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New Extreme Windstorm Catalogue From The Met Office

May 23, 2014

By Paul Homewood

 

h/t Green Sand

 

image

http://www.metoffice.gov.uk/research/news/2014/european-windstorms-catalogue

 

The Met Office have issued this statement.

 

Windstorms are a major source of natural hazard risk for many countries in Europe. These extreme weather systems are the second greatest cause of natural catastrophe insurance losses on the planet. As a recent example, on 28 October 2013 windstorm Christian (St Jude’s Day storm) destroyed infrastructure and interrupted transport and business in several countries across northern Europe. This windstorm is estimated to have caused nearly £1 billion of insured losses across Europe and more than £300 million of insured losses in the UK.

Storm footprints

Looking at past events can help scientists and insurers to better quantify and understand this important source of risk. In particular, to understand the losses caused by past storms, the industry needs reliable, high resolution spatial maps of maximum gusts for each event (known as storm footprints).

The eXtreme WindStorms (XWS) catalogue, available at www.europeanwindstorms.org, contains downloadable storm footprints derived from Met Office Unified Model simulations, with their associated tracks, for 50 of the most extreme winter European windstorms over the period 1979 to 2013 (October to March).

As well as helping to understand the damage caused by individual past events, this consistent historical catalogue can provide information on the variability of storm tracks and footprints. The catalogue also highlights events from the past which may have been not so well studied because they did not hit large urban areas, or because their damage was not well documented at the time.

Major storms

Some of the more major storms included are the Great Storm of ’87, the Burns’ Day storm of 1990 (also known as Daria), and the series of December 1999 storms, Anatol, Lothar and Martin.

 

Storm footprint examples: (a) and (b) maximum 3-second gust footprints of the Great Storm of '87 and Kyrill (January 2007); (c) and (d) mean recalibrated footprints for the same storms.

Storm footprint examples: (a) and (b) maximum 3-second gust footprints of the Great Storm of ’87 and Kyrill (January 2007); (c) and (d) mean recalibrated footprints for the same storms.

 

The images above compare the footprints of the Great Storm of ’87 and the storm Kyrill in January 2007. Both of these events caused similar insured losses (6 billion US dollars and 7 billion US dollars respectively, normalised to 2012 index), yet their footprints are very different; the Great Storm of ’87 has a narrow and intense footprint, whereas Kyrill is less intense but covers a larger area over Europe.

Having a freely available catalogue such as this is in line with the general movement of greater openness of risk assessments within the (re)insurance industry. It is our hope that open access to this data will generate exciting new ideas, collaboration and innovation.

New science

During the creation of the catalogue, many science questions had to be addressed. One of the main issues was how to define an extreme storm, because unlike for hurricanes, there is currently no widely accepted scale for ranking European windstorms.

After much investigation, the XWS storms were selected by taking the top 50 storms as ranked by the index NUmax3, where N is a measure of the damaging area of the storm (the number of land grid points where the maximum gusts exceeds a threshold of 25 ms-1) and Umax is the maximum 925 hPa wind speed given in the storm track. Theoretically Umax3 is a measure of the advection of kinetic energy of the storm. This index was chosen because it was found to be a good meteorological proxy of storm damage as measured by insured loss.

Another important development was the estimation of uncertainty in the maximum wind gusts, enabled by comparing the storm footprints to observations and the development of a recalibration method to correct for model bias. Since it was found that some storms were simulated better than others, the innovation of the statistical method was to allow for storm-to-storm variation in the recalibration model. This was found to perform much better than applying the same recalibration to each storm.

It is hoped that in the future the catalogue can be extended further into the past, giving us even more understanding of these powerful events.

 

 

 

My Analysis

The Met Office data starts in October 1979, and runs through to March 2013. The distribution of storms is shown below.

 

image

http://www.europeanwindstorms.org/cgi-bin/storms/storms.cgi?sort=date&opt=

 

Some things stand out:

1) Some strange results emerge. For instance, the Great Storm of 1987 only ranks 10th, chiefly because of its relatively small footprint.

One possible weakness of the methodology, is that the “N”, the damaging area of the storm, is multiplied by “U max”, the maximum wind speed of the storm. In other words, the result assumes that the max wind speed applies to the whole storm track.

So a storm which briefly hits top speed over a small area, and then trundles over the continent, can attract a higher rating than the 1987 storm, which had higher wind speeds over a much greater area. (Remember, the storm footprint is measured wherever gusts are >25 ms, which amounts to about 56 mph – this is hardly hurricane strength.

It would seem to make more sense summing the wind speeds over the whole area, as the ACE system does for hurricanes.

2) The calculations of footprint are only carried out over land. So a storm which tracks over the sea for much of the time will score lower than one which tracks further south over the continent.

While this may offer a better correlation with insurance losses, it may be less meaningful for climate analysis.

3) Given these issues and the short period covered, the evidence so far suggests that storms were much more frequent, and stronger, in the period up to the mid 1990’s.

1990 suffered the worst, with six of the fifty storms.

4) Top of the list was Jeanette, in October 2002. Jeanette was well down as far as max speed is concerned, but had the biggest footprint of the lot. It also did feature in the list of storms with high insurance losses.

 

Image of Gust speed animation for Jeanette

http://www.europeanwindstorms.org/cgi-bin/storms/storms.cgi?storm1=Jeanette

 

 

This can be compared with the Great Storm of 1987.

 

Image of Gust speed animation for Great Storm of 87

 

http://www.europeanwindstorms.org/cgi-bin/storms/storms.cgi?storm1=Great87

 

 

Rethink

I am not sure how meaningful the Met Office’s analysis is, and would suggest they need to give it a drastic rethink.

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5 Comments
  1. May 23, 2014 6:58 pm

    It is surprising how little wind speed data the is available from the MO to the extent that it is impossible to do any historical comparison in relation to climate change.

    I will have to spend more time on this to work out exactly how they have done this but it is strange how 1987 comes out nothing special on the graph and there certainly appears to be no evidence that wind storms are getting any worse in the short-term.

    I wonder how the “great storm” of 1703 would rank on this basis?

  2. Mikky permalink
    May 23, 2014 7:07 pm

    Sorry, can’t see any hint of a hockey-stick, and no mention of things getting worse in the future, clearly a major malfunction. This also won’t go down well with Lloyd’s, who seem to be employing climate change attack dogs (such as Trevor Maynard) to encourage higher premiums:

    http://www.theguardian.com/business/2014/may/08/lloyds-insurer-account-climate-change-extreme-weather-losses

  3. May 23, 2014 7:26 pm

    Reblogged this on CraigM350.

  4. Andy DC permalink
    May 23, 2014 10:24 pm

    I imagine a small intense storm thru London would do much more damage than a widespread less intense storm further north.

    What would be your top 5 all time and how would they rank against these modern storms? Going back only 35 years is an awfully small sample for a country over 1,000 years old.

  5. John F. Hultquist permalink
    May 24, 2014 4:42 am

    High marks for trying. Like good wine, maybe the thing will improve with time.

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