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the Environment Agency’s Flood Incident Management Investment Review (FIM-IR) give an aggregate value 12 “Estimating the cost of disruption to travel caused by severe winter weather”, K. Johnston, J. Causley and S. Murchie, Transport Scotland. 13 x = 0.14 (616+x) => x=(0.14/0.86)*616=100 Page 13 of 25

Copyright Met Office 29 Event at: xxxxxx Page 2 of 2 Date of event: 12 th June 2016 Rainfall Stations used in assessment. Station Distance (miles) & Direction 09/06/20 16 10/06/20 16 11/06/20 16 12/06/20 16 MISERDEN PARK 6.8 S 0 0.8 2.8 25.8 DOWDESWELL RSERVOIR NO 2 2.6 E 0 4.0 5.6 27.8 EBWORTH 5.9 SSW

that uses this publication as a source. No use of this publication may be made for resale or for any other commercial purpose whatsoever without prior permission in writing from ICIMOD. The views and interpretations in this publication are those of the author(s). They are not attributable to ICIMOD

and Sponsorship The four-day workshop was hosted by Addis Continental Public Institute of Health (ACIPH) and convened by both the Ethiopian Public Health Institute (EPHI) and ACIPH 4 with funding and technical support from the Department of International Development (DFID)’s Weather and Climate Information

believe the forecast(s) to be, based on feedback from market research. Comparative accuracy refers to how accurate the Met Office is compared to other weather providers, and this can be judged via actual accuracy measures or and perceived accuracy measures. However, the definitive comparison is based

of the author(s) and do not necessarily reflect the views of WMO, the UK Met Office, the NMHS, EAC or NCAR. Authors: Paul Watkiss, Robert Powell, Alistair Hunt, Federica Cimato Supported by: Fund Manager: Delivery Partners: Introduction Background and objectives The HIGHWAY project (HIGH impact

fishers of looming danger but sometim es it crows and the wind does not change Sam (small flies) Bad – proceed s a strong wind Low wave Good Good height High wave Dangerous Bad height No rain Good Good How do they obtain this information? water cold, which reduces the catch) Bad Bad Irrelevant - Good

and working change almost overnight, with uncertainty and disruption affecting us in ways we could never have predicted. Two examples of COVID-19’s impact on Met Office operations spring to mind. Firstly, due to reduced aircraft flying, there were fewer observations to feed into the Numerical Weather

for Robusta cultivation may expand in some central parts. • Tree crops in the west offer high potential for food security up to 2030. • Significant risks to water and grazing options (Cameroon). • Substantial increase in heat stress and other heat-related risks (S. Cameroon, Equatorial Guinea

governmental organisations) ▶▶ Level of resources and expertise in the organisation(s) ▶▶ Perceived advantage of using forecasts and warnings particularly in the private sector ▶▶ Ability to develop specific products in regions where forecasts and warnings reliability is higher (e.g. tropics) ▶▶