Author: The Social Metwork

Scientists Acquitted: Examining the Role of Consent in Climate Activism 

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James Fallon – j.fallon@pgr.reading.ac.uk 

Ecosystem collapse and climate change threaten all of our futures. What power do scientists have to avoid this looming catastrophe? 

Last week, a jury at Southwark Crown Court heard statements from two scientists facing charges of criminal damage. They had taken action by calling on members of the Royal Society and the wider scientific community to engage in civil disobedience and non-violent direct action: to act as if the science is real, demonstrating a response commensurate with the catastrophic effects being predicted. 

Figure 1: In September 2020, Scientist Rebellion co-founders Mike and Tim took non-violent direct action at the UK’s most prominent scientific body, The Royal Society – source scientistrebellion.com

The defence rested on legal “consent”: that those working at the institute would, when confronted with the facts, consent to their actions. Had the Royal Society realised the potential of scientists to drive political change through activism, they would have agreed that the damage to their building was justified in pursuit of averting climate and ecological collapse. Dr. Tim Hewlett, astrophysicist, and Mike Lynch-White, former theoretical physicist, were unanimously acquitted on Friday.

Despite new bills being introduced to criminalise protests [1], and available legal defences being constrained, many court juries are in fact still finding climate activists not guilty for non-violent direct action .

In this blog post, I want to introduce some key ideas which explain the power and impact these types of protest can have when used by climate activists today.

Radical Intervention: What needs to happen?

According to Morrison et al. 2020, “meaningful climate action requires interventions that are preventative, effective, and systemic – interventions that are radical rather than conventional” [2]. The term “radical” can assume different definitions, categorised in Figure 2:

Figure 2: Debates about radical intervention invoke at least six different interpretations of ‘radical’. These different interpretations can be viewed as a typology, with each type reflecting the extent to which the intervention disrupts the status quo to address the root drivers of climate change. – Morrison et al. 2022

Current approaches to address climate change focus on what may be considered category “1” and “2” interventions: avoiding systemic changes and focusing on “techno fixes” and soft economic changes (such as carbon accounting). To businesses and politicians, these approaches are often desirable and successful , because they can be rapidly implemented and offer hope. But many of these approaches can suffer from a lack of follow-up, loopholes, or may even inadvertently generate new environmental or social problems.

More radical intervention is challenging, because root drivers of climate and ecological breakdown are “deeply embedded in existing societal structures, practices and values at multiple scales, and manifest in diverse ways; including as constraints on women’s reproductive rights, through irresponsible practices of technological innovation and overconsumption, and via political obsessions with ‘small’ government”.

What might a “deep” (5 and 6) radical intervention look like? Changing our future course from one of climate collapse, to a resilient world will “require disruption of [overlooked drivers including] capitalism, colonialism and global inequality”. We should be actively questioning whether economic systems reliant on infinite growth are sustainable on a planet of finite resources, and then propose new systems that prioritise wellbeing and sustainable development within our planetary boundaries [3].

Legal Consent in Activism

Actions like throwing soup on Van Goghs, gluing a hand to a window, daubing institutions in paint may all seem disconnected from the issues protestors wish to highlight. But these actions put the focus on the absurdity of a system that has greater contempt for property damage than the knowing and wilful destruction of nature. A system where economic inequality is rising whilst the wealthiest individuals are the leading driver of emissions [4].

As Greta Thunberg says, “Our house is on fire”. Defending non-violent actions at the Royal Society and Shell’s London Offices, Dr. Hewlett used this metaphor in his closing statement:

“If I smashed a window to drag you from a burning building, most would consent to that damage; Shell has set our house on fire, and when people understand the full extent of their crimes they do not generally object to a splash of paint, they object to the crime of arson. And when scientists come to appreciate our potential to raise the fire alarm, they generally do not object to the non-violent means used to bring them to that understanding, in fact they are often grateful for having their eyes opened. In order to find us guilty, you must be sure that we did not honestly believe they might consent. If we did not honestly believe in consent, why would we even try to mobilise our community?”

Figure 3: Dr. Tim Hewlett after being acquitted from the Royal Society Case – source: Scientist Rebellion

Although the argument of “consent” was successfully used to reach an acquittal in the case of the Royal Society, during the same trial and facing the same jury, Tim was found guilty of a similar protest at Shell [5]. The deliberations of a jury are known only to the jury members, so we cannot know for sure why this conclusion was reached. Tim is currently seeking legal advice. Out of 45 pieces of evidence against Shell only 2 were accepted by the judge (the rest were hidden from the jury). Expert witnesses were denied the opportunity to talk about Shell’s human rights abuses and the company’s failure to align with the Paris agreement.

Had Tim been allowed to make arguments relevant to his protest in the Shell case, it is likely that he would have been found innocent in this case as well.

“From Publications to Public Actions”: How do we accelerate systemic changes?

Given the urgency of climate and ecological emergency, Gardner et al. 2021 suggest that universities must “expand their conception of how they contribute to the public good, and explicitly recognise engagement with advocacy as part of the work mandate of their academic staff”, and outline how work models should be adapted to support this [6].

In the most read Social Metwork blog post of 2021 [7], Gabriel Perez wrote about how our own ways of thinking are influenced by external factors, and therefore there is a need for us to be aware of our roles as scientists across all levels of politics and society. By supporting and even taking part in different forms of protest, scientists can make uniquely important contributions.

It is possible to lend additional credibility to the demands of climate activists by supporting and engaging with movements. This can range from simply signing a letter, to joining in with “low-risk” activities such as talking about these movements with friends and colleagues, joining in with marches, or engaging in outreach activities. We can even join in provocative non-violent direct actions which may pose risk to our liberty (although not everyone is as equally comfortable to do this, with potential visa issues, childcare commitment, and financial struggles being some of the barriers activists may face).

Figure 4: Una rebelión necesaria: Aprill 2022, Scientists take non-violent direct action at the Spanish congress asking for recommendations of the scientific community to become legally binding objectives with institutional mechanisms that guarantee the real participation of citizens – source: rebelioncientifica.es

As scientists, we each have a powerful toolkit to use in activism: we are trained in statistics and comprehension of complicated reports; we present our findings to different audiences; we might have experience publishing, maintaining websites, communicating across sectors, teaching. These are all valuable skills for activists to have, and we have many of them at once!

Climate activists are sometimes depicted as dangerous radicals. But, the truly dangerous radicals are the countries that are increasing the production of fossil fuels. (António Guterres, UN Secretary-General)

Closing thoughts

Climate and ecological collapse pose existential risks to humanity. If we are to avert the worst effects, and place ourselves in a position best able to support the most impacted, then we need to rethink the purpose of our societies.

Taking non-violent direct action sparks controversy, and is shocking. But as a disruptive tactic, it is successful at initiating debate.

[1] Anita Mureithi 2023 “Scrap plans to give cops more power, say women as David Carrick jailed for life” OpenDemocracy news https://www.opendemocracy.net/en/public-order-bill-metropolitan-police-david-carrick-protest

[2] Morrison, T.H., Adger, W.N., Agrawal, A. et al. Radical interventions for climate-impacted systems. Nat. Clim. Chang. 12, 1100–1106 (2022). https://doi.org/10.1038/s41558-022-01542-y

[3] Doughnut Economics Action Lab – About https://doughnuteconomics.org/about-doughnut-economics

[4] Oxfam “Survival Of The Richest: How We Must Tax The Super-Rich Now To Fight Inequality” 2023 https://www.oxfam.ca/publication/davos-report-2023

[5] Rikki Blue, “Listen to the science. Civil disobedience by 1000 scientists.”, Real Media https://realmedia.press/listen-to-the-science

[6] Gardner, C.J, Thierry, A., Rowlandson, W., Steinberger, J.K. From Publications to Public Actions: The Role of Universities in Facilitating Academic Advocacy and Activism in the Climate and Ecological Emergency. Frontiers in Sustainability. 2, 2022. https://doi.org/10.3389/frsus.2021.679019

[7] Gabriel Perez, Climate Science and Power, The Social Metwork https://socialmetwork.blog/2021/11/05/climate-science-and-power

Fusion energy: what’s the hold up?

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Adam Gainford – a.gainford@pgr.reading.ac.uk

Unless you missed the news late last year that scientists at the National Ignition Facility (NIF) in California reported the first successful ignition experiments, you may be thinking that the world’s energy woes are over, that fusion energy will soon be a common and cheap alternative to fossil fuels, and that the grid will soon be almost fully carbon neutral. Well, it’s not quite that simple. It’s undeniably a huge achievement that the heralded break-even barrier has finally been breached, and the promise of fusion powered reactors are still as tantalising as ever, but even this hasn’t stopped the age-old joke that inertial fusion energy is always ten years away. So why has ignition taken this long to achieve, and why should we be cautious about proclaiming that the world’s energy problems have been solved?

Previous posts on the blog have focussed mostly on the more the traditional forms of clean energy which are already in widespread use throughout the world. But in this post, I’d like to introduce you to the source which some hope will be the future of clean energy production. Specifically, I’ll be explaining the basics behind inertial confinement fusion (ICF) reactions, and explain some of the challenges that researchers have been battling with for more than half a century.

Fusion Basics

After a nuclear reaction occurs, the combined mass of the reactants will always be different to the combined mass of the products. If the total reactant mass is larger than the total product mass, the deficit will be released as energy – this basic principle is the underlying mechanism behind both fission and fusion reactions. But while it’s quite easy to coax a heavy, unstable atom to decay into summatively lighter components, bringing two nuclei close enough together that they can fuse is a much tricker task. All nuclei are positively charged and will experience a repelling electromagnetic (EM) force which scales by the inverse square of the separation between them. But at femtometer (10-15) scales the strong force begins to dominate, and the nuclei will become bound. Creating the high-energy conditions necessary for nuclei to overcome the EM barrier, bind together, and release the excess mass as energy is the fundamental challenge to achieving fusion.

The only realistic way to do this is to heat the fusing material to a large enough temperature that the nuclei gain enough kinetic energy to approach such small separations. The choice of nuclei is also crucially important at this stage. Since the Coulomb barrier scales with the number of protons in the nuclei, using hydrogen isotopes is a necessity. A 50:50 mix of deuterium (3H) and tritium (2H), aka DT, provides the largest reaction cross section and best possible chance to achieve fusion. Each DT reaction releases 17.6 MeV of energy, and produces a helium nucleus and an extra neutron. The high energy neutron interacts weakly with its surroundings and will quickly escape the immediate environment, but the positively charged helium nucleus can scatter off other DT pairs and transfer energy, helping to kickstart further reactions. This extra self-heating is crucial for reaching a sufficient fuel burnup fraction and release more energy than was input to the system.

By considering the energy reabsorbed by helium self-heating against all radiation and conduction losses, the Lawson Criterion can be derived as a metric to assess the reaction performance. The criterion states that if the triple product of the particle number density (n), temperature (T) and confinement time (tau, the length of time over which fusion reactions can realistically occur) exceeds roughly 3 x 1028 Ksm-3, ignition will occur, and net energy gain will be achieved. If we fix the temperature to a realistic value for fusion (roughly 100 million Kelvin), we have a two parameter problem which can be solved in two ways. Either, we aim to compress the fuel to incredibly high densities for only a fraction of a second, as is the approach for inertial confinement fusion (ICF), or we keep the fuel at more manageable densities for a more extended period of time, as is the approach for magnetic confinement fusion (MCF). Historically, both methods have shown promise and have been making incremental progress towards net energy gain, but ultimately it was ICF that won the race to achieve first ignition.

The inertial confinement fusion (ICF) process

In ICF, a solid target of DT fuel surrounded by a plastic shell is irradiated by high-intensity lasers such that the inertia of the ablating material causes a rapid implosion of the interior fuel (fig 1a). As this fuel compresses (fig 1b), the central hotspot region reaches the required 10 keV temperature to begin DT fusion and initiate a burn wave which propagates throughout the rest of the target (fig 1c, d). Confinement of the plasma is entirely due to this inward inertia and lasts for only a few nanoseconds.

The diagram below shows this process in more detail and highlights some of the problems which can arise during the implosion. During the early stages (fig 1a), the interaction between the high-intensity lasers and the coronal plasma can generate laser-plasma instabilities which compromises the implosion by transferring large amounts of energy to electrons in the plasma. These “hot electrons” may penetrate into the DT ice and gas, depositing large amounts of energy. While this may initially sound useful for reaching ignition temperatures, instead, this fuel preheat increases the pressure inside the capsule, meaning that the inward compression is less efficient, and smaller hotspot temperatures are reached. Interestingly though, if these hot electrons have just the right temperature, they may instead be stopped closer to the imploding shell and contribute to the ablation pressures which drive compression.

The other major problem with ICF is ensuring a perfectly symmetric compression, as shown in fig 1b and 1c. Any deformities in the shell or asymmetry in the laser profiles can preferentially deposit more energy on one side of the target than the other, limiting the maximum achievable compression. Rayleigh-Taylor instability can also become a large problem in the inner DT-shell boundary, as mixing of the cold shell and hot fuel will reduce maximum temperatures. This is such a large problem in ICF that it has motivated a shift towards an alternative approach – “Indirect drive ICF”. Instead of irradiating the target directly, the capsule is contained inside a gold hohlraum which emits x-rays when heated by the lasers. The x-rays bathe the target in a more uniform glow, reducing the asymmetry impacts, though this does come at the expense of much smaller conversion efficiency between the laser and the target. The indirect-drive approach ultimately won out over direct-drive, and has shown the world that fusion energy is possible.

The ICF implosion process broken down into four stages.

Ignition at the NIF

Even before the news broke of successful ignition at the NIF, there were hints that a breakthrough was close. A paper published in August 2022 detailed the first experiments to reach the Lawson criterion using indirect drive ICF but only managed to reach target gain (ratio of laser input energy to neutron output energy) of 0.72. Ignition was finally achieved later in the year when a 2.05 MJ laser ignited a target to produce 3.15 MJ of energy, implying a net gain of just over 1.5.

But we are still a long way from being able to hook up a fusion reactor to the grid. Shot cycles still take half a day or more to complete as lasers power up and cool down – in an ideal setting, this would be reduced to mere seconds. And there is still a large amount of additional energy required to cool and operate the lasers which typically is not included in calculations of scientific breakeven. But perhaps the most serious argument restricting ubiquitous fusion energy is an economic one. The UK’s first tokamak for energy production, STEP, is expected to be completed by 2040 for a staggering £10 billion. (As a quick aside, this is expected to achieve ignition through MCF simply by being the biggest tokamak ever built.) This is a huge sum of money, with a large potential for the project to run over-budget, and with large risk involved for investors. In comparison, decentralised renewables like wind and solar offer a much less risky investment with technology that is proven to work, and which is becoming less expensive by the day. Fusion power may once have been the future of energy production, but in my view, these results have come 20 years too late.

Tiger Teams: Using Machine Learning to Improve Urban Heat Wave Predictions

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Adam Gainford – a.gainford@pgr.reading.ac.uk

Brian Lo – brian.lo@pgr.reading.ac.uk

Flynn Ames – f.ames@pgr.reading.ac.uk

Hannah Croad – h.croad@pgr.reading.ac.uk  

Ieuan Higgs  – i.higgs@pgr.reading.ac.uk

What is Tiger Teams?  

You may have heard the term Tiger Teams mentioned around the department by some PhD students, in a SCENARIO DTP weekly update email or even in the department’s pantomime. But what exactly is a tiger team? It is believed the term was coined in a 1964 Aerospace Reliability and Maintainability Conference paper to describe “a team of undomesticated and uninhibited technical specialists, selected for their experience, energy, and imagination, and assigned to track down relentlessly every possible source of failure in a spacecraft subsystem or simulation”.  

This sounds like a perfect team activity for a group of PhD students, although our project had less to do with hunting for flaws in spacecraft subsystems or simulations. Translating the original definition of a tiger team into the SCENARIO DTP activity, “Tiger Teams” is an opportunity for teams of PhD students to apply our skills to real-world challenges supplied by industrial partners.   

The project culminated in a visit to the Met Office to present our work.

Why did we sign up to Tiger Teams?  

In addition to a convincing pitch by our SCENARIO director, we thought that collaborating on a project in an unfamiliar area would be a great way to learn new skills from each other. The cross pollination of ideas and methods would not just be beneficial for our project, it may even help us with our individual PhD work.  

More generally, Tiger Teams was an opportunity to do something slightly different connected to research. Brainstorming ideas together for a specific real-life problem, maintaining a code repository as a group and giving team presentations were not the average experiences one could have as a PhD student. Even when, by chance, we get to collaborate with others, is it ever that different to our PhD? The sight of the same problems …. in the same area of work …everyday …. for months on end, can certainly get tiring. Dedicating one day per week on an unrelated, short-term project which will be completed within a few months helps to break the monotony of the mid-stage PhD blues. This is also much more indicative of how research is conducted in industry, where problems are solved collaboratively, and researchers with different talents are involved in multiple projects at once.

What did we do in this round’s Tiger Teams?  

One project was offered for this round of Tiger Teams: “Crowdsourced Data for Machine Learning Prediction of Urban Heat Wave Temperatures”. The bones of this project started during a machine learning hackathon at the Met Office and was later turned into a Tiger Teams proposal. Essentially, this project aimed to develop a machine learning model which would use amateur observations from the Met Offices Weather Observation Website (WOW), combined with landcover data, to fine-tune model outputs onto higher resolution grids.   

Having various backgrounds from environmental science, meteorology, physics and computer science, we were well equipped to carry out tasks formulated to predict urban heat wave temperatures. Some of the main components included:  

  • Quality control of data – as well as being more spatially dense, amateur observation stations are also more unreliable  
  • Feature selection – which inputs should we select to develop our ML models  
  • Error estimation and visualisation – How do we best assess and visualise the model performance  
  • Spatial predictions – Developing the tools to turn numerical weather prediction model outputs and high resolution landcover data into spatial temperature maps.  

Our supervisor for the project, Lewis Blunn, also provided many of the core ingredients to get this project to work, from retrieving and processing NWP data for our models, to developing a novel method for quantifying upstream land cover to be included in our machine learning models. 

An example of the spatial maps which our ML models can generate. Some key features of London are clearly visible, including the Thames and both Heathrow runways.

What were the deliverables?  

For most projects in industry, the team agrees with the customer (the industrial partner) on end-products to be produced before the conclusion of the project. Our two main deliverables were to (i) develop machine learning models that would predict urban heatwave temperatures across London and (ii) a presentation on our findings at the Met Office headquarters.  

By the end of the project, we had achieved both deliverables. Not only was our seminar at the Met Office attended by more than 120 staff, we also exchanged ideas with scientists from the Informatics Lab and briefly toured around the Met Office HQ and its operational centre. The models we developed as a team are in a shared Git repository, although we admit that we could still add a little more documentation for future development.  

As a bonus deliverable, our supervisor (and us) are consolidating our findings into a publishable paper. This is certainly a good deal considering our team effort in the past few months. Stay tuned for results from our paper perhaps in a future blog post!  

Panto 2022: FORTRANGLED 

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Caleb Miller – c.s.miller@pgr.reading.ac.uk

Jen Stout – j.r.stout@pgr.reading.ac.uk

One of the biggest traditions in the Reading meteorology department is the yearly Christmas pantomime. Because of lockdowns and safety measures in the past several years due to the COVID-19 pandemic, 2022 was the first year to return to a live performance since 2019, and it was a lot of fun! 

FORTRANGLED Poster 

The panto this year was directed by Jen Stout and Caleb Miller. We were asked at the end of the summer by last year’s organizers if we would be interested in the roles of director, and we both agreed to it — most likely only because we didn’t realize how big of a task this would be! 

Plot 

The original idea for this plot was Jen’s idea. They suggested that we create a plot based on the story of Rapunzel, particularly on Disney’s adaptation in the movie Tangled. This turned out to be a well-loved story for many of the PhD students in the department, and when we met early in the autumn term to vote on a plot idea, Tangled won unanimously. 

It wasn’t long before we began to adapt the story to our own department and the field of meteorology. The original movie centers around the story of Rapunzel, a princess who was kidnapped at birth because of her hair’s special abilities, as she escapes the remote tower with the help of an outlaw. We quickly recognized the similarity between the original story and our own department’s move from the old Lyle building to the main Brian Hoskins building which was taking place at the time. 

The Lyle building was famously tall (with many, many stairs), and it was isolated from the majority of the department, much like Rapunzel’s tower. We even had someone to rescue the poor Lyle residents: head of department, Joy Singarayer! 

But who would take the spot of the villain, the woman who owned the tower and held Rapunzel there? Why not the Remote Sensing, Clouds, and Precipitation (“Radar”) research group? Jen and Caleb were both members (as were two of our supervisors), so we could make fun of ourselves, and the group had many members who were still in the Lyle building at the time. 

Soon, the story began to develop. Caleb wrote much of the initial draft and dialog, and several of the seasoned panto writers from last year stepped in and peppered the script with jokes and radar-related puns, much improving the final story! 

In the end, FORTRANGLED told the story of a young PhD student, Rapunzel, who wanted to use her invention, the Handheld Advanced Imaging Radar (“HAIR”), for in situ measurements on a weather balloon, but she is stopped by the Radar group. Thankfully, she is rescued from the lonely Lyle tower by Joy Singarayer, and finally she joins her original supervisor King Professor Sir Brian Hoskins and launches the balloon. 

Songs 

Of course, the panto wouldn’t be the same unless it featured popular songs with brand-new lyrics full of meteorology puns! We decided to use several of the songs from the Tangled film, while adding a few others where they fit.  

The band was headed by Flynn Ames. They began rehearsing months in advance, and their practice paid off enormously. The band performed excellent covers of a wide variety of musical genres and songs, featuring acoustic and electric guitars, bass, drums, keys, cello, and even a trombone! By the time we came to rehearse with the singers, they sounded incredible.  

As for the lyrics to the songs, Jen took charge with most of the writing — once it was realized that “Sheet Nimbostratus” sounded vaguely like “Pina Coladas,” the favourite “Sheet Nimbostratus (Escape)” song (a parody of Rupert Holmes’ Pina Colada Song) was written in under half an hour on a lunch break and ended up working well with theme of escape for the first act. 

Flynn was also a massive help with the songs, especially the last song of the show: Hall & Oates’ – “You Make My Dreams”. When it came to rehearsing the songs with the cast as singers, it was excellent having Flynn as someone who wasn’t rhythmically challenged to help us sort out when to sing the lyrics (as well as what words to sing and what notes to sing them to!), so thank you to Flynn, Beth, and the rest of the band for helping the rest of us sing as best as we could! 

Casting the lead 

Once the script was written, it was time to select the cast. Most of the casting was reasonably quick, but we had one issue: no one wanted to play the lead! Convincing a PhD student to pretend to be a princess in front of the entire department is understandably difficult. We spent at least a week going around the department trying to convince one another to step up for the role.  

However, the role had far too many lines for any one person to commit to, and therefore we settled on the “Rapunzel Roulette,” where a different person would play Rapunzel in each of the scenes. This ended up being a really good move, and meant that instead of the role being high-pressure, it was a rush of excitement and silliness for each act, especially as they had to pass the wig onto the next person before the next act started. 

The Night of the Panto 

The panto turned out to be a lot of fun! We sold over 130 tickets, and this was certainly one of the larger post-lockdown events at the department. Planning for the in-person event required no small amount of admin work, and we were especially helped by Dana Allen, Joy Singarayer, and Andrew Charlton-Perez! 

The event started at 6:30 with a bring-and-share buffet, and doors opened to Madejski Lecture Theatre at 7:30 before the show start. 

The FORTRANGLED cast 

We also had several interval acts, including the latest episode from John Shonk’s famous Mr. Mets series, Blair McGinness’s presentation on the controversial results of a department biscuit ranking tournament, and a musical performance from the faculty! 

After the interval acts, we resumed with the second act of the panto, and finished the result of months’ writing and rehearsals. The inclusion of the “Top Secret” notes and distribution of balloons was a last-minute inclusion, organized by Jen, intended to surprise the rest of the cast except for our excellent Narrator, Natalie, who was told beforehand in case everything went wrong!  

The instructions to the audience were as follows: “In the wilderness: If you see a duck; shout: quack! If you see a goose, shout: honk!,” as well as the extremely vague: “If the stage needs a balloon: please blow up your balloon and throw it towards stage!” 

Surprisingly, especially for a pantomime, the audience was incredibly well-behaved, balloons arrived exactly on cue (despite this not being written into the script whatsoever). As for the command to shout “honk” and “quack” when geese and ducks appeared… the honks went on for much longer than we expected, causing a lot of chaos and confusion both on and off stage! This was undoubtedly Jen’s favourite part of the entire show. 

After the party, we celebrated with an afterparty in the department coffee area led by DJ Shonk. This included some thematically appropriate piña coladas, which may have led to the scattering of the geese and ducks throughout the department… 

Reflections 

As the panto was the first in-person panto since 2019’s The Sonde of Music, and so most of the cast hadn’t seen a live pantomime in the way we did it this year! This made it a massive challenge to organise, and directing the Panto turned out to be a very difficult, but also very rewarding, task. Seeing everyone’s hard work come together on the night was the best part, and we’re glad we contributed to such a long-standing department tradition. 

We’d like to thank everyone who was involved: anyone we convinced to act, sing, play in the band, make props, put on silly outfits, organise the event, perform an interval act, or throw balloons at the stage. We found that this department is full of some very talented people, and it was really fun getting to work in some areas we don’t often get to see. If meteorology research is one day taken over by AI, the members in our department would have no problem finding new jobs on Broadway! 

AGU 2022 in the Windy City

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Lauren James – l.a.james@pgr.reading.ac.uk

AGU Fall Meeting 2022 was held in Chicago, Illinois from 12th – 16th December, and I was fortunate to attend the conference in person to present a poster on my PhD research. At the post-pandemic event, 18,000 attendees were expected to be present throughout the week and more attended online. To date, this was going to be the largest audience to view my research.

No matter how many people tell you how huge the AGU meeting is, it is not until you walk into the venue you understand the extent of this conference. Rows upon rows of poster boards, a sizeable exhibition hall, an AGU centre and relaxation zone, and endless hallways to the seminar rooms. I went by the venue on Sunday afternoon to register and work out the main routes to and around the conference centre. I would recommend this to anyone attending as come Monday morning the registration queue was extraordinarily long, looping across bridges and down staircases. You would have missed any early morning talks you wanted to attend.

Tuesday morning was my allocated time to showcase my work. The poster sessions were 3.5 hours long, but the posters could be kept up on the board for the full day. Whilst there was no requirement to stand next to your poster for the full duration, I did just that as time flew by very quickly. Fellow scientists were eager to discuss the work, learn about new ideas, and find overlaps with their work. I brought along A4 printed versions of the poster (an idea I had picked up from another conference) and it was beneficial to either let attendees take away your work for reference or for allowing people at the back of a crowd to read the poster. For the online attendees, presenters could make an interactive poster (a.k.a iPosters) which was published on the online gallery. This platform allowed videos, gifs, and audio clips as well as no-limit to text in expandable text boxes. Whilst still being mindful of not overcrowding a poster, these additional features made the poster more accessible. For some fortunate presenters, digital poster rows at the conference allowed their iPosters to be viewable in person too. Thus, presenters could use the movies and audio to support their work as well as attendees could easily interact with their displays whilst unmanned. Further, there was no organisation for printing and travelling with a poster and produced no waste. Could this be the future of poster sessions?

Figure 1: An overlooking view of a section of the poster hall on the final day of the conference. The digital poster row can be seen on the closest row.
Figure 2: A picture of myself in front of my poster.

There were so many oral presentations throughout the week that are suitable for your field of research. With the help of the AGU app, I was able to make a schedule for the space physics sessions I wanted to attend and optimise my time at the conference by finding other sessions I would find interesting. This year, for the first time, there was a session on ‘Raising Awareness on Mental Health in the Earth and Space Sciences’. In the last few years, such sessions have become more widely available and I am happy to see that AGU has also taken the opportunity to discuss the importance of healthy work. Of the oral presentation sessions I attended, this one instigated a very engaged audience and highlighted the importance of interdisciplinary discussions. All the oral presentation sessions catered for in-person and online audiences, and have continued to allow online speakers to present and participate in the Q&A. These sessions are also still available to re-watch for all the attendees for a few weeks.

A walk amongst the exhibition hall filled some of the free time between sessions and allowed attendees to discuss careers with academic institutes and businesses working with instrumentation, programming, data accessibility, fieldwork and more. As a postgraduate student in space physics, it was initially overwhelming to see many stands that were advertising topics alien to me. But before you knew it, I had heard about a new state-of-the-art instrument that will rapidly transmit terabytes of data; learnt about ground aquifers by making an Oreo Ice Cream float; and collected a renowned NASA 2023 calendar.

From my understanding, there have been a few changes to the AGU meeting since pre-pandemic times. The colours of your lanyard corresponded to your comfort level of COVID-19 safety, spanning from ‘I need distance’ to ‘Air high fives approved’. Alcoholic refreshments during poster sessions were not provided as a conscious decision to improve attendee well-being and ensure the code of conduct is upheld. And the host city of the meeting will change annually within the US to improve the accessibility of the conference (although for a UK attendee, a long-haul flight is unavoidable regardless of this).

Figure 3: A few memories from my visit to Chicago, including the Oreo ice cream float, the cloud gate (a.k.a., The Bean), and the NHL Ice Hockey game at the Union Center.

Chicago was a lovely city to host this year. The conference centre was easily accessible by train and bus from the downtown area, and even walkable on a good weather day. We were fortunate to have rather pleasant weather throughout the visit, although some rain, snow, and a bitterly cold wind were experienced. Exploring the city was extra special this close to Christmas and aided the glory of city lights after sunset. In the evenings, there was ample time and things to do with early career scientists I’ve met throughout my time as a PhD student and newly made contacts from the US. Watching an NHL ice hockey match, visiting Navy Pier, a competitive evening at the bowling alley, and trying the famous deep dish pizza were just some of the things squeezed into the busy week.

There is no doubt that attending the AGU Fall Meeting has been a highlight in my PhD experience, and one that I would recommend to anyone who has the opportunity to visit in the future. Even if you’re travelling alone, which I did, there were ample opportunities to meet fellow attendees and experience a very enjoyable week in the city. I thank the University of Reading Graduate School for giving me a student travel bursary to help fund this international trip. Next year, this conference is being held in San Francisco, California from the 11th – 15th of December 2023.

Urban observations in Berlin

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Martina Frid – m.a.h.frid@pgr.reading.ac.uk

Beth Saunders – bethany.saunders@pgr.reading.ac.uk

Introduction 

With a large (and growing) proportion of the global population living in cities, research undertaken in urban areas is important; especially in hazardous situations (heatwaves, flooding, etc), which become more severe and frequent due to climate change.  

This post gives an overview of recent work done for The urbisphere; a Synergy Project funded by the European Research Council (urbisphere 2021), aiming to forecast feedbacks between weather, climate and cities.  

Berlin Field Campaign 

The project has included a year-long field campaign (Autumn 2021 – Autumn 2022) undertaken in Berlin (Fig. 1). A smart Urban Observation System was used to take measurements across the city. Sensors used include ceilometers, Doppler wind LIDARs, radiometers, thermal cameras, and large aperture scintillometers (LAS). These measurements were taken to provide new information about the impact of Berlin (and other cities) on the urban boundary layer. The unique observation network was able to provide dense, multi-scale measurements, which will be used to evaluate and inform weather and climate models.  

Figure 1: Locations of the urbisphere senors in Berlin, Germany (urbisphere 2021).

Large Aperture Scintillometry in Berlin

The Berlin field campaign has included 6 LAS paths (Fig. 1). LAS paths consist of a transmitter and receiver mounted in the free atmosphere (Fig. 2), 0.5 – 5 km apart (e.g. Ward et al. 2014).

A beam of near-infrared radiation (wavelength of ~ 850 nm) is passed from the transmitter to receiver, where the beam intensity is measured. Changes in the refractive index of air are used to derive turbulent sensible heat flux. As the received intensity is the result of fluctuations all along the beam, derived quantities are spatially-integrated, and are therefore at a larger-scale compared to other flux measurement techniques (e.g. eddy-covariance).

Figure 2: One of six large aperture scintillometer path (orange) transects. Ground height (blue) is shown between the receiver site (GROP) and transmitter site (OSWE) in Berlin. The Path’s effective beam height is 50 m above ground level.

Our Visit to Berlin

During the first week of August, we travelled to Berlin for three days of fieldwork, to prepare for an intense observation period (IOP). This trip included us installing sensors, and testing they worked as expected. We visited three observation sites: GROP (123 m above sea level, Fig. 2), OSWE (63 m, Fig. 2) and NEUK (60 m).

One of the main purposes of this visit was to align two of the LAS paths (including the one in Fig. 2). Initially, work is undertaken at the transmitter site (Fig. 3, top) to point the instrument in the approximate direction of the receiver using a sight (Fig. 3, right hand side photographs).

At the receiver site (Fig. 3, bottom), the instrument’s measurement of signal strength can be displayed on a monitor in real time. Using this output as a guide, small adjustments to the receiver’s alignment are made by loosening or tightening two bolts on the mount; one which adjusts the receiver’s pitch, and one with adjusts the yaw. This was carried out until we reached a peak reading in signal strength, indicating the path was aligned.

Figure 3: Photographs of the large aperture scintillometer transmitter at site OSWE (top) and receiver at site GROP (bottom).

Our contribution to the IOP

Back in Reading, daily weather forecasts were carried out for the IOP, to determine when ground-based observations could be made. As the field campaign coincided with the central European heat wave, some of the highest temperatures were recorded during the IOP, and there was a need to forecast thunderstorm and the possibility of lightning strikes.

Ideal conditions for observations were clear skies and a consistent wind direction with height. A variety of different wind directions during the IOP was also preferable, to capture different transects of Berlin. For the selected days, group members in Berlin deployed multiple weather balloons simultaneously across multiple sites within the city and the outskirts. This was also timed with satellite overpasses. Observations of the mixing layer height (urban and suburban) were taken using a ceilometer mounted in a van, which drove along different transects of Berlin.

As the field campaign is wrapping up in Berlin, several instruments are now being moved to the new focus city: Paris. We are looking forward to this new period of interesting observations! Thank you and goodbye from us at the top of the GROP observation site!

References

urbisphere, 2021: Project Context and Objectives. http://urbisphere.eu/ (accessed 27/09/22)

Ward, H. C., J. G. Evans, and C. S. B. Grimmond, 2014: Multi-Scale Sensible Heat Fluxes in the Suburban Environment from Large-Aperture Scintillometry and Eddy Covariance. Boundary-Layer Meteorol., 152, 65–89.

Deploying an Instrument to the Reading University Atmospheric Observatory 

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Caleb Miller – c.s.miller@pgr.reading.ac.uk 

In the Reading area, December and January seem to be prime fog season. Since I’m studying the effects of fog on atmospheric electricity, that means that winter is data collection season! However, in order to begin collecting data in the first year of my PhD, there was only a short amount of time to prepare an instrument and deploy it to the observatory before Christmas. 

One of the instruments that I am using to measure fog is called the Optical Cloud Sensor (OCS). It was designed by Giles Harrison and Keri Nicoll, and it is described in more detail in this paper: (Harrison and Nicoll 2014). The OCS has four channels of LEDs which shine light into the surrounding air. When fog is present, the fog droplets scatter light back to the instrument, where the intensity from each channel can be measured. 

Powering the instrument 

The OCS was originally designed to be flown on a weather balloon, which meant that it was meant to be powered by battery and run for only short periods of time. In my case, however, I wanted the device to be able to continuously collect data over a period of weeks or months without interruption. Then, we would be able to catch any fog events, even if they hadn’t been forecasted. That meant the device would need to be powered by the +15V power supply available at the observatory, and my first step was to create a power adapter for the OCS so that this would be possible. 

Initially, I had been considering using an Arduino microcontroller as a datalogger, so I decided to put together a power adapter on an Arduino shield (a small electronic platform) for maximum convenience. I included multiple voltage levels on my power adapter and connected them to different power inputs on the OCS. Once this was completed, the entire system could now be powered with a single power supply that was available at the observatory! 

I was able to find all of the required parts for the power supply in stock in the laboratory in the Meteorology Department, and I soldered it together in a few days. The technical staff of the university were very helpful in this process! A photograph of the power adapter connected to an Arduino is shown in Figure 1. 

Figure 1. The power adapter for the optical cloud sensor, built on an Arduino shield 

Storing data from the instrument 

Once the power supply had been created, the next step was setting up a datalogging system. On a balloon, the data would be streamed in real-time down to a ground station by radio link. But when this system was deployed to the ground, that would no longer be necessary. 

Instead, I decided to use a CR1000X datalogger from Campbell Scientific. This system has a number of voltage inputs which can be programmed using a graphical interface over a USB connection, and it has a port for an SD card. I programmed the datalogger to sample each of the four analog channels coming from the OCS every five seconds and to store the measurements on an SD card. Collecting the measurements was then as simple as removing the SD card from the datalogger and copying the data to my laptop. This could be done without interrupting the datalogger, as it has its own internal storage, and it would continue measuring while the SD card was removed. 

I had also considered simultaneously logging a digital form of the measurements to an Arduino in addition to the analog measurements made by the datalogger. This would give us two redundant logging systems which would decrease the chances of losing valuable information in the event of an instrument malfunction. However, due to a shortage of time and a technical issue with the instrument’s digital channels, I was unable to prepare the Arduino logger by the time we were ready to deploy the OCS, so we used only the analog datalogger. 

Figure 2. The OCS with its new power supply being tested in the laboratory 

Deploying the instrument 

Once the power supply and datalogger were completed, the instrument was ready to be deployed! It was a fairly simple process to get approval to put the instrument in the observatory; then I met with Ian Read to find a suitable location to set up the OCS. There were several posts in the observatory which were free, and I chose one which was close to the temperature and humidity sensors in the hopes that the conditions would be fairly similar in those locations. Once everything was ready, the technicians and I took the OCS and datalogger and set it up in the field site. At first, when we powered it on, nothing happened. Apparently, one of the solder joints on my power adapter had been damaged when I carried it across campus. However, I resoldered that connection with advice from the university technical staff, and it worked beautifully! 

Figure 3. The datalogger inside its enclosure in the observatory 

Figure 4. The OCS attached to its post in the observatory  

Except for a short period of maintenance in January, the OCS has been running continuously from December until May, and it has already captured quite a few fog events! With the data from the OCS, I now have an additional resource to use in analyzing fog. The levels of light backscattered from the four channels of the instrument provide interesting information, which I am combining with electrical and visibility measurements to analyze the microphysical properties of fog development. 

Hopefully, over the next year, we will be able to measure many more fog events with this instrument that will help us to better understand fog! 

Harrison, R. G., and K. A. Nicoll, 2014: Note: Active optical detection of cloud from a balloon platform. Rev. Sci. Instrum., 85, 066104, https://doi.org/10.1063/1.4882318. 

Ensemble Data Assimilation with auto-correlated model error

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Haonan Ren – h.ren@pgr.reading.ac.uk

Data assimilation is a mathematical method to combine forecasts with observations, in order to improve the accuracy of the original forecast. Normally data assimilation methods are performed with the perfect-model assumption (weak-constraint). However, there are different sources that can produce model error, such as missing description of the dynamic system and numerical discretisation. Therefore, in recent years, the model error has been more often considered in the data assimilation process (strong-constraint settings). 

There are several data assimilation methods applied in various fields. My PhD project mainly focuses on the ensemble/Monte Carlo formulation of the Kalman Filter-based methods, more specifically, the ensemble Kalman Smoother (EnKS). Different from the filter, a smoother updates the state of the system using observations from the past, present and possibly the future. The smoother does not only improve the forecast at the observation time, instead, it updates the whole simulation period. 

The main purpose of my research is to investigate the performance of the data assimilation methods with auto-correlated model error. We want to know what will happen if we propose a misspecified auto-correlation in the model error, for both state update and parameter estimation. We start our project with a very simple linear auto-regressive model. As for the auto-correlation in model error, we propose an exponential decaying decorrelation. Naturally, the system has a exponential decaying parameter ωr, and the parameter we use in the forecast and data assimilation is ωg which can be different from the real one. 

A simple example can illuminate the decorrelation issue. In Figure 1, we show results of a smoothing process for a simple one-dimensional system over a time window of 20 nature time steps. We use an ensemble Kalman Smoother with two different observation densities in time. The memories in the nature model and the forecasts models do not coincide. We can see that with ωr = 0.0, when the actual model error is a white-in-time random variable, the evolution of the true state of the system behaves rather erratically with the present model settings. If we do not know the memory and assume the model error is a bias in the data assimilation process (ωg → ∞), the estimation made by the data assimilation method is not even close to the truth, even with very dense observations in the simulation period, as shown in the left two subplots in Figure 1. On the other hand, if the model error in the true model evolution behaves like a bias, and we assume that the model error is white in time in the data assimilation process, the results are quite different with different observation frequencies. As shown in two subplots on the right in Figure 1, with very frequent observations, we can see a fairly good performance of the data assimilation process, but with a single observation, the estimation is still not accurate. 

Figure 1: Plots of the trajectories of the true state of the system (black), three posterior ensemble members (pink, randomly chosen from 200 members), and the posterior ensemble mean (red). The left subplots show results for a true white noise model error and an assumed bias model error for two observation densities. Note that the posterior estimates are poor in both cases. The right subplots depict a bias true model error and an assumed white noise model error. The result with one observation is poor, while if many observations are present the assimilation result is consistent within the ensemble spread.

In order to evaluate the performance of the EnKS, we need to compare the root-mean-square error (RMSE) with the ensemble spread of the posterior. The best performance of the EnKS is when ratio of RMSE over the spread is equal to 1.0. The results are shown in Figure 2. As we can see, the Kalman Smoother works well when ωg = ωr for all the cases, with the ratio of RMSE over the spread equal to 1.0. With relatively high observational frequency, 5 observations or more in the simulation window, the RMSE is larger than the spread when ωg > ωr, and vice versa. In a further investigation, the mismatch between the two timescales ωr and ωg barely has any impact on the RMSE. The ratio is dominated by the ensemble spread.

Figure 2: Ratio of MSE over the posterior variance for the 1-dimensional system, calculated using numerical evaluation of the exact analytical expressions. The different panels show results for different numbers of observations.

Then, we move to estimating the parameter encoded in the auto-correlation of the model error. We estimate the exponential decaying parameter by the state augmentation using the EnKS, and the results are shown in Figure 3. Instead of the exponential parameter, ωg, we use the log scale of the memory timescale to avoid negative memory estimates. The initial log-timescale values are drawn from a normal distribution: ln ωgi ∈ N (ln ωg, 1.0). Hence we assume that the prior distribution of the memory time scale is lognormal distributed. According to Figure 3, with an increasing number of windows we obtain better estimates. Also, the convergence is faster with more observations. And in some cases the solution did not converge to the correct value. This is not surprising given the highly nonlinear character of the parameter estimation problem, especially with only one observation per window. When we observe every time step the convergence is much faster, and the variance in the estimate decreases, as shown in the lower two subplots. In this case we always found fast convergence with different first guess and true timescale combinations, demonstrating that more observations bring us closer to the truth, and hence make the parameter estimation problem more linear.

Figure 3: PDFs of the prior (blue) and posterior (reddish colours) estimated ωg, using an increasing number of assimilation windows. The different panels show results for different observation densities and prior mean larger (a, c) or smaller (b, d) than ωr. The vertical black line denotes the true value ωr.

As the results of the experiments show, the influence of an incorrect decorrelation timescale in the model error can be significant. We found that when the observation density is high, state augmentation is sufficient to obtain converging results. The new element is that online estimation is possible beyond a relatively simple bias estimate of the model error.

As a next step we will explore the influence of incorrectly specified model errors in nonlinear systems, and a more complex auto-correlation in the model error.