Topic Modelling and Sentiment Analysis of Global Warming Tweets

With the increasing extreme weather events and various disasters, people are paying more attention to environmental issues than ever, particularly global warming. Public debate on it has grown on various platforms, including newspapers and social media. This paper examines the topics and sentiments of the discussion of global warming on Twitter over a span of 18 months using two big data analytics techniques—topic modelling and sentiment analysis. There are seven main topics concerning global warming frequently debated on Twitter: factors causing global warming, consequences of global warming, actions necessary to stop global warming, relations between global warming and Covid-19; global warming’s relation with politics, global warming as a hoax, and global warming as a reality. The sentiment analysis shows that most people express positive emotions about global warming, though the most evoked emotion found across the data is fear, followed by trust. The study provides a general and critical view of the public’s principal concerns and their feelings about global warming on Twitter.

Recurrent Rossby waves during Southeast Australian heatwaves and links to quasi-resonant amplification and atmospheric blocks

In the Northern Hemisphere, recurrence of transient Rossby wave packets over periods of days to weeks, termed RRWPs, may repeatedly create similar weather conditions. This recurrence leads to persistent surface anomalies and high-impact weather events. Here, we demonstrate the significance of RRWPs for persistent heatwaves in the Southern Hemisphere (SH). We investigate the relationship between RRWPs, atmospheric blocking, and amplified quasi-stationary Rossby waves with two cases of heatwaves in Southeast Australia (SEA) in 2004 and 2009. This region has seen extraordinary heatwaves in recent years. We also investigate the importance of transient systems such as RRWPs and two other persistent dynamical drivers: atmospheric blocks and quasi-resonant amplification (QRA). We further explore the link between RRWPs, blocks, and QRA in the SH using the ERA-I reanalysis dataset (1979–2018). We find that QRA and RRWPs are strongly associated: 40 % of QRA days feature RRWPs, and QRA events are 13 times more likely to occur with an RRWPs event than without it. Furthermore, days with QRA and RRWPs show high correlations in the composite mean fields of upper-level flows, indicating that both features have a similar hemispheric flow configuration. Blocking frequencies for QRA and RRWP conditions both increase over the south Pacific Ocean but differ substantially over parts of the south Atlantic and Indian Ocean.

Big Data Analytics for Long-Term Meteorological Observations at Hanford Site

A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.

Fatal weather-related carbon monoxide poisonings in the United States

Carbon monoxide (CO) is a colorless, odorless gas that can cause injury or death if inhaled. CO is a frequent secondary hazard induced by the aftereffects of natural hazards as individuals, families, and communities often seek alternative power sources for heating, cooking, lighting, and cleanup during the emergency and recovery phases of a disaster. These alternative power sources—such as portable generators, petroleum-based heaters, and vehicles—exhaust CO that can ultimately build to toxic levels in enclosed areas. Ever-increasing environmental and societal changes combined with an aging infrastructure are growing the odds of power failures during hazardous weather events, which, in turn, are increasing the likelihood of CO exposure, illness, and death. This study analyzed weather-related CO fatalities from 2000 to 2019 in the U.S. using death certificate data, providing one of the longest assessments of this mortality. Results reveal that over 8,300 CO fatalities occurred in the U.S. during the 20-year study period, with 17% of those deaths affiliated with weather perils. Cool-season perils such as ice storms, snowstorms, and extreme cold were the leading hazards that led to situations causing CO fatalities. States in the Southeast and Northeast had the highest CO fatality rates, with winter having the greatest seasonal mortality. In general, these preventable CO poisoning influxes are related to a deficiency of knowledge on generator safety and the absence of working detectors and alarms in the enclosed locations where poisonings occur. Education and prevention programs that target the most vulnerable populations will help prevent future weather-related CO fatalities.

Endless November? Citizen Beliefs Concerning Wood As A Construction Material Under Extreme Weather Events

Climate change places great pressure on the construction sector to decrease its greenhouse gas emissions and to create solutions that perform well in changing weather conditions. In the urbanizing world, wood construction has been identified as one of the opportunities for mitigating these emissions. Our study explores citizen opinions on wood usage as a building material under expected mitigation and adaptation measures aimed at a changing climate and extreme weather events. The data are founded on an internet-based survey material collected from a consumer panel from Finland and Sweden during May–June 2021, with a total of 2015 responses. By employing exploratory factor analysis, we identified similar belief structures for the two countries, consisting of both positive and negative views on wood construction. In linear regressions for predicting these opinions, the perceived seriousness of climate change was found to increase positive views on wood construction but was insignificant for negative views. Both in Finland and Sweden, higher familiarity with wooden multistory construction was found to connect with more positive opinions on the potential of wood in building, e.g., due to carbon storage properties and material attributes. Our findings underline the potential of wood material use as one avenue of climate change adaptation in the built environment. Future research should study how citizens’ concerns for extreme weather events affect their future material preferences in their everyday living environments, also beyond the Nordic region.

A Comparative Assessment of Conventional and Artificial Neural Networks Methods for Electricity Outage Forecasting

The reliability of the power supply depends on the reliability of the structure of the grid. Grid networks are exposed to varying weather events, which makes them prone to faults. There is a growing concern that climate change will lead to increasing numbers and severity of weather events, which will adversely affect grid reliability and electricity supply. Predictive models of electricity reliability have been used which utilize computational intelligence techniques. These techniques have not been adequately explored in forecasting problems related to electricity outages due to weather factors. A model for predicting electricity outages caused by weather events is presented in this study. This uses the back-propagation algorithm as related to the concept of artificial neural networks (ANNs). The performance of the ANN model is evaluated using real-life data sets from Pietermaritzburg, South Africa, and compared with some conventional models. These are the exponential smoothing (ES) and multiple linear regression (MLR) models. The results obtained from the ANN model are found to be satisfactory when compared to those obtained from MLR and ES. The results demonstrate that artificial neural networks are robust and can be used to predict electricity outages with regards to faults caused by severe weather conditions.

Analyze the Outbreak of COVID-19 in Wuhan City Based on Large-Scale Events and Extreme Weather

This article analyzes the impact of large-scale mass activities and extreme weather on the outbreak of COVID-19 in Wuhan, confirming that the South China Seafood Market is indeed the origin of the Wuhan epidemic, and found that the probability of respiratory transmission is low in open space, while food transmission is possible. At the same time, it was found that the outbreaks of SARS in Beijing in 2003 and COVID-19 in Wuhan in 2019 were both related to extreme weather. By investigating genomics and epidemiological data, it was determined that the first COVID-19 case in Wuhan was in November, and the beginning of the epidemic was in late November. Comparing the climate of November, December and January in Wuhan from 2011 to 2020, it is found that there are a lot of extreme weather events in Wuhan from the end of 2019 to the beginning of 2020, including strong winds, heavy rains, large cooling after continuous high temperature, and continuous low temperature and rainy after large cooling, the temperature suddenly rises and then drops rapidly, the wind continues to weaken for many days and then suddenly increases, and long rainy days, etc.