© 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:

© Crown copyright Met Office

Becky Hemingway J. Robbins, J. Mooney and K. Mylne13th EMS / 11th ECAM Meeting 9th-13th September 2013 ECAM5 Session: 12th September 2013

A Vehicle Overturning Model

© Crown copyright Met Office© Crown copyright Met Office

Contents

• The Natural Hazard Partnership (NHP) and Hazard Impact Model (HIM)

• Vehicle Overturning Model

• Improving VOT thresholds

• Visualisation

• Future Work


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.


Vehicle Overturning Model


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


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.


Risk Algorithm – Vehicle Overturning Model

Hazard

Probability of wind gusts exceeding vehicle type gust thresholds. Using 12 MOGREPS-UK ensemble members



Hazard


Vulnerabilit

y

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



Hazard


Vulnerabilit

y

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 UHGVs Number of UHGVs & LGVs Number of All Vehicles

Exposu

re

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


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


Case Study: May 9th 2013

• Deterministic Model

• Asked for by Ops centre

• Enhanced NSWWS yellow wind warning


Improving VOT thresholds:Conditional Probabilities


Accident Data

• HA Report 2009• 1st Jan 2002 – 30th June 2007• 318 wind-induced incidents• Accident data from STATs19

police forms• Blow-overs and Slides• Compared HA and NCIC data


Blow-overs and Slides – Max Gust


Blow-overs Only – Max Gust


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


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)


Visualising VOT Risk Values


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


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


Thank You

Any Questions?


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!!!


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


My Second New Toy

Documents

© 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: