How to write a PhD thesis during a global pandemic

Kaja Milczewska – k.m.milczewska@pgr.reading.ac.uk

Completing a PhD is a momentous task at the best of times, let alone in combination with a year-long global pandemic. Every PhD researcher is different, and as such, everyone has had different circumstantial struggles throughout Covid-19. The lack of human interaction that comes with working in a vibrant academic environment such as the Meteorology Department can make working from home a real struggle. Sometimes it is difficult to find the motivation to get anything useful done; whereas at other times you could squeeze five hours’ worth of work into one. Trying to stay organised is key to getting it done, therefore the following are some of the things that helped me get to the end of my PhD thesis – and it has not been easy. If you are still out there writing and finishing up experiments: read on! Maybe the result is that you might feel a little less alone. The PhD experience can be truly isolating at the best of times, so literally being instructed to isolate from the world is not ideal. The points are numbered for convenience of structuring this post, rather than any order of importance. 

  1. Communicate with your supervisor(s) 

It is tempting to “disappear off the radar” when things are not going well. You could wake up in the morning of the day of your regular weekly meeting, filled with dread that you have not prepared anything for it. Your brain recoils into the depths of your skull as your body recoils back under the safety of the duvet. What are your options? Some of them might be: take a deep gulp and force yourself out of bed with the prospect of coffee before the meeting (where you steer the conversation onto the things you did manage to do); or to postpone the meeting because you need to finish XYZ and thus a later meeting may be more productive; or ignore the meeting altogether. The first one is probably the best option, but it requires mental strength where there might be none. The second one is OK, but you still need to do the work. The last one is a big no. Don’t do it. 

Anxiety will make you believe that ignoring the world and all responsibilities is the most comfortable option in the moment, but the consequences of acting on it could be worse. Supervisors value honesty, and they know well that it is not always possible to complete all the scheduled tasks. Of course, if this happens every week then you might need to introspectively address the reasons for this, and – again, talking with your supervisor is usually a useful thing to do. You might not want them to know your entire life story, but it is helpful for everybody involved if they are aware that you struggle with anxiety / depression / ADHD / *insert any condition here*, which could affect your capacity to complete even the simplest, daily tasks. Being on the same page and having matching expectations is key to any student – supervisor partnership. 

  1.  Reward yourself for the things you have already accomplished. 

Whether that’s mid-week, mid-to-do-list, weekend — whenever. List all the things you have done regularly (either work- or life-related) and recognise that you are trying to survive a pandemic. And trying to complete the monstrous task of writing a PhD thesis. Those are big asks, and the only way to get through them is to break them down into smaller chunks. Putting down “Write thesis” on your to-do list is more likely to intimidate than motivate you. How about breaking it down further: “Re-create plot 4.21”, or “Consolidate supervisor comments on pages 21 – 25” — these are achievable things in a specified length of time. It also means you could tick them off more easily, hopefully resulting in feeling accomplished. Each time this happens, reward yourself in whatever way makes you feel nice. Even just giving yourself a literal pat on the shoulder could feel great – try it! 

  1. Compile supervisor feedback / comments into a spreadsheet  

An Excel spreadsheet – or any other suitable system – will enable you to keep track of what still needs addressing and what has been completed. The beauty of using a colour-coded spreadsheet for feedback comments is that once the required corrections are completed, you have concrete evidence of how much you have already achieved – something to consult if you start feeling inadequate at any point (see previous section!). I found this a much easier system than writing it down in my workbook, although of course this does work for some people, too. Anytime you receive feedback on your work – written or otherwise – note them down. I used brief reminders, such as “See supervisor’s comment on page X” but it was useful to have them all compiled together. Also, I found it useful to classify the comments into ‘writing-type’ corrections and ‘more work required’ corrections. The first one is self-explanatory: these were typos, wrong terminologies, mistakes in equations and minor structural changes. The ‘more work required’ was anything that required me to find citations / literature, major structural changes, issues with my scientific arguments or anything else that required more thought. This meant that if my motivation was lacking, I could turn to the “writing-type” comments and work on them without needing too much brain power. It also meant that I could prioritise the major comments first, which made working to a deadline a little bit easier. 

  1. Break down how long specific things will take 

This is most useful when you are a few weeks away from submission date. With only 5 weeks left, my colour-coded charts were full of outstanding comments; neither my ‘Conclusions’ chapter nor my Abstract had been written; plots needed re-plotting and I still did not know the title of my thesis. Naturally, I was panicking. I knew that the only way I could get through this was to set a schedule — and stick to it. At the time, there were 5 major things to do: complete a final version of each of my 5 thesis chapters. A natural split was to allow each chapter only one week for completion. If I was near to running over my self-prescribed deadline, I would prioritise only the major corrections. If still not done by the end of the allowed week: that’s it! Move on. This can be difficult for any perfectionists out there, but by this point the PhD has definitely taught me that “done” is better than perfect. I also found that some chapters took less time to finish than others, so I had time to return to the things I left not quite finished. Trust yourself, and give it your best. By all means, push through the hardest bit to the end, but remember that there (probably) does not exist a single PhD thesis without any mistakes. 

5. Follow useful Twitter threads 

There exist two groups of people: those who turn off or deactivate all social media when they need to focus on a deadline, and those who get even more absorbed by its ability to divert your attention away from the discomfort of the dreaded task at hand. Some might call it “productive procrastination”. I actually found that social media helped me a little – but only when my state of mind was such that I could resist the urge to fall down a scrolling rabbit hole. If you are on Twitter, you might find hashtags like #phdchat and accounts such as @AcademicChatter , @phdforum @phdvoice useful. 

6. Join a virtual “writing room” 

On the back of the last tip, I have found a virtual writing room helpful for focus. The idea is that you join an organised Zoom meeting full of other PhDs, all of whom are writing at the same time. All microphones are muted, but the chat is active so it is nice to say ‘hello!’ to someone else writing at the same time, anywhere else in the world. The meetings have scheduled breaks, with the organiser announcing when they occur. I found that because I actively chose to be up and start writing at the very early hour of 6am by attending the virtual writing room, I was not going to allow myself to procrastinate. The commitment to being up so early and being in a room full of people also doing the same thing (but virtually, obviously) meant that those were the times that I was probably the most focused. These kinds of rooms are often hosted by @PhDForum on Twitter; there could also be others. An alternative idea could be to set up a “writing meeting” with your group of peers and agree to keep chatter to a minimum (although this is not something I tried myself). 

7. Don’t look at the news 

Or at least, minimise your exposure to them. It is generally a good thing to stay on top of current events, but the final stages of writing a PhD thesis are probably unlike any other time in your life. You need the space and energy to think deeply about your own work right now. Unfortunately, I learnt this the hard way and found that there were days where I could do very little work because my brain was preoccupied with awful events happening around the world. It made me feel pathetic, routinely resulting in staying up late to try and finish whatever I failed to finish during the day. This only deteriorated my wellbeing further with shortened sleep and a constant sense of playing “catch-up”. If this sounds like you, then try switching off your news notifications on your phone or computer, or limit yourself to only checking the news homepage once a day at a designated time.  

8. Be honest when asked about how you are feeling 

Many of us tend to downplay or dismiss our emotions. It can be appealing to keep your feelings to yourself, saving yourself the energy involved in explaining the situation to whomever asked. You might also think that you are saving someone else the hassle of worrying about you. The trouble is that if we continuously paper over the cracks in our mental wellbeing within the handful of conversations we are having (which are especially limited during the pandemic), we could stop acknowledging how we truly feel. This does not necessarily mean spilling all the beans to whomever asked the innocent question, “How are you?”. But the catharsis from opening up to someone and acknowledging that things are not quite right could really offload some weight off your shoulders. If the person on the other end is your PhD supervisor, it can also be helpful for them to know that you are having a terrible time and are therefore unable to complete tasks to your best ability. Submission anxiety can be crippling for some people in the final few weeks, and your supervisor just won’t be able to (and shouldn’t) blindly assume how your mental health is being affected by it, because everyone experiences things differently. This goes back to bullet no.1. 

Hopefully it goes without saying that the above are simply some things that helped me through to the end of the thesis, but everybody is different. I am no counsellor or wellbeing guru; just a recently-finished PhD! Hopefully the above points might offer a little bit of light for anyone else struggling through the storm of that final write-up. Keep your chin up and, as Dory says: just keep swimming. Good luck! 

Better Data… with MetaData!

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

As researchers, we familiarise ourselves with many different datasets. Depending on who put together the dataset, the variable names and definitions that we are already familiar from one dataset may be different in another. These differences can range from subtle annoyances to large structural differences, and it’s not always immediately obvious how best to handle them.

One dataset might be on an hourly time-index, and the other daily. The grid points which tell us the geographic location of data points may be spaced at different intervals, or use entirely different co-ordinate systems!

However most modern datasets come with hidden help in the form of metadata – this information should tell us how the data is to be used, and with the right choice of python modules we can use the metadata to automatically work with different datasets whilst avoiding conversion headaches.

First attempt…

Starting my PhD, my favourite (naïve, inefficient, bug prone,… ) method of reading data with python was with use of the built-in function open() or numpy functions like genfromtxt(). These are quick to set up, and can be good enough. But as soon as we are using data with more than one field, complex coordinates and calendar indexes, or more than one dataset, this line of programming becomes unwieldy and disorderly!

>>> header = np.genfromtxt(fname, delimiter=',', dtype='str', max_rows=1)
>>> print(header)
['Year' 'Month' 'Day' 'Electricity_Demand']
>>> data = np.genfromtxt(fnam, delimiter=',', skip_header=1)
>>> print(data)
array([[2.010e+03, 1.000e+00, 1.000e+00, 0.000e+00],
       [2.010e+03, 1.000e+00, 2.000e+00, 0.000e+00],
       [2.010e+03, 1.000e+00, 3.000e+00, 0.000e+00],
       ...,
       [2.015e+03, 1.200e+01, 2.900e+01, 5.850e+00],
       [2.015e+03, 1.200e+01, 3.000e+01, 6.090e+00],
       [2.015e+03, 1.200e+01, 3.100e+01, 6.040e+00]])

The above code reads in year, month, day data in the first 3 columns, and Electricity_Demand in the last column.

You might be familiar with such a workflow – perhaps you have refined it down to a fine art!

In many cases this is sufficient for what we need, but making use of already available metadata can make the data more readable, and easier to operate on when it comes to complicated collocation and statistics.

Enter pandas!

Pandas

In the previous example, we read in our data to numpy arrays. Numpy arrays are very useful, because they store data more efficiently than a regular python list, they are easier to index, and have many built in operations from simple addition to niche linear algebra techniques.

We stored column labels in an array called header, but this means our metadata has to be handled separately from our data. The dates are stored in three different columns alongside the data – but what if we want to perform an operation on just the data (for example add 5 to every value). It is technically possible but awkward and dangerous – if the column index changes in future our code might break! We are probably better splitting the dates into another separate array, but that means more work to record the column headers, and an increasing number of python variables to keep track of.

Using pandas, we can store all of this information in a single object, and using relevant datatypes:

>>> data = pd.read_csv(fname, parse_dates=[['Year', 'Month', 'Day']], index_col=0)
>>> data
Electricity_Demand
Year_Month_Day      
2010-01-01      0.00
2010-01-02      0.00
2010-01-03      0.00
2010-01-04      0.00
2010-01-05      0.00
...              ...
2015-12-27      5.70
2015-12-28      5.65
2015-12-29      5.85
2015-12-30      6.09
2015-12-31      6.04

[2191 rows x 1 columns]

This may not immediately appear a whole lot different to what we had earlier, but notice the dates are now saved in datetime format, whilst being tied to the data Electricity_Demand. If we want to index the data, we can simultaneously index the time-index without any further code (and possible mistakes leading to errors).

Pandas also makes it really simple to perform some complicated operations. In this example, I am only dealing with one field (Electricity_Demand), but this works with 10, 100, 1000 or more columns!

  • Flip columns with data.T
  • Calculate quantiles with data.quantile
  • Cut to between dates, eg. data.loc['2010-02-03':'2011-01-05']
  • Calculate 7-day rolling mean: data.rolling(7).mean()

We can insert new columns, remove old ones, change the index, perform complex slices, and all the metadata stays stuck to our data!

Whilst pandas does have many maths functions built in, if need-be we can also export directly to numpy using numpy.array(data['Electricity_Demand']) or data.to_numpy().

Pandas can also simplify plotting – particularly convenient when you just want to quickly visualise data without writing import matplotlib.pyplot as plt and other boilerplate code. In this example, I plot my data alongside its 7-day rolling mean:

ax = data.loc['2010'].plot(label='Demand', ylabel='Demand (GW)')
data.loc['2010'].rolling(7).mean().plot(ax=ax, label='Demand rolling mean')
ax.legend()

Now I can visualise the anomalous values at the start of the dataset, a consistent annual trend, a diurnal cycle, and fairly consistent behaviour week to week.

Big datasets

Pandas can read from and write to many different data formats – CSV, HTML, EXCEL, … but some filetypes like netCDF4 that meteorologists like working with aren’t built in.

xarray is an extremely versatile tool that can read in many formats including netCDF, GRIB. As well as having built in functions to export to pandas, xarray is completely capable of handling metadata on its own, and many researchers work directly with objects such as xarray DataArray objects.

There are more xarray features than stars in the universe[citation needed], but some that I find invaluable include:

open_mfdataset – automatically merge multiple files (eg. for different dates or locations)
assign_coords – replace one co-ordinate system with another
where – replace xarray values depending on a condition

Yes you can do all of this with pandas or numpy. But you can pass metadata attributes as arguments, for example we can get the latitude average with my_data.mean('latitude'). No need to work in indexes and hardcoded values – xarray can do all the heavy lifting for you!

Have more useful tips for working effectively with meteorological data? Leave a comment here or send me an email j.fallon@pgr.reading.ac.uk 🙂

The EGU Experience 2021: a PhD student perspective

Max Coleman – m.r.coleman@pgr.reading.ac.uk

Chloe Brimicombe – c.r.brimicombe@pgr.reading.ac.uk

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. 

Summary 

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.  

Coding lessons for the newly initiated

Better coding skills and tooling enable faster, more useful results. 

Daniel Ayers – d.ayers@pgr.reading.ac.uk

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!  

First Steps: a few useful references 

  • A byte of python. A useful and concise reference for the fundamentals. 
  • 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. 

Either way, I also recommend spending some time installing useful plugins as these can make your life easier. My recommendations for VS Code plugins are: Hungry Delete, Rainbow CSV, LaTeX Workshop, Bracket Pair Colorizer 2, Rewrap and Todo Tree

Linters & formatters 

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 d.ayers@pgr.reading.ac.uk. 

Extra conference funding: how to apply and where to look

Shannon Jones – s.jones2@pgr.reading.ac.uk

The current PhD travel budget of £2000 doesn’t go far, especially if you have your eye on attending the AGU Fall Meeting in San Francisco. If the world ever goes back to normal (and fingers crossed it will – though hopefully with more greener travel options, and remote participation in shorter conferences?) you might wonder how you are ever going to afford the conferences your supervisors suggest. Luckily, there are many ways you can supplement your budget. Receiving travel grants not only means more conferences (and more travel!), but it also looks great on your CV. In this blog post I share what I have learnt about applying for conference grants and list the main places to apply.

Sources of funding include…

Graduate School Travel Support Scheme

  • Open to 2nd and 3rd year PhD students at the university (or equivalent year if part-time) 
  • 1 payment per student of up to £200 
  • Usually 3 deadlines throughout the year 

There are two schemes open to all PhD students who are members of the IOP (any PhD student who has a degree in physics or a related subject can apply to become a member)

Research Student Conference Fund

  • Unlimited payments until you have received £300 in total
  • 4 deadlines throughout the year: 1st March, 1st June, 1st September and 1st December 
  • Note: you apply for funding from an IOP group, and the conference must be relevant to the group. For example, most meteorology PhD students would apply for conference funding from the Environmental Physics group. You get to choose which groups to join when you become an IOP member. 

CR Barber Trust

  • 1 payment per student of £100-£300 for an international conference depending on the conference location 
  • Apply anytime as long as there is more than a month before the proposed conference 

Legacies Fund

Conference/Meeting Travel Subsistence

From the conference organiser

  • Finally, many conferences offer their own student support, so it’s always worth checking the conference website to see 
  • Both EGU and AGU offer grants to attend their meetings each year 

Application Tips

Apply early!!!

Many of these schemes take months to let you know whether you have been successful. Becoming a member can also take a while, especially when societies only approve new members at certain times of the year. So, it’s good to talk to your supervisor and make a conference plan early on in your PhD, so you know when to apply. 

Writing your application

Generally, these organisations are keen to give away their funds, you just have to write a good enough application. Keep it simple and short: remember the person reading the application is very unlikely to be an expert in your research. It can be helpful to ask someone who isn’t a scientist (or doesn’t know your work well) to read it and highlight anything that doesn’t make sense to them. 

Estimating your conference expenses

You are usually expected to provide a breakdown of the conference costs with every application. The main costs to account for are: 

  • Accommodation: for non-UK stays must apply for a quote through the university travel agent 
  • Travel: UK train tickets over £100 and all international travel must be booked by university too 
  • Subsistence: i.e. food! University rules used to say this could be a maximum of £30 per day – check current guidelines 
  • Conference Fees: the conference website will usually list this 

The total cost will depend on where the conference is. You are generally expected to choose cheaper options, but there is some flexibility. As a rough guide: a 4-day conference within the UK cost me around £400 (in 2019) and a 5-night stay in San Francisco to attend AGU cost me around £2200 (in 2019).  

Reading PhD students at Union Square, San Francisco for AGU! 

Good luck! Feel free to drop me an email at s.jones2@pgr.reading.ac.uk if you have any questions 😊 

Demonstrating as a PhD student in unprecedented times

Brian Lo – brian.lo@pgr.reading.ac.uk 

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: 

  1. 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. 

  1. 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… 

  1. 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: 

  1. 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… 

  1. 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 coverage of 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… 

  1. 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 brian.lo@pgr.reading.ac.uk if you would like to discuss! 

Visiting Scientist Week Preview: Laure Zanna

Kaja Milczewska – k.m.milczewska@pgr.reading.ac.uk

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!


1In true 2020 curve-ball style, of course.

My journey to Reading: Going from application to newly minted SCENARIO PhD student

George Gunn – g.f.gunn@pgr.reading.ac.uk 

Have you been thinking ‘I’ll never be good enough for a PhD’? Or perhaps you’ve been set on the idea of joining those who push the bounds of knowledge for quite some time, but are feeling daunted by the process? Well, keep reading. 

I started university with the hopes of stretching myself academically and gaining an undergraduate degree. As the degree progressed, I found myself increasingly improving in my marks and abilities. I enjoyed the coursework – researching a topic and the sense of discovery brought about by it. I became deeply interested in climate change and the impact humans have on the environment and was able to begin my dissertation research a year early because I was so motivated within my subject. 

In my final year of undergraduate studies, much of my time was pre-occupied with my role as Student President. Attending social events, board meetings, and lots of other things that didn’t involve a darkened room and a pile of books. I was very much a student who turned up, put the effort in, and then spent the rest of my time as I wished.  

Giving a speech at the Global Youth Strike for Climate, Inverness, as Student President. Extracurricular activities are a worthwhile addition to your application and were considered a lot during the interview! 

I began to look for opportunities for research degrees online, as well as asking almost anyone and everyone I knew academically if they had any ideas. Nothing came to fruition. That was until I received a Twitter notification from my lecturer drawing my attention to what looked to be an ideal PhD studentship. The snag? Applications were due to close within 3 hours of me checking the notification. 

By the time I had read the project particulars, accessed the cited literature and paced around my living room more than a few times, I had around 2 hours to submit an application. Due to my prior unsuccessful searches, I hadn’t previously submitted a PhD application and so had nothing to refer to – but proceed I did.  

Thankfully, the application was relatively straightforward. Standard job application information, details of the grades I had achieved and was predicted to achieve, and two academic references (for me, my personal academic tutor and climate change lecturer). What took time (I would advise anyone considering an application to prepare these earlier than I did!) was the statement of research interest and academic CV. My university careers service had excellent advice and resources to assist in that regard. 

Within minutes of the deadline, my application was in. I had almost forgotten about it by the time a week-or-so later I received an e-mail inviting me to Reading for an interview day. Shocked and excited were the emotions – little old me from the Highlands of Scotland, who hadn’t yet finished his undergraduate degree, was somehow being invited to one of the best Meteorology departments in the world to interview for a PhD studentship.  

No time to spare, my travel to and from Reading was booked. For the next couple of weeks, all I now had to worry about was how to do a PhD interview – though as will become clear, I need not have worried. I sought the advice of academic friends and colleagues (a calming influence for sure) and countless websites and forums (generally a source of unnecessary worry). 

Given the level of conflicting advice on PhD interviews, on arrival at Reading I wasn’t sure what to expect. At the front door I was provided with all the information that I needed for the day. I then made my way to a room with all the other candidates for a welcome talk and the opportunity to learn more about other projects on offer over lunch. 

The interview itself was very relaxed. No ‘stock’ PhD interview questions here – it was very much an opportunity to discuss my previous work and abilities, and how that might fit with the project. Importantly, it was an opportunity to meet my potential supervisors and ‘interview’ them too. If you’re going to spend 3-4 years working together, the connection needs to work well both ways. So, whilst the 30-minute interview slot seemed daunting on paper, the time flew by and it was soon time to leave. 

Fast forward a week or so and I was very surprised to receive an e-mail offering me the studentship that I had applied for: Developing an urban canopy model for improved weather forecasts in cities. And the rest, as they say, is history. 

At my desk in the Department of Meteorology, University of Reading. 

I hope that this blog post has helped you to feel less daunted to begin your PhD journey. Please feel free to get in touch with me by e-mail if you would like to chat further about beginning a PhD, or indeed to let me know how your own interview goes. Good luck!