My tips, strategies and hacks as a PhD student

Email: m.prosser@pgr.reading.ac.uk

Having been a PhD student for a little over 3 months I am perhaps ill-qualified to write such a ‘PhD tips’ type of blog post, but write one I appear to be doing! It’s probably actually more accurately titled ‘study tips in general but ones which are highly relevant to science PhDs.’

The following are just my tips on what have helped me over the course of my studies and may be obvious or not suitable for others, but I write them on the off-chance that something here is useful to someone out there. No doubt I will have many more such strategies by the end of my time here in Reading!

Papers and articles
As a science student you may have encountered these from time to time. The better ones are clearly written and succinct, the worse ones are verbose and obscurantist. If you’re not the quickest reader in the world, getting through papers can end up consuming a great deal of your time.

I’m going to advocate speed reading in a bit but when you start learning speed reading, one of the things they ask you to think about first is “Do I really need to read this?”. If the answer is yes, then the next question is “Do I really need to read all of it?”. Perhaps you only need to glance at just the abstract, figures and conclusion? After all, time spent reading this is time not spent doing something else, something more profitable perhaps, so do check that it really is worth your time before diving in.

So once I’ve ascertained that the article is indeed worth my time, I sit down with a pencil (or the equivalent for a PDF) and read through the sections I’ve decided on. Anything that makes my neurons spike (“oh that’s interesting….”), I underline or highlight. Any thoughts or questions that occur to me, I write in the margin. If I feel the need to criticise the paper for being insufficiently clear then I write down these remarks, too.

Once I get to the end, I put the article away out of sight and sit down with a blank piece of paper (or on a computer) and try and write something very informally about what I’ve just read. Quite often my mind will go helpfully blank at this point, so I try and finish the following sentence: “The biggest thing (if anything) I learned from this article was….”. Completing this one sentence then tends to lead to other stuff tumbling out and in no particular order I jot these all down. Only once the majority of it is down on paper do I take a peek at the annotated piece to see what I missed (For heaven’s sake avoid painting the article yellow with a highlighter!)

Please, please, please, don’t.

This personal blurb that you have produced is then a good way to quickly remind yourself of the contents of that article in the future without having to reread it from scratch. This post-reading exercise need not take more than 15 minutes but if you’re worried about spending this extra time, don’t be. You’ll save yourself a heap of time in future by not having to reread the damn thing.

Random piece of advice – if you are unaware of the Encyclopedia of Atmospheric Sciences, then check it out. Whatever your PhD topic I guarantee there’ll be 10 or so shortish entries which are all highly relevant to your particular PhD topic and consequently worth knowing about!

Speed reading
Really still on the previous paragraph but as is often the way, between the valuable articles that you really should be reading and the stuff for which life’s really too short there’s a grey area.
For such grey areas I am an advocate of speed reading.
For any electronic texts check out this free website:

Just copy, paste and go! https://accelareader.com/

The pace the words flash up doesn’t have to be particularly fast (I suggest trying 300 wpm to start with) but the golden rule is to never press pause once you’ve started. No going back to read stuff you’ve missed (well not until you’ve reached the end first at least!). This method of reading is especially useful for any articles that feel like quagmires into which you are slowly drowning. Paradoxically reading faster in such instances often increases one’s comprehension.

A good way to develop the skill of speed reading is to start on articles you see posted on social media, articles that you are not too fussed about getting every single detail. Just let it wash over you!

Talks and lectures
I have found it useful to make audio recordings of these. I don’t usually tend to listen back, but if there is something that was particularly interesting or dense that might be worth revisiting then it can be very worthwhile. I make a note of the time this something was said at the time it was said and can thus track it down in the recording fairly painlessly afterwards.

One tip about note taking that has stayed with me since I first heard it several years back was the following: after writing down the title, only make notes on what is surprising or interesting to you, just that! This may result in many lines of notes or no lines at all, but whatever you do, don’t just make notes of everything that was said. This advice has been very useful for me.

Organising
Ask me in person if you would like to know my thoughts on this.

Programming to help physical intuition.
This is probably more relevant to students like me who didn’t come from a maths or physics undergrad and consequently aren’t quite as fluent in the old maths….or perhaps undergrads for that matter…
….but in my undergrad (environmental science) I spent quite a lot of the time spent studying maths (and to a lesser extent) physics involved memorising complicated procedures. The best example of this was a lecture on Fourier Series where the professor took the whole hour to work through the process of getting from an input (x^2) to the output (first n terms of the Fourier series). Because it took so much space/effort for me to remember this lengthy process, it ended up crowding out the arguably more important conceptual stuff, such as what a Fourier series actually does and why it is it so useful. When all is said and done and the final exam is handed in, these concepts are what should (ideally) stick with you even if the details of how, don’t.
So here’s where I think programming can come in. Firstly, there’s nothing like coding up some process to check whether you understand the nuts and bolts of it, but more importantly once it has been coded up properly you can then play about with the inputs to see how these affect the graphed outputs. Being able to ‘play’ about like this gives you a more intuitive feel for the model/process that wouldn’t be possible if you had to manually redo the laborious calculations each time you wanted to change the input parameters. 3 examples of where I have done this myself are the following:
1. Getting my head around the thermal inertia of the oceans by varying the depth of the surface and deep layer of the ocean in a simple model.
2. Playing around graphically with dispersion.
3. Convincing myself that it really is true that in the middle of the Northern Hemisphere summer the north pole receives more energy per day than the equator.

And you?
So do you have any hard won study/research tips? If so do email me as I would be interested in hearing about them!
Which study hack do you think I (or others) are most lacking?

AMS Annual Meeting 2019

Email: l.p.blunn@pgr.reading.ac.uk

Between 6th-10th January 2019 I was fortunate enough to attend the 99th American Meteorological Society (AMS) Annual Meeting in their centennial year. It was hosted in the Phoenix, Arizona Convention Center – its vast size was a necessity, seeing as there were 2300 oral presentations and 1100 poster presentations given in 460 sessions! The conferences and symposia covered a wide range of topics such as space weather, hydrology, atmospheric chemistry, climate, meteorological observations and instrumentation, tropical cyclones, monsoons and mesoscale meteorology.

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Me outside one half of Phoenix Convention Center.

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1500 people at the awards banquet.

The theme of this year’s meeting was “Understanding and Building Resilience to Extreme Events by Being Interdisciplinary, International, and Inclusive”. The cost of extreme events has been shown by reinsurance companies to have increased monotonically, with estimated costs for 2017 of $306 billion and 350 lives in the US. Marcia McNutt, President of the National Academy of Science (NAS), gave a town hall talk on the continued importance of evidence-based science in society (view recording). She says that NAS must become more agile at giving advice since the timescales of, for example, hurricanes and poor air quality episodes are very short, but the problems are very complex. There is reason for optimism though, as the new director of the White House Office of Science and Technology Policy is Kelvin Droegemeier, a meteorologist who formerly served as Vice President for Research at the University of Oklahoma.

“Building Resilience to Extreme Events” took on another meaning with the federal shutdown and proved to be the main talking point of this year’s annual meeting. Over 500 people from federally funded organisations such as NOAA could not attend. David Goldston, director of the MIT Washington Office, gave a talk at the presidential forum entitled “Building Resilience to Extreme Political Weather: Advice for Unpredictable Times” (view recording). He made the analogy of both current US political attitude towards climate change and the federal shutdown as being ‘weather’, and thought that politics would return to long-term ‘climate’. He advised scientists to present their facts in a way understandable to public and government, prepare policy proposals, and be clear on why they are not biased. He reassured scientists by saying they have outstanding public support with 76% of the public thinking scientists act in their best interest. During the talk questions were sourced from the audience and could be voted on. The frustration of US scientists with the government was evidently large.

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Questions put forward by the audience and associated votes during Goldston’s talk.

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Ross Herbert (a PDRA in the Reading Meteorology Department) letting his feelings on the federal shutdown be known at the University of Oklahoma after-party.

A growing area of research is artificial and computational intelligence which had its own dedicated conference. As an early career researcher in urban and boundary layer meteorology I was interested to see a talk on “Surface Layer Flux Machine Learning Parametrisations”. By obtaining training data from observational towers it may be possible to improve upon Monin-Obukhov similarity theory in heterogeneous conditions. At the atmospheric chemistry and aerosol keynote talk by Zhanqing Li I learnt that anthropogenic emissions of aerosol can cause a feedback leading to elevated concentration of pollutants. Aerosol reduces solar radiation reaching the surface leading to less turbulence and therefore lower boundary layer height. It also causes warming at the top of the boundary layer creating a stronger capping inversion which inhibits ventilation. Anthropogenic aerosols are not just important for air quality. They affect global warming via their influence on the radiation budget and can lead to more extreme weather through enhancing deep convection.

I particularly enjoyed the poster sessions since they enabled networking with many scientists working in my area. On the first day I bumped into several Reading meteorology undergraduates on their year long exchange at the University of Oklahoma. Like me, I think they were amazed by the scale of the conference and the number of opportunities available as a meteorologist. The exhibition had over 100 organisations showcasing a wide range of products, publications and services. Anemoment (producers of lightweight, compact 3D ultrasonic anemometers) and the University of Oklahoma had stalls showing how instruments attached to drones can be used to profile the boundary layer. This has numerous possible applications such as air quality monitoring and analysing boundary layer dynamics.

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The exhibition (left is a Lockheed-Martin satellite)

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3rd year Reading meteorology undergraduates at the poster session.

Overall, I found the conference very motivating since it reinforced the sense that I have a fantastic opportunity to contribute to an exciting and important area of science. Next year’s annual meeting is the hundredth and will be held in Boston.

Night at the Museum!

On Friday November 30th, Prof. Paul Williams and I ran a ‘pop-up science’ station at the Natural History Museum’s “Lates” event (these are held on the last Friday of each month; the museum is open for all until 10pm, with additional events and activities). Our station was entitled “Turbulence Ahead”, and focused on communicating research under two themes:

  1.  Improving the predictability of clear-air turbulence (CAT) for aviation
  2.  The impact of climate change on aviation, particularly in terms of increasing CAT

There were several other stations, all run by NERC-funded researchers. Our stall went ‘live’ at 6 PM, and from that point on we were speaking almost constantly for the next 3.5 hours – with hundreds (not an exaggeration!) of people coming to our stall to find out more. Neither of us were able to take much of a break, and I’ve never had quite such a sore voice!

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Turbulence ahead? Not on this Friday evening!

Our discussions covered:

  • What is clear-air turbulence (CAT) and why is it hazardous to aviation?
  • How do we predict CAT? How has Paul’s work improved this?
  • How is CAT predicted to change in the future? Why?
  • What other ways does climate change affect aviation?

Those who came to our stall asked some very intelligent questions, and neither of us encountered a ‘climate denier’ – since we were speaking about a very applied impact of climate change, this was heartening. This impact of climate change is not often considered – it’s not as obvious as heatwaves or melting ice, but is a very real threat as shown in recent studies (e.g. Storer et al. 2017). It was a challenge to explain some of these concepts to the general public – some had heard of the jet stream, others had not, whilst some were physicists… and even the director of the British Geological Survey, John Ludden, turned up! It was interesting to hear from so many people who were self-titled “nervous flyers” and deeply concerned about the future potential for more unpleasant journeys.

I found the evening very rewarding; it was interesting to gauge a perspective of how the public perceive a scientist and their work, and it was amazing to see so many curious minds wanting to find out more about subjects with which they are not so familiar.

My involvement with this event stems from my MMet dissertation work with Paul and Tom Frame looking at the North Atlantic jet stream. Changes in the jet stream have large impacts on transatlantic flights (Williams 2016) and the frequency and intensity of CAT. Meanwhile, Paul was a finalist for the 2018 NERC Impact Awards in the Societal Impact category for his work on improving turbulence forecasts – he finished as runner-up in the ceremony which was held on Monday December 3rd.

So, yes, there may indeed be turbulent times ahead – but this Friday evening certainly went smoothly!

Email: s.h.lee@pgr.reading.ac.uk

Twitter: @SimonLeeWx

References

Storer, L. N., P. D. Williams, and M. M. Joshi, 2017: Global Response of Clear-Air Turbulence to Climate Change. Geophys. Res. Lett., 44, 9979-9984, https://doi.org/10.1002/2017GL074618

Williams, P. D., 2016: Transatlantic flight times and climate change. Environ. Res. Lett., 11, 024008, https://doi.org/10.1088/1748-9326/11/2/024008.

Reflecting on starting a PhD

Now that Christmas is just around the corner, and us first year students have settled in to the swing of things, I thought it would be nice to write a short piece on what it’s like starting a PhD.

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Reflecting on my experience so far, my first thought was to remark at how I’d only been a PhD student for a little over two months. Frankly, I don’t think I’ve ever learnt so much in quite so short a time before. This was an encouraging thought, because often in the moment progress can seem very slow indeed. However, when you put it all together and zoom out a little, you realise how far you’ve come. If you feel like you’ve wasted a day lost in code, or just generally lost, it’s never wasted; it’s your PhD and is a constant learning process. I’ve found it really rewarding sometimes to simply explore what I find interesting, or practice different ways of making figures. I would recommend that if something, anything, sparks your interest, investigate it, and read up about it. If it comes to nothing, or if you’re not ready to write a journal paper at the end of the day, well, the skills and familiarities you picked up may eventually go towards doing so! Don’t be afraid of not doing the perfect job first time or making a mistake.

The day-to-day life of doing a PhD is also very dynamic. Perhaps I thought I would only spend long hours sitting at my desk in front of a monitor, but every day is different. Research groups, seminars, and social activities really add variety and inspiration to each and every day, even if it means pulling myself away from my data for a small amount of time! I’d recommend to any first year to get involved as much as possible. It is the best way to get to know people in your department and beyond and how they do science! I will admit it took me a little while at first to treat all these different aspects of a typical day as “PhD work”, but that’s the right mindset to be in.

Finally, getting to know other PhD students and researchers has been one of the best and most eye-opening elements so far. In fact, I would say it is almost a crucial part of the process. I am always grateful to those higher up the academic chain, who, despite being busy, are continually happy to offer advice from the small to the big things. At first, I felt like I would be some sort of annoyance asking other people for help on something, or that I should be able to work it out for myself. However, one comes to realise everyone is delighted to help and share their expertise with you. That’s what being a scientist is about, right? As Google Scholar reminds us on each visit: “Stand on the shoulders of giants.” Those older PhDs may scoff at being referred to as giants, but to someone starting a PhD, daunted at how far there is to go and how much there is to do, well… they’ve done a large part of it!

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It’s not possible to always see the positives all the time, especially with research. However, one thing is for sure: you not only grow huge amounts as a scientist, but generally as a person, and I think if you keep that in mind, it all makes that little bit more sense.

Email: a.j.doyle@pgr.reading.ac.uk

Sea Ice-Ocean Feedbacks in the Antarctic Shelf Seas

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Figure 1: November Antarctic sea ice extent values, showing a small increasing trend. Source: NSIDC

Over the past forty years a small increasing trend in Antarctic sea ice extent has been observed. This is poorly understood, and currently not captured by global climate models which typically simulate a net decrease in Antarctic sea ice extent (Turner et al. 2013). The length of our observational time series in combination with our lack of confidence in global climate model results makes it difficult to assess whether the recent decline of Antarctic sea ice observed in 2016 and 2017 is the start of a new declining trend or just part of natural variability.

The net increase in Antarctic sea ice extent is the sum of stronger, but opposing, regional and highly seasonal trends as shown in Figure 2 (Holland, 2014). The trends grow throughout the spring resulting in the maximum trends in the summer, decaying away throughout the autumn to give negligible trends in the winter. This seasonality implies the role of feedbacks in modulating the observed trends.

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Figure 2: Seasonal maps of sea ice concentration trends from Holland, P. (2014). A-B stands for Amundsen-Bellingshausen Seas.

We have used a highly simplified coupled sea ice—mixed layer model (a schematic is shown in Figure 3) as a tool to help quantify and compare the importance of different feedbacks in two contrasting regions of the Southern Ocean. The Amundsen Sea, which has warm shelf waters, atmospheric conditions that are relatively warm with a high snowfall rate and a diminishing sea ice cover. And the Weddell Sea, which has cold saline shelf waters, cold and dry atmospheric conditions and an expanding sea ice cover.

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Figure 3. Schematic of the 1D sea ice-mixed layer model, adapted from Petty et al. (2013).

We have carried out simulations where we denied different feedbacks in combination with perturbing the surface air temperatures, and compared the results with simulations where the feedback is enabled, and can to respond to the surface air temperature perturbation. We found that in the Weddell Sea the feedback responses were generally smaller than the response of the ice cover to the surface air temperature. However in the Amundsen Sea, we found that the ice cover was very sensitive to the depth of the ocean mixed layer which determines the size of the ocean heat flux under the ice. Whenever the atmosphere warmed we found that the ocean heat flux to the ice decreased (due to a shallower mixed layer), and this acted against the atmospheric changes, buffering changes in the ice volume.

Using a simple model has made it easier to understand the different processes at play in the two regions. However, in order to try to better to understand how these feedbacks link back to the regional trends we will also need to consider spatial variability, which may act to change the importance of some of the feedbacks. Incorporating what we have learnt using the 1D model, we are now working on investigating some of the same processes using the CICE sea ice model, to explore the importance and impact of spatial variability on the feedbacks.

Email: r.frew@pgr.reading.ac.uk

References

Turner et al. (2013), An initial assessment of Antarctic sea ice extent in the CMIP5 models, J. Climate, 26, 1473-1484, doi:10.1175/JCLI-D-12-00068.1

Holland, P. R. (2014), The seasonality of Antarctic sea ice trends, Geophys. Res. Lett., 41, 4230–4237, doi:10.1002/2014GL060172.

Petty et al. (2013), Impact of Atmospheric Forcing on Antarctic Continental Shelf Waters, J. Phys. Ocean., 43, 920-940, doi: 10.1175/JPO-D-12-0172.1

Understanding the Processes Involved in Electrifying Convective Clouds

All clouds within our atmosphere are charged to some extent (Nicoll & Harrison, 2016), caused by the build-up of charge at the cloud edges caused by the charge travelling from the top of the atmosphere towards the surface. On the other hand, nearly all convective clouds are actively charged, caused by charge separation mechanisms that exchange charge between different sized hydrometeors within the cloud (Saunders, 1992). If these actively charged clouds can separate enough charge, then electrical breakdown can occur in the atmosphere and lightning can be initiated.

As lightning is a substantial hazard to both life and infrastructure, understanding the processes that cause a cloud to become charged are crucial for being able to forecast it. Even though most convective clouds within the UK are charged, most will never produce lightning. This puts us in a unique position of observing clouds which can and cannot produce lightning, providing a contrast of the cloud processes involved.

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Figure 1: A conceptual image of a cloud showing the main processes that are thought to be involved in the electrification of a convective cloud. Based on current literature. See MacGorman & Rust (1998) for an overview.

Currently, lightning must be observed before the identification of a thunderstorm can be made, leaving zero lead time. As the first lightning strike can often be the most powerful, (especially in UK winter thunderstorms), improvements to understand the processes that charge a cloud are important to provide a thunderstorm lead time.

There are many processes involved in charging a cloud, a summary of the processes that are thought to have the greatest contribution can be found in Figure 1. The separation of charge through collisions is thought to be the dominant electrification mechanism which occurs in the ice phase of the cloud. The most successful charge separation occurs between growing ice hydrometeors of different sizes (Emersic & Saunders, 2010). The growth of the ice forms an outer shell of supercooled liquid water which contains negative charge. Collisions between different sized hydrometeors cause a net exchange of mass and charge, creating positively and negatively charged ice.

The liquid phase is crucial to maintaining a high moisture content in the ice phase of the cloud brought up by the updraught of the cloud. Once the electric field within the cloud is strong enough, the liquid drops can become polarised which can also help separate charge from collisions of different sized liquid hydrometeors in the later stages of convective cloud development.

Once the charge has been separated, the different polarities must be moved to different regions of the cloud to enhance the electric field. The most well-established method involves gravitational separation (Mason & Dash, 2000). Under the assumption that smaller hydrometeors will often contain negative charge and larger hydrometeors will contain positive charge, there is a distinct separation of hydrometeor sizes. After the formation of an updraught, the smaller hydrometeors can be lifted higher into the cloud overcoming the gravitational forces. Under the right conditions, the larger hydrometeors will be too heavy, and gravity will force the hydrometeors to remain lower in the cloud.

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Figure 2: The Electrostatic Biral Thunderstorm Detector (BTD-300) (a), Electrostatic JCI 131 Field Mill (b), and 35 GHz “Copernicus” Radar installed at Chilbolton Observatory, UK. The Field Mill and BTD-300 were installed on 06/10/16 and 16/11/16 respectively.

To understand if any of the processes discussed in Figure 1 are evident within real-world convective clouds, a field campaign was set-up at Chilbolton Observatory (CO) to measure the properties of the cloud. Two electrical instruments were installed at CO, the Biral Thunderstorm Detector (BTD-300, Figure 2a) and the electrostatic Field Mill (FM, Figure 2b) which can measure the displacement current (jD) and the potential gradient (PG) respectively. The 35GHz “Copernicus” dopplerised radar was used to measure the properties of the cloud. Through a two-year field campaign, 653 convective clouds were identified over CO, with 524 clouds (80.2%) found to be charged and 129 clouds (19.8%) found to be uncharged.

To understand the importance of hydrometeor size for charging a cloud, the 95th percentile of the radar reflectivity (Z) was used. The Z is strongly related to the diameter (sixth power) and number concentration of hydrometeors (first power). Figure 3 shows a boxplot of all 653 clouds, classified by the cloud charge as measured at the surface. For each cloud, the liquid and ice regions were separated (purple and blue boxplots respectively) to highlight the dominant region for charge separation.

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Figure 3: A boxplot of the 95th percentile reflectivity for 635 identified clouds (black box) classified into no charge, small charge and large charge groups. The cloud was also decoupled into the liquid (purple box) and ice (blue box) phases. The box-plot shows the mean (purple bar), median (red bar) and upper and lower quartiles (upper and lower limits of black box).

For all phases of the cloud, there was a substantial increase in the reflectivity for all regions of the cloud, especially the liquid phase. This suggests that the size of the hydrometeors in both the ice and liquid phase are indeed important for charging a cloud.

In the remainder of my PhD, the relative importance of each process discussed in Figure 1 will be addressed to try and decouple each process. Further to these observations made at the surface, ten radiosonde flights will be made (from the Reading University Atmospheric Observatory) inside convectively charged clouds. Measurements of the charge, optical thickness, amount of supercooled liquid water and turbulence will be used to increase the robustness of the results presented in the previous works.

Email: james.gilmore@pgr.reading.ac.uk

References

Bouniol, D., Illingworth, A. J. & Hogan, R. J., 2003. Deriving turbulent kinetic energy dissipation rate within clouds using ground-based 94 GHz radar. Seattle, 31st International Conference on Radar Meteorology.

Emersic, C. & Saunders, C. P., 2010. Further laboratory investigations into the Relative Diffusional Growth Rate theory of thunderstorm electrification. Atmos. Res., Volume 98, pp. 327-340.

MacGorman, D. R. & Rust, D. W., 1998. The Electrical Nature of Storms. 1st ed. New York: Oxford University Press.

Mason, B. L. & Dash, J. G., 2000. Charge and mass transfer in ice–ice collisions: Experimental observations of a mechanism in thunderstorm electrification. J. Geophys. Res., Volume 105, pp. 10185-10192.

Nicoll, K. A. & Harrison, R. G., 2016. Stratiform cloud electrification: comparison of theory with multiple in-cloud measurements. Q.J.R. Meteorol. Soc., Volume 142, pp. 2679-2691.

Renzo, M. D. & Urzay, J., 2018. Aerodynamic generation of electric fields in turbulence laden with charged inertial particles. Nat. Comms., 9(1676).

Saunders, C. P. R., 1992. A Review of Thunderstorm Electrification Processes. J. App. Meteo., Volume 32, pp. 642-655.

A New Aviation Turbulence Forecasting Technique

Anyone that has ever been on a plane will probably have experienced turbulence at some point. Most of the time it is not likely to cause injury, but during severe turbulence unsecured objects (including people) can be thrown around the cabin, costing the airline industry millions of dollars every year in compensation (Sharman and Lane, 2016). Recent research has also indicated that in the future the frequency of clear-air turbulence will increase with climate change. Forecasting turbulence is one of the best ways to reduce the number of injuries by giving pilots and flight planners ample warning, so they can put on the seat-belt sign or avoid the turbulent region altogether. The current method used in creating a turbulence forecast is a single ‘deterministic’ forecast – one forecast model, with one forecast output. This shows the region where they suspect turbulence to be, but because the forecast is not perfect, it would be more ideal to show how certain we are that there is turbulence in that region.

To do this, a probabilistic forecast can be created using an ensemble (a collection of forecast model outputs with slightly different model physics or initial conditions). A probabilistic forecast essentially shows model confidence in the forecast, and therefore how likely it is that there will be turbulence in a given region. For example, if all 10 out of 10 forecast outputs predict turbulence in the same location, the pilots would be confident in taking action (such as avoiding the region altogether). However, if only 1 out of 10 models predict turbulence, then the pilot may choose to turn on the seat-belt sign because there is still a chance of turbulence, but not enough to warrant spending time and fuel to fly around the region. A probabilistic forecast not only provides more information in the certainty of the forecast, but it also increases the chances of forecasting turbulence that a single model might miss.

Gill and Buchanan (2014) showed this ensemble forecast method does improve the forecast skill. In my project we have taken this one step further and created a multi-model ensemble, which is combining two different ensembles, each with their own strengths and weaknesses (Storer et al., 2018). We combine the Met Office Global and Regional Ensemble Prediction System (MOGREPS-G), with the European Centre for Medium Range Weather Forecasting (ECMWF) Ensemble Prediction System (EPS).

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Figure 1: Plot of a moderate-or-greater turbulence event over the possible sources of turbulence: top left: orography, shear turbulence (bottom left: MOGREPS-G and bottom right: ECMWF EPS probability forecast), and top right: convection from satellite data (colour shading indicates deep convection). Both the MOGREPS-G and ECMWF-EPS ensembles forecast the shear turbulence event. The circles indicate turbulence observations with grey indicating no turbulence, orange indicating light turbulence and red indicating moderate or greater turbulence. The convective classification can be found in Francis and Batstone (2013).

There are three main sources of turbulence. The first is mountain wave turbulence, where gravity waves are produced from mountains that ultimately lead to turbulence. The second is convectively-induced turbulence, which includes in-cloud turbulence and also gravity waves produced as a result of deep convection that also lead to turbulence. The third is shear-induced turbulence, which is the one we are trying to forecast in this example. Figure 1 is an example plot showing orography and thus mountain wave turbulence (top left), convection and thus convectively induced turbulence (top right), the MOGREPS-G ensemble forecast of shear turbulence (bottom left) and the ECMWF ensemble forecast of shear turbulence (bottom right). The red circle indicates a ‘moderate or greater’ turbulence event, and we can see that because it is over the North Atlantic it is not a mountain wave turbulence event, and there is no convection nearby, but both the ensemble forecasts correctly predict the location of the shear-induced turbulence. This shows that there is high confidence in the forecast, and action (such as putting the seat-belt sign on) can be taken.

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Figure 2: Value plot with a log scale x-axis of the global turbulence with the 98 convective turbulence cases removed showing the forecast skill of the MOGREPS-G (dot-dash), ECMWF (dot), combined multi-model ensemble (dash) and the maximum value using every threshold of the combined multi-model ensemble (solid). The data used has a forecast lead time between +24 hours and +33 hours between May 2016 and April 2017.

To understand the usefulness of the forecast, Figure 2 is a relative economic value plot. It shows the value of the forecast for a given cost/loss ratio (which will vary depending on the end user). The multi-model ensemble is more valuable than both of the single model ensembles for all cost/loss ratios, showing that every end user will benefit from this forecast. Although our results do show an improvement in forecast skill, it is not statistically significant. However, by combining ensemble forecasts we gain consistency and more operational resilience (i.e., we are still able to produce a forecast if one ensemble is not available), and is therefore still worth implementing in the future.

Email: luke.storer@pgr.reading.ac.uk

References

Gill PG, Buchanan P. 2014. An ensemble based turbulence forecasting system. Meteorol. Appl. 21(1): 12–19.

Sharman R, Lane T. 2016. Aviation Turbulence: Processes, Detection, Prediction. Springer.

Storer, L.N., Gill, P.G. and Williams, P.D., 2018. Multi-Model Ensemble Predictions of Aviation Turbulence. Meteorol. Appl., (Accepted for publication).