Weather Radar for urban pluvial flood forecasting

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Weather Radar for urban pluvial flood forecasting

• Professor Chris Collier
• National Centre for Atmospheric Science, Head of
  Strategic Partnerships
• University of Leeds, UK

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Impact of Flash Floods in Cities
Commercial district of Istanbul, September
2009 At least 20 people died in Istanbul 7
drowned in a minibus going to work
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Urban drainage

• In many urban areas of England the UDS is
  complex, and in parts old and in need of
  refurbishment.
• Sewage discharges to natural water courses
• Accurate high resolution (1km x 1km) rainfall
  measurements and forecasts needed.
• Changes in rainfall patterns and amounts may
  cause problems in UDS management.

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Flood protection and forecasting
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Why radar?

• Wide area measurements of precipitation from a
  single location.
• High resolution.

Courtesy Met Office
Green on this map of Hull UK indicates areas that
are prone to flooding
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Rainfall totals measured at Ruislip, London and
discharge from Yeading Brook West Branch on 8 May
1988
1 in 100 yrs
1 in 25 yrs
63.5 mm in 2.5 h
34.2 mm in 75 min
Bank full
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How radar works
Courtesy Met Office
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The passage of line convection over London as
observed by the Chenies C-band radar 7 December
2006, 1053UTC
Chenies
tornado
Courtesy Met Office
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X-band radar
Advantages
Disadvantages

• Ease of siting
• Cost
• Mobility
• Less ground clutter provided one degree beamwidth
  used.
• Can detect smaller particles including the
  detection of light precipitation such as snow.

• Attenuation through rain, snow and ice (hail) but
  can correct if polarisation capability exists.
• Very limited clear air measurements.

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Polarization techniques offer increased accuracy
for measuring heavy rain
60 dBZ core could be torrential rain or hail
Conventional Radar Reflectivity
Differential Phase Shift
Phase shift indicates torrential rain
Rain gauge confirmed 250mm/hour
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Examples of mobile Doppler dual polarisation
X-band radar
Selex Gematronik
University of Auckland, NZ, Ardmore
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Why do (some) hydrologists still distrust radar
estimates of rainfall? Comparison with raingauges
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Why we need to merge rainfall data?
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Amplification of radar errorsDischarge bias
using radar data input to a stochastic model of
the urban River Croal, UK catchment compared to a
model of the Baron Fork River USA catchment
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Fundamental Limitation of Widely Spaced Long
Range Radars
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High resolution numerical forecasts

• 1-2 km grid lengths now beginning to be used
  operationally.
• Realistic forecasts now being produced, but
  problems remain e.g. Representing sub-grid scale
  processes, although grid lengths of less than 1
  km are also possible.
• These forecasts are expected to replace
  radar-based nowcasts for lead times beyond two
  hours or so.
• However the assimilation of radar data is likely
  to become an essential part of operational
  procedures.

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Illustrating Cobbacombe radar 5 hour total
rainfall (mm) (left panel) and 1 km UM forecast
rainfall (mm) for 12-17 UTC 16 August 2004 (from
Golding et al, 2005) performance due in part to
the dynamic impact of the sea breeze with
orography which introduced a level of
stationarity to the convection
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The problem of issuing an alert under flood
forecasting uncertainty
(courtesy E. Todini)
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Example of the exploitation of new data sources,
data assimilation and ensemble techniques for
storm and flood forecasting Boscastle storm
Case study Boscastle storm (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 (location and 1 km
catchment boundary is given in (a) and (b)).
(courtesy R. Moore and S. Cole)
(a)
(b)
(c)
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Concluding remarks

• Radar data are likely to be the basis of
  forecasts for 1-2 hours ahead. However for longer
  lead times high resolution NWP forecasts
  assimilating radar data, offer the best hope of
  improvement to hydraulic and hydrological
  forecasts.
• It will be necessary to constrain uncertainty
  using both rainfall and hydrological model
  ensembles with statistical procedures.
• Rain Gain will produce a significant step forward
  in using X-band radar data within the context of
  operational radar networks.