Using social media to measure impacts of named storm events in the United Kingdom and Ireland

This is the final version. Available on open access from Wiley via the DOI in this recordDespite increasing use of impact-based weather warnings, the social impacts of extreme weather events lie beyond the reach of conventional meteorological observations and remain difficult to quantify. This presents a challenge for validation of warnings and weather impact models. This study considers the application of social sensing, the systematic analysis of unsolicited social media data to observe real-world events, to determine the impacts of named storms in the United Kingdom and Ireland during the winter storm season 2017–2018. User posts on Twitter are analysed to show that social sensing can robustly detect and locate storm events. Comprehensive filtering of tweets containing weather keywords reveals that ~3% of tweets are relevant to severe weather events and, for those, locations could be derived for about 75%. Impacts of storms on Twitter users are explored using the text content of storm-related tweets to assess changes in sentiment and topics of discussion over the period before, during and after each storm event. Sentiment shows a consistent response to storms, with an increase in expressed negative emotion. Topics of discussion move from warnings as the storm approaches, to local observations and reportage during the storm, to accounts of damage/disruption and sharing of news reports following the event. There is a high level of humour expressed throughout. This study demonstrates a novel methodology for identifying tweets which can be used to assess the impacts of storms and other extreme weather events. Further development could lead to improved understanding of social impacts of storms and impact model validation.Economic and Social Research Council (ESRC)Engineering and Physical Sciences Research Council (EPSRC)Natural Environment Research Council (NERC

Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska

Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has been hypothesized by local ecologists to result in the conversion of forest to grassland and subsequent increased fire danger. This hypothesis stands in contrast to empirical studies in the continental US which suggested that beetle mortality has only a negligible effect on fire danger. In response, we conducted a study using Landsat data and modeling techniques to map land cover change in the Kenai Peninsula and to integrate change maps with other geospatial data to predictively map fire danger for the same region. We collected Landsat imagery to map land cover change at roughly five-year intervals following a severe, mid-1990s beetle infestation to the present. Land cover classification was performed at each time step and used to quantify grassland encroachment patterns over time. The maps of land cover change along with digital elevation models (DEMs), temperature, and historical fire data were used to map and assess wildfire danger across the study area. Results indicate the highest wildfire danger tended to occur in herbaceous and black spruce land cover types, suggesting that the relationship between spruce beetle damage and wildfire danger in costal Alaskan forested ecosystems differs from the relationship between the two in the forests of the coterminous United States. These change detection analyses and fire danger predictions provide the Kenai National Wildlife Refuge (KENWR) ecologists and other forest managers a better understanding of the extent and magnitude of grassland conversion and subsequent change in fire danger following the 1990s spruce beetle outbreak

Social sensing of high-impact rainfall events worldwide: a benchmark comparison against manually curated impact observations

This is the final version. Available on open access from the European Geosciences Union via the DOI in this recordCode and data availability: The Python code is available on request in a private GitHub repository (https://github.com/seda-lab/social_sensing, last access: 17 December 2020) (Seda-lab, 2020), which can be made available on request. Data used in this study were collected using the Twitter API. Due to Twitter's policy on redistributing Twitter content (https://developer.twitter.com/en/developer-terms/more-on-restricted-use-cases, last access: 17 December 2020) (Twitter, 2020), the tweet data cannot be made publicly available but can be provided on request in the form of tweet IDs which can be rehydrated with the tweet content by the requester using the Twitter API.mpact-based weather forecasting and warnings create the need for reliable sources of impact data to generate and evaluate models and forecasts. Here we compare outputs from social sensing – analysis of unsolicited social media data, in this case from Twitter – against a manually curated impact database created by the Met Office. The study focuses on high-impact rainfall events across the globe between January–June 2017. Social sensing successfully identifies most high-impact rainfall events present in the manually curated database, with an overall accuracy of 95 %. Performance varies by location, with some areas of the world achieving 100 % accuracy. Performance is best for severe events and events in English-speaking countries, but good performance is also seen for less severe events and in countries speaking other languages. Social sensing detects a number of additional high-impact rainfall events that are not recorded in the Met Office database, suggesting that social sensing can usefully extend current impact data collection methods and offer more complete coverage. This work provides a novel methodology for the curation of impact data that can be used to support the evaluation of impact-based weather forecasts

Social Sensing of Heatwaves.

This is the final version. Available on open access from MDPI via the DOI in this recordData Availability Statement: The Twitter data used in this word was collected using the official API (https://developer.twitter.com/en/docs/twitter-api (accessed on 20 August 2020)). The temperature data was collected from the NOAA GSOD dataset (https://www.ncei.noaa.gov/access/search/data-search/global-summary-of-the-day (accessed on 15 August 2020)) and the Met Office MIDAS dataset (https://catalogue.ceda.ac.uk/uuid/220a65615218d5c9cc9e4785a3234bd0 (accessed on 15 August 2020)).Heatwaves cause thousands of deaths every year, yet the social impacts of heat are poorly measured. Temperature alone is not sufficient to measure impacts and "heatwaves" are defined differently in different cities/countries. This study used data from the microblogging platform Twitter to detect different scales of response and varying attitudes to heatwaves within the United Kingdom (UK), the United States of America (US) and Australia. At the country scale, the volume of heat-related Twitter activity increased exponentially as temperature increased. The initial social reaction differed between countries, with a larger response to heatwaves elicited from the UK than from Australia, despite the comparatively milder conditions in the UK. Language analysis reveals that the UK user population typically responds with concern for individual wellbeing and discomfort, whereas Australian and US users typically focus on the environmental consequences. At the city scale, differing responses are seen in London, Sydney and New York on governmentally defined heatwave days; sentiment changes predictably in London and New York over a 24-h period, while sentiment is more constant in Sydney. This study shows that social media data can provide robust observations of public response to heat, suggesting that social sensing of heatwaves might be useful for preparedness and mitigation.Engineering and Physical Sciences Research Council (EPSRC

Social sensing of flood impacts in India: A case study of Kerala 2018

This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: The Twitter data used in this word was purchased using the official Twitter PowerTrack API (https://developer.twitter.com/en/docs/twitter-api/enterprise/powertrack-api/overview (accessed on 15 December 2020)). The Telegram data was collected from the Telegram desktop application (https://telegram.org/blog/export-and-more (accessed on 13 October 2020)). The Kerala Rescue data was initially sourced from the RebuildEarth Slack channel (https://rebuildearth.slack.com/(accessed on 15 October 2020)). The Rebuild Kerala data was collected from the Rebuild Kerala Database site (https://rebuild.lsgkerala.gov.in/rebuild2018/(accessed on 6 November 2020)).Flooding is a major hazard that is responsible for substantial damage and risks to human health worldwide. The 2018 flood event in Kerala, India, killed 433 people and displaced more than 1 million people from their homes. Accurate and timely information can help mitigate the impacts of flooding through better preparedness (e.g. forecasting of flood impacts) and situational awareness (e.g. more effective civil response and relief). However, good information on flood impacts is difficult to source; governmental records are often slow and costly to produce, while insurance claim data is commercially sensitive and does not exist for many vulnerable populations. Here we explore “social sensing” – the systematic collection and analysis of social media data to observe real-world events – as a method to locate and characterise the impacts (social, economic and other) of the 2018 Kerala Floods. Data is collected from two social media platforms, Telegram and Twitter, as well as a citizen-produced relief coordination web application, Kerala Rescue, and a government flood damage database, Rebuild Kerala. After careful filtering to retain only flood-related social media posts, content is analysed to map the extent of flood impacts and to identify different kinds of impact (e.g. requests for help, reports of medical or other issues). Maps of flood impacts derived from Telegram and Twitter both show substantial agreement with Kerala Rescue and the damage reports from Rebuild Kerala. Social media content also detects similar kinds of impact to those reported through the more structured Kerala Rescue application. Overall, the results suggest that social sensing can be an effective source of flood impact information that produces outputs in broad agreement with government sources. Furthermore, social sensing information can be produced in near real-time, whereas government records take several months to produce. This suggests that social sensing may be a useful data source to guide decisions around flood relief and emergency response.Newton FundWCSSP IndiaNatural Environment Research Council (NERC)Engineering and Physical Sciences Research Council (EPSRC

Improved methodologies for continuous-flow analysis of stable water isotopes in ice cores

Water isotopes in ice cores are used as a climate proxy for local temperature and regional atmospheric circulation as well as evaporative conditions in moisture source regions. Traditional measurements of water isotopes have been achieved using magnetic sector isotope ratio mass spectrometry (IRMS). However, a number of recent studies have shown that laser absorption spectrometry (LAS) performs as well or better than IRMS. The new LAS technology has been combined with continuous-flow analysis (CFA) to improve data density and sample throughput in numerous prior ice coring projects. Here, we present a comparable semi-automated LAS-CFA system for measuring high-resolution water isotopes of ice cores. We outline new methods for partitioning both system precision and mixing length into liquid and vapor components – useful measures for defining and improving the overall performance of the system. Critically, these methods take into account the uncertainty of depth registration that is not present in IRMS nor fully accounted for in other CFA studies. These analyses are achieved using samples from a South Pole firn core, a Greenland ice core, and the West Antarctic Ice Sheet (WAIS) Divide ice core. The measurement system utilizes a 16-position carousel contained in a freezer to consecutively deliver  ∼  1 m  ×  1.3 cm² ice sticks to a temperature-controlled melt head, where the ice is converted to a continuous liquid stream and eventually vaporized using a concentric nebulizer for isotopic analysis. An integrated delivery system for water isotope standards is used for calibration to the Vienna Standard Mean Ocean Water (VSMOW) scale, and depth registration is achieved using a precise overhead laser distance device with an uncertainty of ±0.2  mm. As an added check on the system, we perform inter-lab LAS comparisons using WAIS Divide ice samples, a corroboratory step not taken in prior CFA studies. The overall results are important for substantiating data obtained from LAS-CFA systems, including optimizing liquid and vapor mixing lengths, determining melt rates for ice cores with different accumulation and thinning histories, and removing system-wide mixing effects that are convolved with the natural diffusional signal that results primarily from water molecule diffusion in the firn column

Social sensing of heatwaves

Heatwaves cause thousands of deaths every year, yet the social impacts of heat are poorly measured. Temperature alone is not sufficient to measure impacts and “heatwaves” are defined differently in different cities/countries. This study used data from the microblogging platform Twitter to detect different scales of response and varying attitudes to heatwaves within the United Kingdom (UK), the United States of America (US) and Australia. At the country scale, the volume of heat-related Twitter activity increased exponentially as temperature increased. The initial social reaction differed between countries, with a larger response to heatwaves elicited from the UK than from Australia, despite the comparatively milder conditions in the UK. Language analysis reveals that the UK user population typically responds with concern for individual wellbeing and discomfort, whereas Australian and US users typically focus on the environmental consequences. At the city scale, differing responses are seen in London, Sydney and New York on governmentally defined heatwave days; sentiment changes predictably in London and New York over a 24-h period, while sentiment is more constant in Sydney. This study shows that social media data can provide robust observations of public response to heat, suggesting that social sensing of heatwaves might be useful for preparedness and mitigation

Social sensing of high-impact rainfall events worldwide: a benchmark comparison against manually curated impact observations

Impact-based weather forecasting and warnings create the need for reliable sources of impact data to generate and evaluate models and forecasts. Here we compare outputs from social sensing – analysis of unsolicited social media data, in this case from Twitter – against a manually curated impact database created by the Met Office. The study focuses on high-impact rainfall events across the globe between January–June 2017. Social sensing successfully identifies most high-impact rainfall events present in the manually curated database, with an overall accuracy of 95 %. Performance varies by location, with some areas of the world achieving 100 % accuracy. Performance is best for severe events and events in English-speaking countries, but good performance is also seen for less severe events and in countries speaking other languages. Social sensing detects a number of additional high-impact rainfall events that are not recorded in the Met Office database, suggesting that social sensing can usefully extend current impact data collection methods and offer more complete coverage. This work provides a novel methodology for the curation of impact data that can be used to support the evaluation of impact-based weather forecasts

polo encodes a protein kinase homolog required for mitosis in Drosophila

We show that mutation in polo leads to a variety of abnormal mitoses in Drosophila larval neuroblasts. These include otherwise normal looking mitotic spindles upon which chromosomes appear overcondensed; normal bipolar spindles with polyploid complements of chromosomes; bipolar spindles in which one pole can be unusually broad; and monopolar spindles. We have cloned the polo gene from a mutant allele carrying a P-element transposon and sequenced cDNAs corresponding to transcripts of the wild-type locus. The sequence shows that polo encodes a 577-amino-acid protein with an amino-terminal domain homologous to a serine-threonine protein kinase. polo transcripts are abundant in tissues and developmental stages in which there is extensive mitotic activity. The transcripts show no obvious spatial pattern of distribution in relation to the mitotic domains of cellularized embryos but are specifically concentrated in dividing cells in larval discs and brains. In the cell cycles of both syncytial and cellularized embryos, the polo kinase undergoes cell cycle-dependent changes in its distribution: It is predominantly cytoplasmic during interphase; it becomes associated with condensed chromosomes toward the end of prophase; and it remains associated with chromosomes until telophase, whereupon it becomes cytoplasmic