Our People
Professor Sarah Dance
NCEO Divisional Director of Data Assimilation and Professor of Data Assimilation
Data Assimilation
Research interests
I am interested in data assimilation, the science of combining mathematical models with observation data. My work covers a broad range of activity, from development of new mathematical approaches, to applications in operational forecasting systems, mostly applied to hazardous weather and flooding.
Recent publications
Assimilation of satellite flood likelihood data improves inundation mapping from a simulation library system. 2024-07-25
DOI: https://doi.org/10.5194/hess-2024-178
Sampling and misspecification errors in the estimation of observation‐error covariance matrices using observation‐minus‐background and observation‐minus‐analysis statistics. 2024-07
DOI: https://doi.org/10.1002/qj.4750
A Novel Localized Fast Multipole Method for Computations With Spatially Correlated Observation Error Statistics in Data Assimilation. 2024-06
DOI: https://doi.org/10.1029/2023MS003871
Deep learning for automated trash screen blockage detection using cameras: Actionable information for flood risk management. 2024-04-01
DOI: https://doi.org/10.2166/hydro.2024.013
Latent Neural Mapping for Hydrological Data Assimilation in Flood Prediction. 2024-03-11
DOI: https://doi.org/10.5194/egusphere-egu24-20164
Assessing the influence of observations on the analysis in ensemble-based data assimilation systems. 2024-03-08
DOI: https://doi.org/10.5194/egusphere-egu24-4082
Assessing the spatial spread–skill of ensemble flood maps with remote-sensing observations. 2023-08-10
DOI: https://doi.org/10.5194/nhess-23-2769-2023
A Novel Numerical Approximation Method for Computations with Spatially Correlated Observation Error Statistics in Data Assimilation. 2023-05-15
DOI: https://doi.org/10.5194/egusphere-egu23-14476
Comparison of deep learning approaches to monitor trash screen blockage from CCTV cameras. 2023-05-15
DOI: https://doi.org/10.5194/egusphere-egu23-3928
Developing a user-focused flood forecast product for a continental-scale system. 2023-05-15
DOI: https://doi.org/10.5194/egusphere-egu23-282
Co-Design and Co-Production of Flood Forecast Products: Summary of a Hybrid Workshop. 2023-05
DOI: http://dx.doi.org/10.1175/bams-d-23-0061.1
Toward improved urban flood detection using Sentinel-1: dependence of the ratio of post- to preflood double scattering cross sections on building orientation. 2023-02-14
DOI: http://dx.doi.org/10.1117/1.jrs.17.016507
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. 2023-01
DOI: https://doi.org/10.1002/asl.1130
Calibrated river-level estimation from river cameras using convolutional neural networks. 2023
DOI: https://doi.org/10.1017/eds.2023.6
Spatial scale evaluation of forecast flood inundation maps. 2022-09
DOI: https://doi.org/10.1016/j.jhydrol.2022.128170
A new skill score for ensemble flood maps: assessing spatial spread-skill with remote sensing observations. 2022-07-14
DOI: https://doi.org/10.5194/nhess-2022-188
Exploring the characteristics of a vehicle‐based temperature dataset for kilometre‐scale data assimilation. 2022-05
DOI: https://doi.org/10.1002/met.2058
Assimilated Watercolours: Pop up art exhibitions in Care Homes. 2022-03-28
DOI: https://doi.org/10.5194/egusphere-egu22-11694
Deep learning approaches to study floods through river cameras. 2022-03-27
DOI: https://doi.org/10.5194/egusphere-egu22-2344
Improved urban flood mapping: dependence of SAR double scattering on building orientation.. 2022-03-27
DOI: https://doi.org/10.5194/egusphere-egu22-1819
Observations and multiple scales in convection permitting data assimilation. 2022-03-27
DOI: https://doi.org/10.5194/egusphere-egu22-2898
Spatial scale evaluation of forecast flood inundation maps using Synthetic Aperture Radar (SAR) images.. 2022-03-26
DOI: https://doi.org/10.5194/egusphere-egu22-721
New bounds on the condition number of the Hessian of the preconditioned variational data assimilation problem. 2022-01
DOI: https://doi.org/10.1002/nla.2405
Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System. 2021-11-10
DOI: https://doi.org/10.5194/hess-2021-539
Efficient computation of matrix–vector products with full observation weighting matrices in data assimilation. 2021-10
DOI: https://doi.org/10.1002/qj.4170
Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning. 2021-08-16
DOI: https://doi.org/10.5194/hess-25-4435-2021
Evaluating errors due to unresolved scales in convection‐permitting numerical weather prediction. 2021-07
DOI: https://doi.org/10.1002/qj.4043
Evaluating the post-processing of the European Flood Awareness System’s continental scale streamflow forecasts. 2021-06-18
DOI: https://doi.org/10.5194/ems2021-178
Improving Urban Flood Mapping by Merging Synthetic Aperture Radar-Derived Flood Footprints with Flood Hazard Maps. 2021-06-02
DOI: https://doi.org/10.3390/w13111577
Comparing diagnosed observation uncertainties with independent estimates: A case study using aircraft‐based observations and a convection‐permitting data assimilation system. 2021-05
DOI: https://doi.org/10.1002/asl.1029
Deep learning for the estimation of water-levels using river cameras. 2021-02-12
DOI: https://doi.org/10.5194/hess-2021-20
The Role of Digital Technologies in Responding to the Grand Challenges of the Natural Environment: The Windermere Accord. 2021-01
DOI: https://doi.org/10.1016/j.patter.2020.100156
Automated Water Segmentation and River Level Detection on Camera Images Using Transfer Learning. 2021
DOI: https://doi.org/10.1007/978-3-030-71278-5_17
The impact of using reconditioned correlated observation‐error covariance matrices in the Met Office 1D‐Var system. 2020-04
DOI: https://doi.org/10.1002/qj.3741
Multi-model data assimilation techniques for flood forecasts. 2020-03-23
DOI: https://doi.org/10.5194/egusphere-egu2020-18472
Accounting for observation uncertainty and bias due to unresolved scales with the Schmidt-Kalman filter. 2020-01-01
DOI: https://doi.org/10.1080/16000870.2020.1831830
Improving the condition number of estimated covariance matrices. 2020-01-01
DOI: http://dx.doi.org/10.1080/16000870.2019.1696646
Assimilating high resolution remotely sensed soil moisture into a distributed hydrologic model to improve runoff prediction. 2020
DOI: https://www.ecmwf.int/node/19548
Collection and extraction of water level information from a digital river camera image dataset. 2020
DOI: http://dx.doi.org/10.1016/j.dib.2020.106338
Towards operational use of aircraft‐derived observations: a case study at London Heathrow airport. 2019-10
DOI: https://doi.org/10.1002/met.1782
Observation Error Statistics for Doppler Radar Radial Wind Superobservations Assimilated into the DWD COSMO-KENDA System. 2019-09-01
DOI: http://dx.doi.org/10.1175/mwr-d-19-0104.1
A pragmatic strategy for implementing spatially correlated observation errors in an operational system: an application to Doppler radial winds. 2019-06-24
DOI: https://doi.org/10.1002/qj.3592
Collecting and utilising crowdsourced data for numerical weather prediction: Propositions from the meeting held in Copenhagen, 4–December 5, 2018. 2019-06-10
DOI: http://dx.doi.org/10.1002/asl.921
Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project. 2019-03
DOI: https://www.mdpi.com/2073-4433/10/3/125
Observation operators for assimilation of satellite observations in fluvial inundation forecasting. 2018-12-20
DOI: https://doi.org/10.5194/hess-2018-589
Robust algorithm for detecting floodwater in urban areas using synthetic aperture radar images. 2018-11-05
DOI: https://doi.org/10.1117/1.JRS.12.045011
The conditioning of least‐squares problems in variational data assimilation. 2018-10
DOI: https://doi.org/10.1002/nla.2165
Observation impact, domain length and parameter estimation in data assimilation for flood forecasting. 2018-06
DOI: https://doi.org/10.1016/j.envsoft.2018.03.013
On the representation error in data assimilation. 2018-04
DOI: https://doi.org/10.1002/qj.3130
Technical note: Analysis of observation uncertainty for flood assimilation and forecasting. 2018-02-01
DOI: https://doi.org/10.5194/hess-2018-43
On the interaction of observation and prior error correlations in data assimilation. 2018-01
DOI: https://doi.org/10.1002/qj.3183
Understanding the effect of disturbance from selective felling on the carbon dynamics of a managed woodland by combining observations with model predictions. 2017-04
DOI: https://doi.org/10.1002/2017JG003760
Diagnosing atmospheric motion vector observation errors for an operational high‐resolution data assimilation system. 2017-01
DOI: https://doi.org/10.1002/qj.2925
Comparison of aircraft‐derived observations with in situ research aircraft measurements. 2016-10
DOI: https://doi.org/10.1002/qj.2864
Diagnosing Horizontal and Inter-Channel Observation Error Correlations for SEVIRI Observations Using Observation-Minus-Background and Observation-Minus-Analysis Statistics. 2016-07
DOI: http://www.mdpi.com/2072-4292/8/7/581
Diagnosing Observation Error Correlations for Doppler Radar Radial Winds in the Met Office UKV Model Using Observation-Minus-Background and Observation-Minus-Analysis Statistics. 2016
Evidence of a topographic signal in surface soil moisture derived from ENVISAT ASAR wide swath data. 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. 2016
DOI: http://www.scopus.com/inward/record.url?eid=2-s2.0-84982684386&partnerID=MN8TOARS
Theoretical insight into diagnosing observation error correlations using observation-minus-background and observation-minus-analysis statistics. 2016
Satellite-supported flood forecasting in river networks: A real case study. 2015
Estimating correlated observation error statistics using an ensemble transform Kalman filter. 2014
Estimating interchannel observation-error correlations for IASI radiance data in the Met Office system. 2014
RMetS Special Interest Group Meeting: high resolution data assimilation. 2014
Representativity error for temperature and humidity using the Met Office high-resolution model. 2014
Data assimilation for state and parameter estimation: application to morphodynamic modelling. 2013
Data assimilation with correlated observation errors: experiments with a 1-D shallow water model. 2013
Scheduling satellite-based SAR acquisition for sequential assimilation of water level observations into flood modelling. 2013
Integration of a 3D variational data assimilation scheme with a coastal area morphodynamic model of Morecambe Bay. 2012
3D-Var Assimilation of Insect-Derived Doppler Radar Radial Winds in Convective Cases Using a High-Resolution Model. 2011
A hybrid data assimilation scheme for model parameter estimation: Application to morphodynamic modelling. 2011
Four-dimensional variational data assimilation for high resolution nested models. 2011
State estimation using the particle filter with mode tracking. 2011
Ensemble-based data assimilation and the localisation problem. 2010
Remote sensing of intertidal morphological change in Morecambe Bay, UK, between 1991 and 2007. 2010
The accuracy of Doppler radar wind retrievals using insects as targets. 2010
DATA ASSIMILATION FOR MORPHODYNAMIC PREDICTION AND PREDICTABILITY. 2009
Estimating surface CO2 fluxes from space-borne CO2 dry air mole fraction observations using an ensemble Kalman Filter. 2009
Variational data assimilation for parameter estimation: application to a simple morphodynamic model. 2009
Correlated observation errors in data assimilation. 2008
Unbiased ensemble square root filters. 2007
DOI: http://dx.doi.org/10.1002/pamm.200700603
Collision barrier effects on the bulk flow in a random suspension. 2004
Issues in high resolution limited area data assimilation for quantitative precipitation forecasting. 2004
Incorporation of lubrication effects into the force-coupling method for particulate two-phase flow. 2003
Particle density stratification in transient sedimentation. 2003
