Activation Functions, and training algorithms. In this paper, we will present a thorough review of the applications of Artificial Neural Networks (ANNs) in weather forecasting, specifically focusing on temperature and rainfall prediction over the past 15 years. To enhance reader comprehension, The work of numerous researchers in this topic is methodically analyzed and compared in in tabular format. The authors aim to facilitate future research decisions by offering an organized review, aiding in the determination of suitable input features, transfer functions, and training algorithms for accurate temperature and rainfall predictions.

ANALISIS DIAGNOSTIK VARIABEL CUACA UNTUK ESTIMASI POLA CURAH HUJAN DI MEDAN MENGGUNAKAN MODEL BAYESIAN VECTOR AUTOREGRESSIVE

The territory of Indonesia is located in a strategic position in the tropics. Indonesia is in a position where the equator passes, making it vulnerable to changes in weather and climate. Big cities are increasingly facing many global challenges, so that the effects of climate change are causing urban areas to become more vulnerable to disasters. The city of Medan is one of the big cities that has been recognized as having different characteristics from the surrounding climate, which still has quite a lot of natural elements. Various community activities in Medan City can change the composition of the atmosphere which causes changes in the characteristics of the microclimate which will affect the weather and climate. Weather and climate have a dynamic relationship with other weather elements such as air humidity, air temperature and rainfall. Weather and climate patterns often do not match the pattern they should and are difficult to predict. The main elements of weather are temperature and rainfall, knowing the temperature and rainfall of an area can be used as material to describe the weather in that area. In expressing rainfall in an area, the relationship between air humidity, air temperature and wind direction and speed is very influential. Knowing the pattern of rainfall is very important to do in several activities. So that a diagnostic analysis of weather variables is needed to estimate rainfall patterns in the city of Medan using the bayesian vector autoregressive (BVAR) model. The estimation results using the Bayesian Vector Autoregressive (BVAR) model for Medan City found that the highest rainfall occurred in September at 571.87 mm and the lowest occurred in January at 54.59 mm with a method accuracy rate of 4.75% which indicates that the use of the BVAR method in estimation is very accurate

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.