Project highlights

  • Determining the drivers of the upper atmosphere, and how these vary by latitudinal region. 
  • Determining the characteristic timescales over which changes in the upper atmosphere occur. 
  • Improving models used to predict the orbits of satellites. 

Overview

The thermosphere is a region of the Earth’s upper atmosphere, spanning altitudes between approximately 80 km and 500 km. It is one of the least understood regions of the terrestrial environment due to the challenges inherent in making observations at these high altitudes. Estimating the thermospheric density is the greatest challenge when predicting satellite orbits. Inaccurate predictions lead to unnecessary collision avoidance manoeuvres which uses fuel and reduces the operational lifetime of the satellite (Hapgood, 2021). 

The European Space Agency’s (ESA) Swarm mission is currently making in-situ observations the thermosphere. Swarm uses a multi-satellite approach and also observes the ionosphere, that is the part of the upper atmosphere which is comprised of plasma, at a range of scale sizes (Jin et al., 2022). This has enabled numerous studies of the multi-scale ionosphere, as reviewed by Wood et al. (2022). 

The University of Birmingham (UoB) have built models of the multi-scale ionosphere using observations from Swarm (Wood et al., 2024; ESA contract 4000130562/20/I-DT). The performance assessment of these models (Spogli et al., 2024) identified both strengths and limitations, and led to ESA awarding an additional 400,000 EUR for model development (ESA contract: 4000143413/23/I-EB). 

One of the model developments, led by the UoB, uses a new high-resolution thermospheric density data product of to drive the ionospheric model. This has led us to consider the inverse question: Could ionospheric measurements be used to infer the properties of the thermosphere? If so, what are the benefits? The purpose of this Ph.D. is to conduct a series of linked studies to determine: 

  1. The statistical relationship between the thermospheric density and the ionospheric variability on different timescales and in different latitudinal regions. This will advance our understanding of the dynamics and morphology of the thermosphere.
  2. To what extent the thermospheric density can be forecasted from ionospheric observations, and the forecast horizon. This will advance our understanding of the fundamental timescales on which the thermosphere fluctuates. 
  3. The improvement to predictions of satellite orbits using this approach. 

Artist’s impression of the Swarm satellites (credit: ESA). This image is purely decorative.

Figure 1: Artist’s impression of the Swarm satellites (credit: ESA). 

Host

University of Birmingham

Theme

  • Dynamic Earth

Supervisors

Project investigator

Co-investigators

How to apply

Methodology

The statistical modelling technique of Generalised Linear Modelling has been applied to the upper atmosphere and space weather (Dorrian et al., 2019; Wood et al., 2024) and will be used as the starting point for this analysis. The student will apply this to determine this statistical relationship between the thermospheric density and the ionospheric variability on different timescales and in different latitudinal regions, and whether statistically significant variations occur between these timescales and regions. The student will then use this technique to build a model to forecast the future behaviour of the thermosphere and assess the performance of this model. Cutting-edge machine learning techniques will also be trialled to establish whether these can improve the model performance. Finally, the thermospheric model, which the student has developed, will be used to drive the orbital propagation tool Orekit (Orekit, nd), to quantify the benefits of this approach for satellite operators. 

Training and skills

DRs will be awarded CENTA Training Credits (CTCs) for participation in CENTA-provided and ‘free choice’ external training. One CTC can be earned per 3 hours training, and DRs must accrue 100 CTCs across the three and a half years of their PhD.  

The Space Environment and Radio Engineering (SERENE) group at UoB run weekly meetings to exchange information and ideas, in which all staff and DRs participate. As part of these meetings, each DR gives four seminars during their studies. Other seminars are given by academic staff, researchers and external visitors. 

A bespoke training programme is developed for each SERENE DR, with activities chosen to meet their needs. Examples from recent years include attendance at national or international summer schools, presentation of work at national or international conferences and participation in short courses in space weather, modelling and programming. 

Further details

Further information on research work conducted across our group is available through our website at: https://serene.bham.ac.uk/ Applicants are welcome to contact the lead supervisor, Dr Alan Wood, to ask informal enquiries via email ([email protected]).

 To apply to this project:  

  • You must include a CV with the names of at least two referees (preferably three) who can comment on your academic abilities.  
  • Please submit your application and complete the host institution application process via: https://sits.bham.ac.uk/lpages/LES068.htm.   Please select the PhD Geography and Environmental Science (CENTA) 2025/26 Apply Now button. The CENTA Studentship Application Form 2025 and CV can be uploaded to the Application Information section of the online form.  Please quote CENTA 2025-B31 when completing the application form.  

 Applications must be submitted by 23:59 GMT on Wednesday 8th January 2025.   

Possible timeline

Year 1

In addition to weekly meetings between the DR and their supervisor, the SERENE research group hold formal progress reviews, which include the student, the lead supervisor, the second supervisor and an independent academic (usually the Head of Group). These take place at the 3, 9, 21 and 33 month marks and are integrated into the proposed timeline as follows: 

Months 0-3: 

  • Literature review of the thermosphere. 
  • Preliminary modelling work, to become familiar with the datasets and statistical modelling techniques. 
  • At the end of this period the DR should have a preliminary literature review and additional training needs relating to the datasets and modelling techniques will be identified. 
  • This period concludes with review meeting 1. 

Months 3-9: 

  • Expand and complete the literature review of the thermosphere, based upon the discussions held during review meeting 1. 
  • Undertake training identified during review meeting 1. 
  • Begin study 1. This should be 50 % complete at the end of this period. 
  • This period concludes with review meeting 2. 

Year 2

Month 9-21: 

  • Modify, develop and complete study 1, based upon feedback from review meeting 2. 
  • Begin study 2. This should be 75 % complete at the end of this period. 
  • This period concludes with review meeting 3. 

Year 3

Month 21-33: 

  • Modify, develop and complete study 2, based upon feedback from review meeting 3. 
  • Undertake study 3. 
  • This period concludes with review meeting 3. 

Month 33 onwards: 

  • Undertake modifications to studies 1-3 as appropriate, based on feedback from review meeting 4 and knowledge gained during the programme. 
  • Complete the writing up of work and submit Ph.D. thesis. 

Further reading

Dorrian, G., Fallows, R., Wood, A., Themens, D. R., Boyde, B., Krankowski, A., et al. (2023). ‘LOFAR observations of substructure within a traveling ionospheric disturbance at mid-latitude.’ Space Weather, 21, e2022SW003198. https://doi.org/10.1029/2022SW003198 

Hapgood, M., Angling, M. J., Attrill, G., Bisi, M., Cannon, P. S., Dyer, C., et al. (2021). ‘Development of space weather reasonable worst-case scenarios for the UK National Risk Assessment.’ Space Weather, 19, e2020SW002593. https://doi.org/10.1029/2020SW002593 

Jin, Y., Kotova, D., Xiong, C., Brask, S. M., Clausen, L. B. N., Kervalishvili, G., et al. (2022). ‘Ionospheric plasma IRregularities – IPIR – Data product based on data from the Swarm satellites’. Journal of Geophysical Research: Space Physics, 127, e2021JA030183. https://doi.org/10.1029/2021JA030183 

Spogli, L., Jin, Y., Urbář, J., Wood, A. G., Donegan-Lawley, E. E., Clausen. L. B. N., Shahtahmassebi, G., Alfonsi, L., Rawlings, J. T., Cicone, A., Kotova, D., Cesaroni, C., Høeg, P., Dorrian, G. D., Nugent, L. D., Elvidge, S., Themens, D. R., Aragón, M., Wojtkiewicz, P. and Miloch, W. J. (2024). ‘Statistical Models of the Variability of Plasma in the Topside Ionosphere: 2: Performance Assessment’, J. Space Weather Space Clim., swsc230023. https://doi.org/10.1051/swsc/2024003 

UKRI (no date) ‘Research areas covered by NERC remit’. Available at: https://www.ukri.org/councils/nerc/guidance-for-applicants/how-to-submit-your-application/research-area-selection-in-je-s/ (Accessed: 17 September 2024). 

Wood, A. G., Donegan-Lawley, E. E., Clausen, L. B. N., Spogli, L., Urbář, J., Jin, Y., Shahtahmassebi, G., Alfonsi, L., Rawlings, J. T., Cicone, A., Kotova, D., Cesaroni, C., Høeg, P., Dorrian, G. D., Nugent, L. D., Elvidge, S., Themens, D. R., Aragón, M. J. B., Wojtkiewicz, P. and Miloch, W. J., (2024). Statistical models of the variability of plasma in the topside ionosphere: 1. Development and optimisation, J. Space Weather Space Clim., 14, 7, DOI: https://doi.org/10.1051/swsc/2024002 

Wood, A. G., Alfonsi, L., Clausen, L. B. N., Jin, Y., Spogli, L., et al., ‘Variability of Ionospheric Plasma: Results from the ESA Swarm Mission’, Space Science Reviews, doi: 10.1007/s11214-022-00916-0, 2022.