Publications and research related activities

Articles in international peer reviewed journals

  1. Hu, G. , Dance, S. L. , Bannister, R., Chipilski, H. G., Guillet, O., Macpherson, B., Weissmann, M. and Yussouf, N. (2022) Progress, challenges and future steps in data assimilation for convection-permitting numerical weather prediction: report on the virtual meeting held on 10 and 12 November 2021. Atmospheric Science Letters In Press
  2. Hooker, H., Dance, S.L., Mason, D.C., Bevington, J. and Shelton, K., (2022) Spatial scale evaluation of forecast flood inundation maps. Journal of Hydrology, 128170.
  3. Matthews, G., Barnard, C., Cloke, H., Dance, S. L., Jurlina, T., Mazzetti, C., and Prudhomme, C.(2022) Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System, Hydrol. Earth Syst. Sci., (In Press)
  4. Bell, Z., Dance, S. L., & Waller, J. A. (2022). Exploring the characteristics of a vehicle-based temperature dataset for kilometre-scale data assimilation. Meteorological Applications. (In Press)
  5. 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. (In Press)
  6. 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: (In Press)
  7. Tabeart, J., Dance, S. , Lawless, A., Nichols, N. , 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)
  8. 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.doi:10.3390/w13111577
  9. 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. doi:10.1002/qj.4043
  10. Mirza, A.K., Dance, S.L., Rooney, G.G., Simonin, D., Stone, E.K., and Waller, J.A., Comparing diagnosed observation uncertainties with independent estimates: a case study using aircraft-based observations and a convection-permitting data assimilation system (2021), Atmos Sci Lett. 2021;el01029. doi:10.1002/asl.1029
  11. Mason, D. C., Dance, S. L. and Cloke, H. L. (2021) 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
  12. 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 Volume 2, Issue 1, 100156,doi:10.1016/j.patter.2020.100156.
  13. 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
  14. 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, doi:10.1016/j.dib.2020.106338.
  15. Jemima M. Tabeart Sarah L. Dance Amos S. Lawless Stefano Migliorini Nancy K. Nichols Fiona Smith Joanne A. Waller (2020) The impact of using reconditioned correlated observation-error covariance matrices in the Met Office 1D-Var system. QJR Meteorol Soc.146: 1372-1390. doi:10.1002/qj.3741
  16. 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
  17. 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
  18. 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. 2019; 145: 2772-2790. doi:10.1002/qj.3592
  19. Cooper, E. S., Dance, S. L., Garcia-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.
  20. Dance, S.L.; Ballard, S.P.; Bannister, R.N.; Clark, P.; Cloke, H.L.; Darlington, T.; Flack, D.L.A.; Gray, S.L.; Hawkness-Smith, L.; Husnoo, N.; Illingworth, A.J.; Kelly, G.A.; Lean, H.W.; Li, D.; Nichols, N.K.; Nicol, J.C.; Oxley, A.; Plant, R.S.; Roberts, N.M.; Roulstone, I.; Simonin, D.; Thompson, R.J.; Waller, J.A. Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project. Atmosphere 2019, 10, 125 doi:10.3390/atmos10030125
  21. 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; doi:10.1002/asl.921
  22. 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
  23. 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. doi: 10.1117/1.JRS.12.045011 (In Press)
  24. 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. ISSN 1027-5606 doi:10.5194/hess-2018-43 (In Press)
  25. 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. ISSN 1364-8152 doi:10.1016/j.envsoft.2018.03.013
  26. 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
  27. 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
  28. Janjic T, Bormann N, Bocquet M, Carton JA, Cohn SE, Dance SL, Losa SN, Nichols NK, Potthast R, Waller JA, Weston P. On the representation error in data assimilation, Q J R Meteorol Soc. 2018;144:12571278.
  29. 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., 143: 2677-2686. doi:10.1002/qj.3117
  30. Pinnington, E. M., Casella, E., Dance, S. L., Lawless, A. S., Morison, J. I. L., Nichols, N. K., Wilkinson, M. and Quaife, T. L. (2017) Understanding the effect of disturbance from selective felling on the carbon dynamics of a managed woodland by combining observations with model predictions. Journal of Geophysical Research: Biogeosciences doi:10.1002/2017JG003760
  31. Cordoba, M., Dance, S. L., Kelly, G. A., Nichols, N. K. and Waller, J. A. (2017), Diagnosing atmospheric motion vector observation errors for an operational high-resolution data assimilation system. Q.J.R. Meteorol. Soc., 143: 333-341. doi:10.1002/qj.2925
  32. Pinnington, E. M., Casella, E., Dance, S. L., Lawless, A. S., Morison, J. I. L., Nichols, N. K., Wilkinson, M. and Quaife, T. L. (2016) Investigating the role of prior and observation error correlations in improving a model forecast of forest carbon balance using Four Dimensional Variational data assimilation. Agricultural and Forest Meteorology, 228-229. pp. 299-314. ISSN 0168-1923 doi: 10.1016/j.agrformet.2016.07.006
  33. Waller, J. A., Ballard, S. P., Dance, S. L., Kelly, G., Nichols, N. K. and Simonin, D. (2016) Diagnosing horizontal and inter-channel observation error correlations for SEVIRI observations using observation-minus-background and observation-minus-analysis statistics. Remote Sensing, 8 (7). 581. ISSN 2072-4292 doi: 10.3390/rs8070581
  34. Mirza, A. K., Ballard, S. P., Dance, S. L., Maisey, P., Rooney, G. G. and Stone, E. K. (2016) Comparison of aircraft derived observations with in situ research aircraft measurements. Quarterly Journal of the Royal Meteorological Society. ISSN 1477-870X doi: 10.1002/qj.2864 (In Press)
  35. Waller, J. A., Simonin, D., Dance, S. L., Nichols, N. K. and Ballard, S. P. (2016) Diagnosing observation error correlations for Doppler radar radial winds in the Met Office UKV model using observation-minus-background and observation-minus-analysis statistics. Monthly Weather Review. ISSN 0027-0644 doi: 10.1175/MWR-D-15-0340.1
  36. Mason, D. C., Garcia-Pintado, J., Cloke, H. L. and Dance, S. L. (2016) Evidence of a topographic signal in surface soil moisture derived from ENVISAT ASAR wide swath data. International Journal of Applied Earth Observation and GeoInformation, 45. pp. 178-186. ISSN 0303-2434 doi: 10.1016/j.jag.2015.02.004
  37. Waller, J. A., Dance, S. L. and Nichols, N. K. (2016) Theoretical insight into diagnosing observation error correlations using observation-minus-background and observation-minus-analysis statistics. Quarterly Journal of the Royal Meteorological Society, 142 (694). pp. 418-431. ISSN 1477-870X doi: 10.1002/qj.2661
  38. David C. Mason, Javier Garcia-Pintado, Hannah L. Cloke, Sarah L. Dance,(2015) The potential of flood forecasting using a variable-resolution global Digital Terrain Model and flood extents from Synthetic Aperture Radar images. Front. Earth Sci. 3 (43) doi:10.3389/feart.2015.00043
  39. Javier Garcia-Pintado, David C. Mason, Sarah L. Dance, Hannah L. Cloke, Jeff C. Neal, Jim Freer, Paul D. Bates (2015), Satellite-supported flood forecasting in river networks: A real case study, J. Hydrol, 523: 706-724, doi: 10.1016/j.jhydrol.2015.01.084
  40. Browne, P. A., Charlton-Perez, C. and Dance, S. L. (2014), RMetS Special Interest Group Meeting: high resolution data assimilation. Atmosph. Sci. Lett., 15: 354-357. doi: 10.1002/asl2.512
  41. Waller, J. A., Dance, S. L., Lawless, A. S. and Nichols, N. K. (2014) Estimating correlated observation error statistics using an ensemble transform Kalman filter. Tellus A, 66, 23294, doi: 10.3402/tellusa.v66.23294
  42. Stewart, L. M., Dance, S. L., Nichols, N. K., Eyre, J. R. and Cameron, J. (2014), Estimating interchannel observation-error correlations for IASI radiance data in the Met Office system. Q.J.R. Meteorol. Soc., 140: 1236-1244, doi: 10.1002/qj.2211
  43. Waller, J. A., Dance, S. L., Lawless, A. S., Nichols, N. K. and Eyre, J. R. (2014), Representativity error for temperature and humidity using the Met Office high-resolution model. Q.J.R. Meteorol. Soc., 140: 1189-1197, doi: 10.1002/qj.2207
  44. Garcia-Pintado, J., Neal, J.C., Mason, D.C., Dance, S.L., Bates, P.D., (2013) Scheduling satellite-based SAR acquisition for sequential assimilation of water level observations into flood modelling, J. Hydrology, 495, pp 252-266, doi: 10.1016/j.jhydrol.2013.03.050
  45. Stewart, L.M., Dance, S.L., Nichols, N.K. (2013) Data assimilation with correlated observation errors: experiments with a 1-D shallow water model, Tellus A 2013, 65, 19546, doi: 10.3402/tellusa.v65i0.19546
  46. Smith, P. J., Thornhill, G. D., Dance, S. L., Lawless, A. S., Mason, D. C. and Nichols, N. K. (2013), Data assimilation for state and parameter estimation: application to morphodynamic modelling. Q.J.R. Meteorol. Soc.. 139:, pp 314-327. doi: 10.1002/qj.1944
  47. Thornhill, G.D., Mason, D.C., Dance, S.L., Lawless, A.S., Nichols, N.K., Forbes, H.R., (2012) Integration of a 3D Variational data assimilation scheme with a coastal area morphodynamic model of Morecambe Bay. Coastal Eng., 69. pp 82-96. doi: 10.1016/j.coastaleng.2012.05.010
  48. Rennie, S.J., Dance, S.L., Illingworth, A.J., Ballard, S.P., and Simonin, D. (2011) 3D-Var assimilation of insect-derived Doppler radar radial winds in convective cases using a high resolution model. Mon. Wea. Rev., 139 (4). pp. 1148-1163. doi: 10.1175/2010MWR3482.1
  49. Baxter, G.M., Dance, S.L., Lawless, A.S., and Nichols, N.K. (2011) Four-dimensional variational data assimilation for high resolution nested models. Computers and Fluids, 46, pp 137-141 doi: 10.1016/j.compfluid.2011.01.023
  50. Smith, P.J., Dance, S.L., Nichols, N.K. (2011) A hybrid data assimilation scheme for model parameter estimation: application to morphodynamic modelling Computers and Fluids, 46, pp 436-441, doi:10.1016/j.compfluid.2011.01.010
  51. Pocock, J.A., Dance, S.L., Lawless, A.S. (2011) State Estimation using the Particle Filter with Mode Tracking Computers and Fluids, 46, pp 392-397 doi:10.1016/j.compfluid.2011.02.009
  52. Petrie, R.E., and Dance, S.L. (2010) Ensemble-based data assimilation and the localisation problem. Weather, 65, 3, pp65-69, doi:10.1002/wea.505
  53. Rennie, S.J., Illingworth, A.J., Dance, S.L., Ballard, S. (2010) The accuracy of Doppler radar wind retrievals using insects as targets. Meteorological Applications, 17(4), pp 419-432, doi: 10.1002/met.174
  54. Mason, D.C., Scott, T.R, Dance, S.L., (2010) Remote sensing of intertidal morphological change in Morecambe Bay, U.K., between 1991 and 2007, Estuarine, Coastal and Shelf Science, 87(3) pp487-496, doi: 10.1016/j.ecss.2010.01.015.
  55. Feng, L., Palmer, P.I., Boesch, H and Dance, S. (2009) Estimating surface CO2 fluxes from space-borne dry air mole fraction observations using an Ensemble Kalman filter. Atmos. Chem. Phys. 9, pp 2619-2633
  56. Smith, P.J., Dance, S.L., Baines, M.J., Nichols N.K, and Scott, T.R. (2009), Variational data assimilation for parameter estimation: application to a simple morphodynamic model. Ocean Dynamics 59 (5), pp697-708, doi:10.1007/s10236-009-0205-6
  57. Stewart, L. M., Dance, S.L. and Nichols, N.K. (2008). Information content and correlated observation errors. Int. J. Num. Meth. Fluid, 56(8), pp 1521-1527 doi: 10.1002/fld.1636
  58. Livings, D.M., Dance, S.L. and Nichols N.K. (2008) Unbiased ensemble square root filters. Physica D 237(8) pp 1021-1028
  59. S.L. Dance (2004) Issues in high resolution limited area data assimilation for quantitative precipitation forecasting. Physica D, 196(1-2) pp 1-27
  60. S.L.Dance, E.Climent and M.R. Maxey (2004) Collision barrier effects on the bulk flow in a homogeneous random suspension. Phys. Fluids,16(3) pp 829-831
  61. S.L.Dance and M.R. Maxey (2003) Particle density stratification in transient sedimentation. Phys. Rev. E 68, 031403
  62. S.L. Dance and M.R. Maxey (2003) Incorporation of lubrication effects into the Force-Coupling Method for particulate two-phase flow, J. Comp. Phys, 189(1) pp212-238.

    Discussion papers

  63. Dance, S.L. and Q. P. Zou, (2010) HESS Opinions "Ensembles, uncertainty and flood prediction" Hydrol. Earth. Syst. Sci. Discuss., 7,pp 3591-3611, doi: 10.5194/hessd-7-3591-2010
  64. Rennie, S. J., A. J. Illingworth, and S. L. Dance, (2010) On the differentiation of ground clutter and insects using single-polarisation C-band radars. Atm. Meas. Tech. Discuss., 3, pp 1843-1860 doi:10.5194/amtd-3-1843-2010

    Peer reviewed conference proceedings

  65. 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.
  66. Scott, T. R., Smith, P.J., Dance, S. L., Mason, D.C., Baines, M.J., Nichols, N.K., Horsburgh, K.J., Sweby, P.K. and Lawless, A.S. (2008) Data assimilation for morphodynamic prediction and predictability. In: Coastal Engineering 2008 Proceedings of the 31st International Conference. World Scientific, pp. 2386-2398. doi: 10.1142/9789814277426_0197
  67. Dance, S.L., Livings, D.M. and Nichols, N. K. (2007) Unbiased ensemble square root filters. PAMM - Proceedings in Applied Mathematics and Mechanics, 7 (1). pp. 1026505-1026506. doi: DOI: 10.1002/pamm.200700603
  68. Magazine articles

  69. D.C. Mason, J. Garcia-Pintado, S.L. Dance, (2014) Improving flood inundation monitoring and modelling using remotely sensed data Civ. Eng. Surv., February 2014, 34-36.

    Technical reports

    (Note that University of Reading Mathematics Reports and Numerical Analysis Reports are available online)
  70. Bell, Z.; Dance, S. L.; Waller, J. A.; O'Boyle, K. (2021) Quality-control of vehicle-based temperature observations and future recommendations, Forecasting Research Technical Report No. 644, Met Office, Exeter.
  71. 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. (2020) ECMWF Tech Memo 867, ECMWF
  72. E.S. Cooper, S.L. Dance, N.K. Nichols, P.J. Smith and J. Garcia-Pintado. Improving Inundation Forecasting using Data Assimilation. Mathematics Report 1/2016, University of Reading
  73. Clifford, D., Dance S.L. and Hogan, R.J., 2D advection in an idealised convection model, Mathematics Report 2/2011, University of Reading
  74. Raoult, N. and Dance, S.L., (2011) Flood modelling and data assimilation, Mathematics Report 1/2011, University of Reading
  75. Smith, P.J., Dance, S.L.,and Nichols, N.K. (2010) A hybrid sequential data assimilation scheme for model state and parameter estimation Mathematics Report 2/2010, University of Reading
  76. Smith,P.J., Dance, S.L., and Nichols, N.K. (2009) Data assimilation for morphodynamic model parameter estimation: a hybrid approach Mathematics Report 2/2009, University of Reading
  77. Stewart,L.M., Cameron,J., Dance, S.L., English, S., Eyre, J, and Nichols, N.K. (2009), Mathematics Report 1/2009, University of Reading
  78. Smith, P.J., Baines, M.J., Dance, S.L., Nichols, N.K., and Scott, T.R. (2008) Data Assimilation for Parameter Estimation with Application to a Simple Morphodynamic Model, Mathematics Report 2/2008, University of Reading
  79. Rennie, S.J. and Dance, S.L., (2008) Radial Velocity Assimilation and Experiments with a Simple Shallow Water Model, Mathematics Report 1/2008, University of Reading
  80. Smith, P.J., Baines,M.J., Dance, S.L., Nichols, N.K. and Scott, T.R. (2007) Simple Models of Changing Bathymetry with Data Assimilation, Numerical Analysis Report, 10/2007 University of Reading
  81. Stewart, L.M., Dance, S.L., and Nichols, N.K. (2007) Correlated observation errors in data assimilation Numerical Analysis Report 3/2007 , University of Reading
  82. Baxter, G.M., Dance, S.L, Lawless, A.S., Nichols, N.K., and Ballard, S.P., (2007) Towards Data Assimilation for High Resolution Nested Models Numerical Analysis Report 2/2007, University of Reading
  83. Baxter, G.M., Dance, S.L, Lawless, A.S., and Nichols, N.K., (2006) Scale Analysis of Observational Information in Data Assimilation, Numerical Analysis Report 11/2006 University of Reading
  84. Livings, D.M., Dance, S.L. and Nichols N.K. (2006) Unbiased ensemble square root filters. Numerical Analysis Report 06/06, University of Reading
  85. Stewart, L.M., Dance, S.L., and Nichols, N.K. (2006) Information Content of Spatially Correlated Observation Errors. Numerical Analysis Report 04/06, University of Reading
  86. Dance, S.L. (2003) Issues in high resolution limited area data assimilation for quantitative precipitation forecasts. Forecasting Research Technical Report No. 420. (Also known as Joint Centre for Mesoscale Meteorology Report No. 139.) Met Office, UK
  87. Dance, S.L. (1999) Forecasting Improvement via Optimal Choice of Sites for Observations: A Toy Model . In Astrophysical and Geophysical Flows as Dynamical Systems; 1998 Summer Study Program in Geophysical Fluid Dynamics,Neil J. Balmforth, Director, Woods Hole Oceanog. Inst. Tech Rept., WHOI 99-01


  88. Dance, S.L. (2002) Particle Sedimentation in Viscous Fluids. PhD thesis, Brown University, Providence RI USA

    Published datasets and software

  89. Hu, Guannan and Dance, Sarah (2021): MATLAB code for the localized Singular Value Decomposition approach of the Fast Multipole Method (the local SVD-FMM). University of Reading. Software.
  90. Vandaele, Remy, Dance, Sarah and Ojha, Varun (2021): Deep learning for the estimation of water-levels using river cameras: networks and datasets. University of Reading. Dataset.
  91. Vetra-Carvalho, Sanita; Dance, Sarah L.; Mason, David ; Waller, Joanne; Smith, Polly; Tabeart, Jemima; Cooper, Elizabeth (2020), River water level height measurements obtained from river cameras near Tewkesbury, Mendeley Data, V1, doi: 10.17632/769cyvdznp.1
  92. Waller, J.A.; Simonin, D.; Dance, S.L.; Nichols, N.K.; Ballard, S.P. (2020): FRANC: Doppler radar radial wind observations and associated observation-minus-model residuals from the Met Office UKV 3D Var assimilation scheme. Centre for Environmental Data Analysis, 05 October 2020. doi:10.5285/5d8221e070e64823891bed7a87840447.
  93. Dance, S.L.; Flack, D.L.A.; Waller, J.A.; Simonin, D.; Nichols, N.K.; Ballard, S.P. (2020): Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection (FRANC): rain radar helical scan data, assimilation versus model residuals and ensemble member model output.. Centre for Environmental Data Analysis, date of citation.
  94. Waller, J.A., Dance, S.L., Lawless, A.S., Nichols, N.K., (2019) An Ensemble transform Kalman filter with observation error covariance estimation: matlab software Github repository. doi: 10.5281/zenodo.3265078

Selected Invited Conference Presentations

S.L. Dance, I. Roulstone and A. C. Lorenc (2003) A review of the theoretical potential and limitations of the Ensemble Kalman Filter and 4D-VAR. EMS workshop on mathematical techniques for improving the forecasting of hazardous weather, Reading, UK. Invited presentation

S. L. Dance, M. Cullen and S. Ballard (2004) Incorporation of surface friction into the control variable transform. SRNWP Workshop, Met Office, Exeter Invited presentation

S.L. Dance (2005) Towards nonlinear data assimilation for hazardous weather prediction. Applied Inverse Problems Conference, Cirencester Invited presentation

S.L.Dance, D. M. Livings and N. K. Nichols (2005) Formulations of the Ensemble Kalman Filter and Bias CTCD Data Assimilation Workshop, Edinburgh Invited presentation

S.L.Dance (2006) Data assimilation theory and practice: lessons learned from the atmosphere. Carbon Fusion Workshop, Edinburgh Invited presentation

S.L.Dance, D.M. Livings, N.K. Nichols (2006) Unbiased ensemble Kalman filters. Mathematics of Data Assimilation Workshop, University of Warwick Invited presentation

S. L. Dance (2008). Data assimilation based on ensembles. ICFD workshop on Mathematics and Applications of Data Assimilation. Reading Invited presentation

S.L. Dance (2008) The Ensemble Kalman Filter: a State Estimation Method for Hazardous Weather Prediction, SAMSI Opening workshop for Program on Sequential Monte Carlo Methods, Research Triangle Park, North Carolina USA. Invited presentation

Dance, S.L. (2008) Advances in DA for convection-resolving forecasts, NCAS/NCEO meeting defining THORPEX-UK research, Royal Cambridge HotelInvited presentation

S. L. Dance, T. R. Scott, M. J. Baines, A. S. Lawless, D. C. Mason, N. K. Nichols, R. Proctor, P. J. Smith, P. K. Sweby, (2008) Changing coastlines: data assimilation for morphodynamic prediction and predictability, HYDRALAB Composite Modelling Workshop, The Deep, Hull Invited presentation

[Some missing data]

S.L. Dance (2016) Invited mini-workshop on Data Assimilation, Maths Foresees General Assembly, University of Edinburgh, September 2016.

S.L. Dance (2017) Invited mini-tutorial on Data Assimilation, SIAM Conference on Dynamical Systems, Snowbird, Utah, USA, May 2017.

S.L. Dance, H.L. Cloke, S. P. Ballard (2017) Flooding from Intense Rainfall,10th HyMeX Workshop, 4-7 July 2017, Barcelona, Spain Invited presentation

S.L. Dance (2018) Characterising and modelling observation errors. ECMWF Annual Seminar, 10-13 September 2018, Reading, UK Invited keynote presentationa

(2019) Invited presentation, 12th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2019), 14-16 December 2019, UCL, London

S.L. Dance (2020) Exploiting new observations and data assimilation techniques for improved forecasting of convective precipitation. RealPEP Online Conference: Precipitation and Flash Flood Prediction from Minutes to Days Invited keynote presentation

S.L. Dance (2020) Estimating observation errors: diagnostics, possibilities, and pitfalls. Virtual Event: ECMWF/EUMETSAT NWP SAF Workshop on the treatment of random and systematic errors in satellite data assimilation for NWP Invited presentation

S.L. Dance (2021) Observation Uncertainty due to Unresolved Scales in Data Assimilation. SIAM Conference on Applications of Dynamical Systems (DS21) (online) May 23-27, 2021 Invited presentation

S L. Dance (2021)Observation uncertainty in data assimilation. The International EnKF Workshop 2021 June 07-11, 2021 (online) Invited keynote presentation

External/Invited Seminars

(1998) Forecasting Improvement via Optimal Choice of Sites for Observations: A Toy Model. GFD Summer Study Program, Woods Hole Oceanographic Institution, MA, USA

(2002) Particle sedimentation in viscous fluids. DEAS, Harvard University,Cambridge, MA, USA

(2003) Go with the flow! Characterising flow dependent background errors in data assimilation.
School of Meteorology, University of Oklahoma, Norman, OK, USA
Mesoscale and Microscale Meteorology, NCAR, Boulder, CO USA
EMC, NCEP, Camp Springs, MD, USA

(2004) Nonlinearity and Data Assimliation.
Neural Computing Research Group, Aston University, Birmingham

(2005) Towards nonlinear data assimilation: estimating initial conditions for hazardous weather prediction. Dept of Mathematics and Statistics, University of Surrey.

(2006) The ensemble Kalman filter: a state estimation method for hazardous weather prediction
Mathematical Institute, University of Oxford.
Dept of Mathematical Sciences, University of Liverpool

(2007) Changing Coastlines: data assimilation for morphodynamic prediction and predictability (with T. R. Scott)
HR Wallingford

(2010) Maximizing the value of observations for data assimilation across a range of scales, Inaugural seminar, University of Reading
Advances in data assimilation for convection-resolving forecasts, NCAS Thorpex Meeting, Cambridge

(2012) Data assimilation, parameter estimation and coastal sediment modelling
70th Birthday Celebration for Prof Nancy Nichols, University of Reading

(2013) Where Mathematics and Meteorology meet: Maximizing the value of observations for data assimilation across a range of scales
University of Surrey
LMS Women in Mathematics Day, Isaac Newton Institute, Cambridge

(2014) FRANC: Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection, NCAS Flash Flooding Symposium, University of Oxford
Correlated Observation Errors in Data Assimilation, ESA Workshop on Correlated Observation Errors in Data Assimilation, University of Reading

(2015) Correlated observation errors in data assimilation, Math and Climate Research Network Joint Data Assimilation Seminar (webinar to international network including University of North Carolina, Chapel Hill and Tata Institute of Fundamental Research)

(2016) Challenges of urban data assimilation, (with Sue Grimmond), Mathematics of Planet Earth Seminars, University of Reading/Imperial College

(2017) End to end flood forecasting: a mathematical tour, University of Leeds, Department of Applied Mathematics

(2021) Using urban observations for numerical weather prediction: crowdsourcing, observation uncertainty and data assimilation. Imperial Collegs, Data Science Institute seminar series

Related professional activities

Workshop organization

  • Joint organiser with Andrew Stuart, Amos Lawless and Nancy Nichols for Mathematics of Data Assimilation workshop, University of Warwick, May 2006
  • Organiser of workshop on "Data assimilation for hydrometeorological hazard prediction" at Royal Meteorological Society 2009 conference
  • Organiser with Qingping Zou (University of Plymouth) of NERC FREE Ensemble workshop, 15,612 funding from NERC
  • Joint organizer for RMetS Data Assimilation Special Interest Group Meeting ,April 2013
  • Joint organizer for 2014 workshop on correlated observation errors with funding from ESA
  • Co-Convenor for European Meteorological Society Conference session on Data Assimilation (2013,2014,2015,2016, 2017, 2018, 2019, 2020)
  • Joint organizer of two workshops at Royal Meteorological Society 2016 Conference, Manchester.
  • Local organising committee member for the International Symposium on Data Assimilation, held in Reading, July 2016
  • Lead organiser for International workshop on data science for high impact weather and flood prediction, Henley, November 2017, funded by EPSRC DARE project - over 30 participants from 5 countries.
  • Scientific Steering Committee for the International Workshop on Sensitivity Analysis and Data Assimilation in Meteorology and Oceanography to be held in Portugal, July 2018
  • Scientific Organizing Committee for by ECMWF/EUMETSAT NWP-SAF Workshop on the treatment of random and systematic errors in satellite data assimilation, Reading, November 2020
  • Scientific Organizing Committee WMO Joint Symposium on Data Assimilation and Reanalysis

Print media

Featured in NERC Planet Earth Magazine Autumn 2006 Supporting Research Careers

Featured in University of Reading Research Review, Issue 10, Summer 2010

Reviewing activities

Editorial board for Atmosphere (since 2019) Guest editor for special issues of 'Advances in Science and Research' (Open access peer reviewed proceedings of the European Meteorological Society) Refereeing journal articles/conference submissions:
  • Quarterly Journal of the Royal Meteorological Society
  • Physica D
  • Ocean Modelling
  • Hydrology and Earth System Sciences (HESS)
  • SIAM Journal on Scientific Computing (SISC)
  • Journal of Geophysical Research- Atmospheres (JGR)
  • Meteorological Zeitschrift
  • Ocean Dynamics
  • Tellus-A
  • Biogeosciences
  • International Conference on Flood Resilience Experiences in Asia and Europe
  • Inverse Problems
  • Journal of the Royal Statistical Society
  • Monthly Weather Review
  • Atmosphere
  • Meteorology and Atmospheric Physics

Member of NERC peer review college since 2014

Member of EPSRC grant review panels

Member of UKRI GCRF grant review panel, Morocco

Refereeing grant applications for EPSRC, NERC, British Council, NSF(USA), Irish Research Council, Kuwait Science Foundation

NERC SCENARIO DTP Selection Board member since 2015.

UN World Meteorological Organization (WMO)

  • Member of Data Assimilation and Observational Systems (DAOS) Working Group - since 2019
  • Member of Study Group on Data Issues and Policies (SG-DIP) - since 2020
  • Judge for WMO 2nd International Verification Challenge - 2021

    PhD examination

    PhD examiner for 7 internal candidates and 7 external candidates (University of Oxford, University of Warwick, University of Leeds(x2), University of Bergen, Norway, University of Paris-Saclay, France, NTNU Norwegian University of Science and Technology, Trondheim, ), independent chair for 1 internal candidate.

    Prizes and awards

    Royal Meteorological Society Adrian Gill Prize 2020

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