Weather Characteristics Research Articles

Interactive effects analysis of road, traffic, and weather characteristics on shared e-bike speeding risk: A data-driven approach

As electric bikes (e-bikes) rapidly develop in China, their traffic safety issues are becoming increasingly prominent. Accurately detecting risky riding behaviors and conducting mechanism analysis on the multiple risk factors are crucial in formulating and implementing precise management policies. The emergence of shared e-bikes and the advancements in interpretable machine learning present new opportunities for accurately analyzing the determinants of risky riding behaviors. The primary objective of this study is to examine and analyze the risk factors related to speeding behavior to aid urban management agencies in crafting necessary management policies. This study utilizes a large-scale dataset of shared e-bike trajectory data to establish a framework for detecting speeding behavior. Subsequently, the extreme gradient boosting (XGBoost) model is employed to identify the level of speeding risk by leveraging its excellent identification ability. Moreover, based on measuring the degree of interaction among road, traffic, and weather characteristics, the investigation of the complex interactive effects of these risk factors on high-risk speeding is conducted using bivariate partial dependence plots (PDP) by its superior parsing ability. Feature importance analysis results indicate that the top five ranked variables that significantly affect the identified results of speed risk levels are land use density, rainfall, road level, curbside parking density, and bike lane width. The interaction analysis results indicate that higher levels of road and bike lane width correspond to an increased possibility of high-risk speeding among riders. Land use density, curbside parking density, and rainfall display a nonlinear effect on high-risk speeding. Introducing road level, bike lane width, and time interval could change the patterns of nonlinear effects in land use density, curbside parking density, and rainfall. Finally, several policy recommendations are proposed to improve e-bike traffic safety by utilizing the extracted feature values associated with a higher probability of high-risk speeding.

Brooding duration does not depend on cat predation risk but is related to weather and phenology in the wandering albatross (Diomedea exulans).

Parental investment increases offspring fitness at the expense of the parent's ability to invest in other offspring. In many animal species, parents guard their offspring after birth. The parental decision over the duration of this period is expected to be triggered by the associated fitness costs and benefits for both offspring and parents. Here, we evaluated the relevance of several intrinsic and environmental variables in determining brooding period duration in the wandering albatross (Diomedea exulans) and questioned whether brooding duration was related to chick subsequent survival and biometry prior to fledging. We used a semi-experimental design to increase the variance in cat abundance, a recent predator of albatross chicks, and predicted that an increased predation risk at the nest scale would trigger longer chick brooding and thus, protection. In addition, we questioned the influence of weather conditions, hatching date, and characteristics of chicks (sex and biometry) and parents (sex and age) on brooding duration. We report no effect of predation risk or parental characteristics on brooding duration. However, the probability for a parent to end brooding decreased with forthcoming unfavorable weather. Our data also revealed reduced brooding duration for late-hatched chicks and a positive association between brooding duration and chick structural size, and between the frequency of shifts between parents and chick structural size. Finally, brooding duration was not associated with chick survival or with chick biometry prior to fledging. We discuss these results in light of pre-existing hypotheses on fitness costs and benefits associated with brooding duration for chicks and parents.

Linking Future Precipitation Changes to Weather Features in CESM2‐LE

AbstractWeather features, such as extratropical cyclones, atmospheric rivers (ARs), and fronts, contribute to substantial amounts of precipitation globally and are associated with different precipitation characteristics. However, future changes in these characteristics, as well as their representation in climate models, remain uncertain. We attribute 6‐hourly accumulated precipitation to cyclones, moisture transport axes (AR‐like features), fronts, and cold air outbreaks, and the combinations thereof in 10 ensemble members of the CESM2‐LE between 1960 and 2100 under the SSP3‐7.0 scenario. We find that, despite some biases in both precipitation and weather features, CESM2‐LE adeptly represents the precipitation characteristics associated with the different combinations of weather features. The combinations of weather features that contribute most to precipitation in the present climate also contribute the most to future changes, both due to changes in intensity as well as frequency. While the increase in precipitation intensity dominates the overall response for total precipitation in the storm track regions, the precipitation intensity for the individual weather features does not necessarily change significantly. Instead, approximately half of the increase in precipitation intensity in the storm track regions can be attributed to a higher occurrence of the more intensely precipitating combinations of weather features, such as the co‐occurrence of extratropical cyclones, fronts, and moisture transport axes.

Cut-to-Length Harvesting Prediction Tool: Machine Learning Model Based on Harvest and Weather Features

Weather is a significant factor influencing forest health, productivity, and the carbon cycle. However, our understanding of these effects is limited for many regions and ecosystems. Assessing the impact of weather variability on harvester productivity from plantation forests may assist in forest planning through the use of data modeling. We investigated whether weather data combined with timber harvesting attributes could be used to create a high-performance model that could accurately predict harvester productivity in Eucalyptus plantations using machine learning. Furthermore, we aimed to provide an online application to assist forest managers in applying the model. For the modeling, we considered 15 weather and timber harvesting attributes. We considered productivity as the target attribute. We subjected the database to 24 common algorithms in default mode and compared them according to error metrics and accuracy. From the timber harvesting features combined with weather features, the Catboost model can predict the productivity of harvesters in a tuned mode, with a coefficient of determination of 0.70. The use of weather data combined with timber harvesting attributes in the model is an accurate approach for predicting harvester productivity in Eucalyptus plantations, allowing for the creation of an online, free application to assist forest managers.

Integration of Price Data in Wind Power Sales Systems Using Association Rule Mining Combined with Deep Learning Algorithms

With the growing attention of the global population towards renewable energy sources such as wind power derived from natural resources such as Solar, Geothermal, and Wind energy, there is a growing need for genuine estimation of wind energy. Wind energy is important in the supply of electricity in the global energy markets because of its enormous importance in the delivery of renewable energy. As such, it is crucial for accurate appreciation of the power of the wind to ably respond to issues that relate to the trading of power while at the same time addressing issues to do with planning, scheduling and strategic positioning of wind power generation. Therefore, the present work aims to develop a new model known as the Association Rule with DL-based Wind Power Generation and Price Prediction (ARDL-WPGPP). It utilizes two datasets: An Energy dataset includes various columns like tie, wind onshore forecast, and price actual, whereas the weather features dataset includes only wind speed. These datasets are combined to create a dataset where each transaction represents 2 hours of wind generation. A data mining approach is employed to uncover hidden patterns, rules, concepts, and correlations within these datasets, operating on various types of data including quantitative, textual, and multimedia formats. To efficiently extract rules from the dataset, an improved Apriori algorithm is introduced. Subsequently, the generated rules incorporating wind speed are passed into an improved LSTM model, which learns by comparing the label data. The price actual value serves as the label data, assigning labels to data points based on their actual price value. High price actual values indicate high wind power prices, while low values indicate low wind power prices.

Predictive Understanding of Links Between Vegetation and Soil Burn Severities Using Physics‐Informed Machine Learning

AbstractBurn severity is fundamental to post‐fire impact assessment and emergency response. Vegetation Burn Severity (VBS) can be derived from satellite observations. However, Soil Burn Severity (SBS) assessment—critical for mitigating hydrologic and geologic hazards—requires costly and laborious field recalibration of VBS maps. Here, we develop a physics‐informed Machine Learning model capable of accurately estimating SBS while revealing the intricate relationships between soil and vegetation burn severities. Our SBS classification model uses VBS, as well as climatological, meteorological, ecological, geological, and topographical wildfire covariates. This model demonstrated an overall accuracy of 89% for out‐of‐sample test data. The model exhibited scalability with additional data, and was able to extract universal functional relationships between vegetation and soil burn severities across the western US. VBS had the largest control on SBS, followed by weather (e.g., wind, fire danger, temperature), climate (e.g., annual precipitation), topography (e.g., elevation), and soil characteristics (e.g., soil organic carbon content). The relative control of processes on SBS changes across regions. Our model revealed nuanced relationships between VBS and SBS; for example, a similar VBS with lower wind speeds—that is, higher fire residence time—translates to a higher SBS. This transferrable model develops reliable and timely SBS maps using satellite and publicly accessible data, providing science‐based insights for managers and diverse stakeholders.

Parking Lot Traffic Prediction Based on Fusion of Multifaceted Spatio-Temporal Features.

With the rapid growth of population and vehicles, issues such as traffic congestion are becoming increasingly apparent. Parking guidance and information (PGI) systems are becoming more critical, with one of the most important tasks being the prediction of traffic flow in parking lots. Predicting parking traffic can effectively improve parking efficiency and alleviate traffic congestion, traffic accidents, and other problems. However, due to the complex characteristics of parking spatio-temporal data, high levels of noise, and the intricate influence of external factors, there are three challenges to predicting parking traffic in a city effectively: (1) how to better model the nonlinear, asymmetric, and complex spatial relationships among parking lots; (2) how to model the temporal autocorrelation of parking flow more accurately for each parking lot, whether periodic or aperiodic; and (3) how to model the correlation between external influences, such as holiday weekends, POIs (points of interest), and weather factors. In this context, this paper proposes a parking lot traffic prediction model based on the fusion of multifaceted spatio-temporal features (MFF-STGCN). The model consists of a feature embedding module, a spatio-temporal attention mechanism module, and a spatio-temporal convolution module. The feature embedding module embeds external features such as weekend holidays, geographic POIs, and weather features into the time series, the spatio-temporal attention mechanism module captures the dynamic spatio-temporal correlation of parking traffic, and the spatio-temporal convolution module captures the spatio-temporal features by using graph convolution and gated recursion units. Finally, the outputs of adjacent time series, daily series, and weekly series are weighted and fused to obtain the final prediction results, thus predicting the parking lot traffic flow more accurately and effectively. Results on real datasets demonstrate that the proposed model enhances prediction performance.

Correlations of chemical weathering indicators with major chemical constituents in sediments to obtain palaeoclimate information from Chaohu Lake, China

The main chemical compositions of 201 surface sediments and 53 deep sediment samples from Chaohu Lake, China, were analysed. Since the surface sediments (0–2 cm depth) in Chaohu Lake are modern sediments, this paper mainly focuses on the deep sediments (50–100 cm depth) in Chaohu Lake. Particle size analysis and magnetization determination of the CH3 and CH4 column sediment samples were carried out. The age determination data of the CH-1 column sediment samples are reported. A systematic study of the rocks and their chemical compositional characteristics in the Chaohu Lake Basin was also carried out. The results of this study show that four positive chemical weathering indicators and one negative chemical weathering indicator are applicable to the study of Chaohu Lake. The mean CIA of the Chaohu Lake sediments was less than 65, indicating that the Chaohu Lake Basin experienced weak chemical weathering and that the palaeoclimate was cold and dry. Vertical variations in the mean grain size and magnetization in the CH3 and CH4 columnar sediments reflect changes in the depositional environment and climate during deposition of the Chaohu Lake sediments. The age data from the CH-1 column sediment samples directly indicate deposition of the deep sediments in Chaohu Lake during the Little Ice Age in eastern China (AD 1380–1880). The Th/U, Sc/Th, Rb/Sr, Na2O/K2O, CaO/MgO and OC/N ratios of the Chaohu sediments reflect palaeoclimate characteristics and the chemical compositions of the source rocks in the Chaohu Lake basin. The correlations of the CIA, CIW, PIA, and CIX with the chemical compositional ratios provide information on the palaeoclimate and the distribution of the chemical compositions. The CIA, CIW, PIA, and CIX were not correlated with Cd, Pb, As, Hg, or P. In contrast, the CIA, CIW, PIA, and CIX were significantly positively correlated with Cr and N. The WIP was inconsistently correlated with the selected chemical components. Therefore, the study of the correlations of chemical weathering indicators with four heavy metals and two eutrophication-related elements is of little significance. The study of the chemical weathering characteristics of deep sediments of inland lakes should be combined with assessment of the geological characteristics of the lake basins, particularly the analysis of the chemical composition of the rocks in the lake basins.

The spatial distribution of heat related hospitalizations and classification of the most dangerous heat events in California at a small-scale level

Many studies have explored the impact of extreme heat on health, but few have investigated localized heat-health outcomes across a wide area. We examined fine-scale variability in vulnerable areas, considering population distribution, local weather, and landscape characteristics. Using 36 different heat event definitions, we identified the most dangerous types of heat events based on minimum, maximum, and diurnal temperatures with varying thresholds and durations. Focusing on California's diverse climate, elevation, and population distribution, we analyzed hospital admissions for various causes of admission (2004–2013). Our matching approach identified vulnerable zip codes, even with small populations, on absolute and relative scales. Bayesian Hierarchical models leveraged spatial correlation. We ranked the 36 heat event types by attributable hospital admissions per zip code and provided code, simulated data, and an interactive web app for reproducibility. Our findings showed high variation in heat-related hospitalizations in coastal cities and substantial heat burdens in the Central Valley. Diurnal heat events had the greatest impact in the Central Valley, while nighttime extreme heat events drove burdens in the southeastern desert. This spatially informed approach guides local policies, prioritizing dangerous heat events to reduce the heat-health burden. The methodology is applicable to other regions, informing early warning systems and characterizing extreme heat impacts.

Tunnel-Shaped Weathering Features in Soil Grains: Why 3D Imaging Must be Performed for Accurate Characterization and Interpretation

Tunnel-Shaped Weathering Features in Soil Grains: Why 3D Imaging Must be Performed for Accurate Characterization and Interpretation

Investigating Arctic Cyclone–Tropopause Polar Vortex Interactions with Idealized Observing System Simulation Experiments

Abstract Arctic cyclones (ACs) are a primary driver of surface weather in the Arctic, contributing to heat and moisture transport and forcing short-term sea ice variability. Still, our understanding of the processes that drive ACs, particularly their large scales and long lifetimes, is limited. ACs are commonly associated with one or more cyclonic tropopause polar vortices (TPVs), potential vorticity (PV) anomalies in the upper troposphere and lower stratosphere that may spur baroclinic development in the surface system, though the exact processes that link the two have yet to be fully explored. In this study, we investigate physical links between TPVs, especially their mesoscale structure and moisture profiles, and ACs with idealized observing system simulation experiments (OSSEs). Starting with a nature run, we simulate different types of dropsonde observations over a TPV during the nascent phase of a nearby AC. The Model for Prediction Across Scales (MPAS) and the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter are then used to run experiments to test the impact of these detailed TPV observations. In addition to a control, five main experiments are conducted, assimilating new observations of temperature and humidity. All experiments reduce forecast errors at the surface and throughout the troposphere. Additional humidity observations alter vertical PV distributions, which in turn impact the development of the AC. Experiments with additional temperature observations exhibit improvements in TPV structure and surrounding PV features and produce stronger surface cyclones with skillful TPV forecasts for up to 36 h longer than the control. Significance Statement Arctic cyclones (ACs) are a weather feature that can produce high winds, precipitation, and changes to sea ice cover in the Arctic. As a result, forecasting these storms accurately is important for human and economic interests in the region; however, there are currently gaps in our understanding of how ACs strengthen and persist. In this study, we explore potential links between ACs and weather features higher up in the atmosphere called tropopause polar vortices (TPVs) using computer modeling experiments. This study shows that there are important connections between the characteristics of TPVs and the development of ACs. These findings will be useful for making more accurate forecasts of future events and advancing our knowledge of how sea ice changes relate to the atmosphere.

Extreme weather characteristics and influences on urban ecosystem services in Wuhan Urban Agglomeration

In recent years, there has been a pronounced increase in the frequency of extreme weather events. To comprehensively examine the impact of extreme weather on ecosystem services within the Wuhan Urban Agglomeration (WUA), this study utilized meteorological station data, the Mann-Kendall (MK) test, and the Standardized Precipitation-Evapotranspiration Index (SPEI) to quantify the variation trends in heatwaves (HW) and droughts from 1961 to 2020. Then the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model was employed to evaluate and compare the differences in water yield and climate regulation ecosystem services under various HW, droughts, and HW-drought combination scenarios. The results show that over the past 60 years, the temperature, duration, and frequency of HW have significantly increased in the WUA. Specifically, the highest HW temperature, total HW days, HW frequency, and average HW temperature showed changing trend of +0.17 ℃/decade, +1.4 day/decade, +0.19 event/decade, and +0.07 ℃/decade, respectively. The year 2000 was identified as a mutation year for HW, characterized by increased frequency and heightened severity thereafter. The SPEI value exhibited an insignificant upward trend, with 1980 marked as a mutation year, indicating a decreasing trend in drought occurrences after 1980. Heatwaves have a weakening effect on both water yield and climate regulation services, while drought significantly weakened water yield and had a relatively modest effect on climate regulation. During HW-drought composite period, the average monthly water yield showed a notable discrepancy of 60 mm compared to humid years. Besides, as heatwaves intensify, the area of low aggregation for ecosystem services expands, whereas the area of high aggregation decreases. This study provides a preliminary understanding of the impact of urban extreme weather on urban ecosystem services under changing climatic conditions.

Assessment of meteorological parameters in predicting seasonal temperature of Dhaka city using ANN

In this study, an attempt has been made to investigate the possibility of a machine learning model, Artificial Neural Network (ANN) for seasonal prediction of the temperature of Dhaka city. Prior knowledge of temperature is essential, especially in tropical regions like Dhaka, as it aids in forecasting heatwaves and implementing effective preparedness schemes. While various machine learning models have been employed for the prediction of hot weather across the world, research specially focused on Bangladesh is limited. Additionally, the application of machine learning models needs to be curated to suit the particular weather features of any region. Therefore, this study approaches ANN method for prediction of the temperature of Dhaka exploring the underlying role of related weather variables. Using the daily data for the months of February to July collected from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data (0.25° × 0.25° global grid) for the years 2011–2020, this study focuses on finding the combination of weather variables in predicting temperatures. The densely populated city, Dhaka, has faced severe consequences due to extreme climate conditions in recent years, and this study will pave a new dimension for further research regarding the topic.

Impact-dispersed Fe–Fe1−xS core–shell particles in Chang’e-5 lunar soil impact glass

Lunar soil undergoes space weathering and accumulates optically active opaque particles (OAOpq) of different sizes, resulting in a darkening or red shift of the reflectance spectrum. The surfaces of weakly weathered objects exhibit spectral characteristics of strong weathering; these mechanisms are still unclear. The causes of OAOpq in lunar soil are complex, especially for submicrometer particles, which account for the largest mass proportion. We found ubiquitous impact-dispersed Fe–Fe1-xS core–shell particles in Chang’e-5 lunar soil impact glass and splatter. The crystal structure, particle size distribution, and chemical composition of OAOpq in the impact glass indicate that these OAOpq consist of sulfides or metals from multiple sources. Thermodynamic evidence, diffusion behavior, and particle dispersion characteristics indicate that impact dispersion is the most likely formation mechanism of these OAOpq. The proposed impact dispersion provides a reason for the large number of OAOpq and the limited products for in situ reactions. This process explains why lunar soil with a low degree of weathering exhibits substantial spectral modification properties. The results provide insights into space weathering of the lunar surface and also imply that impact-dispersed OAOpq may be the primary modification type on asteroid surfaces. The unique chemical properties of Fe–Fe1-xS OAOpq also indicate that the lunar regolith has the potential for resource utilization.

Analysis of the Hydrogeochemical Characteristics and Origins of Groundwater in the Changbai Mountain Region via Inverse Hydrogeochemical Modeling and Unsupervised Machine Learning

This study employed hydrochemical data, traditional hydrogeochemical methods, inverse hydrogeochemical modeling, and unsupervised machine learning techniques to explore the hydrogeochemical traits and origins of groundwater in the Changbai Mountain region. (1) Findings reveal that predominant hydrochemical types include HCO3−Ca·Mg, HCO3−Ca·Na·Mg, HCO3−Mg·Na, and HCO3−Na·Mg. The average metasilicic acid content was found to be at 49.13 mg/L. (2) Rock weathering mechanisms, particularly silicate mineral weathering, primarily shape groundwater chemistry, followed by carbonate dissolution. (3) Water-rock interactions involve volcanic mineral dissolution and cation exchange adsorption. Inverse hydrogeochemical modeling, alongside analysis of the widespread volcanic lithology, underscores the complexity of groundwater reactions, influenced not only by water-rock interactions but also by evaporation and precipitation. (4) Unsupervised machine learning, integrating SOM, PCA, and K-means techniques, elucidates hydrochemical types. SOM component maps reveal a close combination of various hydrochemical components. Principal component analysis (PCA) identifies the first principal component (PC1), explaining 48.15% of the variance. The second (PC2) and third (PC3) principal components, explain 13.2% and 10.8% of the variance, respectively. K clustering categorized samples into three main clusters: one less influenced by basaltic geological processes, another showing strong igneous rock weathering characteristics, and the third affected by other geological processes or anthropogenic factors.

A Two-Stage Operation Strategy for Energy Storage under Extreme-Heat-with-Low-Wind-Speed Scenarios of a Power System

Changes in weather conditions directly impact the output of wind power, photovoltaic systems, and other forms of uncontrollable power generation. During extreme weather events, the output from wind and photovoltaic sources is typically reduced. In light of this, this paper proposes a two-stage operational strategy for energy storage, under scenarios of extreme-heat-with-low-wind-speed, in power systems. Firstly, historical data on wind and solar power, along with weather characteristics, are collected to analyze the power output during multi-day periods of extreme heat and low wind speed. Then, Monte Carlo simulations are employed to generate multi-day load curves with inherent uncertainties, based on regional load characteristics of the power system. Finally, a two-stage operation strategy for energy storage charging and discharging is established. In the first stage, normal operations are conducted to identify periods of power shortage across various types of loads. In the second stage, based on the identified moments of power shortage from the first stage, charging and discharging constraints are applied to the energy storage systems. The feasibility and effectiveness of this two-stage operational strategy are then validated through simulations, using historical data to generate scenarios of multi-day extreme-heat-and-low-wind-speed conditions.

An Autoregulatory Model of Forest Insect Population Dynamics and Forest Stand Damage Dynamics in Different Habitats: An Example of Lymantria dispar L.

This paper addresses the problem of constructing a mathematical model of population density dynamics and the dynamics of forest areas damaged by spongy moth (Lymantria dispar L.) outbreaks in the United States, Europe, Russia, and Japan. The key variable of the model is either the pest population density or the area of forests damaged by spongy moths during a season. This variable can be considered proportional to the total current pest abundance in the study area. For the purposes of modeling, data from a number of different authors was used (see bibliography), as well as data from surveys conducted at the egg or caterpillar stage. The complexity of modeling the dynamics of L. dispar abundance is largely due to the fact that, when studying the dynamics of spongy moth population density, the values of external factors such as parasites, predators, and the amount of available food are often unknown. A simple model was proposed using only two types of data: population density and monthly weather characteristics. Our analysis demonstrated that, even in the absence of knowledge regarding the characteristics of ecosystem components interacting with the spongy moth population (parasites, predators, and the state of forage trees), it is possible to introduce models that characterize the regulatory processes in the population in terms of (i) the presence of negative and positive feedbacks in the system and (ii) the influence of external weather factors. The system under investigation was described as an autoregressive system, whereby the current state of the population is dependent on its state in previous years. The order of autoregression in the system was estimated using the order of the maximum significant partial autocorrelation function. It was found that the regulation of spongy moth population density was characterized by the presence of two feedback loops: positive feedback between the current population density and the population density in the previous season and negative feedback between the current population density and the population density two years ago. To evaluate the model, its stability margin was calculated and found to be directly proportional to the positive feedback coefficient and inversely proportional to the negative feedback coefficient. The model was demonstrated to explain up to 90% of the observed variance of real data. Although the model coefficients for different local populations (North America, Europe, and Asia) differ, the general form of the equation describing both direct data on population densities and indirect data on pest dynamics characterized by areas of stand damage is consistent. Consequently, the form of the ADL model is general, irrespective of the location of the local population.

Effect of transient vortex interactions on the size and strength of Jupiter’s Great Red Spot

Jupiter’s Great Red Spot (GRS) is among the largest discrete weather features in the Solar System. Although observed for at least 200 years, the GRS is shrinking, particularly in the past 50 years. Studies of long-lived terrestrial anticyclonic features, known as blocks, demonstrate that they can be sustained, in part, by interactions with transient vortices. Motivated by these results and observations of the GRS absorbing smaller vortices, we conduct three-dimensional simulations of a GRS-like vortex. Control simulations omit forced vortex interactions, while a set of experimental simulations include GRS interactions with transient anticyclones of varying frequency and intensity. Comparison of these simulations allows isolation of the role of such mergers in the GRS evolution. We find that the GRS is larger following periodic vortex interactions relative to control simulations without mergers. Similarly, analysis of upper troposphere eddy potential vorticity and dynamic tropopause potential temperature anomalies suggests the GRS is strengthened by vortex interactions. The degree to which the GRS is sustained increases with interaction frequency and transient anticyclone intensity. These results are consistent with terrestrial blocking dynamics and support the theory that the GRS is modulated by persistent interactions with smaller vortices.

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.

Solar Power Generation Forecast Using Multivariate Convolution Gated Recurrent Unit Network

For the advancement of smart grids, solar power generation predictions have become an important research topic. In the case of using traditional modeling methods, excessive computational costs may be incurred and it is difficult for these methods to learn the multi-variable dependencies of the data. Therefore, in this paper, a deep learning model was used to combine convolutional neural networks and long short-term memory recurrent network predictions. This method enables hourly power generation one day into the future. Convolutional neural networks are used to extract the features of multiple time series, while long short-term memory neural networks predict multivariate outcomes simultaneously. In order to obtain more accurate prediction results, we performed feature selection on meteorological features and combined the selected weather features to train the prediction model. We further distinguished sunny- and rainy-day models according to the predicted daily rainfall conditions. In the experiment, it was shown that the method of combining meteorological features further reduced the error. Finally, taking into account the differences in climate conditions between the northern and southern regions of Taiwan, the experimental results of case studies involving multiple regions were evaluated to verify the proposed method. The results showed that training combined with selected meteorological features can be widely used in regions with different climates in Taiwan.