Month: March 2026

Honest Reviews of NCAS Training Courses

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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:

Module 1: https://ncasuk.github.io/ncas-isc-shell/06a-environment-variables/index.html

Module 3: https://github.com/ncasuk/ncas-isc/

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.

Course webpage with tutorials: UKCA Chemistry and Aerosol Tutorials at UMvn13.9 – UKCA

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.

Here is the link to the GitHub page: https://ncas-cms.github.io/um-training/index.html

The Secret “Glow” of Thirsty Plants: How Satellites are Learning to Spot Drought Before It Happens

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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.

Left: Plant under daylight (535 nm). Right: Plant under UV light (365 nm), showing the reddish glow. (image source: https://www.exoticaesoterica.com/magazine/plantuvfluorescence)

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.

More details from the paper: https://doi.org/10.1080/01431161.2026.2618097