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Hywel and his collaborators have recently published a paper looking at how crowd-sourced data can be used for weather forecasting. The work is available online.

Crowd-sourced observations (CSO) offer great potential for numerical weather prediction (NWP). This paper offers a synthesis of progress, challenges and opportunities in this area based on a special session of the EWGLAM Meeting in 2019, concentrating on high-resolution limited-area models (LAMs). Two main application areas of CSO are described: data assimilation and verification. One part of data assimilation developments concentrates on smartphone pressure observations, which represent a large volume of data. However, special care has to be taken about data protection and the quality of observations. In this paper, two examples are presented: the SMAPS experiment from Denmark and the uWx experiment from the United States. Another data assimilation topic is citizen observations with low-cost weather sensors; here an example from Norway is presented using Netatmo stations. The other application area is the use of CSO for model verification. One novel method developed in the United Kingdom is applying social media data to detect severe weather events. This approach is especially important because one future application area of LAM NWP models is impact-oriented warnings.

Rudy has recently published a paper exploring how the COVID-19 restrictions have affected the UK job market. The paper is available online.

The COVID-19 global pandemic and the lockdown policies enacted to mitigate it have had profound effects on the labour market. Understanding these effects requires us to obtain and analyse data in as close to real time as possible, especially as rules change rapidly and local lockdowns are enacted. This work studies the UK labour market by analysing data from the online job board Reed.co.uk, using topic modelling and geo-inference methods to break down the data by sector and geography. I also study how the salary, contract type, and mode of work have changed since the COVID-19 crisis hit the UK in March. Overall, vacancies were down by 60 to 70% in the first weeks of lockdown. By the end of the year numbers had recovered somewhat, but the total job ad deficit is measured to be over 40%. Broken down by sector, vacancies for hospitality and graduate jobs are greatly reduced, while there were more care work and nursing vacancies during lockdown. Differences by geography are less significant than between sectors, though there is some indication that local lockdowns stall recovery and less badly hit areas may have experienced a smaller reduction in vacancies. There are also small but significant changes in the salary distribution and number of full time and permanent jobs. As well as the analysis, this work presents an open methodology that enables a rapid and detailed survey of the job market in unsettled conditions and describes a web application jobtrender.com that allows others to query this data set.

Following the publication of ‘Online Misinformation about Climate Change’ in WIRes last year, Kathie was asked to contribute a short video clip answering the question “What can be done to stop or slow the spread of climate misinformation?” for a special focus programme on climate misinformation. The programme including an excerpt of Kathie’s clip was broadcast live on Al Jazeera English and is now available on YouTube.

Tristan, Iain and Hywel have recently published a paper exploring the information sharing ecosystem of climate change on Twitter. This work is available now in open access at PLOS ONE.

Exposure to media content is an important component of opinion formation around climate change. Online social media such as Twitter, the focus of this study, provide an avenue to study public engagement and digital media dissemination related to climate change. Sharing a link to an online article is an indicator of media engagement. Aggregated link-sharing forms a network structure which maps collective media engagement by the user population. Here we construct bipartite networks linking Twitter users to the web pages they shared, using a dataset of approximately 5.3 million English-language tweets by almost 2 million users during an eventful seven-week period centred on the announcement of the US withdrawal from the Paris Agreement on climate change. Community detection indicates that the observed information-sharing network can be partitioned into two weakly connected components, representing subsets of articles shared by a group of users. We characterise these partitions through analysis of web domains and text content from shared articles, finding them to be broadly described as a left-wing/environmentalist group and a right-wing/climate sceptic group. Correlation analysis shows a striking positive association between left/right political ideology and environmentalist/sceptic climate ideology respectively. Looking at information-sharing over time, there is considerable turnover in the engaged user population and the articles that are shared, but the web domain sources and polarised network structure are relatively persistent. This study provides evidence that online sharing of news media content related to climate change is both polarised and politicised, with implications for opinion dynamics and public debate around this important societal challenge.

Josh and Chris have recently published some work with collaborators from the Alan Turing Institute in the Journal of Open Source Software. This paper is pyveg: A Python package for analysing the time evolution of patterned vegetation using Google Earth Engine and is available online.

Periodic vegetation patters occur throughout the world in water limited systems. These patterns take the form of spots, labyrinths and gaps and undergo morphometric changes as they experience long term reductions in rainfall. This provides a useful indicator of the health of drylands. Previous efforts to evaluate these patterns have been qualitative or if quantitative, have been sporadic and have examined small scale sites. These pattern vegetation sites often occur in hard to reach locations. We developed a new way of quantitatively evaluating the state of patterned vegetation and assign a numerical value to their morphology. When applied to satellite data, this enables a long term analysis of pattern vegetation health, as well as their resilience. We have developed a python package, pyveg, which encapsulates this methodology and enables the user to perform long term pattern vegetation analysis of any site in the world.