Data Assimilation for the REsilient City
EPSRC Senior Fellowship in Digital Technology for Living with Environmental Change (DT/LWEC)


Data Assimilation for the REsilient City (DARE) is a research project and network funded by an EPSRC Senior Fellowship in Digital Technology for Living with Environmental Change.


Data assimilation is an emerging mathematical technique for improving predictions from large and complex forecasting models, by combining uncertain model predictions with a diverse set of observational data in a dynamic feedback loop. The project will use advanced data assimilation to combine a range of advanced sensors with state-of-the-art computational models and produce a step-change in the skill of forecasts of urban natural hazards such as floods, snow, ice and heat stress. For more information about the research programme click here.


The Fellowship is held by Prof Sarah L. Dance at the University of Reading and she is working together with a team of other researchers and stakeholders. The Fellowship will influence the future research agenda for how digital technologies can be applied in new and transformative ways to help the human and natural environment be more resilient and adaptable to climate change. In addition to an innovative research programme,  Dr Dance is acting as a Champion for this area, developing outreach activities to other researchers, policy makers and industry through workshops, networks and other mechanisms.




News & views  on data science and the environment

Highlights of the EUMETNET Crowd Sourcing Workshop 2019

EUMETNET, is a grouping of 31 European National Meteorological Services which is provides a framework for collaboration between its members in the meteorological and hydrological fields. You can find out more about EUMETNET and its missions here. ON 12-13th of ...
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ISDA2019 in Japan

by Dr Natalie Douglas, University of Surrey and Dr Alison Fowler, University of Reading and NCEO ISDA2019, the 7th International Symposium for Data Assimilation 2019, was hosted in Kobe, Japan this year from the 21st to the 24th January at ...
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Flood Inundation Mapping with Data Assimilation or Summary of Zhiqi Hu MSc thesis

Due to climate change flooding is predicted to increase in frequency and intensity across the globe and it is imperative we can produce accurate and timely flood forecasts for decision-makers before and during floods. Zhiqi Hu, an MSc in Atmospheric ...
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wCROWN: Workshop on Crowdsourced data in Numerical Weather Prediction

by Sarah Dance On 4-5 December 2018, the Danish Meteorological Institute (DMI) is hosted a workshop on crowdsourced data in numerical weather prediction (NWP), attended by Joanne Waller and Sarah Dance from the DARE project.  DMI  hosted this workshop with ...
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Why? How? What now?

Want to know more about our research? Here are some summaries of our published work written in non-technical language.

How accurate are our atmospheric observations? (pdf)
High speed mathematics: reducing the computation time for weather forecasting (pdf)


Journal publications

Our latest research articles

  • Mirza, A. K., Ballard, S. P., Dance, S. L., Rooney, G. G. and Stone, E. K. (2019), Towards operational use of aircraft‐derived observations: a case study at London Heathrow airport.. Meteorol Appl. Accepted Author Manuscript. doi:10.1002/met.1782
  • J. Holzke and J. A. Waller, ‘Improving Aircraft-Derived Temperature Observations Using Data Assimilation’, Reinvention: an International Journal of Undergraduate Research, Volume 11, Issue 2, 2018, http://centaur.reading.ac.uk/78398/.
  • Mason, D. C., Dance, S. L., Vetra-Carvalho, S. and Cloke, H. L. (2018) Robust algorithm for detecting floodwater in urban areas using Synthetic Aperture Radar images. Journal of Applied Remote Sensing, 12 (4). 045011. doi: 10.1117/1.JRS.12.045011
  • Waller, J. A., Garcia-Pintado, J., Mason, D. C., Dance, S. L. and Nichols, N. K. (2018) Technical note: assessment of observation quality for data assimilation in flood models. Hydrology and Earth System Sciences.  doi: 10.5194/hess-2018-43
  • Cooper ES, Dance SL, Garcia-Pintado J, Nichols NK, Smith PJ (2018)Observation impact, domain length and parameter estimation in data assimilation for flood forecasting. Environmental Modelling and Software. 104. pp. 199-214 doi: 10.1016/j.envsoft.2018.03.013
  • Tabeart JM, Dance SL, Haben SA, Lawless AS, Nichols NK, Waller JA (2018) The conditioning of least-squares problems in variational data assimilation. Numer. Linear Algebra Appl. 2018;e2165. Accepted. doi:10.1002/nla.2165
  • S. Vetra-Carvalho, P. J. Van Leeuwen, L. Nerger, A. Barth, M U. Altaf, P. Brasseur, P. Kirchgessner, J.-M. Beckers, Tellus A: Dynamic Meteorology and Oceanography, (2018). Vol 70:1, p. 1445364. “State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems”
  • Fowler, A. M., Dance, S. L. and Waller, J. A. (2018), On the interaction of observation and prior error correlations in data assimilation. Q.J.R. Meteorol. Soc., 144: 48-62. doi:10.1002/qj.3183
  • Janjić, T., Bormann, N., Bocquet, M., Carton, J. A., Cohn, S. E., Dance, S. L., Losa, S. N., Nichols, N. K., Potthast, R., Waller, J. A. and Weston, P. (2017), On the representation error in data assimilation. Q.J.R. Meteorol. Soc.. doi:10.1002/qj.3130
  • Waller, J. A., Dance, S. L. and Nichols, N. K. (2017), On diagnosing observation-error statistics with local ensemble data assimilation. Q.J.R. Meteorol. Soc.. doi:10.1002/qj.3117



Please contact the DARE project office by email: dare@reading.ac.uk