Mitigating risk through weather and climate intelligence to support business relevant decision making

Transcription

1 Mitigating risk through weather and climate intelligence to support business relevant decision making Caroline Acton, Nicolas Fournier, Paul Newell, Jill Dixon 26 th May 2017

2 Contents Introduction Decisions not data... Weather impacts- UKPN Climate impacts- Infrastructure operator Summary

3 Introducing the Met Office

4 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

5 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 Crown Copyright 2016, Met Office

6 it depends on trust ( skill ) of forecasts Initial condition uncertainty Forecast uncertainty Deterministic forecast Analysis Climatology Crown Copyright 2016, Met Office

7 ...and how forecasts can be used to inform decision making

8 A case study- Weather Impacts

9 It s not just about the weather WEATHER IMPACT DECISION Crown Copyright 2016, Met Office

10 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 Crown Copyright 2016, Met Office

11 Customer data Weather data Quality checking Understand data signals Choose data Create derivatives Create derivatives Building weather impact models No Success Compare / Model Success Apply Results Planning Reporting Forecasting Crown copyright Met Office

12 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?

13 A network fault forecast

14 Making the most of forecasts weeks to months ahead North Atlantic Oscillation (NAO) Crown copyright Met Office

15 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

16 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 Crown Copyright 2016, Met Office

17 Making the most of forecasts weeks to months ahead Crown Copyright 2016, Met Office

18 Case Study- Climate Impacts

19 Some recent extremes... Crown Copyright 2016, Met Office

20 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?

21 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 ( ) thus representing over 1400 years of daily scenarios This means that it contains many more physically plausible extreme events than existing observed records

22 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?

23 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

24 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

25 Caroline Acton Mobile:+44 (0) Crown Copyright 2016, Met Office