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Forecast quality and predictability of severe European cyclones. Jenny Owen Peter Knippertz , Tomasz Trzeciak . University of Leeds, School of Earth and Environment, Leeds, UK. Motivation. Xynthia. Damaging weather Important for Europe Cause fatalities and economic losses. Daria.

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forecast quality and predictability of severe european cyclones
Forecast quality and predictability of severe European cyclones

Jenny Owen

Peter Knippertz, Tomasz Trzeciak.

University of Leeds, School of Earth and Environment, Leeds, UK

motivation
Motivation

Xynthia

  • Damaging weather
  • Important for Europe
  • Cause fatalities and economic losses

Daria

Lothar

Friedhelm

method i
Method I

How well are severe European windstorms forecast?

What factors affect forecast quality?

  • Select historic storms
    • Storm Severity Index:
    • Measures ‘unusualness’ of wind speed
    • Cubed ~ power of the wind ~ damage
  • Track storms automatically
    • Minima in mean sea level pressure (MSLP)
    • Connect together at consecutive timesteps

Daria

selected storms
Selected Storms
  • Daria
  • Nana
  • Vivian
  • Wiebke
  • Udine
  • Verena
  • Agnes
  • Urania
  • Silke
  • Lara
  • Anatol
  • Franz
  • Lothar
  • Martin
  • Kerstin
  • Rebekka
  • Elke
  • Lukas
  • Pawel
  • Jennifer
  • Frieda
  • Jeanette
  • Gero
  • Cyrus
  • Hanno
  • Kyrill
  • Emma
  • Klaus
  • Quinten
  • Xynthia
method ii
Method II
  • Categorise storms:
    • Jet stream shape, relative to the track of the storm
    • Processes that govern deepening, by pressure tendency equation
slide6

Categorising Storms: Jet

Cross Early

Cross Late

Kyrill

Klaus

Edge

Split Jet

Xynthia

Jeanette

  • Based on jet stream (wind speed at 300hPa).
  • Meridional sections that move with the storm track.
  • Similar plots for θe showed no clear groupings.
categorising storms jet
Categorising Storms: Jet
  • Klaus
  • Vivian
  • Wiebke
  • Kyrill
  • Lothar
  • Martin
  • Emma
  • Jeanette
  • Daria
  • Agnes
  • Anatol
  • Udine
  • Rebekka
  • Lara
  • Xynthia
  • Jennifer
  • Gero
  • Hanno
  • Silke
  • Elke
  • Urania
  • Nana
  • Quinten
  • Verena
  • Kerstin
  • Pawel
  • Cyrus
  • Lukas
  • Franz
  • Frieda

Cross Early

Edge

Cross Late

Split

pressure tendency equation
Pressure Tendency Equation
  • Fink et al. (2012, GRL) applied the Pressure Tendency Equation to mid-latitude cyclones
  • 3o x 3o column
  • From surface to 100hPa
  • Box moves along storm track and compares properties from one time step to the next
  • Identify processes that add or remove mass from column and affect core pressure
pressure tendency equation1
Pressure Tendency Equation

Density tendency

Stratosphere

horizvertdiab

Precip

categorising storms pte
Categorising Storms: PTE

PTE terms’ contribution to deepening for ten of the storms

BaroclinicityDiabatic Processes

categorising storms pte1
Categorising Storms: PTE
  • Klaus
  • Vivian
  • Wiebke
  • Kyrill
  • Lothar
  • Martin
  • Emma
  • Jeanette
  • Daria
  • Agnes
  • Anatol
  • Udine
  • Rebekka
  • Lara
  • Xynthia
  • Jennifer
  • Gero
  • Hanno
  • Silke
  • Elke
  • Urania
  • Nana
  • Quinten
  • Verena
  • Kerstin
  • Pawel
  • Cyrus
  • Lukas
  • Franz
  • Frieda

Horiz

Diab

linking categories
Linking Categories
  • Storms that spend longer on the north side of the jet tend to be more baroclinic – stronger temperature gradients.
  • Diabatic storms tend to spend more time on the south side of the jet – warmer and wetter, more potential for latent heat release.
method iii
Method III
  • Run automatic tracker on ECMWF Ensemble Control Forecast
    • Spatial and temporal resolution
    • Initialisation time
  • Match forecast tracks to analysis tracks
    • Quantify best match based on proximity of analysis and forecast tracks at similar time
    • Quality control: reject if tracks > 20 degrees apart at any matched point
matched tracks location
Matched Tracks: Location

Cross Early

Cross Late

Kyrill

Klaus

Edge

Split Jet

Jeanette

Xynthia

  • Some storms are better forecast than others
  • Some tracks are not a good match
matched tracks pressure
Matched Tracks: Pressure

Kyrill

Klaus

Jeanette

Xynthia

  • Storms not always weaker in forecast – but difficult to see big picture
method iv
Method IV
  • Assess how forecast quality varies with lead time
  • Correlations
    • Correlation coefficient, R
    • Test for significance of correlation, T
  • Future Work: Perform more rigorous statistical tests
results latitude longitude
Results: Latitude & Longitude
  • Storms move more slowly W-E in forecasts, than in analysis
  • Storms slightly further south in forecasts
results core pressure
Results: Core Pressure
  • Storms have higher core pressure in forecast => storm less intense in forecast
  • Agrees with previous work e.g. Froude et al.
jet stream type pressure
Jet Stream Type: Pressure
  • Core pressure underprediction stronger in some jet stream types than others
pte type core pressure
PTE Type: Core Pressure
  • Indication that core pressure underprediction stronger in storms where baroclinic processes dominate deepening, than in those where diabatic processes dominate.
  • Needs further statistical testing
resolution core pressure
Resolution: Core Pressure
  • Operational forecast, so forecast system upgraded regularly (dynamics and resolution).
  • Some evidence of relationship between forecast quality and system evolution.
summary i
Summary I
  • Selected 30 European windstorms.
  • Categorised by:
    • Jet stream
    • Processes that dominate deepening (PTE)
  • Assessed forecast quality:
    • Longitude & latitude
    • Core pressure (intensity)
summary ii
Summary II
  • Storms in forecast too slow.
  • Core pressure generally underforecast:
    • Strength of relationship with lead time depends on jet stream type.
    • Baroclinic storms may be more underforecast than diabatic ones.
  • Tendency for improvements of forecast system to affect forecast quality.