Every year, the Met PhD students at the University of Reading invite a scientist from a different university to learn from and talk to about their own project. This year we had the renowned Professor Tim Woolings, who currently researches and teaches at the University of Oxford. Tim’s interests generally revolve around large scale atmospheric dynamics and understanding the impacts of climate change on such features. We, as Met PhD students, were very excited and extremely thankful that Tim donated a week of his time (4th-8th of October) and travelled from Oxford for hybrid events within the Met. building. Tim told us of his own excitement to be back visiting Reading, after completing his PhD here, on isentropic modelling of the atmosphere, and staying on as a researcher and part of the department until 2013.
The week started with Tim presenting “Jet Stream Trends” at the Dynamical Research Group, known as Hoskin’s Half Hour. A large number of PhD students, post-doctorates and supervisors attended, which was to be expected considering Tim has a book dedicated on Jet streams. After a quick turnaround, he spoke at the departmental lunch time seminar on “The role of Rossby waves in polar weather and climate”. Here, Tim did an initial review on Rossby wave theory and then talked about his current fascinating research on the relevance of them within the polar atmosphere. The rest of Tim’s Monday consisted of lunch at park house with Robert Lee and the organising committee, Charlie Suitters, Hannah Croad and Isabel Smith (within picture). Later that evening Tim visited the Three Tuns pub with other staff members, for an important staff meeting! The PhD networking social with Tim on Thursday was a great evening where 15 to–20 students were able to discuss Tim’s research in a less formal setting within Park House pub.
Tim’s Tuesday, Wednesday (morning) and Thursday consisted of virtual and in-person one on one 15-minute meetings with PhD students. Here students explained their research projects and Tim gave them a refreshing outsider perceptive. On Wednesday afternoon, after Tim attended the High-Resolution Climate Modelling research group, he talked about his career in PhD group (A research group for PhD students only, where PhD students present to each other.). Tim explained how his PhD did not work as well as he had initially hoped, and the entire room felt a great weight of relief. His advice on keeping calm and looking for the bigger picture was heard by us all.
On Friday the 8th, a mini conference was put on and six students got to the “virtual” and literal stage and presented their current findings. Topics ranged from changes to Arctic cyclones, blocking, radar and Atmospheric dust. The conference and the week itself were both great successes, with PhD students leaving with inspiring questions to help aid their current studies. All at the University of Reading Department of Meteorology were extremely grateful and we thoroughly enjoyed having Tim here. We wish him all the best in his future endeavours and hope he comes back soon!
Every year, Cambridge and École Polytechnique in Paris alternate hosting duties of the Fluid Dynamics of Sustainability and the Environment (FDSE) summer school. This ran for two weeks earlier in September, and like many other things took place online. After talking to previous attendees of the summer school, I went into the fortnight with excitement but also trepidation, as I had heard that it has an intense programme! Here is my experience of a thoroughly enjoyable couple of weeks.
The summer school brought together around 50 PhD students and a few postdocs from all over the world, from Japan to Europe to Arizona, and I have to admire the determination of those students who attended the school at unsociable times of the day! We all came from different backgrounds – some had a meteorological background like myself, but there were also oceanographers, fluid dynamicists, engineers and geographers to name but a few. It was great to hear from so many students who are passionate about their work in two brief ice-breaker sessions where we introduced ourselves to the group and I got to appreciate how wide-reaching the FDSE community is.
Each day consisted of four 1-hour lectures – normally three ‘core’ subjects (fluid dynamics basics, atmospheric dynamics, climate, oceanography, etc.) and one guest lecturer per day (including our very own Sue Gray who gave us a whistle-stop tour of the mesoscale and extratropical cyclones). After this, there was the opportunity to split into breakout groups and speak to the day’s lecturers to ask them questions and spark discussions in small groups. On the final day, we also had a virtual tour of the various fluid dynamics labs that Cambridge has (there are a lot!) and a few of the students in the labs spoke about their work.
These lectures were given by very engaging specialists including Colm-Cille Caulfield, John Taylor, Alison Ming, Jerome Neufeld and Jean-Marc Chomaz; and provided the perfect opportunity to see lots of pretty videos about fluid flows (Fig. 1). Having done an undergraduate course in Meteorology, a lot of these gave me a refresher of things I should already know, but it was refreshing to see how other lecturers approach the same material.
The most interesting core lectures to me were those regarding renewable energy, given by Riwal Plougonuen and Alex Stegner. Plougonuen lectured us on wind turbines, telling us how they worked and why they are designed like they are – did you know that actually the most efficient wind turbines have 2 blades, but the vast majority have three for better structural stability? On the other hand, Stegner spoke to us about hydroelectricity, and I learned that Norway produces nearly all of its electricity through hydropower. Other highlights from these core lectures include watching a video of a research hut being swamped by an avalanche (Nathalie Vriend, see video at the link here), and seeing Jerome Neufeld demonstrate ice flows using golden syrup (he likes his food!)
For me, the guest lectures were the highlights of my time at the summer school. These lectures often explored things beyond my area of expertise, and demonstrated just how the fluid mechanics we had learned are highly applicable to many different areas of life. We had a lecture about building ventilation from Megan Davies Wykes, which made me realise that adequately ventilating a room is more than simply cracking open a window – you have to consider everything from the size of the room, outside wind speed, how many windows there are, and even the body heat from people inside the room. Davies Wykes’s passion about people using their sash windows correctly will always stick with me – turns out you need to open both the top and the bottom panes for the best ventilation (something she emphasised more than once!), see Fig. 2.
Fittingly, we also had a lecture from Paul Linden about the transmission of Covid, and he demonstrated how effective masks are at preventing transmission using a great visualisation (Fig. 3). It was great to have topics such as these that are relevant in today’s world, and provided yet another real-world application of the fluid dynamics we had learned.
Breakout Discussion Sessions
Every afternoon, the day’s lecturers returned and invited us to ask them questions about their lectures, or just have an intelligent discussion about their area of expertise. Admittedly these sessions could get a little awkward when everyone was too tired to ask anything towards the end of the long two weeks, but these sessions were still incredibly useful. They provided us the means to speak to a professional in their field about their research, and allowed us time to network and ask them some challenging questions.
Of course, over the course of the two weeks we learned so much more than what I described above, and yet again demonstrates the versatility of the field! The summer school as a whole was organised really well and the lecturers were engaging and genuinely interested in hearing about us and our projects. I would highly recommend attending this summer school next year to any PhD student – the scope of the school was so broad that I am sure there will be something for everyone in the programme, and fingers crossed it goes ahead in Paris next year!
Bhagat, R., Davies Wykes, M., Dalziel, S., & Linden, P. (2020). Effects of ventilation on the indoor spread of COVID-19. Journal of Fluid Mechanics, 903, F1. doi:10.1017/jfm.2020.720
The European Geoscience Union General Assembly is one of the big annual conferences for atmospheric science (and Earth sciences more generally). The two of us were fortunate to have the opportunity to attend and present our research at this year’s vEGU21 conference. As has been done in previous years like in 2019 we’re here to give you an account of our EGU experience 😀 (so you can compare our virtual experience with the previous posts if you like 😉)
Entrance hall to virtual EGU (Source: Linda Speight)
What was vEGU21?
EGUv21 was the general assembly for 2021 online. It took place from the 19th to the 30th April EGU. Through an impressive virtual conference center and mostly Zoom.
What was your presentation on?
Chloe – I presented borderless heat stress in the extreme heat events session, which is based on a paper currently under review at Earth’s Future, where we show that heat stress is growing in the area during the month of August. The invited speaker to the session was Laura Suarez-Gutierrez and it was a great presentation on the dynamics of increasing heat extremes with climate change across Europe. I really enjoyed learning about the latest research in the extreme heat area.
Max – I presented on my work using model nudging to study aerosol radiative adjustments. I presented in the session ‘Chemistry, Aerosols and Radiative Forcing in CMIP6-era models’, which was convened and hosted by Reading’s very own Bill Collins. There were many interesting presentations in this session, including presentations on the balance between climate and air quality benefits by Robert Allen and Steve Turnock; a summary of the Aerosol Chemistry Model Intercomparison Project (AerChemMIP) findings by UoR’s Gill Thornhill; and a personal favourite concerned the impacts of different emissions pathways in Africa on local and global climate, and local air pollution effects on mortality, presented by Chris Wells.
Chloe presenting: would win an award for most interesting screenshot. (Source: Maureen Wanzala)
What were your favourite aspects of the conference?
Chloe – Apart from my session one of my favorite’s was on climate services. This focused on the application of meteorological and hydrology data to services for example health heat impacts and growing grapes and olives. I also enjoyed the panel on the climate and ecological emergency in light of COVID-19 including Katherine Hayhoe and the session on equality, diversity and inclusion; it was interesting how ‘listening’ to those impacted was an overlapping theme in these. The weirdest, loveliest experience was my main supervisor sending me a colouring page of her face.
Max – As with any conference it was a great opportunity to learn about the latest research in my specific field, as well as learning about exciting developments in other fields, from machine learning applications in earth science to observational studies of methane emissions. Particularly, it’s a nice change from just reading about them in papers.Having conversations with presenters gives you the opportunity to really dive in and find out what motivated their research initially and discuss future applications. For example, one conversation I had went from discussing their application of unsupervised machine learning in classifying profiles of earth system model output, to learning about it’s potential for use in model intercomparisons.
Katherine Hayhoe in the session Climate and Ecological Emergency: can a pandemic help save us? (Source: Chloe Brimicombe)
What was your least favourite aspect?
Chloe – I did manage to do a little networking. But I’d love to experience an in person conference where I present. I have never presented my research in real life at a conference or research group/department seminar 😱. We also miss out on a lot of free food and pens not going to any in life conferences, which is what research is about 😉. Also, I find it difficult to stay focused on the conference when it’s online.
Max – For me the structure of two minute summaries followed by breakout Zoom rooms for each speaker had some definite drawbacks. For topics outside one’s own field, I found it difficult to really learn much from many of the summaries – it’s not easy to fit something interesting for experts and non-experts into two minutes! In theory you can go speak to presenters in their breakout rooms, but there’s something awkward about entering a zoom breakout room with just you and the presenter, particularly when you aren’t sure exactly how well you understood their two minute summary.
In light of your vEGU21 experience, what are your thoughts on remote vs traditional conferencing?
Max – Overall I think virtual conferencing has a way to go before it can match up to the in person experience. There were the classic technical issues of anything hosted remotely: the ‘I think you’re on mute’ experience, other microphone issues, and even the conference website crashing on the first day of scientific sessions (though the organisers did a swift job getting the conference back up and running). But there’s also the less obvious, such as it feeling actually quite a lonely experience. I’ve only been to a couple of in-person conferences, but there were always some people I knew and could meet up with. But it’s challenging to recreate this online, especially for early career researchers who don’t have as many established connections, and particularly at a big conference like the EGU general assembly. Perhaps a big social media presence can somewhat replace this, but not everyone (including myself!) is a big social media user. .
On the other hand, it’s great that we can still have conferences during a global pandemic, and no doubt is better than an absence of them entirely. Above all else, it’s also much greener and more accessible to those with less available funding for conference travel (though new challenges of accessibility, such as internet quality and access, undoubtedly arise). Plus, the facility to upload various display materials and people to look back at them whenever they like, regardless of time zones, is handy.
Chloe – I’d just add, as great as Twitter is and can be for promoting your research, it’s not the same as going for a good old cup of tea (or cocktail) with someone. Also, you can have the biggest brightest social media, but actually be terrible at conveying your research in person.
Overall it was interesting to take part in vEGU21, and we were both glad we went. It didn’t quite live up to the in person experience – and there is definitely room for improvements for virtual conferencing – but it’s great we can still have these experiences, albeit online.
This post presents a collection of resources and tips that have been most useful to me in the first 18 months I’ve been coding – when I arrived at Reading, my coding ability amounted to using excel formulas. These days, I spend a lot of time coding experiments that test how well machine learning algorithms can provide information on error growth in low-dimensional dynamical systems. This requires fairly heavy use of Scikit-learn, Tensorflow and Pandas. This post would have been optimally useful at the start of the year, but perhaps even the coding veterans will find something of use – or better, they can tell me about something I am yet to discover!
Python Crash Course, Eric Matthes (2019). Detailed, lots of examples, and covers a wider range of topics (including, for example, using git). There are many intro to Python books around; this one has certainly been useful to me.1 There are many good online resources for python, but it can be helpful initially to have a coherent guide in one place.
How did I do that last time…?
Tip: save snippets.
There are often small bits of code that contain key tricks that we use only occasionally. Sometimes it takes a bit of time reading forums or documentation to figure out these tricks. It’s a pain to have to do the legwork again to find the trick a second or third time. There were numerous occasions when I knew I’d worked out how to do something previously, and then spent precious minutes trawling through various bits of code and coursework to find the line where I’d done it. Then I found a better solution: I started saving snippets with an online note taking tool called Supernotes. Here’s an example:
I often find myself searching through my code snippets to remind myself of things.
Text editors, IDEs and plugins.
If you haven’t already, it might be worth trying some different options when it comes to your text editor or IDE. I’ve met many people who swear by PyCharm. Personally, I’ve been getting on well with Visual Studio Code (VS Code) for a year now.
Linters and formatters check your code for syntax errors or style errors. I use the Black formatter, and have it set to run every time I save my file. This seems to save a lot of time, and not only with formatting: it becomes more obvious when I have used incorrect syntax or made a typo. It also makes my code easier to read and look nicer. Here’s an example of Black in anger:
Some other options for linters and formatters include autopep, yapf and pylint.
Metadata for results
Data needs metadata in order to be understood. Does your workflow enable you to understand your data? I tend to work with toy models, so my current approach is to make a new directory for each version of my experiment code. This way I can make notes for each version of the experiment (usually in a markdown file). In other words, what not to do, is to run the code to generate results and then edit the code (excepting, of course, if your code has a bug). At a later stage you may want to understand how your results were calculated, and this cannot be done if you’ve changed the code file since the data was generated (unless you are a git wizard).
A bigger toolbox makes you a more powerful coder
Knowing about the right tool for the job can make life much easier.2 There are many excellent Python packages, and the more you explore, the more likely you’ll know of something that can help you. A good resource for the modules of the Python 3 standard library is Python Module of The Week. Some favourite packages of mine are Pandas (for processing data) and Seaborn (a wrapper on Matplotlib that enables quick and fancy plotting of data). Both are well worth the time spent learning to use them.
Some thoughts on Matplotlib
Frankly some of the most frustrating experiences in my early days with python was trying to plot things with Matplotlib. At times it seemed inanely tedious, and bizarrely difficult to achieve what I wanted given how capable a tool others made it seem. My tips for the uninitiated would be:
Be a minimalist, never a perfectionist. I often managed to spend 80% of my time plotting trying to achieve one obscure change. Ask: Do I really need this bit of the plot to get my point across?
Can you hack it, i.e. can you fix up the plot using something other than Matplotlib? For example, you might spend ages trying to tell Matplotlib to get some spacing right, when for your current purpose you could get the same result by editing the plot in word/pages in a few clicks.
Be patient. I promise, it gets easier with time.
Object oriented programming
I’m curious to know how many of us in the meteorology department code with classes. In simple projects, it is possible to do without classes. That said, there’s a reason classes are a fundamental of modern programming: they enable more elegant and effective problem solving, code structure and testing. As Hans Petter Langtangen states in A Primer on Scientific Programming with Python, “classes often provide better solutions to programming problems.”
What’s more, if you understand classes and object- oriented programming concepts then understanding others’ code is much easier. For example, it can make Matplotlib’s documentation easier to understand and, in the worse caseworst case scenario, if you had to read the Matplotlib source code to understand what was going on under the hood, it will make much more sense if you know how classes work. As with Pandas, classes are worth the time buy in!
Have any suggestions or other useful resources for wannabe pythonistas? Please comment below or email me at email@example.com.
Every year the Met-PhDs put on a Christmas pantomime and perform it to the rest of the department. The autumn term always seems to drag: the mornings are dark; the evenings are darker; and no matter how hard you try, the term just feels so busy! So what better way to finish off the term than with department jokes, terrible singing and unnecessary Benny Hill chase scenes?
Met Panto 2020 virtual group photo
And despite of a global pandemic that is in full swing, this year would be no different – the show must go on! On 10th December we premiered the very first virtual Met panto: The Greatest Storm! – A spin-off of the 2017 film ‘The Greatest Showman’. The Greatest Storm follows Professor Sue Gray Barnum (or PG Barnum for short) on her journey to find the greatest storm. On her way she meets her “misfit” team: Helen Dacre, Pete Inness, Tom Frame and Javier Amezcua, and recruits her right-hand man: Philip-Craig Carlyle. Together they develop a new instrument: DOROTHY, the Data recORding unit fOr in-siTu sting jet measurements High in the skY. But with COVID lurking around every corner, will they ever be able to measure the Greatest Storm? (…although it will actually just be the greatest storm on record…)
Panto 2020 poster – designed and created by Meg Stretton
This year, Max and I were persuaded volunteered for the role of panto organisers, with the promise that running the panto would be ‘much easier’ than previous years as everything would be online. This was partly true, though there was still a lot of last-minute tweaking…
We were very fortunate that Kris Boykin brought forward the idea to recreate The Greatest Showman, with a detailed plan for the plot, which fought off the other (very good) competition for plot ideas. This made the script writing relatively pain-free as we filled in the details and decided on which of the staff should be included.
Next was the song writing: in retrospect, the songs we chose were quite difficult to get right, as it was challenging to stay in time when singing for most of them, especially when we had changed the lyrics to include meteorological puns! In a live panto this might not have been so bad, but as everything had to be recorded individually and put together by our audio editing experts Dominic Jones and Beth Saunders, we can only say, Dom, we’re very sorry…
The next 9 weeks were filled with read throughs, character selection and filming. In a normal year, these weeks would be relatively relaxed, with rehearsals spanning the full 9 weeks, however as we were aware that the video editors Lauren James and Wilson Chan had a lot of work to do in putting all of the scenes together, we tried to film as early as possible to give them more time. Our initial plan was to meet up on a weekend to film the parts in a socially distanced setting, but as the second lockdown was announced, we had to quickly change our plan. Some scenes were filmed individually, but the majority were filmed over Zoom: although this had reduced camera quality, it was much more fun to see each other every week and laugh at everyone’s wacky costumes and improvisation!
The last week leading up to Thursday’s showing (tomorrow as we’re writing this!) was slightly busier, with reviewing footage and making final edits, in the knowledge that in these unprecedented circumstances most of the cast will not have seen a complete run through before the final showing! In the end it all came together with an entirely smooth and seamless virtual viewing experience / it all went horribly wrong and we should never have been entrusted with panto (delete as applicable), which everyone viewing hopefully enjoyed!
Screenshot of scene 2 – the misfits’ entrance.
With that, we’d like to say thank you so much to everyone involved, from script writers, band, editors, cast and everyone that helped both on and off our virtual stage! It has been so lovely to see everyone come together, and although has been a very tiring process, panto 2020 has been a very welcome distraction to the rest of 2020!
This year we did not sell tickets, but instead asked for donations to cover our (reasonably small!) running costs, plus any extra will go to the Reading Meteorology department’s charity: San Francisco Libre Association. If you didn’t donate on the night, but wanted to, here’s a link to our donations page – https://paypal.me/pools/c/8uIzsVEQwB. We were so humbled by everyone that has already donated, both small and large amounts, we really appreciate it!
Thank you to everyone that watched The Greatest Storm on Thursday, we hope you had a fun evening! And we look forward to next year’s panto; who will be next to volunteer for this incredible tradition, with panto 2021…?
Just over a month ago in September 2020, I started my journey as a PhD student. Since then, have I spent all of my working hours solely on research – plotting radar scans of heavy rainfall events and coding up algorithms to analyse the evolution of convective cells? Surely not! Outside my research work, I have also taken on the role of demonstrating this academic year.
What is demonstrating? In the department, PhD students can sign up to facilitate the running of tutorials and problems, synoptic, instrument, and computing laboratory classes. Equipped with a background in Physics and having taken modules as an MSc student at the department in the previous academic year, I signed up to run problem classes for this year’s Atmospheric Physics MSc module.
I have observed quite a few lectures during my undergraduate education at Cambridge, MSc programme at Reading and also a few Massive Open Online Courses (MOOCs) as a student. Each had their unique mode of teaching. At Cambridge, equations were often presented on a physical blackboard in lectures, with problem sheet questions handed in 24 hours before each weekly one-hour “supervision” session as formative assessment. At Reading, there have been less students in each lecture, accompanied by problem classes that are longer and more relaxed, allowing for more informal discussion on problem sheet questions between students. These different forms of teaching were engaging to me in their own ways. I have also given a mix of good and not-as-good tutorial sessions for Year 7s to 13s. Good tutorials included interactive demonstrations, such as exploring parametric equations on an online graphing calculator, whereas the not-as-good ones had content that were pitched at too high of a level. Based on these experiences and having demonstrated for 10 hours, I hopefully can share some tips on demonstrating through describing what one would call a “typical” 9am Atmospheric Physics virtual problems class.
PhD Demonstrating 101
You, a PhD student, have just been allocated the role as demonstrator on Campus Jobs and are excited about the £14.83 per hour pay. With the first problems class happening in just a week’s time, you start thinking about tools you will need to give these MSc students the best learning experience. A pencil, paper, calculator and that handy Thermal Physics of the Atmosphere textbook would certainly suffice for face-to-face classes. The only difference this year: You will be running virtual classes! This means that moist-adiabatic lapse rate equation you have quickly scribbled down on paper may not show well on a pixelated video call due to a “poor (connection) experience” from Blackboard. How are you going to prevent this familiar situation from happening?
Figure 1: Laptop with an iPad with a virtual whiteboard for illustrating diagrams and equations to be shown on Blackboard Collaborate.
In my toolbox, I have an iPad and an Apple pencil for me to draw diagrams and write equations. The laptop’s screen is linked to the iPad with Google Jamboard running and could be shared on Blackboard Collaborate. Here I offer my first tip:
Explore tools available to design workflows for content delivery and decide on one that works well
Days before the problems class, you wonder whether you have done enough preparation. Have you read through and completed the problem sheet; ready to answer those burning questions from the students you will be demonstrating for? It is important you…
Figure 2: Snippet of type-written worked solutions for the Atmospheric Physics MSc module.
Have your worked solutions to refer to during class
A good way to ensure you are able to resolve queries about problem sheet questions is to have a version of your own working. This could be as simple as some written out points, or in my case, fully type-written solutions, just so I have details of each step on hand. In some of my fully worked solutions, I added comments for steps where I found the learning curve was quite steep and annotated places where students may run into potential problems.
Students seem to take interest in these worked solutions, but here I must recommend…
Do not send out or show your entire worked solutions
It is arguable whether worked solutions will help students who have attempted all problems seriously, but the bigger issue lies in those who have not even given the problems a try. As a demonstrator, I often explain the importance of struggling through the multiple steps needed to solve and understand a physics problem. My worked solutions usually present what I consider to be the quick and more refined way to the numerical solution, but usually are not the most intuitive route. On that note, how then are you supposed to help someone stuck on a problem?
It may be tempting to show snippets of your solutions to help someone stuck on a certain part of a problem. Unfortunately, I found this did not work very well. Students can end up disregarding their own attempt and copy down what they regard as the “model answer”. (A cheeky student would have taken multiple screenshots while I scrolled through my worked solutions on the shared screen…) What I found worked better in breakout groups was for the student(s) to explain how they got stuck.
For example, I once had a few students ask me how they should work out the boiling temperature from saturated vapour pressure using Tetens’ formula. However, my worked solutions solved this directly using the Clausius-Clapeyron equation. Instead of showing them my answer, I arrived at the point where they got stuck (red in Figure 3), essentially putting myself in their shoes. From that point, I was able to give small hints in the correct direction. Using their method, we worked together towards a solution for the problem (black in Figure 3). Here is another tip:
Work through the problem from your students’ perspective
Figure 3: Google Jamboard slide showing how Tetens’ formula is rearranged. Red shows where some students got up to in the question, whereas black is further working to reach a solution.
This again illustrates the point on there being no “model answer”. As in many scientific fields, there exist multiple path functions that get you from a problem to a plausible solution, and the preference for such a path is unique to us all.
There will always be a group of diligent students who gave the problem sheet a serious attempt prior to the class. You will find they only take less than 30 minutes to check their understanding and numerical solutions with you, and they might do their own thing afterwards. This is the perfect opportunity to…
Present bonus material to stretch students further
Some ideas include asking for a physical interpretation from their mathematical result, or looking for other (potentially more efficient) methods of deriving their result. For example, I asked students to deduce a cycle describing the Stirling engine on a TS diagram, instead of the pV diagram they had already drawn out as asked by the problem sheet.
Figure 4: A spreadsheet showing the content coverageof each past exam question
I also have a table of past exam questions, with traffic light colours indicating which parts of the syllabus they cover. If a student would like to familiarise themselves with the exam style, I could recommend one or two questions using this spreadsheet.
On the other hand, there may be the occasional group who have no idea where equation (9.11) on page 168 of the notes came from, or a student who would like the extra-reassurance of more mathematical help on a certain problem. As a final tip, I try to cater to these extra requests by…
Staying a little longer to answer a final few questions
The best demonstrators are approachable, and go the extra mile to cater to the needs of the whole range of students they teach, with an understanding of their perspectives. After all, being a demonstrator is not only about students’ learning from teaching, but also your learning by teaching!
I would welcome your ideas about demonstrating as a PhD. Feel free to contact me at firstname.lastname@example.org if you would like to discuss!
As per annual tradition in the Meteorology Department, PhD students have chosen a distinguished scientist to visit the department for one week. Previous years’ visitors include Prof. Tapio Schneider (Caltech), Prof. Olivia Romppainmen-Martius (University of Bern), and Prof. Cecilia Bitz (University of Washington). This year’s winning vote was New York University’s Prof. Laure Zanna, who will be visiting the department virtually1 between 2 – 6th November.
Laure is an oceanographer and climate scientist whose career so far has spanned three continents, won her an American Meteorological Society (AMS) Early Careers’ award for “exceptionally creative” science this year, and netted her 600 citations in the last two years. Her research interests encompass ocean turbulence, climate dynamics, predictability, machine learning and more. Some of the many topics of her published papers include the uncertainty in projections of ocean heat uptake; ocean turbulence parametrisations; predictions of seasonal to decadal sea surface temperatures in the Atlantic using simple statistical models and machine learning to inform prediction of extreme events. Besides being an exceptional scientist, speaker and educator, Laure is a down-to-Earth and friendly person, described by the Climate Scientists podcast’s Dan Jones as ‘a really great person who helps to tie the whole community together’.
As someone who had received their PhD only just over a decade ago, we thought Laure would be the perfect candidate to inspire us and our science through sharing some of her academic experiences with us. Before her visit next week, Laure kindly answered some interview-style questions for this week’s Social Metwork blog post.
Q: What inspired you to research oceanography and climate in the first place?
A: I always enjoyed math and physics. The possibility of using these disciplines to study scientific problems that I could “see” was very appealing.
Q: Why were you drawn to machine learning?
A: The power of machine learning (ML) to advance fields such as natural processing language or computer science is indisputable. I was excited by the premise of ML for climate science. In particular, can ML help deepen our understanding of certain aspects of the climate systems (e.g. interactions between scales or interactions between the ocean and atmosphere)? Can ML improve the representation of small-scale processes in climate models? ML, by itself, is not enough but combined with our physical understanding of the climate system could push the field forward.
Q: Can you give us an idea of what’s the most exciting research you are working on right now?
A: This is impossible. I work on 2 main areas of research right now: understanding and parameterizing ocean mesoscale eddies and understanding the role of the oceans in climate. I am passionate and excited about both topics. Hopefully, you will hear about both of them during the week.
Q: When did you realise/decide you were going to remain in academia?
A: I decided that I wanted to try and stay in academia in the last year of my PhD. I was lucky enough to be able to.
Q: What is your favourite part of your job?
A: Working with my group! The students and postdocs in the group have different expertise but all are passionate about their research. They make the work and the research more fun, more challenging, and more inspiring.
We are honoured to have our invitation accepted by Laure and are eagerly anticipating answers to more of these kind of questions throughout next week’s conversations. Laure will be presenting a seminar titled, “Machine learning for physics-discovery and climate modelling” during the Monday Departmental Seminar series, as well as another seminar in the Climate and Ocean Dynamics research group, titled “Understanding past and future ocean warming”. She will also give a career-focused session at PhD group and, of course, engage with both the PhD students and staff on an individual basis during one-to-one meetings. We are grateful and delighted to be able to welcome Laure to the Meteorology department despite the various difficulties the year 2020 has posed on everyone, so come along to next week’s events!
A challenge most researchers will be familiar with is how do you explain your research to friends and family in a way that’s readily understandable to non-experts? The web comic xkcd decided to try describing the Saturn-V moon rocket (‘Up Goer Five’) using only the ten-hundred most commonly used words.
Feeling that we needed something a bit different during the Covid-19 lockdown, and inspired by our predecessors in a previous blog post, we decided to have a go at explaining our own research using the xkcd ‘simplewriter’ tool in our weekly PhD group meeting. While restricting yourself to only the ten-hundred most commonly used words seemed to make things more confusing at times, it certainly gets one thinking about what complicated words don’t need to be used… and results in some amusing explanations!
Devon Francis – Advanced methods for assimilating satellite data in numerical weather prediction
When we find out how hot or cold it is outside, sometimes it is not right as sometimes it can be too hot and sometimes it can be too cold. If over lots of time it is more often hot than it is cold, or the other way around, then we have to move how much warmer we have found it to be so that we can write down how warm it actually is. We can also imagine how hot or cold it will be by thinking about how hot or cold it has been before, or how hot or cold it is on a day that is like today. But if it was hotter before but we did not notice, then we may think it is also hotter today, so we may need to change what we thought about before as well as what we think about today. Sometimes it is hard to work out why we are wrong about how warm it is today. It could be because we can not decide how warm it is or it could be because it has been too hot or cold in the past. My job is to decide why we think it is too hot or cold, so that tomorrow we can know how warm it will be!
I look at the warming of the sky, and see if that is different from what the sky was like before it was warming. The cool thing is, that we only look at those moments where the sky was attacking us. With us we are talking about humans on the ground. Any form of attack such as warm, cold, strong wind and rain is a thing. I do this by telling a story. The story helps in finding what made the attack happen, if it was the warming of the sky or maybe something else.
Max Coleman – Climate response to short-lived pollutants
In the air are very small things that can hurt us if we breathe them in. However, these things can also change how warm the Earth is by catching light or sending it back to space, and also change how much it rains by changing how much cloud there is in the sky. If humans make more of these things then we will change how warm the Earth is. I study how these small things change how much cloud there is and other things, which in turn change how much light is caught or sent back to space. I do this using a computer which can pretend to be the Earth and works out how much light is caught or sent back to space by changing these things.
Mark Prosser – Using aviation meteorology to improve aircraft safety
My finish-big-school work piece is about crazy wind and sky high-lighting causing problems for flying buses. Crazy wind is already a problem for flying buses which we think will get worse with the Earth hotter than it is now (it is already hotter than before). Didn’t-see-it-coming crazy wind is especially a problem for flying buses because you can’t see into the future. So we brain people use things that big brain animals use on computer brain best guesses, but these aren’t perfect. My work piece is saying good or bad about these things by putting them on real flying buses meeting didn’t-see-coming crazy wind (things really go wrong when these two meet). Day to day I pull down guesses from big computer brain and use not friend long toothy animal to eat these guesses. Right now I am writing up a paper using over leaf and hope it will be put on many bits of paper and seen by some other brain people and liked and talked about.
Jake Bland – The control of cloud and moisture on extratropical cyclone evolution
People use computers to tell them if there will be rain, sun, clouds, or storms in the future, and if it will be hot or cold. To be right about these things the computers need to know about how hot the sky is and how much water is in it, among other things. We get this information by looking with machines on earth, putting machines into the sky, or putting machines in space and telling them to look down at the sky.
You can think of the sky as being in layers, where we are in the lowest layer, and that is where most of our clouds are too. The layer above that is drier, changes less and changes more slowly, but the water in there is still important for being right about the future. It is harder to look at the water in the higher layer than it is to look at how hot it is, or the water in the lower layer, so computers often think there is too much water there. They also think it will be colder up there in the future than it really will be.
I have worked out how wrong computers are about how wet it is and how cold it will be using guesses made four years ago, and machines that were thrown into the sky to look at the same times that were guessed about. (I’m now finishing off writing about this so other people can know about it too!) I have also looked at how the computer being wrong about water and wrong about how cold it is in the future are tied together.
Now I have made a lot of future guesses about those days four years ago, some where the computers have the wrong information like they did before, and some where they have the better information. I am also getting the computer to tell me how it is making the guesses, so I can try to find out what it is wrong with the computer to make it think the higher sky layer is too wet. Using these two sets of guesses I can also find out exactly how important getting how wet it is right for telling the future well.
Linda Toca – Analysis of peatland carbon dynamics using combined optical and microwave satellite data
I study very important wet places and the tiny parts they breath in and out. Most of the time I use space pictures and computer to come up with something to show, but later I plan to go outside to the wet places and see in person how much tiny parts they breath out, and check if it is the same as computer says. I also want to use small flying thing that can make more pictures of wet places from above to see if they can help computer work better. It is very important as the wet places hold huge numbers of tiny parts and we need to know what could happen in the further times with air getting hotter and dry times longer. Do the wet places give much more tiny parts with hotter air and is it enough with space pictures to tell how much, that is what we want to find out.
Wilfred Calder-Potts – Quantifying the impact of increased atmospheric CO2 and climate change on photosynthesis using Solar Induced Fluorescence
When trees accept light they make food. They also make their own light. If you see this light you can guess how much food they are making. Some people have built machines to see this light from space. But we are not sure exactly how much food they are making. I am trying to understand exactly how much food is made, using the light. I am doing this by seeing this light from trees which are hot or cold or have more or less bits of food.