chris tubbs: quantifying uncertainty in an operational environment
DESCRIPTION
Risk SIG conference: 24th October 2013TRANSCRIPT
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Quantifying Uncertainty in an Operational Environment – Risky
weather forecasts
Chris TubbsAssociation of Project Management Thursday 24th October 2013
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Talk structure
• Introduction
• Compiling a forecast – 2-3 days
• Introducing uncertainty – 4-5 days
• Uncertain forecasts; 6-15 day, 16-30 day (monthly) and seasonal forecasting
• Converting uncertainty into risks for customers
• Looking to the future and questions
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Compiling a forecast-Forecasting Process
Observations
4-D winds, rainfall, temperatures…….
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du = ∂p – fvdt ∂xdv = ∂p + fu dt ∂yp = RTρ
Interpretation, Risk Analysis & Communication
Knowledge
70 levels25km
80km high
Creating weather services
Forecast Model
Observations
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Numerical modelling
Weather and Climate Models are huge computer codes based on fundamental mathematical equations of motion, thermodynamics and radiative transfer
These govern:
Flow of air and water - winds in the atmosphere, currents in the ocean.
Exchange of heat between the atmosphere and the earth’s surface / ocean
Release of latent heat by condensation during the formation of clouds and raindrops
Absorption of sunshine and emission of thermal (infra-red) radiation
Numerical methods must conserve mass, energy, momentum, water and tracers
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Operational Forecasting Models: October 2013
Global25km 70L2.5 day forecast twice/day6 day forecast twice/day+24 member EPS at 60km twice/day
N.Atlantic/European (NAE) bec Euro412km bec 4km 70L2.5 bec 5 day f’cast 4 times per day+24 member EPS at 18km twice/day
UK-V1.5km 70L 1.5 day forecast 8 times per day2012: +24 member EPS at 2.2km
Met Office Global Regional Ensemble Prediction System = MOGREPS
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Resolution: Observed & Forecast Accumulations for the Carlisle Flood
12 km
4 km 1 km
Hand analysis of gauges and radar
12 km 1 km
Model Orography
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Resolution: fog prediction
Visibility (m)
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The growth of computer power
LEO 1
MERCURY
IBM 360/195
KDF 9
CYBER 205
ETA 10 CRAY YMP8
CRAY C90
CRAY T3E
10T
1T
100G
10G
1G
100M
10M
1M
100K
10K
1K
100
10
NEC SX-6
NEC SX-8 IBM P6
IBM P7
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Data Assimilation
• The challenge:
• To compute the model state from which the resulting forecast best matches the available observations
T-3 T-2 T-1 T+0 T+1 T+2 T+3 T+144
First guess
Observations
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Global performance by lead timeRMS surface pressure error over the NE Atlantic
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1982 2012
1-day f/c
4-day f/c
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1-2 days ahead
• Additional benefits from our high resolution (1km and 4km) models
• Automated warning products from MOGREPS ensemble system
• Weather system can be monitored before it reaches UK
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3 to 5 days ahead
• Deterministic global model outputs and ensembles
And forecaster interpretation…..’added value’
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3 to 5 days ahead…
Within the current NSWWS timescales
• So we have all of this weather information to enable us to give a ‘most likely’ scenario and potential what if’s
• Increased confidence
• Higher risk can be identified
• Details will still be subject to change and should be treated as best estimates
• Worth realising that the weather system causing the event will often not even have formed yet!
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3 to 5 days ahead…what action can be taken?
• We present these pieces of the jigsaw to other interested parties and partners
• Whole ethos of Hazard Centre
• NSWWS is impact based
• Typical example of Heavy Rain -
• Flood Forecast Centre (FFC) can use rain forecast and add more info in terms of assessing an impact
• Construct a communication plan with key messaging
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Quantifying uncertainty with ensembles
time
Forecast uncertainty
Climatology
Initial Condition Uncertainty
X
Deterministic Forecast
Analysis
CHAOS
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Probability of precipitation >5mm in 12 hours
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Pictorial representation ofuncertainty
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Spaghetti plot of fronts
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Dalmatian plot of lows and depths
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6 to 15 days aheadCan we detect anything at all?
There is often a lot of uncertainty
• Ensemble outputs are main source of information
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Increased reach - 6 to 15 days ahead
• Forecasters are good at:
• - linking atmospheric patterns to potential severe weather
• - identifying trends (e.g. mobile to block, cold to warm)
Sometimes there are hints of the following:
Prolonged rain Windy conditionsSnow Prolonged heat or cold
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Meteogram for 15 days, cloud, precipitation, winds (speed and direction) and temperatures
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MSLP ensemble mean every 12 hours to T+240 (10 days)
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Shannon entropy, a measure of spread
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6 to 15 days ahead…what action can be taken?
• We wouldn’t be able to supply details for any particular region this far out BUT
Heads up information can be useful to open up discussions (e.g. PWS-Advisors, Internal Comms)
These things can be couched in terms of risk, albeit low
Awareness
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Monthly and seasonal forecasts
• Knowing 6-15 day trends, especially if there is high confidence, helps to extend to monthly
• Sometimes ideas around continuation of a spell/climatology can help eg anticyclonic October = warm start, cold end
• Seasonal forecasts rely on Global drivers, eg North Atlantic Oscillation (+ve = mild), El Nino, SST anomalies, Arctic Sea Ice, Tropical Storms
• Most only have skill in winter half of year in Europe. Better in Tropics.
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Our key role as a forecaster is…
Identifying the potential for high impact weather….
• 6 to 15 day lead time
• 3 to 5 day lead time
0 to 2 day lead time
as we approach the ‘event’
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Converting uncertainty into risks for customers
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Weather Impact Matrix
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6 to 15 days ahead…what action can be taken?
• We wouldn’t be able to supply details for any particular region this far out BUT
Heads up information can be useful to open up discussions (e.g. PWS-Advisors, Internal Comms)
These things can be couched in terms of risk, albeit low
Awareness
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3 to 5 days ahead…
Within the current NSWWS timescales
• So we have all of this weather information to enable us to give a ‘most likely’ scenario and potential what if’s
• Increased confidence
• Higher risk can be identified
• Details will still be subject to change and should be treated as best estimates
• Worth realising that the weather system causing the event will often not even have formed yet!
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3 to 5 days ahead…what action can be taken?
• We present these pieces of the jigsaw to other interested parties and partners
• Whole ethos of Hazard Centre
• NSWWS is impact based
• Typical example of Heavy Rain -
• Flood Forecast Centre (FFC) can use rain forecast and add more info in terms of assessing an impact
• Construct a communication plan with key messaging
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Covering risk due to lingering uncertainties
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Example of uncertainty to risks for Met Office customers
• Open Road forecasts for Highways Agencies (HA) & local authorities since mid 1980’s to determine need for overnight winter road gritting
• Tendency of customers and Met Office to be risk averse, why is that?
• Main reason is 10 to one risk ratio, ie cost of service (£20k) is a tenth of average claim (£200k), and 100 to one ration for each gritting run (£2k)
• Therefore whenever the risk of a frost occurring rises above 10% it is cost effective to grit
• More useful to EA when deciding on flood prevention measures
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Looking to the Future
• Better quantification of uncertainty – the UK 2.2km ensemble
• Better nowcasts – an hourly UK NWP cycle
• New and improved specialist models, eg Weymouth Bay 500m model
• Better forecasts for global sites & longer ranges – a higher resolution global model
• Longer forecasts – addition of monthly and seasonal forecast information
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Harbour Wall: 300 (245V332) 09 KT(backed 60 degrees between 1120 and 1200 Z)Buoy: 340 07 KTIsle of Portland: 270 07 KT
1200 Z
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28/09Z UK300 VT 29/0900 Z
Extreme gusts associated with line convection
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So to conclude….
• Improvements in science have given us opportunities to provide useful advice at longer lead times
• We do need to manage expectations (because weather forecasting is not an exact science)
• Communication is key (knowing how/when/what) and also the risks involved for our customers
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Any questions?