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 Status:Ongoing analysis Material  Status: Finalised Material from: Linus, Fernando

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, Tim


 

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Discussed in the following Daily reports:

2018-10-12

2018-10-15

2018-10-18

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Content by Label
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cqllabel = "case0235-tropical-storm-leslie"


1. Impact

Between 13 and 14 October the tropical cyclone Leslie make landfall in Portugal. The passage of Leslie over Portugal left 27 injured, widespread damage on buildings, thousands of falling trees and an entire  borough (Soure) without power (so serious that the power supply company is considering to ask international help). The IPMA (Portuguese weather service) claimed that the all time record of wind gust was observed in Figueira da Foz  weather station. Accordingly with the IPMA web page a gust of 176 km/h (49 m/s) was recorded at 21:40Z,  exceeding the previous maximum of 169 km/h occurred on 17 of October 2015. The same station reported gusts above 100 km/h in several occasions during 40 mins. Other places reported gusts above 100km/h, in particular, Aveiro (north of Figueira) with 120km/h and Coimbra (to east) with 122 km/h between 22:30 and 23:30 local time.

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Left: Airmass RGB composite valid at 21:45 (red arrow show Figueira da Foz). Right: Maximum reflectivity radar (dBZ) valid five min later; arrows represent the sting jet (red) and Figueira da Foz (black)


The maximum gust at Coimbra (=66kts) between 2030 and 2130Z seems to tally with the cause being a sting jet. It coincides on IR imagery with the dark/dry zone that developed hours before (or possibly a mini gust front cloud band that appears within it, similar to the ones Identified by Keith Browning in the October 1987 UK storm). Meanwhile conceptual model considerations, plus radar and satellite imagery above, would ascribe the maximum gust at Figuera da Foz to the boundary region between a sting jet and a cold jet. At inland locations (eg Coimbra) SJ tends to be much stronger than CJ, whilst by coasts (eg Figuera da Foz) they can have similar strengths, due to a different destabilisation mechanism (as described in Hewson and Neu, Tellus, 2015).


3. Predictability

  

3.1 Data assimilation

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4. Experience from general performance/other cases

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5. Good and bad aspects of the forecasts for the event

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