Meteorological Characteristics Research Articles

Influence of hydrometeorological risk factors on child diarrhea and enteropathogens in rural Bangladesh.

A number of studies have detected relationships between weather and diarrhea. Few have investigated associations with specific enteric pathogens. Understanding pathogen-specific relationships with weather is crucial to inform public health in low-resource settings that are especially vulnerable to climate change. Our objectives were to identify weather and environmental risk factors associated with diarrhea and enteropathogen prevalence in young children in rural Bangladesh, a population with high diarrheal disease burden and vulnerability to weather shifts under climate change. We matched temperature, precipitation, surface water, and humidity data to observational longitudinal data from a cluster-randomized trial that measured diarrhea and enteropathogen prevalence in children 6 months-5.5 years from 2012-2016. We fit generalized additive mixed models with cubic regression splines and restricted maximum likelihood estimation for smoothing parameters. Comparing weeks with 30°C versus 15°C average temperature, prevalence was 3.5% higher for diarrhea, 7.3% higher for Shiga toxin-producing Escherichia coli (STEC), 17.3% higher for enterotoxigenic E. coli (ETEC), and 8.0% higher for Cryptosporidium. Above-median weekly precipitation (median: 13mm; range: 0-396mm) was associated with 29% higher diarrhea (adjusted prevalence ratio 1.29, 95% CI 1.07, 1.55); higher Cryptosporidium, ETEC, STEC, Shigella, Campylobacter, Aeromonas, and adenovirus 40/41; and lower Giardia, sapovirus, and norovirus prevalence. Other associations were weak or null. Higher temperatures and precipitation were associated with higher prevalence of diarrhea and multiple enteropathogens; higher precipitation was associated with lower prevalence of some enteric viruses. Our findings emphasize the heterogeneity of the relationships between hydrometeorological variables and specific enteropathogens, which can be masked when looking at composite measures like all-cause diarrhea. Our results suggest that preventive interventions targeted to reduce enteropathogens just before and during the rainy season may more effectively reduce child diarrhea and enteric pathogen carriage in rural Bangladesh and in settings with similar meteorological characteristics, infrastructure, and enteropathogen transmission.

Evaluation of the convection permitting regional climate model CNRM-AROME on the orographically complex island of Corsica

AbstractMeteorological processes over islands with complex orography could be better simulated by Convection Permitting Regional Climate Models (CP-RCMs) thanks to an improved representation of the orography, land–sea contrasts, the combination of coastal and orographic effects, and explicit deep convection. This paper evaluates the ability of the CP-RCM CNRM-AROME (2.5-km horizontal resolution) to simulate relevant meteorological characteristics of the Mediterranean island of Corsica for the 2000–2018 period. These hindcast simulations are compared to their driving Regional Climate Model (RCM) CNRM-ALADIN (12.5-km horizontal resolution and parameterised convection), weather stations for precipitation and wind and gridded precipitation datasets. The main benefits are found in the representation of (i) precipitation extremes resulting mainly from mesoscale convective systems affected by steep mountains during autumn and (ii) the formation of convection through thermally induced diurnal circulations and their interaction with the orography during summer. Simulations of hourly precipitation extremes, the diurnal cycle of precipitation, the distribution of precipitation intensities, the duration of precipitation events, and sea breezes are all improved in the 2.5-km simulations with respect to the RCM, confirming an added value. However, existing differences between model simulations and observations are difficult to explain as the main biases are related to the availability and quality of observations, particularly at high elevations. Overall, better results from the 2.5-km resolution, increase our confidence in CP-RCMs to investigate future climate projections for Corsica and islands with complex terrain.

Application of advanced causal analyses to identify processes governing secondary organic aerosols

Understanding how different physical and chemical atmospheric processes affect the formation of fine particles has been a persistent challenge. Inferring causal relations between the various measured features affecting the formation of secondary organic aerosol (SOA) particles is complicated since correlations between variables do not necessarily imply causality. Here, we apply a state-of-the-art information transfer measure coupled with the Koopman operator framework to infer causal relations between isoprene epoxydiol SOA (IEPOX-SOA) and different chemistry and meteorological variables derived from detailed regional model predictions over the Amazon rainforest. IEPOX-SOA represents one of the most complex SOA formation pathways and is formed by the interactions between natural biogenic isoprene emissions and anthropogenic emissions affecting sulfate, acidity and particle water. Since the regional model captures the known relations of IEPOX-SOA with different chemistry and meteorological features, their simulated time series implicitly include their causal relations. We show that our causal model successfully infers the known major causal relations between total particle phase 2-methyl tetrols (the dominant component of IEPOX-SOA over the Amazon) and input features. We provide the first proof of concept that the application of our causal model better identifies causal relations compared to correlation and random forest analyses performed over the same dataset. Our work has tremendous implications, as our methodology of causal discovery could be used to identify unknown processes and features affecting fine particles and atmospheric chemistry in the Earth’s atmosphere.

МЕТОДЫ ОЦЕНКИ ОЗДОРАВЛИВАЮЩИХ ФАКТОРОВ САНАТОРНО-КУРОРТНОЙ СФЕРЫ

Background. Preventive measures aimed at preserving the health of the country's population should be based on health-resort rehabilitation and treatment using the healing factors of health resorts. In this regard, the study, from the point of view of a systematic approach, considers the data availability assessment of the health-improving factors that affect the activities of the health resort sector in the regions of the country with prioritization of the activities of health resort The purpose of the study is to establish the data availability assessment of health-improving factors that affect the activities of health resort organizations. Material and methods. A retrospective analysis of the sources of official statistical data, the state cadastre systems of natural resources, the results of scientific research in the health resort sector and health resort organizations. The level of data availability assessment of the health-improving factors of health resorts is determined by the method of expert assessment. Results. It is proposed to take into account the system of healing factors affecting the health resort sphere in the following components: natural therapeutic potential (biomedical – meteorological characteristics, severity index, etc.) and natural resource – landscape conditions, mineral waters, therapeutic mud). Medical diagnostic and human resources potential is the medical and diagnostic base of health resorts, staff qualifications. Social and cultural potential is the quality of services provided for health resort treatment and rehabilitation, inextricably linked with social and cultural events, as well as the basis to carry out these events, and qualified staff of health resorts. The interrelation of the components is manifested in both internal and external relations of health resort organizations, awareness and degree of satisfaction with the services of health resort treatment of the population. Conclusion. The methods of assessing the healing factors are grouped, the methodology of an integrated approach – monitoring of health resort organizations and the possibility of establishing permanent contacts between healthcare institutions and health resorts of the country is proposed.

Mapping and Predicting Cassava Mosaic Disease Outbreaks Using Earth Observation and Meteorological Data-driven Approaches

Cassava Mosaic Disease (CMD) poses a significant threat to cassava (Manihot esculenta Crantz) production, both globally and in Thailand. Caused by the Cassava Mosaic Begomovirus (CMB), CMD conventional detection methods involve field-based visual assessment and mapping, which can be time-consuming and labour-intensive.In this study, field-based surveys have been conducted during the early wet season cassava planting period in three different productive regions of Thailand. Five classification models (namely ANN, RF, XGBoost, SVM, and Ensemble learning) were employed to predict CMD occurrence with various sets of optimized predictive meteorological and remote sensing features. Field based CMD observations have been used for modelling validation and accuracy assessment.Utilizing time series of Sentinel-2 MSI Level-2A images, we estimate cassava start of season (SOS) across both before and after planting periods on a gridded basis. Establishing meteorological and remote sensing time frames, we aggregate predictive features through different temporal windows. Employing interactive and multistage correlation analysis, along with feature importance ranking, we optimize the selection of predictive features, effectively reducing multicollinearity. The Ensemble model and the usage of combined meteorological and remote sensing optimized predictive features showed best prediction results achieving an overall accuracy of 90%, F1-score of 0.90, Matthews Correlation Coefficient of 0.71, unit-normalized MCC* of 0.85, and an average central actual accuracy of approximately 0.85.Observed CMD patterns demonstrate that soil-crop suitability and farming practices (including the use of most CMD susceptible cultivars) significantly influence the CMD spread in selected farming areas. However, modelling analysis revealed that, among several meteorological features, CMD occurrence is associated with predominant winds blowing across the entire before and after planting periods, mainly from the first quadrant, and to a lesser extent, humidity. Notably, higher disease incidence areas corresponded with SOS events in January and February. In predicting CMD, individual S2-MSI bands were not crucial, but NDVI and EVI emerged as the most explanatory remote sensing features, followed by GNDVI and PSRI indices.The study’s findings highlight that coupling CMD occurrence modelling, combined remote sensing and meteorological data with conventional field-based observations may significantly enhance targeted disease management protocols and assessing potential impacts on cassava yields.

Enhancing Solar Forecasting Accuracy with Sequential Deep Artificial Neural Network and Hybrid Random Forest and Gradient Boosting Models across Varied Terrains

AbstractEffective solar energy utilization demands improvements in forecasting due to the unpredictable nature of solar irradiance (SI). This study introduces and rigorously tests two innovative forecasting models across different locations: the Sequential Deep Artificial Neural Network (SDANN) and the Deep Hybrid Random Forest Gradient Boosting (RFGB). SDANN, leveraging deep learning, aims to identify complex patterns in weather data, while RFGB, combining Random Forest and Gradient Boosting, proves more effective by offering a superior balance of efficiency and accuracy. The research highlights the SDANN model's deep learning capabilities along with the RFGB model's unique blend and their comparative success over existing models such as eXtreme Gradient Boosting (XGBOOST), Categorical Boosting (CatBOOST), Gated Recurrent Unit (GRU), and a K‐Nearest Neighbors (KNN) and XGBOOST hybrid. With the lowest Mean Squared Error (147.22), Mean Absolute Error (8.77), and a high R2 value (0.80) in a studied region, RFGB stands out. Additionally, detailed ablation studies on meteorological feature impacts on model performance further enhance accuracy and adaptability. By integrating cutting‐edge AI in SI forecasting, this research not only advances the field but also sets the stage for future renewable energy strategies and global policy‐making.

Short-term load forecasting of regional integrated energy system based on spatio-temporal convolutional graph neural network

Accurate short-term load forecasting has significant reference value for the preliminary planning and subsequent coordinated control of regional integrated energy systems. However, main limitations are: (1) Insufficient exploration of the complex correlations among diverse loads and between loads and meteorological factors; and (2) Feature information extraction only from the time dimension, neglecting the spatial dimension, resulting in incomplete feature information extraction. This paper proposes a new method for short-term load forecasting, namely the Attention Spatial–temporal Graph Convolutional Network (ATSTGCN) method. Firstly, the Grey Relational Analysis (GRA) method is used to analyze the correlation between diverse loads, meteorological features, and load features, constructing the input data for the graph neural network. Then, synchronous graph convolution is introduced to extract information between features. Next, the spatio-temporal attention mechanism is incorporated into the synchronous graph convolution to capture spatio-temporal features and reduce information loss using a spatio-temporal window. Finally, a short-term load forecasting method based on ATSTGCN for regional integrated energy systems is constructed. Extensive simulation experiments are conducted on the publicly available dataset from Arizona State University’s online system. The experimental results show that compared to other methods, the proposed method improves MAPE, MAE, and RMSE by 5.6%, 11.7%, and 11.6%.

Prediction of wind and PV power by fusing the multi-stage feature extraction and a PSO-BiLSTM model

Accurate prediction of wind and photovoltaic (PV) power is essential to ensure the stable operation of power systems. However, the conventional power prediction methods face the challenges in capturing the stochastic fluctuations of wind and PV power. A wind and PV power prediction method fusing the multi-stage feature extraction and a particle swarm optimization (PSO)-bidirectional long and short-term memory (BiLSTM) model is developed. To illustrate the oscillation and instability of wind and PV power, the symplectic geometry model decomposition (SGMD) is presented to decompose the feature data and obtain multiple feature sub-sequences. Due to the excessive feature decomposition using the conventional methods, such as empirical modal decomposition, the kernel principal component analysis is utilized to reduce the dimensionality of the nonlinear sub-sequences of the wind and PV power. Compared to the traditional LSTM that ignores the inverse-time correlation between power and meteorological features and is sensitive to the model hyper-parameters, a power prediction model based on PSO-BiLSTM is developed. Using the power data from a power station in Xinjiang Province, China, as an example, the experimental results show that wind and PV power can be accurately forecasted using the developed method with high prediction accuracies of 97.9 % and 98.5 %, respectively.

Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE

The construction of modern power system is key to achieving dual carbon goals, where non-intrusive load monitoring (NILM) plays a vital role in enhancing energy utilization efficiency and energy management. For example, to enable prosumers to better understand the extent of their flexible loads for demand response and peer-to-peer trading, it is essential to be aware of the types and states of loads using the method of NILM. To improve the predictive accuracy and implementation effectiveness of NILM technology, this paper proposes a novel NILM method integrating meteorological and calendar features. It delves deeply into the close connection between external factors such as temperature, precipitation, wind speed, and holidays, and the energy consumption of electrical appliances, constructing additional associative mappings in the training of the Denoising Autoencoder (DAE) model. Test results on the UK-DALE public dataset show that the NILM method proposed in this paper has significant advantages over traditional NILM methods that consider only single-dimensional electrical data features, in terms of load pattern recognition and accuracy in load energy consumption monitoring. This confirms the potential of multi-dimensional feature fusion technology in the application of NILM.

Promising Directions for the Development of the Transport and Logistics Infrastructure of the Northern Sea Route

Relevance. The modern global agenda dictates new rules and principles of building international relations, including international trade. Under these conditions, the reorientation of foreign trade flows becomes an obvious step. This reorientation plays a significant role for Russia in terms of stabilizing the economy, increasing competitiveness and defending national economic interests. The purpose is consideration of the direction of reorientation of Russian foreign trade flows in the context of anti-Russian sanctions.Objectives: describe the composition and tasks of the functioning of the transport and logistics infrastructure of the Northern Sea Route; identify the problems and prospects for the development of the transport and logistics infrastructure of the Northern Sea Route.Methodology. The research methodology included the use of methods for analyzing quantitative and qualitative characteristics, theoretical analysis and generalization of scientific literature, methods of mathematical statistics, analysis of the expert method in open sources, generalization of the analyzed information.Results. The geographical position of Russia and its dominant position in the Arctic makes it promising to use the Northern Sea Route to develop its own transport potential, provided that all the problems that currently prevent an increase in the cargo turnover of this route are solved. Despite the advantages in the length of the route and speed, the meteorological features and complexity of the northern waters, as well as the technological backwardness of the port infrastructure of the Northern Sea Route, make it necessary to make serious investments in the development of both the transport and logistics infrastructure itself and in the creation of a fleet that will carry goods and ice wiring.Conclusions. The creation of an appropriate technological infrastructure for the development of cargo transportation involves the creation of new hub ports. In addition to improving technological infrastructure systems for improving loading and unloading capacities, the expansion of the container fleet is also relevant. To connect the ports of the Northern Sea Route with the railway infrastructure, the implementation of the projects "Northern Latitudinal Passage 1" and "Northern Latitudinal Passage 2" should be continued.

Investigation of Meteorological Characteristics of Tropical Supercyclone Shaheen Insights from High-Resolution Satellite Observations

Investigation of Meteorological Characteristics of Tropical Supercyclone Shaheen Insights from High-Resolution Satellite Observations

Modeling and Predicting PM2.5 Concentration at a Construction Site Using the Artificial Intelligence

Introduction. High concentration of PM2.5 has the adverse effect on people's health. According to the evaluations made by the researchers, the impact of the particulate matter from the construction dust emissions resulted in 18% of deaths from respiratory diseases. Due to the growth of construction production volume and consequent increase of dust emission volumes, there arises the need to expand the scope of using the end-to-end technologies, namely the artificial intelligence technologies, for predicting the fine-dispersed dust particles PM2.5 concentration in dust emissions at the construction site.Materials and methods. To achieve this goal, the measurements of PM2.5 concentration at the construction site were carried out using the Handheld 3016 IAQ particle counter in the period from July 1 to July 6, 2023 taking into account the meteorological characteristics of the territory, which then became the input data for modelling the forecast of dust pollution concentration using such algorithms as ARIMA, EMA, XGBoost, etc., and the ensemble models that included the above machine learning algorithms. The efficiency of using these technologies for predicting was determined by comparing the results of the forecast and the field measurements data.Results. A correlation analysis was performed using the Modeltime program, which determined the relationship between PM2.5 concentration and meteorological variables. Autocorrelation was performed using Pearson correlation. At the first stage, four one-dimensional models based on the artificial intelligence were evaluated to determine the accuracy of mean concentration forecast. The next step was to evaluate the capacity of predicting the mean PM2.5 concentration using the multidimensional models that took into account the relationships between the independent and dependent variables. At the final stage of the research, three most efficient predictive models were included to test the ensemble model.Discussion and conclusion. The reliable predictive models can be the useful tools for understanding the concentration impact factors. In the present research, seven machine learning algorithms were used to predict the concentration of PM2.5. The research, as a whole, presents the evidences of the integrated modeling method efficiency for predicting the air pollution. 

High resolution prediction and explanation of groundwater depletion across India

Food production in much of the world relies on groundwater resources. In many regions, groundwater levels are declining due to a combination of anthropogenic extraction, localized meteorological and geological characteristics, and climate change. Groundwater in India is characteristic of this global trend, with an agricultural sector that is highly dependent on groundwater and increasingly threatened by extraction far in excess of recharge. The complexity of inputs makes groundwater depletion highly heterogeneous across space and time. However, modeling this heterogeneity has thus far proven difficult. Using two ensemble tree-based regression models, we predict district level seasonal groundwater dynamics to an accuracy of R 2 = 0.4–0.6 and Pearson correlations between 0.6 and 0.8. Further using two high-resolution feature importance methods, we demonstrate that atmospheric humidity, groundwater groundwater-based irrigation, and crop cultivation are the most important predictors of seasonal groundwater dynamics at the district level in India. We further demonstrate a shift in the predictors of groundwater depletion over 1998–2014 that is robustly found between the two feature importance methods, namely increasing importance of deep-well irrigation in Central and Eastern India. These areas coincide with districts where groundwater depletion is most severe. Further analysis shows decreases in crop yields per unit of irrigation over those regions, suggesting decreasing marginal returns for largely increasing quantities of groundwater irrigation used. This analysis demonstrates the public policy value of machine learning models for providing high spatiotemporal accuracy in predicting groundwater depletion, while also highlighting how anthropogenic activity impacts groundwater in India, with consequent implications for productivity and well-being.

LSTM-Powered COVID-19 prediction in central Thailand incorporating meteorological and particulate matter data with a multi-feature selection approach

The COVID-19 pandemic has significantly impacted public health and necessitated urgent actions to mitigate its spread. Monitoring and predicting the outbreak's progression have become vital to devise effective strategies and allocate resources efficiently. This study presents a novel approach utilizing Multivariate Long Short-Term Memory (LSTM) to analyze and predict COVID-19 trends in Central Thailand, particularly emphasizing the multi-feature selection process. To consider a comprehensive view of the pandemic's dynamics, our research dataset encompasses epidemiological, meteorological, and particulate matter features, which were gathered from reliable sources. We propose a multi-feature selection technique to identify the most relevant and influential features that significantly impact the spread of COVID-19 in the region to enhance the model's performance. Our results highlight that relative humidity is the key factor driving COVID-19 transmission in Central Thailand. The proposed multi-feature selection technique significantly improves the model's accuracy, ensuring that only the most informative variables contribute to the predictions, avoiding the potential noise or redundancy from less relevant features. The proposed LSTM model demonstrates its capability to forecast COVID-19 cases, facilitating informed decision-making for public health authorities and policymakers.

Role of Lightning Activity in Deciphering Atmospheric Gravity Waves (AGWs) Induced D‐Region Ionospheric Perturbations During Extremely Severe Cyclonic Storm (ESCS) Fani

AbstractThe study presents detailed meteorological characteristics of extremely severe cyclonic storm (ESCS) Fani, and subsequent Atmospheric Gravity Waves (AGWs) induced D‐region ionospheric perturbations and the role of lightning activity in it. The cyclone shaped as a weak disturbance over the north Indian Ocean (2.7°N, 89.7°E) on 25 April 2019. The disturbance intensified and evolved into ESCS Fani over Bay of Bengal (BoB) on 30 April, had landfall on 03 May, and dissipated after 04 May 2019. What makes Fani unique is its long life span of ∼10 days, and only ESCS to occur after ∼30 years over the BoB. Fani attained a minimum cloud top temperature of about −80°C, and a corresponding maximum cloud top altitude of about ∼17 km. Such meteorological conditions presented a strong convection process in the towering cumulonimbus in inner and outer rain bands, resulting in intense lightning activity. The peak lightning flash rate observed was ∼375 min−1. SABER observations confirmed the coupling of atmosphere with ionosphere with strong AGWs in middle atmosphere during Fani. NWC (19.8 kHz) Very Low Frequency signal intersecting the track of Fani is used to decipher D‐region ionospheric perturbations induced by AGWs from Fani. The results show the presence of increased AGW activity in D‐region during the cyclone period when compared to that of the pre‐ and post‐cyclone periods. The periods of the observed gravity waves are between ∼13 and 20 min, highlighting the important role of lightning activity and AGWs in atmosphere‐ionosphere coupling.

Evaluation and Impact Assessment of Meteorological Drought Characteristics over West Africa

When a region receives little precipitation for an extended period of time, droughts occur. The environmental and socioeconomic effects it has make it one of the most devastating natural disasters that can happen to a location. For the purpose of enhancing early warning systems and lowering the risk of disasters caused by drought, research into the effects of meteorological drought characteristics on a location is crucial. Therefore, this study uses the Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI) to assess the effects of meteorological drought characteristics over West Africa. The study area includes West Africa's Saharan, Sahelian, and Sudanese climate zones, and Copernicus Service provided the meteorological information (temperature and precipitation). The data was based on four monthly time scales (3, 6, 12, and 24) and covered 42 years (1979-2021). The data were analyzed and the characteristics of the drought were assessed using SPI and SPEI. According to the results, there was extreme drought between the years 1982 to 1985 and year 2021. However, 1982 was the year with the highest incidence of drought, across nearly all the stations. Additionally, it was found that location 4 experiences drought continuously from 1982 to 2021, whereas all other locations experienced absence of drought for a short period of time. Comparing the SPI and SPEI results, SPI produced results for the drought analysis that were comparable to those of SPEI for each of the monthly time scales for all locations. Majority of the locations will likely experience drought every seven to ten years, with location 4 possibly experiencing at least a mild drought every year. Finally, this study suggests that, in light of the time-scales analysis and drought classification for each location, SPI and SPEI represent the most suitable measures for the region's drought monitoring.

Anomaly Detection in Meteorological Data Using a Hierarchical Temporal Memory Model: A Study on the Case of Kazakhstan

In meteorology, which studies atmospheric events, data representing various properties such as temperature, rainfall, and wind speed are collected regularly over a certain period. Unexpected trends in the data may indicate that an abnormal situation is approaching. Therefore, time series (TS) data play an essential role in the early detection of potential meteorological risks. However, applying effective models by considering many complex parameters in performing accurate analysis and anomaly detection (AD) is an important criterion. In this study, machine learning-based AD is performed using a dataset containing meteorological data on different features collected between January 1, 2019, and June 30, 2023, for Kazakhstan, which has the ninth-largest surface area in the world. The Hierarchical Temporal Memory (HTM) model was used for AD, which can provide more accurate forecasts by modeling long-term dependencies and producing effective results in solving TS problems. Detected anomalies are reported at various levels depending on threshold values. In addition, to analyze the ADs more precisely, correlations are calculated using the Spearman model, which allows us to determine the strength and direction of the monotonic relationship between variables. The study's findings show that the HTM is an effective model for AD using TS data on meteorological features.

A novel scenario generation method of renewable energy using improved VAEGAN with controllable interpretable features

With the high penetration of renewable generation systems in the power grid, the accurate simulation of the uncertainty in renewable energy generation is vital to the safe operation of the power system This paper proposes a novel controllable method for renewable scenario generation based on the improved VAEGAN model. The standard VAEGAN model is first improved using spectral normalization technique and the generator of GAN is trained using VAE. Then, the external and internal interpretable features in the latent space are learned as the controllable vector utilizing the principle of mutual information maximization. Finally, the renewable energy scenarios with overall features are generated using the external universal meteorological features, and renewable energy scenarios with specific features are generated by tuning along the internal interpretable feature of the controllable vector in the latent space. The proposed approach is used to produce real-time series data for renewable energy including wind and solar power. Experiments demonstrate that our method has a better performance in terms of controllable generation and enables the generation of preference patterns covering various statistical features.

Temporal evolution and spatial variation of meteorological drought characteristics in Iran's diverse climates over the past half century

Temporal evolution and spatial variation of meteorological drought characteristics in Iran's diverse climates over the past half century

Meteorological Characteristics of a Continuous Ice-Covered Event on Ultra-High Voltage Transmission Lines in Yunnan Region in 2021

Yunnan plays a pivotal role in transmitting electricity from west to east within China’s Southern Power Grid. During 7–13 January 2021, a large-scale continuous ice-covering event of ultra-high voltage (UHV) transmission lines occurred in the Qujing area of eastern Yunnan Province. Based on ERA5 reanalysis data and meteorological observation data of UHV transmission line icing in China’s Southern Power Grid, the synoptic causes of the icing are comprehensively analyzed from various perspectives, including weather situations, vertical stratification of temperature and humidity, local meteorological elements, and atmospheric circulation indices. The results indicate a strong East Asian trough and a blocking high directing northern airflow southward ahead of the ridge. Cold air enters the Qujing area and combines with warm and moist air from the subtropical high pressure of 50–110° E. As warm and cold air masses form a quasi-stationary front over the northern mountainous area of Qujing due to topographic uplift, the mechanism of “supercooling and warm rain” caused by the “warm–cold” temperature profile structure leads to freezing rain events. Large-scale circulation indices in the Siberian High, East Asian Trough, and 50–110° E Subtropical High regions provided clear precursor signals within 0–2 days before the icing events.