DSNE postdocs and PhD students have been busy this summer with conference papers accepted at renowned conferences and publications such as the Royal Statistical Society and iEMSs. Please see below for a summary.

Daniel Clarkson, DSNE PhD student will discussion a paper for RSS accepted for publication in RSS Series C (Applied Statistics). The discussion session is due to be held on the 14th of September (https://rss.org.uk/training-events/events/key-events/discussion-papers/) and the abstract and title are below.

The importance of context in extreme value analysis with application to extreme temperatures in the USA and Greenland

Statistical extreme value models allow estimation of the frequency, magnitude and spatio-temporal extent of extreme temperature events in the presence of climate change. Unfortunately, the assumptions of many standard methods are not valid for complex environmental data sets, with a realistic statistical model requiring appropriate incorporation of scientific context. We examine two case studies in which the application of routine extreme value methods result in inappropriate models and inaccurate predictions. In the first scenario, record-breaking temperatures experienced in the US in the summer of 2021 are found to exceed the maximum feasible temperature predicted from a standard extreme value analysis of pre-2021 data. Incorporating random effects into the standard methods accounts for additional variability in the model parameters, reflecting shifts in unobserved climatic drivers and permitting greater accuracy in return period prediction. The second scenario examines ice surface temperatures in Greenland. The temperature distribution is found to have a poorly-defined upper tail, with a spike in observations just below 0◦C and an unexpectedly large number of measurements above this value. A Gaussian mixture model fit to the full range of measurements is found to improve fit and predictive abilities in the upper tail when compared to traditional extreme value methods.

Thomas Pinder, DSNE PhD student has recently had GPJaX accepted into the Journal of Open Source Software. This was joint work by Thomas Pinder and Daniel Dodd. The full paper is available at https://joss.theoj.org/papers/10.21105/joss.04455 and the abstract can be found below:

Gaussian processes (GPs, Rasmussen & Williams, 2006) are Bayesian nonparametric models that have been successfully used in applications such as geostatistics (Matheron, 1963), Bayesian optimisation (Mockus et al., 1978), and reinforcement learning (Deisenroth & Rasmussen, 2011). GPJax is a didactic GP library targeted at researchers who wish to develop novel GP methodology. The scope of GPJax is to provide users with a set of composable objects for constructing GP models that closely resemble the underlying maths that one would write on paper. Furthermore, by the virtue of being written in JAX (Bradbury et al., 2018), GPJax natively supports CPUs, GPUs and TPUs through efficient compilation to XLA, automatic differentiation and vectorised operations. Consequently, GPJax provides a modern GP package that can effortlessly be tailored, extended and interleaved with other libraries to meet the individual needs of researchers and scientists.

DSNE post doctoral researcher, Maria Salama is presented “Design and Development of DataLabs for Environment Data Science” at the 11th International Congress on Environmental Modelling and Software (iEMSs 2022) in July 2022.

The conference was organised by the International Environmental Modelling and Software Society (iEMSs), and took place this year in Brussels, Belgium hosted by Vrije University Brussel. The paper was presented in session C.2 – Cloud-based environmental models, data provisioning services, and infrastructures as an online poster. The paper encompassed a brief on the design and technical details of DataLabs, including the hosting and architecture, as well as the tools and functionalities integrated to support environmental data science research.

How to cite: Salama, M., Blair, G.: Design and Development of DataLabs for Environment Data Science 11th International Congress on Environmental Modelling and Software, Brussels, Belgium, Ann van Griensven, Jiri Nossent, Stefan Reis (Eds.), 2022.

Maria Salama presented “Experiences of migrating Environmental Data Science research to Virtual Labs” at the Japan Geoscience Union Meeting 2022.

The abstract titled “Experiences of migrating Environmental Data Science research to Virtual Labs” by Maria Salama and Gordon Blair was accepted for oral presentation at the Japan Geoscience Union Meeting 2022 in the session M-GI30 – Open Science with FAIR Science Data Sharing and Management and e-Infrastructures.

The paper reflected on the study of experiences of DSNE researchers during the migration process of their research work to DataLabs.

How to cite: Salama, M., Blair, G.: Experiences of migrating Environmental Data Science research to Virtual Labs, Japan Geoscience Union Meeting, Chiba, Japan, 29 May – 03 June 2022.

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