1. © Crown copyright Met Office Becky Hemingway J. Robbins, J. Mooney and K. Mylne 13 th EMS / 11 th ECAM Meeting 9 th -13 th September 2013 ECAM5 Session: 12 th September 2013 A Vehicle Overturning Model

2. © Crown copyright Met Office Contents The Natural Hazard Partnership (NHP) and Hazard Impact Model (HIM) Vehicle Overturning Model Improving VOT thresholds Visualisation Future Work

3. © Crown copyright Met Office Natural Hazard Partnership (NHP)

4. © Crown copyright Met Office Natural Hazards Partnership Identify areas and assets which are most vulnerable to hazards Use the Partnerships expertise to asses the vulnerability, exposure and risk of hazards in these areas Create a Hazard Impact Model (HIM) to model this This will help people to prioritise where to deploy ‘responder’ services and aid the decision making process in issuing hazard warnings.

5. © Crown copyright Met Office Vehicle Overturning Model

6. © Crown copyright Met Office Vehicle Overturning Model (VOT) Probabilistic Model using MOGREPS-UK 2.2km gridded wind gust and direction fields 12 Members ensemble out to T+36 Uses VOT thresholds established by Birmingham University (Baker et al. 2008) Unloaded HGVs: 23m/s (51.5mph) Light Goods Vehicles: 26m/s (58mph) Cars: 35m/s (78mph) Loaded HGVs: 36m/s (80.5mph) Aim to calculate risk of disruption on roads across the UK using a combination of hazard, vulnerability and exposure data Will be used in Ops Centre at the Met Office to add value to NSWWS wind warnings

7. © Crown copyright Met Office Wind Direction on the Road Segments The wind direction thresholds are dependent on the individual road segment orientation. Determining whether the wind direction falls within the threshold ranges is calculated at the centre point of the segment.

8. © Crown copyright Met Office Risk Algorithm – Vehicle Overturning Model Hazard Probability of wind gusts exceeding vehicle type gust thresholds. Using 12 MOGREPS-UK ensemble members

9. © Crown copyright Met Office Risk Algorithm – Vehicle Overturning Model Hazard Probability of wind gusts exceeding vehicle type gust thresholds. Using 12 MOGREPS-UK ensemble members Vulnerability Vulnerability of road network. Includes 4 factors: altitude of road segment; number of lanes; aspects of infrastructure (i.e. bridges and tunnels) and road orientation to forecast wind direction x

10. © Crown copyright Met Office Risk Algorithm – Vehicle Overturning Model Hazard Probability of wind gusts exceeding vehicle type gust thresholds. Using 12 MOGREPS-UK ensemble members Vulnerability Vulnerability of road network. Includes 4 factors: altitude of road segment; number of lanes; aspects of infrastructure (i.e. bridges and tunnels) and road orientation to forecast wind direction x Number of UHGVsNumber of UHGVs & LGVsNumber of All Vehicles Exposure Exposure or location of specific vehicle types (UHGVs, LGVs, Cars and LHGVs) determined from Department of Transport traffic flow data. Eventually will change every hour to be more representative x

11. © Crown copyright Met Office

12. Risk Index Hazard Footprint Risk of Disruption – Severity 1 (low impact)= Prob (Hazard >= 1) x Vulnerability x Exposure 1 Risk of Disruption – Severity 2 (low- medium impact)= Prob (Hazard >= 2) x Vulnerability x Exposure 2 Risk of Disruption – Severity 3 (medium-high impact)= Prob (Hazard >= 3) x Vulnerability x Exposure 3 Risk of Disruption – Severity 4 (high impact)= Prob (Hazard = 4) x Vulnerability x Exposure 3 Model Output

13. © Crown copyright Met Office Case Study: May 9 th 2013 Deterministic Model Asked for by Ops centre Enhanced NSWWS yellow wind warning

14. © Crown copyright Met Office Improving VOT thresholds: Conditional Probabilities

15. © Crown copyright Met Office Accident Data HA Report 2009 1 st Jan 2002 – 30 th June 2007 318 wind-induced incidents Accident data from STATs19 police forms Blow-overs and Slides Compared HA and NCIC data

16. © Crown copyright Met Office Blow-overs and Slides – Max Gust

17. © Crown copyright Met Office Blow-overs Only – Max Gust

18. © Crown copyright Met Office Whole Dataset Conditional Probability Data grouped into 5 knot bins Use UK wind gust climatology p(blow-over | wind-speed) At low wind gust values High frequency of occurrence Accident Occurrence Low Conditional Probability LOW At high wind gust values Low frequency of occurring Accident Occurrence Low Conditional Probability HIGH

19. © Crown copyright Met Office Change Spatially? Some areas are more susceptible to high winds than others Split the country in spatial areas? Each with conditional probability charts Currently S-W is a problem Need more data: ~3200 events (2005 - 2011)

20. © Crown copyright Met Office Visualising VOT Risk Values

21. © Crown copyright Met Office Red Lorry, Yellow Lorry Colour code vehicle images Summaries risk of disruption maps (1 to 4) Colour thresholds for Risk Red = 0.75+ Amber = 0.25-0.75 Yellow = >0-0.25 Green = 0 One set of vehicles for each road point Need high zoom to see them as there are ~72,000 points

22. © Crown copyright Met Office Future VOT Plans Get Probabilistic VOT Model running in real time – Nearly Complete Temporally vary exposure field – Testing phase Use Conditional Probability values instead of thresholds? Have ~3200 extra incidents to add to dataset Summary Map and Visualisation – Work in Progress

23. © Crown copyright Met Office Thank You Any Questions?

24. © Crown copyright Met Office My New Toy Colours are the wind footprint - relate to wind thresholds Red = winds over 80.5mph (in real model) This is a mock up image with reduced thresholds to use in testing!!!

25. © Crown copyright Met Office A Good Example – N. Wales Britannia Bridge: Medium-High Risk winds to All Vehicles – May Close Menai Bridge: Low- Medium Risk to All Vehicles –likely to remain open

26. © Crown copyright Met Office My Second New Toy