Combining multiple streams of environmental data into a soil moisture dataset


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 service.

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.

Figure 1: Top layer prior, background, posterior and SMAP satellite observed volumetric soil moisture for Antlers station in Oklahoma Mesonet, for the year 2017.
Figure 2: Distance of prior soil moisture and posterior soil moisture from the concurrent soil moisture SMAP observations explained in Figure 1.

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.

Figure 3: Hind-casted soil moisture for 2018 based on posterior soil texture corresponding to the result obtained from assimilated mesonet soil moisture data for 2017.


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.,

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.

Characterising the seasonal and geographical variability in tropospheric ozone, stratospheric influence and recent changes


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,, 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.

Figure 1 – Seasonal composites of monthly averaged 1000-450 hPa (0-5.5 km) subcolumn O3 (DU) for 2005-2010 (left to right) from (a) OMI, (b) EMAC minus OMI and (c) CMAM minus OMI. Circles denote (a) equivalent ozone-sonde derived subcolumn O3 (DU), (b) EMAC minus ozone-sonde differences and (c) CMAM minus ozone-sonde differences. All data were regridded to 2.5° resolution (~ 275 km). 1 Dobson Unit (DU) equates to a thickness of 0.01 mm if it were compressed at sea level.

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.

Figure 2 – Zonal-mean monthly mean evolution of ozone (O3) volume mixing ratio (ppbv) derived from (a) ozonesondes and (b) EMAC. The evolution of the (c) EMAC stratospheric ozone (O3S) tracer and (d) stratospheric fraction (O3F) (%) are additionally included over the period 1980-2010 for 350 hPa (top row), 500 hPa (middle row) and 850 hPa (bottom row).

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.

Figure 3 – Seasonal change in EMAC ozone volume mixing ratio (O3) (ppbv) between 1980-89 and 2001-10 for MAM (top) and SON (bottom) at (a) 350 hPa, (b) 500 hPa and (c) the surface model level. Stippling denotes regions of statistical significance according to a paired two-sided t-test (p < 0.05).

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.

Figure 4 – Seasonal change in EMAC stratospheric ozone volume mixing ratio (O3S) (ppbv) between 1980-89 and 2001-10 for MAM (top) and SON (bottom) at (a) 350 hPa, (b) 500 hPa and (c) the surface model level. Stippling denotes regions of statistical significance according to a paired two-sided t-test (p < 0.05). Note the scale difference between (a-b) and (c).

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.

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,, 2016.

On relocating to Oklahoma for 3.5 months


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 National Weather Center in Norman, Oklahoma – home to the School of Meteorology.

The National Weather Center (NWC) [first conceived publicly in a 1999 speech by President Bill Clinton in the aftermath of the Bridge Creek-Moore tornado] opened in 2006 and is a vastly bigger building than Reading Meteorology! Alongside the School of Meteorology (SoM), it houses the Oklahoma Mesonet, the NOAA Storm Prediction Center (SPC) (who are responsible for operational severe weather and fire forecasting in the U.S.) and the NOAA National Severe Storms Laboratory (NSSL). SPC and NSSL will be familiar to any of you who have seen the 1996 film Twister. You could think of it as somewhat like a smaller version of the Reading Meteorology department being housed in the Met Office HQ in Exeter.

Inside the NWC.

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!

An EF2-rated wedge tornado on 23 May near Canadian, Texas.

Wisdom from experience: advice for new PhD students

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 life, really.”


“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 expected to.”

“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 ( 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.

SWIFT and YESS International Summer School, Kumasi, Ghana


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.”

Fluid Dynamics of Sustainability and the Environment Summer School


From the 1st – 12th of July 2019, I was fortunate enough to be able to attend the Fluid Dynamics of Sustainability and the Environment (FDSE) summer school held at Ecole Polytechnique on the southern outskirts of Paris. Although it was held at Ecole Polytechnique this year, it alternates with the University of Cambridge, where it will be held in 2020.

As hinted at in the title, the summer school explores the fluid dynamical aspects of planet Earth, including, but not limited to: the atmosphere, the ocean, the cryosphere and the solid Earth, and was of particular relevance to me because I study clear-air turbulence (a fluid dynamical phenomenon) and its impact on aviation. To get a better sense of the summer school, have a watch of this 3-minute promotion video:

It was a busy, action-packed 2 weeks. The days consisted of: 4 hours of lectures held each morning (coffee was provided), followed by either lab or numerical practical sessions in the afternoons and something social (wine was provided) such as a poster session, barbecue, and an environmentally-themed film night followed by a discussion of the film’s (The Day After Tomorrow) fluid dynamical accuracy (or not, as the case may be!). During the mid-programme weekend, we were put up in a hostel in central Paris, treated to an evening on a moored boat on the Seine (champagne was provided) and then left to our own devices to explore Paris.

The boat on the Seine even had its own dance floor.

The other students were great, with all sorts of backgrounds/PhD projects that linked in one way or another to the FDSE theme. Many interesting and diverse conversations were had, as well as a great deal of fun and laughter! No doubt many of the people who met here both this year and others will collaborate scientifically in the future.

Not having come from a maths/physics background, I found a lot of the mathematical content quite challenging, but I made copious notes and my interest in and appreciation for the subject greatly increased. As I progress throughout my PhD (I am currently still in my first year), I feel many of the concepts that I encountered here are likely to resurface in a slow-burn fashion and I can see myself returning to the lecture material as and when I meet related concepts.

In particular, gaining an understanding of what an instability is and studying the different types was eye-opening, and seeing Kelvin-Helmholtz instabilities — which cause the shear that generates the clear-air turbulence I study in my PhD — form in a tube of dyed fluid was a particularly memorable moment for me.

Kelvin-Helmholtz billows forming in a tube.

Apart from being very interesting theoretically, fluid dynamics also has many practical applications. For example, insufficient understanding and modelling of the behaviour of plumes at the Fukushima nuclear reactor led to hydrogen gas concentrations exceeding 8%, resulting in dangerous explosions. Many other such examples could be given.

The summer school was well-organised and many of the lecturers and guest speakers were both highly entertaining and informative, and really bought the subject to life with their enthusiasm for it. I highly recommend it to anyone with a related PhD!

The 2019 cohort in front of Ecole Polytechnique.

The Colour of Climate


Gristey, J.J., J.C. Chiu, R.J. Gurney, K.P. Shine, S. Havemann, J. Thelen, and P.G. Hill, 2019: Shortwave Spectral Radiative Signatures and Their Physical Controls. J. Climate, 32, 4805–4828,

Sunlight reaching the Earth is comprised of many different colours, or wavelengths. Some of these wavelengths cannot be detected by the human eye, such as the ultraviolet (UV) wavelengths which famously cause sunburn. Fortunately for us, the most intense sunlight is found at harmless visible wavelengths and reaches the surface with relative ease, allowing us to see during the daytime. Sometimes nature aligns to dramatically separate these wavelengths, producing beautiful optical phenomena such as rainbows. More often, however, the properties of the atmosphere and surface lead to intricate differences in the wavelengths of sunlight that get reflected back to space (Fig. 1).

Fig. 1. Schematic showing how the spectral structure of reflected sunlight at the top of the atmosphere can emerge via interactions with various atmospheric/surface properties*.

Satellites have observed specific wavelengths of reflected sunlight to infer the properties and evolution of our climate system for decades. Satellites have also independently measured the total amount of reflected sunlight across all wavelengths to track energy flows into and out of the Earth system. It has been less common to make spectrally resolved measurements at many contiguous wavelengths throughout the solar spectrum. In theory, these measurements would simultaneously provide the total energy flow – by integrating over the wavelengths – and the “spectral signature” associated with all atmospheric and surface properties that determined this energy flow. Our recent study puts this theory to the test.

Almost 100,000 spectra of reflected sunlight were computed at the top-of-atmosphere under a diverse variety of conditions. Applying a clustering technique to the computed spectra (which identifies “clusters” in a dataset with similar characteristics) revealed distinct spectral signatures. When we examined the atmospheric and surface properties that were used to compute the spectra belonging to each spectral signature, a remarkable separation of physical properties was found (Fig. 2).

Fig. 2. (top row) Three of the extracted “spectral signatures” of reflected sunlight. (bottom row) Their relationship to the underlying atmospheric/surface properties. Seven others are shown in the published article.

Surprisingly, the separation of physical properties by distinct spectral signatures, as shown in Fig. 2, was found to be robust up to the largest spatial scales tested of 240 km. This is similar to the footprint size of one of the only previous satellite instruments to measure contiguous spectrally resolved reflected sunlight, the SCIAMACHY**, providing an exciting opportunity to investigate spectral signature variability in real observations. We found that the frequency of spectral signatures in real SCIAMACHY observations followed the expected behaviour during the West African monsoon very closely (Fig. 3).

Fig. 3. (left) The annual cycle of precipitation [mm/day] associated with the West African monsoon, and (right) frequency of the three “spectral signatures” shown in Fig. 2 from real satellite observations during 2010 over West Africa.

Overall, the separation of physical properties by distinct spectral signatures demonstrates great promise for monitoring evolution of the Earth system directly from spectral reflected sunlight in the future.

Funding acknowledgement: This work was supported by the Natural Environment Research Council (NERC) SCience of the Environment: Natural and Anthropogenic pRocesses, Impacts and Opportunities (SCENARIO) Doctoral Training Partnership (DTP), Grant NE/L002566/ 1, and from the European Union 7th Framework Programme under Grant Agreement 603502 [EU project Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA)]

*Note several key simplifications in Fig. 1 for the purposes of visual effect: atmospheric properties are separated, but often occur simultaneously and throughout the atmosphere; the depicted path of sunlight is one option, but sunlight emerging at the top of the atmosphere will come from many different paths; sunlight reflected by the surface will need to travel back through the same gases (and likely other properties) on its way back to the top of the atmosphere, which is not shown. The spectra in Fig. 1 are generated with SBDART using a set of arbitrary but realistic atmospheric and surface properties.

** SCIAMACHY = Scanning Imaging Absorption Spectrometer for Atmospheric Chartography.

Jake completed his PhD at Reading in 2018 and now works at the NOAA Earth System Research Laboratory (ESRL) in Boulder, Colorado.