Developing geospatial foundation models for climate model evaluation and the detection of extreme climate events

The DPhil in Computational Discovery is a multidisciplinary programme spanning projects in Advanced Molecular Simulations, Machine Learning and Quantum Computing to develop new tools and methodologies for life sciences discovery.

This innovative course has been developed in close partnership between Oxford University and IBM Research. Each research project has been co-developed by Oxford academics working with IBM scientists. Students will have a named IBM supervisor/s and many opportunities for collaboration with IBM throughout the studentship.

The scientific focus of the programme is at the interface between Physical and Life Sciences. By bringing together advances in data and computing science with large complex sets of experimental data more realistic and predictive computational models can be developed. These new tools and methodologies for computational discovery can drive advances in our understanding of fundamental cellular biology and drug discovery. Projects will span the emerging fields of Advanced Molecular Simulations, Machine Learning and Quantum Computing addressing both fundamental questions in each of these fields as well as at their interfaces.

Students will benefit from the interdisciplinary nature of the course cohort as well as the close interactions with IBM Scientists.

Applicants who are offered places will be provided with a funding package that will include fees at the Home rate, a stipend at the standard Research Council rate (currently £17,668 pa) + £2,400 for four years. 

There are 16 projects available and you may identify up to three projects to be considered for in your application. The details of Project 15 are listed below.

There is no application fee to apply to this course. For information on how to apply and entry requirements, please see DPhil in Computational Discovery | University of Oxford.

Project 15

Title: Developing geospatial foundation models for climate model evaluation and the detection of extreme climate events 

PI:  Philip Stier

Summary:Foundation models are a general class of AI models trained (generally self-supervised) on a large set of multimodal data. The resulting foundation model can be fine-tuned to solve a wide array of downstream tasks. Despite the methodology is general and applicable to different domains and applications, current popular examples are mostly focused on natural language processing (e.g. GPT-3 for natural language and Dall-E for text-to-image tasks). As foundation models are complex and trained on large datasets, they tend to exhibit an emergent property where a system’s behaviour is implicitly induced rather than explicitly specified. This is

especially advantageous for many applications in climate science where the underlying physical processes are sometimes too complex for a limited amount of data to capture, or high quality data for training models able to detect climate events of interest might be scarce. Despite these advantages and the increased availability of large volume of high-resolution climate-related data, the use of foundation models in climate science it is still in its infancy. This is partially due to observed climate datasets (for example, satellite images [e.g. Sentinel and Landsat], time-series [e.g. whether station data and rain gauges], 3D signals [e.g. LiDAR point clouds]) often include spatially heterogeneous and asynchronous data, meaning that not all data modalities are available at the same location and time. Finally, although there is increased availability of data, it is not clear how much data is actually needed to train

foundation models and then obtain good results in downstream climate science-oriented tasks. The aim of this PhD project is to develop new modular deep learning architectures for foundation models that allow one to deal with the multivariate nature of climate data and its spatio-temporal intermittence. The project will explore transformer-based architectures to allow parallelization between modalities before the extracted data representation is recombined. Focusing on training efficiency and computation, the project may also investigate whether it is possible to understand the added value of bringing in an additional modality or sets of training samples.

Ultimately, the foundation models developed during the project will be tested and compared to the regular paradigm (e.g. developing a bespoke model for each application), in downstream tasks. This might include earth observation for climate hazards (e.g. flood, wildfire, landslide, drought) and climate model evaluation against observations. If successful, these foundation models will be an extremely powerful tool that will enable more efficient and accurate climate impact assessment and earth observation.

Further reading:

Bommassani et al 2021: On the opportunities and risks of foundation models

(https://arxiv.org/abs/2108.07258)

Lacoste et al 2021: Toward Foundation Models for Earth Monitoring:

Proposal for a Climate Change Benchmark (https://arxiv.org/pdf/2112.00570.pdf)