Mitigating risk through weather and climate
intelligence to support business relevant decision
making
Caroline Acton, Nicolas Fournier, Paul Newell, Jill Dixon
26th May 2017
Contents
•Introduction
•Decisions not data......
•Weather impacts- UKPN
•Climate impacts- Infrastructure operator
•Summary
Introducing the
Met Office
Leading capabilities
• Provide operational forecasts for the public
and commercial services
• Business Group enables companies to
manage the impact of weather and climate in
their activities – tailored solutions
• Support climate change policy in the UK and
around the world
Decisions not data…..
Risk
Reward
Weather impacts the health and
safety and resilience of the
network
Improve efficiency, health and
safety, customer experience and
sustainability of the network
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…it depends on trust (‘skill’)
of forecasts
Deterministic
forecast
Forecast
uncertainty
Initial condition
uncertainty
Analysis
Climatology
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.....and how forecasts can be
used to inform decision making
A case study- Weather
Impacts
It’s not just about the weather
WEATHER
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IMPACT
DECISION
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Approach
Weather Impact Models
Step 1 –Review and assess impact and weather data
to confirm if a fuller analysis can be carried out
Step 2 - Weather sensitivity analysis to determine if
any stable, plausible relationships exist between the
impact and the most relevant weather parameters
Step 3- Predictive model implemented as an standard
service
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Customer
data
Weather
data
Quality
checking
Choose
data
Understand
data signals
Create
derivatives
No Success
Building
weather impact
models
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Create
derivatives
Compare /
Model
Success
Apply
Results
Planning
Reporting
Forecasting
Case Study
Network Faults
Weather Hazard
Network Impact
Impact on UKPN
Power cuts
High call volume
Unplanned overtime (£)
Disruption to scheduled work
Negative publicity
Increased Health and Safety risk
But what if you
knew the impacts
in advance?
A network fault forecast
Making the most of forecasts weeks to months
ahead
North Atlantic Oscillation (NAO)
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Predictability of the NAO in GloSea5
Retrospective winter forecasts from early November (20 yr hcst)
Ensemble
Member
Observations
(ERA-I)
Ensemble
Mean
NAO correlation: 0.6
Pre-winter uncertainty reduced to 65% of previous
Significant at the 99.5% level
• Folland et al, 2012, Int. J. Climatol,
• MacLachlan et al, 2014, QJRMS,
• Scaife et al, 2014, GRL
Making the most of forecasts
weeks to months ahead
• Efficient operational planning can be further
supported by complementary use of longer-range
tools
• Predictability limits make forecasting of small-scale
weather events all but impossible, but useful
information contained in large-scale circulation types
• Broad-scale circulation types are termed weather
regimes
•Different weather regimes may be correlated with
their suitability for carrying out different types of
operation
• Ensemble members are assigned to the closest
weather regime definition using an automatic patternmatching algorithm, simplifying data into sequence of
probabilities
Neal et al, 2016, Meteorol. Appl
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Making the most of forecasts
weeks to months ahead
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Case Study- Climate
Impacts
Some recent extremes
...
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A Black Swan Event
•The theory was developed by Nassim Nicholas Taleb
•A black swan is an event or occurrence that deviates
beyond what is normally expected of a situation and is
extremely difficult to predict.
•Limitations of observations data to provide the full
spectrum of extreme events
•‘How can we mitigate the risk of events that are
unknown’?
Approach
•The latest version of the Met Office high-resolution climate
model has been used to generate a ‘virtual’ event set consists
of 40 ensembles of 35 years (1980-2014) thus representing
over 1400 years of daily scenarios
•This means that it contains many more physically plausible
extreme events than existing observed records
Case Study
Infrastructure Operator
Climate Hazard
Design Impact
Network Impact
Impact on operator
Increase design cost
Increase build costs
Increase maintenance costs
ROI (£)
Reduces confidence in the EVA
result
What if you could
reduce the
uncertainty within
the data?
Case Study- Infrastructure
Operator
•Use all 1400 years of climate data to reduce the overall uncertainty
of temperature
• Using the virtual dataset significantly lowers the P90 for temperature
to provide a more realistic value
Summary
•Latest techniques in science can be used to mitigate
risk to industry
•A user led approach enables optimization and
calibration of data that is business relevant
•Weather and climate intelligence supports industry
tailored decision making
Caroline Acton
E-mail:
[email protected]
Mobile:+44 (0)7825962870
Tweet: @metoffice
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