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MTMG50: CLIMATE SERVICES AND IMPACT MODELLING

创建日期：2024-04-29 16:38:27

所属分类：物理Physical

标签： MTMG50:

CLIMATE SERVICES AND IMPACT MODELLING

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MTMG50: CLIMATE

SERVICES AND IMPACT
MODELLING

TECHNICAL ASSIGNMENT:
ESTIMATING WIND RISK

DEPARTMENT OF METEOROLOGY
UNIVERSITY OF READING

Climate Service Technical Assignment: A
Estimating risk associated with wind damage over Europe
Setting the scene
One of the most costly natural hazards for Northern Europe arises from extreme winds and
the resulting injury to people and damage to infrastructure, such as buildings, power cables
and so on. Even globally, wind hazard is among the most damaging. In Northern Europe the
strongest winds are associated almost exclusively with the passage of extratropical cyclones
and the mesoscale structures embedded within them, such as fronts and sting jets.

The insurance and re-insurance industry need to estimate the risk of damaging winds across
the regions of interest. Broadly, this includes two components: firstly changes in the hazard
(and whether or not wind extremes might change with changing climate), and, secondly,
changes in the exposure and vulnerability associated with human activity (such as new
buildings, land use change, changes in the distribution and value of insured properties etc).

Defining the problem
In recent years, insurance companies have commissioned climate services to estimate the
risk of extreme winds and associated damage. Direct observational records of wind-storm
damage and insurance loss are relatively short, so re-analyses (and long historical climate
simulations) have been used to estimate the distributions of wind and damage functions
(e.g., the XWS Wind Catalogue, link below). In addition, some companies have started using
seasonal forecasts to estimate risk for the months ahead.

The typical approach to approximating wind damage potential, following Klawa and Pinto
(2003), is to calculate a loss potential or Storm Severity Index (SSI):
= (

98
− 1)
3
for > 98

where loss is only assumed to occur if, V, the local wind speed (at a grid point at a given
time) exceeds the 98th percentile of wind speed at that location, calculated from a long
historical dataset. Note the dependence on “wind excess” cubed. Variable p refers to
population density as a measure of exposure to the hazard, but in this assignment we will
neglect this factor and set p=1.

Klawa and Ulbrich (2003). A model for the estimation of storm losses and the identification
of severe weather storms in Germany, Nat Haz Earth Sys Sci, 3, 725-732.

Leckebusch, G. C., U. Ulbrich, L. Fröhlich, and J. G. Pinto, 2007: Property loss potentials for
European midlatitude storms in a changing climate. Geophys. Res. Lett., 34, L05703,
doi:10.1029/2006GL027663.
3
Questions for this technical assignment
Q1. What does the statistical distribution of observed wind strength look like calculated
from a multi-decadal observational record at three locations (near London, Paris and
Hamburg)? What are the wind speeds corresponding to the 50th, 75th and 98th percentiles of
the wind speed distribution at each location?
Q2. How does the 98th percentile of observed wind speed vary geographically (viewed on a
map) across Northwest Europe?
Q3. How does accumulated loss potential (estimated from the sum of SSI over the historical
record, using daily data) vary geographically across Northwest Europe?
Q4. Using the raw sub-seasonal-to-seasonal model S2S hindcasts, what are the 98th
percentiles of wind speed at the locations near London, Paris and Hamburg? How do they
differ from the 98th percentiles from observations? Do the values of the 98th percentile vary
with forecast lead time?
Q5. Consider the re-analysis data for one high impact cyclone crossing Northwest Europe in
the years where you have S2S hindcast data. What is the loss potential of the storm
estimated from observations?

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