Search

In the present study, the spatiotemporal evaluation of the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite precipitation product is performed in capturing meteorological drought over different climatic regions of Iran. The performance of the product as a high spatial resolution dataset in monitoring drought is evaluated against the 68 meteorological stations from short to long scale (i.e., SPI1, SPI3, SPI6, SPI9, and SPI12) in the period of 1987 to 2017. Besides, the capability of the CHIRPS in detecting drought events is assessed in different drought classes. The results suggest that the climate type, the time scale, and the drought class affect the quality of the CHIRPS performance. The CHIRPS offers the best performance in the detection of all drought events with SPI <  - 1 over the SPI1 (0.69 < POD < 0.85). However, the product provides the worst performance for SPI12 (0.50 < POD < 0.70). At the country level, the highest agreement between the CHIRPS- and observation data-based SPI is found over the SPI6 (CC = 0.56), while the lowest is observed over the SPI12 (CC = 0.47). Based on the temporal evaluation, the G6 (0.18 < CC < 0.44, 1.06 < RMSE < 1.28) and G8 (0.17 < CC < 0.43, 1.06 < RMSE < 1.29) regions located in the southern coast of the Caspian Sea have an inadequate performance. However, the southern parts (G4 region) (0.38 < CC < 0.65, 0.83 < RMSE < 1.27) and the northwestern area (G3 region) (0.53 < CC < 0.62, 0.87 < RMSE < 0.97) of the country offer the best performance. The spatial evaluation describes the high accuracy (CC > 0.7, RMSE < 0.5) in some regions, including the western parts of G1, the northern area of G3, and the southern parts of G4. The research findings provided an important opportunity to advance the understanding of drought monitoring over the different climatic regions based on the high-resolution satellite precipitation products., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The unprecedented increase in anthropogenic activities, coupled with the prevailing climatic conditions, has increased the aerosol load over East Africa (EA). Given this, the present study examined the trends in total, absorption, scattering, and total aerosol extinction optical depth (TAOD, AAOD, SAOD, and TAEOD) over EA, alongside trends in single scattering albedo (SSA). For this purpose, the AOD of different optical properties retrieved from multiple sensors and the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) model between January 2001 to December 2019 were utilized to estimate trends and assess their statistical significance. The spatial patterns of seasonal mean AOD from the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor and MERRA-2 model were generally characterized with high (>0.35) and low (<0.2) AOD centers over EA observed during the local dry and wet seasons, respectively. Also, the spatial trend analysis revealed a general increase in TAOD, being positive and significant over the arid and semi-arid zones of the northeastern part of EA, which is majorly dominated by locally derived dust. The local dry (wet) months generally experienced positive (negative) trends in TAOD, associated with seasonal cycles of rainfall. High and significant positive trends in AAOD were dominated over the study domain, attributed to an increased amount of biomass burning, variations in soil moisture, and changes in the rainfall pattern. The trends in TAEOD showed a distinct pattern, except over some months that depicted significant increasing trends attributed to changes in climatic conditions and anthropogenic activities. At last, the study domain exhibited decreasing trends in SSA, signifying strong absorption of direct solar radiation resulting in a warming effect. The study revealed patterns of trends in aerosol optical properties and forms the basis for further research in aerosols over EA., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This article presents a semester-long, interdisciplinary project-based learning (PBL) suitable for secondary and postsecondary students enrolled in a second-semester (i.e., intermediate) meteorology course. This case-study approach builds on the research question "What atmospheric conditions lead to tornadoes, hail, and damaging winds locally?" Students work collaboratively in a series of five activities, using an online database analyzing map and weather data from a subset of severe weather events. Students synthesize the data by identifying the severe qualitative weather variables that appeared most frequently among the cases and modify, if necessary, accepted threshold values for the quantitative variables. Students finalize the project via an oral presentation and technical paper to transform their newly discovered knowledge into improved severe weather forecasting guidance for societal benefit. An analysis of pre- and postsurvey responses from a small student sample reveals increases in both the comfort and experience of the PBL's components, with the oral presentation showing the most significant impact. After the project, students could identify in greater depth those antecedent atmospheric conditions that generate tornadoes, hail, and strong winds.

Obtaining accurate data on reference crop evapotranspiration (ET 0 ) is important for agricultural water management. A novel Gaussian exponential model (GEM) was developed in this study to predict ET 0 with limited climatic data. The GEM was further compared with the M5 model tree (M5T), extreme learning machine (ELM), and boosted trees (BT) model under local and regional scenarios. Daily meteorological data during 1997-2016 from four stations in Northeast China were used to develop and validate the model. The results showed that the models considering solar radiation and relative humidity demonstrated considerably higher accuracy than those using other inputs. The GEM demonstrated higher accuracy among the four machine learning models for different stations. The accuracy of GEM under local scenarios was higher than that under regional scenarios with the root mean square error (RMSE) reducing by 0.025-0.046 mm/d, relative root mean square error (RRMSE) reducing by 0.879-2.022%, coefficient of efficiency (E ns ) increasing by 0.008-0.026, the coefficients of determination (R 2 ) increasing by 0.008-0.026, and mean absolute error (MAE) reducing by 0.015-0.033 mm/d. The GEM considering solar radiation had the highest accuracy with the global performance indicator (GPI) of 1.876. It can also be seen from the Taylor diagrams that the GEM has the the lowest standard deviation and mean square error and the highest correlation coefficient with the standard values. In general, the GEM considering solar radiation had the lowest error and the highest consistency and could be recommended for ET 0 simulation for Northeast China., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective: We here displayed the global research trends of meteorology and hemorrhagic fever with renal syndrome (HFRS) as a visual knowledge map by using bibliometrics and revealed the research directions, hotspots, trends, and frontiers in this field. Methods: Using Web of Science core collection as the data source and with CiteSpace and VOSviewer software, we collected and analyzed the annual number of papers, cooperative relationships (countries, institutions, authors, etc.), citations (literature citation, literature co-citation, literature publication, etc.), keywords (emergence, clustering, etc.) of meteorology, and HFRSrelated research data for the past 30 years, and drew a visual map. Results: In total, this study included 313 papers investigating the relationship between meteorology and HFRS. The first paper was published in 1992. Globally, United States had the largest number of publications in this field, and the Chinese Center for Disease Control and Prevention was the most influential institution conducting related research (20 articles published, and the mediation centrality was 0.24). Several small author cooperation clusters were formed; however, the number of papers published by the same scholar and the co-citation frequency were low. Cazelles Bernard (7 articles) published the highest number of articles in this field, and Gubler DJ was the author with the most co-citations (55 times). The most frequently cited journal was Emerging Infectious Diseases. In this field, the top three high-frequency keywords were "hemorrhagic fever," "transmission," and "temperature." According to keyword cluster analysis, the top three themes were dengue, dechlorane plus, and bank voles. The timeline spectrum exhibited that dengue clustering had a good temporal continuity. The trend analysis of emergent words revealed that the research on "temperature," "meteorological factors" and "Puumala hantavirus" has gradually appeared in recent years. Conclusion: This study represents the first comprehensive exploration of global trends, hotspots, frontiers, and developments in the relationship between meteorology and HFRS, utilizing CiteSpace and VOSviewer software. The findings of this study are crucial for elucidating the influence of climate change on disease transmission patterns and offering novel insights for forthcoming epidemiological research and public health interventions. [ABSTRACT FROM AUTHOR]

1 Weather and climate information The future risk projections presented in the paper are, as Raftery correctly states in his comment, conditional on a given deterministic representative concentration pathway emission scenario. Authors' reply to the Discussion of 'Assessing present and future risk of water damage using building attributes, meteorology and topography' at the first meeting on "Statistical aspects of climate change" For example, an interaction term between climate covariates and the cellar variable would result in a separate climate risk map for each level of the cellar variable (including the level "unknown"). For our response, we have grouped our comments according to four overarching themes, namely weather and climate information, model specification, model verification, and relation to other work. [Extracted from the article]

This paper examines a particular technique of weaponising smell – the spraying of 'skunk water', a crowd control tool originally developed by the Israeli Police to disperse Palestinian protests – and the olfactory atmospheres of moving matter it extends its violence through. It focuses particularly on ways in which skunk water spraying operates by 'weathering' the air with a stench that sticks on bodies, objects and spaces, often for considerably long periods. By elaborating the two entwined aspects of weathering – the weaponising and the meteorological – the paper shows how skunk water spraying engenders malodourous olfactory durations that move and through their movement extend their violence through meteorological fluidities and moving bodies/objects. The violence of skunk water, we so argue, contains lingering tempos that through material morphoses (water, mist, droplets, dried powder), reactivating/intensifying weather conditions (rain, heat, humidity, wind), and material kinetics (moving bodies, objects and air) spatialise proximities of atmospheric stench, hence targeting the way breathing bodies are immersed in their olfactory environments. By comprehending weathering as weaponised 'matter in motion', the paper offers a novel way of thinking about atmospheric violence through non-linear movements and lingering proximities – namely, as a weaponisation of an olfactory duration of a stinky matter that moves. [ABSTRACT FROM AUTHOR]

Concentrating on the Qingdao Observatory, this paper will explore the role of scientific facility in asserting China's sovereignty during the first half of the twentieth century. Although scholars have explained the efforts of China's internationalization in diplomacy through the perspectives of politics, economics and culture, they have not paid attention to science. Therefore, this paper aims to shed some light on how scientific issues were solved via diplomacy during the Republic of China, while further asserting that the focus in negotiations was not confined to science itself, but rather to sovereignty within a scientific context. In this process, the meaning of sovereignty has also been expanded basing on the improvement of nation's scientific capability. Besides, the participation of different actors involved in sovereignty assertion is investigated by this paper. Although the diplomatic negotiation was held at the international level, the local government and scientific community were main promoters in this case, which calls for attention on the various subjects in sovereignty issue. Consequently, this paper argues that Asian countries, such as Republic of China, could also employ science as a means to negotiate with foreign powers and claim their due rights.

Climate change means coping directly or indirectly with extreme weather conditions for everybody. Therefore, analyzing meteorological data to create precise models is gaining more importance and might become inevitable. Meteorologists have extensive domain knowledge about meteorological data yet lack practical data analysis skills. This paper presents a method to bridge this gap by empowering the data knowledge carriers to analyze the data. The proposed system utilizes symbolic AI, a knowledge base created by experts, and a recommendation expert system to offer suiting data analysis methods or data pre-processing to meteorologists. This paper systematically analyzes the target user group of meteorologists and practical use cases to arrive at a conceptual and technical system design implemented in the CAMeRI prototype. The concepts in this paper are aligned with the AI2VIS4BigData Reference Model and comprise a novel first-order logic knowledge base that represents analysis methods and related pre-processings. The prototype implementation was qualitatively and quantitatively evaluated. This evaluation included recommendation validation for real-world data, a cognitive walkthrough, and measuring computation timings of the different system components. [ABSTRACT FROM AUTHOR]

Over the past decade the use of machine learning in meteorology has grown rapidly. Specifically neural networks and deep learning have been used at an unprecedented rate. To fill the dearth of resources covering neural networks with a meteorological lens, this paper discusses machine learning methods in a plain language format that is targeted to the operational meteorological community. This is the second paper in a pair that aim to serve as a machine learning resource for meteorologists. While the first paper focused on traditional machine learning methods (e.g., random forest), here a broad spectrum of neural networks and deep learning methods is discussed. Specifically, this paper covers perceptrons, artificial neural networks, convolutional neural networks, and U-networks. Like the Part I paper, this manuscript discusses the terms associated with neural networks and their training. Then the manuscript provides some intuition behind every method and concludes by showing each method used in a meteorological example of diagnosing thunderstorms from satellite images (e.g., lightning flashes). This paper is accompanied with an open-source code repository to allow readers to explore neural networks using either the dataset provided (which is used in the paper) or as a template for alternate datasets. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of weather conditions on flight performance and aviation communication. By analyzing the complex relationship between meteorological factors and aviation operations, the importance of understanding and properly managing aviation weather is highlighted. In addition, it examines how communications between pilots and air traffic controllers are influenced by weather conditions and how they can affect flight safety and efficiency. [ABSTRACT FROM AUTHOR]

Estimating precipitation is of critical importance to climate systems and decision-making processes. This paper presents Espresso, a deep learning model designed for estimating precipitation from satellite observations on a global scale. Conventional methods, like ground-based radars, are limited in terms of spatial coverage. Satellite observations, on the other hand, allow global coverage. Combined with deep learning methods, these observations offer the opportunity to address the challenge of estimating precipitation on a global scale. This research paper presents the development of a deep learning model using geostationary satellite data as input and generating instantaneous rainfall rates, calibrated using data from the Global Precipitation Measurement Core Observatory (GPMCO). The performance impact of various input data configurations on Espresso was investigated. These configurations include a sequence of four images from geostationary satellites and the optimal selection of channels. Additional descriptive features were explored to enhance the model's robustness for global applications. When evaluated against the GPMCO test set, Espresso demonstrated highly accurate precipitation estimation, especially within equatorial regions. A comparison against six other operational products using multiple metrics indicated its competitive performance. The model's superior storm localization and intensity estimation were further confirmed through visual comparisons in case studies. Espresso has been incorporated as an operational product at Météo-France, delivering high-quality, real-time global precipitation estimates every 30 min. [ABSTRACT FROM AUTHOR]

Non-professional weather stations are often omitted from the networks of standard/professional stations at various spatial scales. Nevertheless, there are many tasks when such non-professional datasets can serve as the only or the most relevant available source respectively. Its acquisition costs, sufficient quality and capacity together with its moveability represent properties that should be taken into consideration when planning operational usage of various meteorological data. In this paper, we focus on the datasets of air temperatures and relative humidities measured both with professional and nonprofessional devices at nearly the same location. Four years of almost continual measurements (2016-2019) ensure robust sample of mutual comparison, which we analyze in the paper more in detail in order to assess the potential of non-professional datasets for utilization in aviation meteorology. Particular issues such as value difference patterns, large errors occurrence, temporal signal stability and seasonality are elaborated as well [ABSTRACT FROM AUTHOR]

Citizen science has emerged as a potent approach for environmental monitoring, leveraging the collective efforts of volunteers to gather data at unprecedented scales. Within the framework of the I-CHANGE project, MeteoTracker, a citizen science initiative, was employed to collect meteorological measurements. Through MeteoTracker, volunteers contributed to a comprehensive dataset, enabling insights into local weather patterns and trends. This paper presents the analysis and the results of the validation of such observations against the official Italian civil protection in situ weather network, demonstrating the effectiveness of citizen science in generating valuable environmental data. The work discusses the methodology employed, including data collection and statistical analysis techniques, i.e., time-series analysis, spatial and temporal interpolation, and correlation analysis. The overall analysis highlights the high quality and reliability of citizen-generated data as well as the strengths of the MeteoTracker platform. Furthermore, our findings underscore the potential of citizen science to augment traditional monitoring efforts, inform decision-making processes in environmental research and management, and improve the social awareness about environmental and climate issues. [ABSTRACT FROM AUTHOR]

Weather forecasting requires a comprehensive analysis of various types of meteorology data, and with the wide application of deep learning in various fields, deep learning has proved to have powerful feature extraction capabilities. In this paper, from the viewpoint of an image semantic segmentation problem, a deep learning framework based on semantic segmentation is proposed to nowcast Cloud-to-Ground and Intra-Cloud lightning simultaneously within an hour. First, a dataset with spatiotemporal features is constructed using radar echo reflectivity data and lightning observation data. More specifically, each sample in the dataset consists of the past half hour of observations. Then, a Light3DUnet is presented based on 3D U-Net. The three-dimensional structured network can extract spatiotemporal features, and the encoder–decoder structure and the skip connection can handle small targets and recover more details. Due to the sparsity of lightning observations, a weighted cross-loss function was used to evaluate network performance. Finally, Light3DUnet was trained using the dataset to predict Cloud-to-Ground and Intra-Cloud lightning in the next hour. We evaluated the prediction performance of the network using a real-world dataset from middle China. The results show that Light3DUnet has a good ability to nowcast IC and CG lightning. Meanwhile, due to the spatial position coupling of IC and CG on a two-dimensional plane, predictions from summing the probabilistic prediction matrices will be augmented to obtain accurate prediction results for total flashes. [ABSTRACT FROM AUTHOR]

Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Accurately predicting the maximum temperature is essential for studying human comfort, ecological environment development and social progress. However, traditional prediction methods are inefficient and inaccurate when dealing with large volumes of meteorological data. To tackle these challenges, this paper introduces an integrated approach, the ARIMA–LSTM–XGBoost model, which combines the strengths of autoregressive integrated moving average (ARIMA), long short-term memory network (LSTM) and eXtreme Gradient Boosting (XGBoost) to predict the maximum temperature. The proposed model enhances the prediction accuracy and convergence rate through techniques like MAPE reciprocal weight (MAPE-RW) and Schedule Sampling. Additionally, the model selects the best performing model using the early stopping method. This paper compares and analyzes the prediction results of the ARIMA, LSTM, XGBoost and ARIMA–LSTM–XGBoost models. The experimental results indicate that the ARIMA–LSTM–XGBoost model proposed in this paper achieves superior prediction accuracy, performance, and confidence. The ARIMA–LSTM–XGBoost model shows a Root-Mean-Squared Error (RMSE) of 1.381, significantly outperforming the ARIMA model (3.828), LSTM model (3.360) and XGBoost model (1.422). The coefficient of determination ( R 2) is 0.977, surpassing the values of 0.905 for the ARIMA model, 0.922 for the LSTM model and 0.910 for the XGBoost model. The ARIMA–LSTM–XGBoost model also exhibits a higher confidence level compared to the individual models. [ABSTRACT FROM AUTHOR]

The Adrar site is among sites of the highest solar radiation potential in the world. This region is characterized by high ambient temperature in the summer, which in some days is exceed 45 °C, where the high ambient temperature is affects the solar cells.The aim of this paper is to assessments the PV cells types mono crystalline, poly crystalline, micro crystalline amorphous and tripple junction for meteorological variables of Adrar environment.These models were tested by using input data from meteorological station of (URERMS-Adrar),and these inputs are used in software for PV system (PVsyst) to identify the characteristics of these types of PV module. The comparison between PV cells type mono-crystalline and poly-crystalline of same maximum power (250 W) it show that the cells type poly crystalline it more affected by the temperature of than mono crystalline. Finally, have reached that the assessments the performance of PV module is linked to weather (clear sky, overcast sky, shading, cold and hot). [ABSTRACT FROM AUTHOR]

This paper introduces an innovative and eco-friendly computational methodology to assess the wind potential of a location with the aid of high-resolution simulations with a mesoscale numerical weather prediction model (WRF), coupled with the statistical "10% sampling condition". The proposed methodology is tested for a location with complex terrain on the Greek island of Crete, where moderate to strong winds prevail for most of the year. The results are promising, indicating that this method has great potential for studying and assessing areas of interest. Adverse effects and challenges associated with wind energy production may be mitigated with methods such as the proposed one. Mitigating such effects should constitute the main focus and priority in research concerning wind energy production. [ABSTRACT FROM AUTHOR]

This paper explores traditional knowledge in four coastal districts of Odisha to understand its nature, role and relevance in negotiating cyclonic storms. It draws from fieldwork carried out in two phases 2007-2009 and 2015-2019 from the district of Jagatsinghpur, Kendrapara, Ganjam and Puri. In each of these locations, people have experience of cyclonic phenomena both as major disasters and as relatively smaller scale recurrent occurrences. The study shows that there is an extensive, informal rule based traditional knowledge system that makes use of multiple observational attributes in association with meteorological warning. In this perspective, prevalent traditional knowledge is neither privileged nor static and as a matter of fact is in a continual dialogue with meteorological information provided through India Meteorological Department's (IMD) cyclone warning services. Based on findings of this analysis, a framework is proposed that integrates traditional and meteorological knowledge systems for a more comprehensive understanding of local rural communities' response to cyclones. [ABSTRACT FROM AUTHOR]

A digital twin basin serves as a virtual representation of a physical basin, enabling synchronous simulation, virtual–real interaction, and iterative optimization. The construction of a digital twin basin requires a basin database characterized by large-scale coverage, high-precision, high-resolution, and low-latency attributes. The advancements in remote sensing technology present a new technical means for acquiring essential variables of the basin. The purpose of this paper was to provide a comprehensive overview and discussion of the retrieval principle, data status, evaluation and inter-comparison, advantages and challenges, applications, and prospects of remote sensing technology in capturing seven essential variables, i.e., precipitation, surface temperature, evapotranspiration, water level, river discharge, soil moisture, and vegetation. It is indicated that remote sensing can be applied in some digital twin basin functions, such as drought monitoring, precipitation forecasting, and water resources management. However, more effort should be paid to improve the data accuracy, spatiotemporal resolution, and latency through data merging, data assimilation, bias correction, machine learning algorithms, and multi-sensor joint retrieval. This paper will assist in advancing the application of remote sensing technology in constructing a digital twin basin. [ABSTRACT FROM AUTHOR]

Runoff and baseflow are two hydrological elements most closely involved in water-resource management. Defining the response of runoff/baseflow to meteorological drought (MD) is helpful for designing precise drought resisting measures. Thus, Pearson correlation coefficients and mutual information scores between runoff/baseflow and MD in five sub-basins of the Weihe River Basin (WRB) were estimated on a weekly scale, and the best response times of runoff/baseflow to MD on annual and calendar months were determined according to the maximum degree of response. Furthermore, the spatial and seasonal differences in response characteristics in the WRB were discussed and the baseflow index (BFI) was introduced to further explain the propagation process of MD to runoff/baseflow. The results showed that (1) in addition to the response time, the transition sequences of MD propagating to runoff and baseflow varied across basins due to the specific basin properties; (2) Response time of runoff to MD was related to BFI value and showed significant seasonality and hydrological periodicity. In summer and autumn (wet season), the response was faster and stronger, whereas the opposite occurred in winter and spring (normal/dry season); (3) BFI values indicated the main path of drought propagation, explaining the variation in response time between basins and seasons; hence, it can be used to simply and effectively determine the propagation speed of MD to runoff. This study clarified the response characteristics of the runoff process to MD and enhanced our understanding of the drought propagation process, which is crucial for mitigating and managing drought-related hazards., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The influence of the most frequent patterns of synoptic circulation on the dynamics of airborne pollen/spores recorded at the Barcelona Aerobiological Station (BCN) was analysed. Six pollen types (Platanus, Cupressaceae, Olea, Poaceae, Urticaceae and Amaranthaceae), and one fungal spore (Alternaria) were selected for their high allergenic effect in sensitive people. Six synoptic meteorological patterns were identified through cluster analysis of sea level pressure fields as the main responsible of the weather conditions in the Iberian Peninsula. The local meteorological conditions in Barcelona associated with each one of the synoptic types were also stablished. Different statistical methods were applied to analyse possible relationships between concentrations and timing of the recorded aerobiological particles and specific synoptic types. The study, focused in the 19-year period 2001-2019, shows that one of the scenarios, frequent in winter and linked to high stability and air-mass blockage, registered the highest mean and median values for Platanus and Cupressaceae, but it was not very relevant for the other taxa. It was also this scenario that turned out to be the most influent on the pollination timing showing a significant influence on the start occurrence of Urticaceae flowering and on the peak date of Platanus. On the other hand, the most frequent synoptic type in the period, relevant in spring and summer, was linked to sporadic episodes of levels considered to be of high risk of allergy to Platanus, Poaceae, and Urticaceae pollen, and Alternaria fungal spore. This synoptic pattern, characterized by the presence of the Azores anticyclone and the Atlantic low located in the north of the United Kingdom, was associated with high temperatures, low relative humidity and moderate winds from the NW in Barcelona. The identification of an interaction between synoptic meteorology and pollen/spore dynamics will allow better abatement measures, reducing adverse health effects on sensitive population., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Although natural philosophers of Enlightenment Europe shared common ideals, like reliance on reason and natural philosophy, to promote what they deemed to be progress; there were national differences in attitude and disciplinary focus. This paper takes various eligibility criteria as a starting point from which to define a Nordic Enlightenment science; and situates endeavours in climate science within visions of useful science and international conventions for scientific practice. Two perspectives are explored: the make-up of the Nordic Enlightenment science; and the Nordic natural philosopher's various platforms for work and knowledge transfer. While historians differ as to what constitutes Enlightenment thought and spirit, I establish the existence of a Nordic Enlightenment science by identifying and examining several of its indicators. The paper concludes with a more specific discussion of climate science in Norway in which I show how climate observations performed during the eighteenth century by a sample of Norway's clergymen and civil servants bear testimony to an internationally-oriented science, through articles produced for science journals and conventions followed for data presentation and instrumentation. The findings corroborate existing knowledge of a progress-driven, Enlightenment science in Nordic countries; reveal differences between countries, and present Norway's early-modern climate science in an international light.

Urban development creates several inadvertent impacts related to urban climate and human biometeorology. Monitoring systems based on microcontrollers are slowly emerging as an alternative to conventional devices for monitoring outdoor thermal comfort (OTC), thus overcoming limitations imposed by the high costs of commercially available equipment. This review was conducted using the Scopus database, searching for articles and conference papers according to a pre-defined search string, which included the terms "microcontrollers" and "human thermal comfort" up to 2022. From a total sample of 113 articles, 52 papers met the desired criteria (written in English, published in peer-reviewed journals, and within the given time frame). Results show a growing, yet timid trend of published material on low-cost, open-source technologies for diverse applications in human biometeorology., (© 2023. The Author(s) under exclusive licence to International Society of Biometeorology.)

While deep learning (DL) models are effective in rainfall-runoff modelling, their dependence on data and lack of physical mechanisms can limit their use in hydrology. As there is yet no consensus on the consideration of the fundamental water balance for DL models, this paper presents an in-depth investigation of the effects of water balance constraint on the long-short term memory (LSTM) network. Specifically, based on the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset, the LSTM and its architecturally mass-conserving variant (MC-LSTM) are trained basin-wise to provide rainfall-runoff prediction and then the robustness of the LSTM and MC-LSTM against data sparsity, random parameters initialization and contrasting climate conditions are assessed across the contiguous United States. Through large-sample tests, the results show that the water balance constraint evidently improves the robustness of the basin-wise trained LSTM. On the one hand, as the amount of training data increases from 1 year to 15 years, the incorporation of the water balance constraint into the LSTM network decreases the sensitivity from 95.0 % to 32.7 %. On the other hand, the water balance constraint contributes to the stability of the LSTM for 450 (85 %) basins when there are 3 years' training data. In the meantime, the water balance constraint improves the transferability of the LSTM from the driest years to the wettest years for 318 (67 %) basins. Overall, the in-depth investigations of this paper facilitate insights into the use of DL models for rainfall-runoff modelling. [ABSTRACT FROM AUTHOR]

The crucial role of meteorology in military activities is evident both theoretically and practically. Meteorological phenomena profoundly influence the planning and execution of ground, air, and maritime operations, and ignoring them can have devastating consequences. This paper explores the importance of meteorological forecasting in the military domain, emphasizing the need to understand atmospheric phenomena for making informed decisions and anticipating potential risks. By analyzing historical data and climate trends, it highlights the possibility of adapting military strategies to climate change and meteorological variability. Moreover, it proposes the use of historical archives, including military ones, to reconstruct past meteorological and climatic conditions, offering a new perspective on how weather and climate have influenced past military operations and may influence future ones. [ABSTRACT FROM AUTHOR]

Peak wind gust (Wp) is a crucial meteorological variable for wind farm planning and operations. However, for many wind farm sites, there is a dearth of on-site measurements of Wp. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset and, in turn, generates multi-year, site-specific Wp series. Through a systematic feature importance study, we also identify the most relevant meteorological variables for Wp estimation. The INTRIGUE approach outperforms the baseline predictions for all wind gust conditions. However, the performance of this proposed approach and the baselines for extreme conditions (i.e., Wp > 20ms-1) is less satisfactory. [ABSTRACT FROM AUTHOR]

The outbreak of coronavirus disease in 2019 has become a serious threat to human health. Whether meteorological conditions could influence the transmission and virulence of COVID-19 remains controversial. In this study, we systematically reviewed the impact of temperature and humidity on the replication, morbidity, and mortality of COVID-19. We also discussed the main factors underlying the inconsistency across studies. Pubmed, Web of Science, Embase, and Scopus were used to identify papers published up to 7 December 2020. We initially identified 3515 papers, and 28 articles met the inclusion criteria after screening. Most studies showed high temperature and high humidity can partly reduce the reproduction, morbidity, and mortality of COVID-19. But the rest papers failed to identify a significant association. The discrepant results may be related to the difference in the climate context, study design, exposure assessment, policy intervention, socioeconomic status, and public health service. [ABSTRACT FROM AUTHOR]

ESCAP/WMO Typhoon Committee Members are directly or indirectly affected by typhoons every year. Members have accumulated rich experiences dealing with typhoons' negative impact and developed the technologies and measures on typhoon-related disaster risk forecasting and early warning in various ways to reduce the damage caused by typhoon. However, it is still facing many difficulties and challenges to accurately forecast the occurrence of typhoons and warning the potential impacts in an early stage due to the continuously changing weather conditions. With the development of information technology (IT) and computing science, and increasing accumulated hydro-meteorological data in recent decades, scientists, researchers and operationers keep trying to improve forecasting models based on the application of big data and artificial intelligent (AI) technology to promote the capacity of typhoon-related disaster risk forecasting and early warning. This paper reviewed the current status of application of big data and AI technology in the aspect of typhoon-related disaster risk forecasting and early warning, and discussed the challenges and limitations that must be addressed to effectively harness the power of big data and AI technology application in typhoon-related disaster risk reduction in the future. [ABSTRACT FROM AUTHOR]

Evaluation of explosion-proof protected electrical equipment in scope of certification is extremely important considering the risk of explosion that has to be minimized in order to ensure life safety and health of workers and to prevent damaging of property and the environment, as well as free movement of goods when they meet the essential safety requirements at European level. The purpose of this paper is to present aspects regarding the importance of thermal endurance tests performed on the electrical equipment used in potentially explosive atmosphere. The paper also presents laboratory facilities for performing the thermal endurance test. [ABSTRACT FROM AUTHOR]

This paper proposes a modeling approach for characterizing the probability of wildfire ignition caused by faults on power distribution systems. The proposed model serves as a starting point in research literature to illustrate, from an analytical perspective, the many factors that influence wildfire ignitions in power distribution systems. This paper presents the series of events that leads to power-system-related wildfire ignitions, and characterizes the wildfire ignition probability as a combination of the probability that a fault occurs along a power distribution line segment, and the probability that the fault results in the sustained ignition of a vegetation fuel bed surrounding the line. The proposed model integrates a variety of data including environmental conditions, power system protection settings, and power system line flows. A case study is performed on a test 33-bus distribution system using observed historical weather data from a high-threat fire district in California. The California case study is utilized to investigate the effects of three primary factors (wind speed, line congestion, and protection settings) on wildfire ignition probability. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between daily carbon emissions for California's savanna and forest wildfires and regional meteorology over the past 18 years. For each fuel type, the associated weather [daily maximum wind, daily vapor pressure deficit (VPD), and 30-day-prior VPD] is determined for all fire days, the first day of each fire, and the day of maximum emissions of each fire at each fire location. Carbon emissions, used as a marker of wildfire existence and growth, for both savanna and forest wildfires are found to vary greatly with regional meteorology, with the relationship between emissions and meteorology varying with the amount of emissions, fire location, and fuel type. Weak emissions are associated with climatologically typical dryness and wind. For moderate emissions, increasing emissions are associated with higher VPD from increased warming and only display a weak relationship with wind speed. High emissions, which encompass ∼85% of the total emissions but only ∼4% of the fire days, are associated with strong winds and large VPDs. Using spatial meteorological composites for California subregions, we find that weak-to-moderate emissions are associated with modestly warmer-than-normal temperatures and light winds across the domain. In contrast, high emissions are associated with strong winds and substantial temperature anomalies, with colder-than-normal temperatures east of the Sierra Nevada and warmer-than-normal conditions over the coastal zone and the interior of California. Significance Statement: The purpose of this work is to better understand the influence of spatially and temporally variable meteorology and spatially variable surface fuels on California's fires. This is important because much research has focused on large climatic scales that may dilute the true influence of weather (here, high winds and dryness) on fire growth. We use a satellite-recorded fire emissions dataset to quantify daily wildfire existence and growth and to determine the relationship between regional meteorology and wildfires across varying emissions in varying fuels. The result is a novel view of the relationship between California wildfires and rapidly variable, regional meteorology. [ABSTRACT FROM AUTHOR]

This article analyzes weather as enabler of, and able to make, cinematic art. I begin by exploring philosophies of weather and air, and then look at early films where meteorological phenomena receive acute attention alongside related media analyses. Afterwards I analyze two artworks, by Madge Evers and Anna Scime. Each was made by mushrooms sporifying on receptive media, respectively paper and analog film. This article includes original interviews with both artists. I identify fresh ways of understanding cinema but also weatherly and fungal creativity. Analyzing such artworks, I also think about how artists can develop art practices able to galvanize instead of eviscerate futurity. Consequently, I not only investigate more-thanhuman creativity, but explore how cinema can facilitate instead of block ecological healing. [ABSTRACT FROM AUTHOR]

According to GPS radio occultation data from previous studies, the height of the planetary boundary layer (PBLH) is defined as the altitude at which the vertical gradient of refractivity N is at its local minimum, called the gradient approach. As with its density, the atmosphere's refractivity falls broadly exponentially with height. The spherically symmetric refractivity Nss(r) was established to account for the standard deviation of atmospheric refractivity with altitude. Ni is the residual from the fundamental vertical variations of refractivity, defined as Ni(r) = N(r) − Nss(r). In this study, the vertical gradient of N is replaced by the vertical gradient of Ni to optimize the gradient approach, called the local gradient approach. Using the US radiosonde and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultations (ROs) data from 2007–2011, these two PBLH-determining approaches are evaluated. The PBLHs estimated by the gradient approach and the local gradient approach have RMSE values of 0.73 km and 0.65 km, respectively. The PBLH obtained by the local gradient approach is closer to the radiosonde-derived value. In this paper, the COSMIC-2 ROs data and the western Pacific typhoon best track data are collocated in time and space during 2020–2021, and the axisymmetric composite structural characteristics of the tropical cyclone (TC) PBLs are analyzed. The lowest vertical gradients of N and Ni of TCs correspond closely with the average PBLHs. We find that the mean PBLHs of tropical depressions (TD), tropical storms (TS), and typhoons (TY) all have their local maxima at a radial distance of 125 km with heights of 1.03 km, 1.12 km, and 1.36 km, respectively. After 375 km, 575 km, and 935 km of TD, TS, and TY radial distances, the mean PBLHs become stable and cease to vary. The mean PBLH undulations increase significantly with the increase in tropical cyclone intensity. Niwet is the residual from the fundamental vertical variations of wet refractivity, defined as Niwet(r) = Nwet(r) − Nsswet(r). Local minima of Niwet and Ni vertical gradients of TD, TS, and TY have comparable distributions and are concentrated between 0.5 km and 1 km. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting refers to the use of specific meteorological elements to predict precipitation in the next 0-2 h. Existing methods use radar echo maps and the Z-R relationship to directly predict future rainfall rates through deep learning methods, which are not physically constrained, but suffer from severe loss of predicted image details. This paper proposes a new model framework to effectively solve this problem, namely LSTMAtU-Net. It is based on the U-Net architecture, equipped with a Convolutional LSTM (ConvLSTM) unit with the vertical flow direction and depthwise-separable convolution, and we propose a new component, the Efficient Channel and Space Attention (ECSA) module. The ConvLSTM unit with the vertical flow direction memorizes temporal changes by extracting features from different levels of the convolutional layers, while the ECSA module innovatively integrates different structural information of each layer of U-Net into the channelwise attention mechanism to learn channel and spatial information, thereby enhancing attention to the details of precipitation images. The experimental results showed that the performance of the model on the test dataset was better than other examined models and improved the accuracy of medium- and high-intensity precipitation nowcasting.

The climate-health nexus is well documented in the field of biometeorology. Since its inception, Biometeorology has in many ways become the umbrella under which much of this collaborative research has been conducted. Whilst a range of review papers have considered the development of biometeorological research and its coverage in this journal, and a few have reviewed the literature on specific diseases, none have focused on the sub-field of climate and health as a whole. Since its first issue in 1957, the International Journal of Biometeorology has published a total of 2183 papers that broadly consider human health and its relationship with climate. In this review, we identify a total of 180 (8.3%, n = 2183) of these papers that specifically focus on the intersection between meteorological variables and specific, named diagnosable diseases, and explore the publication trends thereof. The number of publications on climate and health in the journal increases considerably since 2011. The largest number of publications on the topic was in 2017 (18) followed by 2021 (17). Of the 180 studies conducted, respiratory diseases accounted for 37.2% of the publications, cardiovascular disease 17%, and cerebrovascular disease 11.1%. The literature on climate and health in the journal is dominated by studies from the global North, with a particular focus on Asia and Europe. Only 2.2% and 8.3% of these studies explore empirical evidence from the African continent and South America respectively. These findings highlight the importance of continued research on climate and human health, especially in low- and lower-middle-income countries, the populations of which are more vulnerable to climate-sensitive illnesses., (© 2023. The Author(s).)

This paper introduces a fast urban microscale meteorological model with a horizontal resolution of O(10) m, named URBAN (Urban Rapid & Building-Aware Neighborhood), which is capable of rapid assessment of meteorological fields over key urban areas, including wind speed, air temperature, humidity and thermal comfort index, with the execution time less than 10 minutes consuming 1 CPU core. URBAN uses a fast wind diagnostic method to construct three-dimensional (3-D) wind fields surrounding complex building clusters with their geometry resolved explicitly To enhance the accuracy of wind reconstruction and the continuity of the initial wind field around irregular buildings, we propose a new parameterization method based on stream functions, which can accurately characterize the influences of complex urban building clusters on the three-dimensional wind field The model can provide various results for the meteorological service of large outdoor activities, including conventional meteorological elements (wind, temperature, humidity, radiation, etc.) and the Universal Thermal Comfort Index, which is derived from the relationship between physiological processes and environmental meteorological conditions. In this paper, URBAN is applied to develop an automatic analysis and forecast system of microscale meteorological elements over the central Beijing region in summer during a large outdoor event. By comparing with the half-hourly observations from three auto weather stations (AWSs) in the region, the root-mean-square errors (RMSEs) of the modeled 10-meter-height wind speed, 2-meter-height air temperature and humidity are 0.98 m s−1,1.37 °C and 7.37%, respectively. [ABSTRACT FROM AUTHOR]

Thunderstorm activity on March 25, 2023 provided a unique opportunity to study the mechanism of lightning events on changes in air pressure. In particular, this event made it possible to study changes in air pressure during thunderstorms using various instruments. This paper presented comprehensive results of infrasound, satellite data, weather radar and weather measurements at the ground during the storm. Observations of lightning events were confirmed using observational data from the International Space Station's Lightning Imaging Sensor (ISS LIS). This work estimated three spectral percentile values on infrasonic sensor data, time series interpolation of standard meteorology profiles, weather radar reflectivity and total radiant energy of lightning from ISS LIS observations during the day and night periods. As a result, during the investigation, it was seen that the recorded infrasound signal in the 0.6–0.8 Hertz (Hz) range was contaminated by background environmental noise, but in the 1–3 Hz band range it was consistent with the appearance of storms that produce high energy blows. Infrasound detection and electromagnetic lightning detection show good correlation up to a distance of 100 km from the infrasonic station. During a thunderstorm, the ISS LIS flight directly above the observation site detected more than 2,000 lightning events. In addition, the application of lightning detection from several independent instruments can provide a complete picture of the observed event. [ABSTRACT FROM AUTHOR]

Global reanalyzes like ERA5 accurately capture atmospheric processes at spatial scales of O(10) $\mathcal{O}(10)$ km or larger. By downscaling ERA5 with large‐eddy simulation (LES), LES can provide details about processes at spatio‐temporal scales down to meters and seconds. Here, we present an open‐source Python package named the "Large‐eddy simulation and Single‐column model—Large‐Scale Dynamics," or (LS)2D in short, designed to simplify the downscaling of ERA5 with doubly‐periodic LES. A validation with observations, for several sensitivity experiments consisting of month‐long LESs over Cabauw (the Netherlands), demonstrates both its usefulness and limitations. The day‐to‐day variability in the weather is well captured by (LS)2D and LES, but the setup under‐performs in conditions with broken or near overcast clouds. As a novel application of this modeling system, we used (LS)2D to study surface solar irradiance variability, as this quantity directly links land‐surface processes, turbulent transport, and clouds, to radiation. At a horizontal resolution of 25 m, the setup reproduces satisfactorily the solar irradiance variability down to a timescale of seconds. This demonstrates that the coupled LES‐ERA5 setup is a useful tool that can provide details on the physics of turbulence and clouds, but can only improve on its host reanalysis when applied to meteorological suitable conditions. Plain Language Summary: Modern global weather models are accurate in predicting atmospheric processes at scales of around 10 km or larger, but are less good at predicting smaller scale processes, like for example, the interaction between solar radiation, individual clouds, and the resulting clouds shadows that are cast onto the land surface. High spatio‐temporal resolution research models are able to capture these smaller scale processes, but require a coupling to a weather model to account for the day‐to‐day variability in our weather. In this paper, we present a framework to couple large to small scale models, and demonstrate both the benefits and challenges of using this coupled model setup. The coupled setup excels in capturing the aforementioned high frequency interactions between small clouds and surface solar radiation. However, the chaotic nature of broken to overcast clouds is proven difficult to represent. The coupled model setup is published as open‐source code, and is therefore freely available to the research community. Key Points: We developed an open‐source Python package named (LS)2D, designed to downscale the ERA5 reanalysis with turbulence and cloud‐resolving large‐eddy simulation (LESs)One month long experiments with (LS)2D and MicroHH over the Netherlands demonstrate both the skill and limitations of the coupled setupCapturing high‐frequency interactions between clouds and surface solar irradiance requires high resolution (O $\mathcal{O}$(10) m) LES [ABSTRACT FROM AUTHOR]

Clean solar energy is one of the best sources of energy. Solar power plants can generate electricity in Iran due to their large number of sunny days. This paper presents a short-term forecasting approach based on artificial neural networks (ANNs) for selected solar power plants in Iran and ranks the input variables of the neural network according to their importance. Two solar power plants in Hamadan province (Amirkabir and Khalij-Fars) were selected for the project. The output of solar power plants is dependent on weather conditions. Solar radiation on the horizontal plane, air temperature, air pressure, day length, number of sunny hours, cloudiness, and airborne dust particles are considered input variables in this study to predict solar power plant output. Forecasting model selection is based on considering zero and nonzero quantities of target variables. The results show that solar production forecasting based on meteorological parameters in the Khalij-Fars is more accurate than Amirkabir. The global solar radiation, air temperature, number of sunny hours, day length, airborne dust particles, cloudiness, air pressure, and dummy variables1 are the order of the most important inputs to solar power generation. Results show simultaneous influences of radiation and temperature on solar power plant production. [ABSTRACT FROM AUTHOR]

The paper highlights the main aspects of designing safety supply chains for grain cargoes and other agricultural commodity delivery. The study's relevance is caused by the fact that periodic port blockades do not allow Ukraine to carry out reliable exports of agricultural products along classical routes. Therefore, there was a need to find new alternatives for exporting agricultural commodities, primarily grain. The study aims to substantiate, describe, structure, and mathematically formalize proposed delivery options and choose the best. The research justifies picking supply chains using automobile and railway transport from Ukraine to the European Union countries. According to three proposed technologies, grain cargoes are exported in batches using containers. The advisability is justified for using each considered delivery option regarding technological aspects. Mathematical modeling, particularly regression analysis, is used to design supply chains. In this case, selecting the best technology is confirmed by corresponding calculations according to designed regression models. The presented option for supplying agricultural commodities is the safest and most reliable but more expensive. However, such logistics will be an excellent solution to reduce the negative consequences of a possible food crisis for the global economy. [ABSTRACT FROM AUTHOR]

This paper examines an alternative view of the use of resources in R&D teams and proposes a way to resolve the dispute between integration versus separation of idea generation and idea implementation concerning the achievement of high levels of product innovation. Contrary to the symmetric approach espoused by earlier studies, the hypotheses here are grounded in the notion that resources have an asymmetric association with idea generation and idea implementation. By asymmetric association we mean that resources have more impact on idea generation than on idea implementation. This paper considers the interactional relationship of two types of resources, time and financial budget, on idea generation and idea implementation. It is based on responses from 214 team members that belong to 40 R&D teams. The results support the existence of an asymmetric mechanism of association between resources and the idea generation and idea implementation climates. Furthermore, they demonstrate that the integration of idea generation and idea implementation is associated with high levels of product innovation when resources are available, while the separation of the two is associated with high levels of product innovation when resources are scarce. [ABSTRACT FROM AUTHOR]

Transmission system operators (TSOs) in recent years have faced challenges in order to ensure maximum transmission capacity of the system to satisfy market needs, while maintaining operational safety and permissible impact on the environment. A great help in the decision-making process was introduced with the Dynamic Thermal Rating (DTR) – an instrument to monitor and predict the maximal allowed ampacity of the power grid based on weather measurements and forecast. However, the introduction of DTR raises a number of questions related to the accuracy and uncertainty of the results of thermal assessment and the level of acceptable risk and its management. In this paper, we present a solution for estimating DTR uncertainty, appropriate for operational use at TSOs. With the help of conductor surface temperature measurements, weather measurements and predicted weather data, we also estimate the error of the weather forecast and the DTR itself. Following the results of the data analyses, we build an operative solution for estimating the ampacity uncertainty based on Monte Carlo random simulations and integrate it into the operational environment of ELES – the operator of the Slovenian electric power transmission network. [ABSTRACT FROM AUTHOR]

Some of the deadliest wildfires in the U.S., such as California’s 2018 wildfires, have been ignited by power systems. In an effort to prevent and minimize the ignition of wildfires, or control them if ignited, energy companies have developed wildfire mitigation plans. This paper provides energy companies and power system operators, engineers, researchers, and suppliers an overview of the state-of-the-art studies that address key topics in these wildfire mitigation plans and compares the wildfire mitigation plans of several energy companies. The key topics include grid design and system hardening, asset management and inspection, situational awareness and forecasting, operational response, vegetation management, public safety power shutoff, and risk-spend efficiency. This paper also presents a comparison of several energy companies’ decision-making criteria for initiating a public safety power shutoff. Finally, we discuss opportunities for future research studies that could help energy companies prevent wildfire ignitions. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) generation is increasing in distribution systems following policies and incentives to promote zero-carbon emission societies. Most residential PV systems are installed behind-the-meter (BTM). Due to single meter deployment that measures the net load only, this PV generation is invisible to distribution system operators causing a negative impact on the distribution system planning and local supply and demand balance. This paper proposes a novel data-driven BTM PV generation disaggregation method using only net load and weather data, without relying on other PV proxies and PV panels’ physical models. Long Short-Term Memory (LSTM) is employed to build a generation difference fitted model (GDFM) and a consumption difference fitted model (CDFM) derived from weather data. Both difference fitted models are refined by a cross-iteration with mutual output. Finally, considering the photoelectric conversion properties, the disaggregated generation results are acquired by the refined GDFM of changing input. The proposed method has been tested with actual smart meter data of Austin, Texas and proves to increase the disaggregated accuracy as compared to current state-of-the-art methods. The proposed method is also applicable to disaggregate BTM PV systems of different manufacturing processes and types. [ABSTRACT FROM AUTHOR]