Variability in the near-Earth solar wind conditions can adversely affect a number of ground- and space-based technologies. Some of these space weather impacts on ground infrastructure are expected to increase primarily with geomagnetic storm intensity, but also storm duration, through time-integrated effects. Forecasting storm duration is also necessary for scheduling the resumption of safe operating of affected infrastructure. It is therefore important to understand the degree to which storm intensity and duration are related.
In this study, we use the recently re-calibrated aa index, aaH to analyse the relationship between geomagnetic storm intensity and storm duration over the past 150 years, further adding to our understanding of the climatology of geomagnetic activity. In particular, we construct and test a simple probabilistic forecast of storm duration based on storm intensity.
Using a peak-above-threshold approach to defining storms, we observe that more intense storms do indeed last longer but with a non-linear relationship (Figure 1).
Next, we analysed the distribution of storm durations in eight different classes of storms dependent on the peak intensity of the storm. We found them to be approximately lognormal with parameters depending on the storm intensity. A lognormal distribution is defined by the mean of the logarithm of the values, μ, and the standard deviation of the logarithm of the values, σ. These parameters were found from the observed durations in each intensity class through Maximum Likelihood Estimation (MLE) and used to create a lognormal distribution, plotted in Figure 2 in dark purple. The light purple distribution shows a histogram of the observed data as an estimate of the probability density function (PDF). By eye, the lognormal distribution provides a reasonable first-order match at all intensity thresholds.
On this basis we created a method to probabilistically predict storm duration given peak intensity. For each of the peak intensity classes, we have calculated the values of μ and σ for the lognormal fits to the duration distributions shown as the black points in Figure 3. It is clear from the points in the left panel of Figure 3 that μ increases as intensity increases, agreeing with the previous results in Figure 1 (i.e., duration increases as intensity increases).
The parameter μ can be approximated as a function of storm intensity by:
μ(intensity) = A ln (B intensity−C)
where A, B and C are free parameters. A least squares fit was implemented, and the coefficients A, B and C were found to be 0.455, 4.632, 283.143 respectively and this curve is plotted, along with uncertainty bars, in Figure 3 (left). Although the fit is based on weighted bin-centres of storm intensity, the equation can be used to interpolate for a given value of intensity. σ can be approximated by a linear fit to give σ as a function of the peak intensity. Figure 3 (right) shows the best fit line which has a shallow gradient of −5.08×10−4 and y-intercept at 0.659.
These equations can be used to find lognormal parameters as a function of storm peak intensity. From these, a distribution of duration can be created and hence a probabilistic estimate of the duration of this storm is available. This can be used to predict the probability a storm will last at least e.g. 24 hours. Figure 4 shows the output of the model for a range of storm peak intensity compared against a test set of the aaH index. The model has good agreement with the observations and provides a robust method for estimating geomagnetic storm duration.
The results demonstrate significant advancements in not only understanding the properties and structure of storms, but also how we can predict and forecast these dynamic and hazardous events.
For more information, please see the open-access paper.
It is often useful to know how much energy is available to generate motion in the atmosphere, for example in storm tracks or tropical cyclones. To this end, Lorenz (1955) developed the theory of Available Potential Energy (APE), which defines the part of the potential energy in the atmosphere that could be converted into kinetic energy.
To calculate the APE of the atmosphere, we first find the minimum total potential energy that could be obtained by adiabatic motion (no heat exchange between parcels of air). The atmospheric setup that gives this minimum is called the reference state. This is illustrated in Figure 1: in the atmosphere on the left, the denser air will move horizontally into the less dense air, but in the reference state on the right, the atmosphere is stable and no motion would occur. No further kinetic energy is expected to be generated once we reach the reference state, and so the APE of the atmosphere is its total potential energy minus the total potential energy of the reference state.
If we think about an atmosphere that only varies in the vertical direction, it is easy to find the reference state if the atmosphere is dry. We assume that the atmosphere consists of a number of air parcels, and then all we have to do is place the parcels in order of increasing potential temperature with height. This ensures that density decreases upwards, so we have a stable atmosphere.
However, if we introduce water vapour into the atmosphere, the situation becomes more complicated. When water vapour condenses, latent heat is released, which increases the temperature of the air, decreasing its density. One moist air parcel can be denser than another at a certain height, but then less dense if they are lifted to a height where the first parcel condenses but the second one does not. The moist reference state therefore depends on the exact method used to sort the parcels by their density.
It is possible to find the rearrangement of the moist air parcels that gives the minimum possible total potential energy, using the Munkres (1957) sorting algorithm, but this takes a very long time for a large number of parcels. Lots of different sorting algorithms have therefore been developed that try to find an approximate moist reference state more quickly (the different types of algorithms are explained by Stansifer (2017) and Harris and Tailleux (2018)). However, these sorting algorithms do not try to analyse whether the parcel movements they are simulating could actually happen in the real atmosphere—for example, many work by lifting all parcels to a fixed level in the atmosphere, without considering whether the parcels could feasibly move there—and there has been little understanding of whether the reference states they find are accurate.
As part of my PhD, I have performed the first assessment of these sorting algorithms across a wide range of atmospheric data, using over 3000 soundings from both tropical island and mid-latitude continental locations (Harris and Tailleux, 2018). This showed that whilst some of the sorting algorithms can provide a good estimate of the minimum potential energy reference state, others are prone to computing a rearrangement that actually has a higher potential energy than the original atmosphere.
We also showed that a new algorithm, which does not rely on sorting procedures, can calculate APE with comparable accuracy to the sorting algorithms. This method finds a layer of near-surface buoyant parcels, and performs the rearrangement by lifting the layer upwards until it is no longer buoyant. The success of this method suggests that we do not need to rely on possibly unphysical sorting algorithms to calculate moist APE, but that we can move towards approaches that consider the physical processes generating motion in a moist atmosphere.
Harris, B. L. and R. Tailleux, 2018: Assessment of algorithms for computing moist available potential energy. Q. J. R. Meteorol. Soc., 144, 1501–1510, https://doi.org/10.1002/qj.3297
Stansifer, E. M., P. A. O’Gorman, and J. I. Holt, 2017: Accurate computation of moist available potential energy with the Munkres algorithm. Q. J. R. Meteorol. Soc., 143, 288–292, https://doi.org/10.1002/qj.2921
An accurate estimate of
soil moisture has a vital role in a number of scientific research areas. It is
important for day to day numerical weather prediction, extreme weather event
forecasting such as for flooding and droughts, crop suitability to a particular
region and crop yield estimation to mention a few. However, in-situ
measurements of soil moisture are generally expensive to obtain, labour
intensive and have sparse spatial coverage. To assist this, satellite
measurements and models are used as a proxy of the ground measurement.
Satellite missions such as SMAP (Soil Moisture Active Passive) observe the soil
moisture content for the top few centimetres from the surface of the earth. On
the other hand, soil moisture estimates from models are prone to errors due to
model errors in representing the physics or the parameter values used.
Data assimilation is a
method of combining numerical models with observed data and its error
statistics. In principle, the state estimate after data assimilation is
expected to be better than the standalone numerical model estimate of the state
or the observations. There are a variety of data assimilation methods:
Variational, Sequential, Monte Carlo methods and a combination of them. The
Joint UK Land Environment Simulator (JULES) is a community land surface model
which calculates several land surface processes such as surface energy balance
and carbon cycle and used by the Met Office – the UK’s national weather
My PhD aims to improve the estimate of soil moisture from the JULES model using satellite data from SMAP and the Four-Dimensional Ensemble Variational (4DEnVar) data assimilation method introduced by Liu et al. (2008) and implemented by Pinnington (2019; under review), a combination of Variational and Ensemble data assimilation methods. In addition to satellite soil moisture data assimilation, ground measurement soil moisture data from Oklahoma Mesoscale Networks (Mesonet) are also assimilated.
The time series of soil moisture from the JULES model (prior), soil moisture obtained after assimilation (posterior) and observed soil moisture for Antlers station in Mesonet are depicted in Figure 1. Figure 2 shows the distance of prior soil moisture estimates and posterior soil moisture estimates from the assimilated observations. The smaller the distance is the better as the primary objective of data assimilation is to optimally fit the model trajectory into the observations and background. From Figure 1 and Figure 2 we can conclude that posterior soil moisture estimates are closer to the observations compared to the prior. Looking at particular months, prior soil moisture is closer to observations compared to the posterior around January and October. This is due to the fact that 4DEnVar considers all the observations to calculate an optimal trajectory which fits observations and background. Hence, it is not surprising to see the prior being closer to the observations than the posterior in some places.
Data assimilation experiments are repeated for different sites in Mesonet with varying soil type, topography and different climate and with different soil moisture dataset. In all the experiments, we have observed that posterior soil moisture estimates are closer to the observations than the prior soil moisture estimates. As a verification, soil moisture reanalysis is calculated for the year 2018 and compared to the observations. Figure 3 is SMAP soil moisture data assimilated into the JULES model and hind-casted for the following year.
Liu, C., Q. Xiao, and B. Wang, 2008: An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test. Mon. Weather Rev., 136 (9), 3363–3373., https://doi.org/10.1175/2008MWR2312.1
Pinnington, E., T. Quaife, A. Lawless, K. Williams, T. Arkebauer, and D. Scoby, 2019: The Land Variational Ensemble Data Assimilation fRamework: LaVEnDAR. Geosci.Model Dev. Discuss.https://doi.org/10.5194/gmd-2019-60
Williams, R. S., Hegglin, M. I., Kerridge, B. J., Jöckel, P., Latter, B. G., and Plummer, D. A.: Characterising the seasonal and geographical variability in tropospheric ozone, stratospheric influence and recent changes, Atmos. Chem. Phys., 19, 3589–3620, https://doi.org/10.5194/acp-19-3589-2019, 2019.
Approximately 90 % of atmospheric ozone (O3) today resides in the stratosphere, which we know as the ozone layer (extending from ~15-35 km), where it plays a critical role in filtering out most of the harmful ultraviolet (UV) rays from the sun. The gradual formation of the ozone layer from around 600 million years ago was key in Earth’s evolutionary history, as it enabled life to flourish on land. Lesser known is the importance of the remaining ~ 10 % of atmospheric ozone, which is found in the troposphere and has implications for air quality, radiative forcing and the oxidation capacity of the troposphere. Whilst ozone is a pollutant at ground level, contributing to an estimated 6 million premature deaths globally per year, it also acts to cleanse the troposphere by breaking down a large number of pollutants, along with some greenhouse gases. Ozone is however a greenhouse gas in itself – where it has a maximum radiative forcing in the upper troposphere. It is an example of a non-well mixed gas, owing to its spatially and temporally highly varying sources and sinks, as well as its relatively short global mean tropospheric lifetime of about 3 weeks.
A major source of tropospheric ozone is the photochemical reactions of emission precursors such as carbon monoxide (CO), nitrogen oxides (NOx) and volatile organic compounds (VOCs), which have both natural and anthropogenic sources, in addition to the natural influx of ozone-rich air from the stratosphere. The magnitude of these two competing influences has been poorly quantified until the recent advent of satellite observations and the development of comprehensive chemistry-climate models (CCMs), which simulate interactive chemistry and are stratospherically well-resolved.
Our study aimed to update and extend the knowledge of a previous key study (Lamarque et al., 1999), that investigated the role of stratosphere-troposphere exchange (STE) on tropospheric ozone, using two contemporary state-of-the-art CCMs (EMAC and CMAM) with stratospheric-tagged ozone tracers as a diagnostic. We first sought to validate the realism of the model ozone estimates with respect to satellite observations from the Ozone Monitoring Instrument (OMI), together with spatially and temporally limited vertical profile information provided from ozonesondes, which we resolved globally on a seasonal basis for the troposphere (1000-450 hPa) (Figure 1).
Whilst we found broad overall agreement with both sets of observations, an overall systematic bias in EMAC of + 2-8 DU (Dobson Units) and regionally and seasonally varying biases in CMAM (± 4 DU) can be seen in the respective difference panels (Figure 1b and 1c). A height-resolved comparison of the models with respect to regionally aggregated ozonesonde observations helped us to understand the origin of these model biases. We showed that apparent closer agreement in CMAM arises due to compensation of a low bias in photochemically produced ozone in the troposphere, resulting from the omission of a group of emission precursors in this model, by excessive smearing of ozone from the lower stratosphere due to an inherent high bias. This smearing is induced when accounting for the satellite observation geometry of OMI, necessary to ensure a direct comparison with vertically well-resolved models, which has limited vertical resolution due to its nadir field of view. The opposite was found to be the case in EMAC, with a high (low) bias in the troposphere (lower stratosphere) relative to ozonesondes. Given the similarity in the emission inventories used in both models, the high bias in this model indicates that excess in situ photochemical production from emission precursors is simulated within the interactive chemistry scheme. These findings emphasise the importance of understanding the origin of such biases, which can help prevent erroneous interpretations of subsequent model-based evaluations.
Noting these model biases, we next exploited the fine scale vertical resolution offered by the CCMs to investigate the regional and seasonal variability of the stratospheric influence. Analysis of the model stratospheric ozone (O3S) tracers revealed large differences in the burden of ozone in the extratropical upper troposphere-lower stratosphere (UTLS) region, with some 50-100 % more ozone in CMAM compared to EMAC. We postulated that CMAM must simulate a stronger lower branch of the Brewer-Dobson Circulation, the meridional stratospheric overturning circulation, since the stratospheric influence is isolated using these simulations. This has implications for the simulated magnitude and distribution of the downward flux of ozone from the stratosphere in each model. Shown in Figure 2 is the zonal-mean monthly evolution of ozone volume mixing ratio (ppbv) from ozonesondes and EMAC over the period 1980-2013 for the upper (350 hPa), middle (500 hPa) and lower (850 hPa) troposphere, together with the EMAC O3S and derived fraction of ozone of stratospheric origin (O3F) (%) evolution.
We found that the ozonesonde evolution closely resembles that of both EMAC and CMAM (not shown) throughout the troposphere. A clear correspondence in the seasonality of ozone is also evident for the EMAC O3S tracer, and in turn the O3F evolution, particularly towards the upper troposphere. Nonetheless, both models imply that over 50 % of near-surface ozone is derived from the stratosphere during wintertime in the extratropics, which is substantially greater than that estimated by Lamarque et al. (1999) (~ 10-20 %), and still considerably higher than more recent studies (~ 30-50 %) (e.g. Banarjee et al., 2016). This indicates that the stratospheric influence may indeed be larger than previously thought and is thus an important consideration when attempting to understand past, present and future trends in tropospheric ozone.
Finally, we analysed height-resolved seasonal changes in both the model O3 and O3S between 1980-89 and 2001-10. The calculated hemispheric springtime (MAM/SON) changes in ozone are shown in Figure 3, and equivalently for O3S in Figure 4, for the upper and middle troposphere (350 and 500 hPa), as well as for the surface model level. A general increase in tropospheric ozone was found worldwide in all seasons, which is maximised overall during spring in both the Northern Hemisphere (~ 4-6 ppbv) and the Southern Hemisphere subtropics (~ 2-6 ppbv), corresponding to a relative increase of about 5-10 %. Respectively, a significant stratospheric contribution to this change of ~ 3-5 ppbv and ~ 1-4 ppbv is estimated using the model O3S tracers (~ 50-80 % of the total change), although with substantial inter-model disagreement over the magnitude and sometimes the sign of the attributable change for any given region or season from the stratosphere.
Although surface ozone changes are dominated by regional changes in precursor emissions between the two periods – the largest, statistically significant increases (> 6 ppbv) being over south-east Asia – the changing influence from the stratosphere were estimated to be up to 1–2 ppbv between the two periods in the Northern Hemisphere, albeit with high regional, seasonal and inter-model variability. In relative terms, the stratosphere can be seen to typically explain 25-30 % of the surface change over regions such as the Himalayas, although locally it may represent the dominant driver (> 50 %) where changes in emission precursors are negligible or even declining due to the enforcement of more stringent air quality regulations over regions such as western Europe and eastern North America in recent years.
To summarise, our paper highlights some of the shortcomings of the EMAC and CMAM CCMs with respect to observations and we emphasise the importance of understanding model bias origins when performing subsequent model-based evaluations. Additionally, our evaluations highlight the necessity of a well-resolved stratosphere in models for quantifying the stratospheric influence on tropospheric ozone. We find evidence that the stratospheric influence may be larger than previously thought, compared with previous model-based studies, which is a highly significant finding for understanding tropospheric ozone trends.
References: Lamarque, J. F., Hess, P. G. and Tie, X. X.: Three‐dimensional model study of the influence of stratosphere‐troposphere exchange and its distribution on tropospheric chemistry., J. Geophys. Res. Atmos., 104(D21), 26363-26372, https://doi:10.1029/1999JD900762, 1999.
Banerjee, A., Maycock, A. C., Archibald, A. T., Abraham, N. L., Telford, P., Braesicke, P., and Pyle, J. A.: Drivers of changes in stratospheric and tropospheric ozone between year 2000 and 2100., Atmos. Chem. Phys., 16, 2727-2746, https://doi.org/10.5194/acp-16-2727-2016, 2016.
From May 4th through August 10th 2019, I relocated to Norman, Oklahoma, where I worked in the School of Meteorology in the National Weather Center (NWC) at the University of Oklahoma (OU). I’m co-supervised by Jason Furtado at OU, and part of my SCENARIO-funded project plan involves visiting OU each summer to work with Dr. Furtado’s research group, while using my time in the U.S. to visit relavant academics and conferences. Prior to my PhD, I studied Reading’s MMet Meteorology and Climate with a Year in Oklahoma degree, and spent 9 months at OU as part of that – so it’s a very familiar place! The two departments have a long-standing link, but this is the first time there has been PhD-supervision collaboration.
The research done at SoM is mostly focussed on mesoscale dynamics, including tornadogenesis, thanks to its location right at the heart of ‘tornado alley’. It’s by no means a typical haunt of someone who researches stratosphere dynamics like I do, but SoM has broadened its focus in recent years with the inception of the Applied Climate Dynamics research group of which I’m a part. Aside from the numerous benefits of being able to speak face-to-face with a supervisor who is otherwise stuck on a TV screen on Skype, I also learnt new skills and new ways of thinking – purely from being at a different institution in a different country. I also used this time to work on the impact of the stratosphere on North America (a paper from this work is currently in review).
I also visited the NOAA Earth System Research Laboratory (ESRL) in Boulder, Colorado to present some of my work, and collaborate on some papers with scientists there. Boulder is an amazing place, and I highly recommend going and hiking up into the mountains if you can (see also this 2018 blog post from Jon Beverley on his visit to Boulder).
As for leisure… I chose to take 2 weeks holiday in late May to, let’s say, do “outdoor atmospheric exploration“. This happened to coincide with the peak of one of the most active tornado seasons in recent years, and I just so happened to see plenty of them. I’m still working on whether or not the stratosphere played a role in the weather patterns responsible for the outbreak!
The new academic year is now underway, and a new bunch of eager first year PhD students are dipping their toes into a three-to-four year journey to their doctorate. So, we’ve collated some advice from the more experienced among us! The idea behind the following tidbits of advice is that they are things we would tell our younger selves if we could go back to day 1…
“Make sure you and
your supervisor set out expectations and at least a vague timeline at the
start, that way you will know you’re on track.”
“Write code as if
you’re giving it to someone else – one day you might have to.”
Even if you don’t give your code to another use, in a year’s time you’ll have forgotten what it does! Related to this, it’s useful to keep good “readme” documents to note where all your code is, how to run things, etcetera. Also, if you think you’re going to present a plot at some point – in a talk, paper, or even your thesis, make a final version at the time (using appropriately accessible colour maps and big enough labels), plus note down where you’ve stored the code you used to make it.
“Learn and use
git/github (or at least get familiar with the 3 basic commands of: git add,
commit, push) ASAP! This means that if you take a wrong turn in your code (you
will), you can painlessly ‘revert’ to a stage before you made a mess.”
“Read papers with your literature review in mind. If you can’t see where the paper will fit in your literature review, either reconsider your literature review… or find a more relevant paper.”
“Write down everything you learn, or facts you are told – you never know when you’ll need a piece of information again.”
But also be
prepared to have not really followed any of this advice properly until you
regurgitate it to new students in your fourth year and wonder why you haven’t
been doing any of it up until now.
“Try to keep up a good routine – it’s much easier to get out of bed when you’re having a slow work week if that’s what your body is used to.”
“You’ll be amazed at
how much you’ll learn and master without even realising.”
“Don’t compare yourself to others.”
Every PhD project is unique, as is every student. During a PhD, you’re looking into the unknown. Maybe you’ll get lucky (with some hard work) and have some really interesting results, or it might be a bit of a battle. Some projects are more suited to regular publications, others less so – this doesn’t necessarily reflect your individual abilities. In addition, everyone has different background knowledge and motivation for doing a PhD.
“Not every day has to
be maximum productivity, that’s okay!”
“Some days are great, others are rubbish. Like
“Make friends with other PhD students. It’s nice to have someone who might make you cake when you feel sad, or happy.”
This is so true. A PhD is quite a unique experience and lots of people don’t really get it, thinking it’s just like another undergrad. Sometimes it’s really useful to have someone who understands the stress of some code just not working, or the dread of a blank page where your monitoring committee report should be. It’s also helpful to get to know people in the years above, or even post-docs, since they’ve probably already gone through what you’re experiencing.
“Make friends and join clubs and societies with people that aren’t doing PhDs.”
Sometimes it’s important to get out of the PhD “bubble” and put things in perspective. Keeping in touch with friends that have “real” jobs (for want of a better word) can be a nice reminder of some of the benefits of PhD life – such as flexible hours (you don’t have to be in before 9 every day) or not having to wear formal business attire.
“Try to keep your weekends free – it’s great for your sanity!”
“Take holiday! You are
“Don’t feel guilty for not cheering up when people tell you everything’s okay. It almost invariably is, but sometimes it all gets a bit much and you’ll feel bad for a while, that’s totally normal!”
Yes, it’s totally okay to have a couple of bad days. Remember, this can often be true of people with ‘real’ jobs, it isn’t just unique to the PhD experience! However, if you’re feeling bad for a long period of time, it’s important to acknowledge that this isn’t okay and you don’t have to feel like that. It might be helpful to let your supervisor know that you’re having a bit of a hard time, for whatever reason, and work might be slow for a while. There are also lots of support systems available. For students at Reading, you can find out more about the Counselling and Wellbeing Service here (http://www.reading.ac.uk/cou/counselling-services-landing.aspx). A PhD is hard work, but it should be a fundamentally enjoyable experience!
“No poking your supervisor with a stick. They don’t appreciate it.”
(…no, we don’t get it either)
Co-written by Simon Lee and Sally Woodhouse, with anonymous pieces of advice collected from various PhD students in the Department of Meteorology.
Last month, from the 21st July until the 3rd August 2019, I was in Ghana attending the African SWIFT and YESS International Summer School. What a catchy name you are probably thinking. SWIFT, or Science for Weather Information and Forecasting Techniques, is a programme of research and capability building, led by the National Centre for Atmospheric Science (NCAS), and funded by the UK Research and Innovation Global Challenges Research Fund. The project aims to improve African weather forecasting, especially on seasonal timescales, as well as build capability in related research. It’s worth a quick Google search at some point, and there are several people involved in the project at the University of Reading. YESS, the Young Earth System Scientists community, is an international, multidisciplinary network of early career researchers. Catchy!
Anyway, all this contributed toward a really remarkable summer school in tropical West Africa, with people from many different institutions and nations across Europe and Africa attending the summer school and science meeting alongside. It was also another chance to get consistently barraged with Brexit questions by a baffled international audience.
The days were long but engaging, with lectures, practical sessions and workshops on a huge variety of topics in tropical meteorology – from Rossby waves, to the monsoon, to remote sensing applications. It was quickly evident how tricky tropical African weather is to forecast. It is largely driven by convection, which is very difficult to forecast accurately on a small spatio-temporal scale, unlike nice, large mid-latitude weather systems. Furthermore, several different atmospheric features are at play. This is where we were introduced to the wonderful West African synoptic analysis/forecast charts (see below for an example of mine). We also had a chance to present our posters, with many of those from the science meeting – experts in their fields – coming round to look, and this was a fantastic networking opportunity. It was really beneficial being around other early career scientists in the same specific field as me, from different places around the world. It cannot be said enough how important this is for PhD students, who for the most part live quite an isolated existence where when you switch from your native English language to your ‘PhD language’, only your supervisors and a few select others can understand you!
For me, it is in the people attending where the strength of the summer school really lies. The people in Kumasi, Ghana were amazing people. They not only keep you going through 2 weeks of long days, 3 dozen lectures, and 400 meals of rice, but they reminded me what it meant to be a scientist. I found, to my discredit, that most of the students there were far more studious than I was, not because they were any less clever or anything like that, but because they simply loved knowledge, and loved applying it (meteorology is great for quickly being able to see how what we know manifests itself in the real world). On reflection, I think they are more aware of the fact millions of people (moreso in Africa than any other continent) simply do not have access to such knowledge, but in Kumasi we were learning about African meteorology from world experts. They did not take it for granted, in fact, it was clearly what drove them. Science wasn’t just an occupation for them, it had tangible importance, which came across in the way they spoke about their science, but also their future ambitions, hopes and plans.
Further to this, meeting people across universities, countries, and continents also brings a different perspective on your work and where it fits into the wider collection of research in the area. One sad point was learning how hard it was for African students to get PhDs. Not only do they typically have to travel much further (i.e. typically to Europe or the US) in order to get one, but they also rely on getting funding, which is often the final obstacle even after they have found the right PhD project. It’s a real shame.
So after 2 long weeks (and a very hot football game on a gravelly pitch with no shoes) I came back physically exhausted, but academically I was refreshed with lots of new ideas floating round, but even more importantly newfound inspiration. In the now famous words of the provost of the college during the closing ceremony, “let your research be SWIFT and YESS.”