I am working within the Space Weather and Atmosphere team at BAS to build a data assimilation system for the BAS Radiation Belt Model (RBM). I will be utilising data assimilation methodologies to incorporate observational data into the BAS-RBM with the aim of improving radiation belt forecasts.

I come from a mathematical background, completing a BSc in Mathematics at the University of Warwick in 2009. I moved to the University of Reading in 2010; firstly to do a Masters in Atmospheres, Ocean and Climate and then to obtain my PhD at the University of Reading on “Model Improvement using Data Assimilation”, supervised by Peter Jan van Leeuwen and Phil Browne between 2012 and 2016.

Following this, I worked at the Laboratoire des Sciences du Climat et de l’Environnement (LSCE) in Paris, working on an intercomparison project to quantify CO₂ fluxes between the land and atmosphere over Europe, along with associated uncertainties, over the previous decade (2006-2015). This work involved collaboration with a number of groups across Europe to generate atmospheric inversions that used data assimilation to incorporate atmospheric carbon measurements into atmospheric transport models, comparing the different groups results and analysing areas where models diverged/converged and over what timescales.

I returned to the University of Reading to work with Matt Owens to develop data assimilation methods for solar wind models. This continued some work that I had done during my PhD, connecting the EMPIRE data assimilation library to the operational solar wind model ENLIL, to perform the first data assimilation experiments in the solar wind field using an advanced data assimilation methodology. Whilst this work had shown that there was potential for data assimilation to improve solar wind forecasts, there were also significant hurdles to overcome. Therefore, I decided to take a step back and use a simplified solar wind model, HUX, and built a variational data assimilation scheme around this to map observational data from the STEREO and ACE spacecraft from Earth’s orbit back to the solar wind model inner boundary, enabling long-lasting forecast improvements in the solar wind estimate. This scheme was called BRaVDA (Burger Radial Variational Data Assimilation), which marked the first data assimilation experiments in the solar wind field that used real solar wind observations. However, the HUX model is not a time-dependent model, hence is unresponsive to fast changes in solar wind structure (eg. from Coronal Mass Ejections). I realised that in order to incorporate more transient features into a data assimilation scheme, a time-dependent model was required, leading to the development of the HUXt solar wind model, a simplified solar wind model that is capable of incorporating CMEs and performing ensemble experiments. HUXt is used in many studies and is currently being transitioned to operational use at the UK Met Office as part of the SWIMMR project.

Research interests

• Data assimilation
• Space weather
  • Radiation belts
  • Solar wind
• Machine Learning techniques

Collaborations

Univeristy of Reading: Matt Owens, Harriet Turner (PhD student, co-supervised by Matt Owens and myself), Chris Scott, Luke Barnard, Mike Lockwood

UK Met Office: Siegfried Gonzi, Mike Marsh, David Jackson

Predictive Science: Pete Riley

1. Radiation Belt Data Assimilation (Radbelt-DA)

   RADBELT-DA applies data assimilation techniques to improve radiation belt forecasts to protect satellites from space weather damage and service disruption.

   Read more of: Radiation Belt Data Assimilation (Radbelt-DA)

   

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