Improving high impact weather forecasts via an international comparison of ObServation error Correlations in data Assimilation (OSCA)

Lead Research Organisation: University of Reading
Department Name: Meteorology


Approximately 4 million properties in the UK are at risk from surface-water flooding which occurs when heavy rainfall overwhelms the drainage capacity of the local area. In the future, as a result of climate change, the frequency and intensity of severe weather events, such as storms and floods, is likely to increase. Accurate forecasts of severe weather provide significant benefit, allowing households and businesses to take mitigating action and emergency services to mobilize resources.

Numerical weather forecasts are obtained by evolving forward the current atmospheric state using computational techniques that solve equations describing atmospheric motions and other physical processes. The current atmospheric state is estimated by a sophisticated mathematical technique known as data assimilation. Data assimilation blends previous forecasts with new atmospheric observations, weighted by their respected uncertainties. The uncertainty in the observations is not well understood, and currently up to 80% of observations are not used in the assimilation because these uncertainties cannnot be properly quantified and accounted for. Working in partnership with the UK Met Office, we have recently demonstrated in the NERC FRANC: Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection project (NE/K008900/1), that it is now feasible to estimate spatial statistics for observation uncertainty. Our previous work in idealized systems has shown that better accounting for these errors in the assimilation is expected to provide significant forecast improvement. There are still a number of fundamental questions to address before the benefits can be realized in operational forecasts.

This proposal to the NERC International Opportunities fund will add value to the work carried out in FRANC, by supporting access to international observation data, numerical weather prediction models and assimilation systems. We will build a new collaboration with the Deutscher Wetterdienst (German Weather Service), and compare observation error statistics for Doppler radar wind data from Deutscher Wetterdienst with those from the UK Met Office. By considering the similarities and differences between the operational forecasting systems, and attributing these to features in the observation error statistics, we will obtain a detailed knowledge of the error sources. By carrying out theoretical and idealized studies and comparing their results with the statistics from the operational systems, we will gain understanding of the impact of differences in the assimilation systems on the diagnostic used to estimate the observation error statistics. In turn, this should allow the observation errors to be reduced, and therefore more of the expensively acquired observations to be utilised, rather than discarded. Ultimately, understanding the observation uncertainty will result in improved forecasts of severe weather events.

Planned Impact

In this project we will be developing a new international collaboration and working in partnership with two national weather services (Met Office, Deutscher Wetterdienst). Our results will also be disseminated to further international weather research services (see Academic beneficiaries). Our results should lead to a step-change in the understanding and treatment of correlated observation errors. This is expected to result in significant improvements in weather forecast skill, particularly for high impact weather such as storms and intense rainfall.

With an understanding of the source of observation error, it can be possible to have an operaiotnal impact on a short timescale. For example, previous research in the FRANC project diagnosed a bias in the SEVIRI satellite data assimilated by the Met Office into their UKV model. A correction was implemented by Met Office staff and made operational within a few months. To reduce other types of error, corrections may require a longer development period, or may not currently be computationally feasible within the time constraints of operational forecast production.

With the appropriate software infra-structure in place, that takes proper account of correlated observation errors, it may be possible to utilize a larger proportion of expensively acquired observations, rather than discarding them. This is expected to improve forecasts as well as providing better value for money for the various government agencies funding these weather services. We note that the UK Met Office has already begun to develop the necessary software infrastructure and we anticipate that this will be in place before the end of this project.

In the UK the Met Office/EA Flood Forecasting Centre (FFC) and Met Office/SEPA Scottish Flood Forecasting Service (SFFS) use direct output from Met Office numerical weather prediction systems to drive river routing models, coastal surge models and surface flooding tools. The output from these tools is used to provide flood warnings to Category 1 responders (fire service, police etc), and to provide driving data for Environment Agency regional offices who carry out predictions and give warnings for individual river catchments (such as those available to the public on EA floodline). The accuracy of all of these warnings is dependent on the accuracy of the numerical weather prediction inputs. Hence the expected improvements in weather forecasting skill due to this project have the potential for significant improvements in flood warnings. Since the FFC and SFFS already have Met Office numerical weather prediction outputs embedded, improvements in flood warning could be felt as soon as the improvements in weather forecasts, although some warning tools may need to be recalibrated to take account of the improved weather forecast skill. Even short lead-time flood warnings can provide useful information for the public and allow time for mitigating actions, evacuation etc. This should help to reduce injury and personal losses.

The University of Reading has recently received funding from HEFCE to create a unique, flagship centre, "The Institute for Environmental Analytics". Harnessing the expertise of world-leading academics and commercial organisations (such as Airbus, Sainsburys and the Lighthill Risk Network), the Institute is developing the technologies and skills that are urgently required to translate cutting-edge environmental research into commercially-relevant solutions. Data assimilation and the use of earth observation data are expected to be a large part of the core business for the centre. The results of this project will feed into market-focussed research and development when the centre begins operations.


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Description The research on this grant has been carrying out an international intercomparison of observation uncertainty for Doppler radar winds. This has required us to investigate a technique for diagnosing observation errors. We have discovered some problems with the method and proposed some possible work-arounds that have been published and tested in the German weather service operational system. The results of the study for the radar data have also been published.
Exploitation Route These findings may be used by national weather services around the world.
Sectors Environment,Government, Democracy and Justice

Description Research has led to improved treatment of radar observations by the German Weather service.
First Year Of Impact 2017
Sector Environment,Government, Democracy and Justice
Impact Types Policy & public services

Description DWD change to radar quality control
Geographic Reach Europe 
Policy Influence Type Influenced training of practitioners or researchers
Impact Our research related to the operational and near operational German public weather forecasting service. Our results highlighted some problems with observation quality control, that have subsequently been rectified, and improved the German weather forecast,
Description EPSRC Senior Fellowship in Digital Technology for Living with Environmental Change
Amount £1,706,722 (GBP)
Funding ID EP/P002331/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 09/2016 
End 08/2021
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 are preparing a jointly authored journal article.
Collaborator Contribution DWD hosted our visit and sent a visitor to us, exchanged data and software and are preparing a jointly authored journal article.
Impact Initial outcomes are a seminar given to DWD. Multidisciplinary: Mathematics and Meteorology.
Start Year 2015
Description Collaboration with NRL 
Organisation United States Naval Research Laboratory
Country United States 
Sector Public 
PI Contribution Nancy Nichols visits NRL, Monterey annually to discuss current research. She has also contributed to writing a joint publication.
Collaborator Contribution Research discussions and joint publication writing.
Impact Outputs: journal article (Hodyss and Nichols, 2015) Multi-disciplinary: Mathematics and Meteorology
Start Year 2006
Description Partnership with the Met Office 
Organisation Meteorological Office UK
Country United Kingdom 
Sector Academic/University 
PI Contribution We are working together with the Met Office on an integrated programme of research.
Collaborator Contribution The Met Office is contributing staff time and expertise, model and data assimilation software, observational data, supercomputing time and support.
Impact The project has resulted in - improvements in the Met Office operational UK weather radar network - changes to the Met Office operational numerical weather prediction software - a number of joint journal publications (listed below) M. Cordoba, S.L. Dance , G.A. Kelly, N.K. Nichols and J.A. Waller (2016) Diagnosing Atmospheric Motion Vector observation errors for an operational high resolution data assimilation system Quarterly Journal of the Royal Meteorological Society (Accepted) 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 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) 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 Flack, D. L. A., Plant, R. S., Gray, S. L., Lean, H. W., Keil, C. and Craig, G. C. (2016), Characterisation of convective regimes over the British Isles. Q.J.R. Meteorol. Soc., 142: 1541-1553. doi: 10.1002/qj.2758
Start Year 2013
Description University of Bremen (Germany) FRANC/SINATRA/OSCA 
Organisation University of Bremen
Department MARUM
Country Germany 
Sector Academic/University 
PI Contribution Research on estimation of correlated observation errors in numerical weather prediction is being applied to a new application of river flood modelling.
Collaborator Contribution Provision of flood model and numerical runs.
Impact Project ongoing.
Start Year 2016
Description Ongoing "Magnificent Maths" Days 
Form Of Engagement Activity Participation in an open day or visit at my research institution
Part Of Official Scheme? Yes
Geographic Reach Regional
Primary Audience Schools
Results and Impact Building on the event funded by this grant, the University of Reading continues to hold an annual "Magnificent Maths" day or 2-day summer school for Year 12 school students, featuring workshops, talks and career panels.

Teachers comment that a Maths related school trip is a rare opportunity. Many of the same schools bring their students year after year, showing the value that they place on this activity for enthusing their students about choosing to continue Maths to A-level as well as further on at University.
Year(s) Of Engagement Activity 2010,2011,2012,2013,2014,2015