Journal publications

Our latest research articles

Journal papers

  • Morrison W, S Kotthaus S Grimmond 2021: Urban surface temperature observations from ground-based thermography: intra- and inter-facet variability.  Urban Climate 35, 100748, https://doi.org/10.1016/j.uclim.2020.100748
  • Hu, G., & Dance, S. L. (2021). Efficient computation of matrix-vector products with full observation weighting matrices in data assimilation. Quarterly Journal of the Royal Meteorological Society. https://doi.org/10.1002/qj.4170 (In Press)
  • Di Mauro​​​​​​​, C., Hostache, R., Matgen, P., Pelich, R., Chini, M., van Leeuwen, P. J., Nichols, N. K., and Blöschl, G.: Assimilation of probabilistic flood maps from SAR data into a coupled hydrologic–hydraulic forecasting model: a proof of concept, Hydrol. Earth Syst. Sci., 25, 4081–4097, https://doi.org/10.5194/hess-25-4081-2021, 2021.
  • Vandaele, R., Dance, S. L.   and Ojha, V.   (2021) Deep learning for automated river-level monitoring through river camera images: an approach based on water segmentation and transfer learning. Hydrology and Earth System Sciences. ISSN 1027-5606 doi: https://doi.org/10.5194/hess-2021-20 (In Press)
  • Tabeart, J., Dance, S.  , Lawless, A., Nichols, N.   and  Waller, J. (2021) New bounds on the condition number of the Hessian of the preconditioned variational data assimilation problem. Numerical Linear Algebra with Applications. ISSN 1099-1506 (In Press)
  • Mason DC, Bevington J, Dance SL, Revilla-Romero B, Smith R, Vetra-Carvalho S, Cloke HL. Improving Urban Flood Mapping by Merging Synthetic Aperture Radar-Derived Flood Footprints with Flood Hazard Maps. Water. 2021; 13(11):1577. https://doi.org/10.3390/w13111577
  • Waller, J.A., Dance, S.L. and Lean, H.W. (2021), Evaluating errors due to unresolved scales in convection permitting numerical weather prediction. Q J R Meteorol Soc. Accepted Author Manuscript. https://doi.org/10.1002/qj.4043
  • Mirza, AK, Dance, SL, Rooney, GG, Simonin, D, Stone, EK, Waller, JA. Comparing diagnosed observation uncertainties with independent estimates: A case study using aircraft‐based observations and a convection‐permitting data assimilation system. Atmos Sci Lett. 2021;el01029. https://doi.org/10.1002/asl.1029
  • David C. Mason, Sarah L. Dance, Hannah L. Cloke, “Floodwater detection in urban areas using Sentinel-1 and WorldDEM data,” J. Appl. Remote Sens. 15(3), 032003 (2021), doi: 10.1117/1.JRS.15.032003.
  • 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., 2021. The Role of Digital Technologies in Responding to the Grand Challenges of the Natural Environment: The Windermere Accord. Patterns.
  • Morrison W, T Yin, N Lauret, J Guilleux, S Kotthaus, JP Gastellu-Etchegorry, L Norford, CSB Grimmond 2020 Atmospheric and emissivity correction for ground-based thermography using 3D radiative transfer modelling, Remote Sensing of Environment, 237, 111524 https://doi.org/10.1016/j.rse.2019.111524
  • Warren E, C Charlton-Perez, S Kotthaus, F Marenco, C Ryder, B Johnson, D Green, H Lean, S Ballard, S Grimmond 2020: Observed aerosol characteristics to improve forward-modelled attenuated backscatter, Atmospheric Environment, 224, 117177,  https://doi.org/10.1016/j.atmosenv.2019.117177.
  • Bokhove O, Kelmanson MA, Kent T, Piton G, Tacnet J-M. A Cost-Effectiveness Protocol for Flood-Mitigation Plans Based on Leeds’ Boxing Day 2015 Floods. Water. 2020; 12(3):652. https://doi.org/10.3390/w12030652
  • 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.
  • 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 Soc. 2020; 146: 1372– 1390. https://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, KS, O’Boyle, K, Dance, SL, et al. Collecting and utilising crowdsourced data for numerical weather prediction: Propositions from the meeting held in Copenhagen, 4–December 5, 2018. Atmos 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

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.L., Ojha V. (2021) Automated Water Segmentation and River Level Detection on Camera Images Using Transfer Learning. In: Akata Z., Geiger A., Sattler T. (eds) Pattern Recognition. DAGM GCPR 2020. Lecture Notes in Computer Science, vol 12544. Springer, Cham. https://doi.org/10.1007/978-3-030-71278-5_17

Pre-print

  • Thomas Kent, Luca Cantarello, Gordon Inverarity, Steven Tobias, Onno Bokhove (2020) Idealized forecast-assimilation experiments for convective-scale Numerical Weather Prediction
  • Thomas Kent, Onno Bokhove (2020) Ensuring ‘well balanced’ shallow water flows via a discontinuous Galerkin finite element method: issues at lowest order