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

DARE

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.

 

 

Blog

News & views  on data science and the environment

1st NCEO-GSSTI Data Assimilation and Earth Observation Training Course

by Javier Amezcua 25 November 2019 I spent the last week in Accra, the capital of Ghana. It was incredibly hot and stuffy for this time of the year (minimum 26C, maximum 31C), which is natural if we consider this ...
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UK weather radar network

Investigating the uncertainty of weather radar data

This blog describes the work of Masters student Vasiliki Kouroupaki, carried out in collaboration with the UK Met Office. In numerical weather prediction, nowcast, hindcast and forecast models can be improved through data assimilation. Data assimilation is the technique which ...
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IBM weather infographic

Operational meteorology for the public good, or for profit?

Most countries have a national weather service, funded by the government. A key role is to provide a public weather service, publishing forecasts to help make everyday business and personal decisions, as well as providing weather warnings for hazardous events ...
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My Met Office Placement

by Laura Mansfield This summer, I spent 10 weeks on a placement at the Met Office, in Exeter. This was part of the Mathematics of Planet Earth training programme and started with a week of lectures and lab sessions thanks ...
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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.

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

  • 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
  • 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

 

Contact

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