Summary Progress Report May 2008
Source: environment.data.gov.uk (2018-06-19)

Flood Risk for Extreme Events (FREE): Radiosonde, Wind Profiles Data and Model Output from the Exploitation of new data sources, data assimilation and ensemble techniques for storm and flood forecasting project. The Exploitation of new data sources, data assimilation and ensemble techniques for storm and flood forecasting Project is a NERC Flood Risk for Extreme Events (FREE) Research Programme project (Round 1 - NE/E002137/1 - Duration January 2007 - April 2010) led by Prof AJ Illingworth, University of Reading. This project investigates possible methods of producing ensemble weather forecasts at high-resolution. These ensembles will be used with raingauge and river flow to improve methods of flood forecasting. The dataset includes radiosonde and wind profiles in England and Wales derived using Doppler radar returns from insects. The radial velocity measurements from insects were converted into VAD profiles by fitting a sinusoid to radial velocities at constant range. All measured profiles have been interpolated to the instrument location. 
Model output files from experiments assimilating radial winds from insects are also available.

Floods in the UK are often caused by extreme rainfall events. At present, weather forecasts can give an indication of a threat of severe storms which might cause flash floods, but are unable to say precisely when and where the downpours will occur, due to the complex range of processes and space-time scales involved. The first stage is to predict the air motions leading to convergence and ascent at a certain location where the precipitation will be initiated, then the development of the precipitation needs to be forecast, and hydrological models used to produce accurate, quantitative, probabilistic flood predictions. Data assimilation is a sophisticated mathematical technique that combines observations with model predictions to give an analysis of the current state of the atmosphere. This analysis may be used to initialise a weather forecast. Although precipitation is well observed by weather radar, attempts to assimilate radar data have had little success; by the time the rain develops the forecast model state is too far from the truth and the air motions are inconsistent with the position of the first radar precipitation echo.

We propose to overcome this problem by assimilating new types of data from weather radars. These provide information on the evolving humidity fields and air motions in the lower atmosphere so that the model can accurately track the developing storm before precipitation appears. The model used will be a new Met Office model that can be run with a resolution (i.e., grid-spacing) of order 1-4km. This enables storm-cloud motions to be explicitly calculated, rather than treated as a sub-grid-scale effect. Furthermore, current operational forecast models are only updated with observations every few hours; in the new approach the model will be updated much more frequently. This should yield weather forecasts with improved locations (in space-time) for rainfall events.

Initialisation errors are not the only cause of inaccuracies in storm-scale weather forecasts. Models are often run only for a small region of the world, and the data on the boundaries of this area provided from a larger-scale model. These data are known as lateral boundary conditions. Errors in these lateral boundary conditions and modelling errors also contribute to the errors in the forecast. Even if these errors were reduced, the nonlinear nature of the storm dynamics ensures that there is a limit, beyond which the value of deterministic forecasts becomes questionable. After that point it becomes important to determine the uncertainties in the forecast precipitation, so an ensemble approach is required. (An ensemble is a collection of perturbed forecasts that may be considered as a statistical sample of the forecast probability distribution.)

The appropriate construction of a storm-scale ensemble is an open question. We propose a structured approach where perturbations will be designed on the basis of physical insight into convective forcing mechanisms. The resulting probabilistic rainfall forecasts can be interfaced to hydrological models used for flood forecasting. For the first time, this project will allow different scales of application of these methods to be supported: ranging from localised flash flooding of small catchments, through to indicative first-alert forecasting with UK-coverage and forecasting of river discharges to the sea. The project will also assess the impacts of improvements in numerical weather prediction on flood forecast performance.

In this project we anticipate fruitful interactions between the different disciplines of observations and measurement, meteorology and hydrology. Radar assimilation software development and ensemble forecasts will take place using Met Office models, so improvements can be implemented operationally very easily. The use of operational radars makes this project well placed to take advantage of data from any extreme events occurring during the period of the study.

Flood Risk from Extreme Events (FREE) 
Summary Progress Report May 2008 
 
Chris. G. Collier 
FREE Science Coordinator, Centre for Environmental Systems Research, 
University of Salford, United Kingdom 
 
INTRODUCTION 
 
Progress reports from all but one of the FREE PIs have been received for the 
quarter ending March 2008. Information from these reports has been 
extracted, and used to generate this report. This information has also formed 
the basis of a paper to be presented by the Science Coordinator at the 
forthcoming Floodrisk2008 Conference 29 September to 3 October 2008 at 
Oxford. Some of the introductory material is of course well known to 
members of the Steering Committee, but is retained here to set the scene. 
Some other material has been omitted as not necessary for this report. It is 
the intention from now on to generate a summary report like this for the 
Steering Committee on approximately a quarterly basis.    
 
  
 
1. PROGRAMME OBJECTIVES 
 
The broad programme objectives embody consideration of the meteorological, 
hydrological and coastal oceanographic processes involved. Such processes include 
floods arising either through the occurrence of extreme rainfall and subsequent flows 
underground, overland or in river channels to the sea, or within estuaries and sea areas 
generated from storm surges around our coast (Figure 1). These components were 
highlighted by major flood events during the summer of 2007 in Tewkesbury and 
Gloucester, and the 9th November 2007 North Sea storm surge which so nearly spelt 
disaster for east coast communities. 
 
 
 
 
Figure 1. The scope of the FREE 
programme (courtesy M. Bramley). 
 
 1 
 

2. PROGRAMME LINKS 
 
The FREE programme is bound together by the use of advanced mathematical 
techniques to represent physical understanding. Numerical representations (models) of 
the physical processes involved in flooding are linked together, and driven by quality 
controlled data inputs. However, the way in which models are driven requires 
mathematical structures to assimilate these data providing the basic state from which 
model equations maybe integrated forward in time. In addition, uncertainty in both the 
forecasts and in the likely occurrence of extreme events requires sometimes complex 
statistical 
approaches 
to 
analyse 
past 
events. 
 
 
Improved modelling 
and forecasts through 
new data assimilation 
methods 
New approaches to 
modelling uncertainty 
in an integrated 
modelling environment 
The statistics of 
extremes and their use 
The Impacts of a 
changing climate on 
the occurrence of 
flooding 
Illingworth – NWP 
and new remote 
sensing observations 
Zou – linking 
meteorological, 
hydrological and 
coastal models using 
ensemble methods 
Svensson – joint 
probability analysis of 
fluvial and estuarine 
floods 
Osborn – changing 
occurrence of rainfall 
Dance – initialisation 
of coastal sediment 
models 
Beven – constraining 
uncertainty in 
hydrologic modelling 
McSharry – 
quantifying flood risk 
using density forecasts 
based on a new digital 
archive and weather 
ensemble predictions 
Cloke – changes in 
hydrological and 
hydraulic flows 
TBC – reducing the 
risk of pluvial flooding 
Wheater – 
groundwater modelling 
of rare events 
Toumi – a hybrid 
model for predicting 
the probability of very 
extreme rainfall 
Reynard – changes in 
fluvial flooding and 
inundation 
 
O’Connell – impact of 
land use on behaviour 
of floods 
 
Williams – changes in 
characteristics of 
coastal floods 
 
Figure 2. FREE Programme at a glance showing the PIs for each consortium project. 
 
 
FREE science is being undertaken within 13 individual projects. Figure 2 shows how 
these projects from a coherent balanced programme of four components with specific 
elements providing knowledge exchange with stakeholders. A data management 
policy has been developed by the British Atmosphere Data Centre (BADC) linking a 
wide range of data sources including data sets managed by the Met Office and the 
Environment Agency. Substantial new datasets will not be produced in FREE.  Any 
data that are collected with output from numerical model runs will be preserved and 
will be fully documented at the BADC, NEODC, BODC, or data centres at CEH, 
Wallingford and BGS as appropriate. An arrangement, known as the FLOod Action 
Team (FLOAT), is also in place to assemble specific data should major extreme 
events occur during the programme as happened during 2007. 
 
 2 
 

 
 
Figure 3. Refractivity change between 03:00 
and 04:00, 30/09/2007 from the 
Cobbacombe Cross radar. A refractivity 
unit (N) is approximately equal to a 1% 
change in relative humidity (courtesy J. 
Nichol).  
 
 
3. ADDRESSING SPECIFIC ISSUES  
 
Flooding is the consequence of the interaction of a number of different physical 
processes. To forecast flooding, be it fluvial, pluvial or coastal, requires numerical 
representations (models) of these processes to be linked together. Such models are 
driven by quality controlled data inputs, but the way in which data are assimilated 
requires mathematical structures which enable models to be initialised, and then 
integrated forward in time in ways which retain numerical stability yet allow changes 
to occur in ways which are physically realistic. 
 
The FREE Programme is addressing the initialisation of Numerical Weather 
Prediction (NWP) forecast models by assimilating new types of weather radar data 
(Figure 3). This will lead to the generation of probabilistic rainfall forecasts using 
ensembles of forecasts achieved through multiple model runs based upon slightly 
different initial conditions. The first set of ensemble simulations at 4km grid spacing 
using a NWP model perturbation technique have been carried out. These forecasts 
have been inserted into a hydrological model to produce probabilistic river flow 
forecasts. This work has been untaken through collaboration between the Joint Centre 
for Mesoscale Meteorology (JCMM, University of Reading) and the Centre for 
Ecology & Hydrology (CEH).  
 
The extreme storm of 16 August 2004 causing catastrophic flooding of Boscastle 
village in Southwest England (HR Wallingford, 2005) has been selected as a case 
study. Deterministic rainfall forecasts using the high-resolution 1 km NWP (Roberts, 
2006) predicted the timing and location of the convective cells well and with 
remarkable lead-times of up to 10-12 hours, although the actual totals were 
underestimated. Good NWP performance at such long lead-times was only possible 
for the Boscastle event due to the particular forcing mechanisms in play (Roberts, 
2006; Golding, 2005); more commonly the location and evolution of convective cells 
cannot be forecast with any certainty beyond 1 to 2 hours.  
 
Notwithstanding the significant improvements in NWP, the resulting rainfall forecasts 
will always possess inherent uncertainties relating to, for example, spatial location, 
rain-rates and evolution. The effect of such uncertainties for flood forecasting and 
 3 
 

warning can be particularly acute when extreme convective rainfall is forecast over 
regions with small fast-responding catchments. This was exactly the case for the 
catchment to Boscastle which drains an area of circa 20 km2 via steep-sided 
convergent valleys. In such a situation, small changes in the location of the forecast 
rainfall of only a couple of kilometres can have a significant impact on the resulting 
flood forecast, particularly when distributed hydrological models are used (Moore et 
l., 2006).  
rigin of the storm randomly within a selected (here, 20 km) 
dius (see Fig. 4(a)).  
location and 1 km 
atchment boundary is given in (a) and (b)) (courtesy R. Moore and S. Cole). 
ps of exceeding a flow threshold of interest, such as the 50-year return 
eriod flood. 
a
 
Ensemble NWP rainfalls used as input to distributed flood forecasting models 
provides an attractive way of quantifying the uncertainty of hydrological forecasts 
attributable to the forecast rainfall input. Whilst more advanced methods for 
generating high-resolution NWP ensembles are being developed (e.g. physically-
based perturbations) a simple method of ‘pseudo-ensemble’ generation has been used 
initially. The method consists of two steps: (i) a scalar multiplier is applied to the 
entire high-resolution NWP forecast, and (ii) ensemble members are generated by 
displacing the spatial o
ra
 
 
 
 
 
 
 
 
 
Figure 4. Example of the exploitation of new data sources, data assimilation and ensemble 
techniques for storm and flood forecasting’ using the Boscastle storm as a case study: (a) a 
‘pseudo-ensemble’ of high-resolution 1 km NWP rainfall, (b) an ensemble of distributed 
hydrological model simulations of river flow using the Grid-to-Grid (G2G) model, (c) comparison 
of G2G ensembles with observations for the River Tamar at Gunnislake (
c
 
 
These ensemble rainfalls have been used as input to the distributed Grid-to-Grid 
(G2G) hydrological model (Moore et al., 2006; Bell et al., 2007). The G2G model has 
been configured to a large part of Southwest England encompassing the ungauged 
Boscastle catchment and the River Tamar catchment gauged at Gunnislake (circa 900 
km2). Figure 4(b) shows the ensemble river flows simulated using the G2G model, 
with the colours reflecting changes in flow magnitude across the river networks. The 
benefits of using the Grid-to-Grid model are that it can provide forecasts ‘everywhere’ 
including gauged and ungauged locations, portrays the spatial evolution of flooding 
across a whole region and is sensitive to the spatial pattern of rainfall input. Ensemble 
forecasting outputs that can aid the flood warning decision-making process range 
from traditional ‘spaghetti’ ensemble plots of hydrographs (see Fig. 4(c)) to spatial 
probability ma
p
 
An alternative approach to improving the quality of predictions of the probability of 
rainfall is to combine advanced methods of statistics, the output from NWP models 
Gunnislake
Boscastle
Gunnislake
Boscastle
Gunnislake
Boscastle
Gunnislake
Boscastle
Gunnislake
Boscastle
Gunnislake
Boscastle
Gunnislake
200
0
1
2
Time (Days)
0
50
100
150
Flow (m3s−1)
G2G Ensembles
Observed flow
(a) 
(c) 
(b) 
 4 
 

and an archive of extreme rainfall events (PI: McSharry, Oxford). So far both 
autoregressive and generalized autoregressive conditional prediction models have 
been tested on rainfall time series with some success. A more sophisticated model has 
been produced making use of Generalized Linear Models based upon the use of non-
aussian densities to produce probabilistic forecasts of daily rainfall totals. 
 satellite and RADARSAT, airborne lidar, swathe 
athymetry and beach transects. 
ore to predict wave overtopping and scour near coastal defence 
tructures. 
 
of GridStix sensors, developed at Lancaster, that measure river depth in 
al-time. 
 been decided upon for modelling the Pang and Lambourn 
atchment in Berkshire. 
us is on the Pontbren (Upper Severn), the Eden and the 
odder (Ribble) catchments. 
 
G
 
A similar, albeit novel, approach is being adopted for coastal sediment models of the 
River Dee estuary and Morecambe Bay. Future bathymetry is predicted and a 
modified version of the Proudman Laboratory hydrodynamic model initialisation is 
achieved via a computational 3D-VAR assimilation structure (PI: Dance, Reading) 
(Figure 5). Data being used include
b
 
An attempt to link meteorological, hydrological and coastal inundation models will be 
made to investigate uncertainties in forecasts using ensemble techniques in the 
Ensemble Prediction of Inundation Risk and Uncertainty arising from Scour 
(EPIRUS) project (PI: Zou, Plymouth). So far the MM5 atmospheric model has been 
used to analyse two historic storms ready for the model output to be used to provide 
input to the POLCOMS model providing forecasts of waves for use with 2-D and 3-D 
surf / scour models. A particular aim will be to investigate wave transformation from 
off-shore to near sh
s
 
However, more detailed investigation of uncertainties, and how to constrain them, in 
the assimilation of data into hydraulic routing models is needed (PI: Beven, 
Lancaster). The models for the Dee and Ribble catchments will incorporate data from 
a network 
re
 
 In addition, floods do not always arise from rivers overtopping, they may result from 
groundwater sources reaching the surface, particularly in Chalk catchments. Extreme 
floods of this type require new approaches to groundwater modelling as their 
occurrence is rare and difficult to forecast. (PI: Wheater, Imperial College, London). 
So far a two dimensional model for simulating the unsaturated / saturated zone 
interactions, and particularly the water table response to extreme events has been 
configured for a hillslope segment through the East Ilsley field site. In addition, a 
model grid structure has
c
 
Although Chalk catchments have a specific type of land use associated with them, 
other catchments attract different types of land use which may have impacts on the 
occurrence and behaviour of floods. This too is being investigated in FREE (PI: 
O’Connell, Newcastle). So far a comprehensive library of local-scale metamodels that 
represent the hydrological response of individual fields / hillslopes has been 
developed. At present the foc
H
 
 5 
 

 
Observed
Modelled 
 
Figure 5. Observed and modelled changes over a three year period of the sea bed morphology in 
Morecambe Bay (from Scott and Mason, 2007). 
 
Forecasting extreme floods using the outcome of FREE research as articulated above 
takes no account of climatic change. Our climate is changing due to human activity. 
Research on how this impacts the occurrence of rainfall (PI: Osborn, UEA), 
hydrological and hydraulic flows linking Hadley Centre and other climate data to the 
Lisflood and Dynamic-TOPMODEL models (PI: Cloke, Kings College, London) 
(Figure 6), the whole of the fluvial flooding chain from rainfall to river inundation 
(PI: Reynard, CEH, Wallingford) and coastal flooding (PI: Williams, Plymouth) are 
being investigated in FREE.  
 
 
 
Figure 6. Modelling structure and uncertainty cascade in the project aimed at assessing 
uncertainty in flood inundation impacts (courtesy H. Cloke). 
 6 
 

 
An enhanced version of the CEH Grid-to-Grid model, incorporating variable soils 
data and lateral flows of soil moisture is being trialled on the Thames region. 
Enhanced 1 km gridded flow directions have led to more realistic artificial river 
networks and catchment areas, leading in turn to reduced errors in the water balance 
and modelled river flows. This and the Newcastle NSRP model will be used with 
future climate data scenarios from the Hadley Centre (PI: Reynard, CEH, 
Wallingford). 
 
 
 
Figure 7. The Sefton coast. Note the 
proximity of the urban area (courtesy J. 
Williams). 
 
The Coastal Flooding by Extreme Events (CoFEE) project (PI: Williams, Plymouth) 
is investigating the impact of severe storms on erosion of the sand dunes along the 
Sefton coast and concomitant flooding (Figure 7). Recent results published by Maraun 
et al (2008) (PI: Osborn, UEA) support the existence of a long-term increase in winter  
precipitation intensity, although the summer rainfall intensity has exhibited changes 
that are more consistent with inter-decadal variability than with any overall trend 
(Figure 8).  
 
Finally, the mathematical recurrence of extreme fluvial or estuarine flooding, 
important for the engineering design of mitigation measures, requires statistical 
analytical approaches such as joint probability analysis which incorporate seasonality 
and geographical variation. This work will concentrate on floods with return periods 
of up to 1000 years and beyond (PI: Svensson, CEH, Wallingford).So far 
autocorrelation and lagged cross-correlations between observed rainfall, observed and 
modelled river flows and modelled soil moisture deficits have been estimated at two 
trial locations. Also regression models for extremes of river flow at a single site have 
been developed, with particular attention being paid to whether pre-processing the 
flow data using such models might improve marginal tail models. In addition the use 
of the Met Office Numerical Weather Prediction model offers a basis for investigating 
the recurrence of extreme rainfall events. Such work began ain January 2008 (PI: 
Toumi, Imperial College, London). Idealised experiments have been undertaken using 
the NCAR Weather Research and Forecasting (WRF) atmospheric model to 
investigate the effect of atmospheric moisture on the individual convective systems 
 7 
 

that contribute to very extreme rainfall events. The results suggest that for an idealised 
squall line there is a near linear relationship between atmospheric  moisture and 
maximum rainfall depth. Future work will employ the Met Office Unified Model 
(UM).   
 
 
 
 
Figure 8. 1961-2006 trends of category 10 
(heavier events) contributions for winter, 
spring, summer and autumn. The results 
are given as the absolute contribution 
change in percent over the whole period 
(from Maraun et al., 2008). 
 
 
4. DATA EXCHANGE 
 
FREE is a basic science research programme, and as such it is reasonable to expect 
the outcomes to focus on advances to our knowledge of how extreme floods occur, 
how we might forecast them, and how climate change may impact their occurrence 
and recurrence. Validated modelling methodologies will form the basis of useful tools 
for weather and flood forecasters, for managers concerned with putting in place 
warning procedures and for civil engineers concerned with designing and building 
flood mitigation measures.  
 
Pluvial floods (overland flows) in both urban and rural areas also present challenges 
for modelling, which will be examined through knowledge transfer activities. A call 
for Knowledge Exchange Projects is being drafted. 
 
Data exchange activities with stakeholders is a central part of the FREE programme. 
Stakeholders directly involved in the programme include the Environment Agency, 
Defra, the Met Office (including the Hadley Centre), HR Wallingford and ECMWF. 
If we needed a reminder on how timely this programme is after the flooding in 2007, 
FREE has already been able to show that it will make a contribution to several of the 
Interim Conclusions and Recommendations from the Pitt Review.  
 
 8 
 

5. PROGRAMME MANAGEMENT 
 
All PIs have been reminded that Quarterly Progress Reports are required. The 
progress reports from the last quarter have been archived on a CD and are available 
for inspection.  
 
REFERENCES  
 
Bell, V.A., Kay, A.L., Jones, R.G. and Moore, R.J. (2007) “Development of a high resolution 
grid-based river flow model for use with regional climate model output”, Hydrol. Earth 
System Sci., 11(1), 532-549. 
 
Golding, B. (ed.) (2005) Boscastle and North Cornwall Post Flood Event Study – 
Meteorological analysis of the conditions leading to flooding on 16th August 2004, Met Office 
Forecasting Research Technical Report No. 459, Met Office, UK, 85pp. 
 
HR Wallingford (2005) Flooding in Boscastle and North Cornwall, August 2004 – Phase 2 
Studies Report, Contract Report EX5160 to the Environment Agency, HR Wallingford, 
170pp. 
 
Maraun, D., Osborn, T.J. and Gillett, N.P. (2008) “United Kingdom daily 
precipitation intensity: improved early data, error estimates and an update from 2000 
to 2006”, Int. J. Climate, DOI: 10.1002/joc.1672 
 
Moore, R.J., Cole, S.J., Bell, V.A. and Jones, D.A. (2006) Issues in flood forecasting: 
ungauged basins, extreme floods and uncertainty. In: I. Tchiguirinskaia, K. N. N. Thein & P. 
Hubert (eds.), Frontiers in Flood Research, 8th Kovacs Colloquium, UNESCO, Paris, 
June/July 2006, IAHS Publ. 305, 103-122. 
 
Roberts, N. (2006) Simulations of extreme rainfall events using the Unified Model with a grid 
spacing of 12, 4 and 1 km, Met Office Forecasting Research Technical Report No. 486, Met 
Office, UK, 49pp. 
 
Scott, T.R. and Mason, D.C. (2007) “Data assimilation for a coastal area 
morphodynamic model: Morecambe Bay”, Coastal Engineering, 54(2), 91-109 
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