Space weather refers to the short-term changes in the space environment of our solar system. We tend to focus on near-Earth space due to its direct impact on human life and infrastructure. Extreme space weather events can cause disruptions to satellite operations, navigation systems, radio communication, power grids, and rail networks. Additionally, they expose humans in space or on high-altitude flights to harmful radiation and energetic particles. Although mitigation procedures are being developed and refined, their efficacy relies on the accuracy of extreme space weather forecasts. As such, understanding causal phenomena such as solar eruptive processes and high-speed solar wind remains a high priority for improving prediction accuracy.
Coronal Mass Ejections (CMEs) are drivers of the most severe space weather. They consist of a large structure of plasma and an accompanying magnetic field that have a typical Sun-Earth transit time of 1-5 days. This range exists because a) CMEs are ejected at different speeds and b) CMEs interact with the background ‘ambient’ solar wind and can be accelerated/decelerated due to drag forces. Current CME transit time predictions possess errors on the order ± 10 hours.
How are forecasts made?
Identifying sources of these errors requires an understanding of how CME transit time forecasts are made. The forecast process is outlined as follows, with a visual summary provided in Figure 1:
Information about the Sun’s magnetic field structure in the form of magnetograms is used as initial data.
These are fed into coronal models to generate ambient solar wind speed profiles at 0.1 Astronomical Units from the Sun (1 AU ~ 150 million kilometers).
CME parameters are derived from white light coronagraph images and extrapolated to 0.1 AU.
Together they serve as initial conditions for heliospheric models that simulate CME propagation to Earth and other planetary bodies.
Figure 1: Schematic of standard space weather forecasting method. From top left to bottom right: A magnetogram, a coronagraph, coronal modelling, derivation of CME parameters, heliospheric modelling to Earth and to outer planets (Owens, M. J., et al. (2026)).
While the models used are imperfect, transit time errors largely stem from initial uncertainties in observations of CME parameters and ambient solar wind conditions.
What did we do?
Though the sources of transit time errors are identified, the extents of their contributions vary, and isolating individual error contributions for observed events is difficult. In particular, the ambient solar wind properties exhibit substantial variability over the solar cycle. Observations show that during solar minimum, Earth intercepts interchangeable fast and slow winds over the course of a month. On the other hand, the solar wind during solar maximum is less stark in its longitudinal gradients. Does this structural difference between solar cycle phases change the ambient solar wind influence on CME propagation? If yes, by how much?
We performed simulations of CME propagation using the Heliospheric Upwind eXtrapolation with time-dependence (HUXt) solar wind model to answer these questions. HUXt approximates the solar wind as a one-dimensional and hydrodynamic flow, which allows for low computational cost. We used realistic solar wind data to simulate a statistically average CME and a fast CME every day between 1975 – 2024 (4.5 solar cycles). This is 18,000 runs and 126,000 simulation days (for each CME)! This gave us a dataset of daily CME transit times to Earth that was later used for the analyses. An example of two such simulations is provided in Figure 2.
Figure 2: Snapshots of solar wind speed in the solar equatorial plane at three different times (from left to right, shown times are 1, 2, and 4 days after CME launch) from two HUXt simulations. The CME is shown by the red outline. Date labels on the left denote initiation time: the top plots were initialized just 1 day before the lower plots, but the arrival times vary a lot!
What did we find out?
From the dataset of daily transit times, we calculated the monthly medians and interquartile ranges. We used the median to characterise the typical transit time, and the interquartile range to represent short-term variability of transit time. The distributions for these metrics are shown in Figure 3 for both types of CMEs.
Figure 3: Top: Distributions of monthly transit time medians, during solar minimum and maximum solar phases for average and fast CMEs. Dotted lines represent median values for the distributions. Bottom: The same for distributions of the monthly interquartile range.
Figure 3 tells us three things:
CMEs arrive faster during solar minimum: the median values show that average CMEs arrive about 5 hours earlier during solar minimum. This is because CMEs encounter either slow or fast wind during solar minimum, but solar maximum presents mostly slow wind that does not accelerate any CME to the same degree.
Transit times are more variable (~6h more variable for an average CME) during solar minimum. The design of our experiment dictates that this effect purely arises from the change in ambient solar wind structure over a solar cycle.
These results are true for both CME types.
In other words, even identical CMEs can exhibit a range of transit times due to changes in the ambient solar wind structure. Moreover, the magnitude of this variability peaks during solar minimum. It implies that in the absence of accurate ambient solar wind conditions, CME arrivals are intrinsically less predictable during solar minimum than solar maximum. Additionally, the penalty for incorrectly modelling the ambient solar wind—for example, small errors in speed gradients or the position of high-speed streams—is greater during solar minimum.
Main Takeaways
During solar minimum, the arrival time of coronal mass ejections at Earth is roughly twice as uncertain due to the influence of the ambient solar wind compared to solar maximum.
Importance of ambient solar wind representation during solar minimum is emphasised.
By Dony Christianto (d.christianto@pgr.reading.ac.uk)
“When do storms really happen?”
It sounds like a simple question—but in the tropics, there is rarely a simple answer.
We often learn this simple process: daytime heating drives peak afternoon rainfall. It is a useful idea—and in many cases, it works. But as we looked more closely at rainfall in Papua, it became clear that the atmosphere was telling a more complex story.
This work began in the field, in southern Papua near Timika. We worked with a network of Automatic Weather Stations (AWS) located across the extraordinary landscape of southern Papua (Figure 1). Across roughly 100 km, the terrain rises from sea level to mountain peaks exceeding 4,700 m. Along this steep transition, particularly in the foothill regions, rainfall reaches some of the highest values in the world, with annual totals of around 12,500 mm.
Figure 1: Automatic Weather Station network across Papua, spanning coast to high mountains, providing critical observations in a complex tropical environment.
This combination of coastline, lowland, and steep mountains creates a highly dynamic environment. Papua is not just another tropical region—it is a natural laboratory where local and large-scale atmospheric processes interact in complex ways.
The AWS data used in this study comes from a collaboration between PT Freeport Indonesia and BMKG (the Indonesian Agency for Meteorology, Climatology, and Geophysics). Maintaining these observations requires field visits, safety briefings, and working in remote areas where conditions can change rapidly. It is a reminder that every dataset has a story behind it—and that reliable ground observations remain essential, especially in regions as complex as Papua. This becomes particularly important when we consider how rainfall is commonly studied today.
Satellite products such as GPM-IMERG are widely used because they provide global coverage. In many studies, they are treated as a reference for understanding rainfall behaviour. But before relying on them, it is important to ask: how well do they represent what actually happens at the surface?
To address this, we compared satellite rainfall estimates with AWS observations in Papua (Figure 2).
Figure 2: Comparison of diurnal rainfall intensity between ground observations (AWS) and satellite estimates (GPM-IMERG), showing a consistent delay in satellite peak timing, with observed rainfall peaking around 16:00 local time and GPM-IMERG peaking later.
What we found was consistent across the dataset: satellite rainfall peaks tend to occur later than those observed at the ground, typically by around one to three hours. This delay reflects that satellites are more sensitive to mature convective systems and therefore detect rainfall after storms have already developed.
At first glance, a difference of a few hours may not seem significant. However, in meteorology, timing is critical.
Satellite datasets are often used to evaluate weather and climate models. If rainfall timing is systematically shifted, models that appear to perform well against satellite data may not accurately represent real surface processes. This also has implications for applications such as early warning systems, where even small timing differences can influence how rainfall events are detected.
Having established this, we then turned to a more fundamental question: when does rainfall actually occur?
While rainfall in Papua often peaks in the late afternoon (around 15:00–18:00 local time), our analysis revealed a second important rainfall regime in the early morning, typically between 03:00 and 09:00 (Figure 3).
Figure 3: Contrasting rainfall regimes in Papua: afternoon storms (15:00–18:00 LT) are associated with offshore propagation from inland regions, while morning storms (09:00–12:00 LT) are linked to onshore flow bringing precipitation inland. The lower panels show meridional wind anomalies, with blue indicating southward (offshore) flow and red indicating northward (onshore) flow.
These two rainfall peaks are not simply different in timing; they are produced by different atmospheric processes.
Afternoon rainfall is largely driven by local processes. Solar heating during the day warms the land surface, causing air to rise, consequently triggering convection. In Papua, this process is strongly enhanced by the presence of mountains, which help initiate and organise storm development. These systems typically form over land and propagate towards the coast.
Morning rainfall, in contrast, often originates over the ocean. During the night, convective systems develop offshore and are transported inland by low-level winds. In this case, rainfall is not initiated locally but carried from the sea.
This leads to a key insight: rainfall timing is closely linked to wind direction.
When winds are directed offshore (from land towards the sea), they favour afternoon and evening rainfall over land. Conversely, when winds are onshore (from sea towards land), they transport moisture inland and are associated with morning rainfall. These alternating wind regimes create distinct “windows” for storm development throughout the day.
The next question is: what controls these wind patterns? The answer lies in larger-scale atmospheric processes.
Phenomena such as the Madden–Julian Oscillation (MJO), equatorial Rossby waves, and seasonal monsoon circulations influence the background wind environment over Papua. For example, different phases of the MJO are associated with shifts in wind direction and moisture transport. Some phases favour onshore flow, increasing the likelihood of morning rainfall, while others favour conditions more conducive to afternoon convection.
This highlights an important point: rainfall in Papua is not controlled by a single mechanism, but by interactions across multiple scales.
This is where the role of local characteristics becomes critical.
Every region has its own unique combination of topography, coastline, and atmospheric conditions. These factors shape local circulation patterns, which determine how that region responds to larger-scale climate phenomena. This is particularly important in the tropical Maritime Continent, where thousands of islands differ in size, shape, elevation, and land–sea distribution. These differences influence surface properties such as albedo, heat capacity, and moisture availability, which in turn affect how energy is distributed within the atmosphere.
As a result, each island develops its own local circulation system. Even under the same large-scale forcing—such as the MJO or monsoon—different regions can exhibit very different rainfall behaviour. The interaction between local geography and atmospheric processes creates a wide range of responses across the region.
Papua, with its steep topography and strong land–sea contrasts, provides a clear example of this complexity.
From field observations to large-scale atmospheric dynamics, this study highlights a simple but important message: understanding rainfall in the tropics requires considering both the local environment and the broader climate system.
Because in regions like Papua, rainfall is determined not by a single process, but by the interaction of many.
By Maia Arama (m.arama@pgr.reading.ac.uk), Molly Macrae(molly.macrae@pgr.reading.ac.uk) and Giacomo Giuliani (g.giuliani@pgr.reading.ac.uk)
A big part of doing a PhD at Reading is the opportunity to complete training courses that expand our skillsets and knowledge. Below, we summarise and review our experiences of different courses offered by the National Centre for Atmospheric Science (NCAS) that we completed at the beginning of our PhDs.
Introduction to Scientific Computing (ISC), Maia Arama
In November 2025, I completed the ISC course run by NCAS. The course consisted of three modules designed to provide students with essential skills and tools for data analysis. Module 1 introduced Linux commands, Module 2 covered an introduction to Python programming, and Module 3 focused on data analysis tools in Python (e.g. numpy, x-array, cf-python). Modules 1 and 2 were aimed at beginners, while Module 3 was aimed at those with some prior experience. After completing a short quiz, I was able to skip Module 2 and complete only Modules 1 and 3. Module 1 was delivered online while Modules 2 and 3 were taught at NCAS headquarters in Leeds.
The most valuable things I learned from Module 1 was how to combine commands using pipes and filters, as well as writing loops and shell scripts. I now regularly use these skills to process data for my PhD project! I did find that more time was spent on easier sections, while more challenging sections felt rushed at times. However, all material was made available to us on Software Carpentry enabling us to practice with the resources after the module. In Module 3, I found it particularly useful to learn how to use cf-python and cf-plot. We also completed a challenge using weather API data where we created NetCDF files, combined our data and produced a plot. The module also introduced version control with Git and the use of remote GitHub repositories. All teaching was done using Jupyter Notebooks on Jasmin.
Overall, I had a fantastic experience attending the course in Leeds. The staff were readily available to answer questions and help with debugging code. It was also great to meet other friendly and motivated PhD students, which created an inspiring working environment. Those with more experience in Python may find some of the material quite basic. However, if you have limited coding experience or want to increase your confidence coding in Python, I would highly recommend this course!
Links to the relevant NCAS ISC GitHub pages can be found here:
Introduction to the UK Chemistry-Aerosol (UKCA) Model, Molly Macrae
UKCA is a community atmospheric chemistry–aerosol model used to study how atmospheric chemistry, aerosols, and climate interact within the Met Office Unified Model, supporting research on climate forcing, air quality, and ozone processes. The ‘Introduction to UK Chemistry Aerosol (UKCA) Model’ course aims to provide experience of running the model through a set of practical, self-paced tutorials. The course was free to attend and took place in-person in Leeds with accommodation, travel and food included.
Over the 3 days, you are provided with a login to Monsoon3 and work through online tutorials at your own pace with expert UKCA users on hand to answer any questions or help with technical issues. These tutorials covered how to use the main parts of the UKCA model, setting up and running experiments, and how to adapt the model to specific purposes. There were also pre-recorded walkthrough videos provided, which were incredibly useful if anything from the instructions was unclear. Additionally, the logins were made available for a couple of weeks beyond the course, and a Slack channel was set up for any queries that arose after the course.
Despite most of the training being individual, I really appreciated that the course was held in-person. It was a great opportunity to meet other people using UKCA and to get guidance and feedback from the experts about how I plan to use UKCA in my project.
Overall, I found the course very useful, and I still go back to the provided resources now as I run the model. As expected for a course introducing the running of a climate model, there is a lot of information to take in, and after a few months, the elements of the course that have stayed with me are the parts I am actually using the model for now. Furthermore, the resources provided are clear and available for returning to at a later date. The self-paced format also allows you to tailor the course by spending longer on tutorials most relevant to your research. I would strongly recommend this course to anyone considering using UKCA in their work.
Introduction to the Unified Model, Giacomo Giuliani
The ‘Introduction to the Unified Model (UM)’ course is a 3-day course organised by NCAS at its headquarters in Leeds. It consists of two complementary parts: a series of lectures on the UM components and their applications, and tutorial sessions focused on working with suites and model outputs.
The UM is the atmospheric model for numerical weather prediction and climate projections. It can be run using different configurations, also called suites. The “how to use it” part mainly focuses on becoming familiar with the (un)intuitive, and sometimes quixotic, architecture of the workflow, as well as how to run simulations. Much of the tutorials involved working on the terminal interface of Archer2 and the UM graphic user interface, in other words, a good refresher for Shell and Bash scripting.
At the end of the course, I proudly scrolled my notebook page of “Commands, Tips and Common Errors when running the UM” noticing how long – and hopefully comprehensive – it was. I think the main takeaway of the course was: to be able to troubleshoot any issues, to create your own model configurations, and to understand the overall process behind a simulation within the UM framework.
The practical part for the UM course can be found on GitHub. Thus, in principle, one could just “learn from home”. However, the lectures are not the only aspect that makes this course extremely valuable. I rapidly assessed in thirty days and several hours between yawns and desperation the amount of time it would take me to complete this course on my own. It was really valuable and helpful to be able to talk to people who develop and use this model every day. On the other hand, this course may be too high-level for a person only interested in using the UM for numerical simulations. It provides a general overview of the architecture of the UM workflow, but it lacks the use of a testbed simulation to simulate a common research situation.
Overall, I would definitely recommend the course to anyone frequently working with the UM, as soon as they have a solid base in Linux.
By Khomkrit (Guy) Onkaew – k.onkaew@pgr.reading.ac.uk
If you look out at a cornfield or a dense forest, you see green. That’s chlorophyll, the pigment plants use to turn sunlight into energy. But there is something else happening in those leaves that the human eye completely misses. While they are basking in the sun, plants are also glowing.
During photosynthesis, plants absorb sunlight to create food. However, they don’t use 100% of the light they take in. A small fraction of that unused solar energy is re-emitted by the plant as a faint, reddish glow. This phenomenon is called Solar-Induced Chlorophyll Fluorescence, or SIF.
Think of SIF as the “heartbeat” of a plant’s metabolism. When a plant is healthy and photosynthesising vigorously, this glow follows a regular pattern. But when a plant gets stressed — perhaps it’s too hot, or it hasn’t rained in weeks — that heartbeat changes.
For years, scientists have used special satellites to measure this glow from space to estimate how well vegetation is growing. Ideally, this glow would tell us exactly how productive the plants are. But there is a catch: a plant’s glow isn’t just determined by how much sun it gets; it is also determined by its “efficiency” in using that light.
This brings us to a question: What happens to that efficiency when the soil dries out?
Imagine you kink a garden hose: as you restrict the water, the flow changes. Similarly, when plants run out of water in the soil, they close their stomata — tiny pores on their leaves — to save moisture. This shuts down photosynthesis. The plant then has to deal with all that incoming sunlight that it can no longer use. To protect itself, the plant dissipates that excess energy as heat, which causes the fluorescence glow to dim or change in efficiency.
In other words, this means that long before a crop turns yellow and dies from drought, its glow changes. If we can understand the relationship between soil moisture and glow, we could potentially predict crop failures and droughts much earlier than we can by just looking at how green the plants are.
Decoding the Signal: A New Study on Africa’s Ecosystems
This is where our study steps in. We focused on the African continent to solve a specific puzzle: How does soil moisture stress change the fluorescence efficiency of plants?
Africa offers the perfect laboratory for this question because it holds almost every type of ecosystem imaginable, from the bone-dry Sahara and the semi-arid savannas to the lush Congo rainforest. We combined satellite data from The TROPOspheric Monitoring Instrument TROPOMI, which measures the SIF glow, with a sophisticated land model, the Joint UK Land Environment Simulator (JULES), which estimates soil moisture deep in the ground.
We tested two different models to see which one better predicted the actual glow observed by satellites:
1. The Baseline Model: Assumed the glow depends only on the light the plant absorbs.
2. The Soil Moisture Model: Assumed the glow is influenced by both the light absorbed and how wet the soil is.
African Plants’ Thirst Strategies
Our study produced some fascinating results regarding how different plants handle thirst. We found that fluorescence efficiency is not one-size-fits-all; it depends entirely on the plant’s “lifestyle”.
1. The “Panickers”: Croplands and Grasslands
We discovered that croplands and grasslands are the “drama queens” of the plant world; they easily panic as soon as the soil dries. These plants show the strongest reaction to soil moisture. When the topsoil dries out, their efficiency plummets; when the soil is wet, their efficiency spikes. This makes sense because crops like maize usually have shallow roots. They live and die by the moisture in the top layer of dirt, making them incredibly sensitive monitors for agricultural drought.
2. The “Resilient”: Evergreen Forests
On the other hand, evergreen forests (like those in the Congo basin) were surprisingly indifferent. Their fluorescence efficiency barely changed even when soil moisture levels changed. Why? These trees have deep, complex root systems that can tap into groundwater reserves far below the surface. They don’t panic when the topsoil gets dry because they have a backup water supply.
3. The “Balancers”: Savannas and Shrublands
Moreover, we found that plants in semi-arid regions like the Sahel have evolved to be adaptive. They ramp up their efficiency quickly at the first sign of rain, but don’t waste extra energy once they have “enough” water.
The Map of Improvement
We found that adding soil moisture data to these models significantly improved their ability to simulate the plant glow in semi-arid regions, such as the Sahel and Southern Africa (the blue area in Figure C). In these water-limited environments, you cannot understand the plant’s light signal without understanding the water in the soil.
However, the study also highlighted where the models fail. In wetlands such as the Okavango Delta and the Sudd Swamp (Locations 5 and 6 in Figure C, respectively), adding soil moisture data worsened the model or yielded no improvement. This is likely because satellite models struggle to understand complex water systems where water flows horizontally or sits just below the surface, keeping plants happy even when the model thinks they should be dry.
Spatial distribution maps of RMSE for two SIF simulation models across Africa. (a) RMSE between observed SIF and the Baseline Model (SIFa), which does not include soil moisture availability (β). (b) RMSE between observed SIF and the Soil Moisture Model (SIFb), which includes β. (c) RMSE difference (ΔRMSE = RMSEb − RMSEa). Blue regions (ΔRMSE < 0) indicate areas where including β improves model performance (Model 2 outperforms), while red areas (ΔRMSE > 0) show regions where including β worsens the fit. Grey areas indicate missing data.
The Takeaway
This research is a step toward “context-dependent” monitoring. We can’t just look at a satellite image and apply a single rule to the whole planet. To truly monitor the health of our food systems and forests from space, we have to treat a shallow-rooted cornfield in a semi-arid zone differently from a deep-rooted tree in a tropical forest. By linking the “glow” of the plants to the water in the soil, we are getting closer to a real-time health check for the Earth’s vegetation.
By Douglas Mulangwa – d.mulangwa@pgr.reading.ac.uk
Between 2019 and 2024, East Africa experienced one of the most persistent high-water periods in modern history: a flood that simply would not recede. Lakes Victoria, Kyoga, and Albert all rose to exceptional levels, and the Sudd Wetland in South Sudan expanded to an unprecedented 163,000 square kilometres in 2022. More than two million people were affected across Uganda and South Sudan as settlements, roads, and farmland remained inundated for months.
At first, 2022 puzzled stakeholders, observers and scientists alike. Rainfall across much of the region was below average that year, yet flooding in the Sudd intensified. This prompted a closer look at the wider hydrological system. Conventional explanations based on local rainfall failed to account for why the water would not recede. The answer, it turned out, lay far upstream and more than a year earlier, hidden within the White Nile’s connected lakes and wetlands.
Figure 1: Map of the White Nile Basin showing delineated sub-catchments, lakes, major rivers, and the Sudd Wetland extent. Sub-catchments are labelled numerically (1–15) with names listed in the legend. Observation stations (A–F) mark key hydrological data collection locations used in this study: Lake Victoria (A), Lake Kyoga (B), River Nile at Masindi Port (C), Lake Albert (D), River Nile at Juba (E), and the Sudd Wetland (F). Background river networks and sub-catchment boundaries are derived from the HydroSHED dataset, and wetland extent is based on MODIS flood mask composites. The map is projected in geographic coordinates (EPSG:4326) with a graduated scale bar for accurate distance representation using UTM Zone 36N.
The White Nile: A Basin with Memory
The White Nile forms one of the world’s most complex lake, river, and wetland systems, extending from Lake Victoria through Lakes Kyoga and Albert into the Sudd. Hydrologically, it is a system of connected reservoirs that store, delay, and gradually release floodwaters downstream.
For decades, operational planning assumed that floodwaters take roughly five months to travel from Lake Victoria to the Sudd. That estimate was never actually tested with data; it originated as a rule of thumb based on Lake Victoria annual maxima in May and peak flooding in South Sudan in September/October.
Our recent study challenged that assumption. By combining daily lake-level and discharge data (1950–2024) with CHIRPS rainfall and MODIS flood-extent records (2002–2024), we tracked how flood peaks propagated through the system, segment by segment. Using an automated peak-matching algorithm, we quantified the lag between successive annual maxima peaks in Lake Victoria, Lake Kyoga, Lake Albert, and the Sudd Wetland.
The unprecedented high-water regime of 2019-2024
Figure 2: Lake Victoria water levels (1950–2024) and Sudd Wetland extents (2002–2024), with the 2019–2024 anomalous period shown in dark blue and earlier observations in black. The orange dotted line marks the pre-2019 maximum, while the solid vermillion line denotes the highest peak observed during 2019–2024. The dashed magenta line represents the reconstructed 1878 Lake Victoria peak (1137.3 m a.s.l.) from Nicholson & Yin (2001). The shaded grey band highlights the 2022 flood year, when the Sudd reached its largest extent in the MODIS record.
Between 2019 and 2024, both Lake Victoria and the Sudd reached record levels. Lake Victoria exceeded its historic 1964 peak in 2020, 2021, and 2024, while the Sudd expanded to more than twice its previous maximum extent. Each year from 2019 to 2024 stayed above any pre-2019 record, revealing that this was not a single flood season but a sustained multi-year regime.
The persistence of the 2019–2024 high-water regime mirrors earlier basin-wide episodes, including the 1961–64 and 1870s floods, when elevated lake levels and wetland extents were sustained across multiple years rather than confined to a single rainy season. However, the 2020s stand out as the most extensive amongst all the episodes since the start of the 20th century. These data confirm that both the headwaters and terminal floodplain remained at record levels for several consecutive years during 2019–2024, highlighting the unprecedented nature of this sustained high-water phase in the modern observational era.
2019–2024: How Multi-Year Rainfall Triggers Propagated a Basin-Wide Flood
The sequence of flood events began with the exceptionally strong positive Indian Ocean Dipole of 2019, which brought extreme rainfall across the Lake Victoria basin. This marked the first in a series of four consecutive anomalous rainfall seasons that sustained elevated inflows into the lake system. The October–December 2019 short rains were among the wettest on record, followed by above-normal rainfall in the March–May 2020 long rains, another wet short-rains season in late 2020, and continued high rainfall through early 2021. Together, these back-to-back wet seasons kept catchments saturated and prevented any significant drawdown of lake levels between seasons. Lake Victoria rose by more than 1.4 metres between September 2019 and May 2020, the highest increase since the 1960s, and remained near the 1960s historical maximum for consecutive years. As that excess water propagated downstream, Lakes Kyoga and Albert filled and stayed high through 2021. Even when regional rainfall weakened in 2022, these upstream lakes continued releasing stored water into the White Nile. The flood peak that reached the Sudd in 2022 corresponded closely to the 2021 Lake Victoria high-water phase.
This sequence shows that the 2022 disaster was not driven by a single rainfall event but by cumulative wetness over multiple seasons. Each lake acted as a slow reservoir that buffered and then released the 2019 to 2021 excess water, resulting in multi-year flooding that persisted long after rainfall had returned to near-normal levels.
Transit Time and Floodwave Propagation
Quantitative tracking showed that it takes an average of 16.8 months for a floodwave to travel from Lake Victoria to the Sudd. The fastest transmission occurs between Victoria and Kyoga (around 4 months), while the slowest and most attenuated segment lies between Albert and the Sudd (around 9 months).
This overturns the long-held assumption of a five-month travel time and reveals a system dominated by floodplain storage and delayed release. The 2019–2021 period showed relatively faster propagation because of high upstream storage, while 2022 exhibited the longest lag as the Sudd absorbed and held vast volumes of water. By establishing this timing empirically, the study offers a more realistic foundation for early-warning systems.
Figure 3: Lake Victoria, Lake Kyoga, and Lake Albert water levels, and Sudd Wetland inundated extent, from 2016 to 2024. Coloured spline curves indicate annual flood-wave trajectories traced from the timing of Lake Victoria annual maxima through the downstream of the White Nile system. Blue shading on the secondary (right) axis shows 180-day rolling rainfall totals over each basin. The panel sequence (Victoria–Kyoga, Kyoga–Albert, Albert–Sudd) highlights the progressive translation of flood waves through the connected lake–river–wetland network.
Wetland Activation and Flood Persistence
Satellite flood-extent maps reveal how the Sudd responded once the inflow arrived. The wetland expanded through multiple activation arms that progressively connected different sub-catchments:
2019: rainfall-fed expansion on the east (Baro–Akobo–Sobat and White Nile sub-basins)
2020–2021: a central-western arm from Bahr el Jebel extending into Bahr el Ghazal and a north-western connection from Bahr el Jebel to Bahr el Arab connected around Bentiu in Unity State.
2022: The two activated arms persisted so the JJAS seasonal rainfall in South Sudan and the inflow from the upstream lakes just compounded the activation leading to the massive flooding in Bentiu, turning the town into an island surrounded by water.
This geometry confirms that the Sudd functions not as a single floodplain but as a network of hydraulically linked basins. Once activated, these wetlands store and recycle water through backwater effects, evaporation, and lateral flow between channels. That internal connectivity explains why flooding persisted long after rainfall declined.
The Bigger Picture
Understanding these long lags is vital for effective flood forecasting and anticipatory humanitarian action. Current early-warning systems in South Sudan and Uganda mainly rely on short-term rainfall forecasts, which cannot capture the multi-season cumulative storage and delayed release that drive multi-year flooding.
By the time floodwaters reach the Sudd Wetland, the hydrological signature of releases from Lake Victoria has been substantially transformed by storage, delay, and attenuation within the intermediate lakes and wetlands. This means that downstream flood conditions are not a direct reflection of upstream releases but the result of cumulative interactions across the basin’s interconnected reservoirs.
The results suggest that antecedent storage conditions in Lakes Victoria, Kyoga, and Albert should be incorporated into regional flood outlooks. When upstream lake levels are exceptionally high, downstream alerts should remain elevated even if rainfall forecasts appear moderate. This approach aligns with impact-based forecasting, where decisions are informed not only by rainfall predictions but also by hydrological memory, system connectivity and potential impact of the floods.
The 2019–2024 high-water regime joins earlier basin-wide flood episodes in the 1870s, 1910s, and 1960s, each linked to multi-year wet phases across the equatorial lakes. The 1961–64 event raised Lake Victoria by about 2.5 metres and reshaped the Nile’s flow for several years. The 1870s flood appears even more extensive, showing that compound, persistent flooding is part of the White Nile’s natural variability.
Climate-change attribution studies indicate that the 2019–2020 rainfall anomaly was intensified by anthropogenic warming, increasing both its magnitude and probability. If such events become more frequent, the basin’s long-memory behaviour could convert short bursts of rainfall into multi-year high-water regimes.
This work reframes how we view the White Nile. It is not a fast, responsive river system but a slow-moving memory corridor in which floodwaves propagate, store, and echo over many months. Recognising this behaviour opens practical opportunities: it enables longer forecast lead times based on upstream indicators, supports coordinated management of lake releases, and strengthens early-action planning for humanitarian agencies across the basin.
It also highlights the need for continued monitoring and data sharing across national borders. Sparse observations remain a major limitation: station gaps, satellite blind spots, and non-public lake-release data all reduce our ability to model the system in real time. Improving this observational backbone is essential if we are to translate scientific insight into effective flood preparedness.
By Douglas Mulangwa (PhD researcher, Department of Meteorology, University of Reading), with contributions from Evet Naturinda, Charles Koboji, Benon T. Zaake, Emily Black, Hannah Cloke, and Elisabeth M. Stephens.
Acknowledgements
This research was conducted under the INFLOW project, funded through the CLARE programme (FCDO and IDRC), with collaboration from the Uganda Ministry of Water and Environment, the South Sudan Ministry of Water Resources and Irrigation, the World Food Programme(WFP), IGAD Climate Prediction and Application Centre (ICPAC), Médecins Sans Frontières (MSF), the Red Cross Red Crescent Climate Centre, Uganda Red Cross Society (URCS), the South Sudan Red Cross Red Crescent Society (SSRCS) and the Red Cross Red Crescent Climate Centre (RCCC).
This September, we attended the NCAS Climate Modelling Summer School (CMSS), held at the University of Cambridge from 8th to 19th September. Five of us from the University of Reading joined this two-week residential programme. It was an intense and inspiring experience, full of lectures, coding sessions, discussions, and social events. In this blog, we would like to share our experiences.
Picture 1: Group Picture of Students and teaching staff. One cohort, many time zones, zero dull moments…
About NCAS CMSS
The NCAS Climate Modelling Summer School (CMSS) is a visionary program, launched in 2007 with funding originating from grant proposals led by Prof. Pier Luigi Vidale. Run by leading researchers from the National Centre for Atmospheric Science and the University of Reading, it’s an immersive, practice-driven program that equips early-career researchers and PhD students with deeper expertise in climate modelling, Earth system science, and state-of-the-art computing. Held biennially in Cambridge, CMSS has trained 350 students from roughly 40 countries worldwide.
The CMSS 2025 brought together around 30 participants, including PhD students and professionals interested in the field of Climate Modelling.
Long Days, Big Ideas: Inside Our Schedule
The school was full of activity from morning to evening. We started around 9:00 AM and usually wrapped up by 8:30 PM, with a good mix of lectures, practical sessions, and discussions that made the long days fly by.
Week 1 was led by Dr Hilary Weller, who ran an excellent series on Numerical Methods for Atmospheric Models. Mornings were devoted to lectures covering core schemes; afternoons shifted to hands-on Python sessions to implement and test the methods. Between blocks, invited talks from leading researchers across universities highlighted key themes in weather and climate modelling. After dinner, each day closed with a thought-provoking discussion on climate modelling, chaired by Prof. Pier Luigi Vidale, where participants shared ideas on improving models and their societal impact.
The week concluded with group presentations summarising the key takeaways from Hilary’s sessions and our first collaborative activity that set the tone for the rest of the school. It was followed by a relaxed barbecue evening, where everyone finally had a chance to unwind, chat freely, and celebrate surviving our first week together.
Picture 2 : Working on our group projects. Looks like NASA, feels like: ‘what’s our team name?’
Week 2 was all about getting hands-on with a climate model and learning how to analyse its output. We moved into group projects using SpeedyWeather.jl to design and run climate model experiments. It is a global atmospheric model with simplified physics, designed as a research playground. One of the developers of SpeedyWeather.jl, Milan Klöwer, was with us throughout the week to guide and support our work. Each team explored a different question, from sensitivity testing to analysing the model outputs, and spent the afternoons debugging, plotting, and comparing results. Evenings featured talks from leading scientists on topics such as the hydrological cycle, land and atmosphere interactions, and the carbon cycle.
The week also included a formal dinner at Sidney Sussex, a welcomed pause before our final presentations. On Friday 19th of September, every group presented its findings before we all headed home. Some slides were finished only seconds before presenting, but the atmosphere was upbeat and supportive. It was a satisfying end to two weeks of hard work, shared learning, and plentyof laughter. A huge thank you to the teaching team for being there, from the “silly” questions to the stubborn bugs. Your patience, clarity, and genuine care made all the difference.
Picture 3: SpeedyWeather, as told by its favourite storyteller Milan, Picture 4: Pier Luigi probably preparing for the next summer school..
Coffee, Culture, and Climate Chat
The best part of the summer school was the people. The group was diverse: PhD students, and professionals from different countries and research areas. We spent nearly every moment together, from breakfast to evening socials, often ending the day with random games of “Would You Rather” or talking about pets. The summer school’s packed schedule brought us closer and sparked rich chats about science and life, everything from AI’s role in climate modelling to the policy levers behind climate action. We left with a lot to think about. Meeting people from around the world exposed us to rich cultural diversity and new perspectives on how science is practiced in different countries, insights that were both fresh and valuable. It went beyond training: we left with skills, new friends, and the seeds of future collaborations, arguably the most important part of research.
Picture 5: Barbecue evening after wrapping up the first week, Picture 6: Formal dinner at Sidney Sussex, one last evening together before the final presentations
Reflections and takeaways
We didn’t become expert modellers in two weeks, but we did get a glimpse of how complex and creative climate modelling can be. The group presentations were chaotic but fun. Different projects, different approaches, and a few slides that weren’t quite finished in time. Some of us improvised more than we planned, but the atmosphere was supportive and full of laughter. More than anything, we learned by doing and by doing it together. The long days, the discussions, and the teamwork made it all worthwhile.
If you ever get the chance to go, take it. You’ll come back with new ideas, good memories, and friends who make science feel a little more human.
For the future participants
The NCAS CMSS usually opens in early spring, with applications closing around June. With limited spots, selection is competitive and merit-based, evaluating both fit for the course and the expected benefit to the student.
Bring curiosity, enthusiasm, and a healthy dose of patience, you’ll need all three. But honestly, that’s what makes it fun. You learn quickly, laugh a lot, and somehow find time to celebrate when a script finally runs without error. By the end, you’ll be tired, happy, and probably a little proud of how much you managed to do (and probably a few new friends who helped you debug along the way).
THERE HAD BEEN murmurings among the cricket lovers of the Meteorology department about resurrecting the Staff v Student cricket match each year when the darling buds of Spring give way to the lush green leaves of Summer and the drying out of the ground underfoot. It was time to get it going. It started with casual conversations over coffee about the logistics of resurrecting this beloved tradition, and then the feelers were sent out. Over 30 people signed up to be notified of developments, so I knew we would have enough players, and it was time to find a venue.
How foolish it was of me to assume that the University would have an on-campus cricket pitch – “a non-starter on campus” I was told by Sport@Reading: turns out they had turfed out their cricketers and exiled them to a far away land the other side of the M4. That would not do. Then came the excellent suggestion from one of the all-time cricketing greats of the department, Reg. Prof. Keith Shine who suggested we could ask over at Sol Joel Park about use of their facilities. A perfectly walkable distance away. But again I was met with an apologetic rebuttal – the facilities were fully booked out by regular-season teams. Looks like we were going to have to MacGyver this together ourselves. The plan was simple: plastic cones for a boundary and stumps on springs on a plain patch of grass near the SportsPark. But then came the revelation that PhD student Eme Dean-Lewis might be able to connect with the lovely folk over at Leighton Park School, just over the road at the Pepper Lane exit. It was all a little last minute, but it was done. The first Staff vs. Student match was to be held on the grounds of Leighton Park school on their beautifully kept ground in front of their adorable Victorian pavilion.
Students’ XI waiting their turn to bat in front the Leighton Park School pavilion. Image Credit: J. Williams
Gameday arrived. Tuesday 17th June 2025. Gloriously hot and dry: a perfect day for a game of cricket. We were to play a 22-over game so that everyone faced 12 balls but we had to be out of the ground by 20:45, so no hanging about! To speed things along, there were no re-bowls for wides or no balls, but a 2-run penalty. A 2 run penalty to the batting team for each wicket lost as well. The Students’ captain E. McKinnon-Gray and Staff captain K. M. R. Hunt went out to inspect the wicket and both had the same first impression. This looks like a subcontinental wicket – hard and dry with a few cracks. Not expecting a lot of bounce or seam movement but the spinners might have a field day…
At the toss, both captains were happy with Students’ XI batting first. The first over was N. Reddy to E. McKinnon-Gray and provided some of the highest quality cricket of the evening. Reddy was bowling fast and straight, but McKinnon-Gray was making use of that pace and managed to score 10 off the first over with a bye for the team taking the opening over score to 11-0. It was looking like a good day to bat. Following that was Shipley to Mammatt with a modest 3 including a wicket. Reddy to McKinnon-Gray was the story of the 3rd over, and two stories of the game as a whole. To make up for his expensive first over, Reddy elected to come over the wicket this time, and first ball pitched up viciously into McKinnon-Gray’s thumb resulting in a painful swelling. Great deal of help those expensive Gray-Nicolls gloves were! The following ball, wicket keeper S. Woolnough suffered the same fate to a searing yorker-length ball just outside off stump that went for a bye. Two fractured thumbs in two balls, Reddy took the pace off a bit and decided to bowl some medium-fast spin. The result was just 2 runs off the 3rd over. McKinnon-Gray and Woolnough continued to play through the pain.
McKinnon-Gray’s smashed thumb
Mammatt followed up with a well earned 3 runs off the bowling of W. Tsai. Up next was the batting pair of S. K. Panda and C. Miller. Like the other pairs, this represents one more and one less experienced player. All the inexperienced players made a great case for themselves and had a good time according to conversations I have had with them today. And that is the most important thing – that people enjoyed playing a team sport with their colleagues (and bosses!).
In the four overs that followed there were a flurry of runs and wickets. Panda crashed a delicious four on the offside which almost got lost in the bushes off Reddy’s fast spin. Miller, after scoring a run off the spin of C. O’Reilly, was caught and bowled by O’Reilly twice in consecutive balls. The eventual player of the match was giving us his first hint that he came here to win this match for team Staff. 5 runs off the Hunt to Panda over, followed by A. Charlton-Perez to Miller which resulted in another wicket, this time a catch by Woolnough off the tall spinner’s bowling.
In at 5 and 6 were the experienced D. Wood and the flying Dutchwoman with 0 games under her belt A. Te Winkel. Wood played a classy innings scoring 4 runs with a variety of classic strokes off the bowling of R. Kandala. Te Winkel scored a steady 3 off Williams’s and Shipley’s bowling. Up next were R. S. Nemani and G. Ching-Johnson. Nemani scored 3 and lost two wickets to the bowling of B. Senapati, while Ching-Johnson scored 1 and weathered the storm against a barrage of spin from Thompson and G. Hu. The best was yet to come for the student batters however – out walked L. Blunn and N. Inatimi with the Students foundering at 62-6 off 16. 4 overs later, they stood at 97-7 after Blunn crashed 21 off his two overs against the bowling of P. M. Inness and N. Reddy. Inatimi endured an eventful two overs, scoring 7 runs including a boundary – the only inexperienced player to do so – and was the 3rd victim of another O’Reilly caught and bowled. Up at 11 to give the Students a boost at the end was J. Charlton-Perez with a beautifully struck 12 from 12 balls (and making his dad run around a fair bit in the field!).
The final student net score of 107 must be caveated here by the fact that the Staff XI was in fact a Staff XIII – two extra players flew under the radar until near the end. With this taken into account, Staff only won by 9 runs, but in their batting innings they certainly made sure that there was no doubt over the winner.
Staff captain Hunt corralling his 13 fielders. Photo: J. Williams
The bowling started well for the Students, with EMG, JCP, Nemani, and Blunn conceding just 16 runs off the opening 4 overs of Hunt and Hu. It was during the staff batting that some creative nominations for player of the match began to be suggested. A very successful chase was about to start, possibly thanks to a seagull roaming around the perimeter. Another suggestion was in the form of umpire K. Shine who, if all the extras were credited to him was the top scorer by 400%! He did however play the full 44 overs – a valiant effort.
In at 3 and 4 were Thompson and Reddy whose mission was to “take off the scoreboard pressure”. And they did exactly that – 36 runs off their stand and Staff were already nearly halfway to Students’ total. No wickets fell in the next pairing of Senapati and Shipley – adding 25 more (mostly extras). The following pairing was what ultimately won the match for the Staff and cemented O’Reilly’s place as player of the match. O’Reilly hit a thunderous 24 off the bowling of Panda and McKinnon-Gray with Tsai contributing 4 more, and extras taking the staff score to within 2 of the Students’. The bowling of Blunn to Tsai also brought up arguably the highest quality piece of bowling and fielding of the match, a beautiful outswinger from Blunn nicked offside straight to the raised hands of Panda at first slip high above his head. A proper test dismissal. JCP’s wicket-keeping debut was off to a flyer with a stumping to end the final over of Tsai’s batting.
Kandala and Inness followed up, and although wickets fell to Panda and Blunn with a great performance from stand-in keeper JCP with a catch to add to his tally, the pair contributed 24 to the Staff score taking the game all but out of reach of the Students. Finally, it was the turn of Williams to bat and perhaps the most village moment of the whole game. While running for his first single off the bowling of Blunn, his box fell out onto the middle of the pitch! We then all politely looked away while he readjusted…! Blunn and J. Charlton-Perez got a wicket each with no extras, but despite their tight bowling, Williams’s 4 runs was enough to make sure of the result.
All in all, a great departmental social event, and hopefully a few more people can count themselves as enjoyers of cricket. See you all at the next one – students, we’ve got revenge to seek!
The making of 2024’s departmental pantomime Stratatouille actually began all the way back in the summer of that year. There had been conversations on some stifling hot days (not that there were many!) around the theme for the current year’s panto. A film viewing had taken place and characters had begun to be assigned. However, come October, no one could have predicted the twist this tale would have taken leading to the majority of PhD students reluctantly(?) donning chef hats and rat ears for the best part of the coming December.
STRATATOUILLE Poster
Our story actually begins even earlier, mere days after the roaring success of sshRACC in December 2023 – as is apparently tradition – the fabled Panto Cupboard Key was foisted upon me by one of last year’s organisers. I won’t name and shame – but my fate was sealed; I was to become the organiser for the 2024 departmental pantomime. But who, pray, would heed my call for a co-organiser in this, my hour of need? It all came down to a rather conniving bit of deception, whereby I managed to trick my co-coordinator into accepting the key when they may have been expecting a tasty treat. Who says the pantomime is begrudgingly organised? But our destiny was well and truly decided and laid out in front of us. In nine to ten short months, we would be organising a corral of unruly PhD students and support staff to put on the world’s greatest annual university meteorology department pantomime.
Coming back to where we began, at the start of the academic year, we had an extremely strong candidate for what we thought was going to be the theme of the panto, but as always, the story for the panto is decided in the second(?) PhD Group Meeting of the year in a democratic process. This is where our original plan got unseated. The strongest proprietor (with many supporters) of the originally planned theme made the fatal mistake of prioritising career development over their wishes for a panto theme and could not make the deciding session. As Rabbie Burns reminds us, “The best laid plans of mice and men often go awry” – this was one such instance. With only half the organising committee present to propose the original idea, a plucky upstart with one good joke took the stage and captured the imagination of the PhD cohort, and so it was decided: Stratatouille would be the theme for this year’s panto. As fatefully predicted in last year’s panto blog post…
Plot
After innumerable lunchtime and evening writing sessions, the bulk of the panto story was baked and ready to consume. It is here where we have to give another massive thank you to Caleb Miller, who spent hours and hours essentially transposing the original story of the film Ratatouille to be based in our department following the terribly cobbled together idea of a story that we had. All we had to do now was pepper it with jokes and puns pertaining to food and/or meteorology and we had a script that even Patton Oswalt would be proud to perform.
Audience review #1
“The best panto I’ve seen for many years”
– Dr. Pete Inness
The story begins with Remi the undergrat realising he feels unable to fulfil his ambition of doing serious research while surrounded by his decidedly unserious fellow undergrats. All they care about is getting drunk off snakebites, but Remi has a dream of becoming a great scientist and doing exceptional and interesting original research. While feeling dejected that he is too inferior to publish original research, he has an apparition of King Sir Professor Brian Hoskins* who gives him a message that anyone can be a scientist if they put their heart into it.
Then we meet Linguini, a floundering PhD student who feels like he isn’t cut out for the work he is undertaking and expected to do. In a moment of serendipity, Linguini leaves his laptop open and unlocked in the BH coffee area where Remi is able to take a look at the work he is carrying out analysing some CheeseCDF files. Remi realises Linguini’s coding is terrible, fixes a few bugs and manages to greatly improve the code Linguini was working on. This leads to Linguini accepting help from Remi to write a paper as part of his PhD.
In the next scene, Linguini is showing Remi around his PhD office, when the WCD (Weekly Cuisine Discussion) bell goes, and all the PhD students diligently trudge down to GU01 to attend. Admittedly the WCD scene doesn’t further the story much apart from giving Remi an insight into the breadth of research done in this meteorology kitchen. But we got a lot of laughs, good jokes and puns, and silly costumes into this scene so it was an audience and cast favourite. It is later in this scene that we meet the terrifying supervisor, brilliantly played by our regular cartoon villain Catherine Toolan. The supervisor is very tough on Linguini with high expectations and little patience. But that is all too easy for Remi who manages to complete the task the supervisor asked for in no time at all. They (Linguini napping with his feet up) spend the next few hours “cooking up some actual research”. When the supervisor returns, she is amazed to see that ‘Linguini’ has disproved the entire concept of PV. Suspicious that he has managed to attain such a level of skill so quickly, she recommends that he first present the work at a conference before they crack on with publishing the work.
At the conference, Linguini gives a great presentation (Remi is giving him slide-by-slide instructions) but makes a fatal error by taking nearly all of the credit and failing to mention he got any help from Remi. This alienates Remi who storms out of the conference to return to the department. Jumping forward in time, when Remi returns to and hatches a cunning plan to derail the entire department – stealing the tea and coffee money box (topical departmental news has appeared in the script!). Back at the conference, Remi is making a total fool of himself by not being able to answer even the simplest of questions from the audience, embarrassing his supervisor in the process. She interrogates him about this and finds out much to her dismay that an undergrat helped with the research. So disgusted is she at this that supervisor and the other staff members strike, leaving the department destitute of senior figures.
This leads to a moment where Remi and Linguini make up thanks to an apology, and Remi recruits a team of undergrats to help finish writing the paper they started. The paper is submitted to the journal Nature: Valley Bar where it is eventually inspected by the feared Reviewer 2, who is so impressed by the work that he recommends it be published with no changes (apart from citing one of his own papers). The story ends with KSPBH* re-appearing and handing Remi the keys to the department and naming the building after him.
Songs
Please Stop Me Now – there was a running theme of ‘difficult to sing but possibly worth the effort since they are well loved tunes’ for most songs this year, and this one was no exception. A parody of Queen’s 1979 mega-hit of Don’t Stop Me Now, our extremely talented band carried our pretty rubbish singing – but that didn’t stop it being some attendees’ favourite part of the show.
Audience review #2
“How did you guys come up with all those song lyrics and make them work? So funny and so impressive!”
– MSc Student
Come on Remi – One of the more singable tunes based on Come on Eileen by Dexys Midnight Runners, all about how much work Remi was going to have to do to get Linguini through his PhD work. In practising this one, we had choir master Catherine bellowing at us to sing louder, a task we all found much easier after a few glasses of boxed wine from the Winnersh Sainsbury’s. The Middle – Jimmy Eat World was the third song which I don’t think we even came up with a spoof title for; a punk-pop particularly catchy tune about the trials and tribulations of poor Linguini the PhD first year who is letting his stress get in the way of enjoying the start of his PhD. Money, Money, Money – an ABBA classic we also didn’t need to change the title of about the rats stealing the money box. We made the bold decision this year to plant much of the songs mid-scene. A directorial choice that I think helped the coherent telling of the story. Special mention here to Nathan’s amazing piano playing skills here – the rendition of Erik Satie’s Gymnopedie during the John Meth-Coq-au-Vin monologue was only improvised in the final dress rehearsal earlier that day! 500 Lines – a version of the Proclaimers’ singalong classic 500 Miles about how many lines Remi has to write to get their paper done! H-O-S-K-I-N-S : I’m not sure how Sir Brian feels about being the subject of the panto or at least a song every year, and this one was a little on the nose; but you really couldn’t ask for a better fit for one of the songs of the summer – Chappell Roan’s Hot to Go had exactly the right mood for what we wanted to sing, and I think it made for a great outro wonderfully delivered by one of the best KSPBH performances we’ve seen in a while by our very own Douglas Mulangwa.
Casting
It can be a bit like pulling teeth trying to cast the leading roles in the panto, and as one of the few first-year PhDs who have shown the extroversion to be able to tackle this and with great stage presence, the inimitable Jake Keller somewhat reluctantly agreed to be Remi with a fateful “if I have to” when asked repeatedly. I think he came around to really enjoying it, and the audience were also quite impressed –
Audience review #3
[To Jake]“You were great!”
– Regius Professor Keith Shine
And Andrea Rivosecchi as Linguini – at first he accepted but then realised he would have to learn even more lines than the main character; so we looked around and found a great doppelganger for the second act – not sure if any of you noticed – but in the second act Linguini was played by a different Italian man in Riccardo Monfardini! Some veterans of the game came through and gave us some great performances with Catherine as Supervisor and Hette Houtman as Pete Dinners. Shout out to Hette as one of last year’s organisers for also helping us with timing and who to contact for various admin duties. The remaining roles had under 5 lines, but all were delivered hilariously and brilliantly, and you all appear to have agreed.
Audience Review #5
“Catherine was quite scary as supervisor”
– Dr Andy Apple Turnover Turner (Catherine’s PhD Supervisor)
The Night (and Day) of the Panto
So as many of you agreed, the Act 1 cameo from our antipodean friend Robbie Marks (the star of last year’s panto) was one of the best moment’s of the panto:
Audience review #4
“I can’t believe Robby came through and made that for us!”
– Gabrielle Ching-Johnson (Undergrat #2)
This is where I would like to make the point that he sent me those videos the morning of the show, and we had to hurriedly stitch together his several renditions of that speech in different locations with the cinematic walk off. Special thanks to Rosie (last year’s co-organiser) for helping with the video editing, and generally for being a great help to us organisers this year by giving advice and keeping us on time (mostly). Robby was sent a video of the mirthful reaction to his cameo with the reaction “F*** yeah, glad I could make an appearance”. The day went much more smoothly than last year, with us occupying the Madejski lecture theatre from 2pm onwards with no interruptions, we had plenty of time to set up the tech and instruments, as well as squeeze in a final full rehearsal. Set up the ticket booth, and we were ready to go! 150 people filed in for a great attendance to our show. Not to forget a great buffet beforehand to get everyone in the mood for the flagship event in the departmental calendar.
Act 1 and Act 2 managed to run for about the same amount of time, 30 mins a piece for an hour-long panto, as we had planned – brilliant! The interval acts, however put paid to that. A mammoth 45-minute session full of controversy and some of the biggest laughs of the night. We saw Stroopwaffels crowned the winner of the big biscuit bracket, however this was vetoed by the head of department who quite rightly pointed out they are not a biscuit and so the runner up chocolate hobnobs was our true champion. Professor Coq-au-Vin was not the only one to take issue with this controversial result. The 3L68 team of Dan Shipley, Jake Bland and Brian Lo made the argument that Bourbons had been wrongfully expelled and would have won this year, and so Dan delivered a hilarious diatribe explaining how they came to decide which Bourbon was best, and therefore the true winner of the biscuit bracket. I don’t remember which one it was in the end (M&S?) – check the video recordings of the night to find out for yourselves.
A pleasant break from the commotion of the biscuit brackets was brought around from some classical piano performed by Amber Te Winkel, and then some might say the only reason they attend the panto – Mr Mets. A blinder of an episode where Peter Clark was apologised to (again – and rejected on his behalf by Humphrey), and insinuated to have signed up to OnlyFans with the most innocent of intentions. The theme of the story was John Methven’s takeover as head of department, with him bumbling along and struggling to fulfil the role while eating copious amounts of ‘free’ food (it’s not free if you use department funds to pay for it, Prof. Methven!). Just to clarify that no one thinks John will struggle to fulfil the role, but as HOD I’m afraid you have to expect a fair bit of derision at these sorts of performances!
Following that, another side-splittingly funny act followed with an after party led by DJ Shonk that included a rare slow number – all in aid of blossoming romance on the dancefloor.
The amazing cast and crew who made STRATATOUILLE happen!
Reflections
As always, the Panto is months of hard work to organise, and things only ever seem to come together in the eleventh hour. But we had a great team and cast and band that really made it come together beautifully. Acting on stage, playing in a live band, organising a production, generally being a thesp isn’t the kind of thing you expect to hear from a large majority of the PhD students of the world’s leading Meteorology department. But it is these experiences, very far outside most of our comfort zones that builds strong and adaptable characters. And I think this experience has probably given us, as organisers and performers alike, more useful skills than we might have realised. This will, however, probably be these director-producers’ debut and final production.
A huge thank you to everyone who attended and contributed to the panto in any way, no matter how small. Your participation is what makes this a great bonding experience for the department, and you are all greatly appreciated!
The Social Metwork 