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. This video tells you more about the use of data assimilation for weather prediction.  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,  Prof 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

DARE contributes evidence to national flooding inquiry

by Tom Kent, University of Leeds Recently, the DARE team at the University of Leeds contributed written evidence to the UK Parliament’s flooding inquiry to inform policy on the Government’s approach to managing flood risk in England. The evidence, which ...
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Dr Joanne Waller wins Royal Meteorological Society LF Richardson Prize

We have been delighted to hear that the Royal Meteorological society has awarded Dr Joanne Waller the LF Richardson prize for her work on observation uncertainty in data assimilation. Jo completed her prize-winning work working as a PDRA in the ...
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The Ensemble Club

by Javier Amezcua Meteorological operational centres have a great responsibility: they produce and revisit forecasts which are periodically released to the public. These forecasts (and their accuracy) are of great importance: from people planning their daily activities to governments allocating ...
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Machine Learning for Landmark Detection in Biomedical Applications

I am Rémy Vandaele, a new post-doc who started working for the DARE project this January 2020. I got a PhD degree in Computer Sciences from the University of Liège in Belgium. I am a researcher who works in the ...
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Why? How? What now?

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

Making the best use of satellite data in river flood mapping ( pdf)
New low-cost aircraft observations for improving weather forecasts (pdf)
Data assimilation: The secret to better weather forecasts (video)
How accurate are our atmospheric observations? (pdf)
High speed mathematics: reducing the computation time for weather forecasting (pdf)
Flood inundation mapping with data assimilation (pdf)


Journal publications

Our latest research articles

Journal papers

  • Blair, G., Bassett, R., Bastin, L., Beevers, L., Borrajo Garcia, M., Brown, M., Dance, S., Dionescu, A., Edwards, L., Ferrario, M.A. and Fraser, R., 2020. The Role of Digital Technologies in Responding to the Grand Challenges of the Natural Environment: The Windermere Accord. Patterns.
  • Zackary Bell, Sarah L. Dance & Joanne A. Waller (2020) Accounting for observation uncertainty and bias due to unresolved scales with the Schmidt-Kalman filter, Tellus A: Dynamic Meteorology and Oceanography, 72:1, 1-21, DOI: 10.1080/16000870.2020.1831830
  • Sanita Vetra-Carvalho, Sarah L. Dance, David C. Mason, Joanne A. Waller, Elizabeth S. Cooper, Polly J. Smith, Jemima M. Tabeart, Collection and extraction of water level information from a digital river camera image dataset, Data in Brief, 2020, 106338, https://doi.org/10.1016/j.dib.2020.106338.
  • Di Mauro, C., Hostache, R., Matgen, P., Pelich, R., Chini, M., van Leeuwen, P. J., Nichols, N., and Blöschl, G.: Assimilation of probabilistic flood maps from SAR data into ahydrologic-hydraulic forecasting model: a proof of concept, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-403, in review, 2020.
  • Tabeart, J. M., Dance, S.L., Lawless, A.S., Migliorini, S., Nichols, N. K., Smith, F. and Waller, J. A. (2020) The impact of using reconditioned correlated observation error covariance matrices in the Met office 1D-Var system. Quarterly Journal of the Royal Meteorological Society. QJR Meteorol Soc20201461372– 1390https://doi.org/10.1002/qj.3741
  • Jemima M. Tabeart, Sarah L. Dance, Amos S. Lawless, Nancy K. Nichols & Joanne A. Waller (2020) Improving the condition number of estimated covariance matrices, Tellus A: Dynamic Meteorology and Oceanography, 72:1, 1-19, doi:10.1080/16000870.2019.1696646
  • Bokhove, O., Kelmanson, M. A., Kent, T., Piton, G., & Tacnet, J. M. (2019): Communicating (nature-based) flood-mitigation schemes using flood-excess volume. River Research and Applications. 35, 1402-1414. DOI. (Preliminary version available on arxiv: https://eartharxiv.org/87z6w/)
  • Waller, J.A., E. Bauernschubert, S.L. Dance, N.K. Nichols, R. Potthast, and D. Simonin, (2019): Observation error statistics for Doppler Radar radial wind superobservations assimilated into the DWD COSMO-KENDA system. Mon. Wea. Rev., doi:10.1175/MWR-D-19-0104.1
  • Simonin, D. , Waller, J. A., Ballard, S. P., Dance, S. L. and Nichols, N. K. (2019), A pragmatic strategy for implementing spatially correlated observation errors in an operational system: an application to Doppler radial winds. Q J R Meteorol Soc. Accepted Author Manuscript. doi:10.1002/qj.3592
  • Hintz, KSO’Boyle, KDance, SLet alCollecting and utilising crowdsourced data for numerical weather prediction: Propositions from the meeting held in Copenhagen, 4–December 5, 2018Atmos Sci Lett.2019;e921. doi:10.1002/asl.921
  • Cooper, E. S., Dance, S. L., García-Pintado, J., Nichols, N. K., and Smith, P. J. (2019) Observation operators for assimilation of satellite observations in fluvial inundation forecasting, Hydrol. Earth Syst. Sci., 23, 2541-2559, doi:10.5194/hess-23-2541-2019
  • 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

Technical reports

  • D. Mason, J. Garcia-Pintado, H. L. Cloke, S. L. Dance. J. Munoz-Sabatier Assimilating high resolution remotely sensed data into a distributed hydrological model to improve run off prediction. (2002) ECMWF Tech Memo 867, ECMWF

Peer reviewed conference proceedings

  • Punitha Jaikumar, Remy Vandaele and Varun Ojha (2021) Transfer Learning for Instance Segmentation of Waste Bottles using Mask R-CNN  To appear in Algorithm Advances in Intelligent Systems and Computing.
  • Vandaele, R., Dance, S. and Ojha, V. (2020) Automated water segmentation and river level detection on camera images using transfer learning. In: 42nd German Conference on Pattern Recognition (DAGM GCPR 2020), 28 Sep – 1 Oct 2020. (In Press)



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