Transmission line galloping often causes structural and electrical failures, which is a serious threat to the security of transmission systems. Through analysing the influence factors of galloping, ...it reveals that weather conditions are the most significant excitation factors and conductors of any voltage level and region may gallop when the apt-galloping weather conditions are satisfied. This study proposes an early warning method for transmission line galloping based on support vector machine (SVM) and AdaBoost bi-level classifiers. First, a prediction model of apt-galloping weather conditions based on an SVM classifier is built through data mining of historical weather parameters in regions where galloping frequently occurred. When the forecast weather conditions of a particular region satisfy the apt-galloping weather conditions, the conductor type, cross-section and span of transmission line are further considered to realise early warning of galloping through an AdaBoost classifier. Finally, the historical galloping events of a power grid are adopted to verify the validity of the proposed methods. The test results indicate that both the accurate classification rate and accurate warning rate are above 90%, whereas the missed warning rate is below 10%. The models are suitable for early warning of transmission line galloping and can provide important decision support for operation staff of power grid.
Changes in global fire activity are influenced by a multitude of factors including land‐cover change, policies, and climatic conditions. This study uses 17 climate models to evaluate when changes in ...fire weather, as realized through the Fire Weather Index, emerge from the expected range of internal variability due to anthropogenic climate change using the time of emergence framework. Anthropogenic increases in extreme Fire Weather Index days emerge for 22% of burnable land area globally by 2019, including much of the Mediterranean and the Amazon. By the midtwenty‐first century, emergence among the different Fire Weather Index metrics occurs for 33–62% of burnable lands. Emergence of heightened fire weather becomes more widespread as a function of global temperature change. At 2 °C above preindustrial levels, the area of emergence is half that for 3 °C. These results highlight increases in fire weather conditions with human‐caused climate change and incentivize local adaptation efforts to limit detrimental fire impacts.
Plain Language Summary
Observed increases in the frequency and severity of fire weather have been observed across portions of the globe over the past half century. We used climate models to identify where and when anthropogenic climate change causes fire weather conditions to exceed that of natural variability. Modeling results show that emergence for some fire weather indices is already under way for a sizable portion of the globe, including much of southern Europe and the Amazon, and with an expansion of this area with continued warming over the twenty‐first century. These findings suggest substantial increases in fire potential in regions where vegetation abundance and ignitions are not limiting, highlighting the urgency to adapt to changes in fire disturbances and hazards.
Key Points
Anthropogenic climate change is projected to enhance fire weather across most burnable global land surfaces during the twenty‐first century
Emergence of fire weather conditions from natural variability is modeled to occur in the first half of the twenty‐first century in many regions
Extent of fire‐weather emergence is twice as large when global temperature surpasses 3 °C, compared to 2 °C, above preindustrial levels
An all-weather land surface temperature (LST) dataset at moderate to high spatial resolutions (e.g. 1 km) has been in urgent need, especially in areas frequently covered in clouds (i.e. the Tibetan ...Plateau). Merging satellite thermal infrared (TIR) and passive microwave (PMW) observations is a widely-adopted approach to derive such LST datasets, whereas the swath gap of the PMW data leads to considerable data deficiency or low reliability in the merged LST, especially at the low-mid latitudes. Fortunately, reanalyzed data provides the spatiotemporally continuous LST and thus, is promising to be merged with TIR data for reconstructing the all-weather LST without this issue. However, few studies along this direction have been reported. In this context, based on the decomposition model of LST time series, this study proposes a novel reanalysis and thermal infrared remote sensing data merging (RTM) method to reconstruct the 1-km all-weather LST. The method was applied to merge Aqua/Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Global/China Land Data Assimilation System (GLDAS/CLDAS) data over the Tibetan Plateau and the surrounding area. Results show that the RTM LST has RMSEs of 2.03–3.98 K and coefficients of determination of 0.82–0.93 under all-weather conditions when validated against the ground measured LST. Besides, from comparison between RTM LST and current existing PMW-TIR merged LST, it is found the former LST efficiently outperforms the latter one in terms of accuracy and image quality, especially over the MW swath gap-covered area. In addition, compared to the MODIS-CLDAS merged all-weather LST based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the two LSTs have comparable accuracy while the RTM LST has higher spatial completeness. The method is promising for generating a long-term all-weather LST record at moderate to high spatiotemporal resolutions at large scales, which would be beneficial to associated studies and applications.
•RTM method is proposed to reconstruct 1-km all-weather LST from TIR and reanalysis data.•RTM LST outperforms TIR-MW merged LST in accuracy and image quality.•RTM LST outperforms the ESTARFM based MODIS-CLDAS merged LST in data integrity.•The method has potential to derive long-term global LST records at moderate/high resolutions.
What role do objective weather conditions play in coastal residents’ perceptions of local climate shifts and how do these perceptions affect attitudes toward climate change? While scholars have ...increasingly investigated the role of weather and climate conditions on climate‐related attitudes and behaviors, they typically assume that residents accurately perceive shifts in local climate patterns. We directly test this assumption using the largest and most comprehensive survey of Gulf Coast residents conducted to date supplemented with monthly temperature data from the U.S. Historical Climatology Network and extreme weather events data from National Climatic Data Center. We find objective conditions have limited explanatory power in determining perceptions of local climate patterns. Only the 15‐ and 19‐year hurricane trends and decadal summer temperature trend have some effects on perceptions of these weather conditions, while the decadal trend of total number of extreme weather events and 15‐ and 19‐year winter temperature trends are correlated with belief in climate change. Partisan affiliation, in contrast, plays a powerful role affecting individual perceptions of changing patterns of air temperatures, flooding, droughts, and hurricanes, as well as belief in the existence of climate change and concern for future consequences. At least when it comes to changing local conditions, “seeing is not believing.” Political orientations rather than local conditions drive perceptions of local weather conditions and these perceptions—rather than objectively measured weather conditions—influence climate‐related attitudes.
•A deep learning model is adopted for predicting block-level parking occupancy 30 min in advance.•The model takes multi-source data as input, e.g., parking, traffic and weather.•The model outperforms ...baseline methods including multi-layer LSTM and LASSO in the case study.•The prediction model works better for business areas than for recreational locations.•Incorporating traffic speed and weather data can significantly improve prediction performance.
A deep learning model is adopted for predicting block-level parking occupancy in real time. The model leverages Graph-Convolutional Neural Networks (GCNN) to extract the spatial relations of traffic flow in large-scale networks, and utilizes Recurrent Neural Networks (RNN) with Long-Short Term Memory (LSTM) to capture the temporal features. In addition, the model is capable of taking multiple heterogeneously structured traffic data sources as input, such as parking meter transactions, traffic speed, and weather conditions. The model performance is evaluated through a case study in Pittsburgh downtown area. The GCNN-based model outperforms other baseline methods including multi-layer LSTM and LASSO with an average testing MAPE of 10.6% when predicting block-level parking occupancies 30 min in advance. The case study also shows that, in generally, the prediction model works better for business areas than for recreational locations. We found that incorporating traffic speed and weather information can significantly improve the prediction performance. Weather data is particularly useful for improving predicting accuracy in recreational areas.
Security and reliability are major concerns for future power systems with distributed generation. A comprehensive evaluation of the risk associated with these systems must consider contingencies ...under normal environmental conditions and also extreme ones. Environmental conditions can strongly influence the operation and performance of distributed generation systems, not only due to the growing shares of renewable-energy generators installed but also for the environment-related contingencies that can damage or deeply degrade the components of the power grid. In this context, the main novelty of this paper is the development of probabilistic risk assessment and risk-cost optimization framework for distributed power generation systems, that take the effects of extreme weather conditions into account. A Monte Carlo non-sequential algorithm is used for generating both normal and severe weather. The probabilistic risk assessment is embedded within a risk-based, bi-objective optimization to find the optimal size of generators distributed on the power grid that minimize both risks and cost associated with severe weather. An application is shown on a case study adapted from the IEEE 13 nodes test system. By comparing the results considering normal environmental conditions and the results considering the effects of extreme weather, the relevance of the latter clearly emerges.
•Probabilistic risk assessed for distributed generation systems.•Extreme weather conditions are included in simulation and effects are quantified.•Risk based optimization to find optimal distributed generation sizes.•Distributed generation systems are confirmed less risky than the radial system.•Optimized DGs are less risky, especially considering extreme weather conditions.
A maximum temperature of 48.8°C (119.8°F) was purportedly recorded for the automated station in Siracusa (Syracuse) Contrada Monasteri, on the island of Sicilia (Sicily) Italy on August 11, 2021. A ...World Meteorological Organization (WMO) ad hoc evaluation committee was assembled to assess the possibility that the Sicilia temperature was the highest recorded temperature in WMO Region VI (continental only). After a detailed review of the site considerations and of the regional synoptic weather conditions, combined with detailed sensor testing and calibration by the Istituto Nazionale di Ricerca Metrologica (INRiM), the WMO evaluation committee concluded (and the Rapporteur accepted) that (1) on August 11, 2021 the high temperature recorded for the automated station in Siracusa C. da Monasteri, did reach a maximum value of 48.8°C (119.8°F), (2) that temperature is recommended to be listed as the WMO official “highest recorded temperature in WMO Region VI (continental only)” and (3) although, as the INRiM testing established, the recorded value of 48.8°C is actually an underestimate of the temperature, the committee recommend that the recorded (likely conservative) value of 48.8°C be the value listed in the Archive. An arbitrated archive of current weather and climate extremes is one means of ensuring that we have the best possible data for climate change analysis and public dissemination.
A maximum temperature of 48.8°C (119.8°F) was purportedly recorded for the automated station in Syracuse on the island of Sicily Italy on August 11, 2021. A World Meteorological Organization evaluation committee assessed the observation and concluded it was highest recorded temperature in WMO Region VI (continental only).
•We analyze the housing characteristics of low income and vulnerable population in Europe.•The indoor environmental quality of low income households during extreme weather conditions is ...examined.•Energy efficiency measures are proposed.
Extreme weather conditions in urban areas have a serious impact on the quality of life, energy consumption and health of urban citizens. In addition energy poverty has a serious impact on the quality of life of low income households. The aim of the present paper is review the actual housing status of low income population in Europe and discuss issues related to the impact of urban overheating and extreme weather phenomena on the specific energy consumption, indoor environmental conditions and health. Finally advanced low cost mitigation and adaptation technologies developed during the last years that offer a serious potential for energy and environmental improvements which can contribute to improve the quality of life of low income population are presented.
Synthetic aperture radar (SAR) sensors represent an indispensable data source for flood disaster planners and responders, given their ability to image the Earth's surface nearly independently of ...weather conditions and time of day. The decision by the European Space Agency (ESA) Copernicus program to open data from its Sentinel-1 SAR satellites to the public marks the first time global, operational SAR data have been made freely available. Combined with the emergence of cloud computing platforms like the Google Earth Engine (GEE), this development presents a tremendous opportunity to the disaster response community, for whom rapid access to analysis-ready data is needed to inform effective flood disaster response interventions and management plans. Here, we present an algorithm that exploits all available Sentinel-1 SAR images in combination with historical Landsat and other auxiliary data sources hosted on the GEE to rapidly map surface inundation during flood events. Our algorithm relies on multi-temporal SAR statistics to identify unexpected floods in near real-time. Additionally, historical Landsat-based surface water class probabilities are used to distinguish unexpected floods from permanent or seasonally occurring surface water. We assessed our algorithm over three recent flood events using coincident very high- spatial resolution imagery and operational flood maps. Using very high resolution optical imagery, we estimated an area-normalized accuracy of 89.8 ± 2.8% (95% c.i.) over Houston, Texas following Hurricane Harvey in late August 2017, representing an improvement of between 1.6% and 9.8% over flood maps derived from a simple backscatter threshold. Additionally, comparison of our results with SAR-derived Copernicus Emergency Management Service (EMS) maps following devastating floods in Thessaly, Greece and Eastern Madagascar in January and March 2018, respectively, yielded overall agreement rates of 98.5% in both cases. Importantly, our algorithm was able to ingest hundreds of SAR and optical images served on the GEE to produce flood maps over affected areas within minutes, circumventing the need for time-consuming data download and pre-processing steps. The flexibility of our algorithm will allow for the rapid processing of future open-access SAR data, including data from future Sentinel-1 missions.
•A new flood detection and monitoring algorithm based on dense Sentinel-1 SAR data is presented.•Temporal backscatter anomalies correct for bias arising from difference in sensor configuration and view angles.•Temporal Z-scores provide an objective measure of change due to flooding.•Integrating Sentinel-1 and Landsat data allow for distinction between seasonal water regimes and new flooding.•Google Earth Engine allows for rapid deployment of algorithm during flood events.
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