Chris Tubbs: Quantifying uncertainty in an operational environment

© Crown copyright Met Office

Quantifying Uncertainty in an Operational Environment – Risky

weather forecasts

Chris TubbsAssociation of Project Management Thursday 24th October 2013


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


Compiling a forecast-Forecasting Process

Observations

4-D winds, rainfall, temperatures…….


du = ∂p – fvdt ∂xdv = ∂p + fu dt ∂yp = RTρ

Interpretation, Risk Analysis & Communication

Knowledge

70 levels25km

80km high

Creating weather services

Forecast Model

Observations


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


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


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


Resolution: fog prediction

Visibility (m)


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


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


Global performance by lead timeRMS surface pressure error over the NE Atlantic


1982 2012

1-day f/c

4-day f/c


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

http://www-nwp/~fren/MOGREPS/past_data/MOGREPS_wx_events/storm_12092011/warnings/mogreps-w_18Z11092011_overall_warning_colour_WG.png


3 to 5 days ahead

• Deterministic global model outputs and ensembles

And forecaster interpretation…..’added value’


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!


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


Quantifying uncertainty with ensembles

time

Forecast uncertainty

Climatology

Initial Condition Uncertainty

X

Deterministic Forecast

Analysis

CHAOS


Probability of precipitation >5mm in 12 hours


Pictorial representation ofuncertainty


Spaghetti plot of fronts


Dalmatian plot of lows and depths


6 to 15 days aheadCan we detect anything at all?

There is often a lot of uncertainty

• Ensemble outputs are main source of information


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


Meteogram for 15 days, cloud, precipitation, winds (speed and direction) and temperatures


MSLP ensemble mean every 12 hours to T+240 (10 days)


Shannon entropy, a measure of spread



• 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


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.


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’


Converting uncertainty into risks for customers


Weather Impact Matrix



• 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


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!



• 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


Covering risk due to lingering uncertainties


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


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


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


28/09Z UK300 VT 29/0900 Z

Extreme gusts associated with line convection


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


Any questions?

Business

Chris Tubbs: Quantifying uncertainty in an operational environment