Abnormal Weather Conditions Research Articles

A Quick Look at the Atmospheric Circulation Leading to Extreme Weather Phenomena on a Continental Scale

The study delves into the primary large-scale atmospheric features contributing to extreme weather events across Europe during early September 2023. The period was examined using a dataset composed by the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis and satellite imagery. In early September 2023, an omega blocking pattern led to the development of a low-pressure system over the Iberian Peninsula producing heavy precipitation and flooding over Spain and acting as a mechanism for a mineral dust outbreak. A second low-pressure system developed over Greece. Extreme precipitation was recorded across Greece, Turkey, and Bulgaria as the system gradually shifted southward over the Mediterranean. The system earned the name “Storm Daniel” as it acquired subtropical characteristics. It caused floods over Libya and its associated circulation favoured the transport of mineral dust over Northern Egypt as it moved eastward. Meanwhile, the high-pressure blocking system associated with the omega pattern induced heatwave temperatures in countries further north. This period was compared with the large-scale circulation observed in mid-September 2020, when severe weather also affected the Mediterranean region. However, the weather systems were not directly connected by the large-scale circulation, as shown in September 2023. Although mesoscale conditions are relevant to formation and intensification of some atmospheric phenomena, the establishment of an omega blocking pattern in early September 2023 showed how large-scale atmospheric dynamics can produce abnormal weather conditions on a continental scale over several days.

End-to-end learning for stochastic preventive dispatch of renewables-rich power systems in abnormal weather conditions

Power systems are currently increasingly threatened by abnormal weather conditions, which are exacerbated by global warming. In particular, the proliferation of renewable energy sources (RES), which are quite variable, might cause power systems to have insufficient energy supply to cope with the risk of severe power imbalances in such conditions. In this paper, a preventive power system dispatch model is proposed that allows power systems with large RES (specifically wind energy) installations to mitigate excessive load curtailments under abnormal weather conditions. Compared to the conventional predict-then-optimize (PTO) framework of power system dispatch that relies on accurate RES forecasting, the proposed end-to-end learning framework mitigates the impact of forecasting errors on decision-making, which reduces the dependence on the accuracy of RES forecasting and effectively enhances the resilience of the power system. The proposed framework consists of a probabilistic RES forecasting module, a preventive dispatch module, and a resilience-oriented assessment module. A backpropagation mechanism based on Karush-Kuhn-Tucker conditions is proposed to unify the dispatch optimization problem as deep neural networks, which ensures the reliability of optimal solutions as well as provides necessary gradients for backpropagation, successfully bridging the information gap among the three modules. The proposed model is updated iteratively in an online implementation scheme composed of the decision-making part and the reflection & training part. The results of numerical experiments on the IEEE 6- and 118-bus systems verify the effectiveness and superiority of the proposed model in maintaining the resilience of RES-based power systems.

Predictive resilience assessment of road networks based on dynamic multi-granularity graph neural network

Due to the influence of global climate anomalies, abnormal weather conditions such as heavy rainfall have become more frequent in recent years, posing a significant threat to the operation of transportation systems. An effective assessment of the resilience of the transportation system before and during heavy rain can alert the transportation department to take necessary emergency actions. However, existing methods for assessing the rainfall resilience of transportation networks mostly suffer from the following problems: (1) Simulation methods for modeling rainfall impacts lack realism; (2) After-the-fact evaluations of resilience cannot offer advance warning prior to or during a heavy rain event. To address above problems, we present a novel resilience assessment methodology for evaluating the resilience of road networks in real-time during heavy rainfall scenarios. In this methodology, we propose the temporal decomposition-based dynamic multi-granularity graph neural network (TD2MG2NN) for long-term traffic speed forecasting, providing a perspective on the future evolution of traffic states for accurate resilience assessment. In addition, we construct a composite traffic resilience indicator, designed to comprehensively reflect changes in the spatial–temporal resilience of the transportation system during heavy rain. Experimental results on four publicly real datasets indicate that the prediction performance of TD2MG2NN outperforms state-of-the-art models. The assessment results for the transportation road network in California demonstrate that the comprehensive resilience indicator is superior to single functional resilience indicator and the real-time methodology for evaluating resilience can accurately depict and predict the operation of the road network system under heavy rainfall scenarios.

Recognition of aggressive driving behavior under abnormal weather based on Convolutional Neural Network and transfer learning

Objectives Aggressive driving behavior can lead to potential traffic collision risks, and abnormal weather conditions can exacerbate this behavior. This study aims to develop recognition models for aggressive driving under various climate conditions, addressing the challenge of collecting sufficient data in abnormal weather. Methods Driving data was collected in a virtual environment using a driving simulator under both normal and abnormal weather conditions. A model was trained on data from normal weather (source domain) and then transferred to foggy and rainy weather conditions (target domains) for retraining and fine-tuning. The K-means algorithm clustered driving behavior instances into three styles: aggressive, normal, and cautious. These clusters were used as labels for each instance in training a CNN model. The pre-trained CNN model was then transferred and fine-tuned for abnormal weather conditions. Results The transferred models showed improved recognition performance, achieving an accuracy score of 0.81 in both foggy and rainy weather conditions. This surpassed the non-transferred models’ accuracy scores of 0.72 and 0.69, respectively. Conclusions The study demonstrates the significant application value of transfer learning in recognizing aggressive driving behaviors with limited data. It also highlights the feasibility of using this approach to address the challenges of driving behavior recognition under abnormal weather conditions.

Near surface camera informed agricultural land monitoring for climate smart agriculture

Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology and responding to climatic and anthropogenic changes. However, the widely used optical satellite remote sensing is limited by revisit cycles and weather conditions, leading to gaps in agricultural monitoring. To address these limitations, we designed and deployed a Near Surface Camera (NSCam) Network across China, and explored its application in agricultural land monitoring and achieving climate-smart agriculture (CSA). By analyzing the image data captured by the NSCam Network, we can accurately assess long-term or abrupt agricultural land changes. According to the preliminary monitoring results, integrating NSCam data with remote sensing imagery greatly enhances the temporal details and accuracy of agricultural monitoring, aiding agricultural managers in making informed decisions. The impacts of abnormal weather conditions and human activities on agricultural land, which are not captured by remote sensing imagery, can be complemented by incorporating our NSCam Network. The successful implementation of this method underscores its potential for broader application in CSA, promoting resilient and sustainable agricultural practices.

Amundsen Sea Ice Loss Contributes to Australian Wildfires.

Wildfires in Australia have attracted extensive attention in recent years, especially for the devastating 2019-2020 fire season. Remote forcing, such as those from tropical oceans, plays an important role in driving the abnormal weather conditions associated with wildfires. However, whether high latitude climate change can impact Australian fires is largely unclear. In this study, we reveal a robust relationship between Antarctic sea ice concentration (SIC), primarily over the Amundsen Sea region, with Australian springtime fire activity, by using reanalysis data sets, AMIP simulation results, and a state-of-the-art climate model simulation. Specifically, a diminished Amundsen SIC leads to the formation of a high-pressure system above Australia as a result of the eastward propagation of Rossby waves. Meanwhile, two strengthened meridional cells originating from the tropic and polar regions also enhance subsiding airflow in Australia, resulting in prolonged arid and high-temperature conditions. This mechanism explains about 28% of the variability of Australian fire weather and contributed more than 40% to the 2019 extreme burning event, especially in the eastern hotspots. These findings contribute to our understanding of polar-low latitude climate teleconnection and have important implications for projecting Australian fires as well as the global environment.

ОСНОВНЫЕ ТРЕНДЫ РОСТА РЕГИОНАЛЬНОГО СЕЛЬСКОГО ХОЗЯЙСТВА: ОТ ОБЪЕМОВ К УСТОЙЧИВОСТИ

The strategic task until 2030 for the agro-economic systems of the country and its regions is to increase the average annual growth rate of volumetric indicators of agricultural production to 3%, which is twice as high as the achieved value. To achieve this aim, it is necessary to develop and implement effective mechanisms to smooth out the abrupt growth of production volumes and ensure the sustainable development of agricultural sectors. The development of the agricultural sector of the Republic of Tatarstan shows positive trends in the growth of volumetric indicators of production over the past five years. The volume of gross agricultural output in the region increased with an annual growth rate of 21.62 billion rubles. At the same time, the volume of production growth in the crop and livestock sectors amounted to 11.11 and 10.49 billion rubles, respectively, which demonstrates the uniform development of the two main industries. The growth in volumetric indicators of product production is mainly due to the intensification of production processes, the introduction and development of innovative technologies. Along with this, abnormal weather conditions have a negative impact on the process of sustainable development of agriculture, as evidenced by the results of 2021, when the total production volume was only 80.1% of the previous year’s level, and crop production decreased by 38.7%. The identified trend once again emphasizes the need to search for adaptive methods of farming in accordance with climate change by moving at a more intensive pace to the creation of drought-resistant varieties, the development of moisture-saving technologies and the active implementation of reclamation measures. Livestock farming is developing at a more sustainable pace, although there is a decline in the numbers of certain animal species. However, this process does not affect production volumes due to increased animal productivity. The industry is in the process of gradually replacing low-productive animals with highly productive breeding stock. To transition to sustainable development of agriculture in the republic, it is necessary to continue work on adapting technological processes to climate change, creating and introducing new varieties, developing genetic technologies for animal breeding, using digitalization and robotization of the industry.

The effect of a microbial consortium on spring barley growth and development in the Kemerovo region, Kuzbass

The Kemerovo region is a region of extreme farming, and therefore requires the development of effective biological methods for improving productivity of cultivated plants adapted to these conditions. The purpose of the current work was to find plant growth-stimulating microorganisms associated with spring barley, as well as to form a microbial consortium on their basis and to conduct field trial. The objects of the study were spring barley seeds selected in the Kemerovo region, growth-stimulating microorganisms isolated from them, and consortia formed on their basis. During the study, there were isolated and tested 9 bacterial isolates for their ability to synthesize growth-stimulating substances (kinetin and indolyl-3-acetic acid). There were selected three strains with the best indicators SHv-2, SHv-5, SHv-6 for further studies. There has been found that the strains are capable of fixing nitrogen (SHv-2 – 800 µg/ml; SHv-5 – 210 µg/ml; SHv-6 – 840 µg/ml). There was also seen an ability to solubilize phosphates (SHv-2 – 2.53; SHv-5 – 1.25; SHv-6 – 3.33). The study of cultural, morphological and biochemical properties made it possible to identify such strains as SHv-2 – Pantoea allii; SHv-5 – Raoultella ornithinolytica; SHv-6 – Pantoea ananatis. Based on these microorganisms, there were made various variants of microbial consortia, which were also valued for their ability to produce growth-promoting substances and fix nitrogen. Consortium MC-7 composed with Pantoea allii, Raoultella ornithinolytica, Pantoea ananatis in a ratio of 1:1:3, respectively, showed maximum activity in terms of the studied parameters. Field trials of this consortium have shown that it successfully improves spring barley productivity. Despite abnormal weather conditions (lack of productive rains), there was an increase in the growth of crops treated by the consortium in comparison with control options. There have been established the most promising options for processing spring barley, options 3 and 4.

농업용 저수지 안전점검 및 등급 체계 개선방안 연구

Accidents in agricultural reservoirs continue to occur due to heavy rain caused by abnormal weather conditions. To prevent reservoir accidents, it is necessary to identify factors affecting collapse and manage them safely through prior inspection and repair. For reservoirs with a total storage capacity of less than 50,000㎥, which accounts for 81% of reservoirs nationwide, regular inspections conducted at least once a quarter are the only safety inspection procedures, and the conduct of regular inspections can be seen as a measure of safety inspections. As a result of the survey, it was determined that the implementation status of regular inspection was low at 46%, and problems were analyzed and improvement measures were proposed. Additionally, if the factor affecting collapse is a member with a low score, it is difficult to reflect the results of regular inspection in the safety grade. In this study, we analyzed the problems with the regular inspection grading system and proposed an improvement plan by borrowing and applying the condition evaluation method of precise safety diagnosis, assessing overflow risk, and considering downstream impacts.

Cost-Benefit Analysis of a Two-Unit Cold Standby System with Imperfect Repair Man and Abnormal Weather Conditions

Cost-Benefit Analysis of a Two-Unit Cold Standby System with Imperfect Repair Man and Abnormal Weather Conditions

CropSow: An integrative remotely sensed crop modeling framework for field-level crop planting date estimation

Crop planting timing is critical in regulating environmental conditions of crop growth throughout the season, and is an essential parameter in crop simulation models for estimating dry matter accumulation and yields. Accurate planting date information is key to characterizing crop growing dynamics under varying farming practices and facilitating agricultural adaptation to climate change. To date, the main methods to acquire planting dates include field survey methods, weather-dependent methods, and remote sensing phenological detecting methods. However, it is still challenging to effectively estimate the crop planting dates at field levels due to the lack of appropriate field-level modeling design as well as the dearth of ground planting reference data. In our study, we develop a novel CropSow modeling framework to estimate field-level planting dates by integrating the remote sensing phenological detecting method with the crop growth model. The remote sensing phenological detecting method is devised to retrieve the critical crop phenological metrics of farm fields from remote sensing time series, which are then integrated into the crop growth model for field planting date estimation in consideration of soil-crop-atmosphere continuum. CropSow leverages the rich physiological knowledge embedded in the crop growth model to scalably interpret satellite observations under a variety of environmental and management conditions for field-level planting date retrievals. With corn in Illinois, US as a case study, the developed CropSow outperforms three advanced benchmark models (i.e., the remote sensing accumulative growing degree day method, the weather-dependent method, and the shape model) in crop planting date estimation at the field level, with R square higher than 0.68, root mean square error (RMSE) lower than 10 days, and mean bias error (MBE) around 5 days from 2016 to 2020. It achieves better generalization performance than the benchmark models, as well as stronger adaptability to abnormal weather conditions with more robust performance in estimating the planting dates of farm fields. CropSow holds considerable promise to extrapolate over space and time for estimating the timing of crop planting of individual farm fields at large scales.

Climate change and Its Impact on coastal communities in india: A quantitative investigation

Climate change has become an international problem. It is having an impact on various facets of our lives. Particularly in coastal areas in India. The increasing sea levels, abnormal weather conditions, and severe weather occasions have become a substantial issue for those communities, representing a significant danger to their jobs and safety. India's extensive coastline spanning over 7,500 kilometersharbors a plethora of individuals who rely on the sea for their subsistence. However, the adverse effects of climate change have imperiled their existence, rendering them vulnerable to various perils. The repercussions of these cataclysmic changes have resulted in severe beach erosion, damage to infrastructure, and property loss, leaving the coastal communities in despair. To ameliorate this bleak predicament. This is imperative to explore sustainable solutions that can aid in mitigating the current situation. climate change has caused devastation to native habitats and wildlife. This influencing the economic situation and diversity of the region. how communities are adjusting to the transforming climate and what steps can be taken to lessen their susceptibility to the climate change effects.

Generation of High Temporal Resolution Full-Coverage Aerosol Optical Depth Based on Remote Sensing and Reanalysis Data

Aerosol Optical Depth (AOD) is a crucial physical parameter used to measure the radiative and scattering properties of the atmosphere. Obtaining full-coverage AOD measurements is essential for a thorough understanding of its impact on climate and air quality. However, satellite-based AOD products can be affected by abnormal weather conditions and high reflectance surfaces, leading to gaps in spatial coverage. To address this issue, we propose a satellite-based AOD filling method based on hourly level-3 Himawari-8 AOD products. In this study, the optimal model with a mean bias error (MBE) less than 0.01 and a root-mean-square error (RMSE) less than 0.1 in most land cover types was selected to generate the full-coverage AOD. The generated full-coverage AOD was validated against in situ measurements from the AERONET sites and compared with the performance of Himawari-8 AOD and MERRA-2 AOD over the AERONET sites. The validation results indicate that the accuracy of full-coverage AOD is comparable to that of the Advanced Himawari Imager (AHI) AOD, and for other land cover types (excluding barren land), the accuracy of full-coverage AOD is superior to that of MERRA-2 AOD. To investigate the practical application of full-coverage AOD, we utilized it as an input parameter to perform radiative transfer simulations in northwestern and southern China. The validation results showed that the simulated at-sensor radiance based on full-coverage AOD was in good agreement with the at-sensor radiance observations from MODIS. These results indicate that complete and accurate measurements of AOD have considerable potential for application in the simulation of at-sensor radiance and other related topics.

A Route Search System to Avoid the Danger to Life in Dynamic Inundation

In recent years, the frequency of torrential rains has increased due to abnormal weather conditions. Torrential rains have caused extensive flooding damage in many areas. As delays in evacuation can pose a threat to life, a quick search for safe evacuation routes has become more important than ever before. In this study, we constructed a new system for searching evacuation routes that incorporates a function that varies the weight of each road in the route search depending on the distance from the flooded area D and the distance that the flood area extends in 10 min D′ (i.e., the flood’s inundation speed). We conducted multiple hypothetical flood simulations with different locations of levee breaches and shelters in the study site (Obihiro City, Japan). Then, we compared the results with the conventional system that does not include the proposed function. The results showed that the system proposed in this study increased the number of successful evacuees by up to 2.16 times compared to the conventional system. In our system, the weight function is set to the Cd power of D/D′; increasing the model parameter Cd selects a route that detours more of the flooded area. The model parameter Cd that maximizes the number of successful evacuees is roughly constant, regardless of the locations of the levee breaches or shelters in the study site.

The reaction of the urban soil-plant complex on abnormal weather conditions

This research is devoted to the study of geochemical features in the soil-plant complex of green spaces in Saint-Petersburg and their changes in abnormal weather conditions, which was the first experience of such work in this region. It included the research of such aspects as accumulation of heavy metals by plants from the soil, photosynthetic pigment apparatus of plants and the content of organic carbon in the soil. The X-ray fluorescence analysis method was used to determine the content of heavy metals, the determination of organic carbon in the soil was carried out in accordance with the Tyurin method, the content of chlorophyll and carotenoids was determined by photometrical method. The study showed that in stressful conditions (abnormal hot weather) there are changes happening in the pigments system of the leaf and in the barrier function in relation to heavy metals.

Assessment of potential risk during gas outflow at above-ground sections of the gas pipeline taking into account abnormal weather conditions of the North

The article presents a model for assessing the potential risk of gas leakage from aboveground sections of the gas pipeline at low temperatures.The influence of abnormal weather conditions on the magnitude of the excess pressure of the shock wave is substantiated. The safe distances during the explosion of a methane gas-air mixture cloud under conditions of temperature inversion are estimated. Based on the data obtained, a model for assessing potential risk has been developed.

Constraints Faced by Cotton Farmers in Management of Cotton Cultivation in Madhya Pradesh, India

The study was conducted in ten purposively selected villages of Khandwa district of Madhya Pradesh on the basis of large area under cotton crop and a total of 200 farmers selected by the proportionate random sampling technique. An interview schedule was prepared for collection data relating to constraint faced by cotton growers. They faced in growing cotton to lack of knowledge about improved scientific practices in cotton crop (89.50%), insufficient training programs (88.00%), insufficient demonstration of improved technology (83.50%), lack of technical knowledge about pheromone and sticking trap (81.00%), high price & poor quality of Bt. cotton seeds (79.50%), lack of information regarding upcoming abnormal weather conditions (78.00%), lack of coordination between farmer and agriculture institution (76.50%), scarcity of labour at the plucking stage (73.50%), a decline in the yield of cotton due to insects (71.00%), lack of technical knowledge about the recommended insecticide, pesticide, fungicide and herbicide (68.00%), high costs of fertilizers (65.50%), uneven electricity supply (65.00%), lack of expertise in identifying the crop's pests, diseases, and weeds (63.50%). Farmer has not proper knowledge about improved cultivation practices of cotton crop. So famer should be provided the various training programmes be organized to provide practical knowledge and field demonstrations should be arranged on improved practices.

Derivation the availability of a system with one unit in normal and abnormal weather conditions by using Laplace transform with application to case of Normal Weather

In this paper We will study the system in good and bad weather by using Laplace transform, the system have one unite in operative, partial failure and total failure modes in two weather conditions normal and stormy resolved by Laplace transform and application to the case of normal Weather only considering existence of preventive maintenance.

Тестирование и перспективы технологии прогнозирования загрязнения воздуха с применением химических транспортных моделей CHIMERE и COSMO-Ru2ART

The technology for numerical air pollution prediction operating in the Hydrometeorological Center of Russia is based on the calculations with CHIMERE and COSMO-ART chemical transport models (CTMs) on a grid with a horizontal step of 2.2 km using the EMEP inventory data on atmospheric emissions updated in 2013 and meteorological data of the COSMO-Ru2 atmosphere model. The paper presents the results of testing model forecasts of pollutant (NO2, CO, PM10, O3) concentrations based on continuous pollution observations in the Moscow region for 12 months. A predominant overestimation of concentrations by the CHIMERE and an underestimation by the COSMO-ART were revealed. Both CTMs mainly underestimate pollution in transport-type areas. The seasonal variability of model deviations with a sign change for each model and each pollutant was identified. The largest model deviations were observed under abnormal weather conditions: during summer heat waves and spring advection of dusty air. Taking into account the model errors established for each CTM, recommendations are formulated for producing an operational consolidated forecast using the integration of simulations with the two CTMs. The development of the technology for numerical air pollution prediction using modified CTM versions and EMEP emissions updated in 2019 on a 0.1×0.1° grid is discussed. Keywords: air pollution, numerical forecast, chemical transport model, CHIMERE, COSMO-Ru2ART, emissions, EMEP, testing

AWMC: Abnormal-Weather Monitoring and Curation Service Based on Dynamic Graph Embedding

This paper presents a system, namely, the abnormal-weather monitoring and curation service (AWMC), which provides people with a better understanding of abnormal weather conditions. The service can analyze a set of multivariate weather datasets (i.e., 7 meteorological datasets from 18 cities in Korea) and show (i) which dates are mostly abnormal in a certain city, and (ii) which cities are mostly abnormal on a certain date. In particular, the dynamic graph-embedding-based anomaly detection method was employed to measure anomaly scores. We implemented the service and conducted evaluations. Regarding the results of monitoring abnormal weather, AWMC shows that the average precision was approximately 90.9%, recall was 93.2%, and F1 score was 92.1% for all the cities.