Data Assimilation for the REsilient City (DARE)
Lead Research Organisation:
University of Reading
Department Name: Meteorology
Abstract
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.
The research will use synthetic aperture radar (SAR) data to develop a tool for real-time detection of flooded urban areas. SAR sensors take images from space over a wide area and can see through clouds. The sensors have resolutions as high as 1m, and are able to image flooded streets. However, substantial areas of urban ground surface may not be visible to the SAR due to shadows caused by buildings. Furthermore, shadowed areas may be misclassified as water even if dry. Our new approach is to use a SAR simulator in conjunction with lidar data. The SAR simulator estimates regions in the image in which water will not be visible due to shadow, and masks these out from the ground surface considered, resulting in a more accurate flood extent. This type of information could be used by first responders to monitor vital infrastructure and understand the extent and depth of the evolving flood.
SAR images can also be used to extract water level observations, which may be assimilated into a flood inundation model, to calibrate the system and keep predictions on track. Our recent ground-breaking work demonstrates the possibility of earth-observation-based flood inundation data assimilation and forecasting over a rural area. In this new project we aim to carry out scientific and mathematical studies to increase the flexibility of our flood data assimilation system, so that it can be straightforwardly applied at any location in the UK (including urban areas). For example, the behaviour of the system is expected to change for larger floods, steeper rivers, faster flow etc. In addition, we will develop techniques to derive new types of water level observations from smartphone photographs, traffic and river CCTV cameras, that can also be assimilated to improve predictive skill.
A number of environmental hazards are caused by the weather (e.g., heat stress, high winds, fog). The skill of numerical weather prediction is strongly constrained by the accuracy of the initial data, as estimated by assimilating expensive observations. There are burgeoning sources of inexpensive datasets of opportunity (citizen science, sensor networks etc.) that could be used, however lack of knowledge about natural variability in urban areas hinders uptake of these data. This proposal addresses uncertainty due to urban natural variability in observation-model comparisons, by considering numerical weather prediction models on a range of scales, and observational data with different "footprints". We will apply these results to citizen science automatic weather station data, car temperature sensors and commercial aircraft reports made to air traffic control (used to derive observations of winds and temperature).
The impact of this research will be guaranteed by working with operational providers of flood warnings and weather forecasts (the Environment Agency and Met Office). Commercialization of aspects of the research will be pursued in conjunction with the Institute for Environmental Analytics.
A network of researchers and industry working with digital technology at the "Living with Environmental Change" interface will be formed. This will have a programme of workshops, webinars, training and industry study groups to cross barriers between academic disciplines, creating bridges between academia and industry and providing space for junior and senior researchers to explore ideas. Funded pilot projects will kick-start activities and help define the future research agenda.
The research will use synthetic aperture radar (SAR) data to develop a tool for real-time detection of flooded urban areas. SAR sensors take images from space over a wide area and can see through clouds. The sensors have resolutions as high as 1m, and are able to image flooded streets. However, substantial areas of urban ground surface may not be visible to the SAR due to shadows caused by buildings. Furthermore, shadowed areas may be misclassified as water even if dry. Our new approach is to use a SAR simulator in conjunction with lidar data. The SAR simulator estimates regions in the image in which water will not be visible due to shadow, and masks these out from the ground surface considered, resulting in a more accurate flood extent. This type of information could be used by first responders to monitor vital infrastructure and understand the extent and depth of the evolving flood.
SAR images can also be used to extract water level observations, which may be assimilated into a flood inundation model, to calibrate the system and keep predictions on track. Our recent ground-breaking work demonstrates the possibility of earth-observation-based flood inundation data assimilation and forecasting over a rural area. In this new project we aim to carry out scientific and mathematical studies to increase the flexibility of our flood data assimilation system, so that it can be straightforwardly applied at any location in the UK (including urban areas). For example, the behaviour of the system is expected to change for larger floods, steeper rivers, faster flow etc. In addition, we will develop techniques to derive new types of water level observations from smartphone photographs, traffic and river CCTV cameras, that can also be assimilated to improve predictive skill.
A number of environmental hazards are caused by the weather (e.g., heat stress, high winds, fog). The skill of numerical weather prediction is strongly constrained by the accuracy of the initial data, as estimated by assimilating expensive observations. There are burgeoning sources of inexpensive datasets of opportunity (citizen science, sensor networks etc.) that could be used, however lack of knowledge about natural variability in urban areas hinders uptake of these data. This proposal addresses uncertainty due to urban natural variability in observation-model comparisons, by considering numerical weather prediction models on a range of scales, and observational data with different "footprints". We will apply these results to citizen science automatic weather station data, car temperature sensors and commercial aircraft reports made to air traffic control (used to derive observations of winds and temperature).
The impact of this research will be guaranteed by working with operational providers of flood warnings and weather forecasts (the Environment Agency and Met Office). Commercialization of aspects of the research will be pursued in conjunction with the Institute for Environmental Analytics.
A network of researchers and industry working with digital technology at the "Living with Environmental Change" interface will be formed. This will have a programme of workshops, webinars, training and industry study groups to cross barriers between academic disciplines, creating bridges between academia and industry and providing space for junior and senior researchers to explore ideas. Funded pilot projects will kick-start activities and help define the future research agenda.
Planned Impact
The project will provide new observations and research software tools for short term prediction and real-time detection of flooded urban areas that are expected to benefit the Environment Agency (EA) and EA/Met Office Flood Forecasting Centre (and their Scottish and Welsh equivalents): the UK public agencies responsible for flood risk guidance and management. The research could be developed into decision tools combining forecasts with geographical information to aid preparedness (e.g., protecting an electricity substation) and guide mitigating actions (e.g., deployment of flood alleviation schemes). Real-time information could be used by first responders to understand the extent of the evolving flood. Data assimilation reanalyses should improve estimates of past floods, allowing better design of new flood defences. Satellite information services such as Copernicus Emergency Management Services, and engineering consultants such as CH2M, JBA may also be interested in commercializing aspects of this research.
Our work on characterizing the uncertainty of urban observations will feed into Met Office operations. Current observing networks employed by the Met Office are very expensive (e.g. the satellite data used by the Met Office costs around £2 billion per year, although these costs are shared internationally). The research will provide the underpinning science that is needed for the use of new inexpensive urban observations and thus has potential for reduction in cost of the Met Office observing network in the future. Outcomes from the research will feed into improved public weather service (PWS) forecasts and are expected to promote better decisions along the existing and wide ranging forecast value chain. The 'Public Weather Service Value for Money Review 2015' indicates that the benefits of the PWS to the UK economy are around £1.5 billion per annum and it is impossible to describe all of the sectors affected in this short summary. However, market reports will be produced during the project to explore and define research questions for new applications of urban meteorology. Some examples relevant to the resilience of urban infrastructure are: better forecasts of fog and high winds would allow better air traffic management; better forecasts of snow and ice would allow transportation managers to target de-icing activities more effectively, and ensure that they are well supplied with materials, equipment and staff at critical times; better forecasts of storms and floods would enable a reduction in damage, reducing costs for the insurance industry; better forecasts of heat stress enable better preparedness for hospitals and public health agencies.
Outcomes from the research will also provide information for policy-makers and their networks such as the London Climate Change Partnership and the UK Natural Hazards Partnership programme. The research will provide evidence for future observing network design and natural hazard modelling strategies, including how to get the best value for money from observations. The project will also produce market reports detailing the needs and opportunities at the digital technology/environment sector interface and road-maps for future research strategy of use to EPSRC and other funders.
The proposed network will feature a range of commercial and public sector partners, and is expected to spin up new partnerships between industry and academia, increasing intellectual capital for UK business and competitiveness in the global economy. Early career scientists will receive training in computer modelling, data assimilation and multi-disciplinary working: skills at the top of the NERC/LWEC "Most Wanted II" list. Public engagement events will aid public understanding and enthusiasm for science, as well as improving personal understanding of what to do when extreme weather hits.
Our work on characterizing the uncertainty of urban observations will feed into Met Office operations. Current observing networks employed by the Met Office are very expensive (e.g. the satellite data used by the Met Office costs around £2 billion per year, although these costs are shared internationally). The research will provide the underpinning science that is needed for the use of new inexpensive urban observations and thus has potential for reduction in cost of the Met Office observing network in the future. Outcomes from the research will feed into improved public weather service (PWS) forecasts and are expected to promote better decisions along the existing and wide ranging forecast value chain. The 'Public Weather Service Value for Money Review 2015' indicates that the benefits of the PWS to the UK economy are around £1.5 billion per annum and it is impossible to describe all of the sectors affected in this short summary. However, market reports will be produced during the project to explore and define research questions for new applications of urban meteorology. Some examples relevant to the resilience of urban infrastructure are: better forecasts of fog and high winds would allow better air traffic management; better forecasts of snow and ice would allow transportation managers to target de-icing activities more effectively, and ensure that they are well supplied with materials, equipment and staff at critical times; better forecasts of storms and floods would enable a reduction in damage, reducing costs for the insurance industry; better forecasts of heat stress enable better preparedness for hospitals and public health agencies.
Outcomes from the research will also provide information for policy-makers and their networks such as the London Climate Change Partnership and the UK Natural Hazards Partnership programme. The research will provide evidence for future observing network design and natural hazard modelling strategies, including how to get the best value for money from observations. The project will also produce market reports detailing the needs and opportunities at the digital technology/environment sector interface and road-maps for future research strategy of use to EPSRC and other funders.
The proposed network will feature a range of commercial and public sector partners, and is expected to spin up new partnerships between industry and academia, increasing intellectual capital for UK business and competitiveness in the global economy. Early career scientists will receive training in computer modelling, data assimilation and multi-disciplinary working: skills at the top of the NERC/LWEC "Most Wanted II" list. Public engagement events will aid public understanding and enthusiasm for science, as well as improving personal understanding of what to do when extreme weather hits.
Organisations
- University of Reading (Lead Research Organisation)
- Danish Meteorological Institute (DMI) (Collaboration)
- Satellite Applications Catapult (Collaboration)
- ASTON UNIVERSITY (Collaboration)
- Jeremy Benn Associates (United Kingdom) (Collaboration, Project Partner)
- Department of Transport (Collaboration)
- Leeds City Council (Collaboration)
- CH2M HILL (Collaboration)
- Farson Digital ltd (Collaboration)
- Meteorological Office UK (Collaboration)
- Transport for London (Collaboration)
- London Climate Change Partnership (Collaboration)
- Environment Agency (Collaboration, Project Partner)
- German Weather Service (Collaboration)
- Aston University (Project Partner)
- Met Office (Project Partner)
- Jacobs (United Kingdom) (Project Partner)
- Climate UK LCCP (Project Partner)
- Flood Forecasting Centre FFC (Project Partner)
Publications
Bell Z
(2020)
Accounting for observation uncertainty and bias due to unresolved scales with the Schmidt-Kalman filter
in Tellus A: Dynamic Meteorology and Oceanography
Bell Z
(2022)
Exploring the characteristics of a vehicle-based temperature dataset for kilometre-scale data assimilation
in Meteorological Applications
Blair GS
(2021)
The Role of Digital Technologies in Responding to the Grand Challenges of the Natural Environment: The Windermere Accord.
in Patterns (New York, N.Y.)
Bokhove O
(2020)
Wetropolis extreme rainfall and flood demonstrator: from mathematical design to outreach
in Hydrology and Earth System Sciences
Bokhove O
(2019)
Communicating (nature-based) flood-mitigation schemes using flood-excess volume
in River Research and Applications
Bokhove O
(2020)
A Cost-Effectiveness Protocol for Flood-Mitigation Plans Based on Leeds' Boxing Day 2015 Floods
in Water
Description | The DARE project has made significant progress in the use of digital technology to improve climate resilience via the use of novel observations for forecasts of high impact weather and flooding. In particular the project has investigated the use of datasets of opportunity alongside scientific observing networks for numerical weather prediction and flood inundation modelling. This required theoretical, mathematical developments alongside investigations using large observational datasets. As a result of our research, wind observations derived from air traffic management reports are now used in Met Office operational forecasts each hour, 24 hours a day. These observations became part of the Met Office operational system in March 2019. In addition, we investigated the characteristics and potential of vehicle-based observations of temperature for improvements of numerical weather predictions and made recommendations for further studies and data-collection protocols. Our work using deep learning techniques with a network of CCTV river-cameras to monitor flooding has been developed as a demonstrator in collaboration with a project partner (small business). |
Exploitation Route | The findings relating to weather prediction are being taken forward by the UK Met Office for operational and strategic developments to improve weather forecasts. Our findings with deep learning and camera images are being developed in conjuction with a number of Local Authorities, funded by the EA/DEFRA FCERM Innovation Programme. |
Sectors | Digital/Communication/Information Technologies (including Software) Environment Government Democracy and Justice Transport |
URL | https://research.reading.ac.uk/dare/whywhatnow/ |
Description | The DARE project has led to a number of non-academic impacts 1) We collaborated with the UK Met Office in investigating a novel source of atmospheric measurements, determined from reports automatically exchanged between aircraft and air traffic control, providing low level information close to airports to improve forecasts of local conditions, such as fog and low level turbulence. This research improves our ability to forecast the weather by efficiently exploiting existing technologies and establishing the error ranges on the new data to ensure best use alongside other sources.The aircraft wind observations have been used for the Met Office hourly weather forecasts since March 2019. 2) Reading's results using deep learning techniques with CCTV river cameras to monitor river levels has been developed as a demonstrator in collaboration with a project partner (small business) 2022. 3) The DARE team at the University of Leeds contributed written evidence to the UK Parliament's flooding inquiry (2020) to inform policy on the Government's approach to managing flood risk in England. The evidence focusses on a novel and innovative way of visualising and analysing complex flood-mitigation schemes. |
First Year Of Impact | 2019 |
Sector | Digital/Communication/Information Technologies (including Software),Environment,Transport |
Impact Types | Economic Policy & public services |
Description | DARE contributes evidence to national flooding enquiry |
Geographic Reach | National |
Policy Influence Type | Contribution to a national consultation/review |
URL | https://research.reading.ac.uk/dare/2020/09/23/dare-contributes-evidence-to-national-flooding-inquir... |
Description | INFCOM 2 citation 2023 |
Geographic Reach | Multiple continents/international |
Policy Influence Type | Citation in other policy documents |
URL | https://library.wmo.int/records/item/66287-commission-for-observation-infrastructure-and-information... |
Description | Umwelt Bundesamt citation 2023 |
Geographic Reach | Europe |
Policy Influence Type | Citation in other policy documents |
URL | https://www.umweltbundesamt.de/publikationen/satellite-based-emission-verification |
Description | A FAIR approach to flood risk |
Amount | £6,000,000 (GBP) |
Organisation | Department For Environment, Food And Rural Affairs (DEFRA) |
Sector | Public |
Country | United Kingdom |
Start | 03/2022 |
End | 03/2027 |
Description | Advancing the Frontiers of Earth System Prediction |
Amount | £500,000 (GBP) |
Organisation | University of Reading |
Sector | Academic/University |
Country | United Kingdom |
Start | 08/2024 |
End | 08/2029 |
Description | Enhancing forecasting flood inundation mapping through data assimilation |
Amount | £65,000 (GBP) |
Funding ID | 2438362 |
Organisation | Natural Environment Research Council |
Sector | Public |
Country | United Kingdom |
Start | 09/2020 |
End | 12/2023 |
Description | Met Office Academic Partnership PDRA |
Amount | £150,000 (GBP) |
Organisation | Meteorological Office UK |
Sector | Academic/University |
Country | United Kingdom |
Start | 07/2022 |
End | 07/2025 |
Description | Multi-Model data assimilation techniques for flood forecasting |
Amount | £35,000 (GBP) |
Funding ID | 2270121 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 09/2019 |
End | 09/2023 |
Description | Transatlantic Data Science Academy Scoping Project |
Amount | £569,705 (GBP) |
Organisation | Meteorological Office UK |
Sector | Academic/University |
Country | United Kingdom |
Start | 11/2023 |
End | 07/2024 |
Title | Deep learning for the estimation of water-levels using river cameras: networks and datasets |
Description | This dataset contains: - The networks weights (weights.zip) that were obtained and used in our papers [1, 2]. We refer to these papers for the methodology used to obtain those weights. Those weights can be used for the binary water semantic segmentation of new images, or for comparison with our methods. - The river camera images (DIGLIS.zip/EVESHAM.zip/STRENSHAM.zip/TEWKESBURY.zip) for the experiments that we presented in [2]. The images can be used for water segmentation and flood analysis purposes. Related publications: [1] Remy Vandaele, Sarah L. Dance, Varun Ojha; Automated water segmentation and river level detection on camera images using transfer learning; 2020; Proceedings of the DAGM German Conference on Pattern Recognition (Accepted) [2] Remy Vandaele, Sarah L. Dance, Varun Ojha; Deep learning for the estimation of water-levels using river cameras; 2020; Hydrology and Earth System Science (in preparation) |
Type Of Material | Database/Collection of data |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | The dataset has just been published. Further impacts are anticipated but not yet realized. |
URL | https://researchdata.reading.ac.uk/id/eprint/282 |
Title | MATLAB code for the localized Singular Value Decomposition approach of the Fast Multipole Method (the local SVD-FMM) |
Description | The local SVD-FMM is a new numerical approximation method for the fast computation of matrix-vector products. The acronym SVD stands for singular value decomposition and FMM stands for fast multipole method. This MATLAB code was written for the numerical experiments in a related publication, which show the application of the method to numerical weather prediction data assimilation. However, the code can be adapted to a wider range of applications. |
Type Of Material | Computer model/algorithm |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | The creation of this software led to a journal publication: 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 |
URL | http://dx.doi.org/10.17864/1947.329 |
Title | River water level height measurements obtained from river cameras near Tewkesbury |
Description | This dataset contains extracted water level observations (WLOs) from four river cameras located on rivers Severn and Avon near Tewkesbury, UK, and owned by Farson Digital Ltd The data is extracted from hourly daylight river camera images between 21st Nov - 5th Dec 2012. The dataset consists of spreadsheets containing extracted water level data for each camera along with necessary metadata, all available Farson Digital Ltd river camera images between 21st Nov - 5th Dec 2012, and 3D point measurements of the location of each camera and locations of all measured points in the field-of-view for each camera. |
Type Of Material | Database/Collection of data |
Year Produced | 2020 |
Provided To Others? | Yes |
Impact | The dataset has been used in further research on automating river level estimation from river cameras. As these data have only recently been published further impacts are expected in future years. |
URL | https://data.mendeley.com/datasets/769cyvdznp/1 |
Title | Wetropolis rainfall and flood demonstrator: developments in hydraulic modelling and visualisation |
Description | This repository contains the source code and documentation on the numerical modelling of Wetropolis. In Bokhove et al. (2020), a numerical model (based on the equations for open channel flow under the kinematic assumption) is used to determine the relevant time and length scales prior to its construction as a physical model -- see the Wetropolis' design and showcase Github page for more info. The original numerical model is crude and inexpensive, suitable for design purposes but less suitable as a predictive model. This page tracks the further development of the numerical modelling and visualisation of Wetropolis, with a view to conducting experiments in data assimilation, flood mitigation, and control. Bokhove, Hicks, Zweers, and Kent (2020): Wetropolis extreme rainfall and flood demonstrator: from mathematical design to outreach, Hydrol. Earth Syst. Sci., 24, 2483-2503, |
Type Of Material | Computer model/algorithm |
Year Produced | 2020 |
Provided To Others? | Yes |
Impact | Publication Bokhove, Hicks, Zweers, and Kent (2020): Wetropolis extreme rainfall and flood demonstrator: from mathematical design to outreach, Hydrol. Earth Syst. Sci., 24, 2483-2503. |
URL | https://github.com/tkent198/hydraulic_wetro |
Description | Aston University (DARE project) |
Organisation | Aston University |
Department | Department of Psychology |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Research on data assimilation relating to urban "big data" |
Collaborator Contribution | Research discussions and attendance at Stakeholder Meetings |
Impact | Project has only recently begun |
Start Year | 2016 |
Description | CH2M (DARE project) |
Organisation | CH2M HILL |
Country | United States |
Sector | Private |
PI Contribution | Flood forecasting and observation research. |
Collaborator Contribution | Scientific discussions and stakeholder meetings, access to software and datasets. |
Impact | Project has only recently begun |
Start Year | 2016 |
Description | Collaboration with DWD |
Organisation | German Weather Service |
Country | Germany |
Sector | Public |
PI Contribution | WE have visited DWD and hosted a visitor from DWD, exchanged data and software with the DWD and have written a jointly authored journal article. |
Collaborator Contribution | DWD hosted our visit and sent a visitor to us, exchanged data and software and have written a jointly authored journal article. |
Impact | The research directly influenced DWD's operational implementations of data assimilation of radar data. This is being used across Germany to give better forecasts of high impact weather such as intense precipitation, winds and hailstorms, Multidisciplinary: Mathematics and Meteorology Journal publication: 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 |
Start Year | 2015 |
Description | Collaboration with UK Met Office (DARE project) |
Organisation | Meteorological Office UK |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | We are working on research relating to the use of urban weather observations, and weather impacts such as flooding. |
Collaborator Contribution | The Met Office has provided a Linux desktop to access their system, supercomputing facilities, observational data, software and expert staff time for research discussions. |
Impact | The project has resulted in changes to operational data assimilation software and strategic developments for observation processing and monitoring used for the Public Weather Service. This has resulted in improved rainfall forecasts. There are a number of joint journal articles relating to this work. Janjic, 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 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 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 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. doi:10.1002/qj.3592 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 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 |
Start Year | 2016 |
Description | DMI workshop and journal article |
Organisation | Danish Meteorological Institute (DMI) |
Country | Denmark |
Sector | Public |
PI Contribution | We attended a workshop and were co-authors on a journal article reviewing the current state of the use of crowdsourced observations in numerical prediction. |
Collaborator Contribution | The Danish Meteorological Institute hosted the workshop and led the writing of the journal article. |
Impact | Journal article Hintz et al (2019) - see publications list. |
Start Year | 2018 |
Description | Environment Agency (DARE project) |
Organisation | Environment Agency |
Country | United Kingdom |
Sector | Public |
PI Contribution | Research in flood prediction and observations. |
Collaborator Contribution | Science discussions and board membership |
Impact | Project has only recently begun. |
Start Year | 2016 |
Description | Farson Digital Watercams |
Organisation | Farson Digital ltd |
Country | United Kingdom |
Sector | Private |
PI Contribution | We are carrying out research on flooding using river camera data to evaluate the use of these images to provide water level observations and for in data assimilation. |
Collaborator Contribution | Farson have provided free access to archived data from river cameras near Tewkesbury. They also facilitated contact with the camera owners and fieldwork at these locations . |
Impact | Journal articles 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. Vandaele R, Dance SL, Ojha V. Calibrated river-level estimation from river cameras using convolutional neural networks. Environmental Data Science. 2023;2:e11. doi:10.1017/eds.2023.6 Vandaele, R., Dance, S. L., and Ojha, V.: Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning, Hydrol. Earth Syst. Sci., 25, 4435-4453, https://doi.org/10.5194/hess-25-4435-2021, 2021. Peer reviewed conference proceedings 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) |
Start Year | 2017 |
Description | Flood Forecasting Centre (FFC - DARE Project) |
Organisation | Meteorological Office UK |
Department | Flood Forecasting Centre |
Country | United Kingdom |
Sector | Public |
PI Contribution | Research in flood prediction and observations. |
Collaborator Contribution | Attendance at meetings and scientific discussions. |
Impact | Project has only recently started |
Start Year | 2016 |
Description | Highways England provision of road weather information station data |
Organisation | Department of Transport |
Department | Highways Agency |
Country | United Kingdom |
Sector | Public |
PI Contribution | Used Highways England data as an independent data source evaluating observations of temperature from vehicles. |
Collaborator Contribution | Provided Roadside Weather Information Station meteorological observation data. |
Impact | Journal article 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, 29(3), e2058. https://doi.org/10.1002/met.2058 |
Start Year | 2020 |
Description | JBA Trust (DARE project) |
Organisation | JBA Trust |
Country | United Kingdom |
Sector | Charity/Non Profit |
PI Contribution | Research on flood inundation data assimilation and remote sensing |
Collaborator Contribution | Attendance at Stakeholder Meetings and scientific discussions, as well as access to proprietary software. Co-supervision and provision of data for Masters Project (summer 2018). Provision of data and co-creation of research for pilot project 2018-19. |
Impact | Masters thesis Lu (2018) - see publications. |
Start Year | 2016 |
Description | Leeds city council traffic cameras |
Organisation | Leeds City Council |
Country | United Kingdom |
Sector | Public |
PI Contribution | We are carrying out research on flooding using traffic camera data. |
Collaborator Contribution | Leeds city council have made camera data available to us free of charge. |
Impact | No outputs yet |
Start Year | 2017 |
Description | London Climate Change Partnership (DARE project) |
Organisation | London Climate Change Partnership |
Country | United Kingdom |
Sector | Learned Society |
PI Contribution | We are carrying out research relating to urban weather and flooding. |
Collaborator Contribution | Attendance at stakeholder meetings, and facilitation of knowledge exchange with further stakeholders |
Impact | The project has only recently started |
Start Year | 2016 |
Description | Satellite Applications Catapult (CORSAIR) |
Organisation | Satellite Applications Catapult |
Country | United Kingdom |
Sector | Charity/Non Profit |
PI Contribution | We have been carrying out research on automatic flood delineation using synthetic aperture radar data provided by the Satellite Applications Catapult. Report to the catapult (March 2018) |
Collaborator Contribution | Synthetic Aperture Radar (SAR) images were provided under the CORSAIR programme. |
Impact | 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 |
Start Year | 2016 |
Description | Transport for London Jam Cams |
Organisation | Transport for London |
Country | United Kingdom |
Sector | Public |
PI Contribution | We are carrying out research using Transport for London "Jam Cams." |
Collaborator Contribution | The traffic camera data is available open access, and we have been provided with some technical support by TfL to access the data. |
Impact | None yet. |
Start Year | 2017 |
Description | VK Masters Project with the Met Office |
Organisation | Meteorological Office UK |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | This Masters project was co-supervised by myself and PDRA Dr Joanne Waller. We initiated the collaboration and met weekly with the student and collaborators; providing key ideas and input to discussions and writing up the work. |
Collaborator Contribution | This Masters project was co-supervised by Dr Lee Hawkness-Smith (Met Office staff member), he attended weekly meeting; providing key ideas and input to discussions and writing up the work. The Met Office provided the radar observations data and supercomputing software; CPU time and model data to support the project. |
Impact | Masters thesis Kourapaki (2019) - see publications section of researchfish. |
Start Year | 2019 |
Title | Operational use of Mode-S EHS at the Met Office |
Description | This collaborative research investigated a novel source of atmospheric measurements, determined from reports automatically exchanged between aircraft and air-traffic control, providing low-level information close to airports to improve forecasts of local conditions, such as fog and low-level turbulence. Analysis of the air-traffic communications provided the ambient wind and temperature at the aircraft's position. The new data source was trialled in the kilometre-scale UK Met Office model after a thorough evaluation of the errors, essential for using the measurements correctly. Additionally, reports from many aircraft were aggregated to construct vertical profiles of temperature for use by forecasters |
Type Of Technology | New/Improved Technique/Technology |
Year Produced | 2019 |
Impact | This research improves our ability to forecast the weather by efficiently exploiting existing technologies and establishing the error ranges on the new data to ensure best use alongside other sources. The aircraft wind observations have been used for the Met Office hourly weather forecasts since March 2019. |
URL | https://research.reading.ac.uk/dare/wp-content/uploads/sites/5/Unorganized/DARE_aircraft_obs_revised... |
Description | DA Training - Portugal |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | Dance was on the organizing committee for the Workshop on Sensitivity Analysis and Data Assimilation in Meteorology and Oceanography, Meliá Ria Hotel Aveiro, Portugal, 1-6 July 2018. The DARE project provided travel scholarships for 6 students (4 UK, 1 Japan, 1 Brazil) to attend a data assimilation training day held as part of the workshop. Each student wrote a blog for the DARE website. |
Year(s) Of Engagement Activity | 2018 |
URL | https://www.morgan.edu/adjoint_workshop |
Description | DARE Watercolours Exhibitions |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Patients, carers and/or patient groups |
Results and Impact | We worked with with artist Hugo Dalton to create a series of mini exhibitions documenting our work with satellite-derived flood observations that have taken place in local residential care homes, raising awareness of climate change impacts with residents, staff and visitors. Each exhibition featured works made during a residency at Reading and with views of the river Thames made on location. The project is in collaboration with the Royal Collection Trust at Windsor. |
Year(s) Of Engagement Activity | 2022,2023 |
URL | https://research.reading.ac.uk/dare/events/dare-watercolour-project/ |
Description | DARE video collection |
Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Public/other audiences |
Results and Impact | Collection of videos made about DARE project research for a lay audience. |
Year(s) Of Engagement Activity | 2019,2020,2021,2022 |
URL | https://www.youtube.com/channel/UCE-cXXxmOqZUYAGQXMuAPvw |
Description | DARE website and blog |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Public/other audiences |
Results and Impact | The DARE website hosts a blog and other generally accessible material about our research and about related work in the Digital Technology/Living with Environmental Change (DT/LWEC) area. |
Year(s) Of Engagement Activity | 2016,2017,2018,2019 |
URL | https://research.reading.ac.uk/dare/ |
Description | Data Assimilation training course |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | The University of Reading organised a 4-day training course in data assimilation, in collaboration with the National Centre for Earth Observation, ECMWF and the DARE project. The course was attended by around 25 international early career researchers, including scientists from universities, research institutes and industry. Many of the students will use the ideas discussed in their own research projects. |
Year(s) Of Engagement Activity | 2019,2020,2021 |
URL | https://research.reading.ac.uk/met-darc/ecmwf2019/ |
Description | Inundation Street |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Public/other audiences |
Results and Impact | Inundation Street is an immersive 360 video experience highlighting the impact of flooding on households. The video demonstrates some simple steps that can be taken to reduce these impacts. The video is available via YouTube and to date (March 2021) has over 1.4 million views. |
Year(s) Of Engagement Activity | 2019,2020 |
URL | https://youtu.be/8oSf7G21fzw |
Description | MOOC - Dare to discover Data Assimilation |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | We developed a Massive Open Online Course (MOOC), explaining the topic of data assimilation, without any prerequisite requirements for technical knowledge. To date over 300 people have participated in the course. Participants have gained a greater understanding of the topic, the state of the art in research as well as the benefits and caveats associated with reanalyses. |
Year(s) Of Engagement Activity | 2022 |
URL | https://discoverda.org/ |
Description | Observer interview February 2024 |
Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Media (as a channel to the public) |
Results and Impact | Guardian journalist Robin McKie has produced a story for The Observer on the University of Reading's involvement in a 15-year research programme, in collaboration with the Met Office and ECMWF, to enhance the accuracy of weather predictions, up to a month in advance. Professor Rowan Sutton, Professor Sarah Dance, Professor Chris Merchant and Professor Pier Luigi Vidale are quoted within the piece, which is republished by TechnoSpace2, Yahoo! News, MSN and Aol. |
Year(s) Of Engagement Activity | 2024 |
URL | https://www.theguardian.com/science/2024/feb/18/the-perfect-storm-for-small-talk-weather-forecasters... |
Description | St Piran's Flooding Workshop |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Schools |
Results and Impact | We delivered a hands-on workshop on flooding to approx 60 Year 6 pupils. The school and parents reported a very enthusiastic response from the children. The team were invited to deliver another workshop next year. |
Year(s) Of Engagement Activity | 2021,2023 |
Description | Tewkesbury Stakeholder Workshop |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Professional Practitioners |
Results and Impact | We held a workshop for landowners of River Camera Sites in the Tewkesbury area including the Environment Agency, The Canal and River Trust, Hampton Ferry, Avon Navigation Trust, and Tewkesbury Marina as welll at the managing director of Farson Digital Ltd (the camera provider). This allowed us to thank the landowners, communicate our research goals, learn their uses for their cameras, as well as to hear about their experience of flood events. |
Year(s) Of Engagement Activity | 2018 |
Description | The Coombes Primary (S-VC 2018) |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Schools |
Results and Impact | Sanita Vetra-Carvalho led an interactive outreach day on flooding for two year 3 classes (65 pupils) at The Coombes Primary C of E School in Arborfield, Berkshire on 12th of June 2018. Sanita delivered an hour long lesson on water cycle and floods to the children followed by running an interactive flood games for the children using Flash Flood game and SandBox table provided by the The River Wey Trust. |
Year(s) Of Engagement Activity | 2018 |
Description | Video - Data Assimilation: The secret to better weather forecasts |
Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Public/other audiences |
Results and Impact | A non-technical video introduction to data assimilation has been produced. This has been posted on YouTube and also used the MOOC "Big Data and the Environment" URL for MOOC https://www.futurelearn.com/courses/big-data-and-the-environment |
Year(s) Of Engagement Activity | 2019 |
URL | https://youtu.be/YPAWYjPf_Pk |
Description | Year 11 conference (SV-C) |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Schools |
Results and Impact | Year 11 students from the Forest School attended a mathematics workshop at the University of Reading. The event included interactive, hands-on activities teaching students about mathematics in different applications, as well as a careers talk about the benefits of studying mathematics at A level and beyond. |
Year(s) Of Engagement Activity | 2017 |